updata micronet

ppocr/modeling/backbones/rec_micronet.py

@@ -1,11 +1,29 @@
+# copyright (c) 2021 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.
+"""
+This code is refer from: 
+https://github.com/liyunsheng13/micronet/blob/main/backbone/micronet.py
+https://github.com/liyunsheng13/micronet/blob/main/backbone/activation.py
+"""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import paddle
 import paddle.nn as nn
 
-from ppocr.optimizer.dyshiftmax import DYShiftMax
 from ppocr.modeling.backbones.det_mobilenet_v3 import make_divisible
 
 M0_cfgs = [
@@ -61,24 +79,6 @@ def get_micronet_config(mode):
     return eval(mode + '_cfgs')
 
 
-class h_sigmoid(nn.Layer):
-    def __init__(self):
-        super(h_sigmoid, self).__init__()
-        self.relu = nn.ReLU6()
-
-    def forward(self, x):
-        return self.relu(x + 3) / 6
-
-
-class h_swish(nn.Layer):
-    def __init__(self):
-        super(h_swish, self).__init__()
-        self.sigmoid = h_sigmoid()
-
-    def forward(self, x):
-        return x * self.sigmoid(x)
-
-
 class MaxGroupPooling(nn.Layer):
     def __init__(self, channel_per_group=2):
         super(MaxGroupPooling, self).__init__()
@@ -220,6 +220,94 @@ def forward(self, x):
         return out
 
 
+class DYShiftMax(nn.Layer):
+    def __init__(self,
+                 inp,
+                 oup,
+                 reduction=4,
+                 act_max=1.0,
+                 act_relu=True,
+                 init_a=[0.0, 0.0],
+                 init_b=[0.0, 0.0],
+                 relu_before_pool=False,
+                 g=None,
+                 expansion=False):
+        super(DYShiftMax, self).__init__()
+        self.oup = oup
+        self.act_max = act_max * 2
+        self.act_relu = act_relu
+        self.avg_pool = nn.Sequential(nn.ReLU() if relu_before_pool == True else
+                                      nn.Sequential(), nn.AdaptiveAvgPool2D(1))
+
+        self.exp = 4 if act_relu else 2
+        self.init_a = init_a
+        self.init_b = init_b
+
+        # determine squeeze
+        squeeze = make_divisible(inp // reduction, 4)
+        if squeeze < 4:
+            squeeze = 4
+
+        self.fc = nn.Sequential(
+            nn.Linear(inp, squeeze),
+            nn.ReLU(), nn.Linear(squeeze, oup * self.exp), nn.Hardsigmoid())
+
+        if g is None:
+            g = 1
+        self.g = g[1]
+        if self.g != 1 and expansion:
+            self.g = inp // self.g
+
+        self.gc = inp // self.g
+        index = paddle.to_tensor([range(inp)])
+        index = paddle.reshape(index, [1, inp, 1, 1])
+        index = paddle.reshape(index, [1, self.g, self.gc, 1, 1])
+        indexgs = paddle.split(index, [1, self.g - 1], axis=1)
+        indexgs = paddle.concat((indexgs[1], indexgs[0]), axis=1)
+        indexs = paddle.split(indexgs, [1, self.gc - 1], axis=2)
+        indexs = paddle.concat((indexs[1], indexs[0]), axis=2)
+        self.index = paddle.reshape(indexs, [inp])
+        self.expansion = expansion
+
+    def forward(self, x):
+        x_in = x
+        x_out = x
+
+        b, c, _, _ = x_in.shape
+        y = self.avg_pool(x_in)
+        y = paddle.reshape(y, [b, c])
+        y = self.fc(y)
+        y = paddle.reshape(y, [b, self.oup * self.exp, 1, 1])
+        y = (y - 0.5) * self.act_max
+
+        n2, c2, h2, w2 = x_out.shape
+        x2 = paddle.to_tensor(x_out.numpy()[:, self.index.numpy(), :, :])
+
+        if self.exp == 4:
+            temp = y.shape
+            a1, b1, a2, b2 = paddle.split(y, temp[1] // self.oup, axis=1)
+
+            a1 = a1 + self.init_a[0]
+            a2 = a2 + self.init_a[1]
+
+            b1 = b1 + self.init_b[0]
+            b2 = b2 + self.init_b[1]
+
+            z1 = x_out * a1 + x2 * b1
+            z2 = x_out * a2 + x2 * b2
+
+            out = paddle.maximum(z1, z2)
+
+        elif self.exp == 2:
+            temp = y.shape
+            a1, b1 = paddle.split(y, temp[1] // self.oup, axis=1)
+            a1 = a1 + self.init_a[0]
+            b1 = b1 + self.init_b[0]
+            out = x_out * a1 + x2 * b1
+
+        return out
+
+
 class DYMicroBlock(nn.Layer):
     def __init__(self,
                  inp,
@@ -347,7 +435,15 @@ def forward(self, x):
 
 
 class MicroNet(nn.Layer):
-    def __init__(self, mode='M0', **kwargs):
+    """
+        the MicroNet backbone network for recognition module.
+        Args:
+            mode(str): {'M0', 'M1', 'M2', 'M3'} 
+                Four models are proposed based on four different computational costs (4M, 6M, 12M, 21M MAdds)
+                Default: 'M3'.
+    """
+
+    def __init__(self, mode='M3', **kwargs):
         super(MicroNet, self).__init__()
 
         self.cfgs = get_micronet_config(mode)