Merge pull request #6043 from LDOUBLEV/dygraph

add CAFPN and FEPAN

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

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/ch_PP-OCR_v3_det/
+  save_epoch_step: 100
+  eval_batch_step:
+  - 0
+  - 400
+  cal_metric_during_train: false
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./checkpoints/det_db/predicts_db.txt
+  distributed: true
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  model_type: det
+  Models:
+    Student:
+      model_type: det
+      algorithm: DB
+      Transform: null
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: true
+      Neck:
+        name: RSEFPN
+        out_channels: 96
+        shortcut: True
+      Head:
+        name: DBHead
+        k: 50
+    Student2:
+      model_type: det
+      algorithm: DB
+      Transform: null
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: true
+      Neck:
+        name: RSEFPN
+        out_channels: 96
+        shortcut: True
+      Head:
+        name: DBHead
+        k: 50
+    Teacher:
+      freeze_params: true
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: ResNet
+        in_channels: 3
+        layers: 50
+      Neck:
+        name: LKPAN
+        out_channels: 256
+      Head:
+        name: DBHead
+        kernel_list: [7,2,2]
+        k: 50
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDilaDBLoss:
+      weight: 1.0
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      - ["Student2", "Teacher"]
+      key: maps
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+  - DistillationDMLLoss:
+      model_name_pairs:
+      - ["Student", "Student2"]
+      maps_name: "thrink_maps"
+      weight: 1.0
+      # act: None
+      model_name_pairs: ["Student", "Student2"]
+      key: maps
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Student2"]
+      # key: maps
+      # name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: L2
+    factor: 5.0e-05
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student"]
+  key: head_out
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - CopyPaste:
+    - IaaAugment:
+        augmenter_args:
+        - type: Fliplr
+          args:
+            p: 0.5
+        - type: Affine
+          args:
+            rotate:
+            - -10
+            - 10
+        - type: Resize
+          args:
+            size:
+            - 0.5
+            - 3
+    - EastRandomCropData:
+        size:
+        - 960
+        - 960
+        max_tries: 50
+        keep_ratio: true
+    - MakeBorderMap:
+        shrink_ratio: 0.4
+        thresh_min: 0.3
+        thresh_max: 0.7
+    - MakeShrinkMap:
+        shrink_ratio: 0.4
+        min_text_size: 8
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - threshold_map
+        - threshold_mask
+        - shrink_map
+        - shrink_mask
+  loader:
+    shuffle: true
+    drop_last: false
+    batch_size_per_card: 8
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - DetResizeForTest: null
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - shape
+        - polys
+        - ignore_tags
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 1
+    num_workers: 2

configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/ch_PP-OCR_V3_det/
+  save_epoch_step: 100
+  eval_batch_step:
+  - 0
+  - 400
+  cal_metric_during_train: false
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./checkpoints/det_db/predicts_db.txt
+  distributed: true
+
+Architecture:
+  model_type: det
+  algorithm: DB
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+    disable_se: True
+  Neck:
+    name: RSEFPN
+    out_channels: 96
+    shortcut: True
+  Head:
+    name: DBHead
+    k: 50
+
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: DiceLoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: L2
+    factor: 5.0e-05
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - IaaAugment:
+        augmenter_args:
+        - type: Fliplr
+          args:
+            p: 0.5
+        - type: Affine
+          args:
+            rotate:
+            - -10
+            - 10
+        - type: Resize
+          args:
+            size:
+            - 0.5
+            - 3
+    - EastRandomCropData:
+        size:
+        - 960
+        - 960
+        max_tries: 50
+        keep_ratio: true
+    - MakeBorderMap:
+        shrink_ratio: 0.4
+        thresh_min: 0.3
+        thresh_max: 0.7
+    - MakeShrinkMap:
+        shrink_ratio: 0.4
+        min_text_size: 8
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - threshold_map
+        - threshold_mask
+        - shrink_map
+        - shrink_mask
+  loader:
+    shuffle: true
+    drop_last: false
+    batch_size_per_card: 8
+    num_workers: 4
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - DetLabelEncode: null
+    - DetResizeForTest: null
+    - NormalizeImage:
+        scale: 1./255.
+        mean:
+        - 0.485
+        - 0.456
+        - 0.406
+        std:
+        - 0.229
+        - 0.224
+        - 0.225
+        order: hwc
+    - ToCHWImage: null
+    - KeepKeys:
+        keep_keys:
+        - image
+        - shape
+        - polys
+        - ignore_tags
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 1
+    num_workers: 2

ppocr/modeling/heads/det_db_head.py

@@ -31,13 +31,14 @@ def get_bias_attr(k):
 
 
 class Head(nn.Layer):
-    def __init__(self, in_channels, name_list):
+    def __init__(self, in_channels, name_list, kernel_list=[3, 2, 2], **kwargs):
         super(Head, self).__init__()
+
         self.conv1 = nn.Conv2D(
             in_channels=in_channels,
             out_channels=in_channels // 4,
-            kernel_size=3,
-            padding=1,
+            kernel_size=kernel_list[0],
+            padding=int(kernel_list[0] // 2),
             weight_attr=ParamAttr(),
             bias_attr=False)
         self.conv_bn1 = nn.BatchNorm(
@@ -50,7 +51,7 @@ class Head(nn.Layer):
         self.conv2 = nn.Conv2DTranspose(
             in_channels=in_channels // 4,
             out_channels=in_channels // 4,
-            kernel_size=2,
+            kernel_size=kernel_list[1],
             stride=2,
             weight_attr=ParamAttr(
                 initializer=paddle.nn.initializer.KaimingUniform()),
@@ -65,7 +66,7 @@ class Head(nn.Layer):
         self.conv3 = nn.Conv2DTranspose(
             in_channels=in_channels // 4,
             out_channels=1,
-            kernel_size=2,
+            kernel_size=kernel_list[2],
             stride=2,
             weight_attr=ParamAttr(
                 initializer=paddle.nn.initializer.KaimingUniform()),
@@ -100,8 +101,8 @@ class DBHead(nn.Layer):
             'conv2d_57', 'batch_norm_49', 'conv2d_transpose_2', 'batch_norm_50',
             'conv2d_transpose_3', 'thresh'
         ]
-        self.binarize = Head(in_channels, binarize_name_list)
-        self.thresh = Head(in_channels, thresh_name_list)
+        self.binarize = Head(in_channels, binarize_name_list, **kwargs)
+        self.thresh = Head(in_channels, thresh_name_list, **kwargs)
 
     def step_function(self, x, y):
         return paddle.reciprocal(1 + paddle.exp(-self.k * (x - y)))

ppocr/modeling/necks/__init__.py

@@ -16,7 +16,7 @@ __all__ = ['build_neck']
 
 
 def build_neck(config):
-    from .db_fpn import DBFPN
+    from .db_fpn import DBFPN, RSEFPN, LKPAN
     from .east_fpn import EASTFPN
     from .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
@@ -26,8 +26,8 @@ def build_neck(config):
     from .fce_fpn import FCEFPN
     from .pren_fpn import PRENFPN
     support_dict = [
-        'FPN', 'FCEFPN', 'DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder',
-        'PGFPN', 'TableFPN', 'PRENFPN'
+        'FPN', 'FCEFPN', 'LKPAN', 'DBFPN', 'RSEFPN', 'EASTFPN', 'SASTFPN',
+        'SequenceEncoder', 'PGFPN', 'TableFPN', 'PRENFPN'
     ]
 
     module_name = config.pop('name')

ppocr/modeling/necks/db_fpn.py

@@ -20,6 +20,88 @@ import paddle
 from paddle import nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../../..')))
+
+from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule
+
+
+class DSConv(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 padding,
+                 stride=1,
+                 groups=None,
+                 if_act=True,
+                 act="relu",
+                 **kwargs):
+        super(DSConv, self).__init__()
+        if groups == None:
+            groups = in_channels
+        self.if_act = if_act
+        self.act = act
+        self.conv1 = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            bias_attr=False)
+
+        self.bn1 = nn.BatchNorm(num_channels=in_channels, act=None)
+
+        self.conv2 = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=int(in_channels * 4),
+            kernel_size=1,
+            stride=1,
+            bias_attr=False)
+
+        self.bn2 = nn.BatchNorm(num_channels=int(in_channels * 4), act=None)
+
+        self.conv3 = nn.Conv2D(
+            in_channels=int(in_channels * 4),
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            bias_attr=False)
+        self._c = [in_channels, out_channels]
+        if in_channels != out_channels:
+            self.conv_end = nn.Conv2D(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                bias_attr=False)
+
+    def forward(self, inputs):
+
+        x = self.conv1(inputs)
+        x = self.bn1(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        if self.if_act:
+            if self.act == "relu":
+                x = F.relu(x)
+            elif self.act == "hardswish":
+                x = F.hardswish(x)
+            else:
+                print("The activation function({}) is selected incorrectly.".
+                      format(self.act))
+                exit()
+
+        x = self.conv3(x)
+        if self._c[0] != self._c[1]:
+            x = x + self.conv_end(inputs)
+        return x
 
 
 class DBFPN(nn.Layer):
@@ -106,3 +188,171 @@ class DBFPN(nn.Layer):
 
         fuse = paddle.concat([p5, p4, p3, p2], axis=1)
         return fuse
+
+
+class RSELayer(nn.Layer):
+    def __init__(self, in_channels, out_channels, kernel_size, shortcut=True):
+        super(RSELayer, self).__init__()
+        weight_attr = paddle.nn.initializer.KaimingUniform()
+        self.out_channels = out_channels
+        self.in_conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=self.out_channels,
+            kernel_size=kernel_size,
+            padding=int(kernel_size // 2),
+            weight_attr=ParamAttr(initializer=weight_attr),
+            bias_attr=False)
+        self.se_block = SEModule(self.out_channels)
+        self.shortcut = shortcut
+
+    def forward(self, ins):
+        x = self.in_conv(ins)
+        if self.shortcut:
+            out = x + self.se_block(x)
+        else:
+            out = self.se_block(x)
+        return out
+
+
+class RSEFPN(nn.Layer):
+    def __init__(self, in_channels, out_channels, shortcut=True, **kwargs):
+        super(RSEFPN, self).__init__()
+        self.out_channels = out_channels
+        self.ins_conv = nn.LayerList()
+        self.inp_conv = nn.LayerList()
+
+        for i in range(len(in_channels)):
+            self.ins_conv.append(
+                RSELayer(
+                    in_channels[i],
+                    out_channels,
+                    kernel_size=1,
+                    shortcut=shortcut))
+            self.inp_conv.append(
+                RSELayer(
+                    out_channels,
+                    out_channels // 4,
+                    kernel_size=3,
+                    shortcut=shortcut))
+
+    def forward(self, x):
+        c2, c3, c4, c5 = x
+
+        in5 = self.ins_conv[3](c5)
+        in4 = self.ins_conv[2](c4)
+        in3 = self.ins_conv[1](c3)
+        in2 = self.ins_conv[0](c2)
+
+        out4 = in4 + F.upsample(
+            in5, scale_factor=2, mode="nearest", align_mode=1)  # 1/16
+        out3 = in3 + F.upsample(
+            out4, scale_factor=2, mode="nearest", align_mode=1)  # 1/8
+        out2 = in2 + F.upsample(
+            out3, scale_factor=2, mode="nearest", align_mode=1)  # 1/4
+
+        p5 = self.inp_conv[3](in5)
+        p4 = self.inp_conv[2](out4)
+        p3 = self.inp_conv[1](out3)
+        p2 = self.inp_conv[0](out2)
+
+        p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
+        p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
+        p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
+
+        fuse = paddle.concat([p5, p4, p3, p2], axis=1)
+        return fuse
+
+
+class LKPAN(nn.Layer):
+    def __init__(self, in_channels, out_channels, mode='large', **kwargs):
+        super(LKPAN, self).__init__()
+        self.out_channels = out_channels
+        weight_attr = paddle.nn.initializer.KaimingUniform()
+
+        self.ins_conv = nn.LayerList()
+        self.inp_conv = nn.LayerList()
+        # pan head
+        self.pan_head_conv = nn.LayerList()
+        self.pan_lat_conv = nn.LayerList()
+
+        if mode.lower() == 'lite':
+            p_layer = DSConv
+        elif mode.lower() == 'large':
+            p_layer = nn.Conv2D
+        else:
+            raise ValueError(
+                "mode can only be one of ['lite', 'large'], but received {}".
+                format(mode))
+
+        for i in range(len(in_channels)):
+            self.ins_conv.append(
+                nn.Conv2D(
+                    in_channels=in_channels[i],
+                    out_channels=self.out_channels,
+                    kernel_size=1,
+                    weight_attr=ParamAttr(initializer=weight_attr),
+                    bias_attr=False))
+
+            self.inp_conv.append(
+                p_layer(
+                    in_channels=self.out_channels,
+                    out_channels=self.out_channels // 4,
+                    kernel_size=9,
+                    padding=4,
+                    weight_attr=ParamAttr(initializer=weight_attr),
+                    bias_attr=False))
+
+            if i > 0:
+                self.pan_head_conv.append(
+                    nn.Conv2D(
+                        in_channels=self.out_channels // 4,
+                        out_channels=self.out_channels // 4,
+                        kernel_size=3,
+                        padding=1,
+                        stride=2,
+                        weight_attr=ParamAttr(initializer=weight_attr),
+                        bias_attr=False))
+            self.pan_lat_conv.append(
+                p_layer(
+                    in_channels=self.out_channels // 4,
+                    out_channels=self.out_channels // 4,
+                    kernel_size=9,
+                    padding=4,
+                    weight_attr=ParamAttr(initializer=weight_attr),
+                    bias_attr=False))
+
+    def forward(self, x):
+        c2, c3, c4, c5 = x
+
+        in5 = self.ins_conv[3](c5)
+        in4 = self.ins_conv[2](c4)
+        in3 = self.ins_conv[1](c3)
+        in2 = self.ins_conv[0](c2)
+
+        out4 = in4 + F.upsample(
+            in5, scale_factor=2, mode="nearest", align_mode=1)  # 1/16
+        out3 = in3 + F.upsample(
+            out4, scale_factor=2, mode="nearest", align_mode=1)  # 1/8
+        out2 = in2 + F.upsample(
+            out3, scale_factor=2, mode="nearest", align_mode=1)  # 1/4
+
+        f5 = self.inp_conv[3](in5)
+        f4 = self.inp_conv[2](out4)
+        f3 = self.inp_conv[1](out3)
+        f2 = self.inp_conv[0](out2)
+
+        pan3 = f3 + self.pan_head_conv[0](f2)
+        pan4 = f4 + self.pan_head_conv[1](pan3)
+        pan5 = f5 + self.pan_head_conv[2](pan4)
+
+        p2 = self.pan_lat_conv[0](f2)
+        p3 = self.pan_lat_conv[1](pan3)
+        p4 = self.pan_lat_conv[2](pan4)
+        p5 = self.pan_lat_conv[3](pan5)
+
+        p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
+        p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
+        p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
+
+        fuse = paddle.concat([p5, p4, p3, p2], axis=1)
+        return fuse