-
Notifications
You must be signed in to change notification settings - Fork 2
/
coco.py
254 lines (213 loc) · 8.08 KB
/
coco.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
"""
Mask R-CNN
Configurations and data loading code for MS COCO.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""
import os
import time
import numpy as np
import config
import pdb
import data as datalib
# Download and install the Python COCO tools from https://github.com/waleedka/coco
# That's a fork from the original https://github.com/pdollar/coco with a bug
# fix for Python 3.
# I submitted a pull request https://github.com/cocodataset/cocoapi/pull/50
# If the PR is merged then use the original repo.
# Note: Edit PythonAPI/Makefile and replace "python" with "python3".
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as maskUtils
import model as modellib
# Root directory of the project
ROOT_DIR = os.getcwd()
# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
DEFAULT_DATASET_YEAR = "2014"
############################################################
# COCO Evaluation
############################################################
def build_coco_results(dataset, image_ids, rois, class_ids, scores, masks):
"""Arrange resutls to match COCO specs in http://cocodataset.org/#format ."""
if rois is None:
return []
results = []
for image_id in image_ids:
for i in range(rois.shape[0]):
class_id = class_ids[i]
score = scores[i]
bbox = np.around(rois[i], 1)
mask = masks[:, :, i]
result = {
"image_id": image_id,
"category_id": dataset.class_id(class_id),
"bbox":
[bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]],
"score": score,
"segmentation": maskUtils.encode(np.asfortranarray(mask))
}
# pdb.set_trace()
# {
# 'image_id': 532481,
# 'category_id': 1,
# 'bbox': [259, 179, 63, 55],
# 'score': 0.99867451,
# 'segmentation': {
# 'size': [426, 640],
# 'counts':
# b'gP^33U=2O2N2O0O2N2O2M2O3L4L5L2N1O1N2N2O1O0O2N2N100O100O101N1001O0001O00001OO2O000O2N101O5J5L1N2O1N101N1O2N2N1O3L4L5JcmT4'
# }
# }
results.append(result)
return results
def evaluate_coco(model, dataset, coco, eval_type="bbox", limit=0, image_ids=None):
"""Run official COCO evaluation.
dataset: A Dataset object with valiadtion data
eval_type: "bbox" or "segm" for bounding box or segmentation evaluation
limit: if not 0, it's the number of images to use for evaluation
"""
# Pick COCO images from the dataset
image_ids = image_ids or dataset.ids
# Limit to a subset
if limit:
image_ids = image_ids[:limit]
t_prediction = 0
t_start = time.time()
results = []
for i, image_id in enumerate(image_ids):
if i % 10 == 0:
print("Evaluating ", eval_type, " ", i + 1, " ... ")
# Load image
image = dataset.load_image(image_id)
# print(image_id, dataset.image_name(dataset.image_index(image_id)))
# Run detection
t = time.time()
class_ids, scores, boxes, masks = model.detect(image)
if class_ids is None:
continue
t_prediction += (time.time() - t)
scores = np.array(scores)
boxes = np.array(boxes).astype(np.int32)
masks = np.array(masks).astype(np.uint8)
masks = masks.transpose(1, 2, 0) # NxHxW--> HxWxN
image_results = build_coco_results(dataset, image_ids[i:i + 1],
boxes, class_ids,
scores, masks)
# pdb.set_trace()
results.extend(image_results)
# Load results. This modifies results with additional attributes.
coco_results = coco.loadRes(results)
# Evaluate
cocoEval = COCOeval(coco, coco_results, eval_type)
cocoEval.params.imgIds = image_ids
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print("Prediction time: {}. Average {}/image".format(
t_prediction, t_prediction / len(image_ids)))
print("Total time: ", time.time() - t_start)
############################################################
# Training
############################################################
if __name__ == '__main__':
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Train/Eval Mask R-CNN Model on MS COCO.')
parser.add_argument(
"command",
metavar="<command>",
help="'train' or 'evaluate' on MS COCO")
parser.add_argument(
'--dataset',
required=True,
metavar="/path/to/coco/",
help='Directory of the MS-COCO dataset')
parser.add_argument(
'--year',
required=False,
default=DEFAULT_DATASET_YEAR,
metavar="<year>",
help='Year of the MS-COCO dataset (2014 or 2017) (default=2014)')
parser.add_argument(
'--model',
required=False,
default="models/mask_rcnn_coco.pth",
metavar="/path/to/weights.pth",
help="Path to weights .pth file or 'coco'")
parser.add_argument(
'--logs',
required=False,
default=DEFAULT_LOGS_DIR,
metavar="/path/to/logs/",
help='Logs and checkpoints directory (default=logs/)')
parser.add_argument(
'--limit',
required=False,
default=500,
metavar="<image count>",
help='Images to use for evaluation (default=500)')
args = parser.parse_args()
print("Command: ", args.command)
print("Model: ", args.model)
print("Dataset: ", args.dataset)
print("Year: ", args.year)
print("Logs: ", args.logs)
# Configurations
if args.command == "train":
config = config.CocoConfig()
else:
config = config.CocoInferenceConfig()
config.display()
# Create model
if args.command == "train":
model = modellib.MaskRCNN(config=config, model_dir=args.logs)
else:
model = modellib.MaskRCNN(config=config, model_dir=args.logs)
if config.GPU_COUNT:
model = model.cuda()
# Load weights
print("Loading weights ", args.model)
model.load_weights(args.model)
# Train or evaluate
if args.command == "train":
# Training dataset. Use the training set and 35K from the
# validation set, as as in the Mask RCNN paper.
dataset_train = datalib.CocoMaskRCNNDataset(args.dataset, "train", args.year, config)
dataset_valid = datalib.CocoMaskRCNNDataset(args.dataset, "minival", args.year, config)
# *** This training schedule is an example. Update to your needs ***
# Training - Stage 1
model.train_model(
dataset_train,
dataset_valid,
learning_rate=config.LEARNING_RATE,
epochs=40,
layers='heads')
# # Training - Stage 2
# # Finetune layers from ResNet stage 4 and up
model.train_model(
dataset_train,
dataset_valid,
learning_rate=config.LEARNING_RATE,
epochs=120,
layers='4+')
# Training - Stage 3
# Fine tune all layers
model.train_model(
dataset_train,
dataset_valid,
learning_rate=config.LEARNING_RATE / 10,
epochs=160,
layers='all')
elif args.command == "evaluate":
# Validation dataset
dataset_valid = datalib.CocoMaskRCNNDataset(args.dataset, "minival", args.year, config)
# dataset_valid.set_filter([532481])
print("Running COCO evaluation on {} images.".format(args.limit))
evaluate_coco(model, dataset_valid, dataset_valid.coco, "bbox", limit=int(args.limit))
evaluate_coco(model, dataset_valid, dataset_valid.coco, "segm", limit=int(args.limit))
else:
print("'{}' is not recognized. "
"Use 'train' or 'evaluate'".format(args.command))