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298 lines
11 KiB
298 lines
11 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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""" |
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Convolution modules |
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""" |
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import math |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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__all__ = [ |
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'Conv', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv', 'ChannelAttention', |
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'SpatialAttention', 'CBAM', 'Concat', 'RepConv'] |
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def autopad(k, p=None, d=1): # kernel, padding, dilation |
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"""Pad to 'same' shape outputs.""" |
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if d > 1: |
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k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size |
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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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class Conv(nn.Module): |
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"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation).""" |
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default_act = nn.SiLU() # default activation |
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): |
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"""Initialize Conv layer with given arguments including activation.""" |
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super().__init__() |
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) |
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self.bn = nn.BatchNorm2d(c2) |
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self.act = self.default_act 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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"""Apply convolution, batch normalization and activation to input tensor.""" |
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return self.act(self.bn(self.conv(x))) |
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def forward_fuse(self, x): |
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"""Perform transposed convolution of 2D data.""" |
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return self.act(self.conv(x)) |
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class Conv2(Conv): |
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"""Simplified RepConv module with Conv fusing.""" |
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def __init__(self, c1, c2, k=3, s=1, p=None, g=1, d=1, act=True): |
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"""Initialize Conv layer with given arguments including activation.""" |
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super().__init__(c1, c2, k, s, p, g=g, d=d, act=act) |
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self.cv2 = nn.Conv2d(c1, c2, 1, s, autopad(1, p, d), groups=g, dilation=d, bias=False) # add 1x1 conv |
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def forward(self, x): |
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"""Apply convolution, batch normalization and activation to input tensor.""" |
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return self.act(self.bn(self.conv(x) + self.cv2(x))) |
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def fuse_convs(self): |
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"""Fuse parallel convolutions.""" |
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w = torch.zeros_like(self.conv.weight.data) |
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i = [x // 2 for x in w.shape[2:]] |
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w[:, :, i[0] - 1:i[0], i[1] - 1:i[1]] = self.cv2.weight.data.clone() |
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self.conv.weight.data += w |
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self.__delattr__('cv2') |
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class LightConv(nn.Module): |
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"""Light convolution with args(ch_in, ch_out, kernel). |
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https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py |
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""" |
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def __init__(self, c1, c2, k=1, act=nn.ReLU()): |
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"""Initialize Conv layer with given arguments including activation.""" |
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super().__init__() |
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self.conv1 = Conv(c1, c2, 1, act=False) |
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self.conv2 = DWConv(c2, c2, k, act=act) |
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def forward(self, x): |
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"""Apply 2 convolutions to input tensor.""" |
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return self.conv2(self.conv1(x)) |
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class DWConv(Conv): |
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"""Depth-wise convolution.""" |
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def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation |
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super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) |
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class DWConvTranspose2d(nn.ConvTranspose2d): |
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"""Depth-wise transpose convolution.""" |
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def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out |
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super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) |
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class ConvTranspose(nn.Module): |
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"""Convolution transpose 2d layer.""" |
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default_act = nn.SiLU() # default activation |
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def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True): |
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"""Initialize ConvTranspose2d layer with batch normalization and activation function.""" |
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super().__init__() |
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self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn) |
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self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity() |
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self.act = self.default_act 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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"""Applies transposed convolutions, batch normalization and activation to input.""" |
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return self.act(self.bn(self.conv_transpose(x))) |
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def forward_fuse(self, x): |
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"""Applies activation and convolution transpose operation to input.""" |
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return self.act(self.conv_transpose(x)) |
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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().__init__() |
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act=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 GhostConv(nn.Module): |
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"""Ghost Convolution https://github.com/huawei-noah/ghostnet.""" |
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups |
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super().__init__() |
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c_ = c2 // 2 # hidden channels |
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self.cv1 = Conv(c1, c_, k, s, None, g, act=act) |
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self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) |
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def forward(self, x): |
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"""Forward propagation through a Ghost Bottleneck layer with skip connection.""" |
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y = self.cv1(x) |
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return torch.cat((y, self.cv2(y)), 1) |
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class RepConv(nn.Module): |
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"""RepConv is a basic rep-style block, including training and deploy status |
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This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py |
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""" |
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default_act = nn.SiLU() # default activation |
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def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False): |
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super().__init__() |
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assert k == 3 and p == 1 |
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self.g = g |
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self.c1 = c1 |
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self.c2 = c2 |
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self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() |
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self.bn = nn.BatchNorm2d(num_features=c1) if bn and c2 == c1 and s == 1 else None |
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self.conv1 = Conv(c1, c2, k, s, p=p, g=g, act=False) |
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self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False) |
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def forward_fuse(self, x): |
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"""Forward process""" |
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return self.act(self.conv(x)) |
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def forward(self, x): |
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"""Forward process""" |
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id_out = 0 if self.bn is None else self.bn(x) |
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return self.act(self.conv1(x) + self.conv2(x) + id_out) |
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def get_equivalent_kernel_bias(self): |
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kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1) |
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kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2) |
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kernelid, biasid = self._fuse_bn_tensor(self.bn) |
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return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid |
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def _avg_to_3x3_tensor(self, avgp): |
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channels = self.c1 |
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groups = self.g |
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kernel_size = avgp.kernel_size |
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input_dim = channels // groups |
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k = torch.zeros((channels, input_dim, kernel_size, kernel_size)) |
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k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2 |
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return k |
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def _pad_1x1_to_3x3_tensor(self, kernel1x1): |
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if kernel1x1 is None: |
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return 0 |
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else: |
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return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1]) |
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def _fuse_bn_tensor(self, branch): |
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if branch is None: |
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return 0, 0 |
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if isinstance(branch, Conv): |
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kernel = branch.conv.weight |
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running_mean = branch.bn.running_mean |
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running_var = branch.bn.running_var |
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gamma = branch.bn.weight |
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beta = branch.bn.bias |
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eps = branch.bn.eps |
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elif isinstance(branch, nn.BatchNorm2d): |
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if not hasattr(self, 'id_tensor'): |
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input_dim = self.c1 // self.g |
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kernel_value = np.zeros((self.c1, input_dim, 3, 3), dtype=np.float32) |
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for i in range(self.c1): |
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kernel_value[i, i % input_dim, 1, 1] = 1 |
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self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) |
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kernel = self.id_tensor |
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running_mean = branch.running_mean |
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running_var = branch.running_var |
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gamma = branch.weight |
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beta = branch.bias |
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eps = branch.eps |
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std = (running_var + eps).sqrt() |
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t = (gamma / std).reshape(-1, 1, 1, 1) |
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return kernel * t, beta - running_mean * gamma / std |
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def fuse_convs(self): |
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if hasattr(self, 'conv'): |
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return |
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kernel, bias = self.get_equivalent_kernel_bias() |
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self.conv = nn.Conv2d(in_channels=self.conv1.conv.in_channels, |
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out_channels=self.conv1.conv.out_channels, |
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kernel_size=self.conv1.conv.kernel_size, |
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stride=self.conv1.conv.stride, |
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padding=self.conv1.conv.padding, |
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dilation=self.conv1.conv.dilation, |
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groups=self.conv1.conv.groups, |
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bias=True).requires_grad_(False) |
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self.conv.weight.data = kernel |
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self.conv.bias.data = bias |
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for para in self.parameters(): |
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para.detach_() |
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self.__delattr__('conv1') |
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self.__delattr__('conv2') |
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if hasattr(self, 'nm'): |
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self.__delattr__('nm') |
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if hasattr(self, 'bn'): |
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self.__delattr__('bn') |
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if hasattr(self, 'id_tensor'): |
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self.__delattr__('id_tensor') |
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class ChannelAttention(nn.Module): |
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"""Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet.""" |
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def __init__(self, channels: int) -> None: |
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super().__init__() |
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self.pool = nn.AdaptiveAvgPool2d(1) |
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self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True) |
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self.act = nn.Sigmoid() |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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return x * self.act(self.fc(self.pool(x))) |
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class SpatialAttention(nn.Module): |
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"""Spatial-attention module.""" |
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def __init__(self, kernel_size=7): |
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"""Initialize Spatial-attention module with kernel size argument.""" |
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super().__init__() |
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assert kernel_size in (3, 7), 'kernel size must be 3 or 7' |
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padding = 3 if kernel_size == 7 else 1 |
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self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) |
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self.act = nn.Sigmoid() |
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def forward(self, x): |
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"""Apply channel and spatial attention on input for feature recalibration.""" |
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return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1))) |
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class CBAM(nn.Module): |
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"""Convolutional Block Attention Module.""" |
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def __init__(self, c1, kernel_size=7): # ch_in, kernels |
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super().__init__() |
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self.channel_attention = ChannelAttention(c1) |
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self.spatial_attention = SpatialAttention(kernel_size) |
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def forward(self, x): |
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"""Applies the forward pass through C1 module.""" |
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return self.spatial_attention(self.channel_attention(x)) |
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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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"""Concatenates a list of tensors along a specified dimension.""" |
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super().__init__() |
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self.d = dimension |
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def forward(self, x): |
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"""Forward pass for the YOLOv8 mask Proto module.""" |
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return torch.cat(x, self.d)
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