class AutoSKUMerger(object):
"""
The class for the training phase of Image classification.
"""
def __init__(self, configer):
self.configer = configer
self.cls_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.runner_state = None
self.round = 1
self._relabel()
def _relabel(self):
label_id = 0
label_dict = dict()
old_label_path = self.configer.get('data', 'label_path')
new_label_path = '{}_new'.format(self.configer.get('data', 'label_path'))
self.configer.update('data.label_path', new_label_path)
fw = open(new_label_path, 'w')
check_valid_dict = dict()
with open(old_label_path, 'r') as fr:
for line in fr.readlines():
line_items = line.strip().split()
if not os.path.exists(os.path.join(self.configer.get('data', 'data_dir'), line_items[0])):
continue
if line_items[1] not in label_dict:
label_dict[line_items[1]] = label_id
label_id += 1
if line_items[0] in check_valid_dict:
Log.error('Duplicate Error: {}'.format(line_items[0]))
exit()
check_valid_dict[line_items[0]] = 1
fw.write('{} {}\n'.format(line_items[0], label_dict[line_items[1]]))
fw.close()
shutil.copy(self.configer.get('data', 'label_path'),
os.path.join(self.configer.get('data', 'merge_dir'), 'ori_label.txt'))
self.configer.update(('data.num_classes'), [label_id])
Log.info('Num Classes is {}...'.format(self.configer.get('data', 'num_classes')))
def _init(self):
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.cls_model_manager = ModelManager(self.configer)
self.cls_data_loader = DataLoader(self.configer)
self.cls_running_score = RunningScore(self.configer)
self.runner_state = dict(iters=0, last_iters=0, epoch=0,
last_epoch=0, performance=0,
val_loss=0, max_performance=0, min_val_loss=0)
self.cls_net = self.cls_model_manager.get_model()
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.solver_dict = self.configer.get(self.configer.get('train', 'solver'))
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.solver_dict)
self.cls_net, self.optimizer = RunnerHelper.to_dtype(self, self.cls_net, self.optimizer)
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.loss = self.cls_model_manager.get_loss()
def _get_parameters(self):
lr_1 = []
lr_2 = []
params_dict = dict(self.cls_net.named_parameters())
for key, value in params_dict.items():
if value.requires_grad:
if 'backbone' in key:
if self.configer.get('network', 'bb_lr_scale') == 0.0:
value.requires_grad = False
else:
lr_1.append(value)
else:
lr_2.append(value)
params = [{'params': lr_1, 'lr': self.solver_dict['lr']['base_lr']*self.configer.get('network', 'bb_lr_scale')},
{'params': lr_2, 'lr': self.solver_dict['lr']['base_lr']}]
return params
def _merge_class(self, cmatrix, fscore_list):
Log.info('Merging class...')
Log.info('Avg F1-score: {}'.format(fscore_list[-1]))
threshold = max(self.configer.get('merge', 'min_thre'),
self.configer.get('merge', 'max_thre') - self.configer.get('merge', 'round_decay') * self.round)
h, w = cmatrix.shape[0], cmatrix.shape[1]
per_class_num = np.sum(cmatrix, 1)
pairs_list = list()
pre_dict = dict()
for i in range(h):
for j in range(w):
if i == j:
continue
if cmatrix[i][j] * 1.0 / per_class_num[i] > threshold:
pairs_list.append([i, j])
pre_dict[i] = i
pre_dict[j] = j
for pair in pairs_list:
root_node = list()
for item in pair:
r = item
while pre_dict[r] != r:
r = pre_dict[r]
i = item
while i != r:
j = pre_dict[i]
pre_dict[i] = r
i = j
root_node.append(r)
if root_node[0] != root_node[1]:
pre_dict[root_node[0]] = root_node[1]
pairs_dict = dict()
for k in pre_dict.keys():
v = k
while pre_dict[v] != v:
v = pre_dict[v]
if v != k:
if v not in pairs_dict:
pairs_dict[v] = [k]
else:
pairs_dict[v].append(k)
mutual_pairs_dict = {}
for k, v in pairs_dict.items():
mutual_pairs_dict[k] = v
if len(v) > 1: # multi relation
for p in v:
mutual_pairs_dict[p] = [k]
for q in v:
if p != q:
mutual_pairs_dict[p].append(q)
else:
mutual_pairs_dict[v[0]] = [k] # mutual relation
id_map_list = [-1] * self.configer.get('data', 'num_classes')[0]
label_cnt = 0
for i in range(self.configer.get('data', 'num_classes')[0]):
if id_map_list[i] != -1:
continue
power = self.round / self.configer.get('merge', 'max_round')
if self.configer.get('merge', 'enable_fscore') and \
fscore_list[i] / fscore_list[-1] < self.configer.get('merge', 'fscore_ratio') * power:
continue
id_map_list[i] = label_cnt
if i in mutual_pairs_dict:
for v in mutual_pairs_dict[i]:
assert id_map_list[v] == -1
id_map_list[v] = label_cnt
label_cnt += 1
fw = open('{}_{}'.format(self.configer.get('data', 'label_path'), self.round), 'w')
with open(self.configer.get('data', 'label_path'), 'r') as fr:
for line in fr.readlines():
path, label = line.strip().split()
if id_map_list[int(label)] == -1:
continue
map_label = id_map_list[int(label)]
fw.write('{} {}\n'.format(path, map_label))
fw.close()
shutil.move('{}_{}'.format(self.configer.get('data', 'label_path'), self.round),
self.configer.get('data', 'label_path'))
shutil.copy(self.configer.get('data', 'label_path'),
os.path.join(self.configer.get('data', 'merge_dir'), 'label_{}.txt'.format(self.round)))
old_label_cnt = self.configer.get('data', 'num_classes')[0]
self.configer.update('data.num_classes', [label_cnt])
return old_label_cnt - label_cnt
def run(self):
last_acc = 0.0
while self.round <= self.configer.get('merge', 'max_round'):
Log.info('Merge Round: {}'.format(self.round))
Log.info('num classes: {}'.format(self.configer.get('data', 'num_classes')))
self._init()
self.train()
acc, cmatrix, fscore_list = self.val(self.cls_data_loader.get_valloader())
merge_cnt = self._merge_class(cmatrix, fscore_list)
if merge_cnt < self.configer.get('merge', 'cnt_thre') \
or (acc - last_acc) < self.configer.get('merge', 'acc_thre'):
break
last_acc = acc
self.round += 1
shutil.copy(self.configer.get('data', 'label_path'),
os.path.join(self.configer.get('data', 'merge_dir'), 'merge_label.txt'))
self._init()
self.train()
Log.info('num classes: {}'.format(self.configer.get('data', 'num_classes')))
def train(self):
"""
Train function of every epoch during train phase.
"""
self.cls_net.train()
start_time = time.time()
while self.runner_state['iters'] < self.solver_dict['max_iters']:
# Adjust the learning rate after every epoch.
self.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self, solver_dict=self.solver_dict)
self.data_time.update(time.time() - start_time)
# Change the data type.
# Forward pass.
out = self.cls_net(data_dict)
# Compute the loss of the train batch & backward.
loss_dict = self.loss(out)
loss = loss_dict['loss']
self.train_losses.update(loss.item(), 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.solver_dict['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.solver_dict['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.solver_dict['lr']['metric'] == 'iters' and self.runner_state['iters'] == self.solver_dict['max_iters']:
self.val()
break
# Check to val the current model.
if self.runner_state['iters'] % self.solver_dict['test_interval'] == 0:
self.val()
def val(self, loader=None):
"""
Validation function during the train phase.
"""
self.cls_net.eval()
start_time = time.time()
loader = self.val_loader if loader is None else loader
list_y_true, list_y_pred = [], []
with torch.no_grad():
for j, data_dict in enumerate(loader):
out = self.cls_net(data_dict)
loss_dict = self.loss(out)
out_dict, label_dict, _ = RunnerHelper.gather(self, out)
# Compute the loss of the val batch.
self.cls_running_score.update(out_dict, label_dict)
y_true = label_dict['out0'].view(-1).cpu().numpy().tolist()
y_pred = out_dict['out0'].max(1)[1].view(-1).cpu().numpy().tolist()
list_y_true.extend(y_true)
list_y_pred.extend(y_pred)
self.val_losses.update(loss_dict['loss'].mean().item(), data_dict['img'].size(0))
# 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.top1_acc.avg['out0'])
self.runner_state['performance'] = self.cls_running_score.top1_acc.avg['out0']
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(batch_time=self.batch_time))
Log.info('Test Set: {} images'.format(len(list_y_true)))
Log.info('TestLoss = {loss.avg:.8f}'.format(loss=self.val_losses))
Log.info('Top1 ACC = {}'.format(self.cls_running_score.top1_acc.avg['out0']))
# Log.info('Top5 ACC = {}'.format(self.cls_running_score.get_top5_acc()))
acc= self.cls_running_score.top1_acc.avg['out0']
cmatrix = confusion_matrix(list_y_true, list_y_pred)
fscore_str = classification_report(list_y_true, list_y_pred, digits=5)
fscore_list = [float(line.strip().split()[-2])
for line in fscore_str.split('\n')[2:] if len(line.strip().split()) > 0]
self.batch_time.reset()
self.val_losses.reset()
self.cls_running_score.reset()
self.cls_net.train()
return acc, cmatrix, fscore_list
class ImageTester(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.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_data_loader = DataLoader(configer)
self.save_dir = self.configer.get('test', 'out_dir')
self.seg_net = None
self.test_loader = None
self.test_size = None
self.infer_time = 0
self.infer_cnt = 0
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
print(f"self.save_dir {self.save_dir}")
# if 'test' in self.save_dir:
# self.test_loader = self.seg_data_loader.get_testloader()
# self.test_size = len(self.test_loader) * self.configer.get('test', 'batch_size')
# print(f"self.test_size {self.test_size}")
# else:
# self.test_loader = self.seg_data_loader.get_valloader()
# self.test_size = len(self.test_loader) * self.configer.get('val', 'batch_size')
self.seg_net.eval()
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 test(self, img_path=None, output_dir=None, data_loader=None):
"""
Validation function during the train phase.
"""
print("test!!!")
self.seg_net.eval()
start_time = time.time()
image_id = 0
Log.info('save dir {}'.format(self.save_dir))
FileHelper.make_dirs(self.save_dir, is_file=False)
colors = get_ade_colors()
# Reader.
if img_path is not None:
input_path = img_path
else:
input_path = self.configer.get('input_image')
input_image = cv2.imread(input_path)
transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(div_value=self.configer.get('normalize', 'div_value'),
mean=self.configer.get('normalize', 'mean'),
std=self.configer.get('normalize', 'std')), ])
aug_val_transform = cv2_aug_transforms.CV2AugCompose(self.configer, split='val')
pre_vis_img = None
pre_lines = None
pre_target_img = None
ori_img = input_image.copy()
h, w, _ = input_image.shape
ori_img_size = [w, h]
# print(img.shape)
input_image = aug_val_transform(input_image)
input_image = input_image[0]
h, w, _ = input_image.shape
border_size = [w, h]
input_image = transform(input_image)
# print(img)
# print(img.shape)
# inputs = data_dict['img']
# names = data_dict['name']
# metas = data_dict['meta']
# print(inputs)
with torch.no_grad():
# Forward pass.
outputs = self.ss_test([input_image])
if isinstance(outputs, torch.Tensor):
outputs = outputs.permute(0, 2, 3, 1).cpu().numpy()
n = outputs.shape[0]
else:
outputs = [output.permute(0, 2, 3, 1).cpu().numpy().squeeze() for output in outputs]
n = len(outputs)
logits = cv2.resize(outputs[0],
tuple(ori_img_size), interpolation=cv2.INTER_CUBIC)
label_img = np.asarray(np.argmax(logits, axis=-1), dtype=np.uint8)
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)
if self.configer.exists('data', 'label_list'):
label_img_ = self.__relabel(label_img)
else:
label_img_ = label_img
label_img_ = Image.fromarray(label_img_, 'P')
input_name = '.'.join(os.path.basename(input_path).split('.')[:-1])
if output_dir is None:
label_path = os.path.join(self.save_dir, 'label_{}.png'.format(input_name))
else:
label_path = os.path.join(output_dir, 'label_{}.png'.format(input_name))
FileHelper.make_dirs(label_path, is_file=True)
# print(f"{label_path}")
ImageHelper.save(label_img_, label_path)
self.batch_time.update(time.time() - start_time)
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(batch_time=self.batch_time))
def offset_test(self, inputs, offset_h_maps, offset_w_maps, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
start = timeit.default_timer()
outputs = self.seg_net.forward(inputs, offset_h_maps, offset_w_maps)
torch.cuda.synchronize()
end = timeit.default_timer()
if (self.configer.get('loss', 'loss_type') == "fs_auxce_loss") or (self.configer.get('loss', 'loss_type') == "triple_auxce_loss"):
outputs = outputs[-1]
elif self.configer.get('loss', 'loss_type') == "pyramid_auxce_loss":
outputs = outputs[1] + outputs[2] + outputs[3] + outputs[4]
outputs = F.interpolate(outputs, size=(h, w), mode='bilinear', align_corners=True)
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ss_test(self, inputs, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
scaled_inputs = F.interpolate(inputs, size=(int(h*scale), int(w*scale)), mode="bilinear", align_corners=True)
start = timeit.default_timer()
outputs = self.seg_net.forward(scaled_inputs)
torch.cuda.synchronize()
end = timeit.default_timer()
outputs = outputs[-1]
outputs = F.interpolate(outputs, size=(h, w), mode='bilinear', align_corners=True)
return outputs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
replicas = nn.parallel.replicate(self.seg_net.module, device_ids)
scaled_inputs, ori_size, outputs = [], [], []
for i, d in zip(inputs, device_ids):
h, w = i.size(1), i.size(2)
ori_size.append((h, w))
i = F.interpolate(i.unsqueeze(0), size=(int(h*scale), int(w*scale)), mode="bilinear", align_corners=True)
scaled_inputs.append(i.cuda(d, non_blocking=True))
scaled_outputs = nn.parallel.parallel_apply(replicas[:len(scaled_inputs)], scaled_inputs)
for i, output in enumerate(scaled_outputs):
outputs.append(F.interpolate(output[-1], size=ori_size[i], mode='bilinear', align_corners=True))
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def flip(self, x, dim):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1, -1, -1,
dtype=torch.long, device=x.device)
return x[tuple(indices)]
def sscrop_test(self, inputs, crop_size, scale=1):
'''
Currently, sscrop_test does not support diverse_size testing
'''
n, c, ori_h, ori_w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
scaled_inputs = F.interpolate(inputs, size=(int(ori_h*scale), int(ori_w*scale)), mode="bilinear", align_corners=True)
n, c, h, w = scaled_inputs.size(0), scaled_inputs.size(1), scaled_inputs.size(2), scaled_inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
count_predictions = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
crop_counter = 0
height_starts = self._decide_intersection(h, crop_size[0])
width_starts = self._decide_intersection(w, crop_size[1])
for height in height_starts:
for width in width_starts:
crop_inputs = scaled_inputs[:, :, height:height+crop_size[0], width:width + crop_size[1]]
prediction = self.ss_test(crop_inputs)
count_predictions[:, :, height:height+crop_size[0], width:width + crop_size[1]] += 1
full_probs[:, :, height:height+crop_size[0], width:width + crop_size[1]] += prediction
crop_counter += 1
Log.info('predicting {:d}-th crop'.format(crop_counter))
full_probs /= count_predictions
full_probs = F.interpolate(full_probs, size=(ori_h, ori_w), mode='bilinear', align_corners=True)
return full_probs
def ms_test(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(self.configer.get('test', 'scale_search'), self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += weight * probs
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index], non_blocking=True)
for index, i in enumerate(inputs)]
flip_inputs = [self.flip(i, 2) for i in inputs]
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(self.configer.get('test', 'scale_search'), self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += weight * (probs[i] + self.flip(flip_probs[i], 3))
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += (probs[i] + self.flip(flip_probs[i], 3))
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ms_test_depth(self, inputs, names):
prob_list = []
scale_list = []
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
prob_list.append(probs)
scale_list.append(scale)
full_probs = self.fuse_with_depth(prob_list, scale_list, names)
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def fuse_with_depth(self, probs, scales, names):
MAX_DEPTH = 63
POWER_BASE = 0.8
if 'test' in self.save_dir:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/test/"
else:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/val/"
n, c, h, w = probs[0].size(0), probs[0].size(1), probs[0].size(2), probs[0].size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for index, name in enumerate(names):
stereo_map = cv2.imread(stereo_path + name + '.png', -1)
depth_map = stereo_map / 256.0
depth_map = 0.5 / depth_map
depth_map = 500 * depth_map
depth_map = np.clip(depth_map, 0, MAX_DEPTH)
depth_map = depth_map // (MAX_DEPTH // len(scales))
for prob, scale in zip(probs, scales):
scale_index = self._locate_scale_index(scale, scales)
weight_map = np.abs(depth_map - scale_index)
weight_map = np.power(POWER_BASE, weight_map)
weight_map = cv2.resize(weight_map, (w, h))
full_probs[index, :, :, :] += torch.from_numpy(np.expand_dims(weight_map, axis=0)).type(torch.cuda.FloatTensor) * prob[index, :, :, :]
return full_probs
@staticmethod
def _locate_scale_index(scale, scales):
for idx, s in enumerate(scales):
if scale == s:
return idx
return 0
def ms_test_wo_flip(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index], non_blocking=True)
for index, i, in enumerate(inputs)]
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
for i in range(len(inputs)):
full_probs[i] += probs[i]
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def mscrop_test(self, inputs, crop_size):
'''
Currently, mscrop_test does not support diverse_size testing
'''
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
Log.info('Scale {0:.2f} prediction'.format(scale))
if scale < 1:
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
else:
probs = self.sscrop_test(inputs, crop_size, scale)
flip_probs = self.sscrop_test(self.flip(inputs, 3), crop_size, scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
def _decide_intersection(self, total_length, crop_length):
stride = crop_length
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 dense_crf_process(self, images, outputs):
'''
Reference: https://github.com/kazuto1011/deeplab-pytorch/blob/master/libs/utils/crf.py
'''
# hyperparameters of the dense crf
# baseline = 79.5
# bi_xy_std = 67, 79.1
# bi_xy_std = 20, 79.6
# bi_xy_std = 10, 79.7
# bi_xy_std = 10, iter_max = 20, v4 79.7
# bi_xy_std = 10, iter_max = 5, v5 79.7
# bi_xy_std = 5, v3 79.7
iter_max = 10
pos_w = 3
pos_xy_std = 1
bi_w = 4
bi_xy_std = 10
bi_rgb_std = 3
b = images.size(0)
mean_vector = np.expand_dims(np.expand_dims(np.transpose(np.array([102.9801, 115.9465, 122.7717])), axis=1), axis=2)
outputs = F.softmax(outputs, dim=1)
for i in range(b):
unary = outputs[i].data.cpu().numpy()
C, H, W = unary.shape
unary = dcrf_utils.unary_from_softmax(unary)
unary = np.ascontiguousarray(unary)
image = np.ascontiguousarray(images[i]) + mean_vector
image = image.astype(np.ubyte)
image = np.ascontiguousarray(image.transpose(1, 2, 0))
d = dcrf.DenseCRF2D(W, H, C)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=pos_xy_std, compat=pos_w)
d.addPairwiseBilateral(sxy=bi_xy_std, srgb=bi_rgb_std, rgbim=image, compat=bi_w)
out_crf = np.array(d.inference(iter_max))
outputs[i] = torch.from_numpy(out_crf).cuda().view(C, H, W)
return outputs
def visualize(self, label_img):
img = label_img.copy()
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_num = 14
img[img == img_num] = 0
out_img = img.copy()
for label in HYUNDAI_POC_CATEGORIES:
red, green, blue = img[:,:,0], img[:,:,1], img[:,:,2]
mask = red == label['id']
out_img[:,:,:3][mask] = label['color']
out_img = cv2.cvtColor(out_img, cv2.COLOR_RGB2BGR)
return out_img
def get_ratio_all(self, anno_img):
total_size = 0
lines = []
for label in HYUNDAI_POC_CATEGORIES:
total = self.get_ratio(anno_img.copy(), label)
lines.append([label['name'], total])
total_size += total
for l in lines:
if total_size:
l[1] = l[1] / total_size * 100
else:
l[1] = 0
return lines
def get_ratio(self, anno_img, label):
total = 0
label_id = label['id']
if label_id == 14:
return total
label_img = (anno_img == label_id).astype(np.uint8)
# label_img = cv2.cvtColor(label_img, cv2.COLOR_BGR2GRAY)
total = np.count_nonzero(label_img)
return total
def visualize_ratio(self, ratios, video_size, ratio_w):
ratio_list = ratios.copy()
ratio_list.insert(0, ['등급','비율'])
RATIO_IMG_W = ratio_w
RATIO_IMG_H = int(video_size[0])
TEXT_MARGIN_H = 20
TEXT_MARGIN_W = 10
row_count = 14
col_count = 2
ratio_img = np.full((RATIO_IMG_H, RATIO_IMG_W, 3), 255, np.uint8)
row_h = RATIO_IMG_H / row_count
col_w = RATIO_IMG_H / row_count
center_w = RATIO_IMG_W / 2
for i in range(1, row_count):
p_y = int(i * row_h)
p_y_n = int((i+1) * row_h)
for label in HYUNDAI_POC_CATEGORIES:
if label['id'] == i:
cv2.rectangle(ratio_img, (0, p_y), (int(center_w), p_y_n), label['color'], cv2.FILLED)
for i in range(1, row_count):
p_y = int(i * row_h)
cv2.line(ratio_img, (0, p_y), (RATIO_IMG_W, p_y), (0,0,0))
cv2.line(ratio_img, (int(center_w), 0), (int(center_w), RATIO_IMG_H), (0,0,0))
for i in range(row_count):
p_y = int(i * row_h) + TEXT_MARGIN_H
p_w = int(center_w) + TEXT_MARGIN_W
ratio_img = Image.fromarray(ratio_img)
font = ImageFont.truetype("NanumGothic.ttf", 15)
draw = ImageDraw.Draw(ratio_img)
color = (0, 0, 0)
# print(ratio_list)
draw.text((0, p_y), ratio_list[i][0], font=font, fill=color)
if isinstance(ratio_list[i][1], str):
draw.text((p_w, p_y), ratio_list[i][1],font=font,fill=color)
else:
draw.text((p_w, p_y), "{:.02f}".format(ratio_list[i][1]),font=font,fill=color)
ratio_img = np.array(ratio_img)
ratio_img = cv2.cvtColor(ratio_img, cv2.COLOR_RGB2BGR)
return ratio_img
class Tester(object):
def __init__(self, configer):
self.crop_size = configer.get('train',
'data_transformer')['input_size']
val_trans_seq = [
x for x in configer.get('val_trans', 'trans_seq')
if 'random' not in x
]
configer.update(('val_trans', 'trans_seq'), val_trans_seq)
configer.get('val', 'data_transformer')['input_size'] = configer.get(
'test', 'data_transformer').get('input_size', None)
configer.update(('train', 'data_transformer'),
configer.get('val', 'data_transformer'))
configer.update(('val', 'batch_size'),
int(os.environ.get('batch_size', 16)))
configer.update(('test', 'batch_size'),
int(os.environ.get('batch_size', 16)))
self.save_dir = configer.get('test', 'out_dir')
self.dataset_name = configer.get('test', 'eval_set')
self.sscrop = configer.get('test', 'sscrop')
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.seg_net = None
self.test_loader = None
self.test_size = None
self.infer_time = 0
self.infer_cnt = 0
self._init_model()
pprint.pprint(configer.params_root)
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
assert self.dataset_name in ('train', 'val',
'test'), 'Cannot infer dataset name'
self.size_mode = self.configer.get(self.dataset_name,
'data_transformer')['size_mode']
if self.dataset_name != 'test':
self.test_loader = self.seg_data_loader.get_valloader(
self.dataset_name)
else:
self.test_loader = self.seg_data_loader.get_testloader(
self.dataset_name)
self.test_size = len(self.test_loader) * self.configer.get(
'val', 'batch_size')
def test(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
image_id = 0
Log.info('save dir {}'.format(self.save_dir))
FileHelper.make_dirs(self.save_dir, is_file=False)
print('Total batches', len(self.test_loader))
for j, data_dict in enumerate(self.test_loader):
inputs = [data_dict['img']]
names = data_dict['name']
metas = data_dict['meta']
dest_dir = self.save_dir
with torch.no_grad():
offsets, logits = self.extract_offset(inputs)
print([x.shape for x in logits])
for k in range(len(inputs[0])):
image_id += 1
ori_img_size = metas[k]['ori_img_size']
border_size = metas[k]['border_size']
offset = offsets[k].squeeze().cpu().numpy()
offset = cv2.resize(
offset[:border_size[1], :border_size[0]],
tuple(ori_img_size),
interpolation=cv2.INTER_NEAREST)
print(image_id)
os.makedirs(dest_dir, exist_ok=True)
if names[k].rpartition('.')[0]:
dest_name = names[k].rpartition('.')[0] + '.mat'
else:
dest_name = names[k] + '.mat'
dest_name = os.path.join(dest_dir, dest_name)
print('Shape:', offset.shape, 'Saving to', dest_name)
data_dict = {'mat': offset}
scipy.io.savemat(dest_name, data_dict, do_compression=True)
try:
scipy.io.loadmat(dest_name)
except Exception as e:
print(e)
scipy.io.savemat(dest_name,
data_dict,
do_compression=False)
self.batch_time.update(time.time() - start_time)
start_time = time.time()
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
def extract_offset(self, inputs):
if self.sscrop:
outputs = self.sscrop_test(inputs, self.crop_size)
elif self.configer.get('test', 'mode') == 'ss_test':
outputs = self.ss_test(inputs)
offsets = []
logits = []
for mask_logits, dir_logits, img in zip(*outputs[:2], inputs[0]):
h, w = img.shape[1:]
mask_logits = F.interpolate(mask_logits.unsqueeze(0),
size=(h, w),
mode='bilinear',
align_corners=True)
dir_logits = F.interpolate(dir_logits.unsqueeze(0),
size=(h, w),
mode='bilinear',
align_corners=True)
logit = torch.softmax(dir_logits, dim=1)
zero_mask = mask_logits.argmax(dim=1, keepdim=True) == 0
logits.append(mask_logits[:, 1])
offset = self._get_offset(mask_logits, dir_logits)
offsets.append(offset)
print([x.shape for x in offsets])
return offsets, logits
def _get_offset(self, mask_logits, dir_logits):
edge_mask = mask_logits[:, 1] > 0.5
dir_logits = torch.softmax(dir_logits, dim=1)
n, _, h, w = dir_logits.shape
keep_mask = edge_mask
dir_label = torch.argmax(dir_logits, dim=1).float()
offset = DTOffsetHelper.label_to_vector(dir_label)
offset = offset.permute(0, 2, 3, 1)
offset[~keep_mask, :] = 0
return offset
def _flip(self, x, dim=-1):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1,
-1,
-1,
dtype=torch.long,
device=x.device)
return x[tuple(indices)]
def _flip_offset(self, x):
x = self._flip(x, dim=-1)
if len(x.shape) == 4:
return x[:, DTOffsetHelper.flipping_indices()]
else:
return x[DTOffsetHelper.flipping_indices()]
def _flip_inputs(self, inputs):
if self.size_mode == 'fix_size':
return [self._flip(x, -1) for x in inputs]
else:
return [[self._flip(x, -1) for x in xs] for xs in inputs]
def _flip_outputs(self, outputs):
funcs = [self._flip, self._flip_offset]
if self.size_mode == 'fix_size':
return [f(x) for f, x in zip(funcs, outputs)]
else:
return [[f(x) for x in xs] for f, xs in zip(funcs, outputs)]
def _tuple_sum(self, tup1, tup2, tup2_weight=1):
"""
tup1 / tup2: tuple of tensors or tuple of list of tensors
"""
if tup1 is None:
if self.size_mode == 'fix_size':
return [y * tup2_weight for y in tup2]
else:
return [[y * tup2_weight for y in ys] for ys in tup2]
else:
if self.size_mode == 'fix_size':
return [x + y * tup2_weight for x, y in zip(tup1, tup2)]
else:
return [[x + y * tup2_weight for x, y in zip(xs, ys)]
for xs, ys in zip(tup1, tup2)]
def _scale_ss_inputs(self, inputs, scale):
n, c, h, w = inputs[0].shape
size = (int(h * scale), int(w * scale))
return [
F.interpolate(inputs[0],
size=size,
mode="bilinear",
align_corners=True),
], (h, w)
def sscrop_test(self, inputs, crop_size, scale=1):
'''
Currently, sscrop_test does not support diverse_size testing
'''
scaled_inputs = inputs
img = scaled_inputs[0]
n, c, h, w = img.size(0), img.size(1), img.size(2), img.size(3)
ori_h, ori_w = h, w
full_probs = [
torch.cuda.FloatTensor(n, dim, h, w).fill_(0) for dim in (2, 8)
]
count_predictions = [
torch.cuda.FloatTensor(n, dim, h, w).fill_(0) for dim in (2, 8)
]
crop_counter = 0
height_starts = self._decide_intersection(h, crop_size[0])
width_starts = self._decide_intersection(w, crop_size[1])
for height in height_starts:
for width in width_starts:
crop_inputs = [
x[..., height:height + crop_size[0],
width:width + crop_size[1]] for x in scaled_inputs
]
prediction = self.ss_test(crop_inputs)
for j in range(2):
count_predictions[j][:, :, height:height + crop_size[0],
width:width + crop_size[1]] += 1
full_probs[j][:, :, height:height + crop_size[0],
width:width + crop_size[1]] += prediction[j]
crop_counter += 1
Log.info('predicting {:d}-th crop'.format(crop_counter))
for j in range(2):
full_probs[j] /= count_predictions[j]
full_probs[j] = F.interpolate(full_probs[j],
size=(ori_h, ori_w),
mode='bilinear',
align_corners=True)
return full_probs
def _scale_ss_outputs(self, outputs, size):
return [
F.interpolate(x, size=size, mode="bilinear", align_corners=True)
for x in outputs
]
def ss_test(self, inputs, scale=1):
if self.size_mode == 'fix_size':
scaled_inputs, orig_size = self._scale_ss_inputs(inputs, scale)
print([x.shape for x in scaled_inputs])
start = timeit.default_timer()
outputs = list(self.seg_net.forward(*scaled_inputs))
if len(outputs) == 3:
outputs = (outputs[0], outputs[2])
else:
outputs[0] = F.softmax(outputs[0], dim=1)
torch.cuda.synchronize()
end = timeit.default_timer()
return self._scale_ss_outputs(outputs, orig_size)
else:
device_ids = self.configer.get('gpu')
replicas = nn.parallel.replicate(self.seg_net.module, device_ids)
scaled_inputs, ori_sizes, outputs = [], [], []
for *i, d in zip(*inputs, device_ids):
scaled_i, ori_size_i = self._scale_ss_inputs(
[x.unsqueeze(0) for x in i], scale)
scaled_inputs.append(
[x.cuda(d, non_blocking=True) for x in scaled_i])
ori_sizes.append(ori_size_i)
scaled_outputs = nn.parallel.parallel_apply(
replicas[:len(scaled_inputs)], scaled_inputs)
for o, ori_size in zip(scaled_outputs, ori_sizes):
o = self._scale_ss_outputs(o, ori_size)
if len(o) == 3:
o = (o[0], o[2])
outputs.append([x.squeeze(0) for x in o])
outputs = list(map(list, zip(*outputs)))
return outputs
def _decide_intersection(self,
total_length,
crop_length,
crop_stride_ratio=1 / 3):
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
class Tester(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.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_data_loader = DataLoader(configer)
self.save_dir = self.configer.get('test', 'out_dir')
self.seg_net = None
self.test_loader = None
self.test_size = None
self.infer_time = 0
self.infer_cnt = 0
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
if 'test' in self.save_dir:
self.test_loader = self.seg_data_loader.get_testloader()
self.test_size = len(self.test_loader) * self.configer.get(
'test', 'batch_size')
else:
self.test_loader = self.seg_data_loader.get_valloader()
self.test_size = len(self.test_loader) * self.configer.get(
'val', 'batch_size')
self.seg_net.eval()
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 test(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
image_id = 0
Log.info('save dir {}'.format(self.save_dir))
FileHelper.make_dirs(self.save_dir, is_file=False)
if self.configer.get('dataset') in ['cityscapes', 'gta5']:
colors = get_cityscapes_colors()
elif self.configer.get('dataset') == 'ade20k':
colors = get_ade_colors()
elif self.configer.get('dataset') == 'lip':
colors = get_lip_colors()
elif self.configer.get('dataset') == 'pascal_context':
colors = get_pascal_context_colors()
elif self.configer.get('dataset') == 'pascal_voc':
colors = get_pascal_voc_colors()
elif self.configer.get('dataset') == 'coco_stuff':
colors = get_cocostuff_colors()
else:
raise RuntimeError("Unsupport colors")
save_prob = False
if self.configer.get('test', 'save_prob'):
save_prob = self.configer.get('test', 'save_prob')
def softmax(X, axis=0):
max_prob = np.max(X, axis=axis, keepdims=True)
X -= max_prob
X = np.exp(X)
sum_prob = np.sum(X, axis=axis, keepdims=True)
X /= sum_prob
return X
for j, data_dict in enumerate(self.test_loader):
inputs = data_dict['img']
names = data_dict['name']
metas = data_dict['meta']
if 'val' in self.save_dir and os.environ.get('save_gt_label'):
labels = data_dict['labelmap']
with torch.no_grad():
# Forward pass.
if self.configer.exists('data',
'use_offset') and self.configer.get(
'data', 'use_offset') == 'offline':
offset_h_maps = data_dict['offsetmap_h']
offset_w_maps = data_dict['offsetmap_w']
outputs = self.offset_test(inputs, offset_h_maps,
offset_w_maps)
elif self.configer.get('test', 'mode') == 'ss_test':
outputs = self.ss_test(inputs)
elif self.configer.get('test', 'mode') == 'ms_test':
outputs = self.ms_test(inputs)
elif self.configer.get('test', 'mode') == 'ms_test_depth':
outputs = self.ms_test_depth(inputs, names)
elif self.configer.get('test', 'mode') == 'sscrop_test':
crop_size = self.configer.get('test', 'crop_size')
outputs = self.sscrop_test(inputs, crop_size)
elif self.configer.get('test', 'mode') == 'mscrop_test':
crop_size = self.configer.get('test', 'crop_size')
outputs = self.mscrop_test(inputs, crop_size)
elif self.configer.get('test', 'mode') == 'crf_ss_test':
outputs = self.ss_test(inputs)
outputs = self.dense_crf_process(inputs, outputs)
if isinstance(outputs, torch.Tensor):
outputs = outputs.permute(0, 2, 3, 1).cpu().numpy()
n = outputs.shape[0]
else:
outputs = [
output.permute(0, 2, 3, 1).cpu().numpy().squeeze()
for output in outputs
]
n = len(outputs)
for k in range(n):
image_id += 1
ori_img_size = metas[k]['ori_img_size']
border_size = metas[k]['border_size']
logits = cv2.resize(
outputs[k][:border_size[1], :border_size[0]],
tuple(ori_img_size),
interpolation=cv2.INTER_CUBIC)
# save the logits map
if self.configer.get('test', 'save_prob'):
prob_path = os.path.join(self.save_dir, "prob/",
'{}.npy'.format(names[k]))
FileHelper.make_dirs(prob_path, is_file=True)
np.save(prob_path, softmax(logits, axis=-1))
label_img = np.asarray(np.argmax(logits, axis=-1),
dtype=np.uint8)
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)
if self.configer.exists('data', 'label_list'):
label_img_ = self.__relabel(label_img)
else:
label_img_ = label_img
label_img_ = Image.fromarray(label_img_, 'P')
Log.info('{:4d}/{:4d} label map generated'.format(
image_id, self.test_size))
label_path = os.path.join(self.save_dir, "label/",
'{}.png'.format(names[k]))
FileHelper.make_dirs(label_path, is_file=True)
ImageHelper.save(label_img_, label_path)
# colorize the label-map
if os.environ.get('save_gt_label'):
if self.configer.exists(
'data',
'reduce_zero_label') and self.configer.get(
'data', 'reduce_zero_label'):
label_img = labels[k] + 1
label_img = np.asarray(label_img, dtype=np.uint8)
color_img_ = Image.fromarray(label_img)
color_img_.putpalette(colors)
vis_path = os.path.join(self.save_dir, "gt_vis/",
'{}.png'.format(names[k]))
FileHelper.make_dirs(vis_path, is_file=True)
ImageHelper.save(color_img_, save_path=vis_path)
else:
color_img_ = Image.fromarray(label_img)
color_img_.putpalette(colors)
vis_path = os.path.join(self.save_dir, "vis/",
'{}.png'.format(names[k]))
FileHelper.make_dirs(vis_path, is_file=True)
ImageHelper.save(color_img_, save_path=vis_path)
self.batch_time.update(time.time() - start_time)
start_time = time.time()
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
def offset_test(self, inputs, offset_h_maps, offset_w_maps, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
start = timeit.default_timer()
outputs = self.seg_net.forward(inputs, offset_h_maps,
offset_w_maps)
torch.cuda.synchronize()
end = timeit.default_timer()
if (self.configer.get('loss', 'loss_type')
== "fs_auxce_loss") or (self.configer.get(
'loss', 'loss_type') == "triple_auxce_loss"):
outputs = outputs[-1]
elif self.configer.get('loss',
'loss_type') == "pyramid_auxce_loss":
outputs = outputs[1] + outputs[2] + outputs[3] + outputs[4]
outputs = F.interpolate(outputs,
size=(h, w),
mode='bilinear',
align_corners=True)
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ss_test(self, inputs, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
scaled_inputs = F.interpolate(inputs,
size=(int(h * scale),
int(w * scale)),
mode="bilinear",
align_corners=True)
start = timeit.default_timer()
outputs = self.seg_net.forward(scaled_inputs)
torch.cuda.synchronize()
end = timeit.default_timer()
outputs = outputs[-1]
outputs = F.interpolate(outputs,
size=(h, w),
mode='bilinear',
align_corners=True)
return outputs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
replicas = nn.parallel.replicate(self.seg_net.module, device_ids)
scaled_inputs, ori_size, outputs = [], [], []
for i, d in zip(inputs, device_ids):
h, w = i.size(1), i.size(2)
ori_size.append((h, w))
i = F.interpolate(i.unsqueeze(0),
size=(int(h * scale), int(w * scale)),
mode="bilinear",
align_corners=True)
scaled_inputs.append(i.cuda(d, non_blocking=True))
scaled_outputs = nn.parallel.parallel_apply(
replicas[:len(scaled_inputs)], scaled_inputs)
for i, output in enumerate(scaled_outputs):
outputs.append(
F.interpolate(output[-1],
size=ori_size[i],
mode='bilinear',
align_corners=True))
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def flip(self, x, dim):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1,
-1,
-1,
dtype=torch.long,
device=x.device)
return x[tuple(indices)]
def sscrop_test(self, inputs, crop_size, scale=1):
'''
Currently, sscrop_test does not support diverse_size testing
'''
n, c, ori_h, ori_w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
scaled_inputs = F.interpolate(inputs,
size=(int(ori_h * scale),
int(ori_w * scale)),
mode="bilinear",
align_corners=True)
n, c, h, w = scaled_inputs.size(0), scaled_inputs.size(
1), scaled_inputs.size(2), scaled_inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
count_predictions = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
crop_counter = 0
height_starts = self._decide_intersection(h, crop_size[0])
width_starts = self._decide_intersection(w, crop_size[1])
for height in height_starts:
for width in width_starts:
crop_inputs = scaled_inputs[:, :, height:height + crop_size[0],
width:width + crop_size[1]]
prediction = self.ss_test(crop_inputs)
count_predictions[:, :, height:height + crop_size[0],
width:width + crop_size[1]] += 1
full_probs[:, :, height:height + crop_size[0],
width:width + crop_size[1]] += prediction
crop_counter += 1
Log.info('predicting {:d}-th crop'.format(crop_counter))
full_probs /= count_predictions
full_probs = F.interpolate(full_probs,
size=(ori_h, ori_w),
mode='bilinear',
align_corners=True)
return full_probs
def ms_test(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(
self.configer.get('test', 'scale_search'),
self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += weight * probs
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [
torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index],
non_blocking=True)
for index, i in enumerate(inputs)
]
flip_inputs = [self.flip(i, 2) for i in inputs]
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(
self.configer.get('test', 'scale_search'),
self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += weight * (probs[i] +
self.flip(flip_probs[i], 3))
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += (probs[i] +
self.flip(flip_probs[i], 3))
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ms_test_depth(self, inputs, names):
prob_list = []
scale_list = []
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
prob_list.append(probs)
scale_list.append(scale)
full_probs = self.fuse_with_depth(prob_list, scale_list, names)
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def fuse_with_depth(self, probs, scales, names):
MAX_DEPTH = 63
POWER_BASE = 0.8
if 'test' in self.save_dir:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/test/"
else:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/val/"
n, c, h, w = probs[0].size(0), probs[0].size(1), probs[0].size(
2), probs[0].size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for index, name in enumerate(names):
stereo_map = cv2.imread(stereo_path + name + '.png', -1)
depth_map = stereo_map / 256.0
depth_map = 0.5 / depth_map
depth_map = 500 * depth_map
depth_map = np.clip(depth_map, 0, MAX_DEPTH)
depth_map = depth_map // (MAX_DEPTH // len(scales))
for prob, scale in zip(probs, scales):
scale_index = self._locate_scale_index(scale, scales)
weight_map = np.abs(depth_map - scale_index)
weight_map = np.power(POWER_BASE, weight_map)
weight_map = cv2.resize(weight_map, (w, h))
full_probs[index, :, :, :] += torch.from_numpy(
np.expand_dims(weight_map, axis=0)).type(
torch.cuda.FloatTensor) * prob[index, :, :, :]
return full_probs
@staticmethod
def _locate_scale_index(scale, scales):
for idx, s in enumerate(scales):
if scale == s:
return idx
return 0
def ms_test_wo_flip(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [
torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index],
non_blocking=True)
for index, i, in enumerate(inputs)
]
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
for i in range(len(inputs)):
full_probs[i] += probs[i]
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def mscrop_test(self, inputs, crop_size):
'''
Currently, mscrop_test does not support diverse_size testing
'''
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
Log.info('Scale {0:.2f} prediction'.format(scale))
if scale < 1:
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
else:
probs = self.sscrop_test(inputs, crop_size, scale)
flip_probs = self.sscrop_test(self.flip(inputs, 3), crop_size,
scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
def _decide_intersection(self, total_length, crop_length):
stride = crop_length
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 dense_crf_process(self, images, outputs):
'''
Reference: https://github.com/kazuto1011/deeplab-pytorch/blob/master/libs/utils/crf.py
'''
# hyperparameters of the dense crf
# baseline = 79.5
# bi_xy_std = 67, 79.1
# bi_xy_std = 20, 79.6
# bi_xy_std = 10, 79.7
# bi_xy_std = 10, iter_max = 20, v4 79.7
# bi_xy_std = 10, iter_max = 5, v5 79.7
# bi_xy_std = 5, v3 79.7
iter_max = 10
pos_w = 3
pos_xy_std = 1
bi_w = 4
bi_xy_std = 10
bi_rgb_std = 3
b = images.size(0)
mean_vector = np.expand_dims(np.expand_dims(np.transpose(
np.array([102.9801, 115.9465, 122.7717])),
axis=1),
axis=2)
outputs = F.softmax(outputs, dim=1)
for i in range(b):
unary = outputs[i].data.cpu().numpy()
C, H, W = unary.shape
unary = dcrf_utils.unary_from_softmax(unary)
unary = np.ascontiguousarray(unary)
image = np.ascontiguousarray(images[i]) + mean_vector
image = image.astype(np.ubyte)
image = np.ascontiguousarray(image.transpose(1, 2, 0))
d = dcrf.DenseCRF2D(W, H, C)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=pos_xy_std, compat=pos_w)
d.addPairwiseBilateral(sxy=bi_xy_std,
srgb=bi_rgb_std,
rgbim=image,
compat=bi_w)
out_crf = np.array(d.inference(iter_max))
outputs[i] = torch.from_numpy(out_crf).cuda().view(C, H, W)
return outputs
class Trainer(object):
"""
The class for Pose Estimation. Include train, val, val & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.foward_time = AverageMeter()
self.backward_time = AverageMeter()
self.loss_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.data_loader = DataLoader(configer)
self.optim_scheduler = OptimScheduler(configer)
self.data_helper = DataHelper(configer, self)
self.evaluator = get_evaluator(configer, self)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.running_score = None
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
Log.info('Params Group Method: {}'.format(self.configer.get('optim', 'group_method')))
if self.configer.get('optim', 'group_method') == 'decay':
params_group = self.group_weight(self.seg_net)
else:
assert self.configer.get('optim', 'group_method') is None
params_group = self._get_parameters()
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(params_group)
self.train_loader = self.data_loader.get_trainloader()
self.val_loader = self.data_loader.get_valloader()
self.pixel_loss = self.loss_manager.get_seg_loss()
if is_distributed():
self.pixel_loss = self.module_runner.to_device(self.pixel_loss)
@staticmethod
def group_weight(module):
group_decay = []
group_no_decay = []
for m in module.modules():
if isinstance(m, nn.Linear):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, nn.modules.conv._ConvNd):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
else:
if hasattr(m, 'weight'):
group_no_decay.append(m.weight)
if hasattr(m, 'bias'):
group_no_decay.append(m.bias)
assert len(list(module.parameters())) == len(group_decay) + len(group_no_decay)
groups = [dict(params=group_decay), dict(params=group_no_decay, weight_decay=.0)]
return groups
def _get_parameters(self):
bb_lr = []
nbb_lr = []
params_dict = dict(self.seg_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
nbb_lr.append(value)
else:
bb_lr.append(value)
params = [{'params': bb_lr, 'lr': self.configer.get('lr', 'base_lr')},
{'params': nbb_lr, 'lr': self.configer.get('lr', 'base_lr') * self.configer.get('lr', 'nbb_mult')}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
self.pixel_loss.train()
start_time = time.time()
if "swa" in self.configer.get('lr', 'lr_policy'):
normal_max_iters = int(self.configer.get('solver', 'max_iters') * 0.75)
swa_step_max_iters = (self.configer.get('solver', 'max_iters') - normal_max_iters) // 5 + 1
if hasattr(self.train_loader.sampler, 'set_epoch'):
self.train_loader.sampler.set_epoch(self.configer.get('epoch'))
for i, data_dict in enumerate(self.train_loader):
if self.configer.get('lr', 'metric') == 'iters':
self.scheduler.step(self.configer.get('iters'))
else:
self.scheduler.step(self.configer.get('epoch'))
if self.configer.get('lr', 'is_warm'):
self.module_runner.warm_lr(
self.configer.get('iters'),
self.scheduler, self.optimizer, backbone_list=[0,]
)
(inputs, targets), batch_size = self.data_helper.prepare_data(data_dict)
self.data_time.update(time.time() - start_time)
foward_start_time = time.time()
outputs = self.seg_net(*inputs)
self.foward_time.update(time.time() - foward_start_time)
loss_start_time = time.time()
if is_distributed():
import torch.distributed as dist
def reduce_tensor(inp):
"""
Reduce the loss from all processes so that
process with rank 0 has the averaged results.
"""
world_size = get_world_size()
if world_size < 2:
return inp
with torch.no_grad():
reduced_inp = inp
dist.reduce(reduced_inp, dst=0)
return reduced_inp
loss = self.pixel_loss(outputs, targets)
backward_loss = loss
display_loss = reduce_tensor(backward_loss) / get_world_size()
else:
backward_loss = display_loss = self.pixel_loss(outputs, targets, gathered=self.configer.get('network', 'gathered'))
self.train_losses.update(display_loss.item(), batch_size)
self.loss_time.update(time.time() - loss_start_time)
backward_start_time = time.time()
self.optimizer.zero_grad()
backward_loss.backward()
self.optimizer.step()
self.backward_time.update(time.time() - backward_start_time)
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get('solver', 'display_iter') == 0 and \
(not is_distributed() or get_rank() == 0):
Log.info('Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Forward Time {foward_time.sum:.3f}s / {2}iters, ({foward_time.avg:.3f})\t'
'Backward Time {backward_time.sum:.3f}s / {2}iters, ({backward_time.avg:.3f})\t'
'Loss Time {loss_time.sum:.3f}s / {2}iters, ({loss_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.configer.get('epoch'), self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.module_runner.get_lr(self.optimizer), batch_time=self.batch_time,
foward_time=self.foward_time, backward_time=self.backward_time, loss_time=self.loss_time,
data_time=self.data_time, loss=self.train_losses))
self.batch_time.reset()
self.foward_time.reset()
self.backward_time.reset()
self.loss_time.reset()
self.data_time.reset()
self.train_losses.reset()
# save checkpoints for swa
if 'swa' in self.configer.get('lr', 'lr_policy') and \
self.configer.get('iters') > normal_max_iters and \
((self.configer.get('iters') - normal_max_iters) % swa_step_max_iters == 0 or \
self.configer.get('iters') == self.configer.get('solver', 'max_iters')):
self.optimizer.update_swa()
if self.configer.get('iters') == self.configer.get('solver', 'max_iters'):
break
# Check to val the current model.
# if self.configer.get('epoch') % self.configer.get('solver', 'test_interval') == 0:
if self.configer.get('iters') % self.configer.get('solver', 'test_interval') == 0:
self.__val()
self.configer.plus_one('epoch')
def __val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
self.pixel_loss.eval()
start_time = time.time()
replicas = self.evaluator.prepare_validaton()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
if j % 10 == 0:
Log.info('{} images processed\n'.format(j))
if self.configer.get('dataset') == 'lip':
(inputs, targets, inputs_rev, targets_rev), batch_size = self.data_helper.prepare_data(data_dict, want_reverse=True)
else:
(inputs, targets), batch_size = self.data_helper.prepare_data(data_dict)
with torch.no_grad():
if self.configer.get('dataset') == 'lip':
inputs = torch.cat([inputs[0], inputs_rev[0]], dim=0)
outputs = self.seg_net(inputs)
outputs_ = self.module_runner.gather(outputs)
if isinstance(outputs_, (list, tuple)):
outputs_ = outputs_[-1]
outputs = outputs_[0:int(outputs_.size(0)/2),:,:,:].clone()
outputs_rev = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),:,:,:].clone()
if outputs_rev.shape[1] == 20:
outputs_rev[:,14,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),15,:,:]
outputs_rev[:,15,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),14,:,:]
outputs_rev[:,16,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),17,:,:]
outputs_rev[:,17,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),16,:,:]
outputs_rev[:,18,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),19,:,:]
outputs_rev[:,19,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),18,:,:]
outputs_rev = torch.flip(outputs_rev, [3])
outputs = (outputs + outputs_rev) / 2.
self.evaluator.update_score(outputs, data_dict['meta'])
elif self.data_helper.conditions.diverse_size:
outputs = nn.parallel.parallel_apply(replicas[:len(inputs)], inputs)
for i in range(len(outputs)):
loss = self.pixel_loss(outputs[i], targets[i])
self.val_losses.update(loss.item(), 1)
outputs_i = outputs[i]
if isinstance(outputs_i, torch.Tensor):
outputs_i = [outputs_i]
self.evaluator.update_score(outputs_i, data_dict['meta'][i:i+1])
else:
outputs = self.seg_net(*inputs)
try:
loss = self.pixel_loss(
outputs, targets,
gathered=self.configer.get('network', 'gathered')
)
except AssertionError as e:
print(len(outputs), len(targets))
if not is_distributed():
outputs = self.module_runner.gather(outputs)
self.val_losses.update(loss.item(), batch_size)
self.evaluator.update_score(outputs, data_dict['meta'])
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.evaluator.update_performance()
self.configer.update(['val_loss'], self.val_losses.avg)
self.module_runner.save_net(self.seg_net, save_mode='performance')
self.module_runner.save_net(self.seg_net, save_mode='val_loss')
cudnn.benchmark = True
# Print the log info & reset the states.
if not is_distributed() or get_rank() == 0:
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.evaluator.print_scores()
self.batch_time.reset()
self.val_losses.reset()
self.evaluator.reset()
self.seg_net.train()
self.pixel_loss.train()
def train(self):
# cudnn.benchmark = True
# self.__val()
if self.configer.get('network', 'resume') is not None:
if self.configer.get('network', 'resume_val'):
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
return
elif self.configer.get('network', 'resume_train'):
self.__val(data_loader=self.data_loader.get_valloader(dataset='train'))
return
# return
if self.configer.get('network', 'resume') is not None and self.configer.get('network', 'resume_val'):
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
return
while self.configer.get('iters') < self.configer.get('solver', 'max_iters'):
self.__train()
# use swa to average the model
if 'swa' in self.configer.get('lr', 'lr_policy'):
self.optimizer.swap_swa_sgd()
self.optimizer.bn_update(self.train_loader, self.seg_net)
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
def summary(self):
from lib.utils.summary import get_model_summary
import torch.nn.functional as F
self.seg_net.eval()
for j, data_dict in enumerate(self.train_loader):
print(get_model_summary(self.seg_net, data_dict['img'][0:1]))
return
class MultiTaskClassifier(object):
"""
The class for the training phase of Image classification.
"""
def __init__(self, configer):
self.configer = configer
self.runner_state = dict(iters=0,
last_iters=0,
epoch=0,
last_epoch=0,
performance=0,
val_loss=0,
max_performance=0,
min_val_loss=0)
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = DictAverageMeter()
self.val_losses = DictAverageMeter()
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.running_score = RunningScore(configer)
self.cls_net = self.cls_model_manager.get_model()
self.solver_dict = self.configer.get(
self.configer.get('train', 'solver'))
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(),
self.solver_dict)
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.cls_net, self.optimizer = RunnerHelper.to_dtype(
self, self.cls_net, self.optimizer)
self.train_loaders = dict()
self.val_loaders = dict()
for source in range(self.configer.get('data', 'num_data_sources')):
self.train_loaders[source] = self.cls_data_loader.get_trainloader(
source=source)
self.val_loaders[source] = self.cls_data_loader.get_valloader(
source=source)
if self.configer.get('data', 'mixup'):
assert (self.configer.get('data', 'num_data_sources') == 2
), "mixup only support src0 and src1 load the same dataset"
self.loss = self.cls_model_manager.get_loss()
def _get_parameters(self):
lr_1 = []
lr_2 = []
params_dict = dict(self.cls_net.named_parameters())
for key, value in params_dict.items():
if value.requires_grad:
if 'backbone' in key:
if self.configer.get('network', 'bb_lr_scale') == 0.0:
value.requires_grad = False
else:
lr_1.append(value)
else:
lr_2.append(value)
params = [{
'params':
lr_1,
'lr':
self.solver_dict['lr']['base_lr'] *
self.configer.get('network', 'bb_lr_scale')
}, {
'params': lr_2,
'lr': self.solver_dict['lr']['base_lr']
}]
return params
def run(self):
"""
Train function of every epoch during train phase.
"""
if self.configer.get('network', 'resume_val'):
self.val()
self.cls_net.train()
train_loaders = dict()
for source in self.train_loaders:
train_loaders[source] = iter(self.train_loaders[source])
start_time = time.time()
# Adjust the learning rate after every epoch.
while self.runner_state['iters'] < self.solver_dict['max_iters']:
data_dict = dict()
for source in train_loaders:
try:
tmp_data_dict = next(train_loaders[source])
# Log.info('iter={}, source={}'.format(self.runner_state['iters'], source))
except StopIteration:
if source == 0 or source == '0':
self.runner_state['epoch'] += 1
# Log.info('Repeat: iter={}, source={}'.format(self.runner_state['iters'], source))
train_loaders[source] = iter(self.train_loaders[source])
tmp_data_dict = next(train_loaders[source])
for k, v in tmp_data_dict.items():
data_dict['src{}_{}'.format(source, k)] = v
if self.configer.get('data', 'multiscale') is not None:
scale_ratios = self.configer.get('data', 'multiscale')
scale_ratio = random.uniform(scale_ratios[0], scale_ratios[-1])
for key in data_dict:
if key.endswith('_img'):
data_dict[key] = F.interpolate(
data_dict[key],
scale_factor=[scale_ratio, scale_ratio],
mode='bilinear',
align_corners=True)
if self.configer.get('data', 'mixup'):
src0_resize = F.interpolate(data_dict['src0_img'],
scale_factor=[
random.uniform(0.4, 0.6),
random.uniform(0.4, 0.6)
],
mode='bilinear',
align_corners=True)
b, c, h, w = src0_resize.shape
pos = random.randint(0, 3)
if pos == 0: # top-left
data_dict['src1_img'][:, :, 0:h, 0:w] = src0_resize
elif pos == 1: # top-right
data_dict['src1_img'][:, :, 0:h, -w:] = src0_resize
elif pos == 2: # bottom-left
data_dict['src1_img'][:, :, -h:, 0:w] = src0_resize
else: # bottom-right
data_dict['src1_img'][:, :, -h:, -w:] = src0_resize
data_dict = RunnerHelper.to_device(self, data_dict)
Trainer.update(
self,
warm_list=(0, ),
warm_lr_list=(self.solver_dict['lr']['base_lr'] *
self.configer.get('network', 'bb_lr_scale'), ),
solver_dict=self.solver_dict)
self.data_time.update(time.time() - start_time)
# Forward pass.
out = self.cls_net(data_dict)
loss_dict, loss_weight_dict = self.loss(out)
# Compute the loss of the train batch & backward.
loss = loss_dict['loss']
self.train_losses.update(
{key: loss.item()
for key, loss in loss_dict.items()},
data_dict['src0_img'].size(0))
self.optimizer.zero_grad()
if self.configer.get('dtype') == 'fp16':
with amp.scale_loss(loss, self.optimizer) as scaled_losses:
scaled_losses.backward()
else:
loss.backward()
if self.configer.get('network', 'clip_grad'):
RunnerHelper.clip_grad(self.cls_net, 10.)
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.solver_dict[
'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 = {5}\tLoss = {3}\nLossWeight = {4}\n'.
format(self.runner_state['epoch'],
self.runner_state['iters'],
self.solver_dict['display_iter'],
self.train_losses.info(),
loss_weight_dict,
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.solver_dict['lr'][
'metric'] == 'iters' and self.runner_state[
'iters'] == self.solver_dict['max_iters']:
if self.configer.get('local_rank') == 0:
RunnerHelper.save_net(self, self.cls_net, postfix='final')
break
if self.runner_state['iters'] % self.solver_dict[
'save_iters'] == 0 and self.configer.get(
'local_rank') == 0:
RunnerHelper.save_net(self, self.cls_net)
# Check to val the current model.
if self.runner_state['iters'] % self.solver_dict[
'test_interval'] == 0:
self.val()
if self.configer.get('local_rank') == 0:
RunnerHelper.save_net(
self,
self.cls_net,
performance=self.runner_state['performance'])
self.val()
def val(self):
"""
Validation function during the train phase.
"""
self.cls_net.eval()
start_time = time.time()
val_loaders = dict()
val_to_end = dict()
all_to_end = False
for source in self.val_loaders:
val_loaders[source] = iter(self.val_loaders[source])
val_to_end[source] = False
with torch.no_grad():
while not all_to_end:
data_dict = dict()
for source in val_loaders:
try:
tmp_data_dict = next(val_loaders[source])
except StopIteration:
val_to_end[source] = True
val_loaders[source] = iter(self.val_loaders[source])
tmp_data_dict = next(val_loaders[source])
for k, v in tmp_data_dict.items():
data_dict['src{}_{}'.format(source, k)] = v
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.cls_net(data_dict)
loss_dict, loss_weight_dict = self.loss(out)
out_dict, label_dict, _ = RunnerHelper.gather(self, out)
# Compute the loss of the val batch.
self.running_score.update(out_dict, label_dict)
self.val_losses.update(
{
key: loss.mean().item()
for key, loss in loss_dict.items()
}, data_dict['src0_img'].size(0))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
# check whether scan over all data sources
all_to_end = True
for source in val_to_end:
if not val_to_end[source]:
all_to_end = False
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
Log.info('TestLoss = {}'.format(self.val_losses.info()))
Log.info('TestLossWeight = {}'.format(loss_weight_dict))
Log.info('Top1 ACC = {}'.format(self.running_score.get_top1_acc()))
Log.info('Top3 ACC = {}'.format(self.running_score.get_top3_acc()))
Log.info('Top5 ACC = {}'.format(self.running_score.get_top5_acc()))
top1_acc = yaml.load(self.running_score.get_top1_acc())
for key in top1_acc:
if 'src0_label0' in key:
self.runner_state['performance'] = top1_acc[key]
Log.info('Use acc of {} to compare performace'.format(key))
break
self.running_score.reset()
self.val_losses.reset()
self.batch_time.reset()
self.cls_net.train()
class ImageClassifier(object):
"""
The class for the training phase of Image classification.
"""
def __init__(self, configer):
self.configer = configer
self.runner_state = dict(iters=0,
last_iters=0,
epoch=0,
last_epoch=0,
performance=0,
val_loss=0,
max_performance=0,
min_val_loss=0)
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = DictAverageMeter()
self.val_losses = DictAverageMeter()
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.running_score = RunningScore(configer)
self.cls_net = self.cls_model_manager.get_model()
self.solver_dict = self.configer.get(
self.configer.get('train', 'solver'))
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(),
self.solver_dict)
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.cls_net, self.optimizer = RunnerHelper.to_dtype(
self, self.cls_net, self.optimizer)
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.loss = self.cls_model_manager.get_loss()
def _get_parameters(self):
lr_1 = []
lr_2 = []
params_dict = dict(self.cls_net.named_parameters())
for key, value in params_dict.items():
if value.requires_grad:
if 'backbone' in key:
if self.configer.get('network', 'bb_lr_scale') == 0.0:
value.requires_grad = False
else:
lr_1.append(value)
else:
lr_2.append(value)
params = [{
'params':
lr_1,
'lr':
self.solver_dict['lr']['base_lr'] *
self.configer.get('network', 'bb_lr_scale')
}, {
'params': lr_2,
'lr': self.solver_dict['lr']['base_lr']
}]
return params
def run(self):
if self.configer.get('network', 'resume_val'):
self.val()
while self.runner_state['iters'] < self.solver_dict['max_iters']:
self.train()
self.val()
def train(self):
"""
Train function of every epoch during train phase.
"""
self.cls_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
data_dict = {'src0_{}'.format(k): v for k, v in data_dict.items()}
Trainer.update(
self,
warm_list=(0, ),
warm_lr_list=(self.solver_dict['lr']['base_lr'] *
self.configer.get('network', 'bb_lr_scale'), ),
solver_dict=self.solver_dict)
self.data_time.update(time.time() - start_time)
data_dict = RunnerHelper.to_device(self, data_dict)
# Forward pass.
out = self.cls_net(data_dict)
loss_dict, _ = self.loss(out)
# Compute the loss of the train batch & backward.
loss = loss_dict['loss']
self.train_losses.update(
{key: loss.item()
for key, loss in loss_dict.items()},
data_dict['src0_img'].size(0))
self.optimizer.zero_grad()
loss.backward()
if self.configer.get('network', 'clip_grad'):
RunnerHelper.clip_grad(self.cls_net, 10.)
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.solver_dict[
'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.solver_dict['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.solver_dict['lr'][
'metric'] == 'iters' and self.runner_state[
'iters'] == self.solver_dict['max_iters']:
break
if self.runner_state['iters'] % self.solver_dict[
'save_iters'] == 0 and self.configer.get(
'local_rank') == 0:
RunnerHelper.save_net(self, self.cls_net)
# Check to val the current model.
# if self.runner_state['iters'] % self.solver_dict['test_interval'] == 0 \
# and not self.configer.get('distributed'):
# self.val()
if self.runner_state['iters'] % self.solver_dict[
'test_interval'] == 0:
self.val()
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):
data_dict = {
'src0_{}'.format(k): v
for k, v in data_dict.items()
}
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.cls_net(data_dict)
loss_dict = self.loss(out)
out_dict, label_dict, _ = RunnerHelper.gather(self, out)
self.running_score.update(out_dict, label_dict)
self.val_losses.update(
{key: loss.item()
for key, loss in loss_dict.items()},
data_dict['src0_img'].size(0))
# 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) # only local_rank=0 can save net
# 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 = {}'.format(self.val_losses.info()))
Log.info('Top1 ACC = {}'.format(self.running_score.get_top1_acc()))
Log.info('Top3 ACC = {}'.format(self.running_score.get_top3_acc()))
Log.info('Top5 ACC = {}'.format(self.running_score.get_top5_acc()))
self.batch_time.reset()
self.batch_time.reset()
self.val_losses.reset()
self.running_score.reset()
self.cls_net.train()
class Trainer(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.running_score = RunningScore(configer)
self.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.data_loader = DataLoader(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
Log.info('Params Group Method: {}'.format(
self.configer.get('optim', 'group_method')))
if self.configer.get('optim', 'group_method') == 'decay':
params_group = self.group_weight(self.seg_net)
else:
assert self.configer.get('optim', 'group_method') is None
params_group = self._get_parameters()
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
params_group)
self.train_loader = self.data_loader.get_trainloader()
self.val_loader = self.data_loader.get_valloader()
self.pixel_loss = self.loss_manager.get_seg_loss()
@staticmethod
def group_weight(module):
group_decay = []
group_no_decay = []
for m in module.modules():
if isinstance(m, nn.Linear):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, nn.modules.conv._ConvNd):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
else:
if hasattr(m, 'weight'):
group_no_decay.append(m.weight)
if hasattr(m, 'bias'):
group_no_decay.append(m.bias)
assert len(list(
module.parameters())) == len(group_decay) + len(group_no_decay)
groups = [
dict(params=group_decay),
dict(params=group_no_decay, weight_decay=.0)
]
return groups
def _get_parameters(self):
bb_lr = []
nbb_lr = []
params_dict = dict(self.seg_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
nbb_lr.append(value)
else:
bb_lr.append(value)
params = [{
'params': bb_lr,
'lr': self.configer.get('lr', 'base_lr')
}, {
'params':
nbb_lr,
'lr':
self.configer.get('lr', 'base_lr') *
self.configer.get('lr', 'nbb_mult')
}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
start_time = time.time()
for i, data_dict in enumerate(self.train_loader):
if self.configer.get('lr', 'metric') == 'iters':
self.scheduler.step(self.configer.get('iters'))
else:
self.scheduler.step(self.configer.get('epoch'))
if self.configer.get('lr', 'is_warm'):
self.module_runner.warm_lr(self.configer.get('iters'),
self.scheduler,
self.optimizer,
backbone_list=[
0,
])
inputs = data_dict['img']
targets = data_dict['labelmap']
self.data_time.update(time.time() - start_time)
# Change the data type.
# inputs, targets = self.module_runner.to_device(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'))
if self.configer.exists('train', 'loader') and self.configer.get(
'train', 'loader') == 'ade20k':
batch_size = self.configer.get(
'train', 'batch_size') * self.configer.get(
'train', 'batch_per_gpu')
self.train_losses.update(loss.item(), batch_size)
else:
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.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('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.configer.get('epoch'),
self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.module_runner.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('iters') == self.configer.get(
'solver', 'max_iters'):
break
# Check to val the current model.
if self.configer.get('iters') % self.configer.get(
'solver', 'test_interval') == 0:
self.__val()
self.configer.plus_one('epoch')
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 = self.module_runner.to_device(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 = self.module_runner.gather(outputs)
self.val_losses.update(loss.item(), inputs.size(0))
self._update_running_score(outputs[-1], data_dict['meta'])
# self.seg_running_score.update(pred.max(1)[1].cpu().numpy(), targets.cpu().numpy())
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.update(['performance'],
self.running_score.get_mean_iou())
self.configer.update(['val_loss'], self.val_losses.avg)
self.module_runner.save_net(self.seg_net, save_mode='performance')
self.module_runner.save_net(self.seg_net, save_mode='val_loss')
# 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.running_score.get_mean_iou()))
Log.info('Pixel ACC: {}\n'.format(self.running_score.get_pixel_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.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.running_score.update(labelmap[None], ori_target[None])
def train(self):
# cudnn.benchmark = True
if self.configer.get('network',
'resume') is not None and self.configer.get(
'network', 'resume_val'):
self.__val()
while self.configer.get('iters') < self.configer.get(
'solver', 'max_iters'):
self.__train()
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
self.__val(data_loader=self.data_loader.get_valloader(dataset='train'))