Reformulate via black
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D-X-Y
@@ -5,110 +5,148 @@ from copy import deepcopy
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import torch
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import numpy as np
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from collections import OrderedDict
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from nas_201_api import NASBench201API as API
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def test_nas_api():
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from nas_201_api import ArchResults
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xdata = torch.load('/home/dxy/FOR-RELEASE/NAS-Projects/output/NAS-BENCH-201-4/simplifies/architectures/000157-FULL.pth')
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for key in ['full', 'less']:
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print ('\n------------------------- {:} -------------------------'.format(key))
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archRes = ArchResults.create_from_state_dict(xdata[key])
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print(archRes)
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print(archRes.arch_idx_str())
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print(archRes.get_dataset_names())
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print(archRes.get_comput_costs('cifar10-valid'))
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# get the metrics
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print(archRes.get_metrics('cifar10-valid', 'x-valid', None, False))
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print(archRes.get_metrics('cifar10-valid', 'x-valid', None, True))
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print(archRes.query('cifar10-valid', 777))
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from nas_201_api import ArchResults
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xdata = torch.load(
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"/home/dxy/FOR-RELEASE/NAS-Projects/output/NAS-BENCH-201-4/simplifies/architectures/000157-FULL.pth"
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)
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for key in ["full", "less"]:
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print("\n------------------------- {:} -------------------------".format(key))
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archRes = ArchResults.create_from_state_dict(xdata[key])
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print(archRes)
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print(archRes.arch_idx_str())
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print(archRes.get_dataset_names())
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print(archRes.get_comput_costs("cifar10-valid"))
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# get the metrics
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print(archRes.get_metrics("cifar10-valid", "x-valid", None, False))
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print(archRes.get_metrics("cifar10-valid", "x-valid", None, True))
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print(archRes.query("cifar10-valid", 777))
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OPS = ['skip-connect', 'conv-1x1', 'conv-3x3', 'pool-3x3']
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COLORS = ['chartreuse' , 'cyan' , 'navyblue', 'chocolate1']
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OPS = ["skip-connect", "conv-1x1", "conv-3x3", "pool-3x3"]
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COLORS = ["chartreuse", "cyan", "navyblue", "chocolate1"]
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def plot(filename):
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from graphviz import Digraph
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g = Digraph(
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format='png',
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edge_attr=dict(fontsize='20', fontname="times"),
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node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
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engine='dot')
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g.body.extend(['rankdir=LR'])
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from graphviz import Digraph
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steps = 5
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for i in range(0, steps):
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if i == 0:
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g.node(str(i), fillcolor='darkseagreen2')
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elif i+1 == steps:
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g.node(str(i), fillcolor='palegoldenrod')
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else: g.node(str(i), fillcolor='lightblue')
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g = Digraph(
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format="png",
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edge_attr=dict(fontsize="20", fontname="times"),
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node_attr=dict(
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style="filled",
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shape="rect",
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align="center",
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fontsize="20",
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height="0.5",
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width="0.5",
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penwidth="2",
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fontname="times",
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),
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engine="dot",
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)
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g.body.extend(["rankdir=LR"])
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for i in range(1, steps):
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for xin in range(i):
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op_i = random.randint(0, len(OPS)-1)
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#g.edge(str(xin), str(i), label=OPS[op_i], fillcolor=COLORS[op_i])
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g.edge(str(xin), str(i), label=OPS[op_i], color=COLORS[op_i], fillcolor=COLORS[op_i])
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#import pdb; pdb.set_trace()
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g.render(filename, cleanup=True, view=False)
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steps = 5
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for i in range(0, steps):
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if i == 0:
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g.node(str(i), fillcolor="darkseagreen2")
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elif i + 1 == steps:
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g.node(str(i), fillcolor="palegoldenrod")
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else:
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g.node(str(i), fillcolor="lightblue")
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for i in range(1, steps):
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for xin in range(i):
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op_i = random.randint(0, len(OPS) - 1)
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# g.edge(str(xin), str(i), label=OPS[op_i], fillcolor=COLORS[op_i])
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g.edge(str(xin), str(i), label=OPS[op_i], color=COLORS[op_i], fillcolor=COLORS[op_i])
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# import pdb; pdb.set_trace()
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g.render(filename, cleanup=True, view=False)
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def test_auto_grad():
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class Net(torch.nn.Module):
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def __init__(self, iS):
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super(Net, self).__init__()
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self.layer = torch.nn.Linear(iS, 1)
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def forward(self, inputs):
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outputs = self.layer(inputs)
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outputs = torch.exp(outputs)
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return outputs.mean()
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net = Net(10)
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inputs = torch.rand(256, 10)
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loss = net(inputs)
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first_order_grads = torch.autograd.grad(loss, net.parameters(), retain_graph=True, create_graph=True)
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first_order_grads = torch.cat([x.view(-1) for x in first_order_grads])
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second_order_grads = []
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for grads in first_order_grads:
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s_grads = torch.autograd.grad(grads, net.parameters())
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second_order_grads.append( s_grads )
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class Net(torch.nn.Module):
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def __init__(self, iS):
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super(Net, self).__init__()
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self.layer = torch.nn.Linear(iS, 1)
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def forward(self, inputs):
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outputs = self.layer(inputs)
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outputs = torch.exp(outputs)
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return outputs.mean()
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net = Net(10)
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inputs = torch.rand(256, 10)
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loss = net(inputs)
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first_order_grads = torch.autograd.grad(loss, net.parameters(), retain_graph=True, create_graph=True)
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first_order_grads = torch.cat([x.view(-1) for x in first_order_grads])
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second_order_grads = []
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for grads in first_order_grads:
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s_grads = torch.autograd.grad(grads, net.parameters())
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second_order_grads.append(s_grads)
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def test_one_shot_model(ckpath, use_train):
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from models import get_cell_based_tiny_net, get_search_spaces
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from datasets import get_datasets, SearchDataset
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from config_utils import load_config, dict2config
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from utils.nas_utils import evaluate_one_shot
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use_train = int(use_train) > 0
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#ckpath = 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/seed-11416-basic.pth'
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#ckpath = 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/seed-28640-basic.pth'
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print ('ckpath : {:}'.format(ckpath))
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ckp = torch.load(ckpath)
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xargs = ckp['args']
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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#config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, None)
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config = load_config('./configs/nas-benchmark/algos/DARTS.config', {'class_num': class_num, 'xshape': xshape}, None)
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if xargs.dataset == 'cifar10':
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cifar_split = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
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xvalid_data = deepcopy(train_data)
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xvalid_data.transform = valid_data.transform
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valid_loader= torch.utils.data.DataLoader(xvalid_data, batch_size=2048, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar_split.valid), num_workers=12, pin_memory=True)
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else: raise ValueError('invalid dataset : {:}'.format(xargs.dataseet))
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search_space = get_search_spaces('cell', xargs.search_space_name)
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model_config = dict2config({'name': 'SETN', 'C': xargs.channel, 'N': xargs.num_cells,
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'max_nodes': xargs.max_nodes, 'num_classes': class_num,
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'space' : search_space,
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'affine' : False, 'track_running_stats': True}, None)
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search_model = get_cell_based_tiny_net(model_config)
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search_model.load_state_dict( ckp['search_model'] )
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search_model = search_model.cuda()
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api = API('/home/dxy/.torch/NAS-Bench-201-v1_0-e61699.pth')
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archs, probs, accuracies = evaluate_one_shot(search_model, valid_loader, api, use_train)
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from models import get_cell_based_tiny_net, get_search_spaces
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from datasets import get_datasets, SearchDataset
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from config_utils import load_config, dict2config
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from utils.nas_utils import evaluate_one_shot
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use_train = int(use_train) > 0
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# ckpath = 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/seed-11416-basic.pth'
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# ckpath = 'output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/seed-28640-basic.pth'
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print("ckpath : {:}".format(ckpath))
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ckp = torch.load(ckpath)
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xargs = ckp["args"]
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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# config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, None)
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config = load_config("./configs/nas-benchmark/algos/DARTS.config", {"class_num": class_num, "xshape": xshape}, None)
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if xargs.dataset == "cifar10":
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cifar_split = load_config("configs/nas-benchmark/cifar-split.txt", None, None)
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xvalid_data = deepcopy(train_data)
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xvalid_data.transform = valid_data.transform
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valid_loader = torch.utils.data.DataLoader(
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xvalid_data,
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batch_size=2048,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar_split.valid),
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num_workers=12,
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pin_memory=True,
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)
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else:
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raise ValueError("invalid dataset : {:}".format(xargs.dataseet))
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search_space = get_search_spaces("cell", xargs.search_space_name)
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model_config = dict2config(
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{
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"name": "SETN",
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"C": xargs.channel,
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"N": xargs.num_cells,
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"max_nodes": xargs.max_nodes,
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"num_classes": class_num,
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"space": search_space,
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"affine": False,
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"track_running_stats": True,
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},
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None,
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)
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search_model = get_cell_based_tiny_net(model_config)
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search_model.load_state_dict(ckp["search_model"])
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search_model = search_model.cuda()
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api = API("/home/dxy/.torch/NAS-Bench-201-v1_0-e61699.pth")
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archs, probs, accuracies = evaluate_one_shot(search_model, valid_loader, api, use_train)
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if __name__ == '__main__':
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#test_nas_api()
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#for i in range(200): plot('{:04d}'.format(i))
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#test_auto_grad()
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test_one_shot_model(sys.argv[1], sys.argv[2])
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if __name__ == "__main__":
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# test_nas_api()
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# for i in range(200): plot('{:04d}'.format(i))
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# test_auto_grad()
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test_one_shot_model(sys.argv[1], sys.argv[2])
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