def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_parser = SegParser(configer)
self.seg_model_manager = SegModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')
self.seg_net = None
self._init_model()
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.seg_running_score = SegRunningScore(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_model_manager = ModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.runner_state = dict()
self._init_model()
class FCNSegmentor(object):
"""
The class for Pose Estimation. Include train, val, val & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.seg_running_score = SegRunningScore(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_model_manager = ModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.runner_state = dict()
self._init_model()
def _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.optimizer, self.scheduler = Trainer.init(
self._get_parameters(), self.configer.get('solver'))
self.train_loader = self.seg_data_loader.get_trainloader()
self.val_loader = self.seg_data_loader.get_valloader()
self.pixel_loss = self.seg_model_manager.get_seg_loss()
def _get_parameters(self):
lr_1 = []
lr_10 = []
params_dict = dict(self.seg_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
lr_10.append(value)
else:
lr_1.append(value)
params = [{
'params': lr_1,
'lr': self.configer.get('solver', 'lr')['base_lr']
}, {
'params': lr_10,
'lr': self.configer.get('solver', 'lr')['base_lr'] * 1.0
}]
return params
def train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self,
backbone_list=(0, ),
solver_dict=self.configer.get('solver'))
inputs = data_dict['img']
targets = data_dict['labelmap']
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, targets = RunnerHelper.to_device(self, inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# outputs = self.module_utilizer.gather(outputs)
# Compute the loss of the train batch & backward.
loss = self.pixel_loss(outputs,
targets,
gathered=self.configer.get(
'network', 'gathered'))
self.train_losses.update(loss.item(), inputs.size(0))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['iters'] += 1
# Print the log info & reset the states.
if self.runner_state['iters'] % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.runner_state['epoch'],
self.runner_state['iters'],
self.configer.get('solver', 'display_iter'),
RunnerHelper.get_lr(self.optimizer),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
if self.configer.get('solver', 'lr')['metric'] == 'iters' \
and self.runner_state['iters'] == self.configer.get('solver', 'max_iters'):
break
# Check to val the current model.
if self.runner_state['iters'] % self.configer.get(
'solver', 'test_interval') == 0:
self.val()
self.runner_state['epoch'] += 1
def val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
inputs = data_dict['img']
targets = data_dict['labelmap']
with torch.no_grad():
# Change the data type.
inputs, targets = RunnerHelper.to_device(self, inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss = self.pixel_loss(outputs,
targets,
gathered=self.configer.get(
'network', 'gathered'))
outputs = RunnerHelper.gather(self, outputs)
self.val_losses.update(loss.item(), inputs.size(0))
self._update_running_score(outputs[-1], data_dict['meta'])
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['performance'] = self.seg_running_score.get_mean_iou(
)
self.runner_state['val_loss'] = self.val_losses.avg
RunnerHelper.save_net(
self,
self.seg_net,
performance=self.seg_running_score.get_mean_iou(),
val_loss=self.val_losses.avg)
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time,
loss=self.val_losses))
Log.info('Mean IOU: {}\n'.format(
self.seg_running_score.get_mean_iou()))
Log.info('Pixel ACC: {}\n'.format(
self.seg_running_score.get_pixel_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.seg_running_score.reset()
self.seg_net.train()
def _update_running_score(self, pred, metas):
pred = pred.permute(0, 2, 3, 1)
for i in range(pred.size(0)):
ori_img_size = metas[i]['ori_img_size']
border_size = metas[i]['border_size']
ori_target = metas[i]['ori_target']
total_logits = cv2.resize(
pred[i, :border_size[1], :border_size[0]].cpu().numpy(),
tuple(ori_img_size),
interpolation=cv2.INTER_CUBIC)
labelmap = np.argmax(total_logits, axis=-1)
self.seg_running_score.update(labelmap[None], ori_target[None])
class FCNSegmentorTest(object):
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_parser = SegParser(configer)
self.seg_model_manager = ModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.test_loader = TestDataLoader(configer)
self.device = torch.device(
'cpu' if self.configer.get('gpu') is None else 'cuda')
self.seg_net = None
self._init_model()
def _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.seg_net.eval()
def test(self, test_dir, out_dir):
for i, data_dict in enumerate(
self.test_loader.get_testloader(test_dir=test_dir)):
total_logits = None
if self.configer.get('test', 'mode') == 'ss_test':
total_logits = self.ss_test(data_dict)
elif self.configer.get('test', 'mode') == 'sscrop_test':
total_logits = self.sscrop_test(data_dict)
elif self.configer.get('test', 'mode') == 'ms_test':
total_logits = self.ms_test(data_dict)
elif self.configer.get('test', 'mode') == 'mscrop_test':
total_logits = self.mscrop_test(data_dict)
else:
Log.error('Invalid test mode:{}'.format(
self.configer.get('test', 'mode')))
exit(1)
meta_list = DCHelper.tolist(data_dict['meta'])
img_list = DCHelper.tolist(data_dict['img'])
for i in range(len(meta_list)):
filename = meta_list[i]['img_path'].split('/')[-1].split(
'.')[0]
label_map = np.argmax(total_logits[i], axis=-1)
label_img = np.array(label_map, dtype=np.uint8)
ori_img_bgr = self.blob_helper.tensor2bgr(img_list[i][0])
ori_img_bgr = ImageHelper.resize(
ori_img_bgr,
target_size=meta_list[i]['ori_img_size'],
interpolation='linear')
image_canvas = self.seg_parser.colorize(
label_img, image_canvas=ori_img_bgr)
ImageHelper.save(image_canvas,
save_path=os.path.join(
out_dir, 'vis/{}.png'.format(filename)))
if self.configer.exists('data', 'label_list'):
label_img = self.__relabel(label_img)
if self.configer.exists(
'data', 'reduce_zero_label') and self.configer.get(
'data', 'reduce_zero_label'):
label_img = label_img + 1
label_img = label_img.astype(np.uint8)
label_img = Image.fromarray(label_img, 'P')
label_path = os.path.join(out_dir,
'label/{}.png'.format(filename))
Log.info('Label Path: {}'.format(label_path))
ImageHelper.save(label_img, label_path)
def ss_test(self, in_data_dict):
data_dict = self.blob_helper.get_blob(in_data_dict, scale=1.0)
results = self._predict(data_dict)
return results
def sscrop_test(self, in_data_dict):
data_dict = self.blob_helper.get_blob(in_data_dict, scale=1.0)
crop_size = self.configer.get('test', 'crop_size')
if any(image.size()[3] < crop_size[0] or image.size()[2] < crop_size[1]
for image in DCHelper.tolist(data_dict['img'])):
results = self._predict(data_dict)
else:
results = self._crop_predict(data_dict, crop_size)
return results
def mscrop_test(self, in_data_dict):
total_logits = [
np.zeros((meta['ori_img_size'][1], meta['ori_img_size'][0],
self.configer.get('data', 'num_classes')), np.float32)
for meta in DCHelper.tolist(in_data_dict['meta'])
]
for scale in self.configer.get('test', 'scale_search'):
data_dict = self.blob_helper.get_blob(in_data_dict, scale=scale)
crop_size = self.configer.get('test', 'crop_size')
if any(image.size()[3] < crop_size[0]
or image.size()[2] < crop_size[1]
for image in DCHelper.tolist(data_dict['img'])):
results = self._predict(data_dict)
else:
results = self._crop_predict(data_dict, crop_size)
for i in range(len(total_logits)):
total_logits[i] += results[i]
for scale in self.configer.get('test', 'scale_search'):
data_dict = self.blob_helper.get_blob(in_data_dict,
scale=scale,
flip=True)
crop_size = self.configer.get('test', 'crop_size')
if any(image.size()[3] < crop_size[0]
or image.size()[2] < crop_size[1]
for image in DCHelper.tolist(data_dict['img'])):
results = self._predict(data_dict)
else:
results = self._crop_predict(data_dict, crop_size)
for i in range(len(total_logits)):
total_logits[i] += results[i][:, ::-1]
return total_logits
def ms_test(self, in_data_dict):
total_logits = [
np.zeros((meta['ori_img_size'][1], meta['ori_img_size'][0],
self.configer.get('data', 'num_classes')), np.float32)
for meta in DCHelper.tolist(in_data_dict['meta'])
]
for scale in self.configer.get('test', 'scale_search'):
data_dict = self.blob_helper.get_blob(in_data_dict, scale=scale)
results = self._predict(data_dict)
for i in range(len(total_logits)):
total_logits[i] += results[i]
for scale in self.configer.get('test', 'scale_search'):
data_dict = self.blob_helper.get_blob(in_data_dict,
scale=scale,
flip=True)
results = self._predict(data_dict)
for i in range(len(total_logits)):
total_logits[i] += results[i][:, ::-1]
return total_logits
def _crop_predict(self, data_dict, crop_size):
split_batch = list()
height_starts_list = list()
width_starts_list = list()
hw_list = list()
for image in DCHelper.tolist(data_dict['img']):
height, width = image.size()[2:]
hw_list.append([height, width])
np_image = image.squeeze(0).permute(1, 2, 0).cpu().numpy()
height_starts = self._decide_intersection(height, crop_size[1])
width_starts = self._decide_intersection(width, crop_size[0])
split_crops = []
for height in height_starts:
for width in width_starts:
image_crop = np_image[height:height + crop_size[1],
width:width + crop_size[0]]
split_crops.append(image_crop[np.newaxis, :])
height_starts_list.append(height_starts)
width_starts_list.append(width_starts)
split_crops = np.concatenate(
split_crops,
axis=0) # (n, crop_image_size, crop_image_size, 3)
inputs = torch.from_numpy(split_crops).permute(0, 3, 1,
2).to(self.device)
split_batch.append(inputs)
out_list = list()
with torch.no_grad():
results = self.seg_net.forward(
DCHelper.todc(split_batch, stack=True, samples_per_gpu=1))
for res in results:
out_list.append(res[-1].permute(0, 2, 3, 1).cpu().numpy())
total_logits = [
np.zeros((hw[0], hw[1], self.configer.get('data', 'num_classes')),
np.float32) for hw in hw_list
]
count_predictions = [
np.zeros((hw[0], hw[1], self.configer.get('data', 'num_classes')),
np.float32) for hw in hw_list
]
for i in range(len(height_starts_list)):
index = 0
for height in height_starts_list[i]:
for width in width_starts_list[i]:
total_logits[i][height:height + crop_size[1], width:width +
crop_size[0]] += out_list[i][index]
count_predictions[i][height:height + crop_size[1],
width:width + crop_size[0]] += 1
index += 1
for i in range(len(total_logits)):
total_logits[i] /= count_predictions[i]
for i, meta in enumerate(DCHelper.tolist(data_dict['meta'])):
total_logits[i] = cv2.resize(
total_logits[i][:meta['border_hw'][0], :meta['border_hw'][1]],
(meta['ori_img_size'][0], meta['ori_img_size'][1]),
interpolation=cv2.INTER_CUBIC)
return total_logits
def _decide_intersection(self, total_length, crop_length):
stride = int(crop_length * self.configer.get(
'test', 'crop_stride_ratio')) # set the stride as the paper do
times = (total_length - crop_length) // stride + 1
cropped_starting = []
for i in range(times):
cropped_starting.append(stride * i)
if total_length - cropped_starting[-1] > crop_length:
cropped_starting.append(total_length -
crop_length) # must cover the total image
return cropped_starting
def _predict(self, data_dict):
with torch.no_grad():
total_logits = list()
results = self.seg_net.forward(data_dict['img'])
for res in results:
total_logits.append(res[-1].squeeze(0).permute(
1, 2, 0).cpu().numpy())
for i, meta in enumerate(DCHelper.tolist(data_dict['meta'])):
total_logits[i] = cv2.resize(
total_logits[i]
[:meta['border_hw'][0], :meta['border_hw'][1]],
(meta['ori_img_size'][0], meta['ori_img_size'][1]),
interpolation=cv2.INTER_CUBIC)
return total_logits
def __relabel(self, label_map):
height, width = label_map.shape
label_dst = np.zeros((height, width), dtype=np.uint8)
for i in range(self.configer.get('data', 'num_classes')):
label_dst[label_map == i] = self.configer.get(
'data', 'label_list')[i]
label_dst = np.array(label_dst, dtype=np.uint8)
return label_dst
def debug(self, vis_dir):
count = 0
for i, data_dict in enumerate(self.seg_data_loader.get_trainloader()):
inputs = data_dict['img']
targets = data_dict['labelmap']
for j in range(inputs.size(0)):
count = count + 1
if count > 20:
exit(1)
image_bgr = self.blob_helper.tensor2bgr(inputs[j])
label_map = targets[j].numpy()
image_canvas = self.seg_parser.colorize(label_map,
image_canvas=image_bgr)
cv2.imwrite(
os.path.join(vis_dir, '{}_{}_vis.png'.format(i, j)),
image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()
class FCNSegmentorTest(object):
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_parser = SegParser(configer)
self.seg_model_manager = SegModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')
self.seg_net = None
self._init_model()
def _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.seg_net.eval()
def _get_blob(self, ori_image, scale=None):
assert scale is not None
image = None
if self.configer.exists('test', 'input_size'):
image = self.blob_helper.make_input(image=ori_image,
input_size=self.configer.get('test', 'input_size'),
scale=scale)
elif self.configer.exists('test', 'min_side_length') and not self.configer.exists('test', 'max_side_length'):
image = self.blob_helper.make_input(image=ori_image,
min_side_length=self.configer.get('test', 'min_side_length'),
scale=scale)
elif not self.configer.exists('test', 'min_side_length') and self.configer.exists('test', 'max_side_length'):
image = self.blob_helper.make_input(image=ori_image,
max_side_length=self.configer.get('test', 'max_side_length'),
scale=scale)
elif self.configer.exists('test', 'min_side_length') and self.configer.exists('test', 'max_side_length'):
image = self.blob_helper.make_input(image=ori_image,
min_side_length=self.configer.get('test', 'min_side_length'),
max_side_length=self.configer.get('test', 'max_side_length'),
scale=scale)
else:
Log.error('Test setting error')
exit(1)
b, c, h, w = image.size()
border_hw = [h, w]
if self.configer.exists('test', 'fit_stride'):
stride = self.configer.get('test', 'fit_stride')
pad_w = 0 if (w % stride == 0) else stride - (w % stride) # right
pad_h = 0 if (h % stride == 0) else stride - (h % stride) # down
expand_image = torch.zeros((b, c, h + pad_h, w + pad_w)).to(image.device)
expand_image[:, :, 0:h, 0:w] = image
image = expand_image
return image, border_hw
def test_img(self, image_path, label_path, vis_path, raw_path):
Log.info('Image Path: {}'.format(image_path))
ori_image = ImageHelper.read_image(image_path,
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
total_logits = None
if self.configer.get('test', 'mode') == 'ss_test':
total_logits = self.ss_test(ori_image)
elif self.configer.get('test', 'mode') == 'sscrop_test':
total_logits = self.sscrop_test(ori_image)
elif self.configer.get('test', 'mode') == 'ms_test':
total_logits = self.ms_test(ori_image)
elif self.configer.get('test', 'mode') == 'mscrop_test':
total_logits = self.mscrop_test(ori_image)
else:
Log.error('Invalid test mode:{}'.format(self.configer.get('test', 'mode')))
exit(1)
label_map = np.argmax(total_logits, axis=-1)
label_img = np.array(label_map, dtype=np.uint8)
ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get('data', 'input_mode'))
image_canvas = self.seg_parser.colorize(label_img, image_canvas=ori_img_bgr)
ImageHelper.save(image_canvas, save_path=vis_path)
ImageHelper.save(ori_image, save_path=raw_path)
if self.configer.exists('data', 'label_list'):
label_img = self.__relabel(label_img)
if self.configer.exists('data', 'reduce_zero_label') and self.configer.get('data', 'reduce_zero_label'):
label_img = label_img + 1
label_img = label_img.astype(np.uint8)
label_img = Image.fromarray(label_img, 'P')
Log.info('Label Path: {}'.format(label_path))
ImageHelper.save(label_img, label_path)
def ss_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
image, border_hw = self._get_blob(ori_image, scale=1.0)
results = self._predict(image)
results = cv2.resize(results[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def sscrop_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
image, _ = self._get_blob(ori_image, scale=1.0)
crop_size = self.configer.get('test', 'crop_size')
if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]:
results = self._crop_predict(image, crop_size)
else:
results = self._predict(image)
results = cv2.resize(results, (ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def mscrop_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
for scale in self.configer.get('test', 'scale_search'):
image, _ = self._get_blob(ori_image, scale=scale)
crop_size = self.configer.get('test', 'crop_size')
if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]:
results = self._crop_predict(image, crop_size)
else:
results = self._predict(image)
results = cv2.resize(results, (ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def ms_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
for scale in self.configer.get('test', 'scale_search'):
image, border_hw = self._get_blob(ori_image, scale=scale)
results = self._predict(image)
results = cv2.resize(results[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
if self.configer.get('data', 'image_tool') == 'cv2':
mirror_image = cv2.flip(ori_image, 1)
else:
mirror_image = ori_image.transpose(Image.FLIP_LEFT_RIGHT)
image, border_hw = self._get_blob(mirror_image, scale=1.0)
results = self._predict(image)
results = results[:border_hw[0], :border_hw[1]]
results = cv2.resize(results[:, ::-1], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def _crop_predict(self, image, crop_size):
height, width = image.size()[2:]
np_image = image.squeeze(0).permute(1, 2, 0).cpu().numpy()
height_starts = self._decide_intersection(height, crop_size[1])
width_starts = self._decide_intersection(width, crop_size[0])
split_crops = []
for height in height_starts:
for width in width_starts:
image_crop = np_image[height:height + crop_size[1], width:width + crop_size[0]]
split_crops.append(image_crop[np.newaxis, :])
split_crops = np.concatenate(split_crops, axis=0) # (n, crop_image_size, crop_image_size, 3)
inputs = torch.from_numpy(split_crops).permute(0, 3, 1, 2).to(self.device)
with torch.no_grad():
results = self.seg_net.forward(inputs)
results = results[-1].permute(0, 2, 3, 1).cpu().numpy()
reassemble = np.zeros((np_image.shape[0], np_image.shape[1], results.shape[-1]), np.float32)
index = 0
for height in height_starts:
for width in width_starts:
reassemble[height:height+crop_size[1], width:width+crop_size[0]] += results[index]
index += 1
return reassemble
def _decide_intersection(self, total_length, crop_length):
stride = int(crop_length * self.configer.get('test', 'crop_stride_ratio')) # set the stride as the paper do
times = (total_length - crop_length) // stride + 1
cropped_starting = []
for i in range(times):
cropped_starting.append(stride*i)
if total_length - cropped_starting[-1] > crop_length:
cropped_starting.append(total_length - crop_length) # must cover the total image
return cropped_starting
def _predict(self, inputs):
with torch.no_grad():
results = self.seg_net.forward(inputs)
results = results[-1].squeeze(0).permute(1, 2, 0).cpu().numpy()
return results
def __relabel(self, label_map):
height, width = label_map.shape
label_dst = np.zeros((height, width), dtype=np.uint8)
for i in range(self.configer.get('data', 'num_classes')):
label_dst[label_map == i] = self.configer.get('data', 'label_list')[i]
label_dst = np.array(label_dst, dtype=np.uint8)
return label_dst
def debug(self, vis_dir):
count = 0
for i, data_dict in enumerate(self.seg_data_loader.get_trainloader()):
inputs = data_dict['img']
targets = data_dict['labelmap']
for j in range(inputs.size(0)):
count = count + 1
if count > 20:
exit(1)
image_bgr = self.blob_helper.tensor2bgr(inputs[j])
label_map = targets[j].numpy()
image_canvas = self.seg_parser.colorize(label_map, image_canvas=image_bgr)
cv2.imwrite(os.path.join(vis_dir, '{}_{}_vis.png'.format(i, j)), image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()
