-
Notifications
You must be signed in to change notification settings - Fork 1
/
evaluate.py
362 lines (314 loc) · 14.3 KB
/
evaluate.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
# -*- coding: utf-8 -*-
"""SANet training routines."""
# Standard lib imports
import os
import time
import argparse
import os.path as osp
from urllib.parse import urlparse
# PyTorch imports
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision.transforms import Compose, ToTensor, Normalize, Resize
# Local imports
from networks.sanet import SANet
from referit_loader import ReferDataset
from utils.pyt_utils import load_model
from engine import Engine
# Other imports
import numpy as np
from tqdm import tqdm
import cv2
from PIL import Image
import skimage
parser = argparse.ArgumentParser(
description='Structured Attention Network for Referring Image Segmentation')
# Dataloading-related settings
parser.add_argument('--data', type=str, default='datasets/refer',
help='path to ReferIt splits data folder')
parser.add_argument('--split-root', type=str, default='data',
help='path to dataloader splits data folder')
parser.add_argument('--snapshot', default='models/deeplab_resnet.pth.tar',
help='path to weight snapshot file')
parser.add_argument('--dataset', default='unc', type=str,
help='dataset used to train network')
parser.add_argument('--val', default='val', type=str,
help='name of the dataset split used to validate')
parser.add_argument('-j', '--num_workers', default=1, type=int, metavar='N',
help='number of data loading workers (default: 4)')
# Training procedure settings
parser.add_argument('--no-cuda', default=False, action='store_true',
help='Do not use cuda to train model')
parser.add_argument('--batch-size', default=1, type=int,
help='Batch size for each gpu')
parser.add_argument('--sync-bn', action='store_true', default=False,
help='Use sync batchnorm. Default False')
parser.add_argument('--pin-memory', default=False, action='store_true',
help='enable CUDA memory pin on DataLoader')
# Model settings
parser.add_argument('--size', default=320, type=int,
help='image size')
parser.add_argument('--os', default=16, type=int,
help='output stride. Default 16')
parser.add_argument('--time', default=20, type=int,
help='maximum time steps per batch')
parser.add_argument('--emb-size', default=300, type=int,
help='word embedding dimensions')
parser.add_argument('--hid-size', default=256, type=int,
help='language model hidden size')
parser.add_argument('--vis-size', default=256, type=int,
help='visual feature dimensions')
parser.add_argument('--mix-size', default=256, type=int,
help='multimodal feature dimensions')
parser.add_argument('--tree-hid-size', default=256, type=int,
help='tree-gru hidden state dimensions')
parser.add_argument('--lang-layers', default=1, type=int,
help='number of language model (Bi-LSTM) stacked layers')
parser.add_argument('--backbone', default='resnet101', type=str,
help='(resnet101, dpn92)')
# Other settings
parser.add_argument('--tensorboard', action='store_true', default=False,
help='Using tensorboard for visualization. Default False')
parser.add_argument('--visual-interval', default=100, type=int,
help='Using tensorboard for visualization. Default False')
engine = Engine(custom_parser=parser)
args = parser.parse_args()
verbose = 0
if (not engine.distributed) or (engine.distributed and engine.local_rank ==0):
verbose = 1
# print argument settings
args_dict = vars(args)
if verbose == 1:
print('Argument list to program')
print('\n'.join(['--{0} {1}'.format(arg, args_dict[arg])
for arg in args_dict]))
print('\n\n')
if args.tensorboard:
from tensorboardX import SummaryWriter
# Writer will output to ./runs/ directory by default
writer = SummaryWriter()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.backends.cudnn.benchmark = True
image_size = (args.size, args.size)
input_transform = Compose([
Resize(image_size),
ToTensor(),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
target_transform = Compose([
Resize(image_size),
ToTensor()
])
totensor = ToTensor()
refer_val = ReferDataset(data_root=args.data,
dataset=args.dataset,
split_root=args.split_root,
split=args.val,
transform=input_transform,
annotation_transform=target_transform,
max_query_len=args.time)
val_loader, val_sampler = engine.get_test_loader(refer_val)
net = SANet(dict_size=len(refer_val.corpus),
emb_size=args.emb_size,
hid_size=args.hid_size,
vis_size=args.vis_size,
mix_size=args.mix_size,
tree_hid_size = args.tree_hid_size,
lang_layers=args.lang_layers,
output_stride=args.os,
num_classes=1,
pretrained_backbone=False,
pretrained_embedding=None,
dataset=args.dataset,
backbone=args.backbone)
if osp.exists(args.snapshot):
print('Loading state dict from: {0}'.format(args.snapshot))
net = load_model(model=net, model_file=args.snapshot, is_restore=True)
else:
ValueError("{} not exists.".format(args.snapshot))
cuda = torch.cuda.is_available() if args.cuda else False
if cuda:
net.cuda()
if verbose == 1:
if cuda:
current_device = torch.cuda.current_device()
print("Running on", torch.cuda.get_device_name(current_device))
else:
print("Running on CPU")
#base_params = list(map(id, net.backbone.parameters()))
#new_params = filter(lambda p: id(p) not in base_params, net.parameters())
net = engine.data_parallel(net)
def compute_mask_IU(masks, target):
assert(target.shape[-2:] == masks.shape[-2:])
temp = (masks * target)
intersection = temp.sum()
union = ((masks + target) - temp).sum()
return intersection, union
# def compute_mask_IU(masks, target):
# assert(target.shape[-2:] == masks.shape[-2:])
# I = torch.sum(masks.int() & target.int())
# U = torch.sum(masks.int() | target.int())
# return I, U
def compute_mask_IU_np(masks, target):
assert(target.shape[-2:] == masks.shape[-2:])
I = np.sum(np.logical_and(masks, target))
U = np.sum(np.logical_or(masks, target))
return I, U
def visualize_data(imgs, masks, words, words_len, out, att):
visual_imgs = imgs.detach().cpu()
visual_gt = masks.detach().cpu()
visual_out = out.detach().cpu()
visual_att = att.detach().cpu()
img_grid = torchvision.utils.make_grid(visual_imgs, nrow=4, normalize=True, pad_value=1)
gt_grid = torchvision.utils.make_grid(visual_gt, nrow=4, normalize=True, pad_value=1)
out_grid = torchvision.utils.make_grid(visual_out, nrow=4, normalize=True, pad_value=1)
att_grid = torchvision.utils.make_grid(visual_att, nrow=4, normalize=True, pad_value=1)
phrase = ""
for i in range(words.size(0)):
words_idx = words[i].detach().cpu().tolist()
words_list = refer_val.corpus.dictionary.__getitem__(words_idx)
phrase += "({})".format(str(i)) + " ".join(words_list[j] for j in range(words_len[i])) + '; '
return img_grid, gt_grid, out_grid, att_grid, phrase
def evaluate(epoch=0):
net.eval()
score_thresh = np.concatenate([np.arange(start=0.00, stop=0.96,
step=0.025)]).tolist()
cum_I = torch.zeros(len(score_thresh)).cuda()
cum_U = torch.zeros(len(score_thresh)).cuda()
eval_seg_iou_list = [.5, .6, .7, .8, .9]
seg_correct = torch.zeros(len(eval_seg_iou_list), len(score_thresh)).cuda()
seg_total = 0
with tqdm(total=len(val_loader),
dynamic_ncols=True,
desc='Validation Epoch #{}'.format(epoch),
disable=not verbose) as t:
for batch_idx, (imgs, masks, words, adjs, words_len) in enumerate(val_loader):
if cuda:
imgs = imgs.cuda()
masks = masks.cuda()
words = words.cuda()
adjs = adjs.cuda()
words_len = words_len.cuda()
with torch.no_grad():
out, _, out_att = net(imgs, words, adjs, words_len)
out = torch.sigmoid(out)
b_cum_I = torch.zeros(len(score_thresh)).cuda()
b_cum_U = torch.zeros(len(score_thresh)).cuda()
b_seg_correct = torch.zeros(len(eval_seg_iou_list), len(score_thresh)).cuda()
for i in range(imgs.size(0)):
inter = torch.zeros(len(score_thresh)).cuda()
union = torch.zeros(len(score_thresh)).cuda()
mask_path = osp.join(refer_val.mask_dir, refer_val.images[seg_total][1])
mask = Image.open(mask_path)
mask = totensor(mask).unsqueeze(0).cuda()
pred = F.interpolate(out[i].unsqueeze(0), size=mask.shape[2:], mode='bilinear', align_corners=True).squeeze(0)
for idx, thresh in enumerate(score_thresh):
thresholded_out = (pred > thresh).float()
try:
inter[idx], union[idx] = compute_mask_IU(thresholded_out, mask)
except AssertionError:
inter[idx] = 0
union[idx] = mask.sum()
this_iou = inter / union
for idx, seg_iou in enumerate(eval_seg_iou_list):
for jdx in range(len(score_thresh)):
b_seg_correct[idx, jdx] += (this_iou[jdx] >= seg_iou)
seg_total += 1
b_cum_I += inter
b_cum_U += union
if verbose == 1 and args.tensorboard and epoch >= 5 and this_iou.max() < 0.1:
failed_idx = [i]
img_grid, gt_grid, out_grid, att_grid, phrase = visualize_data(imgs[failed_idx], \
masks[failed_idx], words[failed_idx], words_len[failed_idx], out[failed_idx], out_att[failed_idx])
n_iter = epoch*len(val_loader) + batch_idx + i
writer.add_image('val_failed/images', img_grid, n_iter)
writer.add_image('val_failed/gts', gt_grid, n_iter)
writer.add_image('val_failed/output', out_grid, n_iter)
writer.add_image('val_failed/att', att_grid, n_iter)
writer.add_text('val_failed/phrase', phrase, n_iter)
if engine.distributed:
seg_correct += engine.all_reduce_tensor(b_seg_correct.float().detach())
cum_I += engine.all_reduce_tensor(b_cum_I.float().detach())
cum_U += engine.all_reduce_tensor(b_cum_U.float().detach())
else:
seg_correct += b_seg_correct.float().detach()
cum_I += b_cum_I.float().detach()
cum_U += b_cum_U.float().detach()
# Tensorboard
if verbose == 1 and args.tensorboard and batch_idx % args.visual_interval == 0:
img_grid, gt_grid, out_grid, att_grid, phrase = visualize_data(imgs, masks, words, words_len, out, out_att)
n_iter = epoch*len(val_loader) + batch_idx
writer.add_image('val/images', img_grid, n_iter)
writer.add_image('val/gts', gt_grid, n_iter)
writer.add_image('val/output', out_grid, n_iter)
writer.add_image('val/att', att_grid, n_iter)
writer.add_text('val/phrase', phrase, n_iter)
t.set_postfix({'IoU@{:.2f}'.format(score_thresh[20]): '{:.3f}'.format(float(this_iou[20]))})
t.update(1)
# Print final accumulated IoUs
final_ious = cum_I / cum_U
max_iou, max_idx = torch.max(final_ious, 0)
max_iou = float(max_iou.detach().cpu().numpy())
max_idx = int(max_idx.detach().cpu().numpy())
# Evaluation finished. Compute total IoUs and threshold that maximizes
if verbose == 1:
print('-' * 26)
for jdx, thresh in enumerate(score_thresh):
print('prec@X for Threshold {:.3f}'.format(thresh))
for idx, seg_iou in enumerate(eval_seg_iou_list):
print('prec@{:s} = {:2.2%}'.format(
str(seg_iou), seg_correct[idx, jdx] / seg_total))
print('prec@X for Threshold {:.3f}'.format(score_thresh[max_idx]))
for idx, seg_iou in enumerate(eval_seg_iou_list):
print('prec@{:s} = {:2.2%}'.format(
str(seg_iou), seg_correct[idx, max_idx] / seg_total))
print('-' * 26 + '\n' + '')
print('FINAL accumulated IoUs at different thresholds:')
print('{:4}| {:3} |'.format('Thresholds', 'mIoU'))
print('-' * 26)
for idx, thresh in enumerate(score_thresh):
print('{:.3f}| {:<2.2%} |'.format(thresh, final_ious[idx]))
print('-' * 26)
# Print maximum IoU
print('Maximum Overall IoU: {:2.2%} - Threshold: {:.3f}'.format(
max_iou, score_thresh[max_idx]))
if args.tensorboard:
writer.add_scalar('val/max_iou', max_iou, epoch)
writer.add_scalar('val/max_iou_threshold', score_thresh[max_idx], epoch)
return max_iou
# average metrics from distributed training.
class Metric(object):
def __init__(self, name, engine):
self.name = name
self.sum = torch.tensor(0.)
self.n = torch.tensor(0.)
self.engine = engine
def update(self, value, n=1):
value = value.detach()
if self.engine.distributed:
value = engine.all_reduce_tensor(value)
self.sum += value
self.n += n
def reset(self):
self.sum = torch.tensor(0.)
self.n = torch.tensor(0.)
@property
def avg(self):
return self.sum / self.n
if __name__ == '__main__':
try:
evaluate(0)
except KeyboardInterrupt:
if args.tensorboard and verbose == 1:
writer.close()
torch.cuda.empty_cache()
print('-' * 89)
print('Exiting from validation early')