add user defined function support in quantization and unittest and demo (#319)

Author: slf12

demo/models/__init__.py

@@ -5,6 +5,7 @@
 from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2
 from .pvanet import PVANet
 from .slimfacenet import SlimFaceNet_A_x0_60, SlimFaceNet_B_x0_75, SlimFaceNet_C_x0_75
+from .mobilenet_v3 import *
 __all__ = [
     "model_list", "MobileNet", "ResNet34", "ResNet50", "MobileNetV2", "PVANet",
     "ResNet50_vd", "ResNet101_vd", "MobileNetV2_x0_25"
@@ -13,3 +14,6 @@
     'MobileNet', 'ResNet34', 'ResNet50', 'MobileNetV2', 'PVANet',
     'ResNet50_vd', "ResNet101_vd", "MobileNetV2_x0_25"
 ]
+
+__all__ += mobilenet_v3.__all__
+model_list += mobilenet_v3.__all__

demo/models/mobilenet_v3.py

@@ -0,0 +1,303 @@
+import paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+import math
+
+__all__ = [
+    'MobileNetV3', 'MobileNetV3_small_x0_25', 'MobileNetV3_small_x0_5',
+    'MobileNetV3_small_x0_75', 'MobileNetV3_small_x1_0',
+    'MobileNetV3_small_x1_25', 'MobileNetV3_large_x0_25',
+    'MobileNetV3_large_x0_5', 'MobileNetV3_large_x0_75',
+    'MobileNetV3_large_x1_0', 'MobileNetV3_large_x1_25',
+    'MobileNetV3_large_x2_0'
+]
+
+
+class MobileNetV3():
+    def __init__(self, scale=1.0, model_name='small'):
+        self.scale = scale
+        self.inplanes = 16
+        if model_name == "large":
+            self.cfg = [
+                # k, exp, c,  se,     nl,  s,
+                [3, 16, 16, False, 'relu', 1],
+                [3, 64, 24, False, 'relu', 2],
+                [3, 72, 24, False, 'relu', 1],
+                [5, 72, 40, True, 'relu', 2],
+                [5, 120, 40, True, 'relu', 1],
+                [5, 120, 40, True, 'relu', 1],
+                [3, 240, 80, False, 'hard_swish', 2],
+                [3, 200, 80, False, 'hard_swish', 1],
+                [3, 184, 80, False, 'hard_swish', 1],
+                [3, 184, 80, False, 'hard_swish', 1],
+                [3, 480, 112, True, 'hard_swish', 1],
+                [3, 672, 112, True, 'hard_swish', 1],
+                [5, 672, 160, True, 'hard_swish', 2],
+                [5, 960, 160, True, 'hard_swish', 1],
+                [5, 960, 160, True, 'hard_swish', 1],
+            ]
+            self.cls_ch_squeeze = 960
+            self.cls_ch_expand = 1280
+        elif model_name == "small":
+            self.cfg = [
+                # k, exp, c,  se,     nl,  s,
+                [3, 16, 16, True, 'relu', 2],
+                [3, 72, 24, False, 'relu', 2],
+                [3, 88, 24, False, 'relu', 1],
+                [5, 96, 40, True, 'hard_swish', 2],
+                [5, 240, 40, True, 'hard_swish', 1],
+                [5, 240, 40, True, 'hard_swish', 1],
+                [5, 120, 48, True, 'hard_swish', 1],
+                [5, 144, 48, True, 'hard_swish', 1],
+                [5, 288, 96, True, 'hard_swish', 2],
+                [5, 576, 96, True, 'hard_swish', 1],
+                [5, 576, 96, True, 'hard_swish', 1],
+            ]
+            self.cls_ch_squeeze = 576
+            self.cls_ch_expand = 1280
+        else:
+            raise NotImplementedError
+
+    def net(self, input, class_dim=1000):
+        scale = self.scale
+        inplanes = self.inplanes
+        cfg = self.cfg
+        cls_ch_squeeze = self.cls_ch_squeeze
+        cls_ch_expand = self.cls_ch_expand
+
+        #conv1
+        conv = self.conv_bn_layer(
+            input,
+            filter_size=3,
+            #num_filters=int(scale*inplanes),
+            num_filters=inplanes if scale <= 1.0 else int(inplanes * scale),
+            stride=2,
+            padding=1,
+            num_groups=1,
+            if_act=True,
+            act='hard_swish',
+            name='conv1')
+        print(conv.shape)
+        i = 0
+        for layer_cfg in cfg:
+            conv = self.residual_unit(
+                input=conv,
+                num_in_filter=inplanes,
+                num_mid_filter=int(scale * layer_cfg[1]),
+                num_out_filter=int(scale * layer_cfg[2]),
+                act=layer_cfg[4],
+                stride=layer_cfg[5],
+                filter_size=layer_cfg[0],
+                use_se=layer_cfg[3],
+                name='conv' + str(i + 2))
+
+            inplanes = int(scale * layer_cfg[2])
+            i += 1
+
+        conv = self.conv_bn_layer(
+            input=conv,
+            filter_size=1,
+            num_filters=int(scale * cls_ch_squeeze),
+            stride=1,
+            padding=0,
+            num_groups=1,
+            if_act=True,
+            act='hard_swish',
+            name='conv_last')
+        conv = fluid.layers.pool2d(
+            input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
+        conv = fluid.layers.conv2d(
+            input=conv,
+            num_filters=cls_ch_expand,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            act=None,
+            param_attr=ParamAttr(name='last_1x1_conv_weights'),
+            bias_attr=False)
+        #conv = fluid.layers.hard_swish(conv)
+        conv = self.hard_swish(conv)
+        out = fluid.layers.fc(input=conv,
+                              size=class_dim,
+                              act='softmax',
+                              param_attr=ParamAttr(name='fc_weights'),
+                              bias_attr=ParamAttr(name='fc_offset'))
+        return out
+
+    def conv_bn_layer(self,
+                      input,
+                      filter_size,
+                      num_filters,
+                      stride,
+                      padding,
+                      num_groups=1,
+                      if_act=True,
+                      act=None,
+                      name=None,
+                      use_cudnn=True):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            act=None,
+            use_cudnn=use_cudnn,
+            param_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+        bn_name = name + '_bn'
+        bn = fluid.layers.batch_norm(
+            input=conv,
+            param_attr=ParamAttr(
+                name=bn_name + "_scale",
+                regularizer=fluid.regularizer.L2DecayRegularizer(
+                    regularization_coeff=0.0)),
+            bias_attr=ParamAttr(
+                name=bn_name + "_offset",
+                regularizer=fluid.regularizer.L2DecayRegularizer(
+                    regularization_coeff=0.0)),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+        if if_act:
+            if act == 'relu':
+                bn = fluid.layers.relu(bn)
+            elif act == 'hard_swish':
+                #bn = fluid.layers.hard_swish(bn)
+                bn = self.hard_swish(bn)
+        return bn
+
+    def hard_swish(self, x):
+        return x * fluid.layers.relu6(x + 3) / 6.
+
+    def se_block(self, input, num_out_filter, ratio=4, name=None):
+        num_mid_filter = int(num_out_filter // ratio)
+        pool = fluid.layers.pool2d(
+            input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
+        conv1 = fluid.layers.conv2d(
+            input=pool,
+            filter_size=1,
+            num_filters=num_mid_filter,
+            act='relu',
+            param_attr=ParamAttr(name=name + '_1_weights'),
+            bias_attr=ParamAttr(name=name + '_1_offset'))
+        conv2 = fluid.layers.conv2d(
+            input=conv1,
+            filter_size=1,
+            num_filters=num_out_filter,
+            act='hard_sigmoid',
+            param_attr=ParamAttr(name=name + '_2_weights'),
+            bias_attr=ParamAttr(name=name + '_2_offset'))
+
+        scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
+        return scale
+
+    def residual_unit(self,
+                      input,
+                      num_in_filter,
+                      num_mid_filter,
+                      num_out_filter,
+                      stride,
+                      filter_size,
+                      act=None,
+                      use_se=False,
+                      name=None):
+
+        input_data = input
+        conv0 = self.conv_bn_layer(
+            input=input,
+            filter_size=1,
+            num_filters=num_mid_filter,
+            stride=1,
+            padding=0,
+            if_act=True,
+            act=act,
+            name=name + '_expand')
+
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            filter_size=filter_size,
+            num_filters=num_mid_filter,
+            stride=stride,
+            padding=int((filter_size - 1) // 2),
+            if_act=True,
+            act=act,
+            num_groups=num_mid_filter,
+            use_cudnn=False,
+            name=name + '_depthwise')
+
+        if use_se:
+            with fluid.name_scope('se_block_skip'):
+                conv1 = self.se_block(
+                    input=conv1,
+                    num_out_filter=num_mid_filter,
+                    name=name + '_se')
+
+        conv2 = self.conv_bn_layer(
+            input=conv1,
+            filter_size=1,
+            num_filters=num_out_filter,
+            stride=1,
+            padding=0,
+            if_act=False,
+            name=name + '_linear')
+        if num_in_filter != num_out_filter or stride != 1:
+            return conv2
+        else:
+            return fluid.layers.elementwise_add(
+                x=input_data, y=conv2, act=None)
+
+
+def MobileNetV3_small_x0_25():
+    model = MobileNetV3(model_name='small', scale=0.25)
+    return model
+
+
+def MobileNetV3_small_x0_5():
+    model = MobileNetV3(model_name='small', scale=0.5)
+    return model
+
+
+def MobileNetV3_small_x0_75():
+    model = MobileNetV3(model_name='small', scale=0.75)
+    return model
+
+
+def MobileNetV3_small_x1_0():
+    model = MobileNetV3(model_name='small', scale=1.0)
+    return model
+
+
+def MobileNetV3_small_x1_25():
+    model = MobileNetV3(model_name='small', scale=1.25)
+    return model
+
+
+def MobileNetV3_large_x0_25():
+    model = MobileNetV3(model_name='large', scale=0.25)
+    return model
+
+
+def MobileNetV3_large_x0_5():
+    model = MobileNetV3(model_name='large', scale=0.5)
+    return model
+
+
+def MobileNetV3_large_x0_75():
+    model = MobileNetV3(model_name='large', scale=0.75)
+    return model
+
+
+def MobileNetV3_large_x1_0():
+    model = MobileNetV3(model_name='large', scale=1.0)
+    return model
+
+
+def MobileNetV3_large_x1_25():
+    model = MobileNetV3(model_name='large', scale=1.25)
+    return model
+
+
+def MobileNetV3_large_x2_0():
+    model = MobileNetV3(model_name='large', scale=2.0)
+    return model