def tst(arg, test_loader, net, write_image=False):
bbox_format = list()
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[0],
arg.model.flt_anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[1],
arg.model.flt_anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
net.eval()
target_dict = dict()
predict_list = list()
print('testing......')
image_counter = 0
for batch_idx, batch_data_dict in tqdm(enumerate(test_loader)):
# if batch_idx > 10:
# break
batch_image = batch_data_dict['image']
batch_label = batch_data_dict['label']
batch_image_path = batch_data_dict['image_path']
np_im = torch_im_to_cv2_im(batch_image[0]).copy()
label_list = list()
for label_box in batch_label[0]:
cls, xmid, ymid, w, h = label_box.tolist()
if xmid + ymid + w + h > 0.000001:
im_w, im_h = arg.model.net.im_size
label_list.append(
[cls, xmid * im_w, ymid * im_h, w * im_w, h * im_h])
xmin, ymin, xmax, ymax = cxcywh_to_x1y1x2y2(xmid, ymid, w, h)
xmin, ymin, xmax, ymax = int(xmin * im_w), int(
ymin * im_h), int(xmax * im_w), int(ymax * im_h)
np_im = cv2.rectangle(np_im, (xmin, ymin), (xmax, ymax),
(0, 255, 0))
target_dict[str(batch_idx) + '.jpg'] = label_list
batch_image, batch_label = list(
map(lambda x: x.to(arg.train.device), (batch_image, batch_label)))
net_out = net.forward(batch_image)
predict_bbox_list = list()
confidence_list = list()
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(
feature, num_anchors=arg.model.net.num_anchor)
batch_predict_bbox, batch_confidence = bbox_format[
model_out_idx].to_bbox(*batch_predict)
predict_bbox_list.append(
batch_predict_bbox[0]) # notice: batch size == 1
confidence_list.append(
batch_confidence[0]) # notice: batch size == 1
predict_bboxes = torch.cat(predict_bbox_list, 0)
predict_bboxes = cxcywh_to_x1y1x2y2(predict_bboxes)
confidences = torch.cat(confidence_list, 0)
bboxes, confidences = confidence_filter(
predict_bboxes, confidences,
arg.model.out_confidence_filter_threshold)
bboxes, confidences = none_max_suppression(bboxes, confidences,
arg.model.nms_iou_threshold)
for box, conf in zip(bboxes, confidences):
xmin, ymin, xmax, ymax = box.tolist()
conf = conf.item()
predict_list.append([
str(batch_idx) + '.jpg', 0, conf, (xmin + xmax) // 2,
(ymin + ymax) // 2, xmax - xmin, ymax - ymin
])
if write_image:
for box, conf in zip(bboxes, confidences):
xmin, ymin, xmax, ymax = box.tolist()
conf = conf.item()
xmin = round(xmin)
xmax = round(xmax)
ymin = round(ymin)
ymax = round(ymax)
np_im = cv2.rectangle(np_im, (xmin, ymin), (xmax, ymax),
(255, 255, 0))
np_im = cv2.putText(np_im, str(round(conf, 3)),
((xmin + xmax) // 2, (ymin + ymax) // 2),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255),
1)
cv2.imshow('', np_im)
cv2.waitKey()
os.makedirs('image_out', exist_ok=True)
cv2.imwrite('image_out/' + str(image_counter) + '.jpg', np_im)
image_counter += 1
rec, prec, ap = det_eval(predict_list, target_dict, 0, 0.5)
return ap
def tst():
arg = ARG()
net = YoloV3_Tiny(class_num=arg.model.net.num_class,
anchors_num=arg.model.net.num_anchor)
net = net.to(arg.train.device)
if arg.model.weight_path is not None:
net.load_state_dict(one_card_model(torch.load(arg.model.weight_path)))
net = torch.nn.DataParallel(net)
net.eval()
bbox_format = list()
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[0],
arg.model.anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[1],
arg.model.anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
for image_path in os.listdir(arg.test.image_dir):
abs_image_path = os.path.join(arg.test.image_dir, image_path)
image = cv2.imread(abs_image_path)
assert image is not None
torch_im, pad_hwc = yolo_mode_cv2_im_to_torch_im(
image, arg.model.net.im_size)
batch_image = torch_im.unsqueeze(0) # make up as a batch
with torch.no_grad():
net_out = net.forward(batch_image)
predict_bbox_list = list()
confidence_list = list()
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(feature,
num_anchors=len(
arg.model.anchor))
batch_predict_bbox, batch_confidence = bbox_format[
model_out_idx].to_bbox(*batch_predict)
predict_bbox_list.append(
batch_predict_bbox[0]) # notice: batch size == 1
confidence_list.append(
batch_confidence[0]) # notice: batch size == 1
predict_bboxes = torch.cat(predict_bbox_list, 0)
predict_bboxes = cxcywh_to_x1y1x2y2(predict_bboxes)
confidences = torch.cat(confidence_list, 0)
bboxes, confidences = confidence_filter(
predict_bboxes, confidences,
arg.model.out_confidence_filter_threshold)
bboxes, confidences = none_max_suppression(bboxes, confidences,
arg.model.nms_iou_threshold)
np_im = torch_im_to_cv2_im(torch_im).copy()
for box in bboxes:
x1, y1, x2, y2 = list(map(lambda x: int(x), box))
image_pre_show = cv2.rectangle(np_im, (x1, y1), (x2, y2),
(255, 255, 0))
# test_out_dir = arg.test.out_dir
# cv2.imwrite(test_out_dir+'/'+image_path, image_pre_show)
cv2.imshow(image_path, image_pre_show)
cv2.waitKey()
def step_vis(self, batch_data_dict, net_out, whole_loss, losses_list,
predict_list, target_list):
batch_image = batch_data_dict['image'].to(
next(self.net.parameters()).device)
batch_label = batch_data_dict['label'].to(
next(self.net.parameters()).device)
predict_bbox_list = list()
confidence_list = list()
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(
feature, num_anchors=self.arg.model.net.num_anchor)
batch_predict_bbox, batch_confidence = self.bbox_format[
model_out_idx].to_bbox(*batch_predict)
predict_bbox_list.append(batch_predict_bbox[0])
confidence_list.append(batch_confidence[0])
predict_bboxes = torch.cat(predict_bbox_list, 0)
predict_bboxes = cxcywh_to_x1y1x2y2(predict_bboxes)
confidences = torch.cat(confidence_list, 0)
bboxes, confidences = confidence_filter(
predict_bboxes, confidences,
self.arg.model.out_confidence_filter_threshold)
np_im = torch_im_to_cv2_im(batch_image[0]).copy()
for box in bboxes:
x1, y1, x2, y2 = list(map(lambda x: int(x), box))
np_im = cv2.rectangle(np_im, (x1, y1), (x2, y2), (255, 255, 0))
self.vis.image('_0_predict_before_nms', np_im)
bboxes, confidences = none_max_suppression(
bboxes, confidences, self.arg.model.nms_iou_threshold)
np_im = torch_im_to_cv2_im(batch_image[0]).copy()
for box, conf in zip(bboxes, confidences):
x1, y1, x2, y2 = list(map(lambda x: int(x), box.tolist()))
np_im = cv2.rectangle(np_im, (x1, y1), (x2, y2), (0, 255, 255))
np_im = cv2.putText(np_im, str(round(conf.item(), 3)),
((x1 + x2) // 2, (y1 + y2) // 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
self.vis.image('_0_predict_after_nms', np_im)
image_show = torch_im_to_cv2_im(batch_image[0]).copy()
for hand_idx in range(batch_label[0].shape[0]):
cx, cy, w, h = (batch_label[0][hand_idx].data.cpu().numpy()[1:])
x1, y1, x2, y2 = cxcywh_to_x1y1x2y2(cx, cy, w, h)
x1, y1, x2, y2 = list(
map(lambda x: int(x * self.arg.model.net.im_size[0]),
(x1, y1, x2, y2)))
image_show = cv2.rectangle(image_show, (x1, y1), (x2, y2),
(0, 255, 0))
self.vis.image('_0_target', image_show)
self.vis.line('0_train_loss', y=whole_loss.item())
for layer_idx, (losses, batch_predict, batch_target) in enumerate(
zip(losses_list, predict_list, target_list)):
mask, negative_mask, tar_dx, tar_dy, tar_w, tar_h, tar_confidence, tar_class = batch_target
pre_dx, pre_dy, pre_w, pre_h, pre_confidence, pre_class = batch_predict
batch_show_idx = 0
for anchor_idx in range(self.arg.model.net.num_anchor):
self.vis.image(
'layer' + str(layer_idx) + '_anchor' + str(anchor_idx) +
'_confidence/target', tar_confidence[batch_show_idx,
anchor_idx])
self.vis.image(
'layer' + str(layer_idx) + '_anchor' + str(anchor_idx) +
'_confidence/predict', pre_confidence[batch_show_idx,
anchor_idx])
# self.vis.image('layer'+str(layer_idx)+'_anchor'+str(anchor_idx)+'_class/target', tar_class[batch_show_idx, anchor_idx, :, :, 0])
# self.vis.image('layer'+str(layer_idx)+'_anchor'+str(anchor_idx)+'_class/predict', pre_class[batch_show_idx, anchor_idx])
loss, loss_dx, loss_dy, loss_w, loss_h, loss_confidence, loss_class = losses
self.vis.line('layer' + str(layer_idx) + '/loss', y=loss.item())
self.vis.line('layer' + str(layer_idx) + '/loss_dx',
y=loss_dx.item())
self.vis.line('layer' + str(layer_idx) + '/loss_dy',
y=loss_dy.item())
self.vis.line('layer' + str(layer_idx) + '/loss_w',
y=loss_w.item())
self.vis.line('layer' + str(layer_idx) + '/loss_h',
y=loss_h.item())
self.vis.line('layer' + str(layer_idx) + '/loss_confidence',
y=loss_confidence.item())
def tst(self, net, write_image=False):
net.eval()
target_dict = dict()
predict_list = list()
print('testing......')
image_counter = 0
for batch_idx, batch_data_dict in tqdm(enumerate(self.dataloader)):
# # FOR TEST FOR TEST FOR TEST
# if batch_idx > 10:
# break
# # FOR TEST FOR TEST FOR TEST
batch_image = batch_data_dict['image'].to(next(net.parameters()).device)
batch_label = batch_data_dict['label'].to(next(net.parameters()).device)
batch_image_path = batch_data_dict['image_path']
label_list = list()
for label_box in batch_label[0]:
cls, xmid, ymid, w, h = label_box.tolist()
if xmid + ymid + w + h > 0.000001:
im_w, im_h = self.arg.model.net.im_size
label_list.append([cls, xmid * im_w, ymid * im_h, w * im_w, h * im_h])
target_dict[str(batch_idx) + '.jpg'] = label_list
with torch.no_grad():
net_out = net.forward(batch_image)
predict_bbox_list = list()
confidence_list = list()
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(feature, num_anchors=self.arg.model.net.num_anchor)
batch_predict_bbox, batch_confidence = self.bbox_format[model_out_idx].to_bbox(*batch_predict)
predict_bbox_list.append(batch_predict_bbox[0]) # notice: batch size == 1
confidence_list.append(batch_confidence[0]) # notice: batch size == 1
predict_bboxes = torch.cat(predict_bbox_list, 0)
predict_bboxes = cxcywh_to_x1y1x2y2(predict_bboxes)
confidences = torch.cat(confidence_list, 0)
bboxes, confidences = confidence_filter(predict_bboxes, confidences,
self.arg.model.out_confidence_filter_threshold)
bboxes, confidences = none_max_suppression(bboxes, confidences, self.arg.model.nms_iou_threshold)
for box, conf in zip(bboxes, confidences):
xmin, ymin, xmax, ymax = box.tolist()
conf = conf.item()
predict_list.append(
[str(batch_idx) + '.jpg', 0, conf, (xmin + xmax) // 2, (ymin + ymax) // 2, xmax - xmin,
ymax - ymin])
if write_image:
np_im = torch_im_to_cv2_im(batch_image[0]).copy()
np_im = cv2.cvtColor(np_im, cv2.COLOR_RGB2BGR)
np_im = cv2.resize(np_im, (448, 448))
for label_box in batch_label[0]:
cls, xmid, ymid, w, h = label_box.tolist()
if xmid + ymid + w + h > 0.000001:
im_h, im_w, _ = np_im.shape
label_list.append([cls, xmid * im_w, ymid * im_h, w * im_w, h * im_h])
xmin, ymin, xmax, ymax = cxcywh_to_x1y1x2y2(xmid, ymid, w, h)
gt_xmin, gt_ymin, gt_xmax, gt_ymax = int(xmin * im_w), int(ymin * im_h), int(xmax * im_w), int(ymax * im_h)
np_im = cv2.rectangle(np_im, (gt_xmin, gt_ymin), (gt_xmax, gt_ymax), (0, 255, 0))
for box, conf in zip(bboxes, confidences):
xmin, ymin, xmax, ymax = box.tolist()
conf = conf.item()
xmin = round(xmin*2)
xmax = round(xmax*2)
ymin = round(ymin*2)
ymax = round(ymax*2)
np_im = cv2.rectangle(np_im, (xmin, ymin), (xmax, ymax), (255, 255, 0))
np_im = cv2.putText(np_im, str(round(conf, 3)), ((xmin + xmax) // 2, (ymin + ymax) // 2),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 1)
iou = bbox_iou_x1y1x2y2([gt_xmin, gt_ymin, gt_xmax, gt_ymax], [xmin, ymin, xmax, ymax])
np_im = cv2.putText(np_im, str(round(iou, 3)), ((xmin + xmax) // 2, (ymin + ymax) // 2 + 20),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 0, 255), 1)
# cv2.imshow('', np_im)
# cv2.waitKey()
os.makedirs('image_out', exist_ok=True)
cv2.imwrite('image_out/' + str(image_counter) + '.jpg', np_im)
image_counter += 1
rec, prec, ap = det_eval(predict_list, target_dict, 0, 0.75)
net.train()
return ap
def train(arg: ARG,
train_loader,
valid_loader,
net,
loss_func,
optimizer,
lr_decay,
epoch,
vis: VIS = None):
bbox_format = list()
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[0],
arg.model.flt_anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[1],
arg.model.flt_anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
net.train()
for batch_idx, batch_data_dict in enumerate(train_loader):
vis.iteration_counter = batch_idx
print('Epoch:{}, step/all: {}/{}'.format(epoch, batch_idx,
len(train_loader)))
batch_image = batch_data_dict['image']
batch_label = batch_data_dict['label']
batch_image_path = batch_data_dict['image_path']
batch_image, batch_label = list(
map(lambda x: x.to(arg.train.device), (batch_image, batch_label)))
lr = lr_decay.get_lr(global_step=vis.step)
optimizer.param_groups[0]['lr'] = lr
optimizer.zero_grad()
net_out = net.forward(batch_image)
predict_list = list()
target_list = list()
losses_list = list()
whole_loss = 0
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(
feature, num_anchors=arg.model.net.num_anchor)
predict_list.append(batch_predict)
batch_target = bbox_format[model_out_idx].to_model(batch_label)
target_list.append(batch_target)
batch_predict = list(
filter(lambda x: x.shape != torch.Size([0]), batch_predict))
batch_target = list(
filter(lambda x: x.shape != torch.Size([0]), batch_target))
layer_loss = loss_func.forward(batch_predict, batch_target)
losses_list.append(layer_loss)
whole_loss += torch.mean(layer_loss[0]) / len(net_out)
whole_loss.backward()
optimizer.step()
if batch_idx % arg.train.log_iteration_interval == 0:
vis.line('log10_lr', np.log10(lr))
predict_bbox_list = list()
confidence_list = list()
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(
feature, num_anchors=arg.model.net.num_anchor)
batch_predict_bbox, batch_confidence = bbox_format[
model_out_idx].to_bbox(*batch_predict)
predict_bbox_list.append(batch_predict_bbox[0])
confidence_list.append(batch_confidence[0])
predict_bboxes = torch.cat(predict_bbox_list, 0)
predict_bboxes = cxcywh_to_x1y1x2y2(predict_bboxes)
confidences = torch.cat(confidence_list, 0)
bboxes, confidences = confidence_filter(
predict_bboxes, confidences,
arg.model.out_confidence_filter_threshold)
np_im = torch_im_to_cv2_im(batch_image[0]).copy()
for box in bboxes:
x1, y1, x2, y2 = list(map(lambda x: int(x), box))
np_im = cv2.rectangle(np_im, (x1, y1), (x2, y2), (255, 255, 0))
vis.image('_0_predict_before_nms', np_im)
bboxes, confidences = none_max_suppression(
bboxes, confidences, arg.model.nms_iou_threshold)
np_im = torch_im_to_cv2_im(batch_image[0]).copy()
for box, conf in zip(bboxes, confidences):
x1, y1, x2, y2 = list(map(lambda x: int(x), box.tolist()))
np_im = cv2.rectangle(np_im, (x1, y1), (x2, y2), (0, 255, 255))
np_im = cv2.putText(np_im, str(round(conf.item(), 3)),
((x1 + x2) // 2, (y1 + y2) // 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0),
1)
vis.image('_0_predict_after_nms', np_im)
image_show = torch_im_to_cv2_im(batch_image[0]).copy()
for hand_idx in range(batch_label[0].shape[0]):
cx, cy, w, h = (
batch_label[0][hand_idx].data.cpu().numpy()[1:])
x1, y1, x2, y2 = cxcywh_to_x1y1x2y2(cx, cy, w, h)
x1, y1, x2, y2 = list(
map(lambda x: int(x * arg.model.net.im_size[0]),
(x1, y1, x2, y2)))
image_show = cv2.rectangle(image_show, (x1, y1), (x2, y2),
(0, 255, 0))
vis.image('_0_target', image_show)
vis.line('_0_whole_loss', y=whole_loss.item())
for layer_idx, (losses, batch_predict, batch_target) in enumerate(
zip(losses_list, predict_list, target_list)):
mask, negative_mask, tar_dx, tar_dy, tar_w, tar_h, tar_confidence, tar_class = batch_target
pre_dx, pre_dy, pre_w, pre_h, pre_confidence, pre_class = batch_predict
batch_show_idx = 0
for anchor_idx in range(arg.model.net.num_anchor):
vis.image(
'layer' + str(layer_idx) + '_anchor' +
str(anchor_idx) + '_confidence/target',
tar_confidence[batch_show_idx, anchor_idx])
vis.image(
'layer' + str(layer_idx) + '_anchor' +
str(anchor_idx) + '_confidence/predict',
pre_confidence[batch_show_idx, anchor_idx])
# vis.image('layer'+str(layer_idx)+'_anchor'+str(anchor_idx)+'_class/target', tar_class[batch_show_idx, anchor_idx, :, :, 0])
# vis.image('layer'+str(layer_idx)+'_anchor'+str(anchor_idx)+'_class/predict', pre_class[batch_show_idx, anchor_idx])
loss, loss_dx, loss_dy, loss_w, loss_h, loss_confidence, loss_class = losses
vis.line(str(layer_idx) + '/loss', y=sum(loss.tolist()))
vis.line(str(layer_idx) + '/loss_dx', y=sum(loss_dx.tolist()))
vis.line(str(layer_idx) + '/loss_dy', y=sum(loss_dy.tolist()))
vis.line(str(layer_idx) + '/loss_w', y=sum(loss_w.tolist()))
vis.line(str(layer_idx) + '/loss_h', y=sum(loss_h.tolist()))
vis.line(str(layer_idx) + '/loss_confidence',
y=sum(loss_confidence.tolist()))
# vis.line(str(layer_idx)+'/loss_class', y=sum(loss_class.tolist()))
if batch_idx % arg.train.valid_iteration_interval == 1:
valid(arg, valid_loader, net, loss_func, epoch, vis)
net.train()
def train(arg: ARG,
train_loader,
net,
loss_func,
optimizer,
epoch,
vis: VIS = None):
bbox_format = list()
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[0],
arg.model.anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold))
bbox_format.append(
Bbox_Format(arg.model.net.im_size, arg.model.net.feature_size[1],
arg.model.anchor, arg.model.net.num_class,
arg.model.mask_iou_threshold,
arg.model.filter_iou_threshold))
net.train()
for batch_idx, batch_data_dict in enumerate(train_loader):
vis.iteration_counter = batch_idx
print('Epoch:{}, step/all: {}/{}'.format(epoch, batch_idx,
len(train_loader)))
batch_image = batch_data_dict['image']
batch_label = batch_data_dict['label']
batch_image_path = batch_data_dict['image_path']
batch_image, batch_label = list(
map(lambda x: x.to(arg.train.device), (batch_image, batch_label)))
optimizer.zero_grad()
net_out = net.forward(batch_image)
predict_list = list()
target_list = list()
losses_list = list()
whole_loss = 0
for model_out_idx, feature in enumerate(net_out):
batch_predict = model_out_to_model_predict(feature,
num_anchors=len(
arg.model.anchor))
predict_list.append(batch_predict)
batch_target = bbox_format[model_out_idx].to_model(batch_label)
target_list.append(batch_target)
layer_loss = loss_func.forward(batch_predict, batch_target)
losses_list.append(layer_loss)
whole_loss += torch.mean(layer_loss[0]) / len(net_out)
whole_loss.backward()
optimizer.step()
if batch_idx % arg.train.log_iteration_interval == 0:
# # # # # TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TES
# batch_predict_bbox, batch_confidence = bbox_format[model_out_idx].to_bbox(*batch_predict)
#
# image_show = torch_im_to_cv2_im(batch_image[0]).copy()
# for pre_idx in range(batch_predict_bbox.shape[1]):
# cx, cy, w, h = (batch_predict_bbox[0][pre_idx].data.cpu().numpy())
# if np.sum((cx, cy, w, h)) < 0.000001:
# continue
# x1, y1, x2, y2 = cxcywh_to_x1y1x2y2(cx, cy, w, h)
# x1, y1, x2, y2 = list(map(lambda x: int(x), (x1, y1, x2, y2)))
# image_pre_show = cv2.rectangle(image_show, (x1, y1), (x2, y2), (255, 255, 0))
# vis.image('_0_predict'+str(model_out_idx), image_pre_show)
# # cv2.imshow('_0_predict'+str(model_out_idx), image_pre_show)
# # cv2.waitKey()
# # # # # TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST
vis.line('_0_whole_loss', y=whole_loss.item())
image_show = torch_im_to_cv2_im(batch_image[0]).copy()
for hand_idx in range(batch_label[0].shape[0]):
cx, cy, w, h = (
batch_label[0][hand_idx].data.cpu().numpy()[1:])
x1, y1, x2, y2 = cxcywh_to_x1y1x2y2(cx, cy, w, h)
x1, y1, x2, y2 = list(
map(lambda x: int(x * 160), (x1, y1, x2, y2)))
image_show = cv2.rectangle(image_show, (x1, y1), (x2, y2),
(0, 255, 0))
vis.image('_0_target', image_show)
# cv2.imshow('image_show', image_show)
# cv2.waitKey()
for layer_idx, (losses, batch_predict, batch_target) in enumerate(
zip(losses_list, predict_list, target_list)):
mask, negative_mask, tar_dx, tar_dy, tar_w, tar_h, tar_confidence, tar_class = batch_target
pre_dx, pre_dy, pre_w, pre_h, pre_confidence, pre_class = batch_predict
batch_show_idx = 0
for anchor_idx in range(arg.model.net.num_anchor):
vis.image(
'layer' + str(layer_idx) + '_anchor' +
str(anchor_idx) + '_confidence/target',
tar_confidence[batch_show_idx, anchor_idx])
vis.image(
'layer' + str(layer_idx) + '_anchor' +
str(anchor_idx) + '_confidence/predict',
pre_confidence[batch_show_idx, anchor_idx])
# vis.image('layer'+str(layer_idx)+'_anchor'+str(anchor_idx)+'_class/target', tar_class[batch_show_idx, anchor_idx, :, :, 0])
# vis.image('layer'+str(layer_idx)+'_anchor'+str(anchor_idx)+'_class/predict', pre_class[batch_show_idx, anchor_idx])
loss, loss_dx, loss_dy, loss_w, loss_h, loss_confidence, loss_class = losses
vis.line(str(layer_idx) + '/loss', y=sum(loss.tolist()))
vis.line(str(layer_idx) + '/loss_dx', y=sum(loss_dx.tolist()))
vis.line(str(layer_idx) + '/loss_dy', y=sum(loss_dy.tolist()))
vis.line(str(layer_idx) + '/loss_w', y=sum(loss_w.tolist()))
vis.line(str(layer_idx) + '/loss_h', y=sum(loss_h.tolist()))
vis.line(str(layer_idx) + '/loss_confidence',
y=sum(loss_confidence.tolist()))
vis.line(str(layer_idx) + '/loss_class',
y=sum(loss_class.tolist()))
