add fastscnn tutorial

new_tutorials/train/segmentation/fast_scnn.py

+import os
+# 选择使用0号卡
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import paddlex as pdx
+from paddlex.seg import transforms
+
+# 下载和解压视盘分割数据集
+optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
+pdx.utils.download_and_decompress(optic_dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
+train_transforms = transforms.ComposedSegTransforms(
+    mode='train', train_crop_size=[769, 769])
+eval_transforms = transforms.ComposedSegTransforms(mode='eval')
+
+# 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
+train_dataset = pdx.datasets.SegDataset(
+    data_dir='optic_disc_seg',
+    file_list='optic_disc_seg/train_list.txt',
+    label_list='optic_disc_seg/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+eval_dataset = pdx.datasets.SegDataset(
+    data_dir='optic_disc_seg',
+    file_list='optic_disc_seg/val_list.txt',
+    label_list='optic_disc_seg/labels.txt',
+    transforms=eval_transforms)
+
+# 初始化模型，并进行训练
+# 可使用VisualDL查看训练指标
+# VisualDL启动方式: visualdl --logdir output/unet/vdl_log --port 8001
+# 浏览器打开 https://0.0.0.0:8001即可
+# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#hrnet
+num_classes = len(train_dataset.labels)
+model = pdx.seg.FastSCNN(num_classes=num_classes)
+model.train(
+    num_epochs=20,
+    train_dataset=train_dataset,
+    train_batch_size=4,
+    eval_dataset=eval_dataset,
+    learning_rate=0.01,
+    save_dir='output/fastscnn',
+    use_vdl=True)

paddlex/cv/models/base.py

 # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -194,9 +194,8 @@ class BaseAPI:
                 if os.path.exists(pretrain_dir):
                     os.remove(pretrain_dir)
                 os.makedirs(pretrain_dir)
-            if pretrain_weights is not None and \
-                not os.path.isdir(pretrain_weights) \
-                and not os.path.isfile(pretrain_weights):
+            if pretrain_weights is not None and not os.path.exists(
+                    pretrain_weights):
                 if self.model_type == 'classifier':
                     if pretrain_weights not in ['IMAGENET']:
                         logging.warning(
@@ -245,8 +244,8 @@ class BaseAPI:
             logging.info(
                 "Load pretrain weights from {}.".format(pretrain_weights),
                 use_color=True)
-            paddlex.utils.utils.load_pretrain_weights(self.exe, self.train_prog,
-                                                      pretrain_weights, fuse_bn)
+            paddlex.utils.utils.load_pretrain_weights(
+                self.exe, self.train_prog, pretrain_weights, fuse_bn)
         # 进行裁剪
         if sensitivities_file is not None:
             import paddleslim
@@ -350,7 +349,9 @@ class BaseAPI:
         logging.info("Model saved in {}.".format(save_dir))
 
     def export_inference_model(self, save_dir):
-        test_input_names = [var.name for var in list(self.test_inputs.values())]
+        test_input_names = [
+            var.name for var in list(self.test_inputs.values())
+        ]
         test_outputs = list(self.test_outputs.values())
         if self.__class__.__name__ == 'MaskRCNN':
             from paddlex.utils.save import save_mask_inference_model
@@ -387,7 +388,8 @@ class BaseAPI:
 
         # 模型保存成功的标志
         open(osp.join(save_dir, '.success'), 'w').close()
-        logging.info("Model for inference deploy saved in {}.".format(save_dir))
+        logging.info("Model for inference deploy saved in {}.".format(
+            save_dir))
 
     def train_loop(self,
                    num_epochs,
@@ -511,11 +513,13 @@ class BaseAPI:
                         eta = ((num_epochs - i) * total_num_steps - step - 1
                                ) * avg_step_time
                     if time_eval_one_epoch is not None:
-                        eval_eta = (total_eval_times - i // save_interval_epochs
-                                    ) * time_eval_one_epoch
+                        eval_eta = (
+                            total_eval_times - i // save_interval_epochs
+                        ) * time_eval_one_epoch
                     else:
-                        eval_eta = (total_eval_times - i // save_interval_epochs
-                                    ) * total_num_steps_eval * avg_step_time
+                        eval_eta = (
+                            total_eval_times - i // save_interval_epochs
+                        ) * total_num_steps_eval * avg_step_time
                     eta_str = seconds_to_hms(eta + eval_eta)
 
                     logging.info(

paddlex/cv/models/fast_scnn.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+import paddle.fluid as fluid
+import paddlex
+from collections import OrderedDict
+from .deeplabv3p import DeepLabv3p
+
+
+class FastSCNN(DeepLabv3p):
+    """实现Fast SCNN网络的构建并进行训练、评估、预测和模型导出。
+
+    Args:
+        num_classes (int): 类别数。
+        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。
+        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。
+            当use_bce_loss和use_dice_loss都为False时，使用交叉熵损失函数。默认False。
+        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
+            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
+            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，
+            即平时使用的交叉熵损失函数。
+        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。
+        multi_loss_weight (list): 多分支上的loss权重。默认计算一个分支上的loss，即默认值为[1.0]。
+            也支持计算两个分支或三个分支上的loss，权重按[fusion_branch_weight, higher_branch_weight, lower_branch_weight]排列，
+            fusion_branch_weight为空间细节分支和全局上下文分支融合后的分支上的loss权重，higher_branch_weight为空间细节分支上的loss权重，
+            lower_branch_weight为全局上下文分支上的loss权重，若higher_branch_weight和lower_branch_weight未设置则不会计算这两个分支上的loss。
+
+    Raises:
+        ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
+        ValueError: class_weight为list, 但长度不等于num_class。
+            class_weight为str, 但class_weight.low()不等于dynamic。
+        TypeError: class_weight不为None时，其类型不是list或str。
+        TypeError: multi_loss_weight不为list。
+        ValueError: multi_loss_weight为list但长度小于0或者大于3。
+    """
+
+    def __init__(self,
+                 num_classes=2,
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255,
+                 multi_loss_weight=[1.0]):
+        self.init_params = locals()
+        super(DeepLabv3p, self).__init__('segmenter')
+        # dice_loss或bce_loss只适用两类分割中
+        if num_classes > 2 and (use_bce_loss or use_dice_loss):
+            raise ValueError(
+                "dice loss and bce loss is only applicable to binary classfication"
+            )
+
+        if class_weight is not None:
+            if isinstance(class_weight, list):
+                if len(class_weight) != num_classes:
+                    raise ValueError(
+                        "Length of class_weight should be equal to number of classes"
+                    )
+            elif isinstance(class_weight, str):
+                if class_weight.lower() != 'dynamic':
+                    raise ValueError(
+                        "if class_weight is string, must be dynamic!")
+            else:
+                raise TypeError(
+                    'Expect class_weight is a list or string but receive {}'.
+                    format(type(class_weight)))
+
+        if not isinstance(multi_loss_weight, list):
+            raise TypeError(
+                'Expect multi_loss_weight is a list but receive {}'.format(
+                    type(multi_loss_weight)))
+        if len(multi_loss_weight) > 3 or len(multi_loss_weight) < 0:
+            raise ValueError(
+                "Length of multi_loss_weight should be lower than or equal to 3 but greater than 0."
+            )
+
+        self.num_classes = num_classes
+        self.use_bce_loss = use_bce_loss
+        self.use_dice_loss = use_dice_loss
+        self.class_weight = class_weight
+        self.multi_loss_weight = multi_loss_weight
+        self.ignore_index = ignore_index
+        self.labels = None
+        self.fixed_input_shape = None
+
+    def build_net(self, mode='train'):
+        model = paddlex.cv.nets.segmentation.FastSCNN(
+            self.num_classes,
+            mode=mode,
+            use_bce_loss=self.use_bce_loss,
+            use_dice_loss=self.use_dice_loss,
+            class_weight=self.class_weight,
+            ignore_index=self.ignore_index,
+            multi_loss_weight=self.multi_loss_weight,
+            fixed_input_shape=self.fixed_input_shape)
+        inputs = model.generate_inputs()
+        model_out = model.build_net(inputs)
+        outputs = OrderedDict()
+        if mode == 'train':
+            self.optimizer.minimize(model_out)
+            outputs['loss'] = model_out
+        else:
+            outputs['pred'] = model_out[0]
+            outputs['logit'] = model_out[1]
+        return inputs, outputs
+
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=2,
+              eval_dataset=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='CITYSCAPES',
+              optimizer=None,
+              learning_rate=0.01,
+              lr_decay_power=0.9,
+              use_vdl=False,
+              sensitivities_file=None,
+              eval_metric_loss=0.05,
+              early_stop=False,
+              early_stop_patience=5,
+              resume_checkpoint=None):
+        """训练。
+
+        Args:
+            num_epochs (int): 训练迭代轮数。
+            train_dataset (paddlex.datasets): 训练数据读取器。
+            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。
+            eval_dataset (paddlex.datasets): 评估数据读取器。
+            save_interval_epochs (int): 模型保存间隔（单位：迭代轮数）。默认为1。
+            log_interval_steps (int): 训练日志输出间隔（单位：迭代次数）。默认为2。
+            save_dir (str): 模型保存路径。默认'output'。
+            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'CITYSCAPES'
+                则自动下载在CITYSCAPES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'CITYSCAPES'。
+            optimizer (paddle.fluid.optimizer): 优化器。当改参数为None时，使用默认的优化器：使用
+                fluid.optimizer.Momentum优化方法，polynomial的学习率衰减策略。
+            learning_rate (float): 默认优化器的初始学习率。默认0.01。
+            lr_decay_power (float): 默认优化器学习率多项式衰减系数。默认0.9。
+            use_vdl (bool): 是否使用VisualDL进行可视化。默认False。
+            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'，
+                则自动下载在Cityscapes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。
+            eval_metric_loss (float): 可容忍的精度损失。默认为0.05。
+            early_stop (bool): 是否使用提前终止训练策略。默认值为False。
+            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
+                连续下降或持平，则终止训练。默认值为5。
+            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。
+
+        Raises:
+            ValueError: 模型从inference model进行加载。
+        """
+        return super(FastSCNN, self).train(
+            num_epochs, train_dataset, train_batch_size, eval_dataset,
+            save_interval_epochs, log_interval_steps, save_dir,
+            pretrain_weights, optimizer, learning_rate, lr_decay_power,
+            use_vdl, sensitivities_file, eval_metric_loss, early_stop,
+            early_stop_patience, resume_checkpoint)

paddlex/cv/nets/segmentation/fast_scnn.py

+# coding: utf8
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import OrderedDict
+
+import paddle.fluid as fluid
+from .model_utils.libs import scope
+from .model_utils.libs import bn, bn_relu, relu, conv_bn_layer
+from .model_utils.libs import conv, avg_pool
+from .model_utils.libs import separate_conv
+from .model_utils.libs import sigmoid_to_softmax
+from .model_utils.loss import softmax_with_loss
+from .model_utils.loss import dice_loss
+from .model_utils.loss import bce_loss
+
+
+class FastSCNN(object):
+    def __init__(self,
+                 num_classes,
+                 mode='train',
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 multi_loss_weight=[1.0],
+                 ignore_index=255,
+                 fixed_input_shape=None):
+        # dice_loss或bce_loss只适用两类分割中
+        if num_classes > 2 and (use_bce_loss or use_dice_loss):
+            raise ValueError(
+                "dice loss and bce loss is only applicable to binary classfication"
+            )
+
+        if class_weight is not None:
+            if isinstance(class_weight, list):
+                if len(class_weight) != num_classes:
+                    raise ValueError(
+                        "Length of class_weight should be equal to number of classes"
+                    )
+            elif isinstance(class_weight, str):
+                if class_weight.lower() != 'dynamic':
+                    raise ValueError(
+                        "if class_weight is string, must be dynamic!")
+            else:
+                raise TypeError(
+                    'Expect class_weight is a list or string but receive {}'.
+                    format(type(class_weight)))
+
+        self.num_classes = num_classes
+        self.mode = mode
+        self.use_bce_loss = use_bce_loss
+        self.use_dice_loss = use_dice_loss
+        self.class_weight = class_weight
+        self.ignore_index = ignore_index
+        self.multi_loss_weight = multi_loss_weight
+        self.fixed_input_shape = fixed_input_shape
+
+    def build_net(self, inputs):
+        if self.use_dice_loss or self.use_bce_loss:
+            self.num_classes = 1
+        image = inputs['image']
+        size = fluid.layers.shape(image)[2:]
+        with scope('learning_to_downsample'):
+            higher_res_features = self._learning_to_downsample(image, 32, 48,
+                                                               64)
+        with scope('global_feature_extractor'):
+            lower_res_feature = self._global_feature_extractor(
+                higher_res_features, 64, [64, 96, 128], 128, 6, [3, 3, 3])
+        with scope('feature_fusion'):
+            x = self._feature_fusion(higher_res_features, lower_res_feature,
+                                     64, 128, 128)
+        with scope('classifier'):
+            logit = self._classifier(x, 128)
+            logit = fluid.layers.resize_bilinear(logit, size, align_mode=0)
+
+        if len(self.multi_loss_weight) == 3:
+            with scope('aux_layer_higher'):
+                higher_logit = self._aux_layer(higher_res_features,
+                                               self.num_classes)
+                higher_logit = fluid.layers.resize_bilinear(
+                    higher_logit, size, align_mode=0)
+            with scope('aux_layer_lower'):
+                lower_logit = self._aux_layer(lower_res_feature,
+                                              self.num_classes)
+                lower_logit = fluid.layers.resize_bilinear(
+                    lower_logit, size, align_mode=0)
+            logit = (logit, higher_logit, lower_logit)
+        elif len(self.multi_loss_weight) == 2:
+            with scope('aux_layer_higher'):
+                higher_logit = self._aux_layer(higher_res_features,
+                                               self.num_classes)
+                higher_logit = fluid.layers.resize_bilinear(
+                    higher_logit, size, align_mode=0)
+            logit = (logit, higher_logit)
+        else:
+            logit = (logit, )
+
+        if self.num_classes == 1:
+            out = sigmoid_to_softmax(logit[0])
+            out = fluid.layers.transpose(out, [0, 2, 3, 1])
+        else:
+            out = fluid.layers.transpose(logit[0], [0, 2, 3, 1])
+
+        pred = fluid.layers.argmax(out, axis=3)
+        pred = fluid.layers.unsqueeze(pred, axes=[3])
+
+        if self.mode == 'train':
+            label = inputs['label']
+            return self._get_loss(logit, label)
+        elif self.mode == 'eval':
+            label = inputs['label']
+            loss = self._get_loss(logit, label)
+            return loss, pred, label, mask
+        else:
+            if self.num_classes == 1:
+                logit = sigmoid_to_softmax(logit[0])
+            else:
+                logit = fluid.layers.softmax(logit[0], axis=1)
+            return pred, logit
+
+    def generate_inputs(self):
+        inputs = OrderedDict()
+        if self.fixed_input_shape is not None:
+            input_shape = [
+                None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
+            ]
+            inputs['image'] = fluid.data(
+                dtype='float32', shape=input_shape, name='image')
+        else:
+            inputs['image'] = fluid.data(
+                dtype='float32', shape=[None, 3, None, None], name='image')
+        if self.mode == 'train':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        elif self.mode == 'eval':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        return inputs
+
+    def _get_loss(self, logits, label):
+        avg_loss = 0
+        if not (self.use_dice_loss or self.use_bce_loss):
+            for i, logit in enumerate(logits):
+                logit_mask = (
+                    label.astype('int32') != self.ignore_index).astype('int32')
+                loss = softmax_with_loss(
+                    logit,
+                    label,
+                    logit_mask,
+                    num_classes=self.num_classes,
+                    weight=self.class_weight,
+                    ignore_index=self.ignore_index)
+                avg_loss += self.multi_loss_weight[i] * loss
+        else:
+            if self.use_dice_loss:
+                for i, logit in enumerate(logits):
+                    logit_mask = (label.astype('int32') != self.ignore_index
+                                  ).astype('int32')
+                    loss = dice_loss(logit, label, logit_mask)
+                    avg_loss += self.multi_loss_weight[i] * loss
+            if self.use_bce_loss:
+                for i, logit in enumerate(logits):
+                    #logit_label = fluid.layers.resize_nearest(label, logit_shape[2:])
+                    logit_mask = (label.astype('int32') != self.ignore_index
+                                  ).astype('int32')
+                    loss = bce_loss(
+                        logit,
+                        label,
+                        logit_mask,
+                        ignore_index=self.ignore_index)
+                    avg_loss += self.multi_loss_weight[i] * loss
+        return avg_loss
+
+    def _learning_to_downsample(self,
+                                x,
+                                dw_channels1=32,
+                                dw_channels2=48,
+                                out_channels=64):
+        x = relu(bn(conv(x, dw_channels1, 3, 2)))
+        with scope('dsconv1'):
+            x = separate_conv(
+                x, dw_channels2, stride=2, filter=3, act=fluid.layers.relu)
+        with scope('dsconv2'):
+            x = separate_conv(
+                x, out_channels, stride=2, filter=3, act=fluid.layers.relu)
+        return x
+
+    def _shortcut(self, input, data_residual):
+        return fluid.layers.elementwise_add(input, data_residual)
+
+    def _dropout2d(self, input, prob, is_train=False):
+        if not is_train:
+            return input
+        keep_prob = 1.0 - prob
+        shape = fluid.layers.shape(input)
+        channels = shape[1]
+        random_tensor = keep_prob + fluid.layers.uniform_random(
+            [shape[0], channels, 1, 1], min=0., max=1.)
+        binary_tensor = fluid.layers.floor(random_tensor)
+        output = input / keep_prob * binary_tensor
+        return output
+
+    def _inverted_residual_unit(self,
+                                input,
+                                num_in_filter,
+                                num_filters,
+                                ifshortcut,
+                                stride,
+                                filter_size,
+                                padding,
+                                expansion_factor,
+                                name=None):
+        num_expfilter = int(round(num_in_filter * expansion_factor))
+
+        channel_expand = conv_bn_layer(
+            input=input,
+            num_filters=num_expfilter,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            num_groups=1,
+            if_act=True,
+            name=name + '_expand')
+
+        bottleneck_conv = conv_bn_layer(
+            input=channel_expand,
+            num_filters=num_expfilter,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            num_groups=num_expfilter,
+            if_act=True,
+            name=name + '_dwise',
+            use_cudnn=False)
+
+        depthwise_output = bottleneck_conv
+
+        linear_out = conv_bn_layer(
+            input=bottleneck_conv,
+            num_filters=num_filters,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            num_groups=1,
+            if_act=False,
+            name=name + '_linear')
+
+        if ifshortcut:
+            out = self._shortcut(input=input, data_residual=linear_out)
+            return out, depthwise_output
+        else:
+            return linear_out, depthwise_output
+
+    def _inverted_blocks(self, input, in_c, t, c, n, s, name=None):
+        first_block, depthwise_output = self._inverted_residual_unit(
+            input=input,
+            num_in_filter=in_c,
+            num_filters=c,
+            ifshortcut=False,
+            stride=s,
+            filter_size=3,
+            padding=1,
+            expansion_factor=t,
+            name=name + '_1')
+
+        last_residual_block = first_block
+        last_c = c
+
+        for i in range(1, n):
+            last_residual_block, depthwise_output = self._inverted_residual_unit(
+                input=last_residual_block,
+                num_in_filter=last_c,
+                num_filters=c,
+                ifshortcut=True,
+                stride=1,
+                filter_size=3,
+                padding=1,
+                expansion_factor=t,
+                name=name + '_' + str(i + 1))
+        return last_residual_block, depthwise_output
+
+    def _psp_module(self, input, out_features):
+
+        cat_layers = []
+        sizes = (1, 2, 3, 6)
+        for size in sizes:
+            psp_name = "psp" + str(size)
+            with scope(psp_name):
+                pool = fluid.layers.adaptive_pool2d(
+                    input,
+                    pool_size=[size, size],
+                    pool_type='avg',
+                    name=psp_name + '_adapool')
+                data = conv(
+                    pool,
+                    out_features,
+                    filter_size=1,
+                    bias_attr=False,
+                    name=psp_name + '_conv')
+                data_bn = bn(data, act='relu')
+                interp = fluid.layers.resize_bilinear(
+                    data_bn,
+                    out_shape=fluid.layers.shape(input)[2:],
+                    name=psp_name + '_interp',
+                    align_mode=0)
+            cat_layers.append(interp)
+        cat_layers = [input] + cat_layers
+        out = fluid.layers.concat(cat_layers, axis=1, name='psp_cat')
+
+        return out
+
+    def _aux_layer(self, x, num_classes):
+        x = relu(bn(conv(x, 32, 3, padding=1)))
+        x = self._dropout2d(x, 0.1, is_train=(self.mode == 'train'))
+        with scope('logit'):
+            x = conv(x, num_classes, 1, bias_attr=True)
+        return x
+
+    def _feature_fusion(self,
+                        higher_res_feature,
+                        lower_res_feature,
+                        higher_in_channels,
+                        lower_in_channels,
+                        out_channels,
+                        scale_factor=4):
+        shape = fluid.layers.shape(higher_res_feature)
+        w = shape[-1]
+        h = shape[-2]
+        lower_res_feature = fluid.layers.resize_bilinear(
+            lower_res_feature, [h, w], align_mode=0)
+
+        with scope('dwconv'):
+            lower_res_feature = relu(
+                bn(conv(lower_res_feature, out_channels,
+                        1)))  #(lower_res_feature)
+        with scope('conv_lower_res'):
+            lower_res_feature = bn(
+                conv(
+                    lower_res_feature, out_channels, 1, bias_attr=True))
+        with scope('conv_higher_res'):
+            higher_res_feature = bn(
+                conv(
+                    higher_res_feature, out_channels, 1, bias_attr=True))
+        out = higher_res_feature + lower_res_feature
+
+        return relu(out)
+
+    def _global_feature_extractor(self,
+                                  x,
+                                  in_channels=64,
+                                  block_channels=(64, 96, 128),
+                                  out_channels=128,
+                                  t=6,
+                                  num_blocks=(3, 3, 3)):
+        x, _ = self._inverted_blocks(x, in_channels, t, block_channels[0],
+                                     num_blocks[0], 2, 'inverted_block_1')
+        x, _ = self._inverted_blocks(x, block_channels[0], t,
+                                     block_channels[1], num_blocks[1], 2,
+                                     'inverted_block_2')
+        x, _ = self._inverted_blocks(x, block_channels[1], t,
+                                     block_channels[2], num_blocks[2], 1,
+                                     'inverted_block_3')
+        x = self._psp_module(x, block_channels[2] // 4)
+
+        with scope('out'):
+            x = relu(bn(conv(x, out_channels, 1)))
+
+        return x
+
+    def _classifier(self, x, dw_channels, stride=1):
+        with scope('dsconv1'):
+            x = separate_conv(
+                x, dw_channels, stride=stride, filter=3, act=fluid.layers.relu)
+        with scope('dsconv2'):
+            x = separate_conv(
+                x, dw_channels, stride=stride, filter=3, act=fluid.layers.relu)
+
+        x = self._dropout2d(x, 0.1, is_train=self.mode == 'train')
+        x = conv(x, self.num_classes, 1, bias_attr=True)
+        return x