add hrnet_w18_small_v1 for segmentation

Applications/HumanSeg/train.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
+
+# 下载和解压人像分割数据集
+human_seg_data = 'https://paddlex.bj.bcebos.com/humanseg/data/human_seg_data.zip'
+pdx.utils.download_and_decompress(human_seg_data, path='./')
+
+# 下载和解压人像分割预训练模型
+pretrain_weights = 'https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_ckpt.zip'
+pdx.utils.download_and_decompress(
+    pretrain_weights, path='./output/human_seg/pretrain')
+
+# 定义训练和验证时的transforms
+train_transforms = transforms.Compose([
+    transforms.Resize([192, 192]), transforms.RandomHorizontalFlip(),
+    transforms.Normalize()
+])
+
+eval_transforms = transforms.Compose(
+    [transforms.Resize([192, 192]), transforms.Normalize()])
+
+# 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='human_seg_data',
+    file_list='human_seg_data/train_list.txt',
+    label_list='human_seg_data/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+eval_dataset = pdx.datasets.SegDataset(
+    data_dir='human_seg_data',
+    file_list='human_seg_data/val_list.txt',
+    label_list='human_seg_data/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.HRNet(num_classes=num_classes, width='18_small_v1')
+model.train(
+    num_epochs=10,
+    train_dataset=train_dataset,
+    train_batch_size=8,
+    eval_dataset=eval_dataset,
+    learning_rate=0.001,
+    pretrain_weights='./output/human_seg/pretrain/humanseg_mobile_ckpt',
+    save_dir='output/human_seg',
+    use_vdl=True)

docs/apis/models/semantic_segmentation.md

@@ -186,10 +186,10 @@ paddlex.seg.HRNet(num_classes=2, width=18, use_bce_loss=False, use_dice_loss=Fal
 > **参数**
 
 > > - **num_classes** (int): 类别数。
-> > - **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]。
+> > - **width** (int|str): 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, '18_small_v1']。'18_small_v1'是18的轻量级版本。
 > > - **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。
+> > - **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。
 
 ### train 训练接口

paddlex/cv/models/hrnet.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.
@@ -24,11 +24,12 @@ class HRNet(DeepLabv3p):
 
     Args:
         num_classes (int): 类别数。
-        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]。
+        width (int|str): 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, '18_small_v1']。
+            '18_small_v1'是18的轻量级版本。
         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
+        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。
@@ -173,6 +174,6 @@ class HRNet(DeepLabv3p):
         return super(HRNet, 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,
+            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/hrnet.py

@@ -51,15 +51,38 @@ class HRNet(object):
 
         self.width = width
         self.has_se = has_se
+        self.num_modules = {
+            '18_small_v1': [1, 1, 1, 1],
+            '18': [1, 1, 4, 3],
+            '30': [1, 1, 4, 3],
+            '32': [1, 1, 4, 3],
+            '40': [1, 1, 4, 3],
+            '44': [1, 1, 4, 3],
+            '48': [1, 1, 4, 3],
+            '60': [1, 1, 4, 3],
+            '64': [1, 1, 4, 3]
+        }
+        self.num_blocks = {
+            '18_small_v1': [[1], [2, 2], [2, 2, 2], [2, 2, 2, 2]],
+            '18': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '30': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '32': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '40': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '44': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '48': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '60': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]],
+            '64': [[4], [4, 4], [4, 4, 4], [4, 4, 4, 4]]
+        }
         self.channels = {
-            18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]],
-            30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
-            32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]],
-            40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
-            44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]],
-            48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]],
-            60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]],
-            64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]],
+            '18_small_v1': [[32], [16, 32], [16, 32, 64], [16, 32, 64, 128]],
+            '18': [[64], [18, 36], [18, 36, 72], [18, 36, 72, 144]],
+            '30': [[64], [30, 60], [30, 60, 120], [30, 60, 120, 240]],
+            '32': [[64], [32, 64], [32, 64, 128], [32, 64, 128, 256]],
+            '40': [[64], [40, 80], [40, 80, 160], [40, 80, 160, 320]],
+            '44': [[64], [44, 88], [44, 88, 176], [44, 88, 176, 352]],
+            '48': [[64], [48, 96], [48, 96, 192], [48, 96, 192, 384]],
+            '60': [[64], [60, 120], [60, 120, 240], [60, 120, 240, 480]],
+            '64': [[64], [64, 128], [64, 128, 256], [64, 128, 256, 512]],
         }
 
         self.freeze_at = freeze_at
@@ -73,31 +96,38 @@ class HRNet(object):
 
     def net(self, input):
         width = self.width
-        channels_2, channels_3, channels_4 = self.channels[width]
-        num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3
+        channels_1, channels_2, channels_3, channels_4 = self.channels[str(
+            width)]
+        num_modules_1, num_modules_2, num_modules_3, num_modules_4 = self.num_modules[
+            str(width)]
+        num_blocks_1, num_blocks_2, num_blocks_3, num_blocks_4 = self.num_blocks[
+            str(width)]
 
         x = self.conv_bn_layer(
             input=input,
             filter_size=3,
-            num_filters=64,
+            num_filters=channels_1[0],
             stride=2,
             if_act=True,
             name='layer1_1')
         x = self.conv_bn_layer(
             input=x,
             filter_size=3,
-            num_filters=64,
+            num_filters=channels_1[0],
             stride=2,
             if_act=True,
             name='layer1_2')
 
-        la1 = self.layer1(x, name='layer2')
+        la1 = self.layer1(x, num_blocks_1, channels_1, name='layer2')
         tr1 = self.transition_layer([la1], [256], channels_2, name='tr1')
-        st2 = self.stage(tr1, num_modules_2, channels_2, name='st2')
+        st2 = self.stage(
+            tr1, num_modules_2, num_blocks_2, channels_2, name='st2')
         tr2 = self.transition_layer(st2, channels_2, channels_3, name='tr2')
-        st3 = self.stage(tr2, num_modules_3, channels_3, name='st3')
+        st3 = self.stage(
+            tr2, num_modules_3, num_blocks_3, channels_3, name='st3')
         tr3 = self.transition_layer(st3, channels_3, channels_4, name='tr3')
-        st4 = self.stage(tr3, num_modules_4, channels_4, name='st4')
+        st4 = self.stage(
+            tr3, num_modules_4, num_blocks_4, channels_4, name='st4')
 
         # classification
         if self.num_classes:
@@ -139,12 +169,12 @@ class HRNet(object):
         self.end_points = st4
         return st4[-1]
 
-    def layer1(self, input, name=None):
+    def layer1(self, input, num_blocks, channels, name=None):
         conv = input
-        for i in range(4):
+        for i in range(num_blocks[0]):
             conv = self.bottleneck_block(
                 conv,
-                num_filters=64,
+                num_filters=channels[0],
                 downsample=True if i == 0 else False,
                 name=name + '_' + str(i + 1))
         return conv
@@ -178,7 +208,7 @@ class HRNet(object):
         out = []
         for i in range(len(channels)):
             residual = x[i]
-            for j in range(block_num):
+            for j in range(block_num[i]):
                 residual = self.basic_block(
                     residual,
                     channels[i],
@@ -240,10 +270,11 @@ class HRNet(object):
 
     def high_resolution_module(self,
                                x,
+                               num_blocks,
                                channels,
                                multi_scale_output=True,
                                name=None):
-        residual = self.branches(x, 4, channels, name=name)
+        residual = self.branches(x, num_blocks, channels, name=name)
         out = self.fuse_layers(
             residual,
             channels,
@@ -254,6 +285,7 @@ class HRNet(object):
     def stage(self,
               x,
               num_modules,
+              num_blocks,
               channels,
               multi_scale_output=True,
               name=None):
@@ -262,12 +294,13 @@ class HRNet(object):
             if i == num_modules - 1 and multi_scale_output == False:
                 out = self.high_resolution_module(
                     out,
+                    num_blocks,
                     channels,
                     multi_scale_output=False,
                     name=name + '_' + str(i + 1))
             else:
                 out = self.high_resolution_module(
-                    out, channels, name=name + '_' + str(i + 1))
+                    out, num_blocks, channels, name=name + '_' + str(i + 1))
 
         return out
 

paddlex/cv/nets/segmentation/hrnet.py

@@ -83,7 +83,8 @@ class HRNet(object):
         st4[3] = fluid.layers.resize_bilinear(st4[3], out_shape=shape)
 
         out = fluid.layers.concat(st4, axis=1)
-        last_channels = sum(self.backbone.channels[self.backbone.width][-1])
+        last_channels = sum(self.backbone.channels[str(self.backbone.width)][
+            -1])
 
         out = self._conv_bn_layer(
             input=out,