-
Notifications
You must be signed in to change notification settings - Fork 2
/
train_gcam_multiclass_grid_consistency.py
401 lines (332 loc) · 17.3 KB
/
train_gcam_multiclass_grid_consistency.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
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
'''
This code was adapted from https://github.com/allenai/elastic
We introduce a custom CocoDetection class for creating our composite images.
Our model uses both BCE and GCAM L1 consistency loss.
'''
import argparse
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data as data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import os
from PIL import Image
from utils import save_checkpoint, AverageMeter
import coco_dataset_gcam
import resnet_multigpu_multiclass as resnet
model_names = ['resnet18', 'resnet50']
class VanillaCocoDetection(datasets.coco.CocoDetection):
'''
This class is used as the dataloader for the COCO validation set.
'''
def __init__(self, root, annFile, transform=None, target_transform=None):
from pycocotools.coco import COCO
self.root = root
self.coco = COCO(annFile)
self.ids = list(self.coco.imgs.keys())
self.transform = transform
self.target_transform = target_transform
self.cat2cat = dict()
for cat in self.coco.cats.keys():
self.cat2cat[cat] = len(self.cat2cat)
def __getitem__(self, index):
coco = self.coco
img_id = self.ids[index]
ann_ids = coco.getAnnIds(imgIds=img_id)
target = coco.loadAnns(ann_ids)
output = torch.zeros(80, dtype=torch.long)
for obj in target:
output[self.cat2cat[obj['category_id']]] = 1
target = output
path = coco.loadImgs(img_id)[0]['file_name']
img = Image.open(os.path.join(self.root, path)).convert('RGB')
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
parser = argparse.ArgumentParser(description='PyTorch COCO Training with Grad-CAM consistency')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18', choices=model_names,
help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet50)')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
help='number of data loading workers (default: 16)')
parser.add_argument('--epochs', default=36, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=96, type=int,
metavar='N', help='mini-batch size (default: 96)')
parser.add_argument('-g', '--num-gpus', default=4, type=int,
metavar='N', help='number of GPUs to match (default: 4)')
parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--print-freq', '-p', default=117, type=int,
metavar='N', help='print frequency (default: 117)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
help='distributed backend')
parser.add_argument('--lambda', default=1, type=float,
metavar='LAM', help='lambda hyperparameter for GCAM loss', dest='lambda_val')
parser.add_argument('--maxpool', dest='maxpool', action='store_true',
help='use maxpool version of ResNet architecture')
parser.add_argument('--save_dir', default='checkpoint', type=str, metavar='SV_PATH',
help='path to save checkpoints (default: none)')
def main():
global args
args = parser.parse_args()
os.makedirs(args.save_dir, exist_ok=True)
print('config: wd', args.weight_decay, 'lr', args.lr, 'batch_size', args.batch_size, 'num_gpus', args.num_gpus)
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size)
# create model for COCO (80 classes)
print("=> creating model '{}'".format(args.arch))
if args.maxpool:
model = resnet.__dict__[args.arch](num_classes=80, maxpool=True)
else:
model = resnet.__dict__[args.arch](num_classes=80)
model = torch.nn.DataParallel(model).cuda()
bce_criterion = nn.BCEWithLogitsLoss().cuda()
l1_criterion = nn.L1Loss().cuda()
params = list()
for n, p in model.named_parameters():
if '.ups.' not in n:
params.append(p)
optimizer = torch.optim.SGD([{'params': iter(params), 'lr': args.lr},
], lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
resume = ('module.fc.bias' in checkpoint['state_dict'] and
checkpoint['state_dict']['module.fc.bias'].size() == model.module.fc.bias.size()) or \
('module.classifier.bias' in checkpoint['state_dict'] and
checkpoint['state_dict']['module.classifier.bias'].size() == model.module.classifier.bias.size())
if resume:
# True resume: resume training on COCO
model.load_state_dict(checkpoint['state_dict'], strict=False)
optimizer.load_state_dict(checkpoint['optimizer']) if 'optimizer' in checkpoint else print(
'no optimizer found')
args.start_epoch = checkpoint['epoch'] if 'epoch' in checkpoint else args.start_epoch
else:
# Fake resume: transfer from ImageNet
for n, p in list(checkpoint['state_dict'].items()):
if 'classifier' in n or 'fc' in n:
print(n, 'deleted from state_dict')
del checkpoint['state_dict'][n]
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch'] if 'epoch' in checkpoint else 'unknown'))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = coco_dataset_gcam.CocoDetection(os.path.join(args.data, 'train2014'),
os.path.join(args.data, 'annotations/instances_train2014.json'),
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
val_dataset = VanillaCocoDetection(os.path.join(args.data, 'val2014'),
os.path.join(args.data, 'annotations/instances_val2014.json'),
transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
normalize,
]))
train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
validate_multi(val_loader, model, bce_criterion)
return
for epoch in range(args.start_epoch, args.epochs):
coco_adjust_learning_rate(optimizer, epoch)
# train for one epoch
train_multi(train_loader, model, bce_criterion, l1_criterion, optimizer, epoch)
# evaluate on validation set
validate_multi(val_loader, model, bce_criterion)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, False, args)
def train_multi(train_loader, model, bce_criterion, l1_criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
bce_losses = AverageMeter()
gc_losses = AverageMeter()
prec = AverageMeter()
rec = AverageMeter()
# switch to train mode
model.train()
optimizer.zero_grad()
end = time.time()
tp, fp, fn, tn, count = 0, 0, 0, 0, 0
for i, (images, targets, composite_images, gcam_gt_targets, gt_quadrants) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
images = images.cuda(non_blocking=True)
targets = targets.cuda(non_blocking=True)
composite_images = composite_images.cuda(non_blocking=True)
gcam_gt_targets = gcam_gt_targets.cuda(non_blocking=True)
gt_quadrants = gt_quadrants.cuda(non_blocking=True)
# compute output, BCE loss and GCAM loss.
# We delegate multi-gpu GCAM loss handling to DataParallel by computing it within the forward pass.
output, targets, bce_loss, gcam_loss = model(images, composite_images, targets, gcam_gt_targets,
gt_quadrants, bce_criterion, l1_criterion)
bce_loss = bce_loss.mean()
gcam_loss = gcam_loss.mean()
bce_losses.update(bce_loss.item(), images.size(0))
gc_losses.update(gcam_loss.item(), images.size(0))
loss = bce_loss * 80.0 + args.lambda_val * gcam_loss
# measure accuracy and record loss
pred = output.data.gt(0.0).long()
tp += (pred + targets).eq(2).sum(dim=0)
fp += (pred - targets).eq(1).sum(dim=0)
fn += (pred - targets).eq(-1).sum(dim=0)
tn += (pred + targets).eq(0).sum(dim=0)
count += images.size(0)
this_tp = (pred + targets).eq(2).sum()
this_fp = (pred - targets).eq(1).sum()
this_fn = (pred - targets).eq(-1).sum()
this_tn = (pred + targets).eq(0).sum()
this_acc = (this_tp + this_tn).float() / (this_tp + this_tn + this_fp + this_fn).float()
this_prec = this_tp.float() / (this_tp + this_fp).float() * 100.0 if this_tp + this_fp != 0 else 0.0
this_rec = this_tp.float() / (this_tp + this_fn).float() * 100.0 if this_tp + this_fn != 0 else 0.0
losses.update(float(loss), images.size(0))
prec.update(float(this_prec), images.size(0))
rec.update(float(this_rec), images.size(0))
# compute gradient and do SGD step
loss.backward()
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
p_c = [float(tp[i].float() / (tp[i] + fp[i]).float()) * 100.0 if tp[i] > 0 else 0.0 for i in range(len(tp))]
r_c = [float(tp[i].float() / (tp[i] + fn[i]).float()) * 100.0 if tp[i] > 0 else 0.0 for i in range(len(tp))]
f_c = [2 * p_c[i] * r_c[i] / (p_c[i] + r_c[i]) if tp[i] > 0 else 0.0 for i in range(len(tp))]
mean_p_c = sum(p_c) / len(p_c)
mean_r_c = sum(r_c) / len(r_c)
mean_f_c = sum(f_c) / len(f_c)
p_o = tp.sum().float() / (tp + fp).sum().float() * 100.0
r_o = tp.sum().float() / (tp + fn).sum().float() * 100.0
f_o = 2 * p_o * r_o / (p_o + r_o)
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'BCE Loss {bce_loss.val:.4f} ({bce_loss.avg:.4f})\t'
'GC Loss {gc_loss.val:.4f} ({gc_loss.avg:.4f})\t'
'Total Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Precision {prec.val:.2f} ({prec.avg:.2f})\t'
'Recall {rec.val:.2f} ({rec.avg:.2f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, bce_loss=bce_losses, gc_loss=gc_losses, loss=losses, prec=prec, rec=rec))
print('P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
def validate_multi(val_loader, model, bce_criterion):
batch_time = AverageMeter()
losses = AverageMeter()
prec = AverageMeter()
rec = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
tp, fp, fn, tn, count = 0, 0, 0, 0, 0
for i, (images, targets) in enumerate(val_loader):
images = images.cuda(non_blocking=True)
targets = targets.cuda(non_blocking=True)
# compute output
with torch.no_grad():
output = model(images, eval=True)
loss = bce_criterion(output, targets.float())
# measure accuracy and record loss
pred = output.data.gt(0.0).long()
tp += (pred + targets).eq(2).sum(dim=0)
fp += (pred - targets).eq(1).sum(dim=0)
fn += (pred - targets).eq(-1).sum(dim=0)
tn += (pred + targets).eq(0).sum(dim=0)
# three_pred = pred.unsqueeze(1).expand(-1, 3, -1) # n, 3, 80
# tp_size += (three_pred + original_target).eq(2).sum(dim=0)
# fn_size += (three_pred - original_target).eq(-1).sum(dim=0)
count += images.size(0)
this_tp = (pred + targets).eq(2).sum()
this_fp = (pred - targets).eq(1).sum()
this_fn = (pred - targets).eq(-1).sum()
this_tn = (pred + targets).eq(0).sum()
this_acc = (this_tp + this_tn).float() / (this_tp + this_tn + this_fp + this_fn).float()
this_prec = this_tp.float() / (this_tp + this_fp).float() * 100.0 if this_tp + this_fp != 0 else 0.0
this_rec = this_tp.float() / (this_tp + this_fn).float() * 100.0 if this_tp + this_fn != 0 else 0.0
losses.update(float(loss), images.size(0))
prec.update(float(this_prec), images.size(0))
rec.update(float(this_rec), images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
p_c = [float(tp[i].float() / (tp[i] + fp[i]).float()) * 100.0 if tp[i] > 0 else 0.0 for i in range(len(tp))]
r_c = [float(tp[i].float() / (tp[i] + fn[i]).float()) * 100.0 if tp[i] > 0 else 0.0 for i in range(len(tp))]
f_c = [2 * p_c[i] * r_c[i] / (p_c[i] + r_c[i]) if tp[i] > 0 else 0.0 for i in range(len(tp))]
mean_p_c = sum(p_c) / len(p_c)
mean_r_c = sum(r_c) / len(r_c)
mean_f_c = sum(f_c) / len(f_c)
p_o = tp.sum().float() / (tp + fp).sum().float() * 100.0
r_o = tp.sum().float() / (tp + fn).sum().float() * 100.0
f_o = 2 * p_o * r_o / (p_o + r_o)
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Precision {prec.val:.2f} ({prec.avg:.2f})\t'
'Recall {rec.val:.2f} ({rec.avg:.2f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
prec=prec, rec=rec))
print('P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
print('--------------------------------------------------------------------')
print(' * P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
return
def coco_adjust_learning_rate(optimizer, epoch):
if isinstance(optimizer, torch.optim.Adam):
return
lr = args.lr
if epoch >= 24:
lr *= 0.1
if epoch >= 30:
lr *= 0.1
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if __name__ == '__main__':
main()