372 lines
15 KiB
Python
372 lines
15 KiB
Python
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# This file contains modules common to various models
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import math
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import numpy as np
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import requests
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import torch
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import torch.nn as nn
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from PIL import Image, ImageDraw
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from Face_facial.utils.datasets import letterbox
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from Face_facial.utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
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from Face_facial.utils.plots import color_list
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def autopad(k, p=None): # kernel, padding
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# Pad to 'same'
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if p is None:
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p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
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return p
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def channel_shuffle(x, groups):
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batchsize, num_channels, height, width = x.data.size()
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channels_per_group = num_channels // groups
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# reshape
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x = x.view(batchsize, groups, channels_per_group, height, width)
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x = torch.transpose(x, 1, 2).contiguous()
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# flatten
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x = x.view(batchsize, -1, height, width)
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return x
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def DWConv(c1, c2, k=1, s=1, act=True):
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# Depthwise convolution
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return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
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class Conv(nn.Module):
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# Standard convolution
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Conv, self).__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
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self.bn = nn.BatchNorm2d(c2)
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self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
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#self.act = self.act = nn.LeakyReLU(0.1, inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
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def forward(self, x):
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return self.act(self.bn(self.conv(x)))
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def fuseforward(self, x):
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return self.act(self.conv(x))
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class StemBlock(nn.Module):
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def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
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super(StemBlock, self).__init__()
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self.stem_1 = Conv(c1, c2, k, s, p, g, act)
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self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
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self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
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self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True)
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self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
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def forward(self, x):
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stem_1_out = self.stem_1(x)
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stem_2a_out = self.stem_2a(stem_1_out)
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stem_2b_out = self.stem_2b(stem_2a_out)
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stem_2p_out = self.stem_2p(stem_1_out)
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out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1))
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return out
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class Bottleneck(nn.Module):
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# Standard bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super(Bottleneck, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c2, 3, 1, g=g)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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class BottleneckCSP(nn.Module):
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(BottleneckCSP, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
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self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
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self.cv4 = Conv(2 * c_, c2, 1, 1)
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self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
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self.act = nn.LeakyReLU(0.1, inplace=True)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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def forward(self, x):
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y1 = self.cv3(self.m(self.cv1(x)))
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y2 = self.cv2(x)
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return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
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class C3(nn.Module):
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# CSP Bottleneck with 3 convolutions
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(C3, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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def forward(self, x):
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return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
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class ShuffleV2Block(nn.Module):
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def __init__(self, inp, oup, stride):
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super(ShuffleV2Block, self).__init__()
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if not (1 <= stride <= 3):
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raise ValueError('illegal stride value')
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self.stride = stride
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branch_features = oup // 2
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assert (self.stride != 1) or (inp == branch_features << 1)
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if self.stride > 1:
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self.branch1 = nn.Sequential(
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self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
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nn.BatchNorm2d(inp),
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nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.SiLU(),
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)
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else:
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self.branch1 = nn.Sequential()
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self.branch2 = nn.Sequential(
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nn.Conv2d(inp if (self.stride > 1) else branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.SiLU(),
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self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
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nn.BatchNorm2d(branch_features),
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nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.SiLU(),
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)
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@staticmethod
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def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
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return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
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def forward(self, x):
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if self.stride == 1:
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x1, x2 = x.chunk(2, dim=1)
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out = torch.cat((x1, self.branch2(x2)), dim=1)
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else:
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out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
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out = channel_shuffle(out, 2)
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return out
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class SPP(nn.Module):
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# Spatial pyramid pooling layer used in YOLOv3-SPP
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def __init__(self, c1, c2, k=(5, 9, 13)):
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super(SPP, self).__init__()
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c_ = c1 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
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def forward(self, x):
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x = self.cv1(x)
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return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
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class Focus(nn.Module):
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# Focus wh information into c-space
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Focus, self).__init__()
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
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# self.contract = Contract(gain=2)
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def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
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return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
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# return self.conv(self.contract(x))
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class Contract(nn.Module):
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# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
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def __init__(self, gain=2):
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super().__init__()
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self.gain = gain
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def forward(self, x):
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N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
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s = self.gain
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x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
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x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
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return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
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class Expand(nn.Module):
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# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
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def __init__(self, gain=2):
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super().__init__()
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self.gain = gain
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def forward(self, x):
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N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
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s = self.gain
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x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
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x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
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return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
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class Concat(nn.Module):
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# Concatenate a list of tensors along dimension
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def __init__(self, dimension=1):
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super(Concat, self).__init__()
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self.d = dimension
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def forward(self, x):
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return torch.cat(x, self.d)
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class NMS(nn.Module):
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# Non-Maximum Suppression (NMS) module
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conf = 0.25 # confidence threshold
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iou = 0.45 # IoU threshold
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classes = None # (optional list) filter by class
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def __init__(self):
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super(NMS, self).__init__()
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def forward(self, x):
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return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
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class autoShape(nn.Module):
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# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
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img_size = 640 # inference size (pixels)
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conf = 0.25 # NMS confidence threshold
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iou = 0.45 # NMS IoU threshold
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classes = None # (optional list) filter by class
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def __init__(self, model):
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super(autoShape, self).__init__()
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self.model = model.eval()
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def autoshape(self):
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print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
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return self
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def forward(self, imgs, size=640, augment=False, profile=False):
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# Inference from various sources. For height=720, width=1280, RGB images example inputs are:
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# filename: imgs = 'data/samples/zidane.jpg'
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# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
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# PIL: = Image.open('image.jpg') # HWC x(720,1280,3)
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# numpy: = np.zeros((720,1280,3)) # HWC
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# torch: = torch.zeros(16,3,720,1280) # BCHW
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# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
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p = next(self.model.parameters()) # for device and type
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if isinstance(imgs, torch.Tensor): # torch
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return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
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# Pre-process
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n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
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shape0, shape1 = [], [] # image and inference shapes
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for i, im in enumerate(imgs):
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if isinstance(im, str): # filename or uri
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im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im) # open
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im = np.array(im) # to numpy
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if im.shape[0] < 5: # image in CHW
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im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
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im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
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s = im.shape[:2] # HWC
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shape0.append(s) # image shape
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g = (size / max(s)) # gain
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shape1.append([y * g for y in s])
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imgs[i] = im # update
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shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
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x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
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x = np.stack(x, 0) if n > 1 else x[0][None] # stack
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x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
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x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
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# Inference
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with torch.no_grad():
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y = self.model(x, augment, profile)[0] # forward
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y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
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# Post-process
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for i in range(n):
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scale_coords(shape1, y[i][:, :4], shape0[i])
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return Detections(imgs, y, self.names)
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class Detections:
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# detections class for YOLOv5 inference results
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def __init__(self, imgs, pred, names=None):
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super(Detections, self).__init__()
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d = pred[0].device # device
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gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
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self.imgs = imgs # list of images as numpy arrays
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
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self.names = names # class names
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self.xyxy = pred # xyxy pixels
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
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self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
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||
|
|
self.n = len(self.pred)
|
||
|
|
|
||
|
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def display(self, pprint=False, show=False, save=False, render=False):
|
||
|
|
colors = color_list()
|
||
|
|
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
|
||
|
|
str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
|
||
|
|
if pred is not None:
|
||
|
|
for c in pred[:, -1].unique():
|
||
|
|
n = (pred[:, -1] == c).sum() # detections per class
|
||
|
|
str += f'{n} {self.names[int(c)]}s, ' # add to string
|
||
|
|
if show or save or render:
|
||
|
|
img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
|
||
|
|
for *box, conf, cls in pred: # xyxy, confidence, class
|
||
|
|
# str += '%s %.2f, ' % (names[int(cls)], conf) # label
|
||
|
|
ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot
|
||
|
|
if pprint:
|
||
|
|
print(str)
|
||
|
|
if show:
|
||
|
|
img.show(f'Image {i}') # show
|
||
|
|
if save:
|
||
|
|
f = f'results{i}.jpg'
|
||
|
|
str += f"saved to '{f}'"
|
||
|
|
img.save(f) # save
|
||
|
|
if render:
|
||
|
|
self.imgs[i] = np.asarray(img)
|
||
|
|
|
||
|
|
def print(self):
|
||
|
|
self.display(pprint=True) # print results
|
||
|
|
|
||
|
|
def show(self):
|
||
|
|
self.display(show=True) # show results
|
||
|
|
|
||
|
|
def save(self):
|
||
|
|
self.display(save=True) # save results
|
||
|
|
|
||
|
|
def render(self):
|
||
|
|
self.display(render=True) # render results
|
||
|
|
return self.imgs
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.n
|
||
|
|
|
||
|
|
def tolist(self):
|
||
|
|
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||
|
|
x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
|
||
|
|
for d in x:
|
||
|
|
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||
|
|
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class Classify(nn.Module):
|
||
|
|
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||
|
|
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||
|
|
super(Classify, self).__init__()
|
||
|
|
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||
|
|
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
||
|
|
self.flat = nn.Flatten()
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||
|
|
return self.flat(self.conv(z)) # flatten to x(b,c2)
|