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update code styles

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This commit is contained in:
D-X-Y
2020-01-10 17:26:37 +11:00
parent ad34af9913
commit 96152a9904
5 changed files with 17 additions and 12 deletions
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0
CODE_OF_CONDUCT.md → CODE-OF-CONDUCT.md
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2
LICENSE.md
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@@ -1,6 +1,6 @@
MIT License MIT License
Copyright (c) 2019 Xuanyi Dong [GitHub: https://github.com/D-X-Y] Copyright (c) 2019 Xuanyi Dong (GitHub: https://github.com/D-X-Y)
Permission is hereby granted, free of charge, to any person obtaining a copy Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal of this software and associated documentation files (the "Software"), to deal

3
exps/vis/test.py
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@@ -87,7 +87,8 @@ def test_one_shot_model(ckpath, use_train):
ckp = torch.load(ckpath) ckp = torch.load(ckpath)
xargs = ckp['args'] xargs = ckp['args']
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1) train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, None) #config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, None)
config = load_config('./configs/nas-benchmark/algos/DARTS.config', {'class_num': class_num, 'xshape': xshape}, None)
if xargs.dataset == 'cifar10': if xargs.dataset == 'cifar10':
cifar_split = load_config('configs/nas-benchmark/cifar-split.txt', None, None) cifar_split = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
xvalid_data = deepcopy(train_data) xvalid_data = deepcopy(train_data)

18
lib/utils/nas_utils.py
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@@ -15,14 +15,16 @@ def evaluate_one_shot(model, xloader, api, cal_mode, seed=111):
with torch.no_grad(): with torch.no_grad():
logits = nn.functional.log_softmax(model.arch_parameters, dim=-1) logits = nn.functional.log_softmax(model.arch_parameters, dim=-1)
archs = CellStructure.gen_all(model.op_names, model.max_nodes, False) archs = CellStructure.gen_all(model.op_names, model.max_nodes, False)
probs, accuracies, gt_accs = [], [], [] probs, accuracies, gt_accs_10_valid, gt_accs_10_test = [], [], [], []
loader_iter = iter(xloader) loader_iter = iter(xloader)
random.seed(seed) random.seed(seed)
random.shuffle(archs) random.shuffle(archs)
for idx, arch in enumerate(archs): for idx, arch in enumerate(archs):
arch_index = api.query_index_by_arch( arch ) arch_index = api.query_index_by_arch( arch )
metrics = api.get_more_info(arch_index, 'cifar10-valid', None, False, False) metrics = api.get_more_info(arch_index, 'cifar10-valid', None, False, False)
gt_accs.append( metrics['valid-accuracy'] ) gt_accs_10_valid.append( metrics['valid-accuracy'] )
metrics = api.get_more_info(arch_index, 'cifar10', None, False, False)
gt_accs_10_test.append( metrics['test-accuracy'] )
select_logits = [] select_logits = []
for i, node_info in enumerate(arch.nodes): for i, node_info in enumerate(arch.nodes):
for op, xin in node_info: for op, xin in node_info:
@@ -31,8 +33,9 @@ def evaluate_one_shot(model, xloader, api, cal_mode, seed=111):
select_logits.append( logits[model.edge2index[node_str], op_index] ) select_logits.append( logits[model.edge2index[node_str], op_index] )
cur_prob = sum(select_logits).item() cur_prob = sum(select_logits).item()
probs.append( cur_prob ) probs.append( cur_prob )
cor_prob = np.corrcoef(probs, gt_accs)[0,1] cor_prob_valid = np.corrcoef(probs, gt_accs_10_valid)[0,1]
print ('correlation for probabilities : {:}'.format(cor_prob)) cor_prob_test = np.corrcoef(probs, gt_accs_10_test )[0,1]
print ('{:} correlation for probabilities : {:.6f} on CIFAR-10 validation and {:.6f} on CIFAR-10 test'.format(time_string(), cor_prob_valid, cor_prob_test))
for idx, arch in enumerate(archs): for idx, arch in enumerate(archs):
model.set_cal_mode('dynamic', arch) model.set_cal_mode('dynamic', arch)
@@ -45,8 +48,9 @@ def evaluate_one_shot(model, xloader, api, cal_mode, seed=111):
_, preds = torch.max(logits, dim=-1) _, preds = torch.max(logits, dim=-1)
correct = (preds == targets.cuda() ).float() correct = (preds == targets.cuda() ).float()
accuracies.append( correct.mean().item() ) accuracies.append( correct.mean().item() )
if idx != 0 and (idx % 300 == 0 or idx + 1 == len(archs) or idx == 10): if idx != 0 and (idx % 500 == 0 or idx + 1 == len(archs)):
cor_accs = np.corrcoef(accuracies, gt_accs[:idx+1])[0,1] cor_accs_valid = np.corrcoef(accuracies, gt_accs_10_valid[:idx+1])[0,1]
print ('{:} {:03d}/{:03d} mode={:5s}, correlation : accs={:.4f}, arch={:}'.format(time_string(), idx, len(archs), 'Train' if cal_mode else 'Eval', cor_accs, arch)) cor_accs_test = np.corrcoef(accuracies, gt_accs_10_test [:idx+1])[0,1]
print ('{:} {:05d}/{:05d} mode={:5s}, correlation : accs={:.5f} for CIFAR-10 valid, {:.5f} for CIFAR-10 test.'.format(time_string(), idx, len(archs), 'Train' if cal_mode else 'Eval', cor_accs_valid, cor_accs_test))
model.load_state_dict(weights) model.load_state_dict(weights)
return archs, probs, accuracies return archs, probs, accuracies