-
Notifications
You must be signed in to change notification settings - Fork 378
/
generate_tsv.py
executable file
·233 lines (189 loc) · 8.38 KB
/
generate_tsv.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
#!/usr/bin/env python
"""Generate bottom-up attention features as a tsv file. Can use multiple gpus, each produces a
separate tsv file that can be merged later (e.g. by using merge_tsv function).
Modify the load_image_ids script as necessary for your data location. """
# Example:
# ./tools/generate_tsv.py --gpu 0,1,2,3,4,5,6,7 --cfg experiments/cfgs/faster_rcnn_end2end_resnet.yml --def models/vg/ResNet-101/faster_rcnn_end2end/test.prototxt --out test2014_resnet101_faster_rcnn_genome.tsv --net data/faster_rcnn_models/resnet101_faster_rcnn_final.caffemodel --split coco_test2014
import _init_paths
from fast_rcnn.config import cfg, cfg_from_file
from fast_rcnn.test import im_detect,_get_blobs
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
import caffe
import argparse
import pprint
import time, os, sys
import base64
import numpy as np
import cv2
import csv
from multiprocessing import Process
import random
import json
csv.field_size_limit(sys.maxsize)
FIELDNAMES = ['image_id', 'image_w','image_h','num_boxes', 'boxes', 'features']
# Settings for the number of features per image. To re-create pretrained features with 36 features
# per image, set both values to 36.
MIN_BOXES = 10
MAX_BOXES = 100
def load_image_ids(split_name):
''' Load a list of (path,image_id tuples). Modify this to suit your data locations. '''
split = []
if split_name == 'coco_test2014':
with open('/data/coco/annotations/image_info_test2014.json') as f:
data = json.load(f)
for item in data['images']:
image_id = int(item['id'])
filepath = os.path.join('/data/test2014/', item['file_name'])
split.append((filepath,image_id))
elif split_name == 'coco_test2015':
with open('/data/coco/annotations/image_info_test2015.json') as f:
data = json.load(f)
for item in data['images']:
image_id = int(item['id'])
filepath = os.path.join('/data/test2015/', item['file_name'])
split.append((filepath,image_id))
elif split_name == 'genome':
with open('/data/visualgenome/image_data.json') as f:
for item in json.load(f):
image_id = int(item['image_id'])
filepath = os.path.join('/data/visualgenome/', item['url'].split('rak248/')[-1])
split.append((filepath,image_id))
else:
print 'Unknown split'
return split
def get_detections_from_im(net, im_file, image_id, conf_thresh=0.2):
im = cv2.imread(im_file)
scores, boxes, attr_scores, rel_scores = im_detect(net, im)
# Keep the original boxes, don't worry about the regresssion bbox outputs
rois = net.blobs['rois'].data.copy()
# unscale back to raw image space
blobs, im_scales = _get_blobs(im, None)
cls_boxes = rois[:, 1:5] / im_scales[0]
cls_prob = net.blobs['cls_prob'].data
pool5 = net.blobs['pool5_flat'].data
# Keep only the best detections
max_conf = np.zeros((rois.shape[0]))
for cls_ind in range(1,cls_prob.shape[1]):
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, cfg.TEST.NMS))
max_conf[keep] = np.where(cls_scores[keep] > max_conf[keep], cls_scores[keep], max_conf[keep])
keep_boxes = np.where(max_conf >= conf_thresh)[0]
if len(keep_boxes) < MIN_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MIN_BOXES]
elif len(keep_boxes) > MAX_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MAX_BOXES]
return {
'image_id': image_id,
'image_h': np.size(im, 0),
'image_w': np.size(im, 1),
'num_boxes' : len(keep_boxes),
'boxes': base64.b64encode(cls_boxes[keep_boxes]),
'features': base64.b64encode(pool5[keep_boxes])
}
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Generate bbox output from a Fast R-CNN network')
parser.add_argument('--gpu', dest='gpu_id', help='GPU id(s) to use',
default='0', type=str)
parser.add_argument('--def', dest='prototxt',
help='prototxt file defining the network',
default=None, type=str)
parser.add_argument('--net', dest='caffemodel',
help='model to use',
default=None, type=str)
parser.add_argument('--out', dest='outfile',
help='output filepath',
default=None, type=str)
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file', default=None, type=str)
parser.add_argument('--split', dest='data_split',
help='dataset to use',
default='karpathy_train', type=str)
parser.add_argument('--set', dest='set_cfgs',
help='set config keys', default=None,
nargs=argparse.REMAINDER)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
def generate_tsv(gpu_id, prototxt, weights, image_ids, outfile):
# First check if file exists, and if it is complete
wanted_ids = set([int(image_id[1]) for image_id in image_ids])
found_ids = set()
if os.path.exists(outfile):
with open(outfile) as tsvfile:
reader = csv.DictReader(tsvfile, delimiter='\t', fieldnames = FIELDNAMES)
for item in reader:
found_ids.add(int(item['image_id']))
missing = wanted_ids - found_ids
if len(missing) == 0:
print 'GPU {:d}: already completed {:d}'.format(gpu_id, len(image_ids))
else:
print 'GPU {:d}: missing {:d}/{:d}'.format(gpu_id, len(missing), len(image_ids))
if len(missing) > 0:
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
net = caffe.Net(prototxt, caffe.TEST, weights=weights)
with open(outfile, 'ab') as tsvfile:
writer = csv.DictWriter(tsvfile, delimiter = '\t', fieldnames = FIELDNAMES)
_t = {'misc' : Timer()}
count = 0
for im_file,image_id in image_ids:
if int(image_id) in missing:
_t['misc'].tic()
writer.writerow(get_detections_from_im(net, im_file, image_id))
_t['misc'].toc()
if (count % 100) == 0:
print 'GPU {:d}: {:d}/{:d} {:.3f}s (projected finish: {:.2f} hours)' \
.format(gpu_id, count+1, len(missing), _t['misc'].average_time,
_t['misc'].average_time*(len(missing)-count)/3600)
count += 1
def merge_tsvs():
test = ['/work/data/tsv/test2015/resnet101_faster_rcnn_final_test.tsv.%d' % i for i in range(8)]
outfile = '/work/data/tsv/merged.tsv'
with open(outfile, 'ab') as tsvfile:
writer = csv.DictWriter(tsvfile, delimiter = '\t', fieldnames = FIELDNAMES)
for infile in test:
with open(infile) as tsv_in_file:
reader = csv.DictReader(tsv_in_file, delimiter='\t', fieldnames = FIELDNAMES)
for item in reader:
try:
writer.writerow(item)
except Exception as e:
print e
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
gpu_id = args.gpu_id
gpu_list = gpu_id.split(',')
gpus = [int(i) for i in gpu_list]
print('Using config:')
pprint.pprint(cfg)
assert cfg.TEST.HAS_RPN
image_ids = load_image_ids(args.data_split)
random.seed(10)
random.shuffle(image_ids)
# Split image ids between gpus
image_ids = [image_ids[i::len(gpus)] for i in range(len(gpus))]
caffe.init_log()
caffe.log('Using devices %s' % str(gpus))
procs = []
for i,gpu_id in enumerate(gpus):
outfile = '%s.%d' % (args.outfile, gpu_id)
p = Process(target=generate_tsv,
args=(gpu_id, args.prototxt, args.caffemodel, image_ids[i], outfile))
p.daemon = True
p.start()
procs.append(p)
for p in procs:
p.join()