def _process_feature_extraction_v2(self,
output,
im_scales,
im_infos,
feature_name="fc6",
conf_thresh=0):
from maskrcnn_benchmark.layers import nms
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(boxes) for boxes in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feature_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
info_list = []
for i in range(batch_size):
dets = output[0]["proposals"][i].bbox / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros((scores.shape[0])).to(cur_device)
conf_thresh_tensor = torch.full_like(max_conf, conf_thresh)
start_index = 1
# Column 0 of the scores matrix is for the background class
for cls_ind in range(start_index, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(
# Better than max one till now and minimally greater than conf_thresh
(cls_scores[keep] > max_conf[keep])
# & (cls_scores[keep] > conf_thresh_tensor[keep])
,
cls_scores[keep],
max_conf[keep],
)
sorted_scores, sorted_indices = torch.sort(max_conf,
descending=True)
num_boxes = (sorted_scores[:self.num_features] != 0).sum()
keep_boxes = sorted_indices[:self.num_features]
feat_list.append(feats[i][keep_boxes])
bbox = output[0]["proposals"][i][keep_boxes].bbox / im_scales[i]
# Predict the class label using the scores
objects = torch.argmax(scores[keep_boxes][start_index:], dim=1)
cls_prob = torch.max(scores[keep_boxes][start_index:], dim=1)
info_list.append({
"bbox": bbox.cpu().numpy(),
# "boxes": bbox.cpu().numpy(),
# "num_boxes": num_boxes.item(),
# "objects": objects.cpu().numpy(),
"image_width": im_infos[i]["width"],
"image_height": im_infos[i]["height"],
# "image_h": im_infos[i]["height"],
# "image_w": im_infos[i]["width"],
"cls_prob": scores[keep_boxes].cpu().numpy(),
"max_features": num_boxes.item(),
})
return feat_list, info_list
def _process_feature_extraction(
self, output, im_scales, feat_name="fc6", conf_thresh=0.2
):
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(_) for _ in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feat_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
for i in range(batch_size):
dets = output[0]["proposals"][i].bbox / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros((scores.shape[0])).to(cur_device)
for cls_ind in range(1, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(
cls_scores[keep] > max_conf[keep], cls_scores[keep], max_conf[keep]
)
keep_boxes = torch.argsort(max_conf, descending=True)[:100]
feat_list.append(feats[i][keep_boxes])
return feat_list
def process_feature_extraction(output,
im_scales,
max_boxes=100,
get_boxes=False,
feat_name='fc6',
conf_thresh=0.2):
# TODO: Add docstring and explain get_boxes
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(_) for _ in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feat_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
boxes_list = []
classes_list = []
conf_list = []
for i in range(batch_size):
# bbox below stays on the device where it was generated
dets = output[0]["proposals"][i].bbox.to(cur_device) / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros((scores.shape[0])).to(cur_device)
if get_boxes:
max_cls = torch.zeros((scores.shape[0]),
dtype=torch.long).to(cur_device)
max_box = torch.zeros((scores.shape[0], 4)).to(cur_device)
for cls_ind in range(1, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
if get_boxes:
max_cls[keep] = torch.where(
cls_scores[keep] > max_conf[keep],
torch.tensor(cls_ind).to(cur_device), max_cls[keep])
max_box[keep] = torch.where(
(cls_scores[keep] > max_conf[keep]).view(-1, 1),
dets[keep], max_box[keep])
max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
keep_boxes = torch.argsort(max_conf, descending=True)[:max_boxes]
feat_list.append(feats[i][keep_boxes])
if not get_boxes:
return feat_list
conf_list.append(max_conf[keep_boxes])
boxes_list.append(max_box[keep_boxes])
classes_list.append(max_cls[keep_boxes])
return [boxes_list, feat_list, classes_list, conf_list]
def _process_feature_extraction(self,
output,
im_scales,
im_infos,
feature_name="fc6",
conf_thresh=0):
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(boxes) for boxes in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feature_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
info_list = []
for i in range(batch_size):
dets = output[0]["proposals"][i].bbox / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros((scores.shape[0])).to(cur_device)
conf_thresh_tensor = torch.full_like(max_conf, conf_thresh)
start_index = 1
# Column 0 of the scores matrix is for the background class
if self.args.background:
start_index = 0
for cls_ind in range(start_index, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(
# Better than max one till now and minimally greater than conf_thresh
(cls_scores[keep] > max_conf[keep])
& (cls_scores[keep] > conf_thresh_tensor[keep]),
cls_scores[keep],
max_conf[keep],
)
feat_list.append(feats[i])
num_boxes = len(feats[i])
bbox = output[0]["proposals"][i]
bbox = bbox.resize(((im_infos[i]["width"], im_infos[i]["height"])))
bbox = bbox.bbox
# Predict the class label using the scores
objects = torch.argmax(scores[:, start_index:], dim=1)
info_list.append({
"bbox": bbox.cpu().numpy(),
"num_boxes": num_boxes,
"objects": objects.cpu().numpy(),
"image_width": im_infos[i]["width"],
"image_height": im_infos[i]["height"],
"cls_prob": scores.cpu().numpy(),
})
return feat_list, info_list
def _process_feature_extraction(self,
output,
im_scales,
im_infos,
feature_name="fc6",
conf_thresh=0):
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(boxes) for boxes in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feature_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
info_list = []
for i in range(batch_size):
dets = output[0]["proposals"][i].bbox / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros((scores.shape[0])).to(cur_device)
conf_thresh_tensor = torch.full_like(max_conf, conf_thresh)
start_index = 1
# Column 0 of the scores matrix is for the background class
if self.args.background:
start_index = 0
for cls_ind in range(start_index, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(
# Better than max one till now and minimally greater than conf_thresh
(cls_scores[keep] > max_conf[keep])
& (cls_scores[keep] > conf_thresh_tensor[keep]),
cls_scores[keep],
max_conf[keep],
)
sorted_scores, sorted_indices = torch.sort(max_conf,
descending=True)
num_boxes = (sorted_scores[:self.args.num_features] != 0).sum()
keep_boxes = sorted_indices[:self.args.num_features]
feat = feats[i][keep_boxes]
feat_list.append(feat)
bbox = output[0]["proposals"][i][keep_boxes].bbox / im_scales[i]
# Normalize the boxes (to 0 ~ 1)
img_h, img_w = im_infos[i]['height'], im_infos[i]['width']
# boxes = boxes.copy()
bbox[:, (0, 2)] /= img_w
bbox[:, (1, 3)] /= img_h
info_list.append(bbox)
# print('size:', bbox.size(), feat.size())
return feat_list, info_list
def _features_extraction(self, output,
im_scales,
feature_name='fc6',
conf_thresh=0.5):
batch_size = len(output[0]["proposals"])
# list[num_of_boxes_per_image]
n_boxes_per_image = [len(_) for _ in output[0]["proposals"]]
# list[Tensor: (n_boxes_per_image, num_classes)]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
# list[Tensor: (n_boxes_per_image, 2048)]
features = output[0][feature_name].split(n_boxes_per_image)
# list[Tensor: (num_features_selected_per_image, 2048)]
# list contain selected features per image
features_list = []
for i in range(batch_size):
# reshape the bounding box to original size/coordinate
dets = output[0]["proposals"][i].bbox / im_scales[i]
# Tensor: (n_boxes_per_image, num_classes)
scores = score_list[i]
# Tensor: (n_boxes_per_image, )
# max_conf record the heightest probs of the class
# associate with each bounding box. If the heightest prob
# of a box (say i) is smaller than threshold (conf_thresh),
# this box will not be select and max_conf[i] will be set
# to 0
max_conf = torch.zeros((scores.shape[0])).to(device)
for cls_ind in range(1, scores.shape[1]):
# Tensor: (n_boxes_per_image, 1)
# score for a specified class
cls_scores = scores[:, cls_ind]
# index of boxes that will be keep
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep],
max_conf[keep])
# select the top 100 boxes which contain an onject with
# probability greater than conf_thresh(usually 0.5)
keep_boxes = torch.argsort(max_conf, descending=True)[:100]
features_per_image = features[i][keep_boxes]
features_list.append(features_per_image)
return features_list
def _process_feature_extraction(self,
output,
im_scales,
feature_name="fc6",
conf_thresh=0.2):
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(boxes) for boxes in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feature_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
info_list = []
for i in range(batch_size):
dets = output[0]["proposals"][i].bbox / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros((scores.shape[0])).to(cur_device)
for cls_ind in range(1, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
keep_boxes = torch.argsort(max_conf,
descending=True)[:self.NUM_FEATURES]
feat_list.append(feats[i][keep_boxes])
bbox = output[0]["proposals"][i][keep_boxes].bbox / im_scales[i]
objects = torch.argmax(scores[keep_boxes], dim=1)
image_width = output[0]["proposals"][i].size[0] / im_scales[i]
image_height = output[0]["proposals"][i].size[1] / im_scales[i]
info_list.append({
"bbox": bbox.cpu().numpy(),
"objects": objects.cpu().numpy(),
"image_width": image_width,
"image_height": image_height,
})
return feat_list, info_list
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4, method=-1):
"""
Removes detections with lower object confidence score than 'conf_thres'
Non-Maximum Suppression to further filter detections.
Returns detections with shape:
(x1, y1, x2, y2, object_conf, class_score, class_pred)
"""
output = [None for _ in range(len(prediction))]
for image_i, pred in enumerate(prediction):
# Filter out confidence scores below threshold
# Get score and class with highest confidence
v = pred[:, 4] > conf_thres
v = v.nonzero().squeeze()
if len(v.shape) == 0:
v = v.unsqueeze(0)
pred = pred[v]
# If none are remaining => process next image
nP = pred.shape[0]
if not nP:
continue
# From (center x, center y, width, height) to (x1, y1, x2, y2)
pred[:, :4] = xywh2xyxy(pred[:, :4])
# Non-maximum suppression
if method == -1:
nms_indices = nms(pred[:, :4], pred[:, 4], nms_thres)
else:
dets = pred[:, :5].clone().contiguous().data.cpu().numpy()
nms_indices = soft_nms(dets, Nt=nms_thres, method=method)
det_max = pred[nms_indices]
if len(det_max) > 0:
# Add max detections to outputs
output[
image_i] = det_max if output[image_i] is None else torch.cat(
(output[image_i], det_max))
return output
def _process_feature_extraction(self, output, im_scales, feat_name='fc6'):
bboxes, scores, feats = output[0]['proposals'][0].bbox, output[0][
'scores'], output[0]['fc6']
scores = F.softmax(scores, dim=1)
feat_list = []
dets = bboxes / im_scales[0]
max_conf = torch.zeros((scores.shape[0])).cuda()
for cls_ind in range(1, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
keep_boxes = torch.argsort(max_conf, descending=True)[:100]
return feats[keep_boxes], bboxes[keep_boxes]
def _process_nms_bbox(self, output, point, im_scale, feat_name):
bboxes, scores, feat = output[0]['proposals'][0].bbox, output[0][
'scores'], output[0][feat_name]
bboxes = bboxes / im_scale
scores = F.softmax(scores, dim=1)
xmin, ymin, xmax, ymax = bboxes.split(1, dim=1)
filt_pt = ((point['x'] >= xmin) & (point['x'] <= xmax) &
(point['y'] >= ymin) & (point['y'] <= ymax)).flatten()
bboxes, scores, feat = bboxes[filt_pt], scores[filt_pt], feat[filt_pt]
# check if no bounding boxes contain that point
if bboxes.nelement() == 0:
return torch.zeros(100, 2048), torch.zeros(100, 4)
# perform nms
max_conf = torch.zeros((scores.shape[0])).cuda()
for cls_ind in range(1, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(bboxes, cls_scores, 0.5)
max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
max_scores, _ = scores[:, 1:].max(dim=-1)
filt_nms = (max_scores == max_conf)
bboxes, scores, feat = bboxes[filt_nms], scores[filt_nms], feat[
filt_nms]
max_scores, _ = scores[:, 1:].max(dim=-1)
sorted_boxes = torch.argsort(max_scores, descending=True)
return feat[sorted_boxes], bboxes[sorted_boxes]
def _process_feature_extraction(
self,
output: torch.Tensor,
im_scales: List[float],
im_infos: List[Dict[str, int]],
feature_name: str = "fc6",
conf_thresh: int = 0,
):
"""
Post-process feature extraction from the detection model.
:param output:
output from the detection model
:param im_scales:
list of scales for the processed images
:param im_infos:
list of dicts containing width/height for images
:param feature_name:
which feature to extract for the image
:param conf_thresh:
threshold for bounding box scores (?)
:return (feature_list, info_list):
return list of processed image features, and list of information for each image
"""
from maskrcnn_benchmark.layers import nms
batch_size = len(output[0]["proposals"])
n_boxes_per_image = [len(boxes) for boxes in output[0]["proposals"]]
score_list = output[0]["scores"].split(n_boxes_per_image)
score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
feats = output[0][feature_name].split(n_boxes_per_image)
cur_device = score_list[0].device
feat_list = []
info_list = []
for i in range(batch_size):
dets = output[0]["proposals"][i].bbox / im_scales[i]
scores = score_list[i]
max_conf = torch.zeros(scores.shape[0]).to(cur_device)
conf_thresh_tensor = torch.full_like(max_conf, conf_thresh)
start_index = 1
# Column 0 of the scores matrix is for the background class
for cls_ind in range(start_index, scores.shape[1]):
cls_scores = scores[:, cls_ind]
keep = nms(dets, cls_scores, 0.5)
max_conf[keep] = torch.where(
# Better than max one till now and minimally greater
# than conf_thresh
(cls_scores[keep] > max_conf[keep])
& (cls_scores[keep] > conf_thresh_tensor[keep]),
cls_scores[keep],
max_conf[keep],
)
sorted_scores, sorted_indices = torch.sort(max_conf, descending=True)
num_boxes = (sorted_scores[: self.num_features] != 0).sum()
keep_boxes = sorted_indices[: self.num_features]
feat_list.append(feats[i][keep_boxes])
bbox = output[0]["proposals"][i][keep_boxes].bbox / im_scales[i]
# Predict the class label using the scores
objects = torch.argmax(scores[keep_boxes][:, start_index:], dim=1)
info_list.append(
{
"bbox": bbox.cpu().numpy(),
"num_boxes": num_boxes.item(),
"objects": objects.cpu().numpy(),
"cls_prob": scores[keep_boxes][:, start_index:].cpu().numpy(),
"image_width": im_infos[i]["width"],
"image_height": im_infos[i]["height"],
}
)
return feat_list, info_list
