初始化以及适配mbv3并且实现从paddle模型的导入

networks/backbones/mobilenet_v3.py

@@ -0,0 +1,234 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import OrderedDict
+
+import torch.nn.functional as F
+from torch import nn
+import torch
+
+
+class HSwish(nn.Module):
+    def forward(self, x):
+        out = x * F.relu6(x + 3, inplace=True) / 6
+        return out
+
+
+class ConvBNACT(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, num_groups=1, act=None):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
+                              stride=stride, padding=padding, groups=num_groups,
+                              bias=False)
+        self.bn = nn.BatchNorm2d(out_channels)
+        if act == 'relu':
+            self.act = nn.ReLU()
+        elif act == 'hard_swish':
+            self.act = HSwish()
+        elif act is None:
+            self.act = None
+
+    def load_3rd_state_dict(self, _3rd_name, _state, _name_prefix):
+        to_load_state_dict = OrderedDict()
+        if _3rd_name == 'paddle':
+            to_load_state_dict['conv.weight'] = torch.Tensor(_state[f'{_name_prefix}_weights'])
+            to_load_state_dict['bn.weight'] = torch.Tensor(_state[f'{_name_prefix}_bn_scale'])
+            to_load_state_dict['bn.bias'] = torch.Tensor(_state[f'{_name_prefix}_bn_offset'])
+            to_load_state_dict['bn.running_mean'] = torch.Tensor(_state[f'{_name_prefix}_bn_mean'])
+            to_load_state_dict['bn.running_var'] = torch.Tensor(_state[f'{_name_prefix}_bn_variance'])
+            self.load_state_dict(to_load_state_dict)
+        else:
+            pass
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.act is not None:
+            x = self.act(x)
+        return x
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, num_in_filter, num_mid_filter, num_out_filter, stride, kernel_size, act=None, use_se=False):
+        super().__init__()
+        self.conv0 = ConvBNACT(in_channels=num_in_filter, out_channels=num_mid_filter, kernel_size=1, stride=1,
+                               padding=0, act=act)
+
+        self.conv1 = ConvBNACT(in_channels=num_mid_filter, out_channels=num_mid_filter, kernel_size=kernel_size,
+                               stride=stride,
+                               padding=int((kernel_size - 1) // 2), act=act, num_groups=num_mid_filter)
+        if use_se:
+            self.se = SEBlock(in_channels=num_mid_filter, out_channels=num_mid_filter)
+        else:
+            self.se = None
+
+        self.conv2 = ConvBNACT(in_channels=num_mid_filter, out_channels=num_out_filter, kernel_size=1, stride=1,
+                               padding=0)
+        self.not_add = num_in_filter != num_out_filter or stride != 1
+
+    def load_3rd_state_dict(self, _3rd_name, _state, _convolution_index):
+        if _3rd_name == 'paddle':
+            self.conv0.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_expand')
+            self.conv1.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_depthwise')
+            if self.se is not None:
+                self.se.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_se')
+            self.conv2.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_linear')
+        else:
+            pass
+        pass
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        if self.se is not None:
+            y = self.se(y)
+        y = self.conv2(y)
+        if self.not_add == False:
+            y = x + y
+        return y
+
+
+class SEBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, ratio=4):
+        super().__init__()
+        num_mid_filter = out_channels // ratio
+        self.pool = nn.AdaptiveAvgPool2d(1)
+        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=num_mid_filter, kernel_size=1, bias=True)
+        self.relu1 = nn.ReLU()
+        self.conv2 = nn.Conv2d(in_channels=num_mid_filter, kernel_size=1, out_channels=out_channels, bias=True)
+        self.relu2 = HSwish()
+
+    def load_3rd_state_dict(self, _3rd_name, _state, _name_prefix):
+        to_load_state_dict = OrderedDict()
+        if _3rd_name == 'paddle':
+            to_load_state_dict['conv1.weight'] = torch.Tensor(_state[f'{_name_prefix}_1_weights'])
+            to_load_state_dict['conv2.weight'] = torch.Tensor(_state[f'{_name_prefix}_2_weights'])
+            to_load_state_dict['conv1.bias'] = torch.Tensor(_state[f'{_name_prefix}_1_offset'])
+            to_load_state_dict['conv2.bias'] = torch.Tensor(_state[f'{_name_prefix}_2_offset'])
+            self.load_state_dict(to_load_state_dict)
+        else:
+            pass
+
+    def forward(self, x):
+        attn = self.pool(x)
+        attn = self.conv1(attn)
+        attn = self.relu1(attn)
+        attn = self.conv2(attn)
+        attn = self.relu2(attn)
+        return x * attn
+
+
+class MobileNetV3(nn.Module):
+    def __init__(self, in_channels, **kwargs):
+        super().__init__()
+        self.scale = kwargs.get('scale', 0.5)
+        model_name = kwargs.get('model_name', 'large')
+        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, 1)],
+                [3, 72, 24, False, 'relu', 1],
+                [5, 72, 40, True, 'relu', (2, 1)],
+                [5, 120, 40, True, 'relu', 1],
+                [5, 120, 40, True, 'relu', 1],
+                [3, 240, 80, False, 'hard_swish', 1],
+                [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, 1)],
+                [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, 1)],
+                [3, 72, 24, False, 'relu', (2, 1)],
+                [3, 88, 24, False, 'relu', 1],
+                [5, 96, 40, True, 'hard_swish', (2, 1)],
+                [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, 1)],
+                [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("mode[" + model_name +
+                                      "_model] is not implemented!")
+
+        supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
+        assert self.scale in supported_scale, "supported scale are {} but input scale is {}".format(supported_scale,
+                                                                                                    self.scale)
+
+        scale = self.scale
+        inplanes = self.inplanes
+        cfg = self.cfg
+        cls_ch_squeeze = self.cls_ch_squeeze
+        cls_ch_expand = self.cls_ch_expand
+        # conv1
+        self.conv1 = ConvBNACT(in_channels=in_channels,
+                               out_channels=self.make_divisible(inplanes * scale),
+                               kernel_size=3,
+                               stride=2,
+                               padding=1,
+                               num_groups=1,
+                               act='hard_swish')
+        i = 0
+        inplanes = self.make_divisible(inplanes * scale)
+        block_list = []
+        for layer_cfg in cfg:
+            block = ResidualUnit(num_in_filter=inplanes,
+                                 num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
+                                 num_out_filter=self.make_divisible(scale * layer_cfg[2]),
+                                 act=layer_cfg[4],
+                                 stride=layer_cfg[5],
+                                 kernel_size=layer_cfg[0],
+                                 use_se=layer_cfg[3])
+            block_list.append(block)
+            inplanes = self.make_divisible(scale * layer_cfg[2])
+
+        self.block_list = nn.Sequential(*block_list)
+        self.conv2 = ConvBNACT(in_channels=inplanes,
+                               out_channels=self.make_divisible(scale * cls_ch_squeeze),
+                               kernel_size=1,
+                               stride=1,
+                               padding=0,
+                               num_groups=1,
+                               act='hard_swish')
+
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+
+    def make_divisible(self, v, divisor=8, min_value=None):
+        if min_value is None:
+            min_value = divisor
+        new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+        if new_v < 0.9 * v:
+            new_v += divisor
+        return new_v
+
+    def load_3rd_state_dict(self, _3rd_name, _state):
+        if _3rd_name == 'paddle':
+            self.conv1.load_3rd_state_dict(_3rd_name, _state, 'conv1')
+            for m_block_index, m_block in enumerate(self.block_list, 2):
+                m_block.load_3rd_state_dict(_3rd_name, _state, m_block_index)
+            self.conv2.load_3rd_state_dict(_3rd_name, _state, 'conv_last')
+        else:
+            pass
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.block_list(x)
+        x = self.conv2(x)
+        x = self.pool(x)
+        return x