forked from tmllab/2021_ICCV_Me-Momentum
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Clothing.py
291 lines (235 loc) · 11.8 KB
/
Clothing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
import os
import os.path
import argparse
import random
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
import torchvision
import torchvision.transforms as transforms
from common.tools import getTime, train, evaluate
np.seterr(divide='ignore', invalid='ignore')
np.set_printoptions(linewidth=np.inf)
np.set_printoptions(formatter={'float': '{: 0.2f}'.format})
print("PyTorch version:", torch.__version__)
os.system('nvidia-smi')
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, help='seed number', default=1)
parser.add_argument('--lr', type=float, help='initial learning rate', default=0.005)
parser.add_argument('--weight_decay', type=float, help='weight_decay for training', default=0.001)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_classes', type=int, default=14)
parser.add_argument('--pretrain', action='store_false', help='pretrain')
parser.add_argument('--model_dir', type=str, help='dir to save model files', default='model')
parser.add_argument('--data_root', type=str, help='data location', default='data/Clothing1M_Official/')
parser.add_argument('--beta', type=float, help='beta for scores', default=0.45)
parser.add_argument('--n_epoch', type=int, default=5)
parser.add_argument('--max_inner_loop', type=int, help='max inner round', default=6)
parser.add_argument('--max_outer_loop', type=int, help='max outer round', default=3)
args = parser.parse_args()
print(args)
class Clothing1M_Dataset(Dataset):
def __init__(self, data, labels, root_dir, transform=None):
self.train_data = np.array(data)
self.train_labels = np.array(labels)
self.root_dir = root_dir
self.length = len(self.train_labels)
if transform is None:
self.transform = transforms.ToTensor()
else:
self.transform = transform
# print("Dataset length:", self.length)
def __getitem__(self, index):
img_paths, target = self.train_data[index], self.train_labels[index]
img_paths = os.path.join(self.root_dir, img_paths)
img = Image.open(img_paths).convert('RGB')
if self.transform is not None:
img = self.transform(img)
return img, target
def __len__(self):
return self.length
def getData(self):
return self.train_data, self.train_labels
def createModel(pretrained):
model = torchvision.models.resnet50(pretrained=pretrained)
model.fc = nn.Linear(2048, args.num_classes)
return model.cuda()
def evaluate_val(test_loader, model, loss_func):
model.eval()
total = 0
test_loss = 0
correct = 0
class_correct = np.zeros(args.num_classes)
class_total = np.zeros(args.num_classes)
class_pred = np.zeros(args.num_classes)
with torch.no_grad():
for images, labels in test_loader:
if torch.cuda.is_available:
images = images.cuda()
labels = labels.cuda()
logits = model(images)
loss = loss_func(logits, labels)
test_loss += loss.item()
outputs = F.softmax(logits, dim=1)
_, pred = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (pred == labels).sum()
preds = pred.cpu()
labels = labels.cpu()
for i in range(labels.size(0)):
class_total[labels[i]] += 1
class_pred[preds[i]] += 1
if(preds[i] == labels[i]):
class_correct[labels[i]] += 1
loss = 100 * (test_loss / total)
acc = 100 * float(correct) / float(total)
std = np.std(100 * class_correct / class_total)
acc_category = np.around(100 * class_correct / class_total, decimals=2)
precision_category = np.around(100 * class_correct / class_pred, decimals=2)
print(getTime(), 'Val Loss: {:.2f}, Acc: {:.2f}, Std: {:.2f}'.format(loss, acc, std))
return loss, acc, std, acc_category, precision_category
def combinateModels(modelList, model_best_scores, modelsIndexs, dataset):
labels = []
data = []
label_sizes = []
imagePaths, noise_labels = dataset.getData()
for j in set(modelsIndexs):
alist = np.argwhere(modelsIndexs == j)
print("Load " + modelList[int(j)] + ", label classes: " + str(alist.squeeze().tolist()))
model = createModel(args.pretrain)
model.load_state_dict(torch.load(modelList[int(j)]))
for i in alist:
labels_index = np.argwhere(noise_labels == i).squeeze()
get_data = np.take(imagePaths, labels_index).squeeze()
get_labels = np.take(noise_labels, labels_index).squeeze()
pred_data, pred_labels, pred_rates = predictByTarget(get_data, get_labels, model, i)
data.extend(pred_data.tolist())
labels.extend(pred_labels.tolist())
label_sizes.append(len(pred_labels))
print('combinate label_sizes', label_sizes)
return np.array(data), np.array(labels)
def predictByTarget(get_data, get_labels, model, target):
model.eval()
preds = []
rates = []
# Prepare new data loader by class
transform_test = transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),])
new_dataset = Clothing1M_Dataset(get_data, get_labels, args.data_root, transform_test)
new_dataset_loader = DataLoader(dataset=new_dataset, batch_size=args.batch_size, num_workers=8, shuffle=False, pin_memory=True)
with torch.no_grad():
for images, labels in new_dataset_loader:
if torch.cuda.is_available:
images = images.cuda()
labels = labels.cuda()
logits = model(images)
outputs = F.softmax(logits, dim=1)
rate, pred = torch.max(outputs.data, 1)
preds.append(pred)
rates.append(rate)
preds = torch.cat(preds, dim=0).cpu().numpy()
rates = torch.cat(rates, dim=0).cpu().numpy()
labels_index = np.argwhere(preds == target).squeeze()
data = np.take(get_data, labels_index).squeeze()
preds = np.take(preds, labels_index).squeeze()
rates = np.take(rates, labels_index).squeeze()
return data, preds, rates
def main():
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
model_save_dir = args.model_dir
if not os.path.exists(model_save_dir):
os.system('mkdir -p %s' % (model_save_dir))
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop(256, padding=32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
transform_test = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# Load data index file
kvDic = np.load(args.data_root + 'Clothing1m-data.npy', allow_pickle=True).item()
# Prepare train data loader
original_train_data = kvDic['train_data']
original_train_labels = kvDic['train_labels']
shuffle_index = np.arange(len(original_train_labels), dtype=int)
np.random.shuffle(shuffle_index)
train_data = original_train_data[shuffle_index]
train_labels = original_train_labels[shuffle_index]
train_dataset = Clothing1M_Dataset(train_data, train_labels, args.data_root, transform)
train_loader = DataLoader(dataset=train_dataset, batch_size=args.batch_size, num_workers=8, shuffle=True, pin_memory=True)
predict_dataset = Clothing1M_Dataset(train_data, train_labels, args.data_root, transform_test)
val_data = kvDic['clean_val_data']
val_labels = kvDic['clean_val_labels']
val_dataset = Clothing1M_Dataset(val_data, val_labels, args.data_root, transform_test)
val_loader = DataLoader(dataset=val_dataset, batch_size=args.batch_size, num_workers=8, shuffle=False, pin_memory=True)
test_data = kvDic['test_data']
test_labels = kvDic['test_labels']
test_dataset = Clothing1M_Dataset(test_data, test_labels, args.data_root, transform_test)
test_loader = DataLoader(dataset=test_dataset, batch_size=args.batch_size, num_workers=8, shuffle=False, pin_memory=True)
# Loss function
train_nums = np.zeros(args.num_classes, dtype=int)
val_nums = np.zeros(args.num_classes, dtype=int)
for item in val_labels:
val_nums[item] += 1
for item in train_labels:
train_nums[item] += 1
class_weights = torch.FloatTensor(np.mean(train_nums) / train_nums * val_nums / np.mean(val_nums)).cuda()
ceriation = nn.CrossEntropyLoss(weight=class_weights).cuda()
best_val_acc = 0
best_test_acc = 0
best_model_name = ""
for outer_loop in range(args.max_outer_loop):
model = createModel(args.pretrain)
modelList = np.array([""])
model_best_scores = np.zeros(args.num_classes, dtype=float)
model_indexs = np.zeros(args.num_classes, dtype=int)
optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=0.9)
scheduler = MultiStepLR(optimizer, milestones=[10, 20], gamma=0.1)
for inner_loop in range(args.max_inner_loop):
print(getTime(), "Outer", outer_loop, "Inner", inner_loop, "begin...")
for epoch in range(args.n_epoch):
train(model, train_loader, optimizer, ceriation, epoch)
val_loss, val_acc, val_std, val_class_acc, val_class_precision = evaluate_val(val_loader, model, ceriation)
scheduler.step()
model_scores = args.beta * val_class_acc + (1 - args.beta) * val_class_precision
filepath = model_save_dir + "/" + str(outer_loop) + "-" + str(inner_loop) + "-" + str(epoch) + "-" + str(round(val_acc, 2)) + ".hdf5"
for i in range(args.num_classes):
if(model_scores[i] > model_best_scores[i]):
model_best_scores[i] = model_scores[i]
model_indexs[i] = len(modelList)
if(val_acc > best_val_acc):
test_acc, _ = evaluate(model, test_loader, ceriation, "Epoch " + str(epoch) + " Test Acc:")
best_val_acc = val_acc
best_test_acc = test_acc
best_model_name = filepath
# save model
modelList = np.append(modelList, filepath)
torch.save(model.state_dict(), filepath)
# update train dataset
# print(getTime(), "Model_best_scores", np.around(model_best_scores, decimals=2), np.around(np.average(model_best_scores), decimals=2), "model indexs", model_indexs, "modelList", modelList)
train_data, train_labels = combinateModels(modelList, model_best_scores, model_indexs, predict_dataset)
train_dataset = Clothing1M_Dataset(train_data, train_labels, args.data_root, transform)
train_loader = DataLoader(dataset=train_dataset, batch_size=args.batch_size, num_workers=8, shuffle=True, pin_memory=True)
# update loss function
train_nums = np.zeros(args.num_classes, dtype=int)
for item in train_labels:
train_nums[int(item)] += 1
class_weights = torch.FloatTensor(np.mean(train_nums) / train_nums * val_nums / np.mean(val_nums)).cuda()
ceriation = nn.CrossEntropyLoss(weight=class_weights).cuda()
print("Best_test_accuracy:", best_test_acc, ", best_model_name:", best_model_name)
if __name__ == '__main__':
main()