# 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