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+#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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+#
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+#Licensed under the Apache License, Version 2.0 (the "License");
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+#you may not use this file except in compliance with the License.
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+#You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+#Unless required by applicable law or agreed to in writing, software
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+#distributed under the License is distributed on an "AS IS" BASIS,
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+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+#See the License for the specific language governing permissions and
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+#limitations under the License.
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+
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+from __future__ import absolute_import
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+import paddlex
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+from collections import OrderedDict
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+from .deeplabv3p import DeepLabv3p
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+
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+
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+class HRNet(DeepLabv3p):
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+ """实现HRNet网络的构建并进行训练、评估、预测和模型导出。
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+
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+ Args:
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+ num_classes (int): 类别数。
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+ width (int): 高分辨率分支中特征层的通道数量。默认值为18。可选择取值为[18, 30, 32, 40, 44, 48, 60, 64]。
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+ use_bce_loss (bool): 是否使用bce loss作为网络的损失函数，只能用于两类分割。可与dice loss同时使用。默认False。
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+ use_dice_loss (bool): 是否使用dice loss作为网络的损失函数，只能用于两类分割，可与bce loss同时使用。
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+ 当use_bce_loss和use_dice_loss都为False时，使用交叉熵损失函数。默认False。
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+ class_weight (list/str): 交叉熵损失函数各类损失的权重。当class_weight为list的时候，长度应为
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+ num_classes。当class_weight为str时， weight.lower()应为'dynamic'，这时会根据每一轮各类像素的比重
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+ 自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1，
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+ 即平时使用的交叉熵损失函数。
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+ ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。默认255。
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+
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+ Raises:
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+ ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
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+ ValueError: class_weight为list, 但长度不等于num_class。
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+ class_weight为str, 但class_weight.low()不等于dynamic。
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+ TypeError: class_weight不为None时，其类型不是list或str。
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+ """
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+
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+ def __init__(self,
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+ num_classes=2,
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+ width=18,
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+ use_bce_loss=False,
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+ use_dice_loss=False,
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+ class_weight=None,
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+ ignore_index=255):
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+ self.init_params = locals()
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+ super(DeepLabv3p, self).__init__('segmenter')
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+ # dice_loss或bce_loss只适用两类分割中
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+ if num_classes > 2 and (use_bce_loss or use_dice_loss):
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+ raise ValueError(
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+ "dice loss and bce loss is only applicable to binary classfication"
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+ )
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+
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+ if class_weight is not None:
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+ if isinstance(class_weight, list):
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+ if len(class_weight) != num_classes:
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+ raise ValueError(
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+ "Length of class_weight should be equal to number of classes"
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+ )
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+ elif isinstance(class_weight, str):
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+ if class_weight.lower() != 'dynamic':
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+ raise ValueError(
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+ "if class_weight is string, must be dynamic!")
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+ else:
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+ raise TypeError(
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+ 'Expect class_weight is a list or string but receive {}'.
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+ format(type(class_weight)))
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+ self.num_classes = num_classes
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+ self.width = width
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+ self.use_bce_loss = use_bce_loss
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+ self.use_dice_loss = use_dice_loss
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+ self.class_weight = class_weight
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+ self.ignore_index = ignore_index
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+ self.labels = None
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+
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+ def build_net(self, mode='train'):
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+ model = paddlex.cv.nets.segmentation.HRNet(
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+ self.num_classes,
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+ width=self.width,
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+ mode=mode,
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+ use_bce_loss=self.use_bce_loss,
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+ use_dice_loss=self.use_dice_loss,
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+ class_weight=self.class_weight,
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+ ignore_index=self.ignore_index)
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+ inputs = model.generate_inputs()
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+ model_out = model.build_net(inputs)
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+ outputs = OrderedDict()
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+ if mode == 'train':
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+ self.optimizer.minimize(model_out)
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+ outputs['loss'] = model_out
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+ elif mode == 'eval':
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+ outputs['loss'] = model_out[0]
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+ outputs['pred'] = model_out[1]
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+ outputs['label'] = model_out[2]
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+ outputs['mask'] = model_out[3]
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+ else:
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+ outputs['pred'] = model_out[0]
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+ outputs['logit'] = model_out[1]
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+ return inputs, outputs
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+
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+ def train(self,
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+ num_epochs,
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+ train_dataset,
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+ train_batch_size=2,
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+ eval_dataset=None,
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+ save_interval_epochs=1,
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+ log_interval_steps=2,
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+ save_dir='output',
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+ pretrain_weights='IMAGENET',
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+ optimizer=None,
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+ learning_rate=0.01,
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+ lr_decay_power=0.9,
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+ use_vdl=False,
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+ sensitivities_file=None,
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+ eval_metric_loss=0.05,
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+ early_stop=False,
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+ early_stop_patience=5,
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+ resume_checkpoint=None):
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+ """训练。
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+
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+ Args:
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+ num_epochs (int): 训练迭代轮数。
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+ train_dataset (paddlex.datasets): 训练数据读取器。
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+ train_batch_size (int): 训练数据batch大小。同时作为验证数据batch大小。默认2。
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+ eval_dataset (paddlex.datasets): 评估数据读取器。
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+ save_interval_epochs (int): 模型保存间隔（单位：迭代轮数）。默认为1。
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+ log_interval_steps (int): 训练日志输出间隔（单位：迭代次数）。默认为2。
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+ save_dir (str): 模型保存路径。默认'output'。
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+ pretrain_weights (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'IMAGENET'，
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+ 则自动下载在IMAGENET图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认为'IMAGENET'。
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+ optimizer (paddle.fluid.optimizer): 优化器。当改参数为None时，使用默认的优化器：使用
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+ fluid.optimizer.Momentum优化方法，polynomial的学习率衰减策略。
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+ learning_rate (float): 默认优化器的初始学习率。默认0.01。
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+ lr_decay_power (float): 默认优化器学习率多项式衰减系数。默认0.9。
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+ use_vdl (bool): 是否使用VisualDL进行可视化。默认False。
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+ sensitivities_file (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，
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+ 则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。
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+ eval_metric_loss (float): 可容忍的精度损失。默认为0.05。
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+ early_stop (bool): 是否使用提前终止训练策略。默认值为False。
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+ early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
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+ 连续下降或持平，则终止训练。默认值为5。
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+ resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
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+
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+ Raises:
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+ ValueError: 模型从inference model进行加载。
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+ """
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+ return super(HRNet, self).train(
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+ num_epochs, train_dataset, train_batch_size, eval_dataset,
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+ save_interval_epochs, log_interval_steps, save_dir,
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+ pretrain_weights, optimizer, learning_rate, lr_decay_power,
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+ use_vdl, sensitivities_file, eval_metric_loss, early_stop,
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+ early_stop_patience, resume_checkpoint)
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