/
detection.py
228 lines (167 loc) · 8.67 KB
/
detection.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
import torch
import torch.nn.functional as F
from ..box_utils import decode, jaccard, index2d
from utils import timer
from data import cfg, mask_type
import numpy as np
class Detect(object):
"""At test time, Detect is the final layer of SSD. Decode location preds,
apply non-maximum suppression to location predictions based on conf
scores and threshold to a top_k number of output predictions for both
confidence score and locations, as the predicted masks.
"""
# TODO: Refactor this whole class away. It needs to go.
def __init__(self, num_classes, bkg_label, top_k, conf_thresh, nms_thresh):
self.num_classes = num_classes
self.background_label = bkg_label
self.top_k = top_k
# Parameters used in nms.
self.nms_thresh = nms_thresh
if nms_thresh <= 0:
raise ValueError('nms_threshold must be non negative.')
self.conf_thresh = conf_thresh
self.use_cross_class_nms = False
self.use_fast_nms = False
def __call__(self, predictions, net):
"""
Args:
loc_data: (tensor) Loc preds from loc layers
Shape: [batch, num_priors, 4]
conf_data: (tensor) Shape: Conf preds from conf layers
Shape: [batch, num_priors, num_classes]
mask_data: (tensor) Mask preds from mask layers
Shape: [batch, num_priors, mask_dim]
prior_data: (tensor) Prior boxes and variances from priorbox layers
Shape: [num_priors, 4]
proto_data: (tensor) If using mask_type.lincomb, the prototype masks
Shape: [batch, mask_h, mask_w, mask_dim]
Returns:
output of shape (batch_size, top_k, 1 + 1 + 4 + mask_dim)
These outputs are in the order: class idx, confidence, bbox coords, and mask.
Note that the outputs are sorted only if cross_class_nms is False
"""
loc_data = predictions['loc']
conf_data = predictions['conf']
mask_data = predictions['mask']
prior_data = predictions['priors']
proto_data = predictions['proto'] if 'proto' in predictions else None
inst_data = predictions['inst'] if 'inst' in predictions else None
out = []
with timer.env('Detect'):
batch_size = loc_data.size(0)
num_priors = prior_data.size(0)
conf_preds = conf_data.view(batch_size, num_priors, self.num_classes).transpose(2, 1).contiguous()
for batch_idx in range(batch_size):
decoded_boxes = decode(loc_data[batch_idx], prior_data)
result = self.detect(batch_idx, conf_preds, decoded_boxes, mask_data, inst_data)
if result is not None and proto_data is not None:
result['proto'] = proto_data[batch_idx]
out.append({'detection': result, 'net': net})
return out
def detect(self, batch_idx, conf_preds, decoded_boxes, mask_data, inst_data):
""" Perform nms for only the max scoring class that isn't background (class 0) """
cur_scores = conf_preds[batch_idx, 1:, :]
conf_scores, _ = torch.max(cur_scores, dim=0)
keep = (conf_scores > self.conf_thresh)
scores = cur_scores[:, keep]
boxes = decoded_boxes[keep, :]
masks = mask_data[batch_idx, keep, :]
if inst_data is not None:
inst = inst_data[batch_idx, keep, :]
if scores.size(1) == 0:
return None
if self.use_fast_nms:
if self.use_cross_class_nms:
boxes, masks, classes, scores = self.cc_fast_nms(boxes, masks, scores, self.nms_thresh, self.top_k)
else:
boxes, masks, classes, scores = self.fast_nms(boxes, masks, scores, self.nms_thresh, self.top_k)
else:
boxes, masks, classes, scores = self.traditional_nms(boxes, masks, scores, self.nms_thresh, self.conf_thresh)
if self.use_cross_class_nms:
print('Warning: Cross Class Traditional NMS is not implemented.')
return {'box': boxes, 'mask': masks, 'class': classes, 'score': scores}
def cc_fast_nms(self, boxes, masks, scores, iou_threshold:float=0.5, top_k:int=200):
# Collapse all the classes into 1
scores, classes = scores.max(dim=0)
_, idx = scores.sort(0, descending=True)
idx = idx[:top_k]
boxes_idx = boxes[idx]
# Compute the pairwise IoU between the boxes
iou = jaccard(boxes_idx, boxes_idx)
# Zero out the lower triangle of the cosine similarity matrix and diagonal
iou.triu_(diagonal=1)
# Now that everything in the diagonal and below is zeroed out, if we take the max
# of the IoU matrix along the columns, each column will represent the maximum IoU
# between this element and every element with a higher score than this element.
iou_max, _ = torch.max(iou, dim=0)
# Now just filter out the ones greater than the threshold, i.e., only keep boxes that
# don't have a higher scoring box that would supress it in normal NMS.
idx_out = idx[iou_max <= iou_threshold]
return boxes[idx_out], masks[idx_out], classes[idx_out], scores[idx_out]
def fast_nms(self, boxes, masks, scores, iou_threshold:float=0.5, top_k:int=200, second_threshold:bool=False):
scores, idx = scores.sort(1, descending=True)
idx = idx[:, :top_k].contiguous()
scores = scores[:, :top_k]
num_classes, num_dets = idx.size()
boxes = boxes[idx.view(-1), :].view(num_classes, num_dets, 4)
masks = masks[idx.view(-1), :].view(num_classes, num_dets, -1)
iou = jaccard(boxes, boxes)
iou.triu_(diagonal=1)
iou_max, _ = iou.max(dim=1)
# Now just filter out the ones higher than the threshold
keep = (iou_max <= iou_threshold)
# We should also only keep detections over the confidence threshold, but at the cost of
# maxing out your detection count for every image, you can just not do that. Because we
# have such a minimal amount of computation per detection (matrix mulitplication only),
# this increase doesn't affect us much (+0.2 mAP for 34 -> 33 fps), so we leave it out.
# However, when you implement this in your method, you should do this second threshold.
if second_threshold:
keep *= (scores > self.conf_thresh)
# Assign each kept detection to its corresponding class
classes = torch.arange(num_classes, device=boxes.device)[:, None].expand_as(keep)
classes = classes[keep]
boxes = boxes[keep]
masks = masks[keep]
scores = scores[keep]
# Only keep the top cfg.max_num_detections highest scores across all classes
scores, idx = scores.sort(0, descending=True)
idx = idx[:cfg.max_num_detections]
scores = scores[:cfg.max_num_detections]
classes = classes[idx]
boxes = boxes[idx]
masks = masks[idx]
return boxes, masks, classes, scores
def traditional_nms(self, boxes, masks, scores, iou_threshold=0.5, conf_thresh=0.05):
import pyximport
pyximport.install(setup_args={"include_dirs":np.get_include()}, reload_support=True)
from utils.cython_nms import nms as cnms
num_classes = scores.size(0)
idx_lst = []
cls_lst = []
scr_lst = []
# Multiplying by max_size is necessary because of how cnms computes its area and intersections
boxes = boxes * cfg.max_size
for _cls in range(num_classes):
cls_scores = scores[_cls, :]
conf_mask = cls_scores > conf_thresh
idx = torch.arange(cls_scores.size(0), device=boxes.device)
cls_scores = cls_scores[conf_mask]
idx = idx[conf_mask]
if cls_scores.size(0) == 0:
continue
preds = torch.cat([boxes[conf_mask], cls_scores[:, None]], dim=1).cpu().numpy()
keep = cnms(preds, iou_threshold)
keep = torch.Tensor(keep, device=boxes.device).long()
idx_lst.append(idx[keep])
cls_lst.append(keep * 0 + _cls)
scr_lst.append(cls_scores[keep])
idx = torch.cat(idx_lst, dim=0)
classes = torch.cat(cls_lst, dim=0)
scores = torch.cat(scr_lst, dim=0)
scores, idx2 = scores.sort(0, descending=True)
idx2 = idx2[:cfg.max_num_detections]
scores = scores[:cfg.max_num_detections]
idx = idx[idx2]
classes = classes[idx2]
# Undo the multiplication above
return boxes[idx] / cfg.max_size, masks[idx], classes, scores