-
Notifications
You must be signed in to change notification settings - Fork 1
/
ssd_utils.py
352 lines (314 loc) · 13 KB
/
ssd_utils.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
import torch
import torch.nn as nn
import os
import logging
import math
from functools import wraps
import time
import datetime
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
def point_form_box(boxes):
""" convert center formed boxes to point formed boxes
Arg:
boxes: (tensor)boxes with center and width/height formation
Return:
boxes: (tensor) boxes with left_top point and right_bottom point formation
"""
bounding = torch.cat(
(boxes[:, :2]-boxes[:, 2:]/2, boxes[:, :2]+boxes[:, 2:]/2), dim=1)
bounding.clamp_(max=1, min=0)
return bounding
def center_size(boxes):
""" Convert prior_boxes to (cx, cy, w, h)
representation for comparison to center-size form ground truth data.
Args:
boxes: (tensor) point_form boxes
Return:
boxes: (tensor) Converted xcenter, ycenter, width, height form of boxes.
"""
return torch.cat(((boxes[:, :2]+boxes[:, 2:])/2, (boxes[:, :2]-boxes[:, 2:])/2), dim=1)
def intersect(box_a, box_b):
""" We resize both tensors to [A,B,2] without new malloc:
[A,2] -> [A,1,2] -> [A,B,2]
[B,2] -> [1,B,2] -> [A,B,2]
Then we compute the area of intersect between box_a and box_b.
Args:
box_a: (tensor) bounding boxes, Shape: [A,4].
box_b: (tensor) bounding boxes, Shape: [B,4].
Return:
(tensor) intersection area, Shape: [A,B].
"""
A = box_a.size(0)
B = box_b.size(0)
min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(
A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(
A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
inter = torch.clamp((max_xy-min_xy), min=0)
return inter[:, :, 0] * inter[:, :, 1]
def jaccard(box_a, box_b):
"""Compute the jaccard overlap of two sets of boxes. The jaccard overlap
is simply the intersection over union of two boxes. Here we operate on
ground truth boxes and default boxes.
E.g.:
A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
Args:
box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
Return:
jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
"""
inter = intersect(box_a, box_b)
area_a = ((box_a[:, 2]-box_a[:, 0]) *
(box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter)
# print((box_a[:, 2]-box_a[:, 0]), (box_a[:, 3] -
# box_a[:, 1]), area_a.max(), area_a.min())
area_b = ((box_b[:, 2]-box_b[:, 0]) *
(box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter)
# print((box_b[:, 2]-box_b[:, 0]).min(), (box_b[:, 3] -
# box_b[:, 1]).min(), area_b.max(), area_b.min())
union = area_a+area_b-inter
return inter/union
def encode(matched, priors, variances):
"""Encode the variances from the priorbox layers into the ground truth boxes
we have matched (based on jaccard overlap) with the prior boxes.
Args:
matched: (tensor) Coords of ground truth for each prior in point-form
Shape: [num_priors, 4].
priors: (tensor) Prior boxes in center-offset form
Shape: [num_priors,4].
variances: (list[float]) Variances of loc regression
Return:
encoded boxes (tensor), Shape: [num_priors, 4]
"""
return torch.cat(
[(matched[:, :2]+matched[:, 2:]/2-priors[:, :2])/(variances[0]*priors[:, 2:]),
torch.log((matched[:, 2:]-matched[:, :2])/priors[:, 2:])/variances[1]], dim=1)
# Adapted from https://github.com/Hakuyume/chainer-ssd
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location regression predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of loc regression
Return:
decoded bounding box predictions
"""
pred_box = torch.cat((priors[:, :2]+loc[:, :2]*priors[:, 2:]*variances[0],
priors[:, 2:]*torch.exp(loc[:, 2:]*variances[1])), dim=1)
pred_box[:, :2] -= pred_box[:, 2:]/2
pred_box[:, 2:] += pred_box[:, :2]
return pred_box
def log_sum_exp(x):
"""Utility function for computing log_sum_exp while determining
This will be used to determine unaveraged confidence loss across
all examples in a batch.
Args:
x (Variable(tensor)): conf_preds from conf layers
"""
x_max = x.data.max()
return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max
def nms(boxes, scores, overlap=0.5, top_k=200):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The overlap thresh for suppressing unnecessary boxes.
top_k: (int) The Maximum number of box preds to consider.
Return:
The indices of the kept boxes with respect to num_priors.
"""
keep = scores.new(scores.size(0)).zero_().long()
if boxes.numel() == 0:
return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
# I = I[v >= 0.01]
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
# keep = torch.Tensor()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
return keep, count
def match(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):
"""Match each prior box with the ground truth box of the highest jaccard
overlap, encode the bounding boxes, then return the matched indices
corresponding to both confidence and location preds.
Args:
threshold: (float) The overlap threshold used when mathing boxes.
truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors].
priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
variances: (tensor) Variances corresponding to each prior coord,
Shape: [num_priors, 4].
labels: (tensor) All the class labels for the image, Shape: [num_obj].
loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
idx: (int) current batch index
Return:
The matched indices corresponding to 1)location and 2)confidence preds.
"""
# jaccard index
overlaps = jaccard(truths, point_form_box(priors))
best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
best_truth_idx.squeeze_(0)
best_truth_overlap.squeeze_(0)
best_prior_idx.squeeze_(1)
best_prior_overlap.squeeze_(1)
best_truth_overlap.index_fill_(0, best_prior_idx, 2)
for j in range(best_prior_idx.size(0)):
best_truth_idx[best_prior_idx[j]] = j
matches = truths[best_truth_idx]
conf = labels[best_truth_idx]+1
conf[best_truth_overlap < threshold] = 0
loc = encode(matches, priors, variances)
loc_t[idx] = loc
conf_t[idx] = conf
class Logger():
""" log object print log at stdout and save it to local disk\n
Args:
log_dir: local directory for save log
name: log file's name
Returns:
logger: logger
"""
def __init__(self, log_dir, name):
self.logger = logging.getLogger(name)
self.logger.setLevel(level=logging.INFO)
formater = logging.Formatter(
'%(asctime)s %(levelname)s %(message)s', '%m-%d %H:%M:%S')
if not os.path.exists(log_dir):
os.mkdir(log_dir)
now = datetime.datetime.now()
log_path = os.path.join(
log_dir, '{}{:%Y%m%dT%H%M}.log'.format(name, now))
handler = logging.FileHandler(log_path)
handler.setFormatter(formater)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
self.logger.addHandler(handler)
self.logger.addHandler(console)
self.logger.info("Start print log")
def __call__(self, log):
self.logger.info(log)
def time_count(func):
""" count time before and after function execute\n
Args:
func: funcation execute
Returns:
timeCount: time count
"""
@wraps(func)
def wrapper(*args, **kwargs):
start = time.time()
func(*args, **kwargs)
end = time.time()
return end-start
return wrapper
def conv_weight_uniform(module):
if isinstance(module, nn.Conv2d):
fan_in, fan_out = torch.nn.init._calculate_fan_in_and_fan_out(
module.weight)
bound = math.sqrt(6./(fan_in+fan_out))
module.weight.data.uniform_(-bound, bound)
if module.bias is not None:
module.bias.data.zero_()
def feature_visual(func):
""" visualize generated feature map for network bottom to top data flow
func must be nn.Module's foward member function with feature maps returned."""
@wraps(func)
def wrapper(*args, **kwargs):
outputs = func(*args, **kwargs)
if isinstance(outputs, list):
# DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
for idx, output in enumerate(outputs, start=1):
if torch.is_tensor(output):
img = torch.Tensor.cpu(output)
img = img.view(img.shape[1:]).permute(1, 2, 0)
weight = torch.ones((3, img.shape[2]), dtype=torch.float32)
weight.normal_()
img = nn.functional.linear(img, weight)
img = img.numpy()
channels_min = np.min(img, axis=(0, 1))
channels_max = np.max(img, axis=(0, 1))
for channel in range(img.shape[2]):
img[:, :, channel] = (
img[:, :, channel] - channels_min[channel]) /\
(channels_max[channel]-channels_min[channel])
print(img.shape)
fig = plt.figure(figsize=(16, 16))
ax = plt.subplot(111)
ax.imshow(img)
ax.set_xticks([]), ax.set_yticks([])
ax.set_title('特征图 %d' % (idx+1))
return outputs
else:
return outputs
return wrapper
def boxes_visual(image, bboxes, labels):
""" image must have 3 channels """
# change image formation to channels_last from channels_first,
if image.shape[0] == 3:
image = np.transpose(image, [1, 2, 0])
# fig = plt.figure(figsize=(16, 16))
ax = plt.subplot(111)
for idx, lbl in enumerate(labels):
p = patches.Rectangle((bboxes[idx][0], bboxes[idx][1]),
bboxes[idx][2]-bboxes[idx][0],
bboxes[idx][3]-bboxes[idx][1],
linewidth=2,
fill=False,
edgecolor=[0, 1, 0],
alpha=1)
ax.text(bboxes[idx][0]+10, bboxes[idx][1]+10, lbl, fontsize=12, family="simsun", color=[0, 0, 1], style="italic",
weight="bold", bbox=dict(facecolor=[0, 0.2, 0.1], alpha=0.2))
ax.add_patch(p)
ax.imshow(image)
# ax.set_xticks([]), ax.set_yticks([])
ax.set_title('目标检测box')