add data_format selection support to ocr

configs/cls/cls_mv3.yml

@@ -23,10 +23,12 @@ Architecture:
     name: MobileNetV3
     scale: 0.35
     model_name: small
+    data_format: NHWC
   Neck:
   Head:
     name: ClsHead
     class_dim: 2
+    data_format: NHWC
 
 Loss:
   name: ClsLoss

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml

@@ -35,13 +35,16 @@ Architecture:
         scale: 0.5
         model_name: large
         disable_se: true
+        data_format: NHWC
       Neck:
         name: RSEFPN
         out_channels: 96
         shortcut: True
+        data_format: NHWC
       Head:
         name: DBHead
         k: 50
+        data_format: NHWC
     Student2:
       pretrained:
       model_type: det
@@ -52,13 +55,16 @@ Architecture:
         scale: 0.5
         model_name: large
         disable_se: true
+        data_format: NHWC
       Neck:
         name: RSEFPN
         out_channels: 96
         shortcut: True
+        data_format: NHWC
       Head:
         name: DBHead
         k: 50
+        data_format: NHWC
     Teacher:
       freeze_params: true
       return_all_feats: false
@@ -68,13 +74,16 @@ Architecture:
         name: ResNet_vd
         in_channels: 3
         layers: 50
+        data_format: NHWC
       Neck:
         name: LKPAN
         out_channels: 256
+        data_format: NHWC
       Head:
         name: DBHead
         kernel_list: [7,2,2]
         k: 50
+        data_format: NHWC
 
 Loss:
   name: CombinedLoss

configs/det/det_mv3_db.yml

@@ -25,12 +25,15 @@ Architecture:
     name: MobileNetV3
     scale: 0.5
     model_name: large
+    data_format: NHWC
   Neck:
     name: DBFPN
     out_channels: 256
+    data_format: NHWC
   Head:
     name: DBHead
     k: 50
+    data_format: NHWC
 
 Loss:
   name: DBLoss
@@ -64,7 +67,7 @@ Metric:
 Train:
   dataset:
     name: SimpleDataSet
-    data_dir: ./train_data/icdar2015/text_localization/
+    data_dir: ./
     label_file_list:
       - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
     ratio_list: [1.0]
@@ -107,7 +110,7 @@ Train:
 Eval:
   dataset:
     name: SimpleDataSet
-    data_dir: ./train_data/icdar2015/text_localization/
+    data_dir: ./
     label_file_list:
       - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
     transforms:

configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml

@@ -44,6 +44,7 @@ Architecture:
     last_conv_stride: [1, 2]
     last_pool_type: avg
     last_pool_kernel_size: [2, 2]
+    data_format: 'NHWC'
   Head:
     name: MultiHead
     head_list:
@@ -59,6 +60,7 @@ Architecture:
       - SARHead:
           enc_dim: 512
           max_text_length: *max_text_length
+    data_format: 'NHWC'
 
 Loss:
   name: MultiLoss

configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml

@@ -54,6 +54,7 @@ Architecture:
         last_conv_stride: [1, 2]
         last_pool_type: avg
         last_pool_kernel_size: [2, 2]
+        data_format: 'NHWC'
       Head:
         name: MultiHead
         head_list:
@@ -69,6 +70,7 @@ Architecture:
           - SARHead:
               enc_dim: 512
               max_text_length: *max_text_length
+        data_format: 'NHWC'
     Student:
       pretrained:
       freeze_params: false
@@ -82,6 +84,7 @@ Architecture:
         last_conv_stride: [1, 2]
         last_pool_type: avg
         last_pool_kernel_size: [2, 2]
+        data_format: 'NHWC'
       Head:
         name: MultiHead
         head_list:
@@ -97,6 +100,7 @@ Architecture:
           - SARHead:
               enc_dim: 512
               max_text_length: *max_text_length
+        data_format: 'NHWC'
 Loss:
   name: CombinedLoss
   loss_config_list:

ppocr/modeling/backbones/det_mobilenet_v3.py

@@ -35,7 +35,13 @@ def make_divisible(v, divisor=8, min_value=None):
 
 class MobileNetV3(nn.Layer):
     def __init__(
-        self, in_channels=3, model_name="large", scale=0.5, disable_se=False, **kwargs
+        self,
+        in_channels=3,
+        model_name="large",
+        scale=0.5,
+        disable_se=False,
+        data_format="NCHW",
+        **kwargs,
     ):
         """
         the MobilenetV3 backbone network for detection module.
@@ -46,6 +52,7 @@ def __init__(
 
         self.disable_se = disable_se
 
+        self.nchw = data_format == "NCHW"
         if model_name == "large":
             cfg = [
                 # k, exp, c,  se,     nl,  s,
@@ -102,6 +109,7 @@ def __init__(
             groups=1,
             if_act=True,
             act="hardswish",
+            data_format=data_format,
         )
 
         self.stages = []
@@ -125,6 +133,7 @@ def __init__(
                     stride=s,
                     use_se=se,
                     act=nl,
+                    data_format=data_format,
                 )
             )
             inplanes = make_divisible(scale * c)
@@ -139,6 +148,7 @@ def __init__(
                 groups=1,
                 if_act=True,
                 act="hardswish",
+                data_format=data_format,
             )
         )
         self.stages.append(nn.Sequential(*block_list))
@@ -147,6 +157,8 @@ def __init__(
             self.add_sublayer(sublayer=stage, name="stage{}".format(i))
 
     def forward(self, x):
+        if not self.nchw:
+            x = x.transpose([0, 2, 3, 1])
         x = self.conv(x)
         out_list = []
         for stage in self.stages:
@@ -166,6 +178,7 @@ def __init__(
         groups=1,
         if_act=True,
         act=None,
+        data_format="NCHW",
     ):
         super(ConvBNLayer, self).__init__()
         self.if_act = if_act
@@ -178,9 +191,12 @@ def __init__(
             padding=padding,
             groups=groups,
             bias_attr=False,
+            data_format=data_format,
         )
 
-        self.bn = nn.BatchNorm(num_channels=out_channels, act=None)
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels, act=None, data_layout=data_format
+        )
 
     def forward(self, x):
         x = self.conv(x)
@@ -210,6 +226,7 @@ def __init__(
         stride,
         use_se,
         act=None,
+        data_format="NCHW",
     ):
         super(ResidualUnit, self).__init__()
         self.if_shortcut = stride == 1 and in_channels == out_channels
@@ -223,6 +240,7 @@ def __init__(
             padding=0,
             if_act=True,
             act=act,
+            data_format=data_format,
         )
         self.bottleneck_conv = ConvBNLayer(
             in_channels=mid_channels,
@@ -233,9 +251,10 @@ def __init__(
             groups=mid_channels,
             if_act=True,
             act=act,
+            data_format=data_format,
         )
         if self.if_se:
-            self.mid_se = SEModule(mid_channels)
+            self.mid_se = SEModule(mid_channels, data_format=data_format)
         self.linear_conv = ConvBNLayer(
             in_channels=mid_channels,
             out_channels=out_channels,
@@ -244,6 +263,7 @@ def __init__(
             padding=0,
             if_act=False,
             act=None,
+            data_format=data_format,
         )
 
     def forward(self, inputs):
@@ -258,22 +278,24 @@ def forward(self, inputs):
 
 
 class SEModule(nn.Layer):
-    def __init__(self, in_channels, reduction=4):
+    def __init__(self, in_channels, reduction=4, data_format="NCHW"):
         super(SEModule, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2D(1)
+        self.avg_pool = nn.AdaptiveAvgPool2D(1, data_format=data_format)
         self.conv1 = nn.Conv2D(
             in_channels=in_channels,
             out_channels=in_channels // reduction,
             kernel_size=1,
             stride=1,
             padding=0,
+            data_format=data_format,
         )
         self.conv2 = nn.Conv2D(
             in_channels=in_channels // reduction,
             out_channels=in_channels,
             kernel_size=1,
             stride=1,
             padding=0,
+            data_format=data_format,
         )
 
     def forward(self, inputs):