def forward(self, x):
# prepare
probs, x_reg_pred, img_info = x
batch_size = probs.size(0)
feature_height, feature_width = probs.size(2), probs.size(3)
# get bg and fg probs
bg_probs = probs[:, :self.num_anchors, :, :]
fg_probs = probs[:, self.num_anchors:, :, :]
# get shift
shift_x = np.arange(0, feature_width) * self.feature_stride
shift_y = np.arange(0, feature_height) * self.feature_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = torch.from_numpy(
np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose())
shifts = shifts.contiguous().type_as(fg_probs).float()
# get anchors
anchors = self.anchors.type_as(fg_probs)
anchors = anchors.view(1, self.num_anchors, 4) + shifts.view(
shifts.size(0), 1, 4)
anchors = anchors.view(1, self.num_anchors * shifts.size(0),
4).expand(batch_size,
self.num_anchors * shifts.size(0), 4)
# format x_reg_pred
bbox_deltas = x_reg_pred.permute(0, 2, 3, 1).contiguous()
bbox_deltas = bbox_deltas.view(batch_size, -1, 4)
# format fg_probs
fg_probs = fg_probs.permute(0, 2, 3, 1).contiguous()
fg_probs = fg_probs.view(batch_size, -1)
# convert anchors to proposals
proposals = BBoxFunctions.anchors2Proposals(anchors, bbox_deltas)
# clip predicted boxes to image
proposals = BBoxFunctions.clipBoxes(proposals, img_info)
# do nms
scores = fg_probs
_, order = torch.sort(scores, 1, True)
output = scores.new(batch_size, self.post_nms_topN, 5).zero_()
for i in range(batch_size):
proposals_single = proposals[i]
scores_single = scores[i]
order_single = order[i]
if self.pre_nms_topN > 0 and self.pre_nms_topN < scores.numel():
order_single = order_single[:self.pre_nms_topN]
proposals_single = proposals_single[order_single, :]
scores_single = scores_single[order_single].view(-1, 1)
_, keep_idxs = nms(torch.cat((proposals_single, scores_single), 1),
self.nms_thresh)
keep_idxs = keep_idxs.long().view(-1)
if self.post_nms_topN > 0:
keep_idxs = keep_idxs[:self.post_nms_topN]
proposals_single = proposals_single[keep_idxs, :]
scores_single = scores_single[keep_idxs, :]
num_proposals = proposals_single.size(0)
output[i, :, 0] = i
output[i, :num_proposals, 1:] = proposals_single
return output
def forward(self, x): # parse x probs_list, x_reg_list, rpn_features_shapes, img_info = x # obtain proposals batch_size = probs_list[0].size(0) outputs = probs_list[0].new(batch_size, self.post_nms_topN, 5).zero_() for i in range(batch_size): output = [] for probs, x_reg, rpn_features_shape, anchor_generator, feature_stride in zip(probs_list, x_reg_list, rpn_features_shapes, self.anchor_generators, self.feature_strides): # --get fg probs fg_probs = probs[i, :, 0] # --get anchors anchors = anchor_generator.generate(feature_shape=rpn_features_shape, feature_stride=feature_stride, device=fg_probs.device).type_as(fg_probs) num_anchors = anchors.size(0) anchors = anchors.view(1, num_anchors, 4) # --format x_reg bbox_deltas = x_reg[i:i+1, ...] # --convert anchors to proposals proposals = BBoxFunctions.anchors2Proposals(anchors, bbox_deltas) # --clip predicted boxes to image proposals = BBoxFunctions.clipBoxes(proposals, img_info[i:i+1, ...]) # --do nms proposals = proposals[0] scores = fg_probs _, order = torch.sort(scores, 0, True) if self.pre_nms_topN > 0 and self.pre_nms_topN < scores.numel(): order = order[:self.pre_nms_topN] proposals = proposals[order] scores = scores[order].view(-1, 1) proposals = torch.cat((proposals, scores), dim=-1) _, keep_idxs = nms(proposals, self.nms_thresh) if self.post_nms_topN > 0: keep_idxs = keep_idxs[:self.post_nms_topN] proposals = proposals[keep_idxs] output.append(proposals) # --merge multi-level proposals output = torch.cat(output, dim=0) _, order = torch.sort(output[:, 4], 0, True) if (output.size(0) > self.post_nms_topN) and (self.post_nms_topN > 0): order = order[:self.post_nms_topN] output = output[order] proposals = output[:, :4] num_proposals = proposals.size(0) outputs[i, :, 0] = i outputs[i, :num_proposals, 1:] = proposals # return the proposal outputs return outputs
def test():
# prepare base things
args = parseArgs()
cfg, cfg_file_path = getCfgByDatasetAndBackbone(
datasetname=args.datasetname, backbonename=args.backbonename)
checkDir(cfg.TEST_BACKUPDIR)
logger_handle = Logger(cfg.TEST_LOGFILE)
use_cuda = torch.cuda.is_available()
clsnames = loadclsnames(cfg.CLSNAMESPATH)
# prepare dataset
if args.datasetname == 'coco':
dataset = COCODataset(rootdir=cfg.DATASET_ROOT_DIR,
image_size_dict=cfg.IMAGESIZE_DICT,
max_num_gt_boxes=-1,
use_color_jitter=False,
img_norm_info=cfg.IMAGE_NORMALIZE_INFO,
mode='TEST',
datasettype=args.datasettype,
annfilepath=args.annfilepath)
else:
raise ValueError('Unsupport datasetname <%s> now...' %
args.datasetname)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=0)
# prepare model
if args.backbonename.find('resnet') != -1:
model = RetinanetFPNResNets(mode='TEST',
cfg=cfg,
logger_handle=logger_handle)
else:
raise ValueError('Unsupport backbonename <%s> now...' %
args.backbonename)
if use_cuda:
model = model.cuda()
# load checkpoints
checkpoints = loadCheckpoints(args.checkpointspath, logger_handle)
model.load_state_dict(checkpoints['model'])
model.eval()
# test mAP
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
results = []
img_ids = []
for batch_idx, samples in enumerate(dataloader):
logger_handle.info('detect %s/%s...' %
(batch_idx + 1, len(dataloader)))
# --do detect
img_id, img, w_ori, h_ori, gt_boxes, img_info, num_gt_boxes = samples
img_id, w_ori, h_ori, scale_factor = int(
img_id.item()), w_ori.item(), h_ori.item(), img_info[0][-1].item()
img_ids.append(img_id)
with torch.no_grad():
output = model(x=img.type(FloatTensor),
gt_boxes=gt_boxes.type(FloatTensor),
img_info=img_info.type(FloatTensor),
num_gt_boxes=num_gt_boxes.type(FloatTensor))
anchors = output[0].data.view(1, -1, 4)
preds_cls = output[1].data
preds_reg = output[2].data
# --parse the results
preds_reg = preds_reg.view(-1, 4) * torch.FloatTensor(
cfg.BBOX_NORMALIZE_STDS).type(FloatTensor) + torch.FloatTensor(
cfg.BBOX_NORMALIZE_MEANS).type(FloatTensor)
preds_reg = preds_reg.view(1, -1, 4)
boxes_pred = BBoxFunctions.decodeBboxes(anchors, preds_reg)
boxes_pred = BBoxFunctions.clipBoxes(
boxes_pred,
torch.from_numpy(
np.array(
[h_ori * scale_factor, w_ori * scale_factor,
scale_factor])).unsqueeze(0).type(FloatTensor).data)
boxes_pred = boxes_pred.squeeze()
scores = preds_cls.squeeze()
thresh = 0.05
for j in range(cfg.NUM_CLASSES - 1):
idxs = torch.nonzero(scores[:, j] > thresh).view(-1)
if idxs.numel() > 0:
cls_scores = scores[:, j][idxs]
_, order = torch.sort(cls_scores, 0, True)
cls_boxes = boxes_pred[idxs, :]
cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
cls_dets = cls_dets[order]
cls_dets, _ = nms(cls_dets, args.nmsthresh)
for cls_det in cls_dets:
category_id = dataset.clsids2cococlsids_dict.get(j)
x1, y1, x2, y2, score = cls_det
x1 = x1.item() / scale_factor
x2 = x2.item() / scale_factor
y1 = y1.item() / scale_factor
y2 = y2.item() / scale_factor
bbox = [x1, y1, x2, y2]
bbox[2] = bbox[2] - bbox[0]
bbox[3] = bbox[3] - bbox[1]
image_result = {
'image_id': img_id,
'category_id': int(category_id),
'score': float(score.item()),
'bbox': bbox
}
results.append(image_result)
json.dump(results, open(cfg.TEST_BBOXES_SAVE_PATH, 'w'), indent=4)
if args.datasettype in ['val2017']:
dataset.doDetectionEval(img_ids, cfg.TEST_BBOXES_SAVE_PATH)
def demo():
# prepare base things
args = parseArgs()
cfg, cfg_file_path = getCfgByDatasetAndBackbone(datasetname=args.datasetname, backbonename=args.backbonename)
checkDir(cfg.TEST_BACKUPDIR)
logger_handle = Logger(cfg.TEST_LOGFILE)
use_cuda = torch.cuda.is_available()
clsnames = loadclsnames(cfg.CLSNAMESPATH)
# prepare model
if args.backbonename.find('resnet') != -1:
model = FasterRCNNResNets(mode='TEST', cfg=cfg, logger_handle=logger_handle)
else:
raise ValueError('Unsupport backbonename <%s> now...' % args.backbonename)
if use_cuda:
model = model.cuda()
# load checkpoints
checkpoints = loadCheckpoints(args.checkpointspath, logger_handle)
model.load_state_dict(checkpoints['model'])
model.eval()
# do detect
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
img = Image.open(args.imagepath)
if args.datasetname == 'coco':
input_img, scale_factor, target_size = COCODataset.preprocessImage(img, use_color_jitter=False, image_size_dict=cfg.IMAGESIZE_DICT, img_norm_info=cfg.IMAGE_NORMALIZE_INFO, use_caffe_pretrained_model=cfg.USE_CAFFE_PRETRAINED_MODEL)
else:
raise ValueError('Unsupport datasetname <%s> now...' % args.datasetname)
input_img = input_img.unsqueeze(0).type(FloatTensor)
gt_boxes = torch.FloatTensor([1, 1, 1, 1, 0]).unsqueeze(0).type(FloatTensor)
img_info = torch.from_numpy(np.array([target_size[0], target_size[1], scale_factor])).unsqueeze(0).type(FloatTensor)
num_gt_boxes = torch.FloatTensor([0]).unsqueeze(0).type(FloatTensor)
with torch.no_grad():
output = model(x=input_img, gt_boxes=gt_boxes, img_info=img_info, num_gt_boxes=num_gt_boxes)
rois = output[0].data[..., 1:5]
cls_probs = output[1].data
bbox_preds = output[2].data
# parse the results
if cfg.IS_CLASS_AGNOSTIC:
box_deltas = bbox_preds.view(-1, 4) * torch.FloatTensor(cfg.TEST_BBOX_NORMALIZE_STDS).type(FloatTensor) + torch.FloatTensor(cfg.TEST_BBOX_NORMALIZE_MEANS).type(FloatTensor)
box_deltas = box_deltas.view(1, -1, 4)
else:
box_deltas = bbox_preds.view(-1, 4) * torch.FloatTensor(cfg.TEST_BBOX_NORMALIZE_STDS).type(FloatTensor) + torch.FloatTensor(cfg.TEST_BBOX_NORMALIZE_MEANS).type(FloatTensor)
box_deltas = box_deltas.view(1, -1, 4*cfg.NUM_CLASSES)
boxes_pred = BBoxFunctions.decodeBboxes(rois, box_deltas)
boxes_pred = BBoxFunctions.clipBoxes(boxes_pred, img_info.data)
boxes_pred = boxes_pred.squeeze()
scores = cls_probs.squeeze()
thresh = 0.05
for j in range(1, cfg.NUM_CLASSES):
idxs = torch.nonzero(scores[:, j] > thresh).view(-1)
if idxs.numel() > 0:
cls_scores = scores[:, j][idxs]
_, order = torch.sort(cls_scores, 0, True)
if cfg.IS_CLASS_AGNOSTIC:
cls_boxes = boxes_pred[idxs, :]
else:
cls_boxes = boxes_pred[idxs][:, j*4: (j+1)*4]
cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
cls_dets = cls_dets[order]
_, keep_idxs = nms(cls_dets, args.nmsthresh)
cls_dets = cls_dets[keep_idxs.view(-1).long()]
for cls_det in cls_dets:
if cls_det[-1] > args.confthresh:
x1, y1, x2, y2 = cls_det[:4]
x1 = x1.item() / scale_factor
x2 = x2.item() / scale_factor
y1 = y1.item() / scale_factor
y2 = y2.item() / scale_factor
label = clsnames[j-1]
logger_handle.info('Detect a %s in confidence %.4f...' % (label, cls_det[-1].item()))
color = (0, 255, 0)
draw = ImageDraw.Draw(img)
draw.line([(x1, y1), (x2, y1), (x2, y2), (x1, y2), (x1, y1)], width=2, fill=color)
font = ImageFont.truetype('libs/font.TTF', 25)
draw.text((x1+5, y1), label, fill=color, font=font)
img.save(os.path.join(cfg.TEST_BACKUPDIR, 'demo_output.jpg'))