Merge pull request #952 from WenmuZhou/dygraph

使用PaddleClass的resnet_vd

configs/det/det_mv3_db.yml

@@ -3,7 +3,7 @@ Global:
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 2
-  save_model_dir: ./output/20201010/
+  save_model_dir: ./output/db_mv3/
   save_epoch_step: 1200
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: 8
@@ -66,9 +66,9 @@ Metric:
 TRAIN:
   dataset:
     name: SimpleDataSet
-    data_dir: /home/zhoujun20/detection/
+    data_dir: ./detection/
     file_list:
-      - /home/zhoujun20/detection/train_icdar2015_label.txt # dataset1
+      - ./detection/train_icdar2015_label.txt # dataset1
     ratio_list: [1.0]
     transforms:
       - DecodeImage: # load image
@@ -103,14 +103,14 @@ TRAIN:
     shuffle: True
     drop_last: False
     batch_size: 16
-    num_workers: 6
+    num_workers: 8
 
 EVAL:
   dataset:
     name: SimpleDataSet
-    data_dir: /home/zhoujun20/detection/
+    data_dir: ./detection/
     file_list:
-      - /home/zhoujun20/detection/test_icdar2015_label.txt
+      - ./detection/test_icdar2015_label.txt
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
@@ -130,4 +130,4 @@ EVAL:
     shuffle: False
     drop_last: False
     batch_size: 1 # must be 1
-    num_workers: 6
+    num_workers: 8

configs/det/det_r50_vd_db.yml

@@ -3,14 +3,14 @@ Global:
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 2
-  save_model_dir: ./output/20201010/
+  save_model_dir: ./output/20201015_r50/
   save_epoch_step: 1200
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: 8
   # if pretrained_model is saved in static mode, load_static_weights must set to True
   load_static_weights: True
   cal_metric_during_train: False
-  pretrained_model: /home/zhoujun20/pretrain_models/MobileNetV3_large_x0_5_pretrained
+  pretrained_model: /home/zhoujun20/pretrain_models/ResNet50_vd_ssld_pretrained/
   checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
   save_inference_dir:
   use_visualdl: True
@@ -102,7 +102,7 @@ TRAIN:
     shuffle: True
     drop_last: False
     batch_size: 16
-    num_workers: 6
+    num_workers: 8
 
 EVAL:
   dataset:
@@ -129,4 +129,4 @@ EVAL:
     shuffle: False
     drop_last: False
     batch_size: 1 # must be 1
-    num_workers: 6
+    num_workers: 8

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -84,7 +84,7 @@ TRAIN:
     batch_size: 256
     shuffle: True
     drop_last: True
-    num_workers: 6
+    num_workers: 8
 
 EVAL:
   dataset:
@@ -105,4 +105,4 @@ EVAL:
     shuffle: False
     drop_last: False
     batch_size: 256
-    num_workers: 6
+    num_workers: 8

configs/rec/rec_mv3_none_bilstm_ctc_lmdb.yml

@@ -83,7 +83,7 @@ TRAIN:
     batch_size: 256
     shuffle: True
     drop_last: True
-    num_workers: 6
+    num_workers: 8
 
 EVAL:
   dataset:
@@ -103,4 +103,4 @@ EVAL:
     shuffle: False
     drop_last: False
     batch_size: 256
-    num_workers: 6
+    num_workers: 8

configs/rec/rec_mv3_none_none_ctc_lmdb.yml

@@ -0,0 +1,105 @@
+Global:
+  use_gpu: false
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 1
+  save_model_dir: ./output/rec/test/
+  save_epoch_step: 500
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: 1016
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  load_static_weights: True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: #output/rec/rec_crnn/best_accuracy
+  save_inference_dir:
+  use_visualdl: True
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  max_text_length: 80
+  character_dict_path: /home/zhoujun20/rec/lmdb/dict.txt
+  character_type: 'en'
+  use_space_char: True
+  infer_mode: False
+  use_tps: False
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  learning_rate:
+    name: Cosine
+    lr: 0.0005
+    warmup_epoch: 1
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [ 1, 2, 2, 2 ]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTC
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+TRAIN:
+  dataset:
+    name: LMDBDateSet
+    file_list:
+      - /Users/zhoujun20/Downloads/evaluation_new # dataset1
+    ratio_list: [ 0.4,0.6 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecAug:
+      - RecResizeImg:
+          image_shape: [ 3,32,320 ]
+      - keepKeys:
+          keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
+  loader:
+    batch_size: 256
+    shuffle: True
+    drop_last: True
+    num_workers: 8
+
+EVAL:
+  dataset:
+    name: LMDBDateSet
+    file_list:
+      - /home/zhoujun20/rec/lmdb/val
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [ 3,32,320 ]
+      - keepKeys:
+          keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size: 256
+    num_workers: 8

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -42,7 +42,7 @@ Architecture:
   Transform:
   Backbone:
     name: ResNet
-    layers: 200
+    layers: 34
   Neck:
     name: SequenceEncoder
     encoder_type: fc
@@ -82,7 +82,7 @@ TRAIN:
     batch_size: 256
     shuffle: True
     drop_last: True
-    num_workers: 6
+    num_workers: 8
 
 EVAL:
   dataset:
@@ -103,4 +103,4 @@ EVAL:
     shuffle: False
     drop_last: False
     batch_size: 256
-    num_workers: 6
+    num_workers: 8

ppocr/modeling/architectures/model.py

@@ -94,13 +94,11 @@ def check_static():
     from ppocr.utils.logging import get_logger
     from tools import program
 
-    config = program.load_config('configs/det/det_r50_vd_db.yml')
+    config = program.load_config('configs/rec/rec_r34_vd_none_bilstm_ctc.yml')
 
-    # import cv2
-    # data = cv2.imread('doc/imgs/1.jpg')
-    # data = normalize(data)
     logger = get_logger()
-    data = np.zeros((1, 3, 640, 640), dtype=np.float32)
+    np.random.seed(0)
+    data = np.random.rand(1, 3, 32, 320).astype(np.float32)
     paddle.disable_static()
 
     config['Architecture']['in_channels'] = 3
@@ -110,17 +108,15 @@ def check_static():
     load_dygraph_pretrain(
         model,
         logger,
-        '/Users/zhoujun20/Desktop/code/PaddleOCR/db/db',
+        '/Users/zhoujun20/Desktop/code/PaddleOCR/cnn_ctc/cnn_ctc',
         load_static_weights=True)
-    x = paddle.to_variable(data)
+    x = paddle.to_tensor(data)
     y = model(x)
     for y1 in y:
         print(y1.shape)
-    #
+
-    # # from matplotlib import pyplot as plt
+    static_out = np.load(
-    # # plt.imshow(y.numpy())
+        '/Users/zhoujun20/Desktop/code/PaddleOCR/output/conv.npy')
-    # # plt.show()
-    static_out = np.load('/Users/zhoujun20/Desktop/code/PaddleOCR/db/db.npy')
     diff = y.numpy() - static_out
     print(y.shape, static_out.shape, diff.mean())
 

ppocr/modeling/backbones/det_resnet_vd.py

@@ -16,143 +16,30 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from paddle import nn
+import paddle
-from paddle.nn import functional as F
 from paddle import ParamAttr
+import paddle.nn as nn
 
 __all__ = ["ResNet"]
 
 
-class ResNet(nn.Layer):
-    def __init__(self, in_channels=3, layers=50, **kwargs):
-        """
-        the Resnet backbone network for detection module.
-        Args:
-            params(dict): the super parameters for network build
-        """
-        super(ResNet, self).__init__()
-        supported_layers = {
-            18: {
-                'depth': [2, 2, 2, 2],
-                'block_class': BasicBlock
-            },
-            34: {
-                'depth': [3, 4, 6, 3],
-                'block_class': BasicBlock
-            },
-            50: {
-                'depth': [3, 4, 6, 3],
-                'block_class': BottleneckBlock
-            },
-            101: {
-                'depth': [3, 4, 23, 3],
-                'block_class': BottleneckBlock
-            },
-            152: {
-                'depth': [3, 8, 36, 3],
-                'block_class': BottleneckBlock
-            },
-            200: {
-                'depth': [3, 12, 48, 3],
-                'block_class': BottleneckBlock
-            }
-        }
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers.keys(), layers)
-        is_3x3 = True
-
-        depth = supported_layers[layers]['depth']
-        block_class = supported_layers[layers]['block_class']
-
-        num_filters = [64, 128, 256, 512]
-
-        conv = []
-        if is_3x3 == False:
-            conv.append(
-                ConvBNLayer(
-                    in_channels=in_channels,
-                    out_channels=64,
-                    kernel_size=7,
-                    stride=2,
-                    act='relu'))
-        else:
-            conv.append(
-                ConvBNLayer(
-                    in_channels=3,
-                    out_channels=32,
-                    kernel_size=3,
-                    stride=2,
-                    act='relu',
-                    name='conv1_1'))
-            conv.append(
-                ConvBNLayer(
-                    in_channels=32,
-                    out_channels=32,
-                    kernel_size=3,
-                    stride=1,
-                    act='relu',
-                    name='conv1_2'))
-            conv.append(
-                ConvBNLayer(
-                    in_channels=32,
-                    out_channels=64,
-                    kernel_size=3,
-                    stride=1,
-                    act='relu',
-                    name='conv1_3'))
-        self.conv1 = nn.Sequential(*conv)
-        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.stages = []
-        self.out_channels = []
-        in_ch = 64
-        for block_index in range(len(depth)):
-            block_list = []
-            for i in range(depth[block_index]):
-                if layers >= 50:
-                    if layers in [101, 152, 200] and block_index == 2:
-                        if i == 0:
-                            conv_name = "res" + str(block_index + 2) + "a"
-                        else:
-                            conv_name = "res" + str(block_index +
-                                                    2) + "b" + str(i)
-                    else:
-                        conv_name = "res" + str(block_index + 2) + chr(97 + i)
-                else:
-                    conv_name = "res" + str(block_index + 2) + chr(97 + i)
-                block_list.append(
-                    block_class(
-                        in_channels=in_ch,
-                        out_channels=num_filters[block_index],
-                        stride=2 if i == 0 and block_index != 0 else 1,
-                        if_first=block_index == i == 0,
-                        name=conv_name))
-                in_ch = block_list[-1].out_channels
-            self.out_channels.append(in_ch)
-            self.stages.append(nn.Sequential(*block_list))
-        for i, stage in enumerate(self.stages):
-            self.add_sublayer(sublayer=stage, name="stage{}".format(i))
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.pool(x)
-        out_list = []
-        for stage in self.stages:
-            x = stage(x)
-            out_list.append(x)
-        return out_list
-
-
 class ConvBNLayer(nn.Layer):
-    def __init__(self,
+    def __init__(
-                 in_channels,
+            self,
-                 out_channels,
+            in_channels,
-                 kernel_size,
+            out_channels,
-                 stride=1,
+            kernel_size,
-                 groups=1,
+            stride=1,
-                 act=None,
+            groups=1,
-                 name=None):
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
         super(ConvBNLayer, self).__init__()
-        self.conv = nn.Conv2d(
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2d(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2d(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=kernel_size,
@@ -165,87 +52,32 @@ class ConvBNLayer(nn.Layer):
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        self.bn = nn.BatchNorm(
+        self._batch_norm = nn.BatchNorm(
-            num_channels=out_channels,
+            out_channels,
             act=act,
-            param_attr=ParamAttr(name=bn_name + "_scale"),
+            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(name=bn_name + "_offset"),
+            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + "_mean",
+            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + "_variance")
+            moving_variance_name=bn_name + '_variance')
 
-    def __call__(self, x):
+    def forward(self, inputs):
-        x = self.conv(x)
+        if self.is_vd_mode:
-        x = self.bn(x)
+            inputs = self._pool2d_avg(inputs)
-        return x
+        y = self._conv(inputs)
-
+        y = self._batch_norm(y)
-
+        return y
-class ConvBNLayerNew(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 groups=1,
-                 act=None,
-                 name=None):
-        super(ConvBNLayerNew, self).__init__()
-        self.pool = nn.AvgPool2d(
-            kernel_size=2, stride=2, padding=0, ceil_mode=True)
-
-        self.conv = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-            groups=groups,
-            weight_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        self.bn = nn.BatchNorm(
-            num_channels=out_channels,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + "_scale"),
-            bias_attr=ParamAttr(name=bn_name + "_offset"),
-            moving_mean_name=bn_name + "_mean",
-            moving_variance_name=bn_name + "_variance")
-
-    def __call__(self, x):
-        x = self.pool(x)
-        x = self.conv(x)
-        x = self.bn(x)
-        return x
-
-
-class ShortCut(nn.Layer):
-    def __init__(self, in_channels, out_channels, stride, name, if_first=False):
-        super(ShortCut, self).__init__()
-        self.use_conv = True
-        if in_channels != out_channels or stride != 1:
-            if if_first:
-                self.conv = ConvBNLayer(
-                    in_channels, out_channels, 1, stride, name=name)
-            else:
-                self.conv = ConvBNLayerNew(
-                    in_channels, out_channels, 1, stride, name=name)
-        elif if_first:
-            self.conv = ConvBNLayer(
-                in_channels, out_channels, 1, stride, name=name)
-        else:
-            self.use_conv = False
-
-    def forward(self, x):
-        if self.use_conv:
-            x = self.conv(x)
-        return x
 
 
 class BottleneckBlock(nn.Layer):
-    def __init__(self, in_channels, out_channels, stride, name, if_first):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
         super(BottleneckBlock, self).__init__()
+
         self.conv0 = ConvBNLayer(
             in_channels=in_channels,
             out_channels=out_channels,
@@ -266,32 +98,46 @@ class BottleneckBlock(nn.Layer):
             act=None,
             name=name + "_branch2c")
 
-        self.short = ShortCut(
+        if not shortcut:
-            in_channels=in_channels,
+            self.short = ConvBNLayer(
-            out_channels=out_channels * 4,
+                in_channels=in_channels,
-            stride=stride,
+                out_channels=out_channels * 4,
-            if_first=if_first,
+                kernel_size=1,
-            name=name + "_branch1")
+                stride=1,
-        self.out_channels = out_channels * 4
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
 
-    def forward(self, x):
+        self.shortcut = shortcut
-        y = self.conv0(x)
+
-        y = self.conv1(y)
+    def forward(self, inputs):
-        y = self.conv2(y)
+        y = self.conv0(inputs)
-        y = y + self.short(x)
+        conv1 = self.conv1(y)
-        y = F.relu(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
         return y
 
 
 class BasicBlock(nn.Layer):
-    def __init__(self, in_channels, out_channels, stride, name, if_first):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
         super(BasicBlock, self).__init__()
+        self.stride = stride
         self.conv0 = ConvBNLayer(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=3,
-            act='relu',
             stride=stride,
+            act='relu',
             name=name + "_branch2a")
         self.conv1 = ConvBNLayer(
             in_channels=out_channels,
@@ -299,31 +145,133 @@ class BasicBlock(nn.Layer):
             kernel_size=3,
             act=None,
             name=name + "_branch2b")
-        self.short = ShortCut(
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.elementwise_add(x=short, y=conv1, act='relu')
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self, in_channels=3, layers=50, **kwargs):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv1_1 = ConvBNLayer(
             in_channels=in_channels,
-            out_channels=out_channels,
+            out_channels=32,
-            stride=stride,
+            kernel_size=3,
-            if_first=if_first,
+            stride=2,
-            name=name + "_branch1")
+            act='relu',
-        self.out_channels = out_channels
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            in_channels=32,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            in_channels=32,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_3")
+        self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 
-    def forward(self, x):
+        self.stages = []
-        y = self.conv0(x)
+        self.out_channels = []
-        y = self.conv1(y)
+        if layers >= 50:
-        y = y + self.short(x)
+            for block in range(len(depth)):
-        return F.relu(y)
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(bottleneck_block)
+                self.out_channels.append(num_filters[block] * 4)
+                self.stages.append(nn.Sequential(*block_list))
+        else:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(basic_block)
+                self.out_channels.append(num_filters[block])
+                self.stages.append(nn.Sequential(*block_list))
 
-
+    def forward(self, inputs):
-if __name__ == '__main__':
+        y = self.conv1_1(inputs)
-    import paddle
+        y = self.conv1_2(y)
-
+        y = self.conv1_3(y)
-    paddle.disable_static()
+        y = self.pool2d_max(y)
-    x = paddle.zeros([1, 3, 640, 640])
+        out = []
-    x = paddle.to_variable(x)
+        for block in self.stages:
-    print(x.shape)
+            y = block(y)
-    net = ResNet(layers=18)
+            out.append(y)
-    y = net(x)
+        return out
-
-    for stage in y:
-        print(stage.shape)
-    # paddle.save(net.state_dict(),'1.pth')

ppocr/modeling/backbones/rec_resnet_vd.py

@@ -16,184 +16,34 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from paddle import nn, ParamAttr
+import paddle
-from paddle.nn import functional as F
+from paddle import ParamAttr
+import paddle.nn as nn
 
 __all__ = ["ResNet"]
 
 
-class ResNet(nn.Layer):
-    def __init__(self, in_channels=3, layers=34):
-        super(ResNet, self).__init__()
-        supported_layers = {
-            18: {
-                'depth': [2, 2, 2, 2],
-                'block_class': BasicBlock
-            },
-            34: {
-                'depth': [3, 4, 6, 3],
-                'block_class': BasicBlock
-            },
-            50: {
-                'depth': [3, 4, 6, 3],
-                'block_class': BottleneckBlock
-            },
-            101: {
-                'depth': [3, 4, 23, 3],
-                'block_class': BottleneckBlock
-            },
-            152: {
-                'depth': [3, 8, 36, 3],
-                'block_class': BottleneckBlock
-            },
-            200: {
-                'depth': [3, 12, 48, 3],
-                'block_class': BottleneckBlock
-            }
-        }
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers.keys(), layers)
-        is_3x3 = True
-
-        num_filters = [64, 128, 256, 512]
-        depth = supported_layers[layers]['depth']
-        block_class = supported_layers[layers]['block_class']
-        conv = []
-        if is_3x3 == False:
-            conv.append(
-                ConvBNLayer(
-                    in_channels=in_channels,
-                    out_channels=64,
-                    kernel_size=7,
-                    stride=1,
-                    act='relu'))
-        else:
-            conv.append(
-                ConvBNLayer(
-                    in_channels=in_channels,
-                    out_channels=32,
-                    kernel_size=3,
-                    stride=1,
-                    act='relu',
-                    name='conv1_1'))
-            conv.append(
-                ConvBNLayer(
-                    in_channels=32,
-                    out_channels=32,
-                    kernel_size=3,
-                    stride=1,
-                    act='relu',
-                    name='conv1_2'))
-            conv.append(
-                ConvBNLayer(
-                    in_channels=32,
-                    out_channels=64,
-                    kernel_size=3,
-                    stride=1,
-                    act='relu',
-                    name='conv1_3'))
-        self.conv1 = nn.Sequential(*conv)
-
-        self.pool = nn.MaxPool2d(
-            kernel_size=3,
-            stride=2,
-            padding=1, )
-
-        block_list = []
-        in_ch = 64
-        for block_index in range(len(depth)):
-            for i in range(depth[block_index]):
-                if layers >= 50:
-                    if layers in [101, 152, 200] and block_index == 2:
-                        if i == 0:
-                            conv_name = "res" + str(block_index + 2) + "a"
-                        else:
-                            conv_name = "res" + str(block_index +
-                                                    2) + "b" + str(i)
-                    else:
-                        conv_name = "res" + str(block_index + 2) + chr(97 + i)
-                else:
-                    conv_name = "res" + str(block_index + 2) + chr(97 + i)
-                if i == 0 and block_index != 0:
-                    stride = (2, 1)
-                else:
-                    stride = (1, 1)
-                block_list.append(
-                    block_class(
-                        in_channels=in_ch,
-                        out_channels=num_filters[block_index],
-                        stride=stride,
-                        if_first=block_index == i == 0,
-                        name=conv_name))
-                in_ch = block_list[-1].out_channels
-        self.block_list = nn.Sequential(*block_list)
-        self.add_sublayer(sublayer=self.block_list, name="block_list")
-        self.pool_out = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
-        self.out_channels = in_ch
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.pool(x)
-        x = self.block_list(x)
-        x = self.pool_out(x)
-        return x
-
-
 class ConvBNLayer(nn.Layer):
-    def __init__(self,
+    def __init__(
-                 in_channels,
+            self,
-                 out_channels,
+            in_channels,
-                 kernel_size,
+            out_channels,
-                 stride=1,
+            kernel_size,
-                 groups=1,
-                 act=None,
-                 name=None):
-        super(ConvBNLayer, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=(kernel_size - 1) // 2,
-            groups=groups,
-            weight_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        self.bn = nn.BatchNorm(
-            num_channels=out_channels,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + "_scale"),
-            bias_attr=ParamAttr(name=bn_name + "_offset"),
-            moving_mean_name=bn_name + "_mean",
-            moving_variance_name=bn_name + "_variance")
-
-    def __call__(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        return x
-
-
-class ConvBNLayerNew(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 groups=1,
-                 act=None,
-                 name=None):
-        super(ConvBNLayerNew, self).__init__()
-        self.pool = nn.AvgPool2d(
-            kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
-
-        self.conv = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
             stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2d(
+            kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=1 if is_vd_mode else stride,
             padding=(kernel_size - 1) // 2,
             groups=groups,
             weight_attr=ParamAttr(name=name + "_weights"),
@@ -202,48 +52,32 @@ class ConvBNLayerNew(nn.Layer):
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        self.bn = nn.BatchNorm(
+        self._batch_norm = nn.BatchNorm(
-            num_channels=out_channels,
+            out_channels,
             act=act,
-            param_attr=ParamAttr(name=bn_name + "_scale"),
+            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(name=bn_name + "_offset"),
+            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + "_mean",
+            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + "_variance")
+            moving_variance_name=bn_name + '_variance')
 
-    def __call__(self, x):
+    def forward(self, inputs):
-        x = self.pool(x)
+        if self.is_vd_mode:
-        x = self.conv(x)
+            inputs = self._pool2d_avg(inputs)
-        x = self.bn(x)
+        y = self._conv(inputs)
-        return x
+        y = self._batch_norm(y)
-
+        return y
-
-class ShortCut(nn.Layer):
-    def __init__(self, in_channels, out_channels, stride, name, if_first=False):
-        super(ShortCut, self).__init__()
-        self.use_conv = True
-
-        if in_channels != out_channels or stride[0] != 1:
-            if if_first:
-                self.conv = ConvBNLayer(
-                    in_channels, out_channels, 1, stride, name=name)
-            else:
-                self.conv = ConvBNLayerNew(
-                    in_channels, out_channels, 1, stride, name=name)
-        elif if_first:
-            self.conv = ConvBNLayer(
-                in_channels, out_channels, 1, stride, name=name)
-        else:
-            self.use_conv = False
-
-    def forward(self, x):
-        if self.use_conv:
-            x = self.conv(x)
-        return x
 
 
 class BottleneckBlock(nn.Layer):
-    def __init__(self, in_channels, out_channels, stride, name, if_first):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
         super(BottleneckBlock, self).__init__()
+
         self.conv0 = ConvBNLayer(
             in_channels=in_channels,
             out_channels=out_channels,
@@ -264,32 +98,47 @@ class BottleneckBlock(nn.Layer):
             act=None,
             name=name + "_branch2c")
 
-        self.short = ShortCut(
+        if not shortcut:
-            in_channels=in_channels,
+            self.short = ConvBNLayer(
-            out_channels=out_channels * 4,
+                in_channels=in_channels,
-            stride=stride,
+                out_channels=out_channels * 4,
-            if_first=if_first,
+                kernel_size=1,
-            name=name + "_branch1")
+                stride=stride,
-        self.out_channels = out_channels * 4
+                is_vd_mode=not if_first and stride[0] != 1,
+                name=name + "_branch1")
 
-    def forward(self, x):
+        self.shortcut = shortcut
-        y = self.conv0(x)
+
-        y = self.conv1(y)
+    def forward(self, inputs):
-        y = self.conv2(y)
+        y = self.conv0(inputs)
-        y = y + self.short(x)
+
-        y = F.relu(y)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
         return y
 
 
 class BasicBlock(nn.Layer):
-    def __init__(self, in_channels, out_channels, stride, name, if_first):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
         super(BasicBlock, self).__init__()
+        self.stride = stride
         self.conv0 = ConvBNLayer(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=3,
-            act='relu',
             stride=stride,
+            act='relu',
             name=name + "_branch2a")
         self.conv1 = ConvBNLayer(
             in_channels=out_channels,
@@ -297,16 +146,138 @@ class BasicBlock(nn.Layer):
             kernel_size=3,
             act=None,
             name=name + "_branch2b")
-        self.short = ShortCut(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            stride=stride,
-            if_first=if_first,
-            name=name + "_branch1")
-        self.out_channels = out_channels
 
-    def forward(self, x):
+        if not shortcut:
-        y = self.conv0(x)
+            self.short = ConvBNLayer(
-        y = self.conv1(y)
+                in_channels=in_channels,
-        y = y + self.short(x)
+                out_channels=out_channels,
-        return F.relu(y)
+                kernel_size=1,
+                stride=stride,
+                is_vd_mode=not if_first and stride[0] != 1,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.elementwise_add(x=short, y=conv1, act='relu')
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self, in_channels=3, layers=50, **kwargs):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv1_1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            in_channels=32,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            in_channels=32,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_3")
+        self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.block_list = []
+        if layers >= 50:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152, 200] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+
+                    if i == 0 and block != 0:
+                        stride = (2, 1)
+                    else:
+                        stride = (1, 1)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            out_channels=num_filters[block],
+                            stride=stride,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    self.block_list.append(bottleneck_block)
+                self.out_channels = num_filters[block]
+        else:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    if i == 0 and block != 0:
+                        stride = (2, 1)
+                    else:
+                        stride = (1, 1)
+
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            out_channels=num_filters[block],
+                            stride=stride,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    self.block_list.append(basic_block)
+                self.out_channels = num_filters[block]
+        self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+
+    def forward(self, inputs):
+        y = self.conv1_1(inputs)
+        y = self.conv1_2(y)
+        y = self.conv1_3(y)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.out_pool(y)
+        return y

ppocr/modeling/necks/rnn.py

@@ -116,7 +116,7 @@ class EncoderWithFC(nn.Layer):
 
 
 class SequenceEncoder(nn.Layer):
-    def __init__(self, in_channels, encoder_type, hidden_size, **kwargs):
+    def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs):
         super(SequenceEncoder, self).__init__()
         self.encoder_reshape = EncoderWithReshape(in_channels)
         self.out_channels = self.encoder_reshape.out_channels

tools/train.py

@@ -88,20 +88,23 @@ def main(config, device, logger, vdl_writer):
                   best_model_dict, logger, vdl_writer)
 
 
-def test_reader(config, place, logger):
+def test_reader(config, place, logger, global_config):
-    train_loader = build_dataloader(config['TRAIN'], place)
+    train_loader, _ = build_dataloader(
+        config['TRAIN'], place, global_config=global_config)
     import time
     starttime = time.time()
     count = 0
     try:
-        for data in train_loader():
+        for data in train_loader:
             count += 1
             if count % 1 == 0:
                 batch_time = time.time() - starttime
                 starttime = time.time()
-                logger.info("reader: {}, {}, {}".format(count,
+                logger.info("reader: {}, {}, {}".format(
-                                                        len(data), batch_time))
+                    count, len(data[0]), batch_time))
     except Exception as e:
+        import traceback
+        traceback.print_exc()
         logger.info(e)
     logger.info("finish reader: {}, Success!".format(count))
 
@@ -130,7 +133,7 @@ def dis_main():
                                                             device))
 
     main(config, device, logger, vdl_writer)
-    # test_reader(config, place, logger)
+    # test_reader(config, device, logger, config['Global'])
 
 
 if __name__ == '__main__':