Spaces:
Runtime error
Runtime error
File size: 6,102 Bytes
7734d5b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import torch
import torch.nn as nn
class SiLU(nn.Module):
"""export-friendly version of nn.SiLU()"""
@staticmethod
def forward(x):
return x * torch.sigmoid(x)
def get_activation(name="silu", inplace=True):
if name == "silu":
module = nn.SiLU(inplace=inplace)
elif name == "relu":
module = nn.ReLU(inplace=inplace)
elif name == "lrelu":
module = nn.LeakyReLU(0.1, inplace=inplace)
else:
raise AttributeError("Unsupported act type: {}".format(name))
return module
class BaseConv(nn.Module):
"""A Conv2d -> Batchnorm -> silu/leaky relu block"""
def __init__(
self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"
):
super().__init__()
# same padding
pad = (ksize - 1) // 2
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=ksize,
stride=stride,
padding=pad,
groups=groups,
bias=bias,
)
self.bn = nn.BatchNorm2d(out_channels)
self.act = get_activation(act, inplace=True)
def forward(self, x):
return self.act(self.bn(self.conv(x)))
def fuseforward(self, x):
return self.act(self.conv(x))
class DWConv(nn.Module):
"""Depthwise Conv + Conv"""
def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"):
super().__init__()
self.dconv = BaseConv(
in_channels,
in_channels,
ksize=ksize,
stride=stride,
groups=in_channels,
act=act,
)
self.pconv = BaseConv(
in_channels, out_channels, ksize=1, stride=1, groups=1, act=act
)
def forward(self, x):
x = self.dconv(x)
return self.pconv(x)
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(
self,
in_channels,
out_channels,
shortcut=True,
expansion=0.5,
depthwise=False,
act="silu",
):
super().__init__()
hidden_channels = int(out_channels * expansion)
Conv = DWConv if depthwise else BaseConv
self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)
self.use_add = shortcut and in_channels == out_channels
def forward(self, x):
y = self.conv2(self.conv1(x))
if self.use_add:
y = y + x
return y
class ResLayer(nn.Module):
"Residual layer with `in_channels` inputs."
def __init__(self, in_channels: int):
super().__init__()
mid_channels = in_channels // 2
self.layer1 = BaseConv(
in_channels, mid_channels, ksize=1, stride=1, act="lrelu"
)
self.layer2 = BaseConv(
mid_channels, in_channels, ksize=3, stride=1, act="lrelu"
)
def forward(self, x):
out = self.layer2(self.layer1(x))
return x + out
class SPPBottleneck(nn.Module):
"""Spatial pyramid pooling layer used in YOLOv3-SPP"""
def __init__(
self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"
):
super().__init__()
hidden_channels = in_channels // 2
self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)
self.m = nn.ModuleList(
[
nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2)
for ks in kernel_sizes
]
)
conv2_channels = hidden_channels * (len(kernel_sizes) + 1)
self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)
def forward(self, x):
x = self.conv1(x)
x = torch.cat([x] + [m(x) for m in self.m], dim=1)
x = self.conv2(x)
return x
class CSPLayer(nn.Module):
"""C3 in yolov5, CSP Bottleneck with 3 convolutions"""
def __init__(
self,
in_channels,
out_channels,
n=1,
shortcut=True,
expansion=0.5,
depthwise=False,
act="silu",
):
"""
Args:
in_channels (int): input channels.
out_channels (int): output channels.
n (int): number of Bottlenecks. Default value: 1.
"""
# ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
hidden_channels = int(out_channels * expansion) # hidden channels
self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)
module_list = [
Bottleneck(
hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act
)
for _ in range(n)
]
self.m = nn.Sequential(*module_list)
def forward(self, x):
x_1 = self.conv1(x)
x_2 = self.conv2(x)
x_1 = self.m(x_1)
x = torch.cat((x_1, x_2), dim=1)
return self.conv3(x)
class Focus(nn.Module):
"""Focus width and height information into channel space."""
def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"):
super().__init__()
self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)
def forward(self, x):
# shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
patch_top_left = x[..., ::2, ::2]
patch_top_right = x[..., ::2, 1::2]
patch_bot_left = x[..., 1::2, ::2]
patch_bot_right = x[..., 1::2, 1::2]
x = torch.cat(
(
patch_top_left,
patch_bot_left,
patch_top_right,
patch_bot_right,
),
dim=1,
)
return self.conv(x)
|