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 get_blob(self, data_dict, scale=None, flip=False):
assert scale is not None
img_list, meta_list = [], []
for image, meta in zip(DCHelper.tolist(data_dict['img']), DCHelper.tolist(data_dict['meta'])):
c, h, w = image.size()
border_hw = [int(h*scale), int(w*scale)]
meta['border_hw'] = border_hw
image = TensorHelper.resize(image, border_hw, mode='bilinear', align_corners=True)
if flip:
image = image.flip([2])
if self.configer.get('test.fit_stride', default=0) > 0:
stride = self.configer.get('test', 'fit_stride')
pad_w = 0 if (border_hw[1] % stride == 0) else stride - (border_hw[1] % stride) # right
pad_h = 0 if (border_hw[0] % stride == 0) else stride - (border_hw[0] % stride) # down
expand_image = torch.zeros((c, border_hw[0] + pad_h, border_hw[1] + pad_w)).to(image.device)
expand_image[:, 0:border_hw[0], 0:border_hw[1]] = image
image = expand_image
img_list.append(image)
meta_list.append(meta)
new_data_dict = dict(
img=DCHelper.todc(img_list, stack=True, samples_per_gpu=True),
meta=DCHelper.todc(meta_list, samples_per_gpu=True, cpu_only=True)
)
return new_data_dict
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.append(inputs)
assert len(split_batch) == torch.cuda.device_count(), 'Only support one image per gpu.'
out_list = list()
with torch.no_grad():
results = self.seg_net(dict(img=DCHelper.todc(split_batch, stack=False, samples_per_gpu=True, concat=True)))
results = results if isinstance(results, (list, tuple)) else [results]
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_wh'][1], :meta['border_wh'][0]],
tuple(meta['ori_img_size']), interpolation=cv2.INTER_CUBIC)
return total_logits
def val(self):
"""
Validation function during the train phase.
"""
self.det_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.det_net(data_dict)
loss_dict = self.det_loss(out)
# Compute the loss of the train batch & backward.
loss = loss_dict['loss'].mean()
out_dict, _ = RunnerHelper.gather(self, out)
self.val_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = out_dict[
'test_group']
batch_detections = FastRCNNTest.decode(
test_roi_locs, test_roi_scores, test_indices_and_rois,
test_rois_num, self.configer,
DCHelper.tolist(data_dict['meta']))
batch_pred_bboxes = self.__get_object_list(batch_detections)
self.det_running_score.update(batch_pred_bboxes, [
item['ori_bboxes']
for item in DCHelper.tolist(data_dict['meta'])
], [
item['ori_labels']
for item in DCHelper.tolist(data_dict['meta'])
])
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self,
self.det_net,
iters=self.runner_state['iters'])
# 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('Val mAP: {}\n'.format(self.det_running_score.get_mAP()))
self.det_running_score.reset()
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
def val(self):
"""
Validation function during the train phase.
"""
self.gan_net.eval()
start_time = time.time()
for j, data_dict in enumerate(self.val_loader):
with torch.no_grad():
# Forward pass.
out_dict = self.gan_net(data_dict)
# Compute the loss of the val batch.
self.val_losses.update(
out_dict['loss_G'].mean().item() +
out_dict['loss_D'].mean().item(),
len(DCHelper.tolist(data_dict['meta'])))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self, self.gan_net, 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))
self.batch_time.reset()
self.val_losses.reset()
self.gan_net.train()
def test(self, test_dir, out_dir):
for _, data_dict in enumerate(
self.test_loader.get_testloader(test_dir=test_dir)):
data_dict['testing'] = True
data_dict = RunnerHelper.to_device(self, data_dict)
out_dict = self.det_net(data_dict)
meta_list = DCHelper.tolist(data_dict['meta'])
test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = out_dict[
'test_group']
batch_detections = self.decode(test_roi_locs, test_roi_scores,
test_indices_and_rois,
test_rois_num, self.configer,
meta_list)
for i in range(len(meta_list)):
ori_img_bgr = ImageHelper.read_image(meta_list[i]['img_path'],
tool='cv2',
mode='BGR')
json_dict = self.__get_info_tree(batch_detections[i])
image_canvas = self.det_parser.draw_bboxes(
ori_img_bgr.copy(),
json_dict,
conf_threshold=self.configer.get('res', 'vis_conf_thre'))
ImageHelper.save(image_canvas,
save_path=os.path.join(
out_dir, 'vis/{}.png'.format(
meta_list[i]['filename'])))
Log.info('Json Path: {}'.format(
os.path.join(
out_dir,
'json/{}.json'.format(meta_list[i]['filename']))))
JsonHelper.save_file(json_dict,
save_path=os.path.join(
out_dir, 'json/{}.json'.format(
meta_list[i]['filename'])))
def train(self):
"""
Train function of every epoch during train phase.
"""
self.det_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.runner_state['epoch'] += 1
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self,
warm_list=(0, ),
warm_lr_list=(self.configer.get('solver',
'lr')['base_lr'], ),
solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
out_dict = self.det_net(data_dict)
# Compute the loss of the train batch & backward.
loss = out_dict['loss'].mean()
self.train_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
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()
def val(self):
"""
Validation function during the train phase.
"""
self.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for i, data_dict in enumerate(self.val_loader):
# Forward pass.
out_dict = self.pose_net(data_dict)
# Compute the loss of the val batch.
loss_dict = self.mse_loss(out_dict, data_dict, gathered=self.configer.get('network', 'gathered'))
self.val_losses.update(loss_dict['loss'].mean().item(), len(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['val_loss'] = self.val_losses.avg
RunnerHelper.save_net(self, self.pose_net, 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))
self.batch_time.reset()
self.val_losses.reset()
self.val_loss_heatmap.reset()
self.val_loss_associate.reset()
self.pose_net.train()
def val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.gan_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):
with torch.no_grad():
# Forward pass.
out_dict = self.gan_net(data_dict)
# Compute the loss of the val batch.
self.val_losses.update(out_dict['loss'].mean().item(),
len(DCHelper.tolist(data_dict['meta'])))
meta_list = DCHelper.tolist(data_dict['meta'])
probe_features = []
gallery_features = []
probe_labels = []
gallery_labels = []
for idx in range(len(meta_list)):
gallery_features.append(out_dict['featB'][idx].cpu().numpy())
gallery_labels.append(meta_list[idx]['labelB'])
probe_features.append(out_dict['featA'][idx].cpu().numpy())
probe_labels.append(meta_list[idx]['labelA'])
rank_1, vr_far_001 = FaceGANTest.decode(probe_features,
gallery_features,
probe_labels,
gallery_labels)
Log.info('Rank1 accuracy is {}'.format(rank_1))
Log.info('VR@FAR=0.1% accuracy is {}'.format(vr_far_001))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self, self.gan_net, 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))
self.batch_time.reset()
self.val_losses.reset()
self.gan_net.train()
def val(self):
"""
Validation function during the train phase.
"""
self.det_net.eval()
start_time = time.time()
with torch.no_grad():
for i, data_dict in enumerate(self.val_loader):
# Forward pass.
out_dict = self.det_net(data_dict)
# Compute the loss of the val batch.
loss = out_dict['loss'].mean()
self.val_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
batch_detections = YOLOv3Test.decode(
out_dict['dets'], self.configer,
DCHelper.tolist(data_dict['meta']))
batch_pred_bboxes = self.__get_object_list(batch_detections)
self.det_running_score.update(batch_pred_bboxes, [
item['ori_bboxes']
for item in DCHelper.tolist(data_dict['meta'])
], [
item['ori_labels']
for item in DCHelper.tolist(data_dict['meta'])
])
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self,
self.det_net,
iters=self.runner_state['iters'])
# 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('Val mAP: {}'.format(self.det_running_score.get_mAP()))
self.det_running_score.reset()
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
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 val(self):
"""
Validation function during the train phase.
"""
self.cls_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
# Forward pass.
out_dict = self.cls_net(data_dict)
# Compute the loss of the val batch.
loss = self.ce_loss(out_dict,
data_dict,
gathered=self.configer.get(
'network', 'gathered'))
out_dict = RunnerHelper.gather(self, out_dict)
self.cls_running_score.update(
out_dict['out'], DCHelper.tolist(data_dict['labels']))
self.val_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(
self,
self.cls_net,
performance=self.cls_running_score.get_top1_acc())
self.runner_state[
'performance'] = self.cls_running_score.get_top1_acc()
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
Log.info('TestLoss = {loss.avg:.8f}'.format(loss=self.val_losses))
Log.info('Top1 ACC = {}'.format(
self.cls_running_score.get_top1_acc()))
Log.info('Top5 ACC = {}'.format(
self.cls_running_score.get_top5_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.cls_running_score.reset()
self.cls_net.train()
def test(self, test_dir, out_dir):
if self.configer.exists('test', 'mode') and self.configer.get('test', 'mode') == 'nir2vis':
jsonA_path = os.path.join(test_dir, 'val_label{}A.json'.format(self.configer.get('data', 'tag')))
test_loader_A = self.test_loader.get_testloader(json_path=jsonA_path) if os.path.exists(jsonA_path) else None
jsonB_path = os.path.join(test_dir, 'val_label{}B.json'.format(self.configer.get('data', 'tag')))
test_loader_B = self.test_loader.get_testloader(json_path=jsonB_path) if os.path.exists(jsonB_path) else None
elif self.configer.exists('test', 'mode') and self.configer.get('test', 'mode') == 'pix2pix':
imgA_dir = os.path.join(test_dir, 'imageA')
test_loader_A = self.test_loader.get_testloader(test_dir=imgA_dir) if os.path.exists(imgA_dir) else None
imgB_dir = os.path.join(test_dir, 'imageB')
test_loader_B = self.test_loader.get_testloader(test_dir=imgB_dir) if os.path.exists(imgB_dir) else None
else:
imgA_dir = os.path.join(test_dir, 'imageA')
test_loader_A = self.test_loader.get_testloader(test_dir=imgA_dir) if os.path.exists(imgA_dir) else None
imgB_dir = os.path.join(test_dir, 'imageB')
test_loader_B = self.test_loader.get_testloader(test_dir=imgB_dir) if os.path.exists(imgB_dir) else None
if test_loader_A is not None:
for data_dict in test_loader_A:
new_data_dict = dict(imgA=data_dict['img'], testing=True)
with torch.no_grad():
out_dict = self.gan_net(new_data_dict)
meta_list = DCHelper.tolist(data_dict['meta'])
for key, value in out_dict.items():
for i in range(len(value)):
img_bgr = self.blob_helper.tensor2bgr(value[i])
img_path = meta_list[i]['img_path']
Log.info('Image Path: {}'.format(img_path))
ImageHelper.save(img_bgr, os.path.join(out_dir, '{}_{}.jpg'.format(meta_list[i]['filename'], key)))
if test_loader_B is not None:
for data_dict in test_loader_B:
new_data_dict = dict(imgB=data_dict['img'], testing=True)
with torch.no_grad():
out_dict = self.gan_net(new_data_dict)
meta_list = DCHelper.tolist(data_dict['meta'])
for key, value in out_dict.items():
for i in range(len(value)):
img_bgr = self.blob_helper.tensor2bgr(value[i])
img_path = meta_list[i]['img_path']
Log.info('Image Path: {}'.format(img_path))
ImageHelper.save(img_bgr, os.path.join(out_dir, '{}_{}.jpg'.format(meta_list[i]['filename'], key)))
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 val(self, data_loader=None):
"""
Validation function during the train phase.
"""
if self.configer.get('local_rank') != 0:
return
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 _predict(self, data_dict):
with torch.no_grad():
total_logits = list()
results = self.seg_net(data_dict)
results = results if isinstance(results, (list, tuple)) else [results]
for res in results:
assert res['out'].size(0) == 1, 'Only support one image per gpu.'
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_wh'][1], :meta['border_wh'][0]],
tuple(meta['ori_img_size']), interpolation=cv2.INTER_CUBIC)
return total_logits
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 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 _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()
print(data_dict['img'].data[0].shape, len(split_batch))
with torch.no_grad():
_len_base = 64
if len(split_batch) > _len_base:
#print('my_test')
results = []
for i in range(0, len(split_batch) - 1, _len_base):
#print(i)
torch.cuda.empty_cache()
tmp_results = self.seg_net(
dict(img=DCHelper.todc(
split_batch[i:min(i +
_len_base, len(split_batch))],
stack=True,
samples_per_gpu=True)))
results.append(
torch.cat([
ele['out'].detach().cpu().permute(0, 2, 3, 1)
for ele in tmp_results
]))
del tmp_results
results = torch.cat(results)
results = results.view(len(height_starts_list), -1,
results.shape[1], results.shape[2],
results.shape[3])
out_list = [
results[i].numpy() for i in range(len(height_starts_list))
]
else:
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'].detach().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_wh'][1], :meta['border_wh'][0]],
tuple(meta['ori_img_size']),
interpolation=cv2.INTER_CUBIC)
return total_logits
def test(self, test_dir, out_dir):
if self.configer.exists('test', 'mode') and self.configer.get('test', 'mode') == 'nir2vis':
jsonA_path = os.path.join(test_dir, 'val_label{}A.json'.format(self.configer.get('data', 'tag')))
test_loader_A = self.test_loader.get_testloader(json_path=jsonA_path) if os.path.exists(jsonA_path) else None
jsonB_path = os.path.join(test_dir, 'val_label{}B.json'.format(self.configer.get('data', 'tag')))
test_loader_B = self.test_loader.get_testloader(json_path=jsonB_path) if os.path.exists(jsonB_path) else None
else:
test_loader_A, test_loader_B = None, None
Log.error('Test Mode not Exists!')
exit(1)
assert test_loader_A is not None and test_loader_B is not None
probe_features = []
gallery_features = []
probe_labels = []
gallery_labels = []
for data_dict in test_loader_A:
new_data_dict = dict(imgA=data_dict['img'])
with torch.no_grad():
out_dict = self.gan_net(new_data_dict, testing=True)
meta_list = DCHelper.tolist(data_dict['meta'])
for idx in range(len(meta_list)):
probe_features.append(out_dict['featA'][idx].cpu().numpy())
probe_labels.append(meta_list[idx]['label'])
for key, value in out_dict.items():
for i in range(len(value)):
if 'feat' in key:
continue
img_bgr = self.blob_helper.tensor2bgr(value[i])
img_path = meta_list[i]['img_path']
Log.info('Image Path: {}'.format(img_path))
img_bgr = ImageHelper.resize(img_bgr,
target_size=self.configer.get('test', 'out_size'),
interpolation='linear')
ImageHelper.save(img_bgr, os.path.join(out_dir, key, meta_list[i]['filename']))
for data_dict in test_loader_B:
new_data_dict = dict(imgB=data_dict['img'])
with torch.no_grad():
out_dict = self.gan_net(new_data_dict, testing=True)
meta_list = DCHelper.tolist(data_dict['meta'])
for idx in range(len(meta_list)):
gallery_features.append(out_dict['feat'][idx].cpu().numpy())
gallery_labels.append(meta_list[idx]['label'])
for key, value in out_dict.items():
for i in range(len(value)):
if 'feat' in key:
continue
img_bgr = self.blob_helper.tensor2bgr(value[i])
img_path = meta_list[i]['img_path']
Log.info('Image Path: {}'.format(img_path))
img_bgr = ImageHelper.resize(img_bgr,
target_size=self.configer.get('test', 'out_size'),
interpolation='linear')
ImageHelper.save(img_bgr, os.path.join(out_dir, key, meta_list[i]['filename']))
r_acc, tpr = self.decode(probe_features, gallery_features, probe_labels, gallery_labels)
Log.info('Final Rank1 accuracy is {}'.format(r_acc))
Log.info('Final VR@FAR=0.1% accuracy is {}'.format(tpr))
def train(self):
"""
Train function of every epoch during train phase.
"""
self.gan_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.scheduler_G.step(self.runner_state['epoch'])
self.scheduler_D.step(self.runner_state['epoch'])
for i, data_dict in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Forward pass.
out_dict = self.gan_net(data_dict)
# outputs = self.module_utilizer.gather(outputs)
self.optimizer_G.zero_grad()
loss_G = out_dict['loss_G'].mean()
loss_G.backward()
self.optimizer_G.step()
self.optimizer_D.zero_grad()
loss_D = out_dict['loss_D'].mean()
loss_D.backward()
self.optimizer_D.step()
loss = loss_G + loss_D
self.train_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
# 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_G),
RunnerHelper.get_lr(self.optimizer_D)
],
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
