From 3f6d16e791ed198bbe38c64dbca42a44f77931a2 Mon Sep 17 00:00:00 2001 From: HamsterMimi Date: Tue, 23 Jan 2024 10:08:45 +0800 Subject: [PATCH] update --- .idea/MeCo.iml | 2 +- .idea/deployment.xml | 289 ++++++- .idea/misc.xml | 5 +- correlation/NAS_Bench_201.py | 73 +- correlation/compute_rho.py | 24 + correlation/foresight/__init__.py | 16 + correlation/foresight/dataset.py | 133 +++ correlation/foresight/h5py_dataset.py | 55 ++ correlation/foresight/imagenet16.py | 142 ++++ correlation/foresight/models/__init__.py | 19 + correlation/foresight/models/nasbench1.py | 251 ++++++ correlation/foresight/models/nasbench1_ops.py | 83 ++ .../foresight/models/nasbench1_spec.py | 295 +++++++ correlation/foresight/models/nasbench2.py | 140 ++++ correlation/foresight/models/nasbench2_ops.py | 166 ++++ correlation/foresight/pruners/__init__.py | 19 + .../foresight/pruners/measures/__init__.py | 69 ++ correlation/foresight/pruners/measures/cor.py | 53 ++ .../foresight/pruners/measures/cova.py | 67 ++ .../foresight/pruners/measures/fisher.py | 107 +++ .../foresight/pruners/measures/grad_norm.py | 38 + .../foresight/pruners/measures/gradsign.py | 76 ++ .../foresight/pruners/measures/grasp.py | 87 ++ .../foresight/pruners/measures/jacob_cov.py | 57 ++ .../foresight/pruners/measures/l2_norm.py | 22 + .../foresight/pruners/measures/mean.py | 63 ++ .../foresight/pruners/measures/meco.py | 73 ++ .../foresight/pruners/measures/norm.py | 55 ++ correlation/foresight/pruners/measures/ntk.py | 94 +++ .../foresight/pruners/measures/param_count.py | 16 + .../foresight/pruners/measures/pearson.py | 71 ++ .../foresight/pruners/measures/plain.py | 44 + .../foresight/pruners/measures/snip.py | 69 ++ .../foresight/pruners/measures/synflow.py | 69 ++ correlation/foresight/pruners/measures/var.py | 55 ++ correlation/foresight/pruners/measures/zen.py | 110 +++ .../foresight/pruners/measures/zico.py | 106 +++ correlation/foresight/pruners/p_utils.py | 83 ++ correlation/foresight/pruners/predictive.py | 116 +++ correlation/foresight/version.py | 51 ++ correlation/foresight/weight_initializers.py | 84 ++ correlation/models/CifarDenseNet.py | 117 +++ correlation/models/CifarResNet.py | 180 ++++ correlation/models/CifarWideResNet.py | 115 +++ correlation/models/ImageNet_MobileNetV2.py | 117 +++ correlation/models/ImageNet_ResNet.py | 217 +++++ correlation/models/SharedUtils.py | 37 + correlation/models/__init__.py | 329 ++++++++ correlation/models/cell_infers/__init__.py | 5 + correlation/models/cell_infers/cells.py | 155 ++++ .../models/cell_infers/nasnet_cifar.py | 118 +++ .../models/cell_infers/tiny_network.py | 63 ++ correlation/models/cell_operations.py | 553 +++++++++++++ correlation/models/cell_searchs/__init__.py | 33 + .../models/cell_searchs/_test_module.py | 14 + .../models/cell_searchs/generic_model.py | 366 +++++++++ correlation/models/cell_searchs/genotypes.py | 274 +++++++ .../models/cell_searchs/search_cells.py | 267 ++++++ .../models/cell_searchs/search_model_darts.py | 122 +++ .../cell_searchs/search_model_darts_nasnet.py | 178 ++++ .../models/cell_searchs/search_model_enas.py | 114 +++ .../cell_searchs/search_model_enas_utils.py | 74 ++ .../models/cell_searchs/search_model_gdas.py | 142 ++++ .../search_model_gdas_frc_nasnet.py | 200 +++++ .../cell_searchs/search_model_gdas_nasnet.py | 197 +++++ .../cell_searchs/search_model_random.py | 102 +++ .../models/cell_searchs/search_model_setn.py | 178 ++++ .../cell_searchs/search_model_setn_nasnet.py | 205 +++++ correlation/models/clone_weights.py | 74 ++ correlation/models/initialization.py | 16 + .../models/shape_infers/InferCifarResNet.py | 287 +++++++ .../shape_infers/InferCifarResNet_depth.py | 263 ++++++ .../shape_infers/InferCifarResNet_width.py | 277 +++++++ .../shape_infers/InferImagenetResNet.py | 324 ++++++++ .../models/shape_infers/InferMobileNetV2.py | 176 ++++ .../models/shape_infers/InferTinyCellNet.py | 74 ++ correlation/models/shape_infers/__init__.py | 9 + .../models/shape_infers/shared_utils.py | 5 + .../models/shape_searchs/SearchCifarResNet.py | 760 +++++++++++++++++ .../shape_searchs/SearchCifarResNet_depth.py | 515 ++++++++++++ .../shape_searchs/SearchCifarResNet_width.py | 619 ++++++++++++++ .../shape_searchs/SearchImagenetResNet.py | 766 ++++++++++++++++++ .../shape_searchs/SearchSimResNet_width.py | 466 +++++++++++ .../models/shape_searchs/SoftSelect.py | 128 +++ correlation/models/shape_searchs/__init__.py | 9 + .../generic_size_tiny_cell_model.py | 209 +++++ correlation/result/sss_cf100_meco.p | Bin 0 -> 4271872 bytes correlation/result/sss_cf100_meco_opt.p | Bin 0 -> 4271872 bytes correlation/result/sss_cf10_meco.p | Bin 0 -> 4271872 bytes correlation/result/sss_cf10_meco_opt.p | Bin 0 -> 4271872 bytes correlation/result/sss_im120_meco.p | Bin 0 -> 4271872 bytes correlation/result/sss_im120_meco_opt.p | Bin 0 -> 4271872 bytes 92 files changed, 12855 insertions(+), 41 deletions(-) create mode 100644 correlation/compute_rho.py create mode 100644 correlation/foresight/__init__.py create mode 100644 correlation/foresight/dataset.py create mode 100644 correlation/foresight/h5py_dataset.py create mode 100644 correlation/foresight/imagenet16.py create mode 100644 correlation/foresight/models/__init__.py create mode 100644 correlation/foresight/models/nasbench1.py create mode 100644 correlation/foresight/models/nasbench1_ops.py create mode 100644 correlation/foresight/models/nasbench1_spec.py create mode 100644 correlation/foresight/models/nasbench2.py create mode 100644 correlation/foresight/models/nasbench2_ops.py create mode 100644 correlation/foresight/pruners/__init__.py create mode 100644 correlation/foresight/pruners/measures/__init__.py create mode 100644 correlation/foresight/pruners/measures/cor.py create mode 100644 correlation/foresight/pruners/measures/cova.py create mode 100644 correlation/foresight/pruners/measures/fisher.py create mode 100644 correlation/foresight/pruners/measures/grad_norm.py create mode 100644 correlation/foresight/pruners/measures/gradsign.py create mode 100644 correlation/foresight/pruners/measures/grasp.py create mode 100644 correlation/foresight/pruners/measures/jacob_cov.py create mode 100644 correlation/foresight/pruners/measures/l2_norm.py create mode 100644 correlation/foresight/pruners/measures/mean.py create mode 100644 correlation/foresight/pruners/measures/meco.py create mode 100644 correlation/foresight/pruners/measures/norm.py create mode 100644 correlation/foresight/pruners/measures/ntk.py create mode 100644 correlation/foresight/pruners/measures/param_count.py create mode 100644 correlation/foresight/pruners/measures/pearson.py create mode 100644 correlation/foresight/pruners/measures/plain.py create mode 100644 correlation/foresight/pruners/measures/snip.py create mode 100644 correlation/foresight/pruners/measures/synflow.py create mode 100644 correlation/foresight/pruners/measures/var.py create mode 100644 correlation/foresight/pruners/measures/zen.py create mode 100644 correlation/foresight/pruners/measures/zico.py create mode 100644 correlation/foresight/pruners/p_utils.py create mode 100644 correlation/foresight/pruners/predictive.py create mode 100644 correlation/foresight/version.py create mode 100644 correlation/foresight/weight_initializers.py create mode 100644 correlation/models/CifarDenseNet.py create mode 100644 correlation/models/CifarResNet.py create mode 100644 correlation/models/CifarWideResNet.py create mode 100644 correlation/models/ImageNet_MobileNetV2.py create mode 100644 correlation/models/ImageNet_ResNet.py create mode 100644 correlation/models/SharedUtils.py create mode 100644 correlation/models/__init__.py create mode 100644 correlation/models/cell_infers/__init__.py create mode 100644 correlation/models/cell_infers/cells.py create mode 100644 correlation/models/cell_infers/nasnet_cifar.py create mode 100644 correlation/models/cell_infers/tiny_network.py create mode 100644 correlation/models/cell_operations.py create mode 100644 correlation/models/cell_searchs/__init__.py create mode 100644 correlation/models/cell_searchs/_test_module.py create mode 100644 correlation/models/cell_searchs/generic_model.py create mode 100644 correlation/models/cell_searchs/genotypes.py create mode 100644 correlation/models/cell_searchs/search_cells.py create mode 100644 correlation/models/cell_searchs/search_model_darts.py create mode 100644 correlation/models/cell_searchs/search_model_darts_nasnet.py create mode 100644 correlation/models/cell_searchs/search_model_enas.py create mode 100644 correlation/models/cell_searchs/search_model_enas_utils.py create mode 100644 correlation/models/cell_searchs/search_model_gdas.py create mode 100644 correlation/models/cell_searchs/search_model_gdas_frc_nasnet.py create mode 100644 correlation/models/cell_searchs/search_model_gdas_nasnet.py create mode 100644 correlation/models/cell_searchs/search_model_random.py create mode 100644 correlation/models/cell_searchs/search_model_setn.py create mode 100644 correlation/models/cell_searchs/search_model_setn_nasnet.py create mode 100644 correlation/models/clone_weights.py create mode 100644 correlation/models/initialization.py create mode 100644 correlation/models/shape_infers/InferCifarResNet.py create mode 100644 correlation/models/shape_infers/InferCifarResNet_depth.py create mode 100644 correlation/models/shape_infers/InferCifarResNet_width.py create mode 100644 correlation/models/shape_infers/InferImagenetResNet.py create mode 100644 correlation/models/shape_infers/InferMobileNetV2.py create mode 100644 correlation/models/shape_infers/InferTinyCellNet.py create mode 100644 correlation/models/shape_infers/__init__.py create mode 100644 correlation/models/shape_infers/shared_utils.py create mode 100644 correlation/models/shape_searchs/SearchCifarResNet.py create mode 100644 correlation/models/shape_searchs/SearchCifarResNet_depth.py create mode 100644 correlation/models/shape_searchs/SearchCifarResNet_width.py create mode 100644 correlation/models/shape_searchs/SearchImagenetResNet.py create mode 100644 correlation/models/shape_searchs/SearchSimResNet_width.py create mode 100644 correlation/models/shape_searchs/SoftSelect.py create mode 100644 correlation/models/shape_searchs/__init__.py create mode 100644 correlation/models/shape_searchs/generic_size_tiny_cell_model.py create mode 100644 correlation/result/sss_cf100_meco.p create mode 100644 correlation/result/sss_cf100_meco_opt.p create mode 100644 correlation/result/sss_cf10_meco.p create mode 100644 correlation/result/sss_cf10_meco_opt.p create mode 100644 correlation/result/sss_im120_meco.p create mode 100644 correlation/result/sss_im120_meco_opt.p diff --git a/.idea/MeCo.iml b/.idea/MeCo.iml index d0876a7..0ea713a 100644 --- a/.idea/MeCo.iml +++ b/.idea/MeCo.iml @@ -2,7 +2,7 @@ - + \ No newline at end of file diff --git a/.idea/deployment.xml b/.idea/deployment.xml index bbf356c..073ed50 100644 --- a/.idea/deployment.xml +++ b/.idea/deployment.xml @@ -1,120 +1,372 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -122,5 +374,6 @@ + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml index aa4591c..fe2ec2b 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,7 @@ - + + + \ No newline at end of file diff --git a/correlation/NAS_Bench_201.py b/correlation/NAS_Bench_201.py index 2e5f5ae..9bab538 100644 --- a/correlation/NAS_Bench_201.py +++ b/correlation/NAS_Bench_201.py @@ -11,24 +11,60 @@ from models import get_cell_based_tiny_net import pickle +def get_score(net, x, device, measure='meco'): + result_list = [] + + def forward_hook(module, data_input, data_output): + fea = data_output[0].clone().detach() + n = torch.tensor(fea.shape[0]) + fea = fea.reshape(n, -1) + if measure == 'meco': + corr = torch.corrcoef(fea) + corr[torch.isnan(corr)] = 0 + corr[torch.isinf(corr)] = 0 + values = torch.linalg.eig(corr)[0] + result = torch.min(torch.real(values)) + elif measure == 'meco_opt': + idxs = random.sample(range(n), 8) + fea = fea[idxs, :] + corr = torch.corrcoef(fea) + corr[torch.isnan(corr)] = 0 + corr[torch.isinf(corr)] = 0 + values = torch.linalg.eig(corr)[0] + result = torch.min(torch.real(values)) * n / 8 + result_list.append(result) + for name, modules in net.named_modules(): + modules.register_forward_hook(forward_hook) + x = x.to(device) + net(x) + results = torch.tensor(result_list) + results = results[torch.logical_not(torch.isnan(results))] + results = results[torch.logical_not(torch.isinf(results))] + res = torch.sum(results) + result_list.clear() + + return res.item() + def get_num_classes(args): return 100 if args.dataset == 'cifar100' else 10 if args.dataset == 'cifar10' else 120 def parse_arguments(): parser = argparse.ArgumentParser(description='Zero-cost Metrics for NAS-Bench-201') - parser.add_argument('--api_loc', default='../data/NAS-Bench-201-v1_0-e61699.pth', - type=str, help='path to API') + # parser.add_argument('--api_loc', default='../data/NAS-Bench-201-v1_0-e61699.pth', + # type=str, help='path to API') parser.add_argument('--outdir', default='./', type=str, help='output directory') + parser.add_argument('--search_space', type=str, default='tss', choices=['tss', 'sss']) parser.add_argument('--init_w_type', type=str, default='none', help='weight initialization (before pruning) type [none, xavier, kaiming, zero, one]') parser.add_argument('--init_b_type', type=str, default='none', help='bias initialization (before pruning) type [none, xavier, kaiming, zero, one]') - parser.add_argument('--batch_size', default=64, type=int) - parser.add_argument('--dataset', type=str, default='ImageNet16-120', + parser.add_argument('--measure', type=str, default='meco', choices=['meco', 'meco_opt']) + parser.add_argument('--batch_size', default=1, type=int) + parser.add_argument('--dataset', type=str, default='cifar10', help='dataset to use [cifar10, cifar100, ImageNet16-120]') - parser.add_argument('--gpu', type=int, default=5, help='GPU index to work on') + parser.add_argument('--gpu', type=int, default=0, help='GPU index to work on') parser.add_argument('--data_size', type=int, default=32, help='data_size') parser.add_argument('--num_data_workers', type=int, default=2, help='number of workers for dataloaders') parser.add_argument('--dataload', type=str, default='appoint', help='random, grasp, appoint supported') @@ -49,11 +85,9 @@ if __name__ == '__main__': args = parse_arguments() print(args.device) - if args.noacc: - api = pickle.load(open(args.api_loc,'rb')) - else: - from nas_201_api import NASBench201API as API - api = API(args.api_loc) + from nats_bench import create + + api = create(None, args.search_space, fast_mode=True, verbose=False) torch.manual_seed(args.seed) torch.backends.cudnn.deterministic = True @@ -61,9 +95,6 @@ if __name__ == '__main__': train_loader, val_loader = get_cifar_dataloaders(args.batch_size, args.batch_size, args.dataset, args.num_data_workers, resize=args.data_size) x, y = next(iter(train_loader)) - # random data - # x = torch.rand((args.batch_size, 3, args.data_size, args.data_size)) - # y = 0 cached_res = [] pre = 'cf' if 'cifar' in args.dataset else 'im' @@ -81,7 +112,6 @@ if __name__ == '__main__': break res = {'i': i, 'arch': arch_str} - # print(arch_str) if args.search_space == 'tss': net = nasbench2.get_model_from_arch_str(arch_str, get_num_classes(args)) arch_str2 = nasbench2.get_arch_str_from_model(net) @@ -91,21 +121,22 @@ if __name__ == '__main__': raise ValueError elif args.search_space == 'sss': config = api.get_net_config(i, args.dataset) - # print(config) net = get_cell_based_tiny_net(config) net.to(args.device) - # print(net) init_net(net, args.init_w_type, args.init_b_type) - # print(x.size(), y) - measures = get_score(net, x, i, args.device) + measures = get_score(net, x, args.device, measure=args.measure) - res['meco'] = measures + res[f'{args.measure}'] = measures if not args.noacc: - info = api.get_more_info(i, 'cifar10-valid' if args.dataset == 'cifar10' else args.dataset, iepoch=None, - hp='200', is_random=False) + if args.search_space == 'tss': + info = api.get_more_info(i, 'cifar10-valid' if args.dataset == 'cifar10' else args.dataset, iepoch=None, + hp='200', is_random=False) + else: + info = api.get_more_info(i, 'cifar10-valid' if args.dataset == 'cifar10' else args.dataset, iepoch=None, + hp='90', is_random=False) trainacc = info['train-accuracy'] valacc = info['valid-accuracy'] diff --git a/correlation/compute_rho.py b/correlation/compute_rho.py new file mode 100644 index 0000000..6558891 --- /dev/null +++ b/correlation/compute_rho.py @@ -0,0 +1,24 @@ +import pandas as pd +import pickle + +# path = 'result/sss_cf10_meco.p' +path = 'nb2_sss_cf10_seed42_dlappoint_dlinfo1_initwnone_initbnone_1.p' +meco = [] +accs = [] +with open(path, 'rb') as f: + while True: + try: + fl = pickle.load(f) + meco.append(fl['meco']) + accs.append(fl['testacc']) + except: + break + +N = len(meco) +print(N) +df = pd.DataFrame({ + 'meco':meco[:N], + 'acc': accs[:N] + }) +print(df.corr(method='spearman')) + diff --git a/correlation/foresight/__init__.py b/correlation/foresight/__init__.py new file mode 100644 index 0000000..978f032 --- /dev/null +++ b/correlation/foresight/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +from .version import * diff --git a/correlation/foresight/dataset.py b/correlation/foresight/dataset.py new file mode 100644 index 0000000..cea71ac --- /dev/null +++ b/correlation/foresight/dataset.py @@ -0,0 +1,133 @@ + +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + + +from torchvision.datasets import MNIST, CIFAR10, CIFAR100, SVHN +from torchvision.transforms import Compose, ToTensor, Normalize +from torchvision import transforms +from torch.utils.data import TensorDataset, DataLoader +import torch + +from .imagenet16 import * + + +def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_workers, resize=None, datadir='_dataset'): + # print(dataset) + if 'ImageNet16' in dataset: + mean = [x / 255 for x in [122.68, 116.66, 104.01]] + std = [x / 255 for x in [63.22, 61.26 , 65.09]] + size, pad = 16, 2 + elif 'cifar' in dataset: + mean = (0.4914, 0.4822, 0.4465) + std = (0.2023, 0.1994, 0.2010) + size, pad = 32, 4 + elif 'svhn' in dataset: + mean = (0.5, 0.5, 0.5) + std = (0.5, 0.5, 0.5) + size, pad = 32, 0 + elif dataset == 'ImageNet1k': + from .h5py_dataset import H5Dataset + size,pad = 224,2 + mean = (0.485, 0.456, 0.406) + std = (0.229, 0.224, 0.225) + #resize = 256 + elif 'random' in dataset: + mean = (0.5, 0.5, 0.5) + std = (1, 1, 1) + size, pad = 32, 0 + + if resize is None: + resize = size + + train_transform = transforms.Compose([ + transforms.RandomCrop(size, padding=pad), + transforms.Resize(resize), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean,std), + ]) + + test_transform = transforms.Compose([ + transforms.Resize(resize), + transforms.ToTensor(), + transforms.Normalize(mean,std), + ]) + + if dataset == 'cifar10': + train_dataset = CIFAR10(datadir, True, train_transform, download=True) + test_dataset = CIFAR10(datadir, False, test_transform, download=True) + elif dataset == 'cifar100': + train_dataset = CIFAR100(datadir, True, train_transform, download=True) + test_dataset = CIFAR100(datadir, False, test_transform, download=True) + elif dataset == 'svhn': + train_dataset = SVHN(datadir, split='train', transform=train_transform, download=True) + test_dataset = SVHN(datadir, split='test', transform=test_transform, download=True) + elif dataset == 'ImageNet16-120': + train_dataset = ImageNet16(os.path.join(datadir, 'ImageNet16'), True , train_transform, 120) + test_dataset = ImageNet16(os.path.join(datadir, 'ImageNet16'), False, test_transform , 120) + elif dataset == 'ImageNet1k': + train_dataset = H5Dataset(os.path.join(datadir, 'imagenet-train-256.h5'), transform=train_transform) + test_dataset = H5Dataset(os.path.join(datadir, 'imagenet-val-256.h5'), transform=test_transform) + + else: + raise ValueError('There are no more cifars or imagenets.') + + train_loader = DataLoader( + train_dataset, + train_batch_size, + shuffle=True, + num_workers=num_workers, + pin_memory=True) + test_loader = DataLoader( + test_dataset, + test_batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=True) + + + return train_loader, test_loader + + +def get_mnist_dataloaders(train_batch_size, val_batch_size, num_workers): + + data_transform = Compose([transforms.ToTensor()]) + + # Normalise? transforms.Normalize((0.1307,), (0.3081,)) + + train_dataset = MNIST("_dataset", True, data_transform, download=True) + test_dataset = MNIST("_dataset", False, data_transform, download=True) + + train_loader = DataLoader( + train_dataset, + train_batch_size, + shuffle=True, + num_workers=num_workers, + pin_memory=True) + test_loader = DataLoader( + test_dataset, + val_batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=True) + + return train_loader, test_loader + +if __name__ == '__main__': + tr, te = get_cifar_dataloaders(64, 64, 'random', 2, resize=None, datadir='_dataset') + for x, y in tr: + print(x.size(), y.size()) + break \ No newline at end of file diff --git a/correlation/foresight/h5py_dataset.py b/correlation/foresight/h5py_dataset.py new file mode 100644 index 0000000..63a88fe --- /dev/null +++ b/correlation/foresight/h5py_dataset.py @@ -0,0 +1,55 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import h5py +import numpy as np +from PIL import Image + + +import torch +from torch.utils.data import Dataset, DataLoader + +class H5Dataset(Dataset): + def __init__(self, h5_path, transform=None): + self.h5_path = h5_path + self.h5_file = None + self.length = len(h5py.File(h5_path, 'r')) + self.transform = transform + + def __getitem__(self, index): + + #loading in getitem allows us to use multiple processes for data loading + #because hdf5 files aren't pickelable so can't transfer them across processes + # https://discuss.pytorch.org/t/hdf5-a-data-format-for-pytorch/40379 + # https://discuss.pytorch.org/t/dataloader-when-num-worker-0-there-is-bug/25643/16 + # TODO possible look at __getstate__ and __setstate__ as a more elegant solution + if self.h5_file is None: + self.h5_file = h5py.File(self.h5_path, 'r') + + record = self.h5_file[str(index)] + + if self.transform: + x = Image.fromarray(record['data'][()]) + x = self.transform(x) + else: + x = torch.from_numpy(record['data'][()]) + + y = record['target'][()] + y = torch.from_numpy(np.asarray(y)) + + return (x,y) + + def __len__(self): + return self.length diff --git a/correlation/foresight/imagenet16.py b/correlation/foresight/imagenet16.py new file mode 100644 index 0000000..c2f1f03 --- /dev/null +++ b/correlation/foresight/imagenet16.py @@ -0,0 +1,142 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import hashlib +import os +import sys + +import numpy as np +import torch.utils.data as data +from PIL import Image + +if sys.version_info[0] == 2: + import cPickle as pickle +else: + import pickle + + +def calculate_md5(fpath, chunk_size=1024 * 1024): + md5 = hashlib.md5() + with open(fpath, 'rb') as f: + for chunk in iter(lambda: f.read(chunk_size), b''): + md5.update(chunk) + return md5.hexdigest() + + +def check_md5(fpath, md5, **kwargs): + return md5 == calculate_md5(fpath, **kwargs) + + +def check_integrity(fpath, md5=None): + if not os.path.isfile(fpath): + print(fpath) + return False + if md5 is None: + return True + else: + return check_md5(fpath, md5) + + +class ImageNet16(data.Dataset): + # http://image-net.org/download-images + # A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets + # https://arxiv.org/pdf/1707.08819.pdf + + train_list = [ + ['train_data_batch_1', '27846dcaa50de8e21a7d1a35f30f0e91'], + ['train_data_batch_2', 'c7254a054e0e795c69120a5727050e3f'], + ['train_data_batch_3', '4333d3df2e5ffb114b05d2ffc19b1e87'], + ['train_data_batch_4', '1620cdf193304f4a92677b695d70d10f'], + ['train_data_batch_5', '348b3c2fdbb3940c4e9e834affd3b18d'], + ['train_data_batch_6', '6e765307c242a1b3d7d5ef9139b48945'], + ['train_data_batch_7', '564926d8cbf8fc4818ba23d2faac7564'], + ['train_data_batch_8', 'f4755871f718ccb653440b9dd0ebac66'], + ['train_data_batch_9', 'bb6dd660c38c58552125b1a92f86b5d4'], + ['train_data_batch_10', '8f03f34ac4b42271a294f91bf480f29b'], + ] + valid_list = [ + ['val_data', '3410e3017fdaefba8d5073aaa65e4bd6'], + ] + + def __init__(self, root, train, transform, use_num_of_class_only=None): + self.root = root + self.transform = transform + self.train = train # training set or valid set + if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.') + + if self.train: + downloaded_list = self.train_list + else: + downloaded_list = self.valid_list + self.data = [] + self.targets = [] + + # now load the picked numpy arrays + for i, (file_name, checksum) in enumerate(downloaded_list): + file_path = os.path.join(self.root, file_name) + # print ('Load {:}/{:02d}-th : {:}'.format(i, len(downloaded_list), file_path)) + with open(file_path, 'rb') as f: + if sys.version_info[0] == 2: + entry = pickle.load(f) + else: + entry = pickle.load(f, encoding='latin1') + self.data.append(entry['data']) + self.targets.extend(entry['labels']) + self.data = np.vstack(self.data).reshape(-1, 3, 16, 16) + self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC + if use_num_of_class_only is not None: + assert isinstance(use_num_of_class_only, + int) and use_num_of_class_only > 0 and use_num_of_class_only < 1000, 'invalid use_num_of_class_only : {:}'.format( + use_num_of_class_only) + new_data, new_targets = [], [] + for I, L in zip(self.data, self.targets): + if 1 <= L <= use_num_of_class_only: + new_data.append(I) + new_targets.append(L) + self.data = new_data + self.targets = new_targets + # self.mean.append(entry['mean']) + # self.mean = np.vstack(self.mean).reshape(-1, 3, 16, 16) + # self.mean = np.mean(np.mean(np.mean(self.mean, axis=0), axis=1), axis=1) + # print ('Mean : {:}'.format(self.mean)) + # temp = self.data - np.reshape(self.mean, (1, 1, 1, 3)) + # std_data = np.std(temp, axis=0) + # std_data = np.mean(np.mean(std_data, axis=0), axis=0) + # print ('Std : {:}'.format(std_data)) + + def __getitem__(self, index): + img, target = self.data[index], self.targets[index] - 1 + + img = Image.fromarray(img) + + if self.transform is not None: + img = self.transform(img) + + return img, target + + def __len__(self): + return len(self.data) + + def _check_integrity(self): + root = self.root + for fentry in (self.train_list + self.valid_list): + filename, md5 = fentry[0], fentry[1] + fpath = os.path.join(root, filename) + if not check_integrity(fpath, md5): + return False + return True + + +# +if __name__ == '__main__': + train = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', True, None) + valid = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', False, None) + + print(len(train)) + print(len(valid)) + image, label = train[111] + trainX = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', True, None, 200) + validX = ImageNet16('/data02/dongxuanyi/.torch/cifar.python/ImageNet16', False, None, 200) + print(len(trainX)) + print(len(validX)) + # import pdb; pdb.set_trace() diff --git a/correlation/foresight/models/__init__.py b/correlation/foresight/models/__init__.py new file mode 100644 index 0000000..fb88309 --- /dev/null +++ b/correlation/foresight/models/__init__.py @@ -0,0 +1,19 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +from os.path import dirname, basename, isfile, join +import glob +modules = glob.glob(join(dirname(__file__), "*.py")) +__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')] \ No newline at end of file diff --git a/correlation/foresight/models/nasbench1.py b/correlation/foresight/models/nasbench1.py new file mode 100644 index 0000000..bd6b9ac --- /dev/null +++ b/correlation/foresight/models/nasbench1.py @@ -0,0 +1,251 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +"""Builds the Pytorch computational graph. +Tensors flowing into a single vertex are added together for all vertices +except the output, which is concatenated instead. Tensors flowing out of input +are always added. +If interior edge channels don't match, drop the extra channels (channels are +guaranteed non-decreasing). Tensors flowing out of the input as always +projected instead. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import math + +from .nasbench1_ops import * + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class Network(nn.Module): + def __init__(self, spec, stem_out, num_stacks, num_mods, num_classes, bn=True): + super(Network, self).__init__() + + self.spec=spec + self.stem_out=stem_out + self.num_stacks=num_stacks + self.num_mods=num_mods + self.num_classes=num_classes + + self.layers = nn.ModuleList([]) + + in_channels = 3 + out_channels = stem_out + + # initial stem convolution + stem_conv = ConvBnRelu(in_channels, out_channels, 3, 1, 1, bn=bn) + self.layers.append(stem_conv) + + in_channels = out_channels + for stack_num in range(num_stacks): + if stack_num > 0: + downsample = nn.MaxPool2d(kernel_size=2, stride=2) + self.layers.append(downsample) + + out_channels *= 2 + + for _ in range(num_mods): + cell = Cell(spec, in_channels, out_channels, bn=bn) + self.layers.append(cell) + in_channels = out_channels + + self.classifier = nn.Linear(out_channels, num_classes) + + self._initialize_weights() + + def forward(self, x): + for _, layer in enumerate(self.layers): + x = layer(x) + out = torch.mean(x, (2, 3)) + out = self.classifier(out) + + return out + + def get_prunable_copy(self, bn=False): + + model_new = Network(self.spec, self.stem_out, self.num_stacks, self.num_mods, self.num_classes, bn=bn) + + #TODO this is quite brittle and doesn't work with nn.Sequential when bn is different + # it is only required to maintain initialization -- maybe init after get_punable_copy? + model_new.load_state_dict(self.state_dict(), strict=False) + model_new.train() + + return model_new + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + n = m.weight.size(1) + m.weight.data.normal_(0, 0.01) + m.bias.data.zero_() + +class Cell(nn.Module): + """ + Builds the model using the adjacency matrix and op labels specified. Channels + controls the module output channel count but the interior channels are + determined via equally splitting the channel count whenever there is a + concatenation of Tensors. + """ + def __init__(self, spec, in_channels, out_channels, bn=True): + super(Cell, self).__init__() + + self.spec = spec + self.num_vertices = np.shape(self.spec.matrix)[0] + + # vertex_channels[i] = number of output channels of vertex i + self.vertex_channels = ComputeVertexChannels(in_channels, out_channels, self.spec.matrix) + #self.vertex_channels = [in_channels] + [out_channels] * (self.num_vertices - 1) + + # operation for each node + self.vertex_op = nn.ModuleList([None]) + for t in range(1, self.num_vertices-1): + op = OP_MAP[spec.ops[t]](self.vertex_channels[t], self.vertex_channels[t], bn=bn) + self.vertex_op.append(op) + + # operation for input on each vertex + self.input_op = nn.ModuleList([None]) + for t in range(1, self.num_vertices): + if self.spec.matrix[0, t]: + self.input_op.append(Projection(in_channels, self.vertex_channels[t], bn=bn)) + else: + self.input_op.append(None) + + def forward(self, x): + tensors = [x] + + out_concat = [] + for t in range(1, self.num_vertices-1): + fan_in = [Truncate(tensors[src], self.vertex_channels[t]) for src in range(1, t) if self.spec.matrix[src, t]] + + if self.spec.matrix[0, t]: + fan_in.append(self.input_op[t](x)) + + # perform operation on node + #vertex_input = torch.stack(fan_in, dim=0).sum(dim=0) + vertex_input = sum(fan_in) + #vertex_input = sum(fan_in) / len(fan_in) + vertex_output = self.vertex_op[t](vertex_input) + + tensors.append(vertex_output) + if self.spec.matrix[t, self.num_vertices-1]: + out_concat.append(tensors[t]) + + if not out_concat: + assert self.spec.matrix[0, self.num_vertices-1] + outputs = self.input_op[self.num_vertices-1](tensors[0]) + else: + if len(out_concat) == 1: + outputs = out_concat[0] + else: + outputs = torch.cat(out_concat, 1) + + if self.spec.matrix[0, self.num_vertices-1]: + outputs += self.input_op[self.num_vertices-1](tensors[0]) + + #if self.spec.matrix[0, self.num_vertices-1]: + # out_concat.append(self.input_op[self.num_vertices-1](tensors[0])) + #outputs = sum(out_concat) / len(out_concat) + + return outputs + +def Projection(in_channels, out_channels, bn=True): + """1x1 projection (as in ResNet) followed by batch normalization and ReLU.""" + return ConvBnRelu(in_channels, out_channels, 1, bn=bn) + +def Truncate(inputs, channels): + """Slice the inputs to channels if necessary.""" + input_channels = inputs.size()[1] + if input_channels < channels: + raise ValueError('input channel < output channels for truncate') + elif input_channels == channels: + return inputs # No truncation necessary + else: + # Truncation should only be necessary when channel division leads to + # vertices with +1 channels. The input vertex should always be projected to + # the minimum channel count. + assert input_channels - channels == 1 + return inputs[:, :channels, :, :] + +def ComputeVertexChannels(in_channels, out_channels, matrix): + """Computes the number of channels at every vertex. + Given the input channels and output channels, this calculates the number of + channels at each interior vertex. Interior vertices have the same number of + channels as the max of the channels of the vertices it feeds into. The output + channels are divided amongst the vertices that are directly connected to it. + When the division is not even, some vertices may receive an extra channel to + compensate. + Returns: + list of channel counts, in order of the vertices. + """ + num_vertices = np.shape(matrix)[0] + + vertex_channels = [0] * num_vertices + vertex_channels[0] = in_channels + vertex_channels[num_vertices - 1] = out_channels + + if num_vertices == 2: + # Edge case where module only has input and output vertices + return vertex_channels + + # Compute the in-degree ignoring input, axis 0 is the src vertex and axis 1 is + # the dst vertex. Summing over 0 gives the in-degree count of each vertex. + in_degree = np.sum(matrix[1:], axis=0) + interior_channels = out_channels // in_degree[num_vertices - 1] + correction = out_channels % in_degree[num_vertices - 1] # Remainder to add + + # Set channels of vertices that flow directly to output + for v in range(1, num_vertices - 1): + if matrix[v, num_vertices - 1]: + vertex_channels[v] = interior_channels + if correction: + vertex_channels[v] += 1 + correction -= 1 + + # Set channels for all other vertices to the max of the out edges, going + # backwards. (num_vertices - 2) index skipped because it only connects to + # output. + for v in range(num_vertices - 3, 0, -1): + if not matrix[v, num_vertices - 1]: + for dst in range(v + 1, num_vertices - 1): + if matrix[v, dst]: + vertex_channels[v] = max(vertex_channels[v], vertex_channels[dst]) + assert vertex_channels[v] > 0 + + # Sanity check, verify that channels never increase and final channels add up. + final_fan_in = 0 + for v in range(1, num_vertices - 1): + if matrix[v, num_vertices - 1]: + final_fan_in += vertex_channels[v] + for dst in range(v + 1, num_vertices - 1): + if matrix[v, dst]: + assert vertex_channels[v] >= vertex_channels[dst] + assert final_fan_in == out_channels or num_vertices == 2 + # num_vertices == 2 means only input/output nodes, so 0 fan-in + + return vertex_channels \ No newline at end of file diff --git a/correlation/foresight/models/nasbench1_ops.py b/correlation/foresight/models/nasbench1_ops.py new file mode 100644 index 0000000..60dcfdb --- /dev/null +++ b/correlation/foresight/models/nasbench1_ops.py @@ -0,0 +1,83 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +"""Base operations used by the modules in this search space.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ConvBnRelu(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, bn=True): + super(ConvBnRelu, self).__init__() + + if bn: + self.conv_bn_relu = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=False) + ) + else: + self.conv_bn_relu = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False), + nn.ReLU(inplace=False) + ) + + def forward(self, x): + return self.conv_bn_relu(x) + +class Conv3x3BnRelu(nn.Module): + """3x3 convolution with batch norm and ReLU activation.""" + def __init__(self, in_channels, out_channels, bn=True): + super(Conv3x3BnRelu, self).__init__() + + self.conv3x3 = ConvBnRelu(in_channels, out_channels, 3, 1, 1, bn=bn) + + def forward(self, x): + x = self.conv3x3(x) + return x + +class Conv1x1BnRelu(nn.Module): + """1x1 convolution with batch norm and ReLU activation.""" + def __init__(self, in_channels, out_channels, bn=True): + super(Conv1x1BnRelu, self).__init__() + + self.conv1x1 = ConvBnRelu(in_channels, out_channels, 1, 1, 0, bn=bn) + + def forward(self, x): + x = self.conv1x1(x) + return x + +class MaxPool3x3(nn.Module): + """3x3 max pool with no subsampling.""" + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, bn=None): + super(MaxPool3x3, self).__init__() + + self.maxpool = nn.MaxPool2d(kernel_size, stride, padding) + + def forward(self, x): + x = self.maxpool(x) + return x + +# Commas should not be used in op names +OP_MAP = { + 'conv3x3-bn-relu': Conv3x3BnRelu, + 'conv1x1-bn-relu': Conv1x1BnRelu, + 'maxpool3x3': MaxPool3x3 +} \ No newline at end of file diff --git a/correlation/foresight/models/nasbench1_spec.py b/correlation/foresight/models/nasbench1_spec.py new file mode 100644 index 0000000..a9e8d77 --- /dev/null +++ b/correlation/foresight/models/nasbench1_spec.py @@ -0,0 +1,295 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +"""Model specification for module connectivity individuals. +This module handles pruning the unused parts of the computation graph but should +avoid creating any TensorFlow models (this is done inside model_builder.py). +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import hashlib +import itertools +import numpy as np + + +# Graphviz is optional and only required for visualization. +try: + import graphviz # pylint: disable=g-import-not-at-top +except ImportError: + pass + +def _ToModelSpec(mat, ops): + return ModelSpec(mat, ops) + +def gen_is_edge_fn(bits): + """Generate a boolean function for the edge connectivity. + Given a bitstring FEDCBA and a 4x4 matrix, the generated matrix is + [[0, A, B, D], + [0, 0, C, E], + [0, 0, 0, F], + [0, 0, 0, 0]] + Note that this function is agnostic to the actual matrix dimension due to + order in which elements are filled out (column-major, starting from least + significant bit). For example, the same FEDCBA bitstring (0-padded) on a 5x5 + matrix is + [[0, A, B, D, 0], + [0, 0, C, E, 0], + [0, 0, 0, F, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]] + Args: + bits: integer which will be interpreted as a bit mask. + Returns: + vectorized function that returns True when an edge is present. + """ + def is_edge(x, y): + """Is there an edge from x to y (0-indexed)?""" + if x >= y: + return 0 + # Map x, y to index into bit string + index = x + (y * (y - 1) // 2) + return (bits >> index) % 2 == 1 + + return np.vectorize(is_edge) + + +def is_full_dag(matrix): + """Full DAG == all vertices on a path from vert 0 to (V-1). + i.e. no disconnected or "hanging" vertices. + It is sufficient to check for: + 1) no rows of 0 except for row V-1 (only output vertex has no out-edges) + 2) no cols of 0 except for col 0 (only input vertex has no in-edges) + Args: + matrix: V x V upper-triangular adjacency matrix + Returns: + True if the there are no dangling vertices. + """ + shape = np.shape(matrix) + + rows = matrix[:shape[0]-1, :] == 0 + rows = np.all(rows, axis=1) # Any row with all 0 will be True + rows_bad = np.any(rows) + + cols = matrix[:, 1:] == 0 + cols = np.all(cols, axis=0) # Any col with all 0 will be True + cols_bad = np.any(cols) + + return (not rows_bad) and (not cols_bad) + + +def num_edges(matrix): + """Computes number of edges in adjacency matrix.""" + return np.sum(matrix) + + +def hash_module(matrix, labeling): + """Computes a graph-invariance MD5 hash of the matrix and label pair. + Args: + matrix: np.ndarray square upper-triangular adjacency matrix. + labeling: list of int labels of length equal to both dimensions of + matrix. + Returns: + MD5 hash of the matrix and labeling. + """ + vertices = np.shape(matrix)[0] + in_edges = np.sum(matrix, axis=0).tolist() + out_edges = np.sum(matrix, axis=1).tolist() + + assert len(in_edges) == len(out_edges) == len(labeling) + hashes = list(zip(out_edges, in_edges, labeling)) + hashes = [hashlib.md5(str(h).encode('utf-8')).hexdigest() for h in hashes] + # Computing this up to the diameter is probably sufficient but since the + # operation is fast, it is okay to repeat more times. + for _ in range(vertices): + new_hashes = [] + for v in range(vertices): + in_neighbors = [hashes[w] for w in range(vertices) if matrix[w, v]] + out_neighbors = [hashes[w] for w in range(vertices) if matrix[v, w]] + new_hashes.append(hashlib.md5( + (''.join(sorted(in_neighbors)) + '|' + + ''.join(sorted(out_neighbors)) + '|' + + hashes[v]).encode('utf-8')).hexdigest()) + hashes = new_hashes + fingerprint = hashlib.md5(str(sorted(hashes)).encode('utf-8')).hexdigest() + + return fingerprint + + +def permute_graph(graph, label, permutation): + """Permutes the graph and labels based on permutation. + Args: + graph: np.ndarray adjacency matrix. + label: list of labels of same length as graph dimensions. + permutation: a permutation list of ints of same length as graph dimensions. + Returns: + np.ndarray where vertex permutation[v] is vertex v from the original graph + """ + # vertex permutation[v] in new graph is vertex v in the old graph + forward_perm = zip(permutation, list(range(len(permutation)))) + inverse_perm = [x[1] for x in sorted(forward_perm)] + edge_fn = lambda x, y: graph[inverse_perm[x], inverse_perm[y]] == 1 + new_matrix = np.fromfunction(np.vectorize(edge_fn), + (len(label), len(label)), + dtype=np.int8) + new_label = [label[inverse_perm[i]] for i in range(len(label))] + return new_matrix, new_label + + +def is_isomorphic(graph1, graph2): + """Exhaustively checks if 2 graphs are isomorphic.""" + matrix1, label1 = np.array(graph1[0]), graph1[1] + matrix2, label2 = np.array(graph2[0]), graph2[1] + assert np.shape(matrix1) == np.shape(matrix2) + assert len(label1) == len(label2) + + vertices = np.shape(matrix1)[0] + # Note: input and output in our constrained graphs always map to themselves + # but this script does not enforce that. + for perm in itertools.permutations(range(0, vertices)): + pmatrix1, plabel1 = permute_graph(matrix1, label1, perm) + if np.array_equal(pmatrix1, matrix2) and plabel1 == label2: + return True + + return False + +class ModelSpec(object): + """Model specification given adjacency matrix and labeling.""" + + def __init__(self, matrix, ops, data_format='channels_last'): + """Initialize the module spec. + Args: + matrix: ndarray or nested list with shape [V, V] for the adjacency matrix. + ops: V-length list of labels for the base ops used. The first and last + elements are ignored because they are the input and output vertices + which have no operations. The elements are retained to keep consistent + indexing. + data_format: channels_last or channels_first. + Raises: + ValueError: invalid matrix or ops + """ + if not isinstance(matrix, np.ndarray): + matrix = np.array(matrix) + shape = np.shape(matrix) + if len(shape) != 2 or shape[0] != shape[1]: + raise ValueError('matrix must be square') + if shape[0] != len(ops): + raise ValueError('length of ops must match matrix dimensions') + if not is_upper_triangular(matrix): + raise ValueError('matrix must be upper triangular') + + # Both the original and pruned matrices are deep copies of the matrix and + # ops so any changes to those after initialization are not recognized by the + # spec. + self.original_matrix = copy.deepcopy(matrix) + # print(self.original_matrix) + self.original_ops = copy.deepcopy(ops) + + self.matrix = copy.deepcopy(matrix) + self.ops = copy.deepcopy(ops) + self.valid_spec = True + self._prune() + + self.data_format = data_format + + def _prune(self): + """Prune the extraneous parts of the graph. + General procedure: + 1) Remove parts of graph not connected to input. + 2) Remove parts of graph not connected to output. + 3) Reorder the vertices so that they are consecutive after steps 1 and 2. + These 3 steps can be combined by deleting the rows and columns of the + vertices that are not reachable from both the input and output (in reverse). + """ + num_vertices = np.shape(self.original_matrix)[0] + + # DFS forward from input + visited_from_input = set([0]) + frontier = [0] + while frontier: + top = frontier.pop() + for v in range(top + 1, num_vertices): + if self.original_matrix[top, v] and v not in visited_from_input: + visited_from_input.add(v) + frontier.append(v) + + # DFS backward from output + visited_from_output = set([num_vertices - 1]) + frontier = [num_vertices - 1] + while frontier: + top = frontier.pop() + for v in range(0, top): + if self.original_matrix[v, top] and v not in visited_from_output: + visited_from_output.add(v) + frontier.append(v) + + # Any vertex that isn't connected to both input and output is extraneous to + # the computation graph. + extraneous = set(range(num_vertices)).difference( + visited_from_input.intersection(visited_from_output)) + + # If the non-extraneous graph is less than 2 vertices, the input is not + # connected to the output and the spec is invalid. + if len(extraneous) > num_vertices - 2: + self.matrix = None + self.ops = None + self.valid_spec = False + return + + self.matrix = np.delete(self.matrix, list(extraneous), axis=0) + self.matrix = np.delete(self.matrix, list(extraneous), axis=1) + for index in sorted(extraneous, reverse=True): + del self.ops[index] + + def hash_spec(self, canonical_ops): + """Computes the isomorphism-invariant graph hash of this spec. + Args: + canonical_ops: list of operations in the canonical ordering which they + were assigned (i.e. the order provided in the config['available_ops']). + Returns: + MD5 hash of this spec which can be used to query the dataset. + """ + # Invert the operations back to integer label indices used in graph gen. + labeling = [-1] + [canonical_ops.index(op) for op in self.ops[1:-1]] + [-2] + return graph_util.hash_module(self.matrix, labeling) + + def visualize(self): + """Creates a dot graph. Can be visualized in colab directly.""" + num_vertices = np.shape(self.matrix)[0] + g = graphviz.Digraph() + g.node(str(0), 'input') + for v in range(1, num_vertices - 1): + g.node(str(v), self.ops[v]) + g.node(str(num_vertices - 1), 'output') + + for src in range(num_vertices - 1): + for dst in range(src + 1, num_vertices): + if self.matrix[src, dst]: + g.edge(str(src), str(dst)) + + return g + + +def is_upper_triangular(matrix): + """True if matrix is 0 on diagonal and below.""" + for src in range(np.shape(matrix)[0]): + for dst in range(0, src + 1): + if matrix[src, dst] != 0: + return False + + return True \ No newline at end of file diff --git a/correlation/foresight/models/nasbench2.py b/correlation/foresight/models/nasbench2.py new file mode 100644 index 0000000..819d3e0 --- /dev/null +++ b/correlation/foresight/models/nasbench2.py @@ -0,0 +1,140 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import os +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +from .nasbench2_ops import * + + +def gen_searchcell_mask_from_arch_str(arch_str): + nodes = arch_str.split('+') + nodes = [node[1:-1].split('|') for node in nodes] + nodes = [[op_and_input.split('~') for op_and_input in node] for node in nodes] + + keep_mask = [] + for curr_node_idx in range(len(nodes)): + for prev_node_idx in range(curr_node_idx+1): + _op = [edge[0] for edge in nodes[curr_node_idx] if int(edge[1]) == prev_node_idx] + assert len(_op) == 1, 'The arch string does not follow the assumption of 1 connection between two nodes.' + for _op_name in OPS.keys(): + keep_mask.append(_op[0] == _op_name) + return keep_mask + + +def get_model_from_arch_str(arch_str, num_classes, use_bn=True, init_channels=16): + keep_mask = gen_searchcell_mask_from_arch_str(arch_str) + net = NAS201Model(arch_str=arch_str, num_classes=num_classes, use_bn=use_bn, keep_mask=keep_mask, stem_ch=init_channels) + return net + + +def get_super_model(num_classes, use_bn=True): + net = NAS201Model(arch_str=arch_str, num_classes=num_classes, use_bn=use_bn) + return net + + +class NAS201Model(nn.Module): + + def __init__(self, arch_str, num_classes, use_bn=True, keep_mask=None, stem_ch=16): + super(NAS201Model, self).__init__() + self.arch_str=arch_str + self.num_classes=num_classes + self.use_bn= use_bn + + self.stem = stem(out_channels=stem_ch, use_bn=use_bn) + self.stack_cell1 = nn.Sequential(*[SearchCell(in_channels=stem_ch, out_channels=stem_ch, stride=1, affine=False, track_running_stats=False, use_bn=use_bn, keep_mask=keep_mask) for i in range(5)]) + self.reduction1 = reduction(in_channels=stem_ch, out_channels=stem_ch*2) + self.stack_cell2 = nn.Sequential(*[SearchCell(in_channels=stem_ch*2, out_channels=stem_ch*2, stride=1, affine=False, track_running_stats=False, use_bn=use_bn, keep_mask=keep_mask) for i in range(5)]) + self.reduction2 = reduction(in_channels=stem_ch*2, out_channels=stem_ch*4) + self.stack_cell3 = nn.Sequential(*[SearchCell(in_channels=stem_ch*4, out_channels=stem_ch*4, stride=1, affine=False, track_running_stats=False, use_bn=use_bn, keep_mask=keep_mask) for i in range(5)]) + # self.top = top(in_dims=stem_ch*4, num_classes=num_classes, use_bn=use_bn) + self.top = top(in_dims=stem_ch*4, use_bn=use_bn) + self.classifier = nn.Linear(stem_ch*4, num_classes) + self.pre_GAP = nn.Sequential(nn.BatchNorm2d(stem_ch * 4), nn.ReLU(inplace=True)) + + def forward(self, x): + x = self.stem(x) + + x = self.stack_cell1(x) + x = self.reduction1(x) + + x = self.stack_cell2(x) + x = self.reduction2(x) + + x = self.stack_cell3(x) + + x = self.top(x) + x = self.classifier(x) + return x + + def forward_pre_GAP(self, x): + x = self.stem(x) + + x = self.stack_cell1(x) + x = self.reduction1(x) + + x = self.stack_cell2(x) + x = self.reduction2(x) + + x = self.stack_cell3(x) + x = self.pre_GAP(x) + return x + + + + def get_prunable_copy(self, bn=False): + model_new = get_model_from_arch_str(self.arch_str, self.num_classes, use_bn=bn) + + #TODO this is quite brittle and doesn't work with nn.Sequential when bn is different + # it is only required to maintain initialization -- maybe init after get_punable_copy? + model_new.load_state_dict(self.state_dict(), strict=False) + model_new.train() + + return model_new + + +def get_arch_str_from_model(net): + search_cell = net.stack_cell1[0].options + keep_mask = net.stack_cell1[0].keep_mask + num_nodes = net.stack_cell1[0].num_nodes + + nodes = [] + idx = 0 + for curr_node in range(num_nodes -1): + edges = [] + for prev_node in range(curr_node+1): # n-1 prev nodes + for _op_name in OPS.keys(): + if keep_mask[idx]: + edges.append(f'{_op_name}~{prev_node}') + idx += 1 + node_str = '|'.join(edges) + node_str = f'|{node_str}|' + nodes.append(node_str) + arch_str = '+'.join(nodes) + return arch_str + + +if __name__ == "__main__": + arch_str = '|nor_conv_3x3~0|+|none~0|none~1|+|avg_pool_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|' + + n = get_model_from_arch_str(arch_str=arch_str, num_classes=10) + print(n.stack_cell1[0]) + + arch_str2 = get_arch_str_from_model(n) + print(arch_str) + print(arch_str2) + print(f'Are the two arch strings same? {arch_str == arch_str2}') diff --git a/correlation/foresight/models/nasbench2_ops.py b/correlation/foresight/models/nasbench2_ops.py new file mode 100644 index 0000000..e39ee92 --- /dev/null +++ b/correlation/foresight/models/nasbench2_ops.py @@ -0,0 +1,166 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import os +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ReLUConvBN(nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, affine, track_running_stats=True, use_bn=True, name='ReLUConvBN'): + super(ReLUConvBN, self).__init__() + self.name = name + if use_bn: + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine), + nn.BatchNorm2d(out_channels, affine=affine, track_running_stats=track_running_stats) + ) + else: + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine) + ) + + def forward(self, x): + return self.op(x) + +class Identity(nn.Module): + def __init__(self, name='Identity'): + self.name = name + super(Identity, self).__init__() + + def forward(self, x): + return x + +class Zero(nn.Module): + + def __init__(self, stride, name='Zero'): + self.name = name + super(Zero, self).__init__() + self.stride = stride + + def forward(self, x): + if self.stride == 1: + return x.mul(0.) + return x[:,:,::self.stride,::self.stride].mul(0.) + +class POOLING(nn.Module): + def __init__(self, kernel_size, stride, padding, name='POOLING'): + super(POOLING, self).__init__() + self.name = name + self.avgpool = nn.AvgPool2d(kernel_size=kernel_size, stride=1, padding=1, count_include_pad=False) + + def forward(self, x): + return self.avgpool(x) + + +class reduction(nn.Module): + def __init__(self, in_channels, out_channels): + super(reduction, self).__init__() + self.residual = nn.Sequential( + nn.AvgPool2d(kernel_size=2, stride=2, padding=0), + nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False)) + + self.conv_a = ReLUConvBN(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1, dilation=1, affine=True, track_running_stats=True) + self.conv_b = ReLUConvBN(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, dilation=1, affine=True, track_running_stats=True) + + def forward(self, x): + basicblock = self.conv_a(x) + basicblock = self.conv_b(basicblock) + residual = self.residual(x) + return residual + basicblock + +class stem(nn.Module): + def __init__(self, out_channels, use_bn=True): + super(stem, self).__init__() + if use_bn: + self.net = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels)) + else: + self.net = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False) + ) + + def forward(self, x): + return self.net(x) + +class top(nn.Module): + # def __init__(self, in_dims, num_classes, use_bn=True): + def __init__(self, in_dims, use_bn=True): + super(top, self).__init__() + if use_bn: + self.lastact = nn.Sequential(nn.BatchNorm2d(in_dims), nn.ReLU(inplace=True)) + else: + self.lastact = nn.ReLU(inplace=True) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + # self.classifier = nn.Linear(in_dims, num_classes) + + def forward(self, x): + x = self.lastact(x) + x = self.global_pooling(x) + x = x.view(x.size(0), -1) + # logits = self.classifier(x) + # return logits + return x + + +class SearchCell(nn.Module): + + def __init__(self, in_channels, out_channels, stride, affine, track_running_stats, use_bn=True, num_nodes=4, keep_mask=None): + super(SearchCell, self).__init__() + self.num_nodes = num_nodes + self.options = nn.ModuleList() + for curr_node in range(self.num_nodes-1): + for prev_node in range(curr_node+1): + for _op_name in OPS.keys(): + op = OPS[_op_name](in_channels, out_channels, stride, affine, track_running_stats, use_bn) + self.options.append(op) + + if keep_mask is not None: + self.keep_mask = keep_mask + else: + self.keep_mask = [True]*len(self.options) + + def forward(self, x): + outs = [x] + + idx = 0 + for curr_node in range(self.num_nodes-1): + edges_in = [] + for prev_node in range(curr_node+1): # n-1 prev nodes + for op_idx in range(len(OPS.keys())): + if self.keep_mask[idx]: + edges_in.append(self.options[idx](outs[prev_node])) + idx += 1 + node_output = sum(edges_in) + outs.append(node_output) + + return outs[-1] + + + +OPS = { + 'none' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Zero(stride, name='none'), + 'avg_pool_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: POOLING(3, 1, 1, name='avg_pool_3x3'), + 'nor_conv_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 3, 1, 1, 1, affine, track_running_stats, use_bn, name='nor_conv_3x3'), + 'nor_conv_1x1' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 1, 1, 0, 1, affine, track_running_stats, use_bn, name='nor_conv_1x1'), + 'skip_connect' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Identity(name='skip_connect'), +} + + diff --git a/correlation/foresight/pruners/__init__.py b/correlation/foresight/pruners/__init__.py new file mode 100644 index 0000000..fb88309 --- /dev/null +++ b/correlation/foresight/pruners/__init__.py @@ -0,0 +1,19 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +from os.path import dirname, basename, isfile, join +import glob +modules = glob.glob(join(dirname(__file__), "*.py")) +__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')] \ No newline at end of file diff --git a/correlation/foresight/pruners/measures/__init__.py b/correlation/foresight/pruners/measures/__init__.py new file mode 100644 index 0000000..19c677b --- /dev/null +++ b/correlation/foresight/pruners/measures/__init__.py @@ -0,0 +1,69 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + + +available_measures = [] +_measure_impls = {} + + +def measure(name, bn=True, copy_net=True, force_clean=True, **impl_args): + def make_impl(func): + def measure_impl(net_orig, device, *args, **kwargs): + if copy_net: + net = net_orig.get_prunable_copy(bn=bn).to(device) + else: + net = net_orig + ret = func(net, *args, **kwargs, **impl_args) + if copy_net and force_clean: + import gc + import torch + del net + torch.cuda.empty_cache() + gc.collect() + return ret + + global _measure_impls + if name in _measure_impls: + raise KeyError(f'Duplicated measure! {name}') + available_measures.append(name) + _measure_impls[name] = measure_impl + return func + return make_impl + + +def calc_measure(name, net, device, *args, **kwargs): + return _measure_impls[name](net, device, *args, **kwargs) + + +def load_all(): + # from . import grad_norm + # from . import snip + # from . import grasp + # from . import fisher + # from . import jacob_cov + # from . import plain + # from . import synflow + # from . import var + # from . import cor + # from . import norm + from . import meco + # from . import zico + # from . import gradsign + # from . import ntk + # from . import zen + + +# TODO: should we do that by default? +load_all() diff --git a/correlation/foresight/pruners/measures/cor.py b/correlation/foresight/pruners/measures/cor.py new file mode 100644 index 0000000..d94ff24 --- /dev/null +++ b/correlation/foresight/pruners/measures/cor.py @@ -0,0 +1,53 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + def forward_hook(module, data_input, data_output): + corr = np.mean(np.corrcoef(data_input[0].detach().cpu().numpy())) + result_list.append(corr) + net.classifier.register_forward_hook(forward_hook) + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + y = net(x[st:en]) + cor = result_list[0].item() + result_list.clear() + return cor + + + +@measure('cor', bn=True) +def compute_norm(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + try: + cor= get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + cor= np.nan + + return cor diff --git a/correlation/foresight/pruners/measures/cova.py b/correlation/foresight/pruners/measures/cova.py new file mode 100644 index 0000000..da43bfa --- /dev/null +++ b/correlation/foresight/pruners/measures/cova.py @@ -0,0 +1,67 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import copy +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +from torch import nn + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + result_t = [] + def forward_hook(module, data_input, data_output): + s = time.time() + fea = data_output[0].detach().cpu().numpy() + fea = fea.reshape(fea.shape[0], -1) + result = 1 / np.var(np.corrcoef(fea)) + e = time.time() + t = e - s + result_list.append(result) + result_t.append(t) + for name, modules in net.named_modules(): + modules.register_forward_hook(forward_hook) + + + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + y = net(x[st:en]) + results = np.array(result_list) + results = results[np.logical_not(np.isnan(results))] + v = np.sum(results) + t = sum(result_t) + result_list.clear() + result_t.clear() + return v, t + + + +@measure('cova', bn=True) +def compute_cova(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + try: + cova, t = get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + cova, t = np.nan, None + return cova, t diff --git a/correlation/foresight/pruners/measures/fisher.py b/correlation/foresight/pruners/measures/fisher.py new file mode 100644 index 0000000..a19081e --- /dev/null +++ b/correlation/foresight/pruners/measures/fisher.py @@ -0,0 +1,107 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import types + +from . import measure +from ..p_utils import get_layer_metric_array, reshape_elements + + +def fisher_forward_conv2d(self, x): + x = F.conv2d(x, self.weight, self.bias, self.stride, + self.padding, self.dilation, self.groups) + #intercept and store the activations after passing through 'hooked' identity op + self.act = self.dummy(x) + return self.act + +def fisher_forward_linear(self, x): + x = F.linear(x, self.weight, self.bias) + self.act = self.dummy(x) + return self.act + +@measure('fisher', bn=True, mode='channel') +def compute_fisher_per_weight(net, inputs, targets, loss_fn, mode, split_data=1): + + device = inputs.device + + if mode == 'param': + raise ValueError('Fisher pruning does not support parameter pruning.') + + net.train() + all_hooks = [] + for layer in net.modules(): + if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): + #variables/op needed for fisher computation + layer.fisher = None + layer.act = 0. + layer.dummy = nn.Identity() + + #replace forward method of conv/linear + if isinstance(layer, nn.Conv2d): + layer.forward = types.MethodType(fisher_forward_conv2d, layer) + if isinstance(layer, nn.Linear): + layer.forward = types.MethodType(fisher_forward_linear, layer) + + #function to call during backward pass (hooked on identity op at output of layer) + def hook_factory(layer): + def hook(module, grad_input, grad_output): + act = layer.act.detach() + grad = grad_output[0].detach() + if len(act.shape) > 2: + g_nk = torch.sum((act * grad), list(range(2,len(act.shape)))) + else: + g_nk = act * grad + del_k = g_nk.pow(2).mean(0).mul(0.5) + if layer.fisher is None: + layer.fisher = del_k + else: + layer.fisher += del_k + del layer.act #without deleting this, a nasty memory leak occurs! related: https://discuss.pytorch.org/t/memory-leak-when-using-forward-hook-and-backward-hook-simultaneously/27555 + return hook + + #register backward hook on identity fcn to compute fisher info + layer.dummy.register_backward_hook(hook_factory(layer)) + + N = inputs.shape[0] + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + + net.zero_grad() + outputs = net(inputs[st:en]) + loss = loss_fn(outputs, targets[st:en]) + loss.backward() + + # retrieve fisher info + def fisher(layer): + if layer.fisher is not None: + return torch.abs(layer.fisher.detach()) + else: + return torch.zeros(layer.weight.shape[0]) #size=ch + + grads_abs_ch = get_layer_metric_array(net, fisher, mode) + + #broadcast channel value here to all parameters in that channel + #to be compatible with stuff downstream (which expects per-parameter metrics) + #TODO cleanup on the selectors/apply_prune_mask side (?) + shapes = get_layer_metric_array(net, lambda l : l.weight.shape[1:], mode) + + grads_abs = reshape_elements(grads_abs_ch, shapes, device) + + return grads_abs diff --git a/correlation/foresight/pruners/measures/grad_norm.py b/correlation/foresight/pruners/measures/grad_norm.py new file mode 100644 index 0000000..9b8c9fc --- /dev/null +++ b/correlation/foresight/pruners/measures/grad_norm.py @@ -0,0 +1,38 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import torch.nn.functional as F + +import copy + +from . import measure +from ..p_utils import get_layer_metric_array + +@measure('grad_norm', bn=True) +def get_grad_norm_arr(net, inputs, targets, loss_fn, split_data=1, skip_grad=False): + net.zero_grad() + N = inputs.shape[0] + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + + outputs = net.forward(inputs[st:en]) + loss = loss_fn(outputs, targets[st:en]) + loss.backward() + + grad_norm_arr = get_layer_metric_array(net, lambda l: l.weight.grad.norm() if l.weight.grad is not None else torch.zeros_like(l.weight), mode='param') + + return grad_norm_arr diff --git a/correlation/foresight/pruners/measures/gradsign.py b/correlation/foresight/pruners/measures/gradsign.py new file mode 100644 index 0000000..9d7e1f1 --- /dev/null +++ b/correlation/foresight/pruners/measures/gradsign.py @@ -0,0 +1,76 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +from torch import nn +import numpy as np + +from . import measure + + +def get_flattened_metric(net, metric): + grad_list = [] + for layer in net.modules(): + if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): + grad_list.append(metric(layer).flatten()) + flattened_grad = np.concatenate(grad_list) + + return flattened_grad + + +def get_grad_conflict(net, inputs, targets, loss_fn): + N = inputs.shape[0] + batch_grad = [] + for i in range(N): + net.zero_grad() + outputs = net.forward(inputs[[i]]) + loss = loss_fn(outputs, targets[[i]]) + loss.backward() + flattened_grad = get_flattened_metric(net, lambda + l: l.weight.grad.data.clone().cpu().numpy() if l.weight.grad is not None else torch.zeros_like( + l.weight).clone().cpu().numpy()) + batch_grad.append(flattened_grad) + batch_grad = np.stack(batch_grad) + direction_code = np.sign(batch_grad) + direction_code = abs(direction_code.sum(axis=0)) + score = np.nansum(direction_code) + return score + + +def get_gradsign(input, target, net, device, loss_fn): + s = [] + net = net.to(device) + x, target = input, target + # x2 = torch.clone(x) + # x2 = x2.to(device) + x, target = x.to(device), target.to(device) + s.append(get_grad_conflict(net=net, inputs=x, targets=target, loss_fn=loss_fn)) + s = np.mean(s) + return s + +@measure('gradsign', bn=True) +def compute_gradsign(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + + try: + gradsign = get_gradsign(inputs, targets, net, device, loss_fn) + except Exception as e: + print(e) + gradsign= np.nan + + return gradsign diff --git a/correlation/foresight/pruners/measures/grasp.py b/correlation/foresight/pruners/measures/grasp.py new file mode 100644 index 0000000..d36ef62 --- /dev/null +++ b/correlation/foresight/pruners/measures/grasp.py @@ -0,0 +1,87 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.autograd as autograd + +from . import measure +from ..p_utils import get_layer_metric_array + + +@measure('grasp', bn=True, mode='param') +def compute_grasp_per_weight(net, inputs, targets, mode, loss_fn, T=1, num_iters=1, split_data=1): + + # get all applicable weights + weights = [] + for layer in net.modules(): + if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): + weights.append(layer.weight) + layer.weight.requires_grad_(True) # TODO isn't this already true? + + # NOTE original code had some input/target splitting into 2 + # I am guessing this was because of GPU mem limit + net.zero_grad() + N = inputs.shape[0] + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + + #forward/grad pass #1 + grad_w = None + for _ in range(num_iters): + #TODO get new data, otherwise num_iters is useless! + outputs = net.forward(inputs[st:en])/T + loss = loss_fn(outputs, targets[st:en]) + grad_w_p = autograd.grad(loss, weights, allow_unused=True) + if grad_w is None: + grad_w = list(grad_w_p) + else: + for idx in range(len(grad_w)): + grad_w[idx] += grad_w_p[idx] + + + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + + # forward/grad pass #2 + outputs = net.forward(inputs[st:en])/T + loss = loss_fn(outputs, targets[st:en]) + grad_f = autograd.grad(loss, weights, create_graph=True, allow_unused=True) + + # accumulate gradients computed in previous step and call backwards + z, count = 0,0 + for layer in net.modules(): + if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): + if grad_w[count] is not None: + z += (grad_w[count].data * grad_f[count]).sum() + count += 1 + z.backward() + + # compute final sensitivity metric and put in grads + def grasp(layer): + if layer.weight.grad is not None: + return -layer.weight.data * layer.weight.grad # -theta_q Hg + #NOTE in the grasp code they take the *bottom* (1-p)% of values + #but we take the *top* (1-p)%, therefore we remove the -ve sign + #EDIT accuracy seems to be negatively correlated with this metric, so we add -ve sign here! + else: + return torch.zeros_like(layer.weight) + + grads = get_layer_metric_array(net, grasp, mode) + + return grads \ No newline at end of file diff --git a/correlation/foresight/pruners/measures/jacob_cov.py b/correlation/foresight/pruners/measures/jacob_cov.py new file mode 100644 index 0000000..9f4835e --- /dev/null +++ b/correlation/foresight/pruners/measures/jacob_cov.py @@ -0,0 +1,57 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import numpy as np + +from . import measure + + +def get_batch_jacobian(net, x, target, device, split_data): + x.requires_grad_(True) + + N = x.shape[0] + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + y = net(x[st:en]) + y.backward(torch.ones_like(y)) + + jacob = x.grad.detach() + x.requires_grad_(False) + return jacob, target.detach() + +def eval_score(jacob, labels=None): + corrs = np.corrcoef(jacob) + v, _ = np.linalg.eig(corrs) + k = 1e-5 + return -np.sum(np.log(v + k) + 1./(v + k)) + +@measure('jacob_cov', bn=True) +def compute_jacob_cov(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + jacobs, labels = get_batch_jacobian(net, inputs, targets, device, split_data=split_data) + jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy() + + try: + jc = eval_score(jacobs, labels) + except Exception as e: + print(e) + jc = np.nan + + return jc diff --git a/correlation/foresight/pruners/measures/l2_norm.py b/correlation/foresight/pruners/measures/l2_norm.py new file mode 100644 index 0000000..564c778 --- /dev/null +++ b/correlation/foresight/pruners/measures/l2_norm.py @@ -0,0 +1,22 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +from . import measure +from ..p_utils import get_layer_metric_array + + +@measure('l2_norm', copy_net=False, mode='param') +def get_l2_norm_array(net, inputs, targets, mode, split_data=1): + return get_layer_metric_array(net, lambda l: l.weight.norm(), mode=mode) diff --git a/correlation/foresight/pruners/measures/mean.py b/correlation/foresight/pruners/measures/mean.py new file mode 100644 index 0000000..cbb48dc --- /dev/null +++ b/correlation/foresight/pruners/measures/mean.py @@ -0,0 +1,63 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + def forward_hook(module, data_input, data_output): + s = time.time() + mean = torch.mean(data_input[0]) + e = time.time() + t = e - s + result_list.append(mean) + result_list.append(t) + net.classifier.register_forward_hook(forward_hook) + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + # t1 = time.time() + y = net(x[st:en]) + # t2 = time.time() + # print('var:', t2-t1) + m = result_list[0].item() + t = result_list[1] + result_list.clear() + return m, t + + + +@measure('mean', bn=True) +def compute_mean(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + # print('var:', features.shape) + try: + mean, t = get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + mean, t = np.nan, None + # print(jc) + # print(f'var time: {t} s') + return mean, t diff --git a/correlation/foresight/pruners/measures/meco.py b/correlation/foresight/pruners/measures/meco.py new file mode 100644 index 0000000..9df5ca5 --- /dev/null +++ b/correlation/foresight/pruners/measures/meco.py @@ -0,0 +1,73 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import copy +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch +from torch import nn + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + x = torch.randn(size=(1, 3, 64, 64)).to(device) + net.to(device) + def forward_hook(module, data_input, data_output): + + fea = data_output[0].detach() + fea = fea.reshape(fea.shape[0], -1) + n = fea.shape[0] + corr = torch.corrcoef(fea) + corr[torch.isnan(corr)] = 0 + corr[torch.isinf(corr)] = 0 + values = torch.linalg.eig(corr)[0] + # result = np.real(np.min(values)) / np.real(np.max(values)) + result = torch.min(torch.real(values)) + result_list.append(result) + + for name, modules in net.named_modules(): + modules.register_forward_hook(forward_hook) + + + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + y = net(x[st:en]) + # break + results = torch.tensor(result_list) + results = results[torch.logical_not(torch.isnan(results))] + v = torch.sum(results) + result_list.clear() + return v.item() + + + +@measure('meco', bn=True) +def compute_meco(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + + try: + meco = get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + meco = np.nan, None + return meco diff --git a/correlation/foresight/pruners/measures/norm.py b/correlation/foresight/pruners/measures/norm.py new file mode 100644 index 0000000..19d7e78 --- /dev/null +++ b/correlation/foresight/pruners/measures/norm.py @@ -0,0 +1,55 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + def forward_hook(module, data_input, data_output): + norm = torch.norm(data_input[0]) + result_list.append(norm) + net.classifier.register_forward_hook(forward_hook) + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + y = net(x[st:en]) + n = result_list[0].item() + result_list.clear() + return n + + + +@measure('norm', bn=True) +def compute_norm(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + # print('var:', feature.shape) + try: + norm, t = get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + norm, t = np.nan, None + # print(jc) + # print(f'norm time: {t} s') + return norm, t diff --git a/correlation/foresight/pruners/measures/ntk.py b/correlation/foresight/pruners/measures/ntk.py new file mode 100644 index 0000000..103d386 --- /dev/null +++ b/correlation/foresight/pruners/measures/ntk.py @@ -0,0 +1,94 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import numpy as np + +from . import measure + + +def recal_bn(network, inputs, targets, recalbn, device): + for m in network.modules(): + if isinstance(m, torch.nn.BatchNorm2d): + m.running_mean.data.fill_(0) + m.running_var.data.fill_(0) + m.num_batches_tracked.data.zero_() + m.momentum = None + network.train() + with torch.no_grad(): + for i, (inputs, targets) in enumerate(zip(inputs, targets)): + if i >= recalbn: break + inputs = inputs.cuda(device=device, non_blocking=True) + _, _ = network(inputs) + return network + + +def get_ntk_n(inputs, targets, network, device, recalbn=0, train_mode=False, num_batch=1): + device = device + # if recalbn > 0: + # network = recal_bn(network, xloader, recalbn, device) + # if network_2 is not None: + # network_2 = recal_bn(network_2, xloader, recalbn, device) + network.eval() + networks = [] + networks.append(network) + ntks = [] + # if train_mode: + # networks.train() + # else: + # networks.eval() + ###### + grads = [[] for _ in range(len(networks))] + for i in range(num_batch): + if num_batch > 0 and i >= num_batch: break + inputs = inputs.cuda(device=device, non_blocking=True) + for net_idx, network in enumerate(networks): + network.zero_grad() + # print(inputs.size()) + inputs_ = inputs.clone().cuda(device=device, non_blocking=True) + logit = network(inputs_) + if isinstance(logit, tuple): + logit = logit[1] # 201 networks: return features and logits + for _idx in range(len(inputs_)): + logit[_idx:_idx + 1].backward(torch.ones_like(logit[_idx:_idx + 1]), retain_graph=True) + grad = [] + for name, W in network.named_parameters(): + if 'weight' in name and W.grad is not None: + grad.append(W.grad.view(-1).detach()) + grads[net_idx].append(torch.cat(grad, -1)) + network.zero_grad() + torch.cuda.empty_cache() + ###### + grads = [torch.stack(_grads, 0) for _grads in grads] + ntks = [torch.einsum('nc,mc->nm', [_grads, _grads]) for _grads in grads] + for ntk in ntks: + eigenvalues, _ = torch.linalg.eigh(ntk) # ascending + conds = np.nan_to_num((eigenvalues[-1] / eigenvalues[0]).item(), copy=True, nan=100000.0) + return conds + +@measure('ntk', bn=True) +def compute_ntk(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + + try: + conds = get_ntk_n(inputs, targets, net, device) + except Exception as e: + print(e) + conds= np.nan + + return conds diff --git a/correlation/foresight/pruners/measures/param_count.py b/correlation/foresight/pruners/measures/param_count.py new file mode 100644 index 0000000..10a880e --- /dev/null +++ b/correlation/foresight/pruners/measures/param_count.py @@ -0,0 +1,16 @@ +import time +import torch + +from . import measure +from ..p_utils import get_layer_metric_array + + + +@measure('param_count', copy_net=False, mode='param') +def get_param_count_array(net, inputs, targets, mode, loss_fn, split_data=1): + s = time.time() + count = get_layer_metric_array(net, lambda l: torch.tensor(sum(p.numel() for p in l.parameters() if p.requires_grad)), mode=mode) + e = time.time() + t = e - s + # print(f'param_count time: {t} s') + return count, t \ No newline at end of file diff --git a/correlation/foresight/pruners/measures/pearson.py b/correlation/foresight/pruners/measures/pearson.py new file mode 100644 index 0000000..5270d3f --- /dev/null +++ b/correlation/foresight/pruners/measures/pearson.py @@ -0,0 +1,71 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import copy +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +from torch import nn +# import pandas as pd + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + result_t = [] + def forward_hook(module, data_input, data_output): + s = time.time() + fea = data_output[0].detach().cpu().numpy() + fea = fea.reshape(fea.shape[0], -1) + # result = 1 / np.var(np.corrcoef(fea)) + result = np.var(np.corrcoef(fea)) + e = time.time() + t = e - s + result_list.append(result) + result_t.append(t) + + for name, modules in net.named_modules(): + modules.register_forward_hook(forward_hook) + + + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + y = net(x[st:en]) + # print(y) + results = np.array(result_list) + results = results[np.logical_not(np.isnan(results))] + v = np.sum(results) + t = sum(result_t) + result_list.clear() + result_t.clear() + return v, t + + + +@measure('pearson', bn=True) +def compute_pearson(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + try: + pearson, t = get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + pearson, t = np.nan, None + return pearson, t diff --git a/correlation/foresight/pruners/measures/plain.py b/correlation/foresight/pruners/measures/plain.py new file mode 100644 index 0000000..d35897d --- /dev/null +++ b/correlation/foresight/pruners/measures/plain.py @@ -0,0 +1,44 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import torch.nn.functional as F + +from . import measure +from ..p_utils import get_layer_metric_array + + +@measure('plain', bn=True, mode='param') +def compute_plain_per_weight(net, inputs, targets, mode, loss_fn, split_data=1): + + net.zero_grad() + N = inputs.shape[0] + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + + outputs = net.forward(inputs[st:en]) + loss = loss_fn(outputs, targets[st:en]) + loss.backward() + + # select the gradients that we want to use for search/prune + def plain(layer): + if layer.weight.grad is not None: + return layer.weight.grad * layer.weight + else: + return torch.zeros_like(layer.weight) + + grads_abs = get_layer_metric_array(net, plain, mode) + return grads_abs diff --git a/correlation/foresight/pruners/measures/snip.py b/correlation/foresight/pruners/measures/snip.py new file mode 100644 index 0000000..b3df43c --- /dev/null +++ b/correlation/foresight/pruners/measures/snip.py @@ -0,0 +1,69 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import copy +import types + +from . import measure +from ..p_utils import get_layer_metric_array + + +def snip_forward_conv2d(self, x): + return F.conv2d(x, self.weight * self.weight_mask, self.bias, + self.stride, self.padding, self.dilation, self.groups) + +def snip_forward_linear(self, x): + return F.linear(x, self.weight * self.weight_mask, self.bias) + +@measure('snip', bn=True, mode='param') +def compute_snip_per_weight(net, inputs, targets, mode, loss_fn, split_data=1): + for layer in net.modules(): + if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): + layer.weight_mask = nn.Parameter(torch.ones_like(layer.weight)) + layer.weight.requires_grad = False + + # Override the forward methods: + if isinstance(layer, nn.Conv2d): + layer.forward = types.MethodType(snip_forward_conv2d, layer) + + if isinstance(layer, nn.Linear): + layer.forward = types.MethodType(snip_forward_linear, layer) + + # Compute gradients (but don't apply them) + net.zero_grad() + N = inputs.shape[0] + for sp in range(split_data): + st=sp*N//split_data + en=(sp+1)*N//split_data + + outputs = net.forward(inputs[st:en]) + loss = loss_fn(outputs, targets[st:en]) + loss.backward() + + # select the gradients that we want to use for search/prune + def snip(layer): + if layer.weight_mask.grad is not None: + return torch.abs(layer.weight_mask.grad) + else: + return torch.zeros_like(layer.weight) + + grads_abs = get_layer_metric_array(net, snip, mode) + + return grads_abs diff --git a/correlation/foresight/pruners/measures/synflow.py b/correlation/foresight/pruners/measures/synflow.py new file mode 100644 index 0000000..c8153fb --- /dev/null +++ b/correlation/foresight/pruners/measures/synflow.py @@ -0,0 +1,69 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch + +from . import measure +from ..p_utils import get_layer_metric_array + + +@measure('synflow', bn=False, mode='param') +@measure('synflow_bn', bn=True, mode='param') +def compute_synflow_per_weight(net, inputs, targets, mode, split_data=1, loss_fn=None): + + device = inputs.device + + #convert params to their abs. Keep sign for converting it back. + @torch.no_grad() + def linearize(net): + signs = {} + for name, param in net.state_dict().items(): + signs[name] = torch.sign(param) + param.abs_() + return signs + + #convert to orig values + @torch.no_grad() + def nonlinearize(net, signs): + for name, param in net.state_dict().items(): + if 'weight_mask' not in name: + param.mul_(signs[name]) + + # keep signs of all params + signs = linearize(net) + + # Compute gradients with input of 1s + net.zero_grad() + net.double() + input_dim = list(inputs[0,:].shape) + inputs = torch.ones([1] + input_dim).double().to(device) + output = net.forward(inputs) + torch.sum(output).backward() + + # select the gradients that we want to use for search/prune + def synflow(layer): + if layer.weight.grad is not None: + return torch.abs(layer.weight * layer.weight.grad) + else: + return torch.zeros_like(layer.weight) + + grads_abs = get_layer_metric_array(net, synflow, mode) + + # apply signs of all params + nonlinearize(net, signs) + + return grads_abs + + diff --git a/correlation/foresight/pruners/measures/var.py b/correlation/foresight/pruners/measures/var.py new file mode 100644 index 0000000..17b99d4 --- /dev/null +++ b/correlation/foresight/pruners/measures/var.py @@ -0,0 +1,55 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch + +from . import measure + + +def get_score(net, x, target, device, split_data): + result_list = [] + def forward_hook(module, data_input, data_output): + var = torch.var(data_input[0]) + result_list.append(var) + net.classifier.register_forward_hook(forward_hook) + + N = x.shape[0] + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + y = net(x[st:en]) + v = result_list[0].item() + result_list.clear() + return v + + + +@measure('var', bn=True) +def compute_var(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + # print('var:', feature.shape) + try: + var= get_score(net, inputs, targets, device, split_data=split_data) + except Exception as e: + print(e) + var= np.nan + # print(jc) + # print(f'var time: {t} s') + return var \ No newline at end of file diff --git a/correlation/foresight/pruners/measures/zen.py b/correlation/foresight/pruners/measures/zen.py new file mode 100644 index 0000000..8b6e64e --- /dev/null +++ b/correlation/foresight/pruners/measures/zen.py @@ -0,0 +1,110 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +from torch import nn +import numpy as np + +from . import measure + + +def network_weight_gaussian_init(net: nn.Module): + with torch.no_grad(): + for n, m in net.named_modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight) + if hasattr(m, 'bias') and m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + try: + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + except: + pass + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight) + if hasattr(m, 'bias') and m.bias is not None: + nn.init.zeros_(m.bias) + else: + continue + + return net + + +def get_zen(gpu, model, mixup_gamma=1e-2, resolution=32, batch_size=64, repeat=32, + fp16=False): + info = {} + nas_score_list = [] + if gpu is not None: + device = torch.device(gpu) + else: + device = torch.device('cpu') + + if fp16: + dtype = torch.half + else: + dtype = torch.float32 + + with torch.no_grad(): + for repeat_count in range(repeat): + network_weight_gaussian_init(model) + input = torch.randn(size=[batch_size, 3, resolution, resolution], device=device, dtype=dtype) + input2 = torch.randn(size=[batch_size, 3, resolution, resolution], device=device, dtype=dtype) + mixup_input = input + mixup_gamma * input2 + output = model.forward_pre_GAP(input) + mixup_output = model.forward_pre_GAP(mixup_input) + + nas_score = torch.sum(torch.abs(output - mixup_output), dim=[1, 2, 3]) + nas_score = torch.mean(nas_score) + + # compute BN scaling + log_bn_scaling_factor = 0.0 + for m in model.modules(): + if isinstance(m, nn.BatchNorm2d): + try: + bn_scaling_factor = torch.sqrt(torch.mean(m.running_var)) + log_bn_scaling_factor += torch.log(bn_scaling_factor) + except: + pass + pass + pass + nas_score = torch.log(nas_score) + log_bn_scaling_factor + nas_score_list.append(float(nas_score)) + + std_nas_score = np.std(nas_score_list) + avg_precision = 1.96 * std_nas_score / np.sqrt(len(nas_score_list)) + avg_nas_score = np.mean(nas_score_list) + + info = float(avg_nas_score) + return info + + + + + +@measure('zen', bn=True) +def compute_zen(net, inputs, targets, split_data=1, loss_fn=None): + device = inputs.device + # Compute gradients (but don't apply them) + net.zero_grad() + + + try: + zen = get_zen(device,net) + except Exception as e: + print(e) + zen= np.nan + + return zen diff --git a/correlation/foresight/pruners/measures/zico.py b/correlation/foresight/pruners/measures/zico.py new file mode 100644 index 0000000..8af2ffa --- /dev/null +++ b/correlation/foresight/pruners/measures/zico.py @@ -0,0 +1,106 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +import time + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch + +from . import measure +from torch import nn + +from ...dataset import get_cifar_dataloaders + + +def getgrad(model: torch.nn.Module, grad_dict: dict, step_iter=0): + if step_iter == 0: + for name, mod in model.named_modules(): + if isinstance(mod, nn.Conv2d) or isinstance(mod, nn.Linear): + # print(mod.weight.grad.data.size()) + # print(mod.weight.data.size()) + try: + grad_dict[name] = [mod.weight.grad.data.cpu().reshape(-1).numpy()] + except: + continue + else: + for name, mod in model.named_modules(): + if isinstance(mod, nn.Conv2d) or isinstance(mod, nn.Linear): + try: + grad_dict[name].append(mod.weight.grad.data.cpu().reshape(-1).numpy()) + except: + continue + return grad_dict + + +def caculate_zico(grad_dict): + allgrad_array = None + for i, modname in enumerate(grad_dict.keys()): + grad_dict[modname] = np.array(grad_dict[modname]) + nsr_mean_sum = 0 + nsr_mean_sum_abs = 0 + nsr_mean_avg = 0 + nsr_mean_avg_abs = 0 + for j, modname in enumerate(grad_dict.keys()): + nsr_std = np.std(grad_dict[modname], axis=0) + # print(grad_dict[modname].shape) + # print(grad_dict[modname].shape, nsr_std.shape) + nonzero_idx = np.nonzero(nsr_std)[0] + nsr_mean_abs = np.mean(np.abs(grad_dict[modname]), axis=0) + tmpsum = np.sum(nsr_mean_abs[nonzero_idx] / nsr_std[nonzero_idx]) + if tmpsum == 0: + pass + else: + nsr_mean_sum_abs += np.log(tmpsum) + nsr_mean_avg_abs += np.log(np.mean(nsr_mean_abs[nonzero_idx] / nsr_std[nonzero_idx])) + return nsr_mean_sum_abs + + +def getzico(network, inputs, targets, loss_fn, split_data=2): + grad_dict = {} + network.train() + device = inputs.device + network.to(device) + N = inputs.shape[0] + split_data = 2 + + for sp in range(split_data): + st = sp * N // split_data + en = (sp + 1) * N // split_data + outputs = network.forward(inputs[st:en]) + loss = loss_fn(outputs, targets[st:en]) + loss.backward() + grad_dict = getgrad(network, grad_dict, sp) + # print(grad_dict) + res = caculate_zico(grad_dict) + return res + + + + + +@measure('zico', bn=True) +def compute_zico(net, inputs, targets, split_data=2, loss_fn=None): + + # Compute gradients (but don't apply them) + net.zero_grad() + + # print('var:', feature.shape) + try: + zico = getzico(net, inputs, targets, loss_fn, split_data=split_data) + except Exception as e: + print(e) + zico= np.nan + # print(jc) + # print(f'var time: {t} s') + return zico \ No newline at end of file diff --git a/correlation/foresight/pruners/p_utils.py b/correlation/foresight/pruners/p_utils.py new file mode 100644 index 0000000..bf5eb73 --- /dev/null +++ b/correlation/foresight/pruners/p_utils.py @@ -0,0 +1,83 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..models import * + +def get_some_data(train_dataloader, num_batches, device): + traindata = [] + dataloader_iter = iter(train_dataloader) + for _ in range(num_batches): + traindata.append(next(dataloader_iter)) + inputs = torch.cat([a for a,_ in traindata]) + targets = torch.cat([b for _,b in traindata]) + inputs = inputs.to(device) + targets = targets.to(device) + return inputs, targets + +def get_some_data_grasp(train_dataloader, num_classes, samples_per_class, device): + datas = [[] for _ in range(num_classes)] + labels = [[] for _ in range(num_classes)] + mark = dict() + dataloader_iter = iter(train_dataloader) + while True: + inputs, targets = next(dataloader_iter) + for idx in range(inputs.shape[0]): + x, y = inputs[idx:idx+1], targets[idx:idx+1] + category = y.item() + if len(datas[category]) == samples_per_class: + mark[category] = True + continue + datas[category].append(x) + labels[category].append(y) + if len(mark) == num_classes: + break + + x = torch.cat([torch.cat(_, 0) for _ in datas]).to(device) + y = torch.cat([torch.cat(_) for _ in labels]).view(-1).to(device) + return x, y + +def get_layer_metric_array(net, metric, mode): + metric_array = [] + + for layer in net.modules(): + if mode=='channel' and hasattr(layer,'dont_ch_prune'): + continue + if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): + metric_array.append(metric(layer)) + + return metric_array + +def reshape_elements(elements, shapes, device): + def broadcast_val(elements, shapes): + ret_grads = [] + for e,sh in zip(elements, shapes): + ret_grads.append(torch.stack([torch.Tensor(sh).fill_(v) for v in e], dim=0).to(device)) + return ret_grads + if type(elements[0]) == list: + outer = [] + for e,sh in zip(elements, shapes): + outer.append(broadcast_val(e,sh)) + return outer + else: + return broadcast_val(elements, shapes) + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + diff --git a/correlation/foresight/pruners/predictive.py b/correlation/foresight/pruners/predictive.py new file mode 100644 index 0000000..7029637 --- /dev/null +++ b/correlation/foresight/pruners/predictive.py @@ -0,0 +1,116 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .p_utils import * +from . import measures + +import types +import copy + + +def no_op(self,x): + return x + +def copynet(self, bn): + net = copy.deepcopy(self) + if bn==False: + for l in net.modules(): + if isinstance(l,nn.BatchNorm2d) or isinstance(l,nn.BatchNorm1d) : + l.forward = types.MethodType(no_op, l) + return net + +def find_measures_arrays(net_orig, trainloader, dataload_info, device, measure_names=None, loss_fn=F.cross_entropy): + if measure_names is None: + measure_names = measures.available_measures + + dataload, num_imgs_or_batches, num_classes = dataload_info + + if not hasattr(net_orig,'get_prunable_copy'): + net_orig.get_prunable_copy = types.MethodType(copynet, net_orig) + + #move to cpu to free up mem + torch.cuda.empty_cache() + net_orig = net_orig.cpu() + torch.cuda.empty_cache() + + #given 1 minibatch of data + if dataload == 'random': + inputs, targets = get_some_data(trainloader, num_batches=num_imgs_or_batches, device=device) + elif dataload == 'grasp': + inputs, targets = get_some_data_grasp(trainloader, num_classes, samples_per_class=num_imgs_or_batches, device=device) + else: + raise NotImplementedError(f'dataload {dataload} is not supported') + + done, ds = False, 1 + measure_values = {} + + while not done: + try: + for measure_name in measure_names: + if measure_name not in measure_values: + val = measures.calc_measure(measure_name, net_orig, device, inputs, targets, loss_fn=loss_fn, split_data=ds) + measure_values[measure_name] = val + + done = True + except RuntimeError as e: + if 'out of memory' in str(e): + done=False + if ds == inputs.shape[0]//2: + raise ValueError(f'Can\'t split data anymore, but still unable to run. Something is wrong') + ds += 1 + while inputs.shape[0] % ds != 0: + ds += 1 + torch.cuda.empty_cache() + print(f'Caught CUDA OOM, retrying with data split into {ds} parts') + else: + raise e + + net_orig = net_orig.to(device).train() + return measure_values + +def find_measures(net_orig, # neural network + dataloader, # a data loader (typically for training data) + dataload_info, # a tuple with (dataload_type = {random, grasp}, number_of_batches_for_random_or_images_per_class_for_grasp, number of classes) + device, # GPU/CPU device used + loss_fn=F.cross_entropy, # loss function to use within the zero-cost metrics + measure_names=None, # an array of measure names to compute, if left blank, all measures are computed by default + measures_arr=None): # [not used] if the measures are already computed but need to be summarized, pass them here + + #Given a neural net + #and some information about the input data (dataloader) + #and loss function (loss_fn) + #this function returns an array of zero-cost proxy metrics. + + def sum_arr(arr): + sum = 0. + for i in range(len(arr)): + sum += torch.sum(arr[i]) + return sum.item() + + if measures_arr is None: + measures_arr = find_measures_arrays(net_orig, dataloader, dataload_info, device, loss_fn=loss_fn, measure_names=measure_names) + + measures = {} + for k,v in measures_arr.items(): + if k in ['jacob_cov', 'var', 'cor', 'norm', 'meco', 'zico', 'ntk', 'gradsign', 'zen']: + measures[k] = v + else: + measures[k] = sum_arr(v) + + return measures diff --git a/correlation/foresight/version.py b/correlation/foresight/version.py new file mode 100644 index 0000000..05a3e3f --- /dev/null +++ b/correlation/foresight/version.py @@ -0,0 +1,51 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +version = '1.0.0' +repo = 'unknown' +commit = 'unknown' +has_repo = False + +try: + import git + from pathlib import Path + + try: + r = git.Repo(Path(__file__).parents[1]) + has_repo = True + + if not r.remotes: + repo = 'local' + else: + repo = r.remotes.origin.url + + commit = r.head.commit.hexsha + if r.is_dirty(): + commit += ' (dirty)' + except git.InvalidGitRepositoryError: + raise ImportError() +except ImportError: + pass + +try: + from . import _dist_info as info + assert not has_repo, '_dist_info should not exist when repo is in place' + assert version == info.version + repo = info.repo + commit = info.commit +except (ImportError, SystemError): + pass + +__all__ = ['version', 'repo', 'commit', 'has_repo'] diff --git a/correlation/foresight/weight_initializers.py b/correlation/foresight/weight_initializers.py new file mode 100644 index 0000000..52cfb3b --- /dev/null +++ b/correlation/foresight/weight_initializers.py @@ -0,0 +1,84 @@ +# Copyright 2021 Samsung Electronics Co., Ltd. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= + +import torch.nn as nn + +def init_net(net, w_type, b_type): + if w_type == 'none': + pass + elif w_type == 'xavier': + net.apply(init_weights_vs) + elif w_type == 'kaiming': + net.apply(init_weights_he) + elif w_type == 'zero': + net.apply(init_weights_zero) + elif w_type == 'one': + net.apply(init_weights_one) + else: + raise NotImplementedError(f'init_type={w_type} is not supported.') + + if b_type == 'none': + pass + elif b_type == 'xavier': + net.apply(init_bias_vs) + elif b_type == 'kaiming': + net.apply(init_bias_he) + elif b_type == 'zero': + net.apply(init_bias_zero) + elif b_type == 'one': + net.apply(init_bias_one) + else: + raise NotImplementedError(f'init_type={b_type} is not supported.') + + + + +def init_weights_vs(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + nn.init.xavier_normal_(m.weight) + +def init_bias_vs(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + if m.bias is not None: + nn.init.xavier_normal_(m.bias) + +def init_weights_he(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + nn.init.kaiming_normal_(m.weight) + +def init_bias_he(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + if m.bias is not None: + nn.init.kaiming_normal_(m.bias) + +def init_weights_zero(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + m.weight.data.fill_(.0) + +def init_weights_one(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + m.weight.data.fill_(1.) + +def init_bias_zero(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + if m.bias is not None: + m.bias.data.fill_(.0) + + +def init_bias_one(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + if m.bias is not None: + m.bias.data.fill_(1.) + diff --git a/correlation/models/CifarDenseNet.py b/correlation/models/CifarDenseNet.py new file mode 100644 index 0000000..eaf8e98 --- /dev/null +++ b/correlation/models/CifarDenseNet.py @@ -0,0 +1,117 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, torch +import torch.nn as nn +import torch.nn.functional as F +from .initialization import initialize_resnet + + +class Bottleneck(nn.Module): + def __init__(self, nChannels, growthRate): + super(Bottleneck, self).__init__() + interChannels = 4 * growthRate + self.bn1 = nn.BatchNorm2d(nChannels) + self.conv1 = nn.Conv2d(nChannels, interChannels, kernel_size=1, bias=False) + self.bn2 = nn.BatchNorm2d(interChannels) + self.conv2 = nn.Conv2d( + interChannels, growthRate, kernel_size=3, padding=1, bias=False + ) + + def forward(self, x): + out = self.conv1(F.relu(self.bn1(x))) + out = self.conv2(F.relu(self.bn2(out))) + out = torch.cat((x, out), 1) + return out + + +class SingleLayer(nn.Module): + def __init__(self, nChannels, growthRate): + super(SingleLayer, self).__init__() + self.bn1 = nn.BatchNorm2d(nChannels) + self.conv1 = nn.Conv2d( + nChannels, growthRate, kernel_size=3, padding=1, bias=False + ) + + def forward(self, x): + out = self.conv1(F.relu(self.bn1(x))) + out = torch.cat((x, out), 1) + return out + + +class Transition(nn.Module): + def __init__(self, nChannels, nOutChannels): + super(Transition, self).__init__() + self.bn1 = nn.BatchNorm2d(nChannels) + self.conv1 = nn.Conv2d(nChannels, nOutChannels, kernel_size=1, bias=False) + + def forward(self, x): + out = self.conv1(F.relu(self.bn1(x))) + out = F.avg_pool2d(out, 2) + return out + + +class DenseNet(nn.Module): + def __init__(self, growthRate, depth, reduction, nClasses, bottleneck): + super(DenseNet, self).__init__() + + if bottleneck: + nDenseBlocks = int((depth - 4) / 6) + else: + nDenseBlocks = int((depth - 4) / 3) + + self.message = "CifarDenseNet : block : {:}, depth : {:}, reduction : {:}, growth-rate = {:}, class = {:}".format( + "bottleneck" if bottleneck else "basic", + depth, + reduction, + growthRate, + nClasses, + ) + + nChannels = 2 * growthRate + self.conv1 = nn.Conv2d(3, nChannels, kernel_size=3, padding=1, bias=False) + + self.dense1 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck) + nChannels += nDenseBlocks * growthRate + nOutChannels = int(math.floor(nChannels * reduction)) + self.trans1 = Transition(nChannels, nOutChannels) + + nChannels = nOutChannels + self.dense2 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck) + nChannels += nDenseBlocks * growthRate + nOutChannels = int(math.floor(nChannels * reduction)) + self.trans2 = Transition(nChannels, nOutChannels) + + nChannels = nOutChannels + self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck) + nChannels += nDenseBlocks * growthRate + + self.act = nn.Sequential( + nn.BatchNorm2d(nChannels), nn.ReLU(inplace=True), nn.AvgPool2d(8) + ) + self.fc = nn.Linear(nChannels, nClasses) + + self.apply(initialize_resnet) + + def get_message(self): + return self.message + + def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck): + layers = [] + for i in range(int(nDenseBlocks)): + if bottleneck: + layers.append(Bottleneck(nChannels, growthRate)) + else: + layers.append(SingleLayer(nChannels, growthRate)) + nChannels += growthRate + return nn.Sequential(*layers) + + def forward(self, inputs): + out = self.conv1(inputs) + out = self.trans1(self.dense1(out)) + out = self.trans2(self.dense2(out)) + out = self.dense3(out) + features = self.act(out) + features = features.view(features.size(0), -1) + out = self.fc(features) + return features, out diff --git a/correlation/models/CifarResNet.py b/correlation/models/CifarResNet.py new file mode 100644 index 0000000..7ab777f --- /dev/null +++ b/correlation/models/CifarResNet.py @@ -0,0 +1,180 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .initialization import initialize_resnet +from .SharedUtils import additive_func + + +class Downsample(nn.Module): + def __init__(self, nIn, nOut, stride): + super(Downsample, self).__init__() + assert stride == 2 and nOut == 2 * nIn, "stride:{} IO:{},{}".format( + stride, nIn, nOut + ) + self.in_dim = nIn + self.out_dim = nOut + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=1, padding=0, bias=False) + + def forward(self, x): + x = self.avg(x) + out = self.conv(x) + return out + + +class ConvBNReLU(nn.Module): + def __init__(self, nIn, nOut, kernel, stride, padding, bias, relu): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d( + nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, bias=bias + ) + self.bn = nn.BatchNorm2d(nOut) + if relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + self.out_dim = nOut + self.num_conv = 1 + + def forward(self, x): + conv = self.conv(x) + bn = self.bn(conv) + if self.relu: + return self.relu(bn) + else: + return bn + + +class ResNetBasicblock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, True) + self.conv_b = ConvBNReLU(planes, planes, 3, 1, 1, False, False) + if stride == 2: + self.downsample = Downsample(inplanes, planes, stride) + elif inplanes != planes: + self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, False) + else: + self.downsample = None + self.out_dim = planes + self.num_conv = 2 + + def forward(self, inputs): + + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, basicblock) + return F.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_1x1 = ConvBNReLU(inplanes, planes, 1, 1, 0, False, True) + self.conv_3x3 = ConvBNReLU(planes, planes, 3, stride, 1, False, True) + self.conv_1x4 = ConvBNReLU( + planes, planes * self.expansion, 1, 1, 0, False, False + ) + if stride == 2: + self.downsample = Downsample(inplanes, planes * self.expansion, stride) + elif inplanes != planes * self.expansion: + self.downsample = ConvBNReLU( + inplanes, planes * self.expansion, 1, 1, 0, False, False + ) + else: + self.downsample = None + self.out_dim = planes * self.expansion + self.num_conv = 3 + + def forward(self, inputs): + + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, bottleneck) + return F.relu(out, inplace=True) + + +class CifarResNet(nn.Module): + def __init__(self, block_name, depth, num_classes, zero_init_residual): + super(CifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + + self.message = "CifarResNet : Block : {:}, Depth : {:}, Layers for each block : {:}".format( + block_name, depth, layer_blocks + ) + self.num_classes = num_classes + self.channels = [16] + self.layers = nn.ModuleList([ConvBNReLU(3, 16, 3, 1, 1, False, True)]) + for stage in range(3): + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 16 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iC, + module.out_dim, + stride, + ) + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(module.out_dim, num_classes) + assert ( + sum(x.num_conv for x in self.layers) + 1 == depth + ), "invalid depth check {:} vs {:}".format( + sum(x.num_conv for x in self.layers) + 1, depth + ) + + self.apply(initialize_resnet) + if zero_init_residual: + for m in self.modules(): + if isinstance(m, ResNetBasicblock): + nn.init.constant_(m.conv_b.bn.weight, 0) + elif isinstance(m, ResNetBottleneck): + nn.init.constant_(m.conv_1x4.bn.weight, 0) + + def get_message(self): + return self.message + + def forward(self, inputs): + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/CifarWideResNet.py b/correlation/models/CifarWideResNet.py new file mode 100644 index 0000000..62e97c3 --- /dev/null +++ b/correlation/models/CifarWideResNet.py @@ -0,0 +1,115 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .initialization import initialize_resnet + + +class WideBasicblock(nn.Module): + def __init__(self, inplanes, planes, stride, dropout=False): + super(WideBasicblock, self).__init__() + + self.bn_a = nn.BatchNorm2d(inplanes) + self.conv_a = nn.Conv2d( + inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + + self.bn_b = nn.BatchNorm2d(planes) + if dropout: + self.dropout = nn.Dropout2d(p=0.5, inplace=True) + else: + self.dropout = None + self.conv_b = nn.Conv2d( + planes, planes, kernel_size=3, stride=1, padding=1, bias=False + ) + + if inplanes != planes: + self.downsample = nn.Conv2d( + inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False + ) + else: + self.downsample = None + + def forward(self, x): + + basicblock = self.bn_a(x) + basicblock = F.relu(basicblock) + basicblock = self.conv_a(basicblock) + + basicblock = self.bn_b(basicblock) + basicblock = F.relu(basicblock) + if self.dropout is not None: + basicblock = self.dropout(basicblock) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + x = self.downsample(x) + + return x + basicblock + + +class CifarWideResNet(nn.Module): + """ + ResNet optimized for the Cifar dataset, as specified in + https://arxiv.org/abs/1512.03385.pdf + """ + + def __init__(self, depth, widen_factor, num_classes, dropout): + super(CifarWideResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + assert (depth - 4) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 4) // 6 + print( + "CifarPreResNet : Depth : {} , Layers for each block : {}".format( + depth, layer_blocks + ) + ) + + self.num_classes = num_classes + self.dropout = dropout + self.conv_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) + + self.message = "Wide ResNet : depth={:}, widen_factor={:}, class={:}".format( + depth, widen_factor, num_classes + ) + self.inplanes = 16 + self.stage_1 = self._make_layer( + WideBasicblock, 16 * widen_factor, layer_blocks, 1 + ) + self.stage_2 = self._make_layer( + WideBasicblock, 32 * widen_factor, layer_blocks, 2 + ) + self.stage_3 = self._make_layer( + WideBasicblock, 64 * widen_factor, layer_blocks, 2 + ) + self.lastact = nn.Sequential( + nn.BatchNorm2d(64 * widen_factor), nn.ReLU(inplace=True) + ) + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(64 * widen_factor, num_classes) + + self.apply(initialize_resnet) + + def get_message(self): + return self.message + + def _make_layer(self, block, planes, blocks, stride): + + layers = [] + layers.append(block(self.inplanes, planes, stride, self.dropout)) + self.inplanes = planes + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, 1, self.dropout)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv_3x3(x) + x = self.stage_1(x) + x = self.stage_2(x) + x = self.stage_3(x) + x = self.lastact(x) + x = self.avgpool(x) + features = x.view(x.size(0), -1) + outs = self.classifier(features) + return features, outs diff --git a/correlation/models/ImageNet_MobileNetV2.py b/correlation/models/ImageNet_MobileNetV2.py new file mode 100644 index 0000000..814ab39 --- /dev/null +++ b/correlation/models/ImageNet_MobileNetV2.py @@ -0,0 +1,117 @@ +# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018 +from torch import nn +from .initialization import initialize_resnet + + +class ConvBNReLU(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1): + super(ConvBNReLU, self).__init__() + padding = (kernel_size - 1) // 2 + self.conv = nn.Conv2d( + in_planes, + out_planes, + kernel_size, + stride, + padding, + groups=groups, + bias=False, + ) + self.bn = nn.BatchNorm2d(out_planes) + self.relu = nn.ReLU6(inplace=True) + + def forward(self, x): + out = self.conv(x) + out = self.bn(out) + out = self.relu(out) + return out + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = int(round(inp * expand_ratio)) + self.use_res_connect = self.stride == 1 and inp == oup + + layers = [] + if expand_ratio != 1: + # pw + layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) + layers.extend( + [ + # dw + ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ] + ) + self.conv = nn.Sequential(*layers) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV2(nn.Module): + def __init__( + self, num_classes, width_mult, input_channel, last_channel, block_name, dropout + ): + super(MobileNetV2, self).__init__() + if block_name == "InvertedResidual": + block = InvertedResidual + else: + raise ValueError("invalid block name : {:}".format(block_name)) + inverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + [6, 160, 3, 2], + [6, 320, 1, 1], + ] + + # building first layer + input_channel = int(input_channel * width_mult) + self.last_channel = int(last_channel * max(1.0, width_mult)) + features = [ConvBNReLU(3, input_channel, stride=2)] + # building inverted residual blocks + for t, c, n, s in inverted_residual_setting: + output_channel = int(c * width_mult) + for i in range(n): + stride = s if i == 0 else 1 + features.append( + block(input_channel, output_channel, stride, expand_ratio=t) + ) + input_channel = output_channel + # building last several layers + features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1)) + # make it nn.Sequential + self.features = nn.Sequential(*features) + + # building classifier + self.classifier = nn.Sequential( + nn.Dropout(dropout), + nn.Linear(self.last_channel, num_classes), + ) + self.message = "MobileNetV2 : width_mult={:}, in-C={:}, last-C={:}, block={:}, dropout={:}".format( + width_mult, input_channel, last_channel, block_name, dropout + ) + + # weight initialization + self.apply(initialize_resnet) + + def get_message(self): + return self.message + + def forward(self, inputs): + features = self.features(inputs) + vectors = features.mean([2, 3]) + predicts = self.classifier(vectors) + return features, predicts diff --git a/correlation/models/ImageNet_ResNet.py b/correlation/models/ImageNet_ResNet.py new file mode 100644 index 0000000..66d830a --- /dev/null +++ b/correlation/models/ImageNet_ResNet.py @@ -0,0 +1,217 @@ +# Deep Residual Learning for Image Recognition, CVPR 2016 +import torch.nn as nn +from .initialization import initialize_resnet + + +def conv3x3(in_planes, out_planes, stride=1, groups=1): + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=1, + groups=groups, + bias=False, + ) + + +def conv1x1(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__( + self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64 + ): + super(BasicBlock, self).__init__() + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__( + self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64 + ): + super(Bottleneck, self).__init__() + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = nn.BatchNorm2d(width) + self.conv2 = conv3x3(width, width, stride, groups) + self.bn2 = nn.BatchNorm2d(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block_name, + layers, + deep_stem, + num_classes, + zero_init_residual, + groups, + width_per_group, + ): + super(ResNet, self).__init__() + + # planes = [int(width_per_group * groups * 2 ** i) for i in range(4)] + if block_name == "BasicBlock": + block = BasicBlock + elif block_name == "Bottleneck": + block = Bottleneck + else: + raise ValueError("invalid block-name : {:}".format(block_name)) + + if not deep_stem: + self.conv = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + ) + else: + self.conv = nn.Sequential( + nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(32), + nn.ReLU(inplace=True), + nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + ) + self.inplanes = 64 + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer( + block, 64, layers[0], stride=1, groups=groups, base_width=width_per_group + ) + self.layer2 = self._make_layer( + block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group + ) + self.layer3 = self._make_layer( + block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group + ) + self.layer4 = self._make_layer( + block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + self.message = ( + "block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}".format( + block, layers, deep_stem, num_classes + ) + ) + + self.apply(initialize_resnet) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride, groups, base_width): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + if stride == 2: + downsample = nn.Sequential( + nn.AvgPool2d(kernel_size=2, stride=2, padding=0), + conv1x1(self.inplanes, planes * block.expansion, 1), + nn.BatchNorm2d(planes * block.expansion), + ) + elif stride == 1: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + nn.BatchNorm2d(planes * block.expansion), + ) + else: + raise ValueError("invalid stride [{:}] for downsample".format(stride)) + + layers = [] + layers.append( + block(self.inplanes, planes, stride, downsample, groups, base_width) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, 1, None, groups, base_width)) + + return nn.Sequential(*layers) + + def get_message(self): + return self.message + + def forward(self, x): + x = self.conv(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.fc(features) + + return features, logits diff --git a/correlation/models/SharedUtils.py b/correlation/models/SharedUtils.py new file mode 100644 index 0000000..adcdf8b --- /dev/null +++ b/correlation/models/SharedUtils.py @@ -0,0 +1,37 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch +import torch.nn as nn + + +def additive_func(A, B): + assert A.dim() == B.dim() and A.size(0) == B.size(0), "{:} vs {:}".format( + A.size(), B.size() + ) + C = min(A.size(1), B.size(1)) + if A.size(1) == B.size(1): + return A + B + elif A.size(1) < B.size(1): + out = B.clone() + out[:, :C] += A + return out + else: + out = A.clone() + out[:, :C] += B + return out + + +def change_key(key, value): + def func(m): + if hasattr(m, key): + setattr(m, key, value) + + return func + + +def parse_channel_info(xstring): + blocks = xstring.split(" ") + blocks = [x.split("-") for x in blocks] + blocks = [[int(_) for _ in x] for x in blocks] + return blocks diff --git a/correlation/models/__init__.py b/correlation/models/__init__.py new file mode 100644 index 0000000..cd5bdb7 --- /dev/null +++ b/correlation/models/__init__.py @@ -0,0 +1,329 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +from os import path as osp +from typing import List, Text +import torch + +__all__ = [ + "change_key", + "get_cell_based_tiny_net", + "get_search_spaces", + "get_cifar_models", + "get_imagenet_models", + "obtain_model", + "obtain_search_model", + "load_net_from_checkpoint", + "CellStructure", + "CellArchitectures", +] + +# useful modules +from xautodl.config_utils import dict2config +from .SharedUtils import change_key +from .cell_searchs import CellStructure, CellArchitectures + + +# Cell-based NAS Models +def get_cell_based_tiny_net(config): + if isinstance(config, dict): + config = dict2config(config, None) # to support the argument being a dict + # print(config) + super_type = getattr(config, "super_type", "basic") + # print(super_type) + group_names = ["DARTS-V1", "DARTS-V2", "GDAS", "SETN", "ENAS", "RANDOM", "generic"] + if super_type == "basic" and config.name in group_names: + from .cell_searchs import nas201_super_nets as nas_super_nets + + try: + return nas_super_nets[config.name]( + config.C, + config.N, + config.max_nodes, + config.num_classes, + config.space, + config.affine, + config.track_running_stats, + ) + except: + return nas_super_nets[config.name]( + config.C, config.N, config.max_nodes, config.num_classes, config.space + ) + elif super_type == "search-shape": + from .shape_searchs import GenericNAS301Model + + genotype = CellStructure.str2structure(config.genotype) + return GenericNAS301Model( + config.candidate_Cs, + config.max_num_Cs, + genotype, + config.num_classes, + config.affine, + config.track_running_stats, + ) + elif super_type == "nasnet-super": + from .cell_searchs import nasnet_super_nets as nas_super_nets + + return nas_super_nets[config.name]( + config.C, + config.N, + config.steps, + config.multiplier, + config.stem_multiplier, + config.num_classes, + config.space, + config.affine, + config.track_running_stats, + ) + elif config.name == "infer.tiny": + from .cell_infers import TinyNetwork + + if hasattr(config, "genotype"): + genotype = config.genotype + elif hasattr(config, "arch_str"): + genotype = CellStructure.str2structure(config.arch_str) + else: + raise ValueError( + "Can not find genotype from this config : {:}".format(config) + ) + return TinyNetwork(config.C, config.N, genotype, config.num_classes) + # sss 网络用到的 + elif config.name == "infer.shape.tiny": + from .shape_infers import DynamicShapeTinyNet + + if isinstance(config.channels, str): + channels = tuple([int(x) for x in config.channels.split(":")]) + else: + channels = config.channels + genotype = CellStructure.str2structure(config.genotype) + return DynamicShapeTinyNet(channels, genotype, config.num_classes) + elif config.name == "infer.nasnet-cifar": + from .cell_infers import NASNetonCIFAR + + raise NotImplementedError + else: + raise ValueError("invalid network name : {:}".format(config.name)) + + +# obtain the search space, i.e., a dict mapping the operation name into a python-function for this op +def get_search_spaces(xtype, name) -> List[Text]: + if xtype == "cell" or xtype == "tss": # The topology search space. + from .cell_operations import SearchSpaceNames + + assert name in SearchSpaceNames, "invalid name [{:}] in {:}".format( + name, SearchSpaceNames.keys() + ) + return SearchSpaceNames[name] + elif xtype == "sss": # The size search space. + if name in ["nats-bench", "nats-bench-size"]: + return {"candidates": [8, 16, 24, 32, 40, 48, 56, 64], "numbers": 5} + else: + raise ValueError("Invalid name : {:}".format(name)) + else: + raise ValueError("invalid search-space type is {:}".format(xtype)) + + +def get_cifar_models(config, extra_path=None): + super_type = getattr(config, "super_type", "basic") + if super_type == "basic": + from .CifarResNet import CifarResNet + from .CifarDenseNet import DenseNet + from .CifarWideResNet import CifarWideResNet + + if config.arch == "resnet": + return CifarResNet( + config.module, config.depth, config.class_num, config.zero_init_residual + ) + elif config.arch == "densenet": + return DenseNet( + config.growthRate, + config.depth, + config.reduction, + config.class_num, + config.bottleneck, + ) + elif config.arch == "wideresnet": + return CifarWideResNet( + config.depth, config.wide_factor, config.class_num, config.dropout + ) + else: + raise ValueError("invalid module type : {:}".format(config.arch)) + elif super_type.startswith("infer"): + from .shape_infers import InferWidthCifarResNet + from .shape_infers import InferDepthCifarResNet + from .shape_infers import InferCifarResNet + from .cell_infers import NASNetonCIFAR + + assert len(super_type.split("-")) == 2, "invalid super_type : {:}".format( + super_type + ) + infer_mode = super_type.split("-")[1] + if infer_mode == "width": + return InferWidthCifarResNet( + config.module, + config.depth, + config.xchannels, + config.class_num, + config.zero_init_residual, + ) + elif infer_mode == "depth": + return InferDepthCifarResNet( + config.module, + config.depth, + config.xblocks, + config.class_num, + config.zero_init_residual, + ) + elif infer_mode == "shape": + return InferCifarResNet( + config.module, + config.depth, + config.xblocks, + config.xchannels, + config.class_num, + config.zero_init_residual, + ) + elif infer_mode == "nasnet.cifar": + genotype = config.genotype + if extra_path is not None: # reload genotype by extra_path + if not osp.isfile(extra_path): + raise ValueError("invalid extra_path : {:}".format(extra_path)) + xdata = torch.load(extra_path) + current_epoch = xdata["epoch"] + genotype = xdata["genotypes"][current_epoch - 1] + C = config.C if hasattr(config, "C") else config.ichannel + N = config.N if hasattr(config, "N") else config.layers + return NASNetonCIFAR( + C, N, config.stem_multi, config.class_num, genotype, config.auxiliary + ) + else: + raise ValueError("invalid infer-mode : {:}".format(infer_mode)) + else: + raise ValueError("invalid super-type : {:}".format(super_type)) + + +def get_imagenet_models(config): + super_type = getattr(config, "super_type", "basic") + if super_type == "basic": + from .ImageNet_ResNet import ResNet + from .ImageNet_MobileNetV2 import MobileNetV2 + + if config.arch == "resnet": + return ResNet( + config.block_name, + config.layers, + config.deep_stem, + config.class_num, + config.zero_init_residual, + config.groups, + config.width_per_group, + ) + elif config.arch == "mobilenet_v2": + return MobileNetV2( + config.class_num, + config.width_multi, + config.input_channel, + config.last_channel, + "InvertedResidual", + config.dropout, + ) + else: + raise ValueError("invalid arch : {:}".format(config.arch)) + elif super_type.startswith("infer"): # NAS searched architecture + assert len(super_type.split("-")) == 2, "invalid super_type : {:}".format( + super_type + ) + infer_mode = super_type.split("-")[1] + if infer_mode == "shape": + from .shape_infers import InferImagenetResNet + from .shape_infers import InferMobileNetV2 + + if config.arch == "resnet": + return InferImagenetResNet( + config.block_name, + config.layers, + config.xblocks, + config.xchannels, + config.deep_stem, + config.class_num, + config.zero_init_residual, + ) + elif config.arch == "MobileNetV2": + return InferMobileNetV2( + config.class_num, config.xchannels, config.xblocks, config.dropout + ) + else: + raise ValueError("invalid arch-mode : {:}".format(config.arch)) + else: + raise ValueError("invalid infer-mode : {:}".format(infer_mode)) + else: + raise ValueError("invalid super-type : {:}".format(super_type)) + + +# Try to obtain the network by config. +def obtain_model(config, extra_path=None): + if config.dataset == "cifar": + return get_cifar_models(config, extra_path) + elif config.dataset == "imagenet": + return get_imagenet_models(config) + else: + raise ValueError("invalid dataset in the model config : {:}".format(config)) + + +def obtain_search_model(config): + if config.dataset == "cifar": + if config.arch == "resnet": + from .shape_searchs import SearchWidthCifarResNet + from .shape_searchs import SearchDepthCifarResNet + from .shape_searchs import SearchShapeCifarResNet + + if config.search_mode == "width": + return SearchWidthCifarResNet( + config.module, config.depth, config.class_num + ) + elif config.search_mode == "depth": + return SearchDepthCifarResNet( + config.module, config.depth, config.class_num + ) + elif config.search_mode == "shape": + return SearchShapeCifarResNet( + config.module, config.depth, config.class_num + ) + else: + raise ValueError("invalid search mode : {:}".format(config.search_mode)) + elif config.arch == "simres": + from .shape_searchs import SearchWidthSimResNet + + if config.search_mode == "width": + return SearchWidthSimResNet(config.depth, config.class_num) + else: + raise ValueError("invalid search mode : {:}".format(config.search_mode)) + else: + raise ValueError( + "invalid arch : {:} for dataset [{:}]".format( + config.arch, config.dataset + ) + ) + elif config.dataset == "imagenet": + from .shape_searchs import SearchShapeImagenetResNet + + assert config.search_mode == "shape", "invalid search-mode : {:}".format( + config.search_mode + ) + if config.arch == "resnet": + return SearchShapeImagenetResNet( + config.block_name, config.layers, config.deep_stem, config.class_num + ) + else: + raise ValueError("invalid model config : {:}".format(config)) + else: + raise ValueError("invalid dataset in the model config : {:}".format(config)) + + +def load_net_from_checkpoint(checkpoint): + assert osp.isfile(checkpoint), "checkpoint {:} does not exist".format(checkpoint) + checkpoint = torch.load(checkpoint) + model_config = dict2config(checkpoint["model-config"], None) + model = obtain_model(model_config) + model.load_state_dict(checkpoint["base-model"]) + return model diff --git a/correlation/models/cell_infers/__init__.py b/correlation/models/cell_infers/__init__.py new file mode 100644 index 0000000..ac1a183 --- /dev/null +++ b/correlation/models/cell_infers/__init__.py @@ -0,0 +1,5 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +from .tiny_network import TinyNetwork +from .nasnet_cifar import NASNetonCIFAR diff --git a/correlation/models/cell_infers/cells.py b/correlation/models/cell_infers/cells.py new file mode 100644 index 0000000..1fa2e98 --- /dev/null +++ b/correlation/models/cell_infers/cells.py @@ -0,0 +1,155 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### + +import torch +import torch.nn as nn +from copy import deepcopy + +from xautodl.models.cell_operations import OPS + + +# Cell for NAS-Bench-201 +class InferCell(nn.Module): + def __init__( + self, genotype, C_in, C_out, stride, affine=True, track_running_stats=True + ): + super(InferCell, self).__init__() + + self.layers = nn.ModuleList() + self.node_IN = [] + self.node_IX = [] + self.genotype = deepcopy(genotype) + for i in range(1, len(genotype)): + node_info = genotype[i - 1] + cur_index = [] + cur_innod = [] + for (op_name, op_in) in node_info: + if op_in == 0: + layer = OPS[op_name]( + C_in, C_out, stride, affine, track_running_stats + ) + else: + layer = OPS[op_name](C_out, C_out, 1, affine, track_running_stats) + cur_index.append(len(self.layers)) + cur_innod.append(op_in) + self.layers.append(layer) + self.node_IX.append(cur_index) + self.node_IN.append(cur_innod) + self.nodes = len(genotype) + self.in_dim = C_in + self.out_dim = C_out + + def extra_repr(self): + string = "info :: nodes={nodes}, inC={in_dim}, outC={out_dim}".format( + **self.__dict__ + ) + laystr = [] + for i, (node_layers, node_innods) in enumerate(zip(self.node_IX, self.node_IN)): + y = [ + "I{:}-L{:}".format(_ii, _il) + for _il, _ii in zip(node_layers, node_innods) + ] + x = "{:}<-({:})".format(i + 1, ",".join(y)) + laystr.append(x) + return ( + string + + ", [{:}]".format(" | ".join(laystr)) + + ", {:}".format(self.genotype.tostr()) + ) + + def forward(self, inputs): + nodes = [inputs] + for i, (node_layers, node_innods) in enumerate(zip(self.node_IX, self.node_IN)): + node_feature = sum( + self.layers[_il](nodes[_ii]) + for _il, _ii in zip(node_layers, node_innods) + ) + nodes.append(node_feature) + return nodes[-1] + + +# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018 +class NASNetInferCell(nn.Module): + def __init__( + self, + genotype, + C_prev_prev, + C_prev, + C, + reduction, + reduction_prev, + affine, + track_running_stats, + ): + super(NASNetInferCell, self).__init__() + self.reduction = reduction + if reduction_prev: + self.preprocess0 = OPS["skip_connect"]( + C_prev_prev, C, 2, affine, track_running_stats + ) + else: + self.preprocess0 = OPS["nor_conv_1x1"]( + C_prev_prev, C, 1, affine, track_running_stats + ) + self.preprocess1 = OPS["nor_conv_1x1"]( + C_prev, C, 1, affine, track_running_stats + ) + + if not reduction: + nodes, concats = genotype["normal"], genotype["normal_concat"] + else: + nodes, concats = genotype["reduce"], genotype["reduce_concat"] + self._multiplier = len(concats) + self._concats = concats + self._steps = len(nodes) + self._nodes = nodes + self.edges = nn.ModuleDict() + for i, node in enumerate(nodes): + for in_node in node: + name, j = in_node[0], in_node[1] + stride = 2 if reduction and j < 2 else 1 + node_str = "{:}<-{:}".format(i + 2, j) + self.edges[node_str] = OPS[name]( + C, C, stride, affine, track_running_stats + ) + + # [TODO] to support drop_prob in this function.. + def forward(self, s0, s1, unused_drop_prob): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + for i, node in enumerate(self._nodes): + clist = [] + for in_node in node: + name, j = in_node[0], in_node[1] + node_str = "{:}<-{:}".format(i + 2, j) + op = self.edges[node_str] + clist.append(op(states[j])) + states.append(sum(clist)) + return torch.cat([states[x] for x in self._concats], dim=1) + + +class AuxiliaryHeadCIFAR(nn.Module): + def __init__(self, C, num_classes): + """assuming input size 8x8""" + super(AuxiliaryHeadCIFAR, self).__init__() + self.features = nn.Sequential( + nn.ReLU(inplace=True), + nn.AvgPool2d( + 5, stride=3, padding=0, count_include_pad=False + ), # image size = 2 x 2 + nn.Conv2d(C, 128, 1, bias=False), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + nn.Conv2d(128, 768, 2, bias=False), + nn.BatchNorm2d(768), + nn.ReLU(inplace=True), + ) + self.classifier = nn.Linear(768, num_classes) + + def forward(self, x): + x = self.features(x) + x = self.classifier(x.view(x.size(0), -1)) + return x diff --git a/correlation/models/cell_infers/nasnet_cifar.py b/correlation/models/cell_infers/nasnet_cifar.py new file mode 100644 index 0000000..2109477 --- /dev/null +++ b/correlation/models/cell_infers/nasnet_cifar.py @@ -0,0 +1,118 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch +import torch.nn as nn +from copy import deepcopy + +from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR + + +# The macro structure is based on NASNet +class NASNetonCIFAR(nn.Module): + def __init__( + self, + C, + N, + stem_multiplier, + num_classes, + genotype, + auxiliary, + affine=True, + track_running_stats=True, + ): + super(NASNetonCIFAR, self).__init__() + self._C = C + self._layerN = N + self.stem = nn.Sequential( + nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(C * stem_multiplier), + ) + + # config for each layer + layer_channels = ( + [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1) + ) + layer_reductions = ( + [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1) + ) + + C_prev_prev, C_prev, C_curr, reduction_prev = ( + C * stem_multiplier, + C * stem_multiplier, + C, + False, + ) + self.auxiliary_index = None + self.auxiliary_head = None + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + cell = InferCell( + genotype, + C_prev_prev, + C_prev, + C_curr, + reduction, + reduction_prev, + affine, + track_running_stats, + ) + self.cells.append(cell) + C_prev_prev, C_prev, reduction_prev = ( + C_prev, + cell._multiplier * C_curr, + reduction, + ) + if reduction and C_curr == C * 4 and auxiliary: + self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes) + self.auxiliary_index = index + self._Layer = len(self.cells) + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.drop_path_prob = -1 + + def update_drop_path(self, drop_path_prob): + self.drop_path_prob = drop_path_prob + + def auxiliary_param(self): + if self.auxiliary_head is None: + return [] + else: + return list(self.auxiliary_head.parameters()) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def forward(self, inputs): + stem_feature, logits_aux = self.stem(inputs), None + cell_results = [stem_feature, stem_feature] + for i, cell in enumerate(self.cells): + cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob) + cell_results.append(cell_feature) + if ( + self.auxiliary_index is not None + and i == self.auxiliary_index + and self.training + ): + logits_aux = self.auxiliary_head(cell_results[-1]) + out = self.lastact(cell_results[-1]) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + if logits_aux is None: + return out, logits + else: + return out, [logits, logits_aux] diff --git a/correlation/models/cell_infers/tiny_network.py b/correlation/models/cell_infers/tiny_network.py new file mode 100644 index 0000000..e8da1e4 --- /dev/null +++ b/correlation/models/cell_infers/tiny_network.py @@ -0,0 +1,63 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch.nn as nn +from ..cell_operations import ResNetBasicblock +from .cells import InferCell + + +# The macro structure for architectures in NAS-Bench-201 +class TinyNetwork(nn.Module): + def __init__(self, C, N, genotype, num_classes): + super(TinyNetwork, self).__init__() + self._C = C + self._layerN = N + + self.stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + C_prev = C + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2, True) + else: + cell = InferCell(genotype, C_prev, C_curr, 1) + self.cells.append(cell) + C_prev = cell.out_dim + self._Layer = len(self.cells) + + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def forward(self, inputs): + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + feature = cell(feature) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_operations.py b/correlation/models/cell_operations.py new file mode 100644 index 0000000..051539c --- /dev/null +++ b/correlation/models/cell_operations.py @@ -0,0 +1,553 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import torch +import torch.nn as nn + +__all__ = ["OPS", "RAW_OP_CLASSES", "ResNetBasicblock", "SearchSpaceNames"] + +OPS = { + "none": lambda C_in, C_out, stride, affine, track_running_stats: Zero( + C_in, C_out, stride + ), + "avg_pool_3x3": lambda C_in, C_out, stride, affine, track_running_stats: POOLING( + C_in, C_out, stride, "avg", affine, track_running_stats + ), + "max_pool_3x3": lambda C_in, C_out, stride, affine, track_running_stats: POOLING( + C_in, C_out, stride, "max", affine, track_running_stats + ), + "nor_conv_7x7": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN( + C_in, + C_out, + (7, 7), + (stride, stride), + (3, 3), + (1, 1), + affine, + track_running_stats, + ), + "nor_conv_3x3": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN( + C_in, + C_out, + (3, 3), + (stride, stride), + (1, 1), + (1, 1), + affine, + track_running_stats, + ), + "nor_conv_1x1": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN( + C_in, + C_out, + (1, 1), + (stride, stride), + (0, 0), + (1, 1), + affine, + track_running_stats, + ), + "dua_sepc_3x3": lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv( + C_in, + C_out, + (3, 3), + (stride, stride), + (1, 1), + (1, 1), + affine, + track_running_stats, + ), + "dua_sepc_5x5": lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv( + C_in, + C_out, + (5, 5), + (stride, stride), + (2, 2), + (1, 1), + affine, + track_running_stats, + ), + "dil_sepc_3x3": lambda C_in, C_out, stride, affine, track_running_stats: SepConv( + C_in, + C_out, + (3, 3), + (stride, stride), + (2, 2), + (2, 2), + affine, + track_running_stats, + ), + "dil_sepc_5x5": lambda C_in, C_out, stride, affine, track_running_stats: SepConv( + C_in, + C_out, + (5, 5), + (stride, stride), + (4, 4), + (2, 2), + affine, + track_running_stats, + ), + "skip_connect": lambda C_in, C_out, stride, affine, track_running_stats: Identity() + if stride == 1 and C_in == C_out + else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats), +} + +CONNECT_NAS_BENCHMARK = ["none", "skip_connect", "nor_conv_3x3"] +NAS_BENCH_201 = ["none", "skip_connect", "nor_conv_1x1", "nor_conv_3x3", "avg_pool_3x3"] +DARTS_SPACE = [ + "none", + "skip_connect", + "dua_sepc_3x3", + "dua_sepc_5x5", + "dil_sepc_3x3", + "dil_sepc_5x5", + "avg_pool_3x3", + "max_pool_3x3", +] + +SearchSpaceNames = { + "connect-nas": CONNECT_NAS_BENCHMARK, + "nats-bench": NAS_BENCH_201, + "nas-bench-201": NAS_BENCH_201, + "darts": DARTS_SPACE, +} + + +class ReLUConvBN(nn.Module): + def __init__( + self, + C_in, + C_out, + kernel_size, + stride, + padding, + dilation, + affine, + track_running_stats=True, + ): + super(ReLUConvBN, self).__init__() + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d( + C_in, + C_out, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=not affine, + ), + nn.BatchNorm2d( + C_out, affine=affine, track_running_stats=track_running_stats + ), + ) + + def forward(self, x): + return self.op(x) + + +class SepConv(nn.Module): + def __init__( + self, + C_in, + C_out, + kernel_size, + stride, + padding, + dilation, + affine, + track_running_stats=True, + ): + super(SepConv, self).__init__() + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d( + C_in, + C_in, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=C_in, + bias=False, + ), + nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=not affine), + nn.BatchNorm2d( + C_out, affine=affine, track_running_stats=track_running_stats + ), + ) + + def forward(self, x): + return self.op(x) + + +class DualSepConv(nn.Module): + def __init__( + self, + C_in, + C_out, + kernel_size, + stride, + padding, + dilation, + affine, + track_running_stats=True, + ): + super(DualSepConv, self).__init__() + self.op_a = SepConv( + C_in, + C_in, + kernel_size, + stride, + padding, + dilation, + affine, + track_running_stats, + ) + self.op_b = SepConv( + C_in, C_out, kernel_size, 1, padding, dilation, affine, track_running_stats + ) + + def forward(self, x): + x = self.op_a(x) + x = self.op_b(x) + return x + + +class ResNetBasicblock(nn.Module): + def __init__(self, inplanes, planes, stride, affine=True, track_running_stats=True): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_a = ReLUConvBN( + inplanes, planes, 3, stride, 1, 1, affine, track_running_stats + ) + self.conv_b = ReLUConvBN( + planes, planes, 3, 1, 1, 1, affine, track_running_stats + ) + if stride == 2: + self.downsample = nn.Sequential( + nn.AvgPool2d(kernel_size=2, stride=2, padding=0), + nn.Conv2d( + inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False + ), + ) + elif inplanes != planes: + self.downsample = ReLUConvBN( + inplanes, planes, 1, 1, 0, 1, affine, track_running_stats + ) + else: + self.downsample = None + self.in_dim = inplanes + self.out_dim = planes + self.stride = stride + self.num_conv = 2 + + def extra_repr(self): + string = "{name}(inC={in_dim}, outC={out_dim}, stride={stride})".format( + name=self.__class__.__name__, **self.__dict__ + ) + return string + + def forward(self, inputs): + + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + return residual + basicblock + + +class POOLING(nn.Module): + def __init__( + self, C_in, C_out, stride, mode, affine=True, track_running_stats=True + ): + super(POOLING, self).__init__() + if C_in == C_out: + self.preprocess = None + else: + self.preprocess = ReLUConvBN( + C_in, C_out, 1, 1, 0, 1, affine, track_running_stats + ) + if mode == "avg": + self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False) + elif mode == "max": + self.op = nn.MaxPool2d(3, stride=stride, padding=1) + else: + raise ValueError("Invalid mode={:} in POOLING".format(mode)) + + def forward(self, inputs): + if self.preprocess: + x = self.preprocess(inputs) + else: + x = inputs + return self.op(x) + + +class Identity(nn.Module): + def __init__(self): + super(Identity, self).__init__() + + def forward(self, x): + return x + + +class Zero(nn.Module): + def __init__(self, C_in, C_out, stride): + super(Zero, self).__init__() + self.C_in = C_in + self.C_out = C_out + self.stride = stride + self.is_zero = True + + def forward(self, x): + if self.C_in == self.C_out: + if self.stride == 1: + return x.mul(0.0) + else: + return x[:, :, :: self.stride, :: self.stride].mul(0.0) + else: + shape = list(x.shape) + shape[1] = self.C_out + zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device) + return zeros + + def extra_repr(self): + return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__) + + +class FactorizedReduce(nn.Module): + def __init__(self, C_in, C_out, stride, affine, track_running_stats): + super(FactorizedReduce, self).__init__() + self.stride = stride + self.C_in = C_in + self.C_out = C_out + self.relu = nn.ReLU(inplace=False) + if stride == 2: + # assert C_out % 2 == 0, 'C_out : {:}'.format(C_out) + C_outs = [C_out // 2, C_out - C_out // 2] + self.convs = nn.ModuleList() + for i in range(2): + self.convs.append( + nn.Conv2d( + C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine + ) + ) + self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0) + elif stride == 1: + self.conv = nn.Conv2d( + C_in, C_out, 1, stride=stride, padding=0, bias=not affine + ) + else: + raise ValueError("Invalid stride : {:}".format(stride)) + self.bn = nn.BatchNorm2d( + C_out, affine=affine, track_running_stats=track_running_stats + ) + + def forward(self, x): + if self.stride == 2: + x = self.relu(x) + y = self.pad(x) + out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1) + else: + out = self.conv(x) + out = self.bn(out) + return out + + def extra_repr(self): + return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__) + + +# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019 +class PartAwareOp(nn.Module): + def __init__(self, C_in, C_out, stride, part=4): + super().__init__() + self.part = 4 + self.hidden = C_in // 3 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.local_conv_list = nn.ModuleList() + for i in range(self.part): + self.local_conv_list.append( + nn.Sequential( + nn.ReLU(), + nn.Conv2d(C_in, self.hidden, 1), + nn.BatchNorm2d(self.hidden, affine=True), + ) + ) + self.W_K = nn.Linear(self.hidden, self.hidden) + self.W_Q = nn.Linear(self.hidden, self.hidden) + + if stride == 2: + self.last = FactorizedReduce(C_in + self.hidden, C_out, 2) + elif stride == 1: + self.last = FactorizedReduce(C_in + self.hidden, C_out, 1) + else: + raise ValueError("Invalid Stride : {:}".format(stride)) + + def forward(self, x): + batch, C, H, W = x.size() + assert H >= self.part, "input size too small : {:} vs {:}".format( + x.shape, self.part + ) + IHs = [0] + for i in range(self.part): + IHs.append(min(H, int((i + 1) * (float(H) / self.part)))) + local_feat_list = [] + for i in range(self.part): + feature = x[:, :, IHs[i] : IHs[i + 1], :] + xfeax = self.avg_pool(feature) + xfea = self.local_conv_list[i](xfeax) + local_feat_list.append(xfea) + part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part) + part_feature = part_feature.transpose(1, 2).contiguous() + part_K = self.W_K(part_feature) + part_Q = self.W_Q(part_feature).transpose(1, 2).contiguous() + weight_att = torch.bmm(part_K, part_Q) + attention = torch.softmax(weight_att, dim=2) + aggreateF = torch.bmm(attention, part_feature).transpose(1, 2).contiguous() + features = [] + for i in range(self.part): + feature = aggreateF[:, :, i : i + 1].expand( + batch, self.hidden, IHs[i + 1] - IHs[i] + ) + feature = feature.view(batch, self.hidden, IHs[i + 1] - IHs[i], 1) + features.append(feature) + features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W) + final_fea = torch.cat((x, features), dim=1) + outputs = self.last(final_fea) + return outputs + + +def drop_path(x, drop_prob): + if drop_prob > 0.0: + keep_prob = 1.0 - drop_prob + mask = x.new_zeros(x.size(0), 1, 1, 1) + mask = mask.bernoulli_(keep_prob) + x = torch.div(x, keep_prob) + x.mul_(mask) + return x + + +# Searching for A Robust Neural Architecture in Four GPU Hours +class GDAS_Reduction_Cell(nn.Module): + def __init__( + self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats + ): + super(GDAS_Reduction_Cell, self).__init__() + if reduction_prev: + self.preprocess0 = FactorizedReduce( + C_prev_prev, C, 2, affine, track_running_stats + ) + else: + self.preprocess0 = ReLUConvBN( + C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats + ) + self.preprocess1 = ReLUConvBN( + C_prev, C, 1, 1, 0, 1, affine, track_running_stats + ) + + self.reduction = True + self.ops1 = nn.ModuleList( + [ + nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d( + C, + C, + (1, 3), + stride=(1, 2), + padding=(0, 1), + groups=8, + bias=not affine, + ), + nn.Conv2d( + C, + C, + (3, 1), + stride=(2, 1), + padding=(1, 0), + groups=8, + bias=not affine, + ), + nn.BatchNorm2d( + C, affine=affine, track_running_stats=track_running_stats + ), + nn.ReLU(inplace=False), + nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine), + nn.BatchNorm2d( + C, affine=affine, track_running_stats=track_running_stats + ), + ), + nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d( + C, + C, + (1, 3), + stride=(1, 2), + padding=(0, 1), + groups=8, + bias=not affine, + ), + nn.Conv2d( + C, + C, + (3, 1), + stride=(2, 1), + padding=(1, 0), + groups=8, + bias=not affine, + ), + nn.BatchNorm2d( + C, affine=affine, track_running_stats=track_running_stats + ), + nn.ReLU(inplace=False), + nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine), + nn.BatchNorm2d( + C, affine=affine, track_running_stats=track_running_stats + ), + ), + ] + ) + + self.ops2 = nn.ModuleList( + [ + nn.Sequential( + nn.MaxPool2d(3, stride=2, padding=1), + nn.BatchNorm2d( + C, affine=affine, track_running_stats=track_running_stats + ), + ), + nn.Sequential( + nn.MaxPool2d(3, stride=2, padding=1), + nn.BatchNorm2d( + C, affine=affine, track_running_stats=track_running_stats + ), + ), + ] + ) + + @property + def multiplier(self): + return 4 + + def forward(self, s0, s1, drop_prob=-1): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + X0 = self.ops1[0](s0) + X1 = self.ops1[1](s1) + if self.training and drop_prob > 0.0: + X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob) + + # X2 = self.ops2[0] (X0+X1) + X2 = self.ops2[0](s0) + X3 = self.ops2[1](s1) + if self.training and drop_prob > 0.0: + X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob) + return torch.cat([X0, X1, X2, X3], dim=1) + + +# To manage the useful classes in this file. +RAW_OP_CLASSES = {"gdas_reduction": GDAS_Reduction_Cell} diff --git a/correlation/models/cell_searchs/__init__.py b/correlation/models/cell_searchs/__init__.py new file mode 100644 index 0000000..0d770cb --- /dev/null +++ b/correlation/models/cell_searchs/__init__.py @@ -0,0 +1,33 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +# The macro structure is defined in NAS-Bench-201 +from .search_model_darts import TinyNetworkDarts +from .search_model_gdas import TinyNetworkGDAS +from .search_model_setn import TinyNetworkSETN +from .search_model_enas import TinyNetworkENAS +from .search_model_random import TinyNetworkRANDOM +from .generic_model import GenericNAS201Model +from .genotypes import Structure as CellStructure, architectures as CellArchitectures + +# NASNet-based macro structure +from .search_model_gdas_nasnet import NASNetworkGDAS +from .search_model_gdas_frc_nasnet import NASNetworkGDAS_FRC +from .search_model_darts_nasnet import NASNetworkDARTS + + +nas201_super_nets = { + "DARTS-V1": TinyNetworkDarts, + "DARTS-V2": TinyNetworkDarts, + "GDAS": TinyNetworkGDAS, + "SETN": TinyNetworkSETN, + "ENAS": TinyNetworkENAS, + "RANDOM": TinyNetworkRANDOM, + "generic": GenericNAS201Model, +} + +nasnet_super_nets = { + "GDAS": NASNetworkGDAS, + "GDAS_FRC": NASNetworkGDAS_FRC, + "DARTS": NASNetworkDARTS, +} diff --git a/correlation/models/cell_searchs/_test_module.py b/correlation/models/cell_searchs/_test_module.py new file mode 100644 index 0000000..cd6fbfb --- /dev/null +++ b/correlation/models/cell_searchs/_test_module.py @@ -0,0 +1,14 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import torch +from search_model_enas_utils import Controller + + +def main(): + controller = Controller(6, 4) + predictions = controller() + + +if __name__ == "__main__": + main() diff --git a/correlation/models/cell_searchs/generic_model.py b/correlation/models/cell_searchs/generic_model.py new file mode 100644 index 0000000..bbbbb1f --- /dev/null +++ b/correlation/models/cell_searchs/generic_model.py @@ -0,0 +1,366 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 # +##################################################### +import torch, random +import torch.nn as nn +from copy import deepcopy +from typing import Text +from torch.distributions.categorical import Categorical + +from ..cell_operations import ResNetBasicblock, drop_path +from .search_cells import NAS201SearchCell as SearchCell +from .genotypes import Structure + + +class Controller(nn.Module): + # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py + def __init__( + self, + edge2index, + op_names, + max_nodes, + lstm_size=32, + lstm_num_layers=2, + tanh_constant=2.5, + temperature=5.0, + ): + super(Controller, self).__init__() + # assign the attributes + self.max_nodes = max_nodes + self.num_edge = len(edge2index) + self.edge2index = edge2index + self.num_ops = len(op_names) + self.op_names = op_names + self.lstm_size = lstm_size + self.lstm_N = lstm_num_layers + self.tanh_constant = tanh_constant + self.temperature = temperature + # create parameters + self.register_parameter( + "input_vars", nn.Parameter(torch.Tensor(1, 1, lstm_size)) + ) + self.w_lstm = nn.LSTM( + input_size=self.lstm_size, + hidden_size=self.lstm_size, + num_layers=self.lstm_N, + ) + self.w_embd = nn.Embedding(self.num_ops, self.lstm_size) + self.w_pred = nn.Linear(self.lstm_size, self.num_ops) + + nn.init.uniform_(self.input_vars, -0.1, 0.1) + nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1) + nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1) + nn.init.uniform_(self.w_embd.weight, -0.1, 0.1) + nn.init.uniform_(self.w_pred.weight, -0.1, 0.1) + + def convert_structure(self, _arch): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + op_index = _arch[self.edge2index[node_str]] + op_name = self.op_names[op_index] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def forward(self): + + inputs, h0 = self.input_vars, None + log_probs, entropys, sampled_arch = [], [], [] + for iedge in range(self.num_edge): + outputs, h0 = self.w_lstm(inputs, h0) + + logits = self.w_pred(outputs) + logits = logits / self.temperature + logits = self.tanh_constant * torch.tanh(logits) + # distribution + op_distribution = Categorical(logits=logits) + op_index = op_distribution.sample() + sampled_arch.append(op_index.item()) + + op_log_prob = op_distribution.log_prob(op_index) + log_probs.append(op_log_prob.view(-1)) + op_entropy = op_distribution.entropy() + entropys.append(op_entropy.view(-1)) + + # obtain the input embedding for the next step + inputs = self.w_embd(op_index) + return ( + torch.sum(torch.cat(log_probs)), + torch.sum(torch.cat(entropys)), + self.convert_structure(sampled_arch), + ) + + +class GenericNAS201Model(nn.Module): + def __init__( + self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats + ): + super(GenericNAS201Model, self).__init__() + self._C = C + self._layerN = N + self._max_nodes = max_nodes + self._stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + C_prev, num_edge, edge2index = C, None, None + self._cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2) + else: + cell = SearchCell( + C_prev, + C_curr, + 1, + max_nodes, + search_space, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self._cells.append(cell) + C_prev = cell.out_dim + self._op_names = deepcopy(search_space) + self._Layer = len(self._cells) + self.edge2index = edge2index + self.lastact = nn.Sequential( + nn.BatchNorm2d( + C_prev, affine=affine, track_running_stats=track_running_stats + ), + nn.ReLU(inplace=True), + ) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self._num_edge = num_edge + # algorithm related + self.arch_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self._mode = None + self.dynamic_cell = None + self._tau = None + self._algo = None + self._drop_path = None + self.verbose = False + + def set_algo(self, algo: Text): + # used for searching + assert self._algo is None, "This functioin can only be called once." + self._algo = algo + if algo == "enas": + self.controller = Controller( + self.edge2index, self._op_names, self._max_nodes + ) + else: + self.arch_parameters = nn.Parameter( + 1e-3 * torch.randn(self._num_edge, len(self._op_names)) + ) + if algo == "gdas": + self._tau = 10 + + def set_cal_mode(self, mode, dynamic_cell=None): + assert mode in ["gdas", "enas", "urs", "joint", "select", "dynamic"] + self._mode = mode + if mode == "dynamic": + self.dynamic_cell = deepcopy(dynamic_cell) + else: + self.dynamic_cell = None + + def set_drop_path(self, progress, drop_path_rate): + if drop_path_rate is None: + self._drop_path = None + elif progress is None: + self._drop_path = drop_path_rate + else: + self._drop_path = progress * drop_path_rate + + @property + def mode(self): + return self._mode + + @property + def drop_path(self): + return self._drop_path + + @property + def weights(self): + xlist = list(self._stem.parameters()) + xlist += list(self._cells.parameters()) + xlist += list(self.lastact.parameters()) + xlist += list(self.global_pooling.parameters()) + xlist += list(self.classifier.parameters()) + return xlist + + def set_tau(self, tau): + self._tau = tau + + @property + def tau(self): + return self._tau + + @property + def alphas(self): + if self._algo == "enas": + return list(self.controller.parameters()) + else: + return [self.arch_parameters] + + @property + def message(self): + string = self.extra_repr() + for i, cell in enumerate(self._cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self._cells), cell.extra_repr() + ) + return string + + def show_alphas(self): + with torch.no_grad(): + if self._algo == "enas": + return "w_pred :\n{:}".format(self.controller.w_pred.weight) + else: + return "arch-parameters :\n{:}".format( + nn.functional.softmax(self.arch_parameters, dim=-1).cpu() + ) + + def extra_repr(self): + return "{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + @property + def genotype(self): + genotypes = [] + for i in range(1, self._max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + with torch.no_grad(): + weights = self.arch_parameters[self.edge2index[node_str]] + op_name = self._op_names[weights.argmax().item()] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def dync_genotype(self, use_random=False): + genotypes = [] + with torch.no_grad(): + alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1) + for i in range(1, self._max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + if use_random: + op_name = random.choice(self._op_names) + else: + weights = alphas_cpu[self.edge2index[node_str]] + op_index = torch.multinomial(weights, 1).item() + op_name = self._op_names[op_index] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def get_log_prob(self, arch): + with torch.no_grad(): + logits = nn.functional.log_softmax(self.arch_parameters, dim=-1) + select_logits = [] + for i, node_info in enumerate(arch.nodes): + for op, xin in node_info: + node_str = "{:}<-{:}".format(i + 1, xin) + op_index = self._op_names.index(op) + select_logits.append(logits[self.edge2index[node_str], op_index]) + return sum(select_logits).item() + + def return_topK(self, K, use_random=False): + archs = Structure.gen_all(self._op_names, self._max_nodes, False) + pairs = [(self.get_log_prob(arch), arch) for arch in archs] + if K < 0 or K >= len(archs): + K = len(archs) + if use_random: + return random.sample(archs, K) + else: + sorted_pairs = sorted(pairs, key=lambda x: -x[0]) + return_pairs = [sorted_pairs[_][1] for _ in range(K)] + return return_pairs + + def normalize_archp(self): + if self.mode == "gdas": + while True: + gumbels = -torch.empty_like(self.arch_parameters).exponential_().log() + logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau + probs = nn.functional.softmax(logits, dim=1) + index = probs.max(-1, keepdim=True)[1] + one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0) + hardwts = one_h - probs.detach() + probs + if ( + (torch.isinf(gumbels).any()) + or (torch.isinf(probs).any()) + or (torch.isnan(probs).any()) + ): + continue + else: + break + with torch.no_grad(): + hardwts_cpu = hardwts.detach().cpu() + return hardwts, hardwts_cpu, index, "GUMBEL" + else: + alphas = nn.functional.softmax(self.arch_parameters, dim=-1) + index = alphas.max(-1, keepdim=True)[1] + with torch.no_grad(): + alphas_cpu = alphas.detach().cpu() + return alphas, alphas_cpu, index, "SOFTMAX" + + def forward(self, inputs): + alphas, alphas_cpu, index, verbose_str = self.normalize_archp() + feature = self._stem(inputs) + for i, cell in enumerate(self._cells): + if isinstance(cell, SearchCell): + if self.mode == "urs": + feature = cell.forward_urs(feature) + if self.verbose: + verbose_str += "-forward_urs" + elif self.mode == "select": + feature = cell.forward_select(feature, alphas_cpu) + if self.verbose: + verbose_str += "-forward_select" + elif self.mode == "joint": + feature = cell.forward_joint(feature, alphas) + if self.verbose: + verbose_str += "-forward_joint" + elif self.mode == "dynamic": + feature = cell.forward_dynamic(feature, self.dynamic_cell) + if self.verbose: + verbose_str += "-forward_dynamic" + elif self.mode == "gdas": + feature = cell.forward_gdas(feature, alphas, index) + if self.verbose: + verbose_str += "-forward_gdas" + elif self.mode == "gdas_v1": + feature = cell.forward_gdas_v1(feature, alphas, index) + if self.verbose: + verbose_str += "-forward_gdas_v1" + else: + raise ValueError("invalid mode={:}".format(self.mode)) + else: + feature = cell(feature) + if self.drop_path is not None: + feature = drop_path(feature, self.drop_path) + if self.verbose and random.random() < 0.001: + print(verbose_str) + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + return out, logits diff --git a/correlation/models/cell_searchs/genotypes.py b/correlation/models/cell_searchs/genotypes.py new file mode 100644 index 0000000..f0ec8f2 --- /dev/null +++ b/correlation/models/cell_searchs/genotypes.py @@ -0,0 +1,274 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +from copy import deepcopy + + +def get_combination(space, num): + combs = [] + for i in range(num): + if i == 0: + for func in space: + combs.append([(func, i)]) + else: + new_combs = [] + for string in combs: + for func in space: + xstring = string + [(func, i)] + new_combs.append(xstring) + combs = new_combs + return combs + + +class Structure: + def __init__(self, genotype): + assert isinstance(genotype, list) or isinstance( + genotype, tuple + ), "invalid class of genotype : {:}".format(type(genotype)) + self.node_num = len(genotype) + 1 + self.nodes = [] + self.node_N = [] + for idx, node_info in enumerate(genotype): + assert isinstance(node_info, list) or isinstance( + node_info, tuple + ), "invalid class of node_info : {:}".format(type(node_info)) + assert len(node_info) >= 1, "invalid length : {:}".format(len(node_info)) + for node_in in node_info: + assert isinstance(node_in, list) or isinstance( + node_in, tuple + ), "invalid class of in-node : {:}".format(type(node_in)) + assert ( + len(node_in) == 2 and node_in[1] <= idx + ), "invalid in-node : {:}".format(node_in) + self.node_N.append(len(node_info)) + self.nodes.append(tuple(deepcopy(node_info))) + + def tolist(self, remove_str): + # convert this class to the list, if remove_str is 'none', then remove the 'none' operation. + # note that we re-order the input node in this function + # return the-genotype-list and success [if unsuccess, it is not a connectivity] + genotypes = [] + for node_info in self.nodes: + node_info = list(node_info) + node_info = sorted(node_info, key=lambda x: (x[1], x[0])) + node_info = tuple(filter(lambda x: x[0] != remove_str, node_info)) + if len(node_info) == 0: + return None, False + genotypes.append(node_info) + return genotypes, True + + def node(self, index): + assert index > 0 and index <= len(self), "invalid index={:} < {:}".format( + index, len(self) + ) + return self.nodes[index] + + def tostr(self): + strings = [] + for node_info in self.nodes: + string = "|".join([x[0] + "~{:}".format(x[1]) for x in node_info]) + string = "|{:}|".format(string) + strings.append(string) + return "+".join(strings) + + def check_valid(self): + nodes = {0: True} + for i, node_info in enumerate(self.nodes): + sums = [] + for op, xin in node_info: + if op == "none" or nodes[xin] is False: + x = False + else: + x = True + sums.append(x) + nodes[i + 1] = sum(sums) > 0 + return nodes[len(self.nodes)] + + def to_unique_str(self, consider_zero=False): + # this is used to identify the isomorphic cell, which rerquires the prior knowledge of operation + # two operations are special, i.e., none and skip_connect + nodes = {0: "0"} + for i_node, node_info in enumerate(self.nodes): + cur_node = [] + for op, xin in node_info: + if consider_zero is None: + x = "(" + nodes[xin] + ")" + "@{:}".format(op) + elif consider_zero: + if op == "none" or nodes[xin] == "#": + x = "#" # zero + elif op == "skip_connect": + x = nodes[xin] + else: + x = "(" + nodes[xin] + ")" + "@{:}".format(op) + else: + if op == "skip_connect": + x = nodes[xin] + else: + x = "(" + nodes[xin] + ")" + "@{:}".format(op) + cur_node.append(x) + nodes[i_node + 1] = "+".join(sorted(cur_node)) + return nodes[len(self.nodes)] + + def check_valid_op(self, op_names): + for node_info in self.nodes: + for inode_edge in node_info: + # assert inode_edge[0] in op_names, 'invalid op-name : {:}'.format(inode_edge[0]) + if inode_edge[0] not in op_names: + return False + return True + + def __repr__(self): + return "{name}({node_num} nodes with {node_info})".format( + name=self.__class__.__name__, node_info=self.tostr(), **self.__dict__ + ) + + def __len__(self): + return len(self.nodes) + 1 + + def __getitem__(self, index): + return self.nodes[index] + + @staticmethod + def str2structure(xstr): + if isinstance(xstr, Structure): + return xstr + assert isinstance(xstr, str), "must take string (not {:}) as input".format( + type(xstr) + ) + nodestrs = xstr.split("+") + genotypes = [] + for i, node_str in enumerate(nodestrs): + inputs = list(filter(lambda x: x != "", node_str.split("|"))) + for xinput in inputs: + assert len(xinput.split("~")) == 2, "invalid input length : {:}".format( + xinput + ) + inputs = (xi.split("~") for xi in inputs) + input_infos = tuple((op, int(IDX)) for (op, IDX) in inputs) + genotypes.append(input_infos) + return Structure(genotypes) + + @staticmethod + def str2fullstructure(xstr, default_name="none"): + assert isinstance(xstr, str), "must take string (not {:}) as input".format( + type(xstr) + ) + nodestrs = xstr.split("+") + genotypes = [] + for i, node_str in enumerate(nodestrs): + inputs = list(filter(lambda x: x != "", node_str.split("|"))) + for xinput in inputs: + assert len(xinput.split("~")) == 2, "invalid input length : {:}".format( + xinput + ) + inputs = (xi.split("~") for xi in inputs) + input_infos = list((op, int(IDX)) for (op, IDX) in inputs) + all_in_nodes = list(x[1] for x in input_infos) + for j in range(i): + if j not in all_in_nodes: + input_infos.append((default_name, j)) + node_info = sorted(input_infos, key=lambda x: (x[1], x[0])) + genotypes.append(tuple(node_info)) + return Structure(genotypes) + + @staticmethod + def gen_all(search_space, num, return_ori): + assert isinstance(search_space, list) or isinstance( + search_space, tuple + ), "invalid class of search-space : {:}".format(type(search_space)) + assert ( + num >= 2 + ), "There should be at least two nodes in a neural cell instead of {:}".format( + num + ) + all_archs = get_combination(search_space, 1) + for i, arch in enumerate(all_archs): + all_archs[i] = [tuple(arch)] + + for inode in range(2, num): + cur_nodes = get_combination(search_space, inode) + new_all_archs = [] + for previous_arch in all_archs: + for cur_node in cur_nodes: + new_all_archs.append(previous_arch + [tuple(cur_node)]) + all_archs = new_all_archs + if return_ori: + return all_archs + else: + return [Structure(x) for x in all_archs] + + +ResNet_CODE = Structure( + [ + (("nor_conv_3x3", 0),), # node-1 + (("nor_conv_3x3", 1),), # node-2 + (("skip_connect", 0), ("skip_connect", 2)), + ] # node-3 +) + +AllConv3x3_CODE = Structure( + [ + (("nor_conv_3x3", 0),), # node-1 + (("nor_conv_3x3", 0), ("nor_conv_3x3", 1)), # node-2 + (("nor_conv_3x3", 0), ("nor_conv_3x3", 1), ("nor_conv_3x3", 2)), + ] # node-3 +) + +AllFull_CODE = Structure( + [ + ( + ("skip_connect", 0), + ("nor_conv_1x1", 0), + ("nor_conv_3x3", 0), + ("avg_pool_3x3", 0), + ), # node-1 + ( + ("skip_connect", 0), + ("nor_conv_1x1", 0), + ("nor_conv_3x3", 0), + ("avg_pool_3x3", 0), + ("skip_connect", 1), + ("nor_conv_1x1", 1), + ("nor_conv_3x3", 1), + ("avg_pool_3x3", 1), + ), # node-2 + ( + ("skip_connect", 0), + ("nor_conv_1x1", 0), + ("nor_conv_3x3", 0), + ("avg_pool_3x3", 0), + ("skip_connect", 1), + ("nor_conv_1x1", 1), + ("nor_conv_3x3", 1), + ("avg_pool_3x3", 1), + ("skip_connect", 2), + ("nor_conv_1x1", 2), + ("nor_conv_3x3", 2), + ("avg_pool_3x3", 2), + ), + ] # node-3 +) + +AllConv1x1_CODE = Structure( + [ + (("nor_conv_1x1", 0),), # node-1 + (("nor_conv_1x1", 0), ("nor_conv_1x1", 1)), # node-2 + (("nor_conv_1x1", 0), ("nor_conv_1x1", 1), ("nor_conv_1x1", 2)), + ] # node-3 +) + +AllIdentity_CODE = Structure( + [ + (("skip_connect", 0),), # node-1 + (("skip_connect", 0), ("skip_connect", 1)), # node-2 + (("skip_connect", 0), ("skip_connect", 1), ("skip_connect", 2)), + ] # node-3 +) + +architectures = { + "resnet": ResNet_CODE, + "all_c3x3": AllConv3x3_CODE, + "all_c1x1": AllConv1x1_CODE, + "all_idnt": AllIdentity_CODE, + "all_full": AllFull_CODE, +} diff --git a/correlation/models/cell_searchs/search_cells.py b/correlation/models/cell_searchs/search_cells.py new file mode 100644 index 0000000..6be7c52 --- /dev/null +++ b/correlation/models/cell_searchs/search_cells.py @@ -0,0 +1,267 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, random, torch +import warnings +import torch.nn as nn +import torch.nn.functional as F +from copy import deepcopy +from ..cell_operations import OPS + + +# This module is used for NAS-Bench-201, represents a small search space with a complete DAG +class NAS201SearchCell(nn.Module): + def __init__( + self, + C_in, + C_out, + stride, + max_nodes, + op_names, + affine=False, + track_running_stats=True, + ): + super(NAS201SearchCell, self).__init__() + + self.op_names = deepcopy(op_names) + self.edges = nn.ModuleDict() + self.max_nodes = max_nodes + self.in_dim = C_in + self.out_dim = C_out + for i in range(1, max_nodes): + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + if j == 0: + xlists = [ + OPS[op_name](C_in, C_out, stride, affine, track_running_stats) + for op_name in op_names + ] + else: + xlists = [ + OPS[op_name](C_in, C_out, 1, affine, track_running_stats) + for op_name in op_names + ] + self.edges[node_str] = nn.ModuleList(xlists) + self.edge_keys = sorted(list(self.edges.keys())) + self.edge2index = {key: i for i, key in enumerate(self.edge_keys)} + self.num_edges = len(self.edges) + + def extra_repr(self): + string = "info :: {max_nodes} nodes, inC={in_dim}, outC={out_dim}".format( + **self.__dict__ + ) + return string + + def forward(self, inputs, weightss): + nodes = [inputs] + for i in range(1, self.max_nodes): + inter_nodes = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + weights = weightss[self.edge2index[node_str]] + inter_nodes.append( + sum( + layer(nodes[j]) * w + for layer, w in zip(self.edges[node_str], weights) + ) + ) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + # GDAS + def forward_gdas(self, inputs, hardwts, index): + nodes = [inputs] + for i in range(1, self.max_nodes): + inter_nodes = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + weights = hardwts[self.edge2index[node_str]] + argmaxs = index[self.edge2index[node_str]].item() + weigsum = sum( + weights[_ie] * edge(nodes[j]) if _ie == argmaxs else weights[_ie] + for _ie, edge in enumerate(self.edges[node_str]) + ) + inter_nodes.append(weigsum) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + # GDAS Variant: https://github.com/D-X-Y/AutoDL-Projects/issues/119 + def forward_gdas_v1(self, inputs, hardwts, index): + nodes = [inputs] + for i in range(1, self.max_nodes): + inter_nodes = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + weights = hardwts[self.edge2index[node_str]] + argmaxs = index[self.edge2index[node_str]].item() + weigsum = weights[argmaxs] * self.edges[node_str](nodes[j]) + inter_nodes.append(weigsum) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + # joint + def forward_joint(self, inputs, weightss): + nodes = [inputs] + for i in range(1, self.max_nodes): + inter_nodes = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + weights = weightss[self.edge2index[node_str]] + # aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel() + aggregation = sum( + layer(nodes[j]) * w + for layer, w in zip(self.edges[node_str], weights) + ) + inter_nodes.append(aggregation) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + # uniform random sampling per iteration, SETN + def forward_urs(self, inputs): + nodes = [inputs] + for i in range(1, self.max_nodes): + while True: # to avoid select zero for all ops + sops, has_non_zero = [], False + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + candidates = self.edges[node_str] + select_op = random.choice(candidates) + sops.append(select_op) + if not hasattr(select_op, "is_zero") or select_op.is_zero is False: + has_non_zero = True + if has_non_zero: + break + inter_nodes = [] + for j, select_op in enumerate(sops): + inter_nodes.append(select_op(nodes[j])) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + # select the argmax + def forward_select(self, inputs, weightss): + nodes = [inputs] + for i in range(1, self.max_nodes): + inter_nodes = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + weights = weightss[self.edge2index[node_str]] + inter_nodes.append( + self.edges[node_str][weights.argmax().item()](nodes[j]) + ) + # inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) ) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + # forward with a specific structure + def forward_dynamic(self, inputs, structure): + nodes = [inputs] + for i in range(1, self.max_nodes): + cur_op_node = structure.nodes[i - 1] + inter_nodes = [] + for op_name, j in cur_op_node: + node_str = "{:}<-{:}".format(i, j) + op_index = self.op_names.index(op_name) + inter_nodes.append(self.edges[node_str][op_index](nodes[j])) + nodes.append(sum(inter_nodes)) + return nodes[-1] + + +# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018 + + +class MixedOp(nn.Module): + def __init__(self, space, C, stride, affine, track_running_stats): + super(MixedOp, self).__init__() + self._ops = nn.ModuleList() + for primitive in space: + op = OPS[primitive](C, C, stride, affine, track_running_stats) + self._ops.append(op) + + def forward_gdas(self, x, weights, index): + return self._ops[index](x) * weights[index] + + def forward_darts(self, x, weights): + return sum(w * op(x) for w, op in zip(weights, self._ops)) + + +class NASNetSearchCell(nn.Module): + def __init__( + self, + space, + steps, + multiplier, + C_prev_prev, + C_prev, + C, + reduction, + reduction_prev, + affine, + track_running_stats, + ): + super(NASNetSearchCell, self).__init__() + self.reduction = reduction + self.op_names = deepcopy(space) + if reduction_prev: + self.preprocess0 = OPS["skip_connect"]( + C_prev_prev, C, 2, affine, track_running_stats + ) + else: + self.preprocess0 = OPS["nor_conv_1x1"]( + C_prev_prev, C, 1, affine, track_running_stats + ) + self.preprocess1 = OPS["nor_conv_1x1"]( + C_prev, C, 1, affine, track_running_stats + ) + self._steps = steps + self._multiplier = multiplier + + self._ops = nn.ModuleList() + self.edges = nn.ModuleDict() + for i in range(self._steps): + for j in range(2 + i): + node_str = "{:}<-{:}".format( + i, j + ) # indicate the edge from node-(j) to node-(i+2) + stride = 2 if reduction and j < 2 else 1 + op = MixedOp(space, C, stride, affine, track_running_stats) + self.edges[node_str] = op + self.edge_keys = sorted(list(self.edges.keys())) + self.edge2index = {key: i for i, key in enumerate(self.edge_keys)} + self.num_edges = len(self.edges) + + @property + def multiplier(self): + return self._multiplier + + def forward_gdas(self, s0, s1, weightss, indexs): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + for i in range(self._steps): + clist = [] + for j, h in enumerate(states): + node_str = "{:}<-{:}".format(i, j) + op = self.edges[node_str] + weights = weightss[self.edge2index[node_str]] + index = indexs[self.edge2index[node_str]].item() + clist.append(op.forward_gdas(h, weights, index)) + states.append(sum(clist)) + + return torch.cat(states[-self._multiplier :], dim=1) + + def forward_darts(self, s0, s1, weightss): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + for i in range(self._steps): + clist = [] + for j, h in enumerate(states): + node_str = "{:}<-{:}".format(i, j) + op = self.edges[node_str] + weights = weightss[self.edge2index[node_str]] + clist.append(op.forward_darts(h, weights)) + states.append(sum(clist)) + + return torch.cat(states[-self._multiplier :], dim=1) diff --git a/correlation/models/cell_searchs/search_model_darts.py b/correlation/models/cell_searchs/search_model_darts.py new file mode 100644 index 0000000..31041b6 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_darts.py @@ -0,0 +1,122 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +######################################################## +# DARTS: Differentiable Architecture Search, ICLR 2019 # +######################################################## +import torch +import torch.nn as nn +from copy import deepcopy +from ..cell_operations import ResNetBasicblock +from .search_cells import NAS201SearchCell as SearchCell +from .genotypes import Structure + + +class TinyNetworkDarts(nn.Module): + def __init__( + self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats + ): + super(TinyNetworkDarts, self).__init__() + self._C = C + self._layerN = N + self.max_nodes = max_nodes + self.stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + C_prev, num_edge, edge2index = C, None, None + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2) + else: + cell = SearchCell( + C_prev, + C_curr, + 1, + max_nodes, + search_space, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev = cell.out_dim + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + + def get_weights(self): + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def get_alphas(self): + return [self.arch_parameters] + + def show_alphas(self): + with torch.no_grad(): + return "arch-parameters :\n{:}".format( + nn.functional.softmax(self.arch_parameters, dim=-1).cpu() + ) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def genotype(self): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + with torch.no_grad(): + weights = self.arch_parameters[self.edge2index[node_str]] + op_name = self.op_names[weights.argmax().item()] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def forward(self, inputs): + alphas = nn.functional.softmax(self.arch_parameters, dim=-1) + + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + if isinstance(cell, SearchCell): + feature = cell(feature, alphas) + else: + feature = cell(feature) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_darts_nasnet.py b/correlation/models/cell_searchs/search_model_darts_nasnet.py new file mode 100644 index 0000000..7cfdb47 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_darts_nasnet.py @@ -0,0 +1,178 @@ +#################### +# DARTS, ICLR 2019 # +#################### +import torch +import torch.nn as nn +from copy import deepcopy +from typing import List, Text, Dict +from .search_cells import NASNetSearchCell as SearchCell + + +# The macro structure is based on NASNet +class NASNetworkDARTS(nn.Module): + def __init__( + self, + C: int, + N: int, + steps: int, + multiplier: int, + stem_multiplier: int, + num_classes: int, + search_space: List[Text], + affine: bool, + track_running_stats: bool, + ): + super(NASNetworkDARTS, self).__init__() + self._C = C + self._layerN = N + self._steps = steps + self._multiplier = multiplier + self.stem = nn.Sequential( + nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(C * stem_multiplier), + ) + + # config for each layer + layer_channels = ( + [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1) + ) + layer_reductions = ( + [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1) + ) + + num_edge, edge2index = None, None + C_prev_prev, C_prev, C_curr, reduction_prev = ( + C * stem_multiplier, + C * stem_multiplier, + C, + False, + ) + + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + cell = SearchCell( + search_space, + steps, + multiplier, + C_prev_prev, + C_prev, + C_curr, + reduction, + reduction_prev, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_normal_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.arch_reduce_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + + def get_weights(self) -> List[torch.nn.Parameter]: + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def get_alphas(self) -> List[torch.nn.Parameter]: + return [self.arch_normal_parameters, self.arch_reduce_parameters] + + def show_alphas(self) -> Text: + with torch.no_grad(): + A = "arch-normal-parameters :\n{:}".format( + nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() + ) + B = "arch-reduce-parameters :\n{:}".format( + nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() + ) + return "{:}\n{:}".format(A, B) + + def get_message(self) -> Text: + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self) -> Text: + return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def genotype(self) -> Dict[Text, List]: + def _parse(weights): + gene = [] + for i in range(self._steps): + edges = [] + for j in range(2 + i): + node_str = "{:}<-{:}".format(i, j) + ws = weights[self.edge2index[node_str]] + for k, op_name in enumerate(self.op_names): + if op_name == "none": + continue + edges.append((op_name, j, ws[k])) + # (TODO) xuanyidong: + # Here the selected two edges might come from the same input node. + # And this case could be a problem that two edges will collapse into a single one + # due to our assumption -- at most one edge from an input node during evaluation. + edges = sorted(edges, key=lambda x: -x[-1]) + selected_edges = edges[:2] + gene.append(tuple(selected_edges)) + return gene + + with torch.no_grad(): + gene_normal = _parse( + torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy() + ) + gene_reduce = _parse( + torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy() + ) + return { + "normal": gene_normal, + "normal_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + "reduce": gene_reduce, + "reduce_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + } + + def forward(self, inputs): + + normal_w = nn.functional.softmax(self.arch_normal_parameters, dim=1) + reduce_w = nn.functional.softmax(self.arch_reduce_parameters, dim=1) + + s0 = s1 = self.stem(inputs) + for i, cell in enumerate(self.cells): + if cell.reduction: + ww = reduce_w + else: + ww = normal_w + s0, s1 = s1, cell.forward_darts(s0, s1, ww) + out = self.lastact(s1) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_enas.py b/correlation/models/cell_searchs/search_model_enas.py new file mode 100644 index 0000000..7ba91d4 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_enas.py @@ -0,0 +1,114 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +########################################################################## +# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 # +########################################################################## +import torch +import torch.nn as nn +from copy import deepcopy +from ..cell_operations import ResNetBasicblock +from .search_cells import NAS201SearchCell as SearchCell +from .genotypes import Structure +from .search_model_enas_utils import Controller + + +class TinyNetworkENAS(nn.Module): + def __init__( + self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats + ): + super(TinyNetworkENAS, self).__init__() + self._C = C + self._layerN = N + self.max_nodes = max_nodes + self.stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + C_prev, num_edge, edge2index = C, None, None + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2) + else: + cell = SearchCell( + C_prev, + C_curr, + 1, + max_nodes, + search_space, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev = cell.out_dim + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + # to maintain the sampled architecture + self.sampled_arch = None + + def update_arch(self, _arch): + if _arch is None: + self.sampled_arch = None + elif isinstance(_arch, Structure): + self.sampled_arch = _arch + elif isinstance(_arch, (list, tuple)): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + op_index = _arch[self.edge2index[node_str]] + op_name = self.op_names[op_index] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + self.sampled_arch = Structure(genotypes) + else: + raise ValueError("invalid type of input architecture : {:}".format(_arch)) + return self.sampled_arch + + def create_controller(self): + return Controller(len(self.edge2index), len(self.op_names)) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def forward(self, inputs): + + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + if isinstance(cell, SearchCell): + feature = cell.forward_dynamic(feature, self.sampled_arch) + else: + feature = cell(feature) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_enas_utils.py b/correlation/models/cell_searchs/search_model_enas_utils.py new file mode 100644 index 0000000..71d5d0f --- /dev/null +++ b/correlation/models/cell_searchs/search_model_enas_utils.py @@ -0,0 +1,74 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +########################################################################## +# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 # +########################################################################## +import torch +import torch.nn as nn +from torch.distributions.categorical import Categorical + + +class Controller(nn.Module): + # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py + def __init__( + self, + num_edge, + num_ops, + lstm_size=32, + lstm_num_layers=2, + tanh_constant=2.5, + temperature=5.0, + ): + super(Controller, self).__init__() + # assign the attributes + self.num_edge = num_edge + self.num_ops = num_ops + self.lstm_size = lstm_size + self.lstm_N = lstm_num_layers + self.tanh_constant = tanh_constant + self.temperature = temperature + # create parameters + self.register_parameter( + "input_vars", nn.Parameter(torch.Tensor(1, 1, lstm_size)) + ) + self.w_lstm = nn.LSTM( + input_size=self.lstm_size, + hidden_size=self.lstm_size, + num_layers=self.lstm_N, + ) + self.w_embd = nn.Embedding(self.num_ops, self.lstm_size) + self.w_pred = nn.Linear(self.lstm_size, self.num_ops) + + nn.init.uniform_(self.input_vars, -0.1, 0.1) + nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1) + nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1) + nn.init.uniform_(self.w_embd.weight, -0.1, 0.1) + nn.init.uniform_(self.w_pred.weight, -0.1, 0.1) + + def forward(self): + + inputs, h0 = self.input_vars, None + log_probs, entropys, sampled_arch = [], [], [] + for iedge in range(self.num_edge): + outputs, h0 = self.w_lstm(inputs, h0) + + logits = self.w_pred(outputs) + logits = logits / self.temperature + logits = self.tanh_constant * torch.tanh(logits) + # distribution + op_distribution = Categorical(logits=logits) + op_index = op_distribution.sample() + sampled_arch.append(op_index.item()) + + op_log_prob = op_distribution.log_prob(op_index) + log_probs.append(op_log_prob.view(-1)) + op_entropy = op_distribution.entropy() + entropys.append(op_entropy.view(-1)) + + # obtain the input embedding for the next step + inputs = self.w_embd(op_index) + return ( + torch.sum(torch.cat(log_probs)), + torch.sum(torch.cat(entropys)), + sampled_arch, + ) diff --git a/correlation/models/cell_searchs/search_model_gdas.py b/correlation/models/cell_searchs/search_model_gdas.py new file mode 100644 index 0000000..82f7b9a --- /dev/null +++ b/correlation/models/cell_searchs/search_model_gdas.py @@ -0,0 +1,142 @@ +########################################################################### +# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 # +########################################################################### +import torch +import torch.nn as nn +from copy import deepcopy +from ..cell_operations import ResNetBasicblock +from .search_cells import NAS201SearchCell as SearchCell +from .genotypes import Structure + + +class TinyNetworkGDAS(nn.Module): + + # def __init__(self, C, N, max_nodes, num_classes, search_space, affine=False, track_running_stats=True): + def __init__( + self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats + ): + super(TinyNetworkGDAS, self).__init__() + self._C = C + self._layerN = N + self.max_nodes = max_nodes + self.stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + C_prev, num_edge, edge2index = C, None, None + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2) + else: + cell = SearchCell( + C_prev, + C_curr, + 1, + max_nodes, + search_space, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev = cell.out_dim + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.tau = 10 + + def get_weights(self): + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def set_tau(self, tau): + self.tau = tau + + def get_tau(self): + return self.tau + + def get_alphas(self): + return [self.arch_parameters] + + def show_alphas(self): + with torch.no_grad(): + return "arch-parameters :\n{:}".format( + nn.functional.softmax(self.arch_parameters, dim=-1).cpu() + ) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def genotype(self): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + with torch.no_grad(): + weights = self.arch_parameters[self.edge2index[node_str]] + op_name = self.op_names[weights.argmax().item()] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def forward(self, inputs): + while True: + gumbels = -torch.empty_like(self.arch_parameters).exponential_().log() + logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau + probs = nn.functional.softmax(logits, dim=1) + index = probs.max(-1, keepdim=True)[1] + one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0) + hardwts = one_h - probs.detach() + probs + if ( + (torch.isinf(gumbels).any()) + or (torch.isinf(probs).any()) + or (torch.isnan(probs).any()) + ): + continue + else: + break + + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + if isinstance(cell, SearchCell): + feature = cell.forward_gdas(feature, hardwts, index) + else: + feature = cell(feature) + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_gdas_frc_nasnet.py b/correlation/models/cell_searchs/search_model_gdas_frc_nasnet.py new file mode 100644 index 0000000..9ca5ce7 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_gdas_frc_nasnet.py @@ -0,0 +1,200 @@ +########################################################################### +# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 # +########################################################################### +import torch +import torch.nn as nn +from copy import deepcopy + +from .search_cells import NASNetSearchCell as SearchCell +from ..cell_operations import RAW_OP_CLASSES + + +# The macro structure is based on NASNet +class NASNetworkGDAS_FRC(nn.Module): + def __init__( + self, + C, + N, + steps, + multiplier, + stem_multiplier, + num_classes, + search_space, + affine, + track_running_stats, + ): + super(NASNetworkGDAS_FRC, self).__init__() + self._C = C + self._layerN = N + self._steps = steps + self._multiplier = multiplier + self.stem = nn.Sequential( + nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(C * stem_multiplier), + ) + + # config for each layer + layer_channels = ( + [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1) + ) + layer_reductions = ( + [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1) + ) + + num_edge, edge2index = None, None + C_prev_prev, C_prev, C_curr, reduction_prev = ( + C * stem_multiplier, + C * stem_multiplier, + C, + False, + ) + + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = RAW_OP_CLASSES["gdas_reduction"]( + C_prev_prev, + C_prev, + C_curr, + reduction_prev, + affine, + track_running_stats, + ) + else: + cell = SearchCell( + search_space, + steps, + multiplier, + C_prev_prev, + C_prev, + C_curr, + reduction, + reduction_prev, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + reduction + or num_edge == cell.num_edges + and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev_prev, C_prev, reduction_prev = ( + C_prev, + cell.multiplier * C_curr, + reduction, + ) + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.tau = 10 + + def get_weights(self): + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def set_tau(self, tau): + self.tau = tau + + def get_tau(self): + return self.tau + + def get_alphas(self): + return [self.arch_parameters] + + def show_alphas(self): + with torch.no_grad(): + A = "arch-normal-parameters :\n{:}".format( + nn.functional.softmax(self.arch_parameters, dim=-1).cpu() + ) + return "{:}".format(A) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def genotype(self): + def _parse(weights): + gene = [] + for i in range(self._steps): + edges = [] + for j in range(2 + i): + node_str = "{:}<-{:}".format(i, j) + ws = weights[self.edge2index[node_str]] + for k, op_name in enumerate(self.op_names): + if op_name == "none": + continue + edges.append((op_name, j, ws[k])) + edges = sorted(edges, key=lambda x: -x[-1]) + selected_edges = edges[:2] + gene.append(tuple(selected_edges)) + return gene + + with torch.no_grad(): + gene_normal = _parse( + torch.softmax(self.arch_parameters, dim=-1).cpu().numpy() + ) + return { + "normal": gene_normal, + "normal_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + } + + def forward(self, inputs): + def get_gumbel_prob(xins): + while True: + gumbels = -torch.empty_like(xins).exponential_().log() + logits = (xins.log_softmax(dim=1) + gumbels) / self.tau + probs = nn.functional.softmax(logits, dim=1) + index = probs.max(-1, keepdim=True)[1] + one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0) + hardwts = one_h - probs.detach() + probs + if ( + (torch.isinf(gumbels).any()) + or (torch.isinf(probs).any()) + or (torch.isnan(probs).any()) + ): + continue + else: + break + return hardwts, index + + hardwts, index = get_gumbel_prob(self.arch_parameters) + + s0 = s1 = self.stem(inputs) + for i, cell in enumerate(self.cells): + if cell.reduction: + s0, s1 = s1, cell(s0, s1) + else: + s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index) + out = self.lastact(s1) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_gdas_nasnet.py b/correlation/models/cell_searchs/search_model_gdas_nasnet.py new file mode 100644 index 0000000..5aff5d3 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_gdas_nasnet.py @@ -0,0 +1,197 @@ +########################################################################### +# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 # +########################################################################### +import torch +import torch.nn as nn +from copy import deepcopy +from .search_cells import NASNetSearchCell as SearchCell + + +# The macro structure is based on NASNet +class NASNetworkGDAS(nn.Module): + def __init__( + self, + C, + N, + steps, + multiplier, + stem_multiplier, + num_classes, + search_space, + affine, + track_running_stats, + ): + super(NASNetworkGDAS, self).__init__() + self._C = C + self._layerN = N + self._steps = steps + self._multiplier = multiplier + self.stem = nn.Sequential( + nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(C * stem_multiplier), + ) + + # config for each layer + layer_channels = ( + [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1) + ) + layer_reductions = ( + [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1) + ) + + num_edge, edge2index = None, None + C_prev_prev, C_prev, C_curr, reduction_prev = ( + C * stem_multiplier, + C * stem_multiplier, + C, + False, + ) + + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + cell = SearchCell( + search_space, + steps, + multiplier, + C_prev_prev, + C_prev, + C_curr, + reduction, + reduction_prev, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_normal_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.arch_reduce_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.tau = 10 + + def get_weights(self): + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def set_tau(self, tau): + self.tau = tau + + def get_tau(self): + return self.tau + + def get_alphas(self): + return [self.arch_normal_parameters, self.arch_reduce_parameters] + + def show_alphas(self): + with torch.no_grad(): + A = "arch-normal-parameters :\n{:}".format( + nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() + ) + B = "arch-reduce-parameters :\n{:}".format( + nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() + ) + return "{:}\n{:}".format(A, B) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def genotype(self): + def _parse(weights): + gene = [] + for i in range(self._steps): + edges = [] + for j in range(2 + i): + node_str = "{:}<-{:}".format(i, j) + ws = weights[self.edge2index[node_str]] + for k, op_name in enumerate(self.op_names): + if op_name == "none": + continue + edges.append((op_name, j, ws[k])) + edges = sorted(edges, key=lambda x: -x[-1]) + selected_edges = edges[:2] + gene.append(tuple(selected_edges)) + return gene + + with torch.no_grad(): + gene_normal = _parse( + torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy() + ) + gene_reduce = _parse( + torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy() + ) + return { + "normal": gene_normal, + "normal_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + "reduce": gene_reduce, + "reduce_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + } + + def forward(self, inputs): + def get_gumbel_prob(xins): + while True: + gumbels = -torch.empty_like(xins).exponential_().log() + logits = (xins.log_softmax(dim=1) + gumbels) / self.tau + probs = nn.functional.softmax(logits, dim=1) + index = probs.max(-1, keepdim=True)[1] + one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0) + hardwts = one_h - probs.detach() + probs + if ( + (torch.isinf(gumbels).any()) + or (torch.isinf(probs).any()) + or (torch.isnan(probs).any()) + ): + continue + else: + break + return hardwts, index + + normal_hardwts, normal_index = get_gumbel_prob(self.arch_normal_parameters) + reduce_hardwts, reduce_index = get_gumbel_prob(self.arch_reduce_parameters) + + s0 = s1 = self.stem(inputs) + for i, cell in enumerate(self.cells): + if cell.reduction: + hardwts, index = reduce_hardwts, reduce_index + else: + hardwts, index = normal_hardwts, normal_index + s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index) + out = self.lastact(s1) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_random.py b/correlation/models/cell_searchs/search_model_random.py new file mode 100644 index 0000000..611dc75 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_random.py @@ -0,0 +1,102 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +############################################################################## +# Random Search and Reproducibility for Neural Architecture Search, UAI 2019 # +############################################################################## +import torch, random +import torch.nn as nn +from copy import deepcopy +from ..cell_operations import ResNetBasicblock +from .search_cells import NAS201SearchCell as SearchCell +from .genotypes import Structure + + +class TinyNetworkRANDOM(nn.Module): + def __init__( + self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats + ): + super(TinyNetworkRANDOM, self).__init__() + self._C = C + self._layerN = N + self.max_nodes = max_nodes + self.stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + C_prev, num_edge, edge2index = C, None, None + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2) + else: + cell = SearchCell( + C_prev, + C_curr, + 1, + max_nodes, + search_space, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev = cell.out_dim + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_cache = None + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def random_genotype(self, set_cache): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + op_name = random.choice(self.op_names) + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + arch = Structure(genotypes) + if set_cache: + self.arch_cache = arch + return arch + + def forward(self, inputs): + + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + if isinstance(cell, SearchCell): + feature = cell.forward_dynamic(feature, self.arch_cache) + else: + feature = cell(feature) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + return out, logits diff --git a/correlation/models/cell_searchs/search_model_setn.py b/correlation/models/cell_searchs/search_model_setn.py new file mode 100644 index 0000000..ce38be9 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_setn.py @@ -0,0 +1,178 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +###################################################################################### +# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 # +###################################################################################### +import torch, random +import torch.nn as nn +from copy import deepcopy +from ..cell_operations import ResNetBasicblock +from .search_cells import NAS201SearchCell as SearchCell +from .genotypes import Structure + + +class TinyNetworkSETN(nn.Module): + def __init__( + self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats + ): + super(TinyNetworkSETN, self).__init__() + self._C = C + self._layerN = N + self.max_nodes = max_nodes + self.stem = nn.Sequential( + nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C) + ) + + layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + C_prev, num_edge, edge2index = C, None, None + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + if reduction: + cell = ResNetBasicblock(C_prev, C_curr, 2) + else: + cell = SearchCell( + C_prev, + C_curr, + 1, + max_nodes, + search_space, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev = cell.out_dim + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.mode = "urs" + self.dynamic_cell = None + + def set_cal_mode(self, mode, dynamic_cell=None): + assert mode in ["urs", "joint", "select", "dynamic"] + self.mode = mode + if mode == "dynamic": + self.dynamic_cell = deepcopy(dynamic_cell) + else: + self.dynamic_cell = None + + def get_cal_mode(self): + return self.mode + + def get_weights(self): + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def get_alphas(self): + return [self.arch_parameters] + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def genotype(self): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + with torch.no_grad(): + weights = self.arch_parameters[self.edge2index[node_str]] + op_name = self.op_names[weights.argmax().item()] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def dync_genotype(self, use_random=False): + genotypes = [] + with torch.no_grad(): + alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1) + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + if use_random: + op_name = random.choice(self.op_names) + else: + weights = alphas_cpu[self.edge2index[node_str]] + op_index = torch.multinomial(weights, 1).item() + op_name = self.op_names[op_index] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def get_log_prob(self, arch): + with torch.no_grad(): + logits = nn.functional.log_softmax(self.arch_parameters, dim=-1) + select_logits = [] + for i, node_info in enumerate(arch.nodes): + for op, xin in node_info: + node_str = "{:}<-{:}".format(i + 1, xin) + op_index = self.op_names.index(op) + select_logits.append(logits[self.edge2index[node_str], op_index]) + return sum(select_logits).item() + + def return_topK(self, K): + archs = Structure.gen_all(self.op_names, self.max_nodes, False) + pairs = [(self.get_log_prob(arch), arch) for arch in archs] + if K < 0 or K >= len(archs): + K = len(archs) + sorted_pairs = sorted(pairs, key=lambda x: -x[0]) + return_pairs = [sorted_pairs[_][1] for _ in range(K)] + return return_pairs + + def forward(self, inputs): + alphas = nn.functional.softmax(self.arch_parameters, dim=-1) + with torch.no_grad(): + alphas_cpu = alphas.detach().cpu() + + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + if isinstance(cell, SearchCell): + if self.mode == "urs": + feature = cell.forward_urs(feature) + elif self.mode == "select": + feature = cell.forward_select(feature, alphas_cpu) + elif self.mode == "joint": + feature = cell.forward_joint(feature, alphas) + elif self.mode == "dynamic": + feature = cell.forward_dynamic(feature, self.dynamic_cell) + else: + raise ValueError("invalid mode={:}".format(self.mode)) + else: + feature = cell(feature) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/cell_searchs/search_model_setn_nasnet.py b/correlation/models/cell_searchs/search_model_setn_nasnet.py new file mode 100644 index 0000000..c406fc3 --- /dev/null +++ b/correlation/models/cell_searchs/search_model_setn_nasnet.py @@ -0,0 +1,205 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +###################################################################################### +# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 # +###################################################################################### +import torch +import torch.nn as nn +from copy import deepcopy +from typing import List, Text, Dict +from .search_cells import NASNetSearchCell as SearchCell + + +# The macro structure is based on NASNet +class NASNetworkSETN(nn.Module): + def __init__( + self, + C: int, + N: int, + steps: int, + multiplier: int, + stem_multiplier: int, + num_classes: int, + search_space: List[Text], + affine: bool, + track_running_stats: bool, + ): + super(NASNetworkSETN, self).__init__() + self._C = C + self._layerN = N + self._steps = steps + self._multiplier = multiplier + self.stem = nn.Sequential( + nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(C * stem_multiplier), + ) + + # config for each layer + layer_channels = ( + [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1) + ) + layer_reductions = ( + [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1) + ) + + num_edge, edge2index = None, None + C_prev_prev, C_prev, C_curr, reduction_prev = ( + C * stem_multiplier, + C * stem_multiplier, + C, + False, + ) + + self.cells = nn.ModuleList() + for index, (C_curr, reduction) in enumerate( + zip(layer_channels, layer_reductions) + ): + cell = SearchCell( + search_space, + steps, + multiplier, + C_prev_prev, + C_prev, + C_curr, + reduction, + reduction_prev, + affine, + track_running_stats, + ) + if num_edge is None: + num_edge, edge2index = cell.num_edges, cell.edge2index + else: + assert ( + num_edge == cell.num_edges and edge2index == cell.edge2index + ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges) + self.cells.append(cell) + C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction + self.op_names = deepcopy(search_space) + self._Layer = len(self.cells) + self.edge2index = edge2index + self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + self.arch_normal_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.arch_reduce_parameters = nn.Parameter( + 1e-3 * torch.randn(num_edge, len(search_space)) + ) + self.mode = "urs" + self.dynamic_cell = None + + def set_cal_mode(self, mode, dynamic_cell=None): + assert mode in ["urs", "joint", "select", "dynamic"] + self.mode = mode + if mode == "dynamic": + self.dynamic_cell = deepcopy(dynamic_cell) + else: + self.dynamic_cell = None + + def get_weights(self): + xlist = list(self.stem.parameters()) + list(self.cells.parameters()) + xlist += list(self.lastact.parameters()) + list( + self.global_pooling.parameters() + ) + xlist += list(self.classifier.parameters()) + return xlist + + def get_alphas(self): + return [self.arch_normal_parameters, self.arch_reduce_parameters] + + def show_alphas(self): + with torch.no_grad(): + A = "arch-normal-parameters :\n{:}".format( + nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() + ) + B = "arch-reduce-parameters :\n{:}".format( + nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() + ) + return "{:}\n{:}".format(A, B) + + def get_message(self): + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def dync_genotype(self, use_random=False): + genotypes = [] + with torch.no_grad(): + alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1) + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = "{:}<-{:}".format(i, j) + if use_random: + op_name = random.choice(self.op_names) + else: + weights = alphas_cpu[self.edge2index[node_str]] + op_index = torch.multinomial(weights, 1).item() + op_name = self.op_names[op_index] + xlist.append((op_name, j)) + genotypes.append(tuple(xlist)) + return Structure(genotypes) + + def genotype(self): + def _parse(weights): + gene = [] + for i in range(self._steps): + edges = [] + for j in range(2 + i): + node_str = "{:}<-{:}".format(i, j) + ws = weights[self.edge2index[node_str]] + for k, op_name in enumerate(self.op_names): + if op_name == "none": + continue + edges.append((op_name, j, ws[k])) + edges = sorted(edges, key=lambda x: -x[-1]) + selected_edges = edges[:2] + gene.append(tuple(selected_edges)) + return gene + + with torch.no_grad(): + gene_normal = _parse( + torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy() + ) + gene_reduce = _parse( + torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy() + ) + return { + "normal": gene_normal, + "normal_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + "reduce": gene_reduce, + "reduce_concat": list( + range(2 + self._steps - self._multiplier, self._steps + 2) + ), + } + + def forward(self, inputs): + normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1) + reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1) + + s0 = s1 = self.stem(inputs) + for i, cell in enumerate(self.cells): + # [TODO] + raise NotImplementedError + if cell.reduction: + hardwts, index = reduce_hardwts, reduce_index + else: + hardwts, index = normal_hardwts, normal_index + s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index) + out = self.lastact(s1) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits diff --git a/correlation/models/clone_weights.py b/correlation/models/clone_weights.py new file mode 100644 index 0000000..9e904ac --- /dev/null +++ b/correlation/models/clone_weights.py @@ -0,0 +1,74 @@ +import torch +import torch.nn as nn + + +def copy_conv(module, init): + assert isinstance(module, nn.Conv2d), "invalid module : {:}".format(module) + assert isinstance(init, nn.Conv2d), "invalid module : {:}".format(init) + new_i, new_o = module.in_channels, module.out_channels + module.weight.copy_(init.weight.detach()[:new_o, :new_i]) + if module.bias is not None: + module.bias.copy_(init.bias.detach()[:new_o]) + + +def copy_bn(module, init): + assert isinstance(module, nn.BatchNorm2d), "invalid module : {:}".format(module) + assert isinstance(init, nn.BatchNorm2d), "invalid module : {:}".format(init) + num_features = module.num_features + if module.weight is not None: + module.weight.copy_(init.weight.detach()[:num_features]) + if module.bias is not None: + module.bias.copy_(init.bias.detach()[:num_features]) + if module.running_mean is not None: + module.running_mean.copy_(init.running_mean.detach()[:num_features]) + if module.running_var is not None: + module.running_var.copy_(init.running_var.detach()[:num_features]) + + +def copy_fc(module, init): + assert isinstance(module, nn.Linear), "invalid module : {:}".format(module) + assert isinstance(init, nn.Linear), "invalid module : {:}".format(init) + new_i, new_o = module.in_features, module.out_features + module.weight.copy_(init.weight.detach()[:new_o, :new_i]) + if module.bias is not None: + module.bias.copy_(init.bias.detach()[:new_o]) + + +def copy_base(module, init): + assert type(module).__name__ in [ + "ConvBNReLU", + "Downsample", + ], "invalid module : {:}".format(module) + assert type(init).__name__ in [ + "ConvBNReLU", + "Downsample", + ], "invalid module : {:}".format(init) + if module.conv is not None: + copy_conv(module.conv, init.conv) + if module.bn is not None: + copy_bn(module.bn, init.bn) + + +def copy_basic(module, init): + copy_base(module.conv_a, init.conv_a) + copy_base(module.conv_b, init.conv_b) + if module.downsample is not None: + if init.downsample is not None: + copy_base(module.downsample, init.downsample) + # else: + # import pdb; pdb.set_trace() + + +def init_from_model(network, init_model): + with torch.no_grad(): + copy_fc(network.classifier, init_model.classifier) + for base, target in zip(init_model.layers, network.layers): + assert ( + type(base).__name__ == type(target).__name__ + ), "invalid type : {:} vs {:}".format(base, target) + if type(base).__name__ == "ConvBNReLU": + copy_base(target, base) + elif type(base).__name__ == "ResNetBasicblock": + copy_basic(target, base) + else: + raise ValueError("unknown type name : {:}".format(type(base).__name__)) diff --git a/correlation/models/initialization.py b/correlation/models/initialization.py new file mode 100644 index 0000000..e82d723 --- /dev/null +++ b/correlation/models/initialization.py @@ -0,0 +1,16 @@ +import torch +import torch.nn as nn + + +def initialize_resnet(m): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) diff --git a/correlation/models/shape_infers/InferCifarResNet.py b/correlation/models/shape_infers/InferCifarResNet.py new file mode 100644 index 0000000..1731392 --- /dev/null +++ b/correlation/models/shape_infers/InferCifarResNet.py @@ -0,0 +1,287 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch.nn as nn +import torch.nn.functional as F + +from ..initialization import initialize_resnet + + +class ConvBNReLU(nn.Module): + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + if has_bn: + self.bn = nn.BatchNorm2d(nOut) + else: + self.bn = None + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + + def forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.bn: + out = self.bn(conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + + return out + + +class ResNetBasicblock(nn.Module): + num_conv = 2 + expansion = 1 + + def __init__(self, iCs, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + assert isinstance(iCs, tuple) or isinstance( + iCs, list + ), "invalid type of iCs : {:}".format(iCs) + assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs) + + self.conv_a = ConvBNReLU( + iCs[0], + iCs[1], + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + residual_in = iCs[0] + if stride == 2: + self.downsample = ConvBNReLU( + iCs[0], + iCs[2], + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + residual_in = iCs[2] + elif iCs[0] != iCs[2]: + self.downsample = ConvBNReLU( + iCs[0], + iCs[2], + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + # self.out_dim = max(residual_in, iCs[2]) + self.out_dim = iCs[2] + + def forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + basicblock + return F.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, iCs, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + assert isinstance(iCs, tuple) or isinstance( + iCs, list + ), "invalid type of iCs : {:}".format(iCs) + assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs) + self.conv_1x1 = ConvBNReLU( + iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + iCs[1], + iCs[2], + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False + ) + residual_in = iCs[0] + if stride == 2: + self.downsample = ConvBNReLU( + iCs[0], + iCs[3], + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + residual_in = iCs[3] + elif iCs[0] != iCs[3]: + self.downsample = ConvBNReLU( + iCs[0], + iCs[3], + 1, + 1, + 0, + False, + has_avg=False, + has_bn=False, + has_relu=False, + ) + residual_in = iCs[3] + else: + self.downsample = None + # self.out_dim = max(residual_in, iCs[3]) + self.out_dim = iCs[3] + + def forward(self, inputs): + + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + bottleneck + return F.relu(out, inplace=True) + + +class InferCifarResNet(nn.Module): + def __init__( + self, block_name, depth, xblocks, xchannels, num_classes, zero_init_residual + ): + super(InferCifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + assert len(xblocks) == 3, "invalid xblocks : {:}".format(xblocks) + + self.message = ( + "InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.xchannels = xchannels + self.layers = nn.ModuleList( + [ + ConvBNReLU( + xchannels[0], + xchannels[1], + 3, + 1, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + ] + ) + last_channel_idx = 1 + for stage in range(3): + for iL in range(layer_blocks): + num_conv = block.num_conv + iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1] + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iCs, stride) + last_channel_idx += num_conv + self.xchannels[last_channel_idx] = module.out_dim + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iCs, + module.out_dim, + stride, + ) + if iL + 1 == xblocks[stage]: # reach the maximum depth + out_channel = module.out_dim + for iiL in range(iL + 1, layer_blocks): + last_channel_idx += num_conv + self.xchannels[last_channel_idx] = module.out_dim + break + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(self.xchannels[-1], num_classes) + + self.apply(initialize_resnet) + if zero_init_residual: + for m in self.modules(): + if isinstance(m, ResNetBasicblock): + nn.init.constant_(m.conv_b.bn.weight, 0) + elif isinstance(m, ResNetBottleneck): + nn.init.constant_(m.conv_1x4.bn.weight, 0) + + def get_message(self): + return self.message + + def forward(self, inputs): + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_infers/InferCifarResNet_depth.py b/correlation/models/shape_infers/InferCifarResNet_depth.py new file mode 100644 index 0000000..c6f9bb3 --- /dev/null +++ b/correlation/models/shape_infers/InferCifarResNet_depth.py @@ -0,0 +1,263 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch.nn as nn +import torch.nn.functional as F +from ..initialization import initialize_resnet + + +class ConvBNReLU(nn.Module): + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + if has_bn: + self.bn = nn.BatchNorm2d(nOut) + else: + self.bn = None + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + + def forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.bn: + out = self.bn(conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + + return out + + +class ResNetBasicblock(nn.Module): + num_conv = 2 + expansion = 1 + + def __init__(self, inplanes, planes, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + + self.conv_a = ConvBNReLU( + inplanes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes + + def forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + basicblock + return F.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, inplanes, planes, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_1x1 = ConvBNReLU( + inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + planes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + planes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes * self.expansion: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=False, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes * self.expansion + + def forward(self, inputs): + + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + bottleneck + return F.relu(out, inplace=True) + + +class InferDepthCifarResNet(nn.Module): + def __init__(self, block_name, depth, xblocks, num_classes, zero_init_residual): + super(InferDepthCifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + assert len(xblocks) == 3, "invalid xblocks : {:}".format(xblocks) + + self.message = ( + "InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True + ) + ] + ) + self.channels = [16] + for stage in range(3): + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 16 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + planes, + module.out_dim, + stride, + ) + if iL + 1 == xblocks[stage]: # reach the maximum depth + break + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(self.channels[-1], num_classes) + + self.apply(initialize_resnet) + if zero_init_residual: + for m in self.modules(): + if isinstance(m, ResNetBasicblock): + nn.init.constant_(m.conv_b.bn.weight, 0) + elif isinstance(m, ResNetBottleneck): + nn.init.constant_(m.conv_1x4.bn.weight, 0) + + def get_message(self): + return self.message + + def forward(self, inputs): + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_infers/InferCifarResNet_width.py b/correlation/models/shape_infers/InferCifarResNet_width.py new file mode 100644 index 0000000..9400f71 --- /dev/null +++ b/correlation/models/shape_infers/InferCifarResNet_width.py @@ -0,0 +1,277 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch.nn as nn +import torch.nn.functional as F +from ..initialization import initialize_resnet + + +class ConvBNReLU(nn.Module): + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + if has_bn: + self.bn = nn.BatchNorm2d(nOut) + else: + self.bn = None + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + + def forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.bn: + out = self.bn(conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + + return out + + +class ResNetBasicblock(nn.Module): + num_conv = 2 + expansion = 1 + + def __init__(self, iCs, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + assert isinstance(iCs, tuple) or isinstance( + iCs, list + ), "invalid type of iCs : {:}".format(iCs) + assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs) + + self.conv_a = ConvBNReLU( + iCs[0], + iCs[1], + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + residual_in = iCs[0] + if stride == 2: + self.downsample = ConvBNReLU( + iCs[0], + iCs[2], + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + residual_in = iCs[2] + elif iCs[0] != iCs[2]: + self.downsample = ConvBNReLU( + iCs[0], + iCs[2], + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + # self.out_dim = max(residual_in, iCs[2]) + self.out_dim = iCs[2] + + def forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + basicblock + return F.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, iCs, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + assert isinstance(iCs, tuple) or isinstance( + iCs, list + ), "invalid type of iCs : {:}".format(iCs) + assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs) + self.conv_1x1 = ConvBNReLU( + iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + iCs[1], + iCs[2], + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False + ) + residual_in = iCs[0] + if stride == 2: + self.downsample = ConvBNReLU( + iCs[0], + iCs[3], + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + residual_in = iCs[3] + elif iCs[0] != iCs[3]: + self.downsample = ConvBNReLU( + iCs[0], + iCs[3], + 1, + 1, + 0, + False, + has_avg=False, + has_bn=False, + has_relu=False, + ) + residual_in = iCs[3] + else: + self.downsample = None + # self.out_dim = max(residual_in, iCs[3]) + self.out_dim = iCs[3] + + def forward(self, inputs): + + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + bottleneck + return F.relu(out, inplace=True) + + +class InferWidthCifarResNet(nn.Module): + def __init__(self, block_name, depth, xchannels, num_classes, zero_init_residual): + super(InferWidthCifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + + self.message = ( + "InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.xchannels = xchannels + self.layers = nn.ModuleList( + [ + ConvBNReLU( + xchannels[0], + xchannels[1], + 3, + 1, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + ] + ) + last_channel_idx = 1 + for stage in range(3): + for iL in range(layer_blocks): + num_conv = block.num_conv + iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1] + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iCs, stride) + last_channel_idx += num_conv + self.xchannels[last_channel_idx] = module.out_dim + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iCs, + module.out_dim, + stride, + ) + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(self.xchannels[-1], num_classes) + + self.apply(initialize_resnet) + if zero_init_residual: + for m in self.modules(): + if isinstance(m, ResNetBasicblock): + nn.init.constant_(m.conv_b.bn.weight, 0) + elif isinstance(m, ResNetBottleneck): + nn.init.constant_(m.conv_1x4.bn.weight, 0) + + def get_message(self): + return self.message + + def forward(self, inputs): + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_infers/InferImagenetResNet.py b/correlation/models/shape_infers/InferImagenetResNet.py new file mode 100644 index 0000000..0415e58 --- /dev/null +++ b/correlation/models/shape_infers/InferImagenetResNet.py @@ -0,0 +1,324 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +import torch.nn as nn +import torch.nn.functional as F +from ..initialization import initialize_resnet + + +class ConvBNReLU(nn.Module): + + num_conv = 1 + + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + if has_bn: + self.bn = nn.BatchNorm2d(nOut) + else: + self.bn = None + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + + def forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.bn: + out = self.bn(conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + + return out + + +class ResNetBasicblock(nn.Module): + num_conv = 2 + expansion = 1 + + def __init__(self, iCs, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + assert isinstance(iCs, tuple) or isinstance( + iCs, list + ), "invalid type of iCs : {:}".format(iCs) + assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs) + + self.conv_a = ConvBNReLU( + iCs[0], + iCs[1], + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + residual_in = iCs[0] + if stride == 2: + self.downsample = ConvBNReLU( + iCs[0], + iCs[2], + 1, + 1, + 0, + False, + has_avg=True, + has_bn=True, + has_relu=False, + ) + residual_in = iCs[2] + elif iCs[0] != iCs[2]: + self.downsample = ConvBNReLU( + iCs[0], + iCs[2], + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + # self.out_dim = max(residual_in, iCs[2]) + self.out_dim = iCs[2] + + def forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + basicblock + return F.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, iCs, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + assert isinstance(iCs, tuple) or isinstance( + iCs, list + ), "invalid type of iCs : {:}".format(iCs) + assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs) + self.conv_1x1 = ConvBNReLU( + iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + iCs[1], + iCs[2], + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False + ) + residual_in = iCs[0] + if stride == 2: + self.downsample = ConvBNReLU( + iCs[0], + iCs[3], + 1, + 1, + 0, + False, + has_avg=True, + has_bn=True, + has_relu=False, + ) + residual_in = iCs[3] + elif iCs[0] != iCs[3]: + self.downsample = ConvBNReLU( + iCs[0], + iCs[3], + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + residual_in = iCs[3] + else: + self.downsample = None + # self.out_dim = max(residual_in, iCs[3]) + self.out_dim = iCs[3] + + def forward(self, inputs): + + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = residual + bottleneck + return F.relu(out, inplace=True) + + +class InferImagenetResNet(nn.Module): + def __init__( + self, + block_name, + layers, + xblocks, + xchannels, + deep_stem, + num_classes, + zero_init_residual, + ): + super(InferImagenetResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "BasicBlock": + block = ResNetBasicblock + elif block_name == "Bottleneck": + block = ResNetBottleneck + else: + raise ValueError("invalid block : {:}".format(block_name)) + assert len(xblocks) == len( + layers + ), "invalid layers : {:} vs xblocks : {:}".format(layers, xblocks) + + self.message = "InferImagenetResNet : Depth : {:} -> {:}, Layers for each block : {:}".format( + sum(layers) * block.num_conv, sum(xblocks) * block.num_conv, xblocks + ) + self.num_classes = num_classes + self.xchannels = xchannels + if not deep_stem: + self.layers = nn.ModuleList( + [ + ConvBNReLU( + xchannels[0], + xchannels[1], + 7, + 2, + 3, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + ] + ) + last_channel_idx = 1 + else: + self.layers = nn.ModuleList( + [ + ConvBNReLU( + xchannels[0], + xchannels[1], + 3, + 2, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ), + ConvBNReLU( + xchannels[1], + xchannels[2], + 3, + 1, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ), + ] + ) + last_channel_idx = 2 + self.layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) + for stage, layer_blocks in enumerate(layers): + for iL in range(layer_blocks): + num_conv = block.num_conv + iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1] + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iCs, stride) + last_channel_idx += num_conv + self.xchannels[last_channel_idx] = module.out_dim + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iCs, + module.out_dim, + stride, + ) + if iL + 1 == xblocks[stage]: # reach the maximum depth + out_channel = module.out_dim + for iiL in range(iL + 1, layer_blocks): + last_channel_idx += num_conv + self.xchannels[last_channel_idx] = module.out_dim + break + assert last_channel_idx + 1 == len(self.xchannels), "{:} vs {:}".format( + last_channel_idx, len(self.xchannels) + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.classifier = nn.Linear(self.xchannels[-1], num_classes) + + self.apply(initialize_resnet) + if zero_init_residual: + for m in self.modules(): + if isinstance(m, ResNetBasicblock): + nn.init.constant_(m.conv_b.bn.weight, 0) + elif isinstance(m, ResNetBottleneck): + nn.init.constant_(m.conv_1x4.bn.weight, 0) + + def get_message(self): + return self.message + + def forward(self, inputs): + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_infers/InferMobileNetV2.py b/correlation/models/shape_infers/InferMobileNetV2.py new file mode 100644 index 0000000..d3db752 --- /dev/null +++ b/correlation/models/shape_infers/InferMobileNetV2.py @@ -0,0 +1,176 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018 +##################################################### +from torch import nn + +from ..initialization import initialize_resnet +from ..SharedUtils import parse_channel_info + + +class ConvBNReLU(nn.Module): + def __init__( + self, + in_planes, + out_planes, + kernel_size, + stride, + groups, + has_bn=True, + has_relu=True, + ): + super(ConvBNReLU, self).__init__() + padding = (kernel_size - 1) // 2 + self.conv = nn.Conv2d( + in_planes, + out_planes, + kernel_size, + stride, + padding, + groups=groups, + bias=False, + ) + if has_bn: + self.bn = nn.BatchNorm2d(out_planes) + else: + self.bn = None + if has_relu: + self.relu = nn.ReLU6(inplace=True) + else: + self.relu = None + + def forward(self, x): + out = self.conv(x) + if self.bn: + out = self.bn(out) + if self.relu: + out = self.relu(out) + return out + + +class InvertedResidual(nn.Module): + def __init__(self, channels, stride, expand_ratio, additive): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2], "invalid stride : {:}".format(stride) + assert len(channels) in [2, 3], "invalid channels : {:}".format(channels) + + if len(channels) == 2: + layers = [] + else: + layers = [ConvBNReLU(channels[0], channels[1], 1, 1, 1)] + layers.extend( + [ + # dw + ConvBNReLU(channels[-2], channels[-2], 3, stride, channels[-2]), + # pw-linear + ConvBNReLU(channels[-2], channels[-1], 1, 1, 1, True, False), + ] + ) + self.conv = nn.Sequential(*layers) + self.additive = additive + if self.additive and channels[0] != channels[-1]: + self.shortcut = ConvBNReLU(channels[0], channels[-1], 1, 1, 1, True, False) + else: + self.shortcut = None + self.out_dim = channels[-1] + + def forward(self, x): + out = self.conv(x) + # if self.additive: return additive_func(out, x) + if self.shortcut: + return out + self.shortcut(x) + else: + return out + + +class InferMobileNetV2(nn.Module): + def __init__(self, num_classes, xchannels, xblocks, dropout): + super(InferMobileNetV2, self).__init__() + block = InvertedResidual + inverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + [6, 160, 3, 2], + [6, 320, 1, 1], + ] + assert len(inverted_residual_setting) == len( + xblocks + ), "invalid number of layers : {:} vs {:}".format( + len(inverted_residual_setting), len(xblocks) + ) + for block_num, ir_setting in zip(xblocks, inverted_residual_setting): + assert block_num <= ir_setting[2], "{:} vs {:}".format( + block_num, ir_setting + ) + xchannels = parse_channel_info(xchannels) + # for i, chs in enumerate(xchannels): + # if i > 0: assert chs[0] == xchannels[i-1][-1], 'Layer[{:}] is invalid {:} vs {:}'.format(i, xchannels[i-1], chs) + self.xchannels = xchannels + self.message = "InferMobileNetV2 : xblocks={:}".format(xblocks) + # building first layer + features = [ConvBNReLU(xchannels[0][0], xchannels[0][1], 3, 2, 1)] + last_channel_idx = 1 + + # building inverted residual blocks + for stage, (t, c, n, s) in enumerate(inverted_residual_setting): + for i in range(n): + stride = s if i == 0 else 1 + additv = True if i > 0 else False + module = block(self.xchannels[last_channel_idx], stride, t, additv) + features.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, Cs={:}, stride={:}, expand={:}, original-C={:}".format( + stage, + i, + n, + len(features), + self.xchannels[last_channel_idx], + stride, + t, + c, + ) + last_channel_idx += 1 + if i + 1 == xblocks[stage]: + out_channel = module.out_dim + for iiL in range(i + 1, n): + last_channel_idx += 1 + self.xchannels[last_channel_idx][0] = module.out_dim + break + # building last several layers + features.append( + ConvBNReLU( + self.xchannels[last_channel_idx][0], + self.xchannels[last_channel_idx][1], + 1, + 1, + 1, + ) + ) + assert last_channel_idx + 2 == len(self.xchannels), "{:} vs {:}".format( + last_channel_idx, len(self.xchannels) + ) + # make it nn.Sequential + self.features = nn.Sequential(*features) + + # building classifier + self.classifier = nn.Sequential( + nn.Dropout(dropout), + nn.Linear(self.xchannels[last_channel_idx][1], num_classes), + ) + + # weight initialization + self.apply(initialize_resnet) + + def get_message(self): + return self.message + + def forward(self, inputs): + features = self.features(inputs) + vectors = features.mean([2, 3]) + predicts = self.classifier(vectors) + return features, predicts diff --git a/correlation/models/shape_infers/InferTinyCellNet.py b/correlation/models/shape_infers/InferTinyCellNet.py new file mode 100644 index 0000000..9fab553 --- /dev/null +++ b/correlation/models/shape_infers/InferTinyCellNet.py @@ -0,0 +1,74 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +from typing import List, Text, Any +import torch.nn as nn + +from ..cell_operations import ResNetBasicblock +from ..cell_infers.cells import InferCell + + +class DynamicShapeTinyNet(nn.Module): + def __init__(self, channels: List[int], genotype: Any, num_classes: int): + super(DynamicShapeTinyNet, self).__init__() + self._channels = channels + if len(channels) % 3 != 2: + raise ValueError("invalid number of layers : {:}".format(len(channels))) + self._num_stage = N = len(channels) // 3 + + self.stem = nn.Sequential( + nn.Conv2d(3, channels[0], kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(channels[0]), + ) + + # layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + c_prev = channels[0] + self.cells = nn.ModuleList() + for index, (c_curr, reduction) in enumerate(zip(channels, layer_reductions)): + if reduction: + cell = ResNetBasicblock(c_prev, c_curr, 2, True) + else: + cell = InferCell(genotype, c_prev, c_curr, 1) + self.cells.append(cell) + c_prev = cell.out_dim + self._num_layer = len(self.cells) + + self.lastact = nn.Sequential(nn.BatchNorm2d(c_prev), nn.ReLU(inplace=True)) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(c_prev, num_classes) + + def get_message(self) -> Text: + string = self.extra_repr() + for i, cell in enumerate(self.cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self.cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(C={_channels}, N={_num_stage}, L={_num_layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def forward(self, inputs): + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + feature = cell(feature) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return logits + + def forward_pre_GAP(self, inputs): + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + feature = cell(feature) + + out = self.lastact(feature) + return out + diff --git a/correlation/models/shape_infers/__init__.py b/correlation/models/shape_infers/__init__.py new file mode 100644 index 0000000..9c305ff --- /dev/null +++ b/correlation/models/shape_infers/__init__.py @@ -0,0 +1,9 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +from .InferCifarResNet_width import InferWidthCifarResNet +from .InferImagenetResNet import InferImagenetResNet +from .InferCifarResNet_depth import InferDepthCifarResNet +from .InferCifarResNet import InferCifarResNet +from .InferMobileNetV2 import InferMobileNetV2 +from .InferTinyCellNet import DynamicShapeTinyNet diff --git a/correlation/models/shape_infers/shared_utils.py b/correlation/models/shape_infers/shared_utils.py new file mode 100644 index 0000000..86ab949 --- /dev/null +++ b/correlation/models/shape_infers/shared_utils.py @@ -0,0 +1,5 @@ +def parse_channel_info(xstring): + blocks = xstring.split(" ") + blocks = [x.split("-") for x in blocks] + blocks = [[int(_) for _ in x] for x in blocks] + return blocks diff --git a/correlation/models/shape_searchs/SearchCifarResNet.py b/correlation/models/shape_searchs/SearchCifarResNet.py new file mode 100644 index 0000000..653051b --- /dev/null +++ b/correlation/models/shape_searchs/SearchCifarResNet.py @@ -0,0 +1,760 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, torch +from collections import OrderedDict +from bisect import bisect_right +import torch.nn as nn +from ..initialization import initialize_resnet +from ..SharedUtils import additive_func +from .SoftSelect import select2withP, ChannelWiseInter +from .SoftSelect import linear_forward +from .SoftSelect import get_width_choices + + +def get_depth_choices(nDepth, return_num): + if nDepth == 2: + choices = (1, 2) + elif nDepth == 3: + choices = (1, 2, 3) + elif nDepth > 3: + choices = list(range(1, nDepth + 1, 2)) + if choices[-1] < nDepth: + choices.append(nDepth) + else: + raise ValueError("invalid nDepth : {:}".format(nDepth)) + if return_num: + return len(choices) + else: + return choices + + +def conv_forward(inputs, conv, choices): + iC = conv.in_channels + fill_size = list(inputs.size()) + fill_size[1] = iC - fill_size[1] + filled = torch.zeros(fill_size, device=inputs.device) + xinputs = torch.cat((inputs, filled), dim=1) + outputs = conv(xinputs) + selecteds = [outputs[:, :oC] for oC in choices] + return selecteds + + +class ConvBNReLU(nn.Module): + num_conv = 1 + + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + self.InShape = None + self.OutShape = None + self.choices = get_width_choices(nOut) + self.register_buffer("choices_tensor", torch.Tensor(self.choices)) + + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + # if has_bn : self.bn = nn.BatchNorm2d(nOut) + # else : self.bn = None + self.has_bn = has_bn + self.BNs = nn.ModuleList() + for i, _out in enumerate(self.choices): + self.BNs.append(nn.BatchNorm2d(_out)) + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + self.in_dim = nIn + self.out_dim = nOut + self.search_mode = "basic" + + def get_flops(self, channels, check_range=True, divide=1): + iC, oC = channels + if check_range: + assert ( + iC <= self.conv.in_channels and oC <= self.conv.out_channels + ), "{:} vs {:} | {:} vs {:}".format( + iC, self.conv.in_channels, oC, self.conv.out_channels + ) + assert ( + isinstance(self.InShape, tuple) and len(self.InShape) == 2 + ), "invalid in-shape : {:}".format(self.InShape) + assert ( + isinstance(self.OutShape, tuple) and len(self.OutShape) == 2 + ), "invalid out-shape : {:}".format(self.OutShape) + # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups + conv_per_position_flops = ( + self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups + ) + all_positions = self.OutShape[0] * self.OutShape[1] + flops = (conv_per_position_flops * all_positions / divide) * iC * oC + if self.conv.bias is not None: + flops += all_positions / divide + return flops + + def get_range(self): + return [self.choices] + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, index, prob = tuple_inputs + index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob) + probability = torch.squeeze(probability) + assert len(index) == 2, "invalid length : {:}".format(index) + # compute expected flop + # coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability) + expected_outC = (self.choices_tensor * probability).sum() + expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6) + if self.avg: + out = self.avg(inputs) + else: + out = inputs + # convolutional layer + out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index]) + out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)] + # merge + out_channel = max([x.size(1) for x in out_bns]) + outA = ChannelWiseInter(out_bns[0], out_channel) + outB = ChannelWiseInter(out_bns[1], out_channel) + out = outA * prob[0] + outB * prob[1] + # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1]) + + if self.relu: + out = self.relu(out) + else: + out = out + return out, expected_outC, expected_flop + + def basic_forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.has_bn: + out = self.BNs[-1](conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + self.OutShape = (out.size(-2), out.size(-1)) + return out + + +class ResNetBasicblock(nn.Module): + expansion = 1 + num_conv = 2 + + def __init__(self, inplanes, planes, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_a = ConvBNReLU( + inplanes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes + self.search_mode = "basic" + + def get_range(self): + return self.conv_a.get_range() + self.conv_b.get_range() + + def get_flops(self, channels): + assert len(channels) == 3, "invalid channels : {:}".format(channels) + flop_A = self.conv_a.get_flops([channels[0], channels[1]]) + flop_B = self.conv_b.get_flops([channels[1], channels[2]]) + if hasattr(self.downsample, "get_flops"): + flop_C = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_C = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_C = ( + channels[0] + * channels[-1] + * self.conv_b.OutShape[0] + * self.conv_b.OutShape[1] + ) + return flop_A + flop_B + flop_C + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2 + out_a, expected_inC_a, expected_flop_a = self.conv_a( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + out_b, expected_inC_b, expected_flop_b = self.conv_b( + (out_a, expected_inC_a, probability[1], indexes[1], probs[1]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[1], indexes[1], probs[1]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out_b) + return ( + nn.functional.relu(out, inplace=True), + expected_inC_b, + sum([expected_flop_a, expected_flop_b, expected_flop_c]), + ) + + def basic_forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, basicblock) + return nn.functional.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, inplanes, planes, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_1x1 = ConvBNReLU( + inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + planes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + planes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes * self.expansion: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes * self.expansion + self.search_mode = "basic" + + def get_range(self): + return ( + self.conv_1x1.get_range() + + self.conv_3x3.get_range() + + self.conv_1x4.get_range() + ) + + def get_flops(self, channels): + assert len(channels) == 4, "invalid channels : {:}".format(channels) + flop_A = self.conv_1x1.get_flops([channels[0], channels[1]]) + flop_B = self.conv_3x3.get_flops([channels[1], channels[2]]) + flop_C = self.conv_1x4.get_flops([channels[2], channels[3]]) + if hasattr(self.downsample, "get_flops"): + flop_D = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_D = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_D = ( + channels[0] + * channels[-1] + * self.conv_1x4.OutShape[0] + * self.conv_1x4.OutShape[1] + ) + return flop_A + flop_B + flop_C + flop_D + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def basic_forward(self, inputs): + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, bottleneck) + return nn.functional.relu(out, inplace=True) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3 + out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( + (out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1]) + ) + out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( + (out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[2], indexes[2], probs[2]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out_1x4) + return ( + nn.functional.relu(out, inplace=True), + expected_inC_1x4, + sum( + [ + expected_flop_1x1, + expected_flop_3x3, + expected_flop_1x4, + expected_flop_c, + ] + ), + ) + + +class SearchShapeCifarResNet(nn.Module): + def __init__(self, block_name, depth, num_classes): + super(SearchShapeCifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + + self.message = ( + "SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.channels = [16] + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True + ) + ] + ) + self.InShape = None + self.depth_info = OrderedDict() + self.depth_at_i = OrderedDict() + for stage in range(3): + cur_block_choices = get_depth_choices(layer_blocks, False) + assert ( + cur_block_choices[-1] == layer_blocks + ), "stage={:}, {:} vs {:}".format(stage, cur_block_choices, layer_blocks) + self.message += ( + "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format( + stage, cur_block_choices, layer_blocks + ) + ) + block_choices, xstart = [], len(self.layers) + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 16 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iC, + module.out_dim, + stride, + ) + # added for depth + layer_index = len(self.layers) - 1 + if iL + 1 in cur_block_choices: + block_choices.append(layer_index) + if iL + 1 == layer_blocks: + self.depth_info[layer_index] = { + "choices": block_choices, + "stage": stage, + "xstart": xstart, + } + self.depth_info_list = [] + for xend, info in self.depth_info.items(): + self.depth_info_list.append((xend, info)) + xstart, xstage = info["xstart"], info["stage"] + for ilayer in range(xstart, xend + 1): + idx = bisect_right(info["choices"], ilayer - 1) + self.depth_at_i[ilayer] = (xstage, idx) + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(module.out_dim, num_classes) + self.InShape = None + self.tau = -1 + self.search_mode = "basic" + # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth) + + # parameters for width + self.Ranges = [] + self.layer2indexRange = [] + for i, layer in enumerate(self.layers): + start_index = len(self.Ranges) + self.Ranges += layer.get_range() + self.layer2indexRange.append((start_index, len(self.Ranges))) + assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format( + len(self.Ranges) + 1, depth + ) + + self.register_parameter( + "width_attentions", + nn.Parameter(torch.Tensor(len(self.Ranges), get_width_choices(None))), + ) + self.register_parameter( + "depth_attentions", + nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))), + ) + nn.init.normal_(self.width_attentions, 0, 0.01) + nn.init.normal_(self.depth_attentions, 0, 0.01) + self.apply(initialize_resnet) + + def arch_parameters(self, LR=None): + if LR is None: + return [self.width_attentions, self.depth_attentions] + else: + return [ + {"params": self.width_attentions, "lr": LR}, + {"params": self.depth_attentions, "lr": LR}, + ] + + def base_parameters(self): + return ( + list(self.layers.parameters()) + + list(self.avgpool.parameters()) + + list(self.classifier.parameters()) + ) + + def get_flop(self, mode, config_dict, extra_info): + if config_dict is not None: + config_dict = config_dict.copy() + # select channels + channels = [3] + for i, weight in enumerate(self.width_attentions): + if mode == "genotype": + with torch.no_grad(): + probe = nn.functional.softmax(weight, dim=0) + C = self.Ranges[i][torch.argmax(probe).item()] + elif mode == "max": + C = self.Ranges[i][-1] + elif mode == "fix": + C = int(math.sqrt(extra_info) * self.Ranges[i][-1]) + elif mode == "random": + assert isinstance(extra_info, float), "invalid extra_info : {:}".format( + extra_info + ) + with torch.no_grad(): + prob = nn.functional.softmax(weight, dim=0) + approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1]) + for j in range(prob.size(0)): + prob[j] = 1 / ( + abs(j - (approximate_C - self.Ranges[i][j])) + 0.2 + ) + C = self.Ranges[i][torch.multinomial(prob, 1, False).item()] + else: + raise ValueError("invalid mode : {:}".format(mode)) + channels.append(C) + # select depth + if mode == "genotype": + with torch.no_grad(): + depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + choices = torch.argmax(depth_probs, dim=1).cpu().tolist() + elif mode == "max" or mode == "fix": + choices = [depth_probs.size(1) - 1 for _ in range(depth_probs.size(0))] + elif mode == "random": + with torch.no_grad(): + depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + choices = torch.multinomial(depth_probs, 1, False).cpu().tolist() + else: + raise ValueError("invalid mode : {:}".format(mode)) + selected_layers = [] + for choice, xvalue in zip(choices, self.depth_info_list): + xtemp = xvalue[1]["choices"][choice] - xvalue[1]["xstart"] + 1 + selected_layers.append(xtemp) + flop = 0 + for i, layer in enumerate(self.layers): + s, e = self.layer2indexRange[i] + xchl = tuple(channels[s : e + 1]) + if i in self.depth_at_i: + xstagei, xatti = self.depth_at_i[i] + if xatti <= choices[xstagei]: # leave this depth + flop += layer.get_flops(xchl) + else: + flop += 0 # do not use this layer + else: + flop += layer.get_flops(xchl) + # the last fc layer + flop += channels[-1] * self.classifier.out_features + if config_dict is None: + return flop / 1e6 + else: + config_dict["xchannels"] = channels + config_dict["xblocks"] = selected_layers + config_dict["super_type"] = "infer-shape" + config_dict["estimated_FLOP"] = flop / 1e6 + return flop / 1e6, config_dict + + def get_arch_info(self): + string = ( + "for depth and width, there are {:} + {:} attention probabilities.".format( + len(self.depth_attentions), len(self.width_attentions) + ) + ) + string += "\n{:}".format(self.depth_info) + discrepancy = [] + with torch.no_grad(): + for i, att in enumerate(self.depth_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.depth_attentions), " ".join(prob) + ) + logt = ["{:.4f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:17s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || discrepancy={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + string += "\n-----------------------------------------------" + for i, att in enumerate(self.width_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.width_attentions), " ".join(prob) + ) + logt = ["{:.3f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:52s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || dis={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + return string, discrepancy + + def set_tau(self, tau_max, tau_min, epoch_ratio): + assert ( + epoch_ratio >= 0 and epoch_ratio <= 1 + ), "invalid epoch-ratio : {:}".format(epoch_ratio) + tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2 + self.tau = tau + + def get_message(self): + return self.message + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, inputs): + flop_width_probs = nn.functional.softmax(self.width_attentions, dim=1) + flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + flop_depth_probs = torch.flip( + torch.cumsum(torch.flip(flop_depth_probs, [1]), 1), [1] + ) + selected_widths, selected_width_probs = select2withP( + self.width_attentions, self.tau + ) + selected_depth_probs = select2withP(self.depth_attentions, self.tau, True) + with torch.no_grad(): + selected_widths = selected_widths.cpu() + + x, last_channel_idx, expected_inC, flops = inputs, 0, 3, [] + feature_maps = [] + for i, layer in enumerate(self.layers): + selected_w_index = selected_widths[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + selected_w_probs = selected_width_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + layer_prob = flop_width_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + x, expected_inC, expected_flop = layer( + (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) + ) + feature_maps.append(x) + last_channel_idx += layer.num_conv + if i in self.depth_info: # aggregate the information + choices = self.depth_info[i]["choices"] + xstagei = self.depth_info[i]["stage"] + # print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist())) + # for A, W in zip(choices, selected_depth_probs[xstagei]): + # print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W)) + possible_tensors = [] + max_C = max(feature_maps[A].size(1) for A in choices) + for tempi, A in enumerate(choices): + xtensor = ChannelWiseInter(feature_maps[A], max_C) + # drop_ratio = 1-(tempi+1.0)/len(choices) + # xtensor = drop_path(xtensor, drop_ratio) + possible_tensors.append(xtensor) + weighted_sum = sum( + xtensor * W + for xtensor, W in zip( + possible_tensors, selected_depth_probs[xstagei] + ) + ) + x = weighted_sum + + if i in self.depth_at_i: + xstagei, xatti = self.depth_at_i[i] + x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop + else: + x_expected_flop = expected_flop + flops.append(x_expected_flop) + flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6)) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = linear_forward(features, self.classifier) + return logits, torch.stack([sum(flops)]) + + def basic_forward(self, inputs): + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_searchs/SearchCifarResNet_depth.py b/correlation/models/shape_searchs/SearchCifarResNet_depth.py new file mode 100644 index 0000000..24c5d83 --- /dev/null +++ b/correlation/models/shape_searchs/SearchCifarResNet_depth.py @@ -0,0 +1,515 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, torch +from collections import OrderedDict +from bisect import bisect_right +import torch.nn as nn +from ..initialization import initialize_resnet +from ..SharedUtils import additive_func +from .SoftSelect import select2withP, ChannelWiseInter +from .SoftSelect import linear_forward +from .SoftSelect import get_width_choices + + +def get_depth_choices(nDepth, return_num): + if nDepth == 2: + choices = (1, 2) + elif nDepth == 3: + choices = (1, 2, 3) + elif nDepth > 3: + choices = list(range(1, nDepth + 1, 2)) + if choices[-1] < nDepth: + choices.append(nDepth) + else: + raise ValueError("invalid nDepth : {:}".format(nDepth)) + if return_num: + return len(choices) + else: + return choices + + +class ConvBNReLU(nn.Module): + num_conv = 1 + + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + self.InShape = None + self.OutShape = None + self.choices = get_width_choices(nOut) + self.register_buffer("choices_tensor", torch.Tensor(self.choices)) + + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + if has_bn: + self.bn = nn.BatchNorm2d(nOut) + else: + self.bn = None + if has_relu: + self.relu = nn.ReLU(inplace=False) + else: + self.relu = None + self.in_dim = nIn + self.out_dim = nOut + + def get_flops(self, divide=1): + iC, oC = self.in_dim, self.out_dim + assert ( + iC <= self.conv.in_channels and oC <= self.conv.out_channels + ), "{:} vs {:} | {:} vs {:}".format( + iC, self.conv.in_channels, oC, self.conv.out_channels + ) + assert ( + isinstance(self.InShape, tuple) and len(self.InShape) == 2 + ), "invalid in-shape : {:}".format(self.InShape) + assert ( + isinstance(self.OutShape, tuple) and len(self.OutShape) == 2 + ), "invalid out-shape : {:}".format(self.OutShape) + # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups + conv_per_position_flops = ( + self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups + ) + all_positions = self.OutShape[0] * self.OutShape[1] + flops = (conv_per_position_flops * all_positions / divide) * iC * oC + if self.conv.bias is not None: + flops += all_positions / divide + return flops + + def forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.bn: + out = self.bn(conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + self.OutShape = (out.size(-2), out.size(-1)) + return out + + +class ResNetBasicblock(nn.Module): + expansion = 1 + num_conv = 2 + + def __init__(self, inplanes, planes, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_a = ConvBNReLU( + inplanes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes + self.search_mode = "basic" + + def get_flops(self, divide=1): + flop_A = self.conv_a.get_flops(divide) + flop_B = self.conv_b.get_flops(divide) + if hasattr(self.downsample, "get_flops"): + flop_C = self.downsample.get_flops(divide) + else: + flop_C = 0 + return flop_A + flop_B + flop_C + + def forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, basicblock) + return nn.functional.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, inplanes, planes, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_1x1 = ConvBNReLU( + inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + planes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + planes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes * self.expansion: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes * self.expansion + self.search_mode = "basic" + + def get_range(self): + return ( + self.conv_1x1.get_range() + + self.conv_3x3.get_range() + + self.conv_1x4.get_range() + ) + + def get_flops(self, divide): + flop_A = self.conv_1x1.get_flops(divide) + flop_B = self.conv_3x3.get_flops(divide) + flop_C = self.conv_1x4.get_flops(divide) + if hasattr(self.downsample, "get_flops"): + flop_D = self.downsample.get_flops(divide) + else: + flop_D = 0 + return flop_A + flop_B + flop_C + flop_D + + def forward(self, inputs): + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, bottleneck) + return nn.functional.relu(out, inplace=True) + + +class SearchDepthCifarResNet(nn.Module): + def __init__(self, block_name, depth, num_classes): + super(SearchDepthCifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + + self.message = ( + "SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.channels = [16] + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True + ) + ] + ) + self.InShape = None + self.depth_info = OrderedDict() + self.depth_at_i = OrderedDict() + for stage in range(3): + cur_block_choices = get_depth_choices(layer_blocks, False) + assert ( + cur_block_choices[-1] == layer_blocks + ), "stage={:}, {:} vs {:}".format(stage, cur_block_choices, layer_blocks) + self.message += ( + "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format( + stage, cur_block_choices, layer_blocks + ) + ) + block_choices, xstart = [], len(self.layers) + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 16 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iC, + module.out_dim, + stride, + ) + # added for depth + layer_index = len(self.layers) - 1 + if iL + 1 in cur_block_choices: + block_choices.append(layer_index) + if iL + 1 == layer_blocks: + self.depth_info[layer_index] = { + "choices": block_choices, + "stage": stage, + "xstart": xstart, + } + self.depth_info_list = [] + for xend, info in self.depth_info.items(): + self.depth_info_list.append((xend, info)) + xstart, xstage = info["xstart"], info["stage"] + for ilayer in range(xstart, xend + 1): + idx = bisect_right(info["choices"], ilayer - 1) + self.depth_at_i[ilayer] = (xstage, idx) + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(module.out_dim, num_classes) + self.InShape = None + self.tau = -1 + self.search_mode = "basic" + # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth) + + self.register_parameter( + "depth_attentions", + nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))), + ) + nn.init.normal_(self.depth_attentions, 0, 0.01) + self.apply(initialize_resnet) + + def arch_parameters(self): + return [self.depth_attentions] + + def base_parameters(self): + return ( + list(self.layers.parameters()) + + list(self.avgpool.parameters()) + + list(self.classifier.parameters()) + ) + + def get_flop(self, mode, config_dict, extra_info): + if config_dict is not None: + config_dict = config_dict.copy() + # select depth + if mode == "genotype": + with torch.no_grad(): + depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + choices = torch.argmax(depth_probs, dim=1).cpu().tolist() + elif mode == "max": + choices = [depth_probs.size(1) - 1 for _ in range(depth_probs.size(0))] + elif mode == "random": + with torch.no_grad(): + depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + choices = torch.multinomial(depth_probs, 1, False).cpu().tolist() + else: + raise ValueError("invalid mode : {:}".format(mode)) + selected_layers = [] + for choice, xvalue in zip(choices, self.depth_info_list): + xtemp = xvalue[1]["choices"][choice] - xvalue[1]["xstart"] + 1 + selected_layers.append(xtemp) + flop = 0 + for i, layer in enumerate(self.layers): + if i in self.depth_at_i: + xstagei, xatti = self.depth_at_i[i] + if xatti <= choices[xstagei]: # leave this depth + flop += layer.get_flops() + else: + flop += 0 # do not use this layer + else: + flop += layer.get_flops() + # the last fc layer + flop += self.classifier.in_features * self.classifier.out_features + if config_dict is None: + return flop / 1e6 + else: + config_dict["xblocks"] = selected_layers + config_dict["super_type"] = "infer-depth" + config_dict["estimated_FLOP"] = flop / 1e6 + return flop / 1e6, config_dict + + def get_arch_info(self): + string = "for depth, there are {:} attention probabilities.".format( + len(self.depth_attentions) + ) + string += "\n{:}".format(self.depth_info) + discrepancy = [] + with torch.no_grad(): + for i, att in enumerate(self.depth_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.depth_attentions), " ".join(prob) + ) + logt = ["{:.4f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:17s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || discrepancy={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + return string, discrepancy + + def set_tau(self, tau_max, tau_min, epoch_ratio): + assert ( + epoch_ratio >= 0 and epoch_ratio <= 1 + ), "invalid epoch-ratio : {:}".format(epoch_ratio) + tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2 + self.tau = tau + + def get_message(self): + return self.message + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, inputs): + flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + flop_depth_probs = torch.flip( + torch.cumsum(torch.flip(flop_depth_probs, [1]), 1), [1] + ) + selected_depth_probs = select2withP(self.depth_attentions, self.tau, True) + + x, flops = inputs, [] + feature_maps = [] + for i, layer in enumerate(self.layers): + layer_i = layer(x) + feature_maps.append(layer_i) + if i in self.depth_info: # aggregate the information + choices = self.depth_info[i]["choices"] + xstagei = self.depth_info[i]["stage"] + possible_tensors = [] + for tempi, A in enumerate(choices): + xtensor = feature_maps[A] + possible_tensors.append(xtensor) + weighted_sum = sum( + xtensor * W + for xtensor, W in zip( + possible_tensors, selected_depth_probs[xstagei] + ) + ) + x = weighted_sum + else: + x = layer_i + + if i in self.depth_at_i: + xstagei, xatti = self.depth_at_i[i] + # print ('layer-{:03d}, stage={:}, att={:}, prob={:}, flop={:}'.format(i, xstagei, xatti, flop_depth_probs[xstagei, xatti].item(), layer.get_flops(1e6))) + x_expected_flop = flop_depth_probs[xstagei, xatti] * layer.get_flops( + 1e6 + ) + else: + x_expected_flop = layer.get_flops(1e6) + flops.append(x_expected_flop) + flops.append( + (self.classifier.in_features * self.classifier.out_features * 1.0 / 1e6) + ) + + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = linear_forward(features, self.classifier) + return logits, torch.stack([sum(flops)]) + + def basic_forward(self, inputs): + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_searchs/SearchCifarResNet_width.py b/correlation/models/shape_searchs/SearchCifarResNet_width.py new file mode 100644 index 0000000..61bee6f --- /dev/null +++ b/correlation/models/shape_searchs/SearchCifarResNet_width.py @@ -0,0 +1,619 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, torch +import torch.nn as nn +from ..initialization import initialize_resnet +from ..SharedUtils import additive_func +from .SoftSelect import select2withP, ChannelWiseInter +from .SoftSelect import linear_forward +from .SoftSelect import get_width_choices as get_choices + + +def conv_forward(inputs, conv, choices): + iC = conv.in_channels + fill_size = list(inputs.size()) + fill_size[1] = iC - fill_size[1] + filled = torch.zeros(fill_size, device=inputs.device) + xinputs = torch.cat((inputs, filled), dim=1) + outputs = conv(xinputs) + selecteds = [outputs[:, :oC] for oC in choices] + return selecteds + + +class ConvBNReLU(nn.Module): + num_conv = 1 + + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + self.InShape = None + self.OutShape = None + self.choices = get_choices(nOut) + self.register_buffer("choices_tensor", torch.Tensor(self.choices)) + + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + # if has_bn : self.bn = nn.BatchNorm2d(nOut) + # else : self.bn = None + self.has_bn = has_bn + self.BNs = nn.ModuleList() + for i, _out in enumerate(self.choices): + self.BNs.append(nn.BatchNorm2d(_out)) + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + self.in_dim = nIn + self.out_dim = nOut + self.search_mode = "basic" + + def get_flops(self, channels, check_range=True, divide=1): + iC, oC = channels + if check_range: + assert ( + iC <= self.conv.in_channels and oC <= self.conv.out_channels + ), "{:} vs {:} | {:} vs {:}".format( + iC, self.conv.in_channels, oC, self.conv.out_channels + ) + assert ( + isinstance(self.InShape, tuple) and len(self.InShape) == 2 + ), "invalid in-shape : {:}".format(self.InShape) + assert ( + isinstance(self.OutShape, tuple) and len(self.OutShape) == 2 + ), "invalid out-shape : {:}".format(self.OutShape) + # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups + conv_per_position_flops = ( + self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups + ) + all_positions = self.OutShape[0] * self.OutShape[1] + flops = (conv_per_position_flops * all_positions / divide) * iC * oC + if self.conv.bias is not None: + flops += all_positions / divide + return flops + + def get_range(self): + return [self.choices] + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, index, prob = tuple_inputs + index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob) + probability = torch.squeeze(probability) + assert len(index) == 2, "invalid length : {:}".format(index) + # compute expected flop + # coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability) + expected_outC = (self.choices_tensor * probability).sum() + expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6) + if self.avg: + out = self.avg(inputs) + else: + out = inputs + # convolutional layer + out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index]) + out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)] + # merge + out_channel = max([x.size(1) for x in out_bns]) + outA = ChannelWiseInter(out_bns[0], out_channel) + outB = ChannelWiseInter(out_bns[1], out_channel) + out = outA * prob[0] + outB * prob[1] + # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1]) + + if self.relu: + out = self.relu(out) + else: + out = out + return out, expected_outC, expected_flop + + def basic_forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.has_bn: + out = self.BNs[-1](conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + self.OutShape = (out.size(-2), out.size(-1)) + return out + + +class ResNetBasicblock(nn.Module): + expansion = 1 + num_conv = 2 + + def __init__(self, inplanes, planes, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_a = ConvBNReLU( + inplanes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes + self.search_mode = "basic" + + def get_range(self): + return self.conv_a.get_range() + self.conv_b.get_range() + + def get_flops(self, channels): + assert len(channels) == 3, "invalid channels : {:}".format(channels) + flop_A = self.conv_a.get_flops([channels[0], channels[1]]) + flop_B = self.conv_b.get_flops([channels[1], channels[2]]) + if hasattr(self.downsample, "get_flops"): + flop_C = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_C = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_C = ( + channels[0] + * channels[-1] + * self.conv_b.OutShape[0] + * self.conv_b.OutShape[1] + ) + return flop_A + flop_B + flop_C + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2 + out_a, expected_inC_a, expected_flop_a = self.conv_a( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + out_b, expected_inC_b, expected_flop_b = self.conv_b( + (out_a, expected_inC_a, probability[1], indexes[1], probs[1]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[1], indexes[1], probs[1]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out_b) + return ( + nn.functional.relu(out, inplace=True), + expected_inC_b, + sum([expected_flop_a, expected_flop_b, expected_flop_c]), + ) + + def basic_forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, basicblock) + return nn.functional.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, inplanes, planes, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_1x1 = ConvBNReLU( + inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + planes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + planes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes * self.expansion: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes * self.expansion + self.search_mode = "basic" + + def get_range(self): + return ( + self.conv_1x1.get_range() + + self.conv_3x3.get_range() + + self.conv_1x4.get_range() + ) + + def get_flops(self, channels): + assert len(channels) == 4, "invalid channels : {:}".format(channels) + flop_A = self.conv_1x1.get_flops([channels[0], channels[1]]) + flop_B = self.conv_3x3.get_flops([channels[1], channels[2]]) + flop_C = self.conv_1x4.get_flops([channels[2], channels[3]]) + if hasattr(self.downsample, "get_flops"): + flop_D = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_D = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_D = ( + channels[0] + * channels[-1] + * self.conv_1x4.OutShape[0] + * self.conv_1x4.OutShape[1] + ) + return flop_A + flop_B + flop_C + flop_D + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def basic_forward(self, inputs): + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, bottleneck) + return nn.functional.relu(out, inplace=True) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3 + out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( + (out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1]) + ) + out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( + (out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[2], indexes[2], probs[2]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out_1x4) + return ( + nn.functional.relu(out, inplace=True), + expected_inC_1x4, + sum( + [ + expected_flop_1x1, + expected_flop_3x3, + expected_flop_1x4, + expected_flop_c, + ] + ), + ) + + +class SearchWidthCifarResNet(nn.Module): + def __init__(self, block_name, depth, num_classes): + super(SearchWidthCifarResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "ResNetBasicblock": + block = ResNetBasicblock + assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110" + layer_blocks = (depth - 2) // 6 + elif block_name == "ResNetBottleneck": + block = ResNetBottleneck + assert (depth - 2) % 9 == 0, "depth should be one of 164" + layer_blocks = (depth - 2) // 9 + else: + raise ValueError("invalid block : {:}".format(block_name)) + + self.message = ( + "SearchWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.channels = [16] + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True + ) + ] + ) + self.InShape = None + for stage in range(3): + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 16 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iC, + module.out_dim, + stride, + ) + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(module.out_dim, num_classes) + self.InShape = None + self.tau = -1 + self.search_mode = "basic" + # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth) + + # parameters for width + self.Ranges = [] + self.layer2indexRange = [] + for i, layer in enumerate(self.layers): + start_index = len(self.Ranges) + self.Ranges += layer.get_range() + self.layer2indexRange.append((start_index, len(self.Ranges))) + assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format( + len(self.Ranges) + 1, depth + ) + + self.register_parameter( + "width_attentions", + nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))), + ) + nn.init.normal_(self.width_attentions, 0, 0.01) + self.apply(initialize_resnet) + + def arch_parameters(self): + return [self.width_attentions] + + def base_parameters(self): + return ( + list(self.layers.parameters()) + + list(self.avgpool.parameters()) + + list(self.classifier.parameters()) + ) + + def get_flop(self, mode, config_dict, extra_info): + if config_dict is not None: + config_dict = config_dict.copy() + # weights = [F.softmax(x, dim=0) for x in self.width_attentions] + channels = [3] + for i, weight in enumerate(self.width_attentions): + if mode == "genotype": + with torch.no_grad(): + probe = nn.functional.softmax(weight, dim=0) + C = self.Ranges[i][torch.argmax(probe).item()] + elif mode == "max": + C = self.Ranges[i][-1] + elif mode == "fix": + C = int(math.sqrt(extra_info) * self.Ranges[i][-1]) + elif mode == "random": + assert isinstance(extra_info, float), "invalid extra_info : {:}".format( + extra_info + ) + with torch.no_grad(): + prob = nn.functional.softmax(weight, dim=0) + approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1]) + for j in range(prob.size(0)): + prob[j] = 1 / ( + abs(j - (approximate_C - self.Ranges[i][j])) + 0.2 + ) + C = self.Ranges[i][torch.multinomial(prob, 1, False).item()] + else: + raise ValueError("invalid mode : {:}".format(mode)) + channels.append(C) + flop = 0 + for i, layer in enumerate(self.layers): + s, e = self.layer2indexRange[i] + xchl = tuple(channels[s : e + 1]) + flop += layer.get_flops(xchl) + # the last fc layer + flop += channels[-1] * self.classifier.out_features + if config_dict is None: + return flop / 1e6 + else: + config_dict["xchannels"] = channels + config_dict["super_type"] = "infer-width" + config_dict["estimated_FLOP"] = flop / 1e6 + return flop / 1e6, config_dict + + def get_arch_info(self): + string = "for width, there are {:} attention probabilities.".format( + len(self.width_attentions) + ) + discrepancy = [] + with torch.no_grad(): + for i, att in enumerate(self.width_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.width_attentions), " ".join(prob) + ) + logt = ["{:.3f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:52s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || dis={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + return string, discrepancy + + def set_tau(self, tau_max, tau_min, epoch_ratio): + assert ( + epoch_ratio >= 0 and epoch_ratio <= 1 + ), "invalid epoch-ratio : {:}".format(epoch_ratio) + tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2 + self.tau = tau + + def get_message(self): + return self.message + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, inputs): + flop_probs = nn.functional.softmax(self.width_attentions, dim=1) + selected_widths, selected_probs = select2withP(self.width_attentions, self.tau) + with torch.no_grad(): + selected_widths = selected_widths.cpu() + + x, last_channel_idx, expected_inC, flops = inputs, 0, 3, [] + for i, layer in enumerate(self.layers): + selected_w_index = selected_widths[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + selected_w_probs = selected_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + layer_prob = flop_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + x, expected_inC, expected_flop = layer( + (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) + ) + last_channel_idx += layer.num_conv + flops.append(expected_flop) + flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6)) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = linear_forward(features, self.classifier) + return logits, torch.stack([sum(flops)]) + + def basic_forward(self, inputs): + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_searchs/SearchImagenetResNet.py b/correlation/models/shape_searchs/SearchImagenetResNet.py new file mode 100644 index 0000000..11da09a --- /dev/null +++ b/correlation/models/shape_searchs/SearchImagenetResNet.py @@ -0,0 +1,766 @@ +import math, torch +from collections import OrderedDict +from bisect import bisect_right +import torch.nn as nn +from ..initialization import initialize_resnet +from ..SharedUtils import additive_func +from .SoftSelect import select2withP, ChannelWiseInter +from .SoftSelect import linear_forward +from .SoftSelect import get_width_choices + + +def get_depth_choices(layers): + min_depth = min(layers) + info = {"num": min_depth} + for i, depth in enumerate(layers): + choices = [] + for j in range(1, min_depth + 1): + choices.append(int(float(depth) * j / min_depth)) + info[i] = choices + return info + + +def conv_forward(inputs, conv, choices): + iC = conv.in_channels + fill_size = list(inputs.size()) + fill_size[1] = iC - fill_size[1] + filled = torch.zeros(fill_size, device=inputs.device) + xinputs = torch.cat((inputs, filled), dim=1) + outputs = conv(xinputs) + selecteds = [outputs[:, :oC] for oC in choices] + return selecteds + + +class ConvBNReLU(nn.Module): + num_conv = 1 + + def __init__( + self, + nIn, + nOut, + kernel, + stride, + padding, + bias, + has_avg, + has_bn, + has_relu, + last_max_pool=False, + ): + super(ConvBNReLU, self).__init__() + self.InShape = None + self.OutShape = None + self.choices = get_width_choices(nOut) + self.register_buffer("choices_tensor", torch.Tensor(self.choices)) + + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + # if has_bn : self.bn = nn.BatchNorm2d(nOut) + # else : self.bn = None + self.has_bn = has_bn + self.BNs = nn.ModuleList() + for i, _out in enumerate(self.choices): + self.BNs.append(nn.BatchNorm2d(_out)) + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + + if last_max_pool: + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + else: + self.maxpool = None + self.in_dim = nIn + self.out_dim = nOut + self.search_mode = "basic" + + def get_flops(self, channels, check_range=True, divide=1): + iC, oC = channels + if check_range: + assert ( + iC <= self.conv.in_channels and oC <= self.conv.out_channels + ), "{:} vs {:} | {:} vs {:}".format( + iC, self.conv.in_channels, oC, self.conv.out_channels + ) + assert ( + isinstance(self.InShape, tuple) and len(self.InShape) == 2 + ), "invalid in-shape : {:}".format(self.InShape) + assert ( + isinstance(self.OutShape, tuple) and len(self.OutShape) == 2 + ), "invalid out-shape : {:}".format(self.OutShape) + # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups + conv_per_position_flops = ( + self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups + ) + all_positions = self.OutShape[0] * self.OutShape[1] + flops = (conv_per_position_flops * all_positions / divide) * iC * oC + if self.conv.bias is not None: + flops += all_positions / divide + return flops + + def get_range(self): + return [self.choices] + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, index, prob = tuple_inputs + index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob) + probability = torch.squeeze(probability) + assert len(index) == 2, "invalid length : {:}".format(index) + # compute expected flop + # coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability) + expected_outC = (self.choices_tensor * probability).sum() + expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6) + if self.avg: + out = self.avg(inputs) + else: + out = inputs + # convolutional layer + out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index]) + out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)] + # merge + out_channel = max([x.size(1) for x in out_bns]) + outA = ChannelWiseInter(out_bns[0], out_channel) + outB = ChannelWiseInter(out_bns[1], out_channel) + out = outA * prob[0] + outB * prob[1] + # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1]) + + if self.relu: + out = self.relu(out) + if self.maxpool: + out = self.maxpool(out) + return out, expected_outC, expected_flop + + def basic_forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.has_bn: + out = self.BNs[-1](conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + self.OutShape = (out.size(-2), out.size(-1)) + if self.maxpool: + out = self.maxpool(out) + return out + + +class ResNetBasicblock(nn.Module): + expansion = 1 + num_conv = 2 + + def __init__(self, inplanes, planes, stride): + super(ResNetBasicblock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_a = ConvBNReLU( + inplanes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_b = ConvBNReLU( + planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=True, + has_relu=False, + ) + elif inplanes != planes: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes + self.search_mode = "basic" + + def get_range(self): + return self.conv_a.get_range() + self.conv_b.get_range() + + def get_flops(self, channels): + assert len(channels) == 3, "invalid channels : {:}".format(channels) + flop_A = self.conv_a.get_flops([channels[0], channels[1]]) + flop_B = self.conv_b.get_flops([channels[1], channels[2]]) + if hasattr(self.downsample, "get_flops"): + flop_C = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_C = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_C = ( + channels[0] + * channels[-1] + * self.conv_b.OutShape[0] + * self.conv_b.OutShape[1] + ) + return flop_A + flop_B + flop_C + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2 + # import pdb; pdb.set_trace() + out_a, expected_inC_a, expected_flop_a = self.conv_a( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + out_b, expected_inC_b, expected_flop_b = self.conv_b( + (out_a, expected_inC_a, probability[1], indexes[1], probs[1]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[1], indexes[1], probs[1]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out_b) + return ( + nn.functional.relu(out, inplace=True), + expected_inC_b, + sum([expected_flop_a, expected_flop_b, expected_flop_c]), + ) + + def basic_forward(self, inputs): + basicblock = self.conv_a(inputs) + basicblock = self.conv_b(basicblock) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, basicblock) + return nn.functional.relu(out, inplace=True) + + +class ResNetBottleneck(nn.Module): + expansion = 4 + num_conv = 3 + + def __init__(self, inplanes, planes, stride): + super(ResNetBottleneck, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv_1x1 = ConvBNReLU( + inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True + ) + self.conv_3x3 = ConvBNReLU( + planes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + self.conv_1x4 = ConvBNReLU( + planes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=True, + has_relu=False, + ) + elif inplanes != planes * self.expansion: + self.downsample = ConvBNReLU( + inplanes, + planes * self.expansion, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes * self.expansion + self.search_mode = "basic" + + def get_range(self): + return ( + self.conv_1x1.get_range() + + self.conv_3x3.get_range() + + self.conv_1x4.get_range() + ) + + def get_flops(self, channels): + assert len(channels) == 4, "invalid channels : {:}".format(channels) + flop_A = self.conv_1x1.get_flops([channels[0], channels[1]]) + flop_B = self.conv_3x3.get_flops([channels[1], channels[2]]) + flop_C = self.conv_1x4.get_flops([channels[2], channels[3]]) + if hasattr(self.downsample, "get_flops"): + flop_D = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_D = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_D = ( + channels[0] + * channels[-1] + * self.conv_1x4.OutShape[0] + * self.conv_1x4.OutShape[1] + ) + return flop_A + flop_B + flop_C + flop_D + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def basic_forward(self, inputs): + bottleneck = self.conv_1x1(inputs) + bottleneck = self.conv_3x3(bottleneck) + bottleneck = self.conv_1x4(bottleneck) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, bottleneck) + return nn.functional.relu(out, inplace=True) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3 + out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( + (out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1]) + ) + out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( + (out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[2], indexes[2], probs[2]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out_1x4) + return ( + nn.functional.relu(out, inplace=True), + expected_inC_1x4, + sum( + [ + expected_flop_1x1, + expected_flop_3x3, + expected_flop_1x4, + expected_flop_c, + ] + ), + ) + + +class SearchShapeImagenetResNet(nn.Module): + def __init__(self, block_name, layers, deep_stem, num_classes): + super(SearchShapeImagenetResNet, self).__init__() + + # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model + if block_name == "BasicBlock": + block = ResNetBasicblock + elif block_name == "Bottleneck": + block = ResNetBottleneck + else: + raise ValueError("invalid block : {:}".format(block_name)) + + self.message = ( + "SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}".format( + sum(layers) * block.num_conv, layers + ) + ) + self.num_classes = num_classes + if not deep_stem: + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, + 64, + 7, + 2, + 3, + False, + has_avg=False, + has_bn=True, + has_relu=True, + last_max_pool=True, + ) + ] + ) + self.channels = [64] + else: + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, 32, 3, 2, 1, False, has_avg=False, has_bn=True, has_relu=True + ), + ConvBNReLU( + 32, + 64, + 3, + 1, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + last_max_pool=True, + ), + ] + ) + self.channels = [32, 64] + + meta_depth_info = get_depth_choices(layers) + self.InShape = None + self.depth_info = OrderedDict() + self.depth_at_i = OrderedDict() + for stage, layer_blocks in enumerate(layers): + cur_block_choices = meta_depth_info[stage] + assert ( + cur_block_choices[-1] == layer_blocks + ), "stage={:}, {:} vs {:}".format(stage, cur_block_choices, layer_blocks) + block_choices, xstart = [], len(self.layers) + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 64 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = block(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iC, + module.out_dim, + stride, + ) + # added for depth + layer_index = len(self.layers) - 1 + if iL + 1 in cur_block_choices: + block_choices.append(layer_index) + if iL + 1 == layer_blocks: + self.depth_info[layer_index] = { + "choices": block_choices, + "stage": stage, + "xstart": xstart, + } + self.depth_info_list = [] + for xend, info in self.depth_info.items(): + self.depth_info_list.append((xend, info)) + xstart, xstage = info["xstart"], info["stage"] + for ilayer in range(xstart, xend + 1): + idx = bisect_right(info["choices"], ilayer - 1) + self.depth_at_i[ilayer] = (xstage, idx) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.classifier = nn.Linear(module.out_dim, num_classes) + self.InShape = None + self.tau = -1 + self.search_mode = "basic" + # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth) + + # parameters for width + self.Ranges = [] + self.layer2indexRange = [] + for i, layer in enumerate(self.layers): + start_index = len(self.Ranges) + self.Ranges += layer.get_range() + self.layer2indexRange.append((start_index, len(self.Ranges))) + + self.register_parameter( + "width_attentions", + nn.Parameter(torch.Tensor(len(self.Ranges), get_width_choices(None))), + ) + self.register_parameter( + "depth_attentions", + nn.Parameter(torch.Tensor(len(layers), meta_depth_info["num"])), + ) + nn.init.normal_(self.width_attentions, 0, 0.01) + nn.init.normal_(self.depth_attentions, 0, 0.01) + self.apply(initialize_resnet) + + def arch_parameters(self, LR=None): + if LR is None: + return [self.width_attentions, self.depth_attentions] + else: + return [ + {"params": self.width_attentions, "lr": LR}, + {"params": self.depth_attentions, "lr": LR}, + ] + + def base_parameters(self): + return ( + list(self.layers.parameters()) + + list(self.avgpool.parameters()) + + list(self.classifier.parameters()) + ) + + def get_flop(self, mode, config_dict, extra_info): + if config_dict is not None: + config_dict = config_dict.copy() + # select channels + channels = [3] + for i, weight in enumerate(self.width_attentions): + if mode == "genotype": + with torch.no_grad(): + probe = nn.functional.softmax(weight, dim=0) + C = self.Ranges[i][torch.argmax(probe).item()] + else: + raise ValueError("invalid mode : {:}".format(mode)) + channels.append(C) + # select depth + if mode == "genotype": + with torch.no_grad(): + depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + choices = torch.argmax(depth_probs, dim=1).cpu().tolist() + else: + raise ValueError("invalid mode : {:}".format(mode)) + selected_layers = [] + for choice, xvalue in zip(choices, self.depth_info_list): + xtemp = xvalue[1]["choices"][choice] - xvalue[1]["xstart"] + 1 + selected_layers.append(xtemp) + flop = 0 + for i, layer in enumerate(self.layers): + s, e = self.layer2indexRange[i] + xchl = tuple(channels[s : e + 1]) + if i in self.depth_at_i: + xstagei, xatti = self.depth_at_i[i] + if xatti <= choices[xstagei]: # leave this depth + flop += layer.get_flops(xchl) + else: + flop += 0 # do not use this layer + else: + flop += layer.get_flops(xchl) + # the last fc layer + flop += channels[-1] * self.classifier.out_features + if config_dict is None: + return flop / 1e6 + else: + config_dict["xchannels"] = channels + config_dict["xblocks"] = selected_layers + config_dict["super_type"] = "infer-shape" + config_dict["estimated_FLOP"] = flop / 1e6 + return flop / 1e6, config_dict + + def get_arch_info(self): + string = ( + "for depth and width, there are {:} + {:} attention probabilities.".format( + len(self.depth_attentions), len(self.width_attentions) + ) + ) + string += "\n{:}".format(self.depth_info) + discrepancy = [] + with torch.no_grad(): + for i, att in enumerate(self.depth_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.depth_attentions), " ".join(prob) + ) + logt = ["{:.4f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:17s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || discrepancy={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + string += "\n-----------------------------------------------" + for i, att in enumerate(self.width_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.width_attentions), " ".join(prob) + ) + logt = ["{:.3f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:52s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || dis={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + return string, discrepancy + + def set_tau(self, tau_max, tau_min, epoch_ratio): + assert ( + epoch_ratio >= 0 and epoch_ratio <= 1 + ), "invalid epoch-ratio : {:}".format(epoch_ratio) + tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2 + self.tau = tau + + def get_message(self): + return self.message + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, inputs): + flop_width_probs = nn.functional.softmax(self.width_attentions, dim=1) + flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1) + flop_depth_probs = torch.flip( + torch.cumsum(torch.flip(flop_depth_probs, [1]), 1), [1] + ) + selected_widths, selected_width_probs = select2withP( + self.width_attentions, self.tau + ) + selected_depth_probs = select2withP(self.depth_attentions, self.tau, True) + with torch.no_grad(): + selected_widths = selected_widths.cpu() + + x, last_channel_idx, expected_inC, flops = inputs, 0, 3, [] + feature_maps = [] + for i, layer in enumerate(self.layers): + selected_w_index = selected_widths[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + selected_w_probs = selected_width_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + layer_prob = flop_width_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + x, expected_inC, expected_flop = layer( + (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) + ) + feature_maps.append(x) + last_channel_idx += layer.num_conv + if i in self.depth_info: # aggregate the information + choices = self.depth_info[i]["choices"] + xstagei = self.depth_info[i]["stage"] + # print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist())) + # for A, W in zip(choices, selected_depth_probs[xstagei]): + # print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W)) + possible_tensors = [] + max_C = max(feature_maps[A].size(1) for A in choices) + for tempi, A in enumerate(choices): + xtensor = ChannelWiseInter(feature_maps[A], max_C) + possible_tensors.append(xtensor) + weighted_sum = sum( + xtensor * W + for xtensor, W in zip( + possible_tensors, selected_depth_probs[xstagei] + ) + ) + x = weighted_sum + + if i in self.depth_at_i: + xstagei, xatti = self.depth_at_i[i] + x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop + else: + x_expected_flop = expected_flop + flops.append(x_expected_flop) + flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6)) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = linear_forward(features, self.classifier) + return logits, torch.stack([sum(flops)]) + + def basic_forward(self, inputs): + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_searchs/SearchSimResNet_width.py b/correlation/models/shape_searchs/SearchSimResNet_width.py new file mode 100644 index 0000000..584ffef --- /dev/null +++ b/correlation/models/shape_searchs/SearchSimResNet_width.py @@ -0,0 +1,466 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, torch +import torch.nn as nn +from ..initialization import initialize_resnet +from ..SharedUtils import additive_func +from .SoftSelect import select2withP, ChannelWiseInter +from .SoftSelect import linear_forward +from .SoftSelect import get_width_choices as get_choices + + +def conv_forward(inputs, conv, choices): + iC = conv.in_channels + fill_size = list(inputs.size()) + fill_size[1] = iC - fill_size[1] + filled = torch.zeros(fill_size, device=inputs.device) + xinputs = torch.cat((inputs, filled), dim=1) + outputs = conv(xinputs) + selecteds = [outputs[:, :oC] for oC in choices] + return selecteds + + +class ConvBNReLU(nn.Module): + num_conv = 1 + + def __init__( + self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu + ): + super(ConvBNReLU, self).__init__() + self.InShape = None + self.OutShape = None + self.choices = get_choices(nOut) + self.register_buffer("choices_tensor", torch.Tensor(self.choices)) + + if has_avg: + self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + else: + self.avg = None + self.conv = nn.Conv2d( + nIn, + nOut, + kernel_size=kernel, + stride=stride, + padding=padding, + dilation=1, + groups=1, + bias=bias, + ) + # if has_bn : self.bn = nn.BatchNorm2d(nOut) + # else : self.bn = None + self.has_bn = has_bn + self.BNs = nn.ModuleList() + for i, _out in enumerate(self.choices): + self.BNs.append(nn.BatchNorm2d(_out)) + if has_relu: + self.relu = nn.ReLU(inplace=True) + else: + self.relu = None + self.in_dim = nIn + self.out_dim = nOut + self.search_mode = "basic" + + def get_flops(self, channels, check_range=True, divide=1): + iC, oC = channels + if check_range: + assert ( + iC <= self.conv.in_channels and oC <= self.conv.out_channels + ), "{:} vs {:} | {:} vs {:}".format( + iC, self.conv.in_channels, oC, self.conv.out_channels + ) + assert ( + isinstance(self.InShape, tuple) and len(self.InShape) == 2 + ), "invalid in-shape : {:}".format(self.InShape) + assert ( + isinstance(self.OutShape, tuple) and len(self.OutShape) == 2 + ), "invalid out-shape : {:}".format(self.OutShape) + # conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups + conv_per_position_flops = ( + self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups + ) + all_positions = self.OutShape[0] * self.OutShape[1] + flops = (conv_per_position_flops * all_positions / divide) * iC * oC + if self.conv.bias is not None: + flops += all_positions / divide + return flops + + def get_range(self): + return [self.choices] + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, index, prob = tuple_inputs + index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob) + probability = torch.squeeze(probability) + assert len(index) == 2, "invalid length : {:}".format(index) + # compute expected flop + # coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability) + expected_outC = (self.choices_tensor * probability).sum() + expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6) + if self.avg: + out = self.avg(inputs) + else: + out = inputs + # convolutional layer + out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index]) + out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)] + # merge + out_channel = max([x.size(1) for x in out_bns]) + outA = ChannelWiseInter(out_bns[0], out_channel) + outB = ChannelWiseInter(out_bns[1], out_channel) + out = outA * prob[0] + outB * prob[1] + # out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1]) + + if self.relu: + out = self.relu(out) + else: + out = out + return out, expected_outC, expected_flop + + def basic_forward(self, inputs): + if self.avg: + out = self.avg(inputs) + else: + out = inputs + conv = self.conv(out) + if self.has_bn: + out = self.BNs[-1](conv) + else: + out = conv + if self.relu: + out = self.relu(out) + else: + out = out + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + self.OutShape = (out.size(-2), out.size(-1)) + return out + + +class SimBlock(nn.Module): + expansion = 1 + num_conv = 1 + + def __init__(self, inplanes, planes, stride): + super(SimBlock, self).__init__() + assert stride == 1 or stride == 2, "invalid stride {:}".format(stride) + self.conv = ConvBNReLU( + inplanes, + planes, + 3, + stride, + 1, + False, + has_avg=False, + has_bn=True, + has_relu=True, + ) + if stride == 2: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=True, + has_bn=False, + has_relu=False, + ) + elif inplanes != planes: + self.downsample = ConvBNReLU( + inplanes, + planes, + 1, + 1, + 0, + False, + has_avg=False, + has_bn=True, + has_relu=False, + ) + else: + self.downsample = None + self.out_dim = planes + self.search_mode = "basic" + + def get_range(self): + return self.conv.get_range() + + def get_flops(self, channels): + assert len(channels) == 2, "invalid channels : {:}".format(channels) + flop_A = self.conv.get_flops([channels[0], channels[1]]) + if hasattr(self.downsample, "get_flops"): + flop_C = self.downsample.get_flops([channels[0], channels[-1]]) + else: + flop_C = 0 + if ( + channels[0] != channels[-1] and self.downsample is None + ): # this short-cut will be added during the infer-train + flop_C = ( + channels[0] + * channels[-1] + * self.conv.OutShape[0] + * self.conv.OutShape[1] + ) + return flop_A + flop_C + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, tuple_inputs): + assert ( + isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5 + ), "invalid type input : {:}".format(type(tuple_inputs)) + inputs, expected_inC, probability, indexes, probs = tuple_inputs + assert ( + indexes.size(0) == 1 and probs.size(0) == 1 and probability.size(0) == 1 + ), "invalid size : {:}, {:}, {:}".format( + indexes.size(), probs.size(), probability.size() + ) + out, expected_next_inC, expected_flop = self.conv( + (inputs, expected_inC, probability[0], indexes[0], probs[0]) + ) + if self.downsample is not None: + residual, _, expected_flop_c = self.downsample( + (inputs, expected_inC, probability[-1], indexes[-1], probs[-1]) + ) + else: + residual, expected_flop_c = inputs, 0 + out = additive_func(residual, out) + return ( + nn.functional.relu(out, inplace=True), + expected_next_inC, + sum([expected_flop, expected_flop_c]), + ) + + def basic_forward(self, inputs): + basicblock = self.conv(inputs) + if self.downsample is not None: + residual = self.downsample(inputs) + else: + residual = inputs + out = additive_func(residual, basicblock) + return nn.functional.relu(out, inplace=True) + + +class SearchWidthSimResNet(nn.Module): + def __init__(self, depth, num_classes): + super(SearchWidthSimResNet, self).__init__() + + assert ( + depth - 2 + ) % 3 == 0, "depth should be one of 5, 8, 11, 14, ... instead of {:}".format( + depth + ) + layer_blocks = (depth - 2) // 3 + self.message = ( + "SearchWidthSimResNet : Depth : {:} , Layers for each block : {:}".format( + depth, layer_blocks + ) + ) + self.num_classes = num_classes + self.channels = [16] + self.layers = nn.ModuleList( + [ + ConvBNReLU( + 3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True + ) + ] + ) + self.InShape = None + for stage in range(3): + for iL in range(layer_blocks): + iC = self.channels[-1] + planes = 16 * (2 ** stage) + stride = 2 if stage > 0 and iL == 0 else 1 + module = SimBlock(iC, planes, stride) + self.channels.append(module.out_dim) + self.layers.append(module) + self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format( + stage, + iL, + layer_blocks, + len(self.layers) - 1, + iC, + module.out_dim, + stride, + ) + + self.avgpool = nn.AvgPool2d(8) + self.classifier = nn.Linear(module.out_dim, num_classes) + self.InShape = None + self.tau = -1 + self.search_mode = "basic" + # assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth) + + # parameters for width + self.Ranges = [] + self.layer2indexRange = [] + for i, layer in enumerate(self.layers): + start_index = len(self.Ranges) + self.Ranges += layer.get_range() + self.layer2indexRange.append((start_index, len(self.Ranges))) + assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format( + len(self.Ranges) + 1, depth + ) + + self.register_parameter( + "width_attentions", + nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))), + ) + nn.init.normal_(self.width_attentions, 0, 0.01) + self.apply(initialize_resnet) + + def arch_parameters(self): + return [self.width_attentions] + + def base_parameters(self): + return ( + list(self.layers.parameters()) + + list(self.avgpool.parameters()) + + list(self.classifier.parameters()) + ) + + def get_flop(self, mode, config_dict, extra_info): + if config_dict is not None: + config_dict = config_dict.copy() + # weights = [F.softmax(x, dim=0) for x in self.width_attentions] + channels = [3] + for i, weight in enumerate(self.width_attentions): + if mode == "genotype": + with torch.no_grad(): + probe = nn.functional.softmax(weight, dim=0) + C = self.Ranges[i][torch.argmax(probe).item()] + elif mode == "max": + C = self.Ranges[i][-1] + elif mode == "fix": + C = int(math.sqrt(extra_info) * self.Ranges[i][-1]) + elif mode == "random": + assert isinstance(extra_info, float), "invalid extra_info : {:}".format( + extra_info + ) + with torch.no_grad(): + prob = nn.functional.softmax(weight, dim=0) + approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1]) + for j in range(prob.size(0)): + prob[j] = 1 / ( + abs(j - (approximate_C - self.Ranges[i][j])) + 0.2 + ) + C = self.Ranges[i][torch.multinomial(prob, 1, False).item()] + else: + raise ValueError("invalid mode : {:}".format(mode)) + channels.append(C) + flop = 0 + for i, layer in enumerate(self.layers): + s, e = self.layer2indexRange[i] + xchl = tuple(channels[s : e + 1]) + flop += layer.get_flops(xchl) + # the last fc layer + flop += channels[-1] * self.classifier.out_features + if config_dict is None: + return flop / 1e6 + else: + config_dict["xchannels"] = channels + config_dict["super_type"] = "infer-width" + config_dict["estimated_FLOP"] = flop / 1e6 + return flop / 1e6, config_dict + + def get_arch_info(self): + string = "for width, there are {:} attention probabilities.".format( + len(self.width_attentions) + ) + discrepancy = [] + with torch.no_grad(): + for i, att in enumerate(self.width_attentions): + prob = nn.functional.softmax(att, dim=0) + prob = prob.cpu() + selc = prob.argmax().item() + prob = prob.tolist() + prob = ["{:.3f}".format(x) for x in prob] + xstring = "{:03d}/{:03d}-th : {:}".format( + i, len(self.width_attentions), " ".join(prob) + ) + logt = ["{:.3f}".format(x) for x in att.cpu().tolist()] + xstring += " || {:52s}".format(" ".join(logt)) + prob = sorted([float(x) for x in prob]) + disc = prob[-1] - prob[-2] + xstring += " || dis={:.2f} || select={:}/{:}".format( + disc, selc, len(prob) + ) + discrepancy.append(disc) + string += "\n{:}".format(xstring) + return string, discrepancy + + def set_tau(self, tau_max, tau_min, epoch_ratio): + assert ( + epoch_ratio >= 0 and epoch_ratio <= 1 + ), "invalid epoch-ratio : {:}".format(epoch_ratio) + tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2 + self.tau = tau + + def get_message(self): + return self.message + + def forward(self, inputs): + if self.search_mode == "basic": + return self.basic_forward(inputs) + elif self.search_mode == "search": + return self.search_forward(inputs) + else: + raise ValueError("invalid search_mode = {:}".format(self.search_mode)) + + def search_forward(self, inputs): + flop_probs = nn.functional.softmax(self.width_attentions, dim=1) + selected_widths, selected_probs = select2withP(self.width_attentions, self.tau) + with torch.no_grad(): + selected_widths = selected_widths.cpu() + + x, last_channel_idx, expected_inC, flops = inputs, 0, 3, [] + for i, layer in enumerate(self.layers): + selected_w_index = selected_widths[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + selected_w_probs = selected_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + layer_prob = flop_probs[ + last_channel_idx : last_channel_idx + layer.num_conv + ] + x, expected_inC, expected_flop = layer( + (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) + ) + last_channel_idx += layer.num_conv + flops.append(expected_flop) + flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6)) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = linear_forward(features, self.classifier) + return logits, torch.stack([sum(flops)]) + + def basic_forward(self, inputs): + if self.InShape is None: + self.InShape = (inputs.size(-2), inputs.size(-1)) + x = inputs + for i, layer in enumerate(self.layers): + x = layer(x) + features = self.avgpool(x) + features = features.view(features.size(0), -1) + logits = self.classifier(features) + return features, logits diff --git a/correlation/models/shape_searchs/SoftSelect.py b/correlation/models/shape_searchs/SoftSelect.py new file mode 100644 index 0000000..3cdfa45 --- /dev/null +++ b/correlation/models/shape_searchs/SoftSelect.py @@ -0,0 +1,128 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import math, torch +import torch.nn as nn + + +def select2withP(logits, tau, just_prob=False, num=2, eps=1e-7): + if tau <= 0: + new_logits = logits + probs = nn.functional.softmax(new_logits, dim=1) + else: + while True: # a trick to avoid the gumbels bug + gumbels = -torch.empty_like(logits).exponential_().log() + new_logits = (logits.log_softmax(dim=1) + gumbels) / tau + probs = nn.functional.softmax(new_logits, dim=1) + if ( + (not torch.isinf(gumbels).any()) + and (not torch.isinf(probs).any()) + and (not torch.isnan(probs).any()) + ): + break + + if just_prob: + return probs + + # with torch.no_grad(): # add eps for unexpected torch error + # probs = nn.functional.softmax(new_logits, dim=1) + # selected_index = torch.multinomial(probs + eps, 2, False) + with torch.no_grad(): # add eps for unexpected torch error + probs = probs.cpu() + selected_index = torch.multinomial(probs + eps, num, False).to(logits.device) + selected_logit = torch.gather(new_logits, 1, selected_index) + selcted_probs = nn.functional.softmax(selected_logit, dim=1) + return selected_index, selcted_probs + + +def ChannelWiseInter(inputs, oC, mode="v2"): + if mode == "v1": + return ChannelWiseInterV1(inputs, oC) + elif mode == "v2": + return ChannelWiseInterV2(inputs, oC) + else: + raise ValueError("invalid mode : {:}".format(mode)) + + +def ChannelWiseInterV1(inputs, oC): + assert inputs.dim() == 4, "invalid dimension : {:}".format(inputs.size()) + + def start_index(a, b, c): + return int(math.floor(float(a * c) / b)) + + def end_index(a, b, c): + return int(math.ceil(float((a + 1) * c) / b)) + + batch, iC, H, W = inputs.size() + outputs = torch.zeros((batch, oC, H, W), dtype=inputs.dtype, device=inputs.device) + if iC == oC: + return inputs + for ot in range(oC): + istartT, iendT = start_index(ot, oC, iC), end_index(ot, oC, iC) + values = inputs[:, istartT:iendT].mean(dim=1) + outputs[:, ot, :, :] = values + return outputs + + +def ChannelWiseInterV2(inputs, oC): + assert inputs.dim() == 4, "invalid dimension : {:}".format(inputs.size()) + batch, C, H, W = inputs.size() + if C == oC: + return inputs + else: + return nn.functional.adaptive_avg_pool3d(inputs, (oC, H, W)) + # inputs_5D = inputs.view(batch, 1, C, H, W) + # otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'area', None) + # otputs = otputs_5D.view(batch, oC, H, W) + # otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'trilinear', False) + # return otputs + + +def linear_forward(inputs, linear): + if linear is None: + return inputs + iC = inputs.size(1) + weight = linear.weight[:, :iC] + if linear.bias is None: + bias = None + else: + bias = linear.bias + return nn.functional.linear(inputs, weight, bias) + + +def get_width_choices(nOut): + xsrange = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] + if nOut is None: + return len(xsrange) + else: + Xs = [int(nOut * i) for i in xsrange] + # xs = [ int(nOut * i // 10) for i in range(2, 11)] + # Xs = [x for i, x in enumerate(xs) if i+1 == len(xs) or xs[i+1] > x+1] + Xs = sorted(list(set(Xs))) + return tuple(Xs) + + +def get_depth_choices(nDepth): + if nDepth is None: + return 3 + else: + assert nDepth >= 3, "nDepth should be greater than 2 vs {:}".format(nDepth) + if nDepth == 1: + return (1, 1, 1) + elif nDepth == 2: + return (1, 1, 2) + elif nDepth >= 3: + return (nDepth // 3, nDepth * 2 // 3, nDepth) + else: + raise ValueError("invalid Depth : {:}".format(nDepth)) + + +def drop_path(x, drop_prob): + if drop_prob > 0.0: + keep_prob = 1.0 - drop_prob + mask = x.new_zeros(x.size(0), 1, 1, 1) + mask = mask.bernoulli_(keep_prob) + x = x * (mask / keep_prob) + # x.div_(keep_prob) + # x.mul_(mask) + return x diff --git a/correlation/models/shape_searchs/__init__.py b/correlation/models/shape_searchs/__init__.py new file mode 100644 index 0000000..15e2260 --- /dev/null +++ b/correlation/models/shape_searchs/__init__.py @@ -0,0 +1,9 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +from .SearchCifarResNet_width import SearchWidthCifarResNet +from .SearchCifarResNet_depth import SearchDepthCifarResNet +from .SearchCifarResNet import SearchShapeCifarResNet +from .SearchSimResNet_width import SearchWidthSimResNet +from .SearchImagenetResNet import SearchShapeImagenetResNet +from .generic_size_tiny_cell_model import GenericNAS301Model diff --git a/correlation/models/shape_searchs/generic_size_tiny_cell_model.py b/correlation/models/shape_searchs/generic_size_tiny_cell_model.py new file mode 100644 index 0000000..c53805d --- /dev/null +++ b/correlation/models/shape_searchs/generic_size_tiny_cell_model.py @@ -0,0 +1,209 @@ +##################################################### +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # +##################################################### +# Here, we utilized three techniques to search for the number of channels: +# - channel-wise interpolation from "Network Pruning via Transformable Architecture Search, NeurIPS 2019" +# - masking + Gumbel-Softmax (mask_gumbel) from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020" +# - masking + sampling (mask_rl) from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020" +from typing import List, Text, Any +import random, torch +import torch.nn as nn + +from ..cell_operations import ResNetBasicblock +from ..cell_infers.cells import InferCell +from .SoftSelect import select2withP, ChannelWiseInter + + +class GenericNAS301Model(nn.Module): + def __init__( + self, + candidate_Cs: List[int], + max_num_Cs: int, + genotype: Any, + num_classes: int, + affine: bool, + track_running_stats: bool, + ): + super(GenericNAS301Model, self).__init__() + self._max_num_Cs = max_num_Cs + self._candidate_Cs = candidate_Cs + if max_num_Cs % 3 != 2: + raise ValueError("invalid number of layers : {:}".format(max_num_Cs)) + self._num_stage = N = max_num_Cs // 3 + self._max_C = max(candidate_Cs) + + stem = nn.Sequential( + nn.Conv2d(3, self._max_C, kernel_size=3, padding=1, bias=not affine), + nn.BatchNorm2d( + self._max_C, affine=affine, track_running_stats=track_running_stats + ), + ) + + layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N + + c_prev = self._max_C + self._cells = nn.ModuleList() + self._cells.append(stem) + for index, reduction in enumerate(layer_reductions): + if reduction: + cell = ResNetBasicblock(c_prev, self._max_C, 2, True) + else: + cell = InferCell( + genotype, c_prev, self._max_C, 1, affine, track_running_stats + ) + self._cells.append(cell) + c_prev = cell.out_dim + self._num_layer = len(self._cells) + + self.lastact = nn.Sequential( + nn.BatchNorm2d( + c_prev, affine=affine, track_running_stats=track_running_stats + ), + nn.ReLU(inplace=True), + ) + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(c_prev, num_classes) + # algorithm related + self.register_buffer("_tau", torch.zeros(1)) + self._algo = None + self._warmup_ratio = None + + def set_algo(self, algo: Text): + # used for searching + assert self._algo is None, "This functioin can only be called once." + assert algo in ["mask_gumbel", "mask_rl", "tas"], "invalid algo : {:}".format( + algo + ) + self._algo = algo + self._arch_parameters = nn.Parameter( + 1e-3 * torch.randn(self._max_num_Cs, len(self._candidate_Cs)) + ) + # if algo == 'mask_gumbel' or algo == 'mask_rl': + self.register_buffer( + "_masks", torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)) + ) + for i in range(len(self._candidate_Cs)): + self._masks.data[i, : self._candidate_Cs[i]] = 1 + + @property + def tau(self): + return self._tau + + def set_tau(self, tau): + self._tau.data[:] = tau + + @property + def warmup_ratio(self): + return self._warmup_ratio + + def set_warmup_ratio(self, ratio: float): + self._warmup_ratio = ratio + + @property + def weights(self): + xlist = list(self._cells.parameters()) + xlist += list(self.lastact.parameters()) + xlist += list(self.global_pooling.parameters()) + xlist += list(self.classifier.parameters()) + return xlist + + @property + def alphas(self): + return [self._arch_parameters] + + def show_alphas(self): + with torch.no_grad(): + return "arch-parameters :\n{:}".format( + nn.functional.softmax(self._arch_parameters, dim=-1).cpu() + ) + + @property + def random(self): + cs = [] + for i in range(self._max_num_Cs): + index = random.randint(0, len(self._candidate_Cs) - 1) + cs.append(str(self._candidate_Cs[index])) + return ":".join(cs) + + @property + def genotype(self): + cs = [] + for i in range(self._max_num_Cs): + with torch.no_grad(): + index = self._arch_parameters[i].argmax().item() + cs.append(str(self._candidate_Cs[index])) + return ":".join(cs) + + def get_message(self) -> Text: + string = self.extra_repr() + for i, cell in enumerate(self._cells): + string += "\n {:02d}/{:02d} :: {:}".format( + i, len(self._cells), cell.extra_repr() + ) + return string + + def extra_repr(self): + return "{name}(candidates={_candidate_Cs}, num={_max_num_Cs}, N={_num_stage}, L={_num_layer})".format( + name=self.__class__.__name__, **self.__dict__ + ) + + def forward(self, inputs): + feature = inputs + + log_probs = [] + for i, cell in enumerate(self._cells): + feature = cell(feature) + # apply different searching algorithms + idx = max(0, i - 1) + if self._warmup_ratio is not None: + if random.random() < self._warmup_ratio: + mask = self._masks[-1] + else: + mask = self._masks[random.randint(0, len(self._masks) - 1)] + feature = feature * mask.view(1, -1, 1, 1) + elif self._algo == "mask_gumbel": + weights = nn.functional.gumbel_softmax( + self._arch_parameters[idx : idx + 1], tau=self.tau, dim=-1 + ) + mask = torch.matmul(weights, self._masks).view(1, -1, 1, 1) + feature = feature * mask + elif self._algo == "tas": + selected_cs, selected_probs = select2withP( + self._arch_parameters[idx : idx + 1], self.tau, num=2 + ) + with torch.no_grad(): + i1, i2 = selected_cs.cpu().view(-1).tolist() + c1, c2 = self._candidate_Cs[i1], self._candidate_Cs[i2] + out_channel = max(c1, c2) + out1 = ChannelWiseInter(feature[:, :c1], out_channel) + out2 = ChannelWiseInter(feature[:, :c2], out_channel) + out = out1 * selected_probs[0, 0] + out2 * selected_probs[0, 1] + if feature.shape[1] == out.shape[1]: + feature = out + else: + miss = torch.zeros( + feature.shape[0], + feature.shape[1] - out.shape[1], + feature.shape[2], + feature.shape[3], + device=feature.device, + ) + feature = torch.cat((out, miss), dim=1) + elif self._algo == "mask_rl": + prob = nn.functional.softmax( + self._arch_parameters[idx : idx + 1], dim=-1 + ) + dist = torch.distributions.Categorical(prob) + action = dist.sample() + log_probs.append(dist.log_prob(action)) + mask = self._masks[action.item()].view(1, -1, 1, 1) + feature = feature * mask + else: + raise ValueError("invalid algorithm : {:}".format(self._algo)) + + out = self.lastact(feature) + out = self.global_pooling(out) + out = out.view(out.size(0), -1) + logits = self.classifier(out) + + return out, logits, log_probs diff --git a/correlation/result/sss_cf100_meco.p b/correlation/result/sss_cf100_meco.p new file mode 100644 index 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