-
Notifications
You must be signed in to change notification settings - Fork 3
/
heatmapping.py
328 lines (263 loc) · 10.5 KB
/
heatmapping.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
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
import os
import shutil
import time
import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import models
from utils import measure_model
from opts import args
# Init Torch/Cuda
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.manual_seed)
torch.manual_seed(args.manual_seed)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
best_prec1 = 0
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def msd_loss(output, target_var, criterion):
losses = []
for out in range(0, len(output)):
losses.append(criterion(output[out], target_var))
mean_loss = sum(losses) / len(output)
return mean_loss
def correctNess(scores, target_var):
cNess = []
for score in scores:
argmax = score.max(-1)[1]
if argmax == target_var:
cNess.append(1)
else:
cNess.append(0)
return cNess
def msdnet_accuracy(output, target, x, val=False):
"""
Calculates multi-classifier accuracy
:param output: A list in the length of the number of classifiers,
including output tensors of size (batch, classes)
:param target: a tensor of length batch_size, including GT
:param x: network input input
:param val: A flag to print per class validation accuracy
:return: mean precision of top1 and top5
"""
top1s = []
top5s = []
if torch.cuda.is_available():
prec1 = torch.FloatTensor([0]).cuda()
prec5 = torch.FloatTensor([0]).cuda()
else:
prec1 = torch.FloatTensor([0])
prec5 = torch.FloatTensor([0])
for out in output:
tprec1, tprec5 = accuracy(out.data, target, topk=(1, 5))
prec1 += tprec1
prec5 += tprec5
top1s.append(tprec1[0])
top5s.append(tprec5[0])
if val:
for c in range(0, len(top1s)):
print("Classifier {} top1: {} top5: {}".
format(c, top1s[c], top5s[c]))
prec1 = prec1 / len(output)
prec5 = prec5 / len(output)
return prec1, prec5, (top1s, top5s)
def load_checkpoint(args):
if args.evaluate_from:
print("Evaluating from model: ", args.evaluate_from)
model_filename = args.evaluate_from
else:
model_dir = os.path.join(args.savedir, 'save_models')
latest_filename = os.path.join(model_dir, 'latest.txt')
if os.path.exists(latest_filename):
with open(latest_filename, 'r') as fin:
model_filename = fin.readlines()[0].strip()
else:
return None
print("=> loading checkpoint '{}'".format(model_filename))
if torch.cuda.is_available():
state = torch.load(model_filename)
else:
state = torch.load(model_filename, map_location=lambda storage, loc: storage)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state['state_dict'].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
state['state_dict'] = new_state_dict
print("=> loaded checkpoint '{}'".format(model_filename))
return state
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def main(**kwargs):
global args, best_prec1
mapper = {
1 : 'airplane',
2 : 'automobile',
3 : 'bird',
4 : 'cat',
5 : 'deer',
6 : 'dog',
7 : 'frog',
8 : 'horse',
9 : 'ship',
10 : 'truck'
}
# Override if needed
for arg, v in kwargs.items():
args.__setattr__(arg, v)
### Calculate FLOPs & Param
model = getattr(models, args.model)(args)
if args.data in ['cifar10', 'cifar100']:
IMAGE_SIZE = 32
else:
IMAGE_SIZE = 224
n_flops, n_params = measure_model(model, IMAGE_SIZE, IMAGE_SIZE, args.debug)
if 'measure_only' in args and args.measure_only:
return n_flops, n_params
print('Starting.. FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6))
args.filename = "%s_%s_%s.txt" % \
(args.model, int(n_params), int(n_flops))
del(model)
# Create model
model = getattr(models, args.model)(args)
if args.debug:
print(args)
print(model)
criterion = nn.CrossEntropyLoss()
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = criterion.cuda()
# Define loss function (criterion) and optimizer
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
checkpoint = load_checkpoint(args)
args.start_epoch = checkpoint['epoch'] + 1
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
cudnn.benchmark = True
### Data loading
if args.data == "cifar10":
train_set = datasets.CIFAR10('../data', train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
]))
val_set = datasets.CIFAR10('../data', train=False,
transform=transforms.Compose([
transforms.ToTensor(),
]))
elif args.data == "cifar100":
train_set = datasets.CIFAR100('../data', train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
]))
val_set = datasets.CIFAR100('../data', train=False,
transform=transforms.Compose([
transforms.ToTensor(),
]))
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=2, shuffle=False,
num_workers=args.workers, pin_memory=True)
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
try:
top1_per_cls = [AverageMeter() for i in range(0, model.module.num_blocks)]
top5_per_cls = [AverageMeter() for i in range(0, model.module.num_blocks)]
except:
top1_per_cls = [AverageMeter() for i in range(0, model.num_blocks)]
top5_per_cls = [AverageMeter() for i in range(0, model.num_blocks)]
model.eval()
end = time.time()
from gradCam import *
for i, (input, target) in enumerate(val_loader):
if args.imgNo > 0 and i!=args.imgNo:
continue
(inputM, targetM) = (input, target)
input, target = input[0], target[0].view(1)
input = input.view(1,input.shape[0], input.shape[1],input.shape[2])
if torch.cuda.is_available():
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input, requires_grad=True)
target_var = torch.autograd.Variable(target)
# ### Compute output
scores, feats = model(input_var, 0.0, p=1)
if args.model == 'msdnet':
loss = msd_loss(scores, target_var, criterion)
else:
loss = criterion(scores, target_var)
#############################################################################
name = 'diags/' + mapper[target_var.cpu().data.numpy()[0]+1] + '_' + str(args.imgNo) + '_' + str(args.maxC)
name2 = 'diags/' + mapper[target_var.cpu().data.numpy()[0]+1] + '_' + str(args.imgNo)
if len(args.classLabel):
if mapper[target_var.cpu().data.numpy()[0]+1] == args.classLabel:
print(correctNess(scores, target_var), i, mapper[target_var.cpu().data.numpy()[0]+1])
elif args.imgNo < 0:
print(correctNess(scores, target_var), i, mapper[target_var.cpu().data.numpy()[0]+1])
elif i == args.imgNo:
import os
if not os.path.exists('diags'):
os.makedirs('diags')
print("Category : {}".format(mapper[target_var.cpu().data.numpy()[0]+1]))
print(correctNess(scores, target_var))
grad_cam = GradCam(model = model)
mask = grad_cam(target_var.cpu().data.numpy()[0], input_var, 0, args.maxC-1)#target_index)
gb_model = GuidedBackpropReLUModel(model)
gb = gb_model(target_var.cpu().data.numpy()[0], input_var, 0, args.maxC-1).transpose(2,0,1)
img = input[0].cpu().data.numpy().transpose(1,2,0)
img = cv2.resize(img, (512, 512))
show_cam_on_image(img, mask, name)
utils.save_image(torch.from_numpy(gb), name +'_gb.jpg')
cam_mask = np.zeros(gb.shape)
for i in range(0, gb.shape[0]):
cam_mask[i, :, :] = mask
cam_gb = np.multiply(cam_mask, gb)
utils.save_image(torch.from_numpy(cam_gb), name +'_cam_gb.jpg')
img = cv2.resize(input.cpu().data.numpy()[0].transpose(1,2,0), (512, 512))
utils.save_image(torch.from_numpy(img.transpose(2,0,1)), name2 + '_input.jpg')
exit()
else:
continue
#############################################################################
if __name__ == '__main__':
main()