def build_model(main_prog=None,
start_prog=None,
phase=ModelPhase.TRAIN,
**kwargs):
if not ModelPhase.is_valid_phase(phase):
raise ValueError("ModelPhase {} is not valid!".format(phase))
if ModelPhase.is_train(phase):
width = cfg.TRAIN_CROP_SIZE[0]
height = cfg.TRAIN_CROP_SIZE[1]
else:
width = cfg.EVAL_CROP_SIZE[0]
height = cfg.EVAL_CROP_SIZE[1]
image_shape = [-1, cfg.DATASET.DATA_DIM, height, width]
grt_shape = [-1, 1, height, width]
class_num = cfg.DATASET.NUM_CLASSES
#with fluid.program_guard(main_prog, start_prog):
# with fluid.unique_name.guard():
# 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程
# 预测部署时只须对输入图像增加batch_size维度即可
if cfg.SLIM.KNOWLEDGE_DISTILL_IS_TEACHER:
image = main_prog.global_block()._clone_variable(
kwargs['image'], force_persistable=False)
label = main_prog.global_block()._clone_variable(
kwargs['label'], force_persistable=False)
mask = main_prog.global_block()._clone_variable(
kwargs['mask'], force_persistable=False)
else:
if ModelPhase.is_predict(phase):
origin_image = fluid.data(name='image',
shape=[-1, -1, -1, cfg.DATASET.DATA_DIM],
dtype='float32')
image, valid_shape, origin_shape = export_preprocess(origin_image)
else:
image = fluid.data(name='image',
shape=image_shape,
dtype='float32')
label = fluid.data(name='label', shape=grt_shape, dtype='int32')
mask = fluid.data(name='mask', shape=grt_shape, dtype='int32')
# use DataLoader.from_generator when doing traning and evaluation
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
data_loader = None
if not cfg.SLIM.KNOWLEDGE_DISTILL_IS_TEACHER:
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[image, label, mask],
capacity=cfg.DATALOADER.BUF_SIZE,
iterable=False,
use_double_buffer=True)
loss_type = cfg.SOLVER.LOSS
if not isinstance(loss_type, list):
loss_type = list(loss_type)
# dice_loss或bce_loss只适用两类分割中
if class_num > 2 and (("dice_loss" in loss_type) or
("bce_loss" in loss_type)):
raise Exception(
"dice loss and bce loss is only applicable to binary classfication"
)
# 在两类分割情况下，当loss函数选择dice_loss或bce_loss的时候，最后logit输出通道数设置为1
if ("dice_loss" in loss_type) or ("bce_loss" in loss_type):
class_num = 1
if "softmax_loss" in loss_type:
raise Exception(
"softmax loss can not combine with dice loss or bce loss")
logits = seg_model(image, class_num)
# 根据选择的loss函数计算相应的损失函数
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
loss_valid = False
avg_loss_list = []
valid_loss = []
if "softmax_loss" in loss_type:
weight = cfg.SOLVER.CROSS_ENTROPY_WEIGHT
avg_loss_list.append(
multi_softmax_with_loss(logits, label, mask, class_num,
weight))
loss_valid = True
valid_loss.append("softmax_loss")
if "dice_loss" in loss_type:
avg_loss_list.append(multi_dice_loss(logits, label, mask))
loss_valid = True
valid_loss.append("dice_loss")
if "bce_loss" in loss_type:
avg_loss_list.append(multi_bce_loss(logits, label, mask))
loss_valid = True
valid_loss.append("bce_loss")
if not loss_valid:
raise Exception(
"SOLVER.LOSS: {} is set wrong. it should "
"include one of (softmax_loss, bce_loss, dice_loss) at least"
" example: ['softmax_loss'], ['dice_loss'], ['bce_loss', 'dice_loss']"
.format(cfg.SOLVER.LOSS))
invalid_loss = [x for x in loss_type if x not in valid_loss]
if len(invalid_loss) > 0:
print(
"Warning: the loss {} you set is invalid. it will not be included in loss computed."
.format(invalid_loss))
avg_loss = 0
for i in range(0, len(avg_loss_list)):
avg_loss += avg_loss_list[i]
#get pred result in original size
if isinstance(logits, tuple):
logit = logits[0]
else:
logit = logits
if logit.shape[2:] != label.shape[2:]:
logit = fluid.layers.resize_bilinear(logit, label.shape[2:])
# return image input and logit output for inference graph prune
if ModelPhase.is_predict(phase):
# 两类分割中，使用dice_loss或bce_loss返回的logit为单通道，进行到两通道的变换
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
# 获取有效部分
logit = fluid.layers.slice(logit,
axes=[2, 3],
starts=[0, 0],
ends=valid_shape)
logit = fluid.layers.resize_bilinear(logit,
out_shape=origin_shape,
align_corners=False,
align_mode=0)
logit = fluid.layers.argmax(logit, axis=1)
return origin_image, logit
if class_num == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if ModelPhase.is_visual(phase):
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
return pred, logit
if ModelPhase.is_eval(phase):
return data_loader, avg_loss, pred, label, mask
if ModelPhase.is_train(phase):
decayed_lr = None
if not cfg.SLIM.KNOWLEDGE_DISTILL:
optimizer = solver.Solver(main_prog, start_prog)
decayed_lr = optimizer.optimise(avg_loss)
# optimizer = solver.Solver(main_prog, start_prog)
# decayed_lr = optimizer.optimise(avg_loss)
return data_loader, avg_loss, decayed_lr, pred, label, mask, image
def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
if not ModelPhase.is_valid_phase(phase):
raise ValueError("ModelPhase {} is not valid!".format(phase))
if ModelPhase.is_train(phase):
width = cfg.TRAIN_CROP_SIZE[0]
height = cfg.TRAIN_CROP_SIZE[1]
else:
width = cfg.EVAL_CROP_SIZE[0]
height = cfg.EVAL_CROP_SIZE[1]
channel_mul = 1
if ModelPhase.is_eval(phase) and cfg.TEST.TEST_AUG:
channel_mul = len(cfg.TEST.TEST_AUG_FLIP_OPS) + len(
cfg.TEST.TEST_AUG_ROTATE_OPS) + 1
image1_shape = [-1, cfg.DATASET.DATA_DIM * channel_mul, height, width]
grt_shape = [-1, 1, height, width]
class_num = cfg.DATASET.NUM_CLASSES
with fluid.program_guard(main_prog, start_prog):
with fluid.unique_name.guard():
# 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程
# 预测部署时只须对输入图像增加batch_size维度即可
image1 = fluid.data(name='image1',
shape=image1_shape,
dtype='float32')
image2 = None
if cfg.DATASET.INPUT_IMAGE_NUM == 2:
image2_shape = [
-1, cfg.DATASET.DATA_DIM * channel_mul, height, width
]
image2 = fluid.data(name='image2',
shape=image2_shape,
dtype='float32')
label = fluid.data(name='label', shape=grt_shape, dtype='int32')
mask = fluid.data(name='mask', shape=grt_shape, dtype='int32')
# use DataLoader when doing traning and evaluation
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
if cfg.DATASET.INPUT_IMAGE_NUM == 1:
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[image1, label, mask],
capacity=cfg.DATALOADER.BUF_SIZE,
iterable=False,
use_double_buffer=True)
else:
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[image1, image2, label, mask],
capacity=cfg.DATALOADER.BUF_SIZE,
iterable=False,
use_double_buffer=True)
loss_type = cfg.SOLVER.LOSS
if not isinstance(loss_type, list):
loss_type = list(loss_type)
# lovasz_hinge_loss或dice_loss或bce_loss只适用两类分割中
if class_num > 2 and (("lovasz_hinge_loss" in loss_type) or
("dice_loss" in loss_type) or
("bce_loss" in loss_type)):
raise Exception(
"lovasz hinge loss, dice loss and bce loss are only applicable to binary classfication."
)
# 在两类分割情况下，当loss函数选择lovasz_hinge_loss或dice_loss或bce_loss的时候，最后logit输出通道数设置为1
if ("dice_loss" in loss_type) or ("bce_loss" in loss_type) or (
"lovasz_hinge_loss" in loss_type):
class_num = 1
if ("softmax_loss" in loss_type) or ("lovasz_softmax_loss"
in loss_type):
raise Exception(
"softmax loss or lovasz softmax loss can not combine with bce loss or dice loss or lovasz hinge loss."
)
cfg.PHASE = phase
if cfg.DATASET.INPUT_IMAGE_NUM == 1:
if cfg.TEST.TEST_AUG:
image_split = fluid.layers.split(
image1, channel_mul,
dim=1) # batch_size, 3 x channel_mul, h, w
image1 = fluid.layers.concat(
image_split,
axis=0) # channel_mul * batch_size, 3, h, w
logits = seg_model(image1, class_num)
logits_split = fluid.layers.split(logits,
channel_mul,
dim=0)
logits = test_aug_inv(logits_split)
else:
logits = seg_model(image1, class_num)
else:
if cfg.TEST.TEST_AUG:
image_split = fluid.layers.split(
image1, channel_mul,
dim=1) # batch_size, 3 x channel_mul, h, w
image1 = fluid.layers.concat(
image_split,
axis=0) # channel_mul * batch_size, 3, h, w
logits1 = seg_model(image1, class_num)
logits1_split = fluid.layers.split(logits1,
channel_mul,
dim=0)
logits1 = test_aug_inv(logits1_split)
image_split = fluid.layers.split(
image2, channel_mul,
dim=1) # batch_size, 3 x channel_mul, h, w
image2 = fluid.layers.concat(
image_split,
axis=0) # channel_mul * batch_size, 3, h, w
logits2 = seg_model(image2, class_num)
logits2_split = fluid.layers.split(logits2,
channel_mul,
dim=0)
logits2 = test_aug_inv(logits2_split)
else:
logits1 = seg_model(image1, class_num)
logits2 = seg_model(image2, class_num)
if ModelPhase.is_visual(phase) and cfg.VIS.SEG_FOR_CD:
logits = fluid.layers.concat([logits1, logits2], axis=0)
else:
logits = fluid.layers.concat([logits1, logits2], axis=1)
logits = fuse(logits, class_num)
# 根据选择的loss函数计算相应的损失函数
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
loss_valid = False
avg_loss_list = []
valid_loss = []
if "softmax_loss" in loss_type:
weight = cfg.SOLVER.CROSS_ENTROPY_WEIGHT
avg_loss_list.append(
multi_softmax_with_loss(logits, label, mask, class_num,
weight))
loss_valid = True
valid_loss.append("softmax_loss")
if "dice_loss" in loss_type:
avg_loss_list.append(multi_dice_loss(logits, label, mask))
loss_valid = True
valid_loss.append("dice_loss")
if "bce_loss" in loss_type:
avg_loss_list.append(multi_bce_loss(logits, label, mask))
loss_valid = True
valid_loss.append("bce_loss")
if "lovasz_hinge_loss" in loss_type:
avg_loss_list.append(
lovasz_hinge(logits, label, ignore=mask))
loss_valid = True
valid_loss.append("lovasz_hinge_loss")
if "lovasz_softmax_loss" in loss_type:
probas = fluid.layers.softmax(logits, axis=1)
avg_loss_list.append(
lovasz_softmax(probas, label, ignore=mask))
loss_valid = True
valid_loss.append("lovasz_softmax_loss")
if not loss_valid:
raise Exception(
"SOLVER.LOSS: {} is set wrong. it should "
"include one of (softmax_loss, bce_loss, dice_loss, lovasz_hinge_loss, lovasz_softmax_loss) at least"
" example: ['softmax_loss'], ['dice_loss'], ['bce_loss', 'dice_loss'], ['lovasz_hinge_loss','bce_loss'], ['lovasz_softmax_loss','softmax_loss']"
.format(cfg.SOLVER.LOSS))
invalid_loss = [x for x in loss_type if x not in valid_loss]
if len(invalid_loss) > 0:
print(
"Warning: the loss {} you set is invalid. it will not be included in loss computed."
.format(invalid_loss))
avg_loss = 0
for i in range(0, len(avg_loss_list)):
loss_name = valid_loss[i].upper()
loss_weight = eval('cfg.SOLVER.LOSS_WEIGHT.' + loss_name)
avg_loss += loss_weight * avg_loss_list[i]
#get pred result in original size
if isinstance(logits, tuple):
logit = logits[0]
else:
logit = logits
if logit.shape[2:] != label.shape[2:]:
logit = fluid.layers.resize_bilinear(logit, label.shape[2:])
# return image input and logit output for inference graph prune
if ModelPhase.is_predict(phase):
# 两类分割中，使用lovasz_hinge_loss或dice_loss或bce_loss返回的logit为单通道，进行到两通道的变换
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
if cfg.DATASET.INPUT_IMAGE_NUM == 1:
return image1, logit
else:
return image1, image2, logit
if class_num == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if ModelPhase.is_visual(phase):
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
pred, _ = fluid.layers.split(logit, 2, dim=1)
return pred, logit
if ModelPhase.is_eval(phase):
if cfg.VIS.VISINEVAL:
logit = softmax(logit)
pred, _ = fluid.layers.split(logit, 2, dim=1)
return data_loader, avg_loss, pred, label, mask
if ModelPhase.is_train(phase):
optimizer = solver.Solver(main_prog, start_prog)
decayed_lr = optimizer.optimise(avg_loss)
return data_loader, avg_loss, decayed_lr, pred, label, mask
def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
if not ModelPhase.is_valid_phase(phase):
raise ValueError("ModelPhase {} is not valid!".format(phase))
if ModelPhase.is_train(phase):
width = cfg.TRAIN_CROP_SIZE[0]
height = cfg.TRAIN_CROP_SIZE[1]
else:
width = cfg.EVAL_CROP_SIZE[0]
height = cfg.EVAL_CROP_SIZE[1]
image_shape = [cfg.DATASET.DATA_DIM, height, width]
grt_shape = [1, height, width]
class_num = cfg.DATASET.NUM_CLASSES
with fluid.program_guard(main_prog, start_prog):
with fluid.unique_name.guard():
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(
name='label', shape=grt_shape, dtype='int32')
mask = fluid.layers.data(
name='mask', shape=grt_shape, dtype='int32')
# use PyReader when doing traning and evaluation
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
py_reader = fluid.io.PyReader(
feed_list=[image, label, mask],
capacity=cfg.DATALOADER.BUF_SIZE,
iterable=False,
use_double_buffer=True)
model_name = map_model_name(cfg.MODEL.MODEL_NAME)
model_func = get_func("modeling." + model_name)
loss_type = cfg.SOLVER.LOSS
if not isinstance(loss_type, list):
loss_type = list(loss_type)
if class_num > 2 and (("dice_loss" in loss_type) or ("bce_loss" in loss_type)):
raise Exception("dice loss and bce loss is only applicable to binary classfication")
if ("dice_loss" in loss_type) or ("bce_loss" in loss_type):
class_num = 1
if "softmax_loss" in loss_type:
raise Exception("softmax loss can not combine with dice loss or bce loss")
logits = model_func(image, class_num)
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
loss_valid = False
avg_loss_list = []
valid_loss = []
if "softmax_loss" in loss_type:
avg_loss_list.append(multi_softmax_with_loss(logits,
label, mask,class_num))
loss_valid = True
valid_loss.append("softmax_loss")
if "dice_loss" in loss_type:
avg_loss_list.append(multi_dice_loss(logits, label, mask))
loss_valid = True
valid_loss.append("dice_loss")
if "bce_loss" in loss_type:
avg_loss_list.append(multi_bce_loss(logits, label, mask))
loss_valid = True
valid_loss.append("bce_loss")
if not loss_valid:
raise Exception("SOLVER.LOSS: {} is set wrong. it should "
"include one of (softmax_loss, bce_loss, dice_loss) at least"
" example: ['softmax_loss'], ['dice_loss'], ['bce_loss', 'dice_loss']".format(cfg.SOLVER.LOSS))
invalid_loss = [x for x in loss_type if x not in valid_loss]
if len(invalid_loss) > 0:
print("Warning: the loss {} you set is invalid. it will not be included in loss computed.".format(invalid_loss))
avg_loss = 0
for i in range(0, len(avg_loss_list)):
avg_loss += avg_loss_list[i]
#get pred result in original size
if isinstance(logits, tuple):
logit = logits[0]
else:
logit = logits
if logit.shape[2:] != label.shape[2:]:
logit = fluid.layers.resize_bilinear(logit, label.shape[2:])
# return image input and logit output for inference graph prune
if ModelPhase.is_predict(phase):
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
return image, logit
if class_num == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if ModelPhase.is_visual(phase):
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
return pred, logit
if ModelPhase.is_eval(phase):
return py_reader, avg_loss, pred, label, mask
if ModelPhase.is_train(phase):
optimizer = solver.Solver(main_prog, start_prog)
decayed_lr = optimizer.optimise(avg_loss)
return py_reader, avg_loss, decayed_lr, pred, label, mask
def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
if not ModelPhase.is_valid_phase(phase):
raise ValueError("ModelPhase {} is not valid!".format(phase))
if ModelPhase.is_train(phase):
width = cfg.TRAIN_CROP_SIZE[0]
height = cfg.TRAIN_CROP_SIZE[1]
else:
width = cfg.EVAL_CROP_SIZE[0]
height = cfg.EVAL_CROP_SIZE[1]
image_shape = [cfg.DATASET.DATA_DIM, height, width]
grt_shape = [1, height, width]
class_num = cfg.DATASET.NUM_CLASSES
with fluid.program_guard(main_prog, start_prog):
with fluid.unique_name.guard():
# 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程
# 预测部署时只须对输入图像增加batch_size维度即可
if ModelPhase.is_predict(phase):
origin_image = fluid.layers.data(name='image',
shape=[ -1, 1, 1, cfg.DATASET.DATA_DIM],
dtype='float32',
append_batch_size=False)
image = fluid.layers.transpose(origin_image, [0, 3, 1, 2])
origin_shape = fluid.layers.shape(image)[-2:]
mean = np.array(cfg.MEAN).reshape(1, len(cfg.MEAN), 1, 1)
mean = fluid.layers.assign(mean.astype('float32'))
std = np.array(cfg.STD).reshape(1, len(cfg.STD), 1, 1)
std = fluid.layers.assign(std.astype('float32'))
image = (image/255 - mean)/std
image = fluid.layers.resize_bilinear(image,
out_shape=[height, width], align_corners=False, align_mode=0)
else:
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(
name='label', shape=grt_shape, dtype='int32')
mask = fluid.layers.data(
name='mask', shape=grt_shape, dtype='int32')
# use PyReader when doing traning and evaluation
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
py_reader = fluid.io.PyReader(
feed_list=[image, label, mask],
capacity=cfg.DATALOADER.BUF_SIZE,
iterable=False,
use_double_buffer=True)
model_name = map_model_name(cfg.MODEL.MODEL_NAME)
model_func = get_func("modeling." + model_name)
loss_type = cfg.SOLVER.LOSS
if not isinstance(loss_type, list):
loss_type = list(loss_type)
# dice_loss或bce_loss只适用两类分割中
if class_num > 2 and (("dice_loss" in loss_type) or ("bce_loss" in loss_type)):
raise Exception("dice loss and bce loss is only applicable to binary classfication")
# 在两类分割情况下，当loss函数选择dice_loss或bce_loss的时候，最后logit输出通道数设置为1
if ("dice_loss" in loss_type) or ("bce_loss" in loss_type):
class_num = 1
if "softmax_loss" in loss_type:
raise Exception("softmax loss can not combine with dice loss or bce loss")
logits = model_func(image, class_num)
# 根据选择的loss函数计算相应的损失函数
if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase):
loss_valid = False
avg_loss_list = []
valid_loss = []
if "softmax_loss" in loss_type:
avg_loss_list.append(multi_softmax_with_loss(logits,
label, mask,class_num))
loss_valid = True
valid_loss.append("softmax_loss")
if "dice_loss" in loss_type:
avg_loss_list.append(multi_dice_loss(logits, label, mask))
loss_valid = True
valid_loss.append("dice_loss")
if "bce_loss" in loss_type:
avg_loss_list.append(multi_bce_loss(logits, label, mask))
loss_valid = True
valid_loss.append("bce_loss")
if not loss_valid:
raise Exception("SOLVER.LOSS: {} is set wrong. it should "
"include one of (softmax_loss, bce_loss, dice_loss) at least"
" example: ['softmax_loss'], ['dice_loss'], ['bce_loss', 'dice_loss']".format(cfg.SOLVER.LOSS))
invalid_loss = [x for x in loss_type if x not in valid_loss]
if len(invalid_loss) > 0:
print("Warning: the loss {} you set is invalid. it will not be included in loss computed.".format(invalid_loss))
avg_loss = 0
for i in range(0, len(avg_loss_list)):
avg_loss += avg_loss_list[i]
#get pred result in original size
if isinstance(logits, tuple):
logit = logits[0]
else:
logit = logits
if logit.shape[2:] != label.shape[2:]:
logit = fluid.layers.resize_bilinear(logit, label.shape[2:])
# return image input and logit output for inference graph prune
if ModelPhase.is_predict(phase):
# 两类分割中，使用dice_loss或bce_loss返回的logit为单通道，进行到两通道的变换
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
logit = fluid.layers.resize_bilinear(logit, out_shape=origin_shape, align_corners=False, align_mode=0)
logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
logit = fluid.layers.argmax(logit, axis=3)
return origin_image, logit
if class_num == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if ModelPhase.is_visual(phase):
if class_num == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = softmax(logit)
return pred, logit
if ModelPhase.is_eval(phase):
return py_reader, avg_loss, pred, label, mask
if ModelPhase.is_train(phase):
optimizer = solver.Solver(main_prog, start_prog)
decayed_lr = optimizer.optimise(avg_loss)
return py_reader, avg_loss, decayed_lr, pred, label, mask