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 = DictAverageMeter()
self.val_losses = DictAverageMeter()
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.get_seg_model()
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.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,
warm_list=(0, ),
solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.seg_net(data_dict)
# Compute the loss of the train batch & backward.
loss_dict = self.loss(out)
loss = loss_dict['loss']
self.train_losses.update(
{key: loss.item()
for key, loss in loss_dict.items()}, data_dict['img'].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 = {4}\tLoss = {3}\n'.format(
self.runner_state['epoch'],
self.runner_state['iters'],
self.configer.get('solver', 'display_iter'),
self.train_losses.info(),
RunnerHelper.get_lr(self.optimizer),
batch_time=self.batch_time,
data_time=self.data_time))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
if self.runner_state['iters'] % self.configer.get('solver.save_iters') == 0 \
and self.configer.get('local_rank') == 0:
RunnerHelper.save_net(self, self.seg_net)
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 \
and not self.configer.get('network.distributed'):
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):
data_dict = RunnerHelper.to_device(self, data_dict)
with torch.no_grad():
# Forward pass.
out = self.seg_net(data_dict)
loss_dict = self.loss(out)
# Compute the loss of the val batch.
out_dict, _ = RunnerHelper.gather(self, out)
self.val_losses.update(
{key: loss.item()
for key, loss in loss_dict.items()}, data_dict['img'].size(0))
self._update_running_score(out_dict['out'],
DCHelper.tolist(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['loss']
RunnerHelper.save_net(
self,
self.seg_net,
performance=self.seg_running_score.get_mean_iou(),
val_loss=self.val_losses.avg['loss'])
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss = {0}\n'.format(self.val_losses.info(),
batch_time=self.batch_time))
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)):
border_size = metas[i]['border_wh']
ori_target = metas[i]['ori_target']
total_logits = cv2.resize(
pred[i, :border_size[1], :border_size[0]].cpu().numpy(),
tuple(metas[i]['ori_img_wh']),
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.get_seg_model()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.seg_net.eval()
def test(self, test_dir, out_dir):
for _, 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,
params_dict=self.configer.get(
'test', 'sscrop_test'))
elif self.configer.get('test', 'mode') == 'ms_test':
total_logits = self.ms_test(data_dict,
params_dict=self.configer.get(
'test', 'ms_test'))
elif self.configer.get('test', 'mode') == 'mscrop_test':
total_logits = self.mscrop_test(data_dict,
params_dict=self.configer.get(
'test', 'mscrop_test'))
else:
Log.error('Invalid test mode:{}'.format(
self.configer.get('test', 'mode')))
exit(1)
meta_list = DCHelper.tolist(data_dict['meta'])
for i in range(len(meta_list)):
label_map = np.argmax(total_logits[i], axis=-1)
label_img = np.array(label_map, dtype=np.uint8)
ori_img_bgr = ImageHelper.read_image(meta_list[i]['img_path'],
tool='cv2',
mode='BGR')
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(
meta_list[i]['filename'])))
if self.configer.get('data.label_list',
default=None) is not None:
label_img = self.__relabel(label_img)
if self.configer.get('data.reduce_zero_label', default=False):
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(meta_list[i]['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 ms_test(self, in_data_dict, params_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 params_dict['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 params_dict['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 sscrop_test(self, in_data_dict, params_dict):
data_dict = self.blob_helper.get_blob(in_data_dict, scale=1.0)
if any(image.size()[2] < params_dict['crop_size'][0]
or image.size()[1] < params_dict['crop_size'][1]
for image in DCHelper.tolist(data_dict['img'])):
results = self._predict(data_dict)
else:
results = self._crop_predict(data_dict, params_dict['crop_size'],
params_dict['crop_stride_ratio'])
return results
def mscrop_test(self, in_data_dict, params_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 params_dict['scale_search']:
data_dict = self.blob_helper.get_blob(in_data_dict, scale=scale)
if any(image.size()[2] < params_dict['crop_size'][0]
or image.size()[1] < params_dict['crop_size'][1]
for image in DCHelper.tolist(data_dict['img'])):
results = self._predict(data_dict)
else:
results = self._crop_predict(data_dict,
params_dict['crop_size'],
params_dict['crop_stride_ratio'])
for i in range(len(total_logits)):
total_logits[i] += results[i]
for scale in params_dict['scale_search']:
data_dict = self.blob_helper.get_blob(in_data_dict,
scale=scale,
flip=True)
if any(image.size()[2] < params_dict['crop_size'][0]
or image.size()[1] < params_dict['crop_size'][1]
for image in DCHelper.tolist(data_dict['img'])):
results = self._predict(data_dict)
else:
results = self._crop_predict(data_dict,
params_dict['crop_size'],
params_dict['crop_stride_ratio'])
for i in range(len(total_logits)):
total_logits[i] += results[i][:, ::-1]
return total_logits
def _crop_predict(self, data_dict, crop_size, crop_stride_ratio):
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()[1:]
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],
crop_stride_ratio)
width_starts = self._decide_intersection(width, crop_size[0],
crop_stride_ratio)
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.extend(list(inputs))
out_list = list()
with torch.no_grad():
results = self.seg_net(
dict(img=DCHelper.todc(
split_batch, stack=True, samples_per_gpu=True)))
for res in results:
out_list.append(res['out'].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,
crop_stride_ratio):
stride = int(crop_length *
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(data_dict)
for res in results:
total_logits.append(res['out'].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