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""" |
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This code is refer from: |
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https://github.com/THU-MIG/RepViT |
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""" |
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import torch.nn as nn |
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import torch |
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from torch.nn.init import constant_ |
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def _make_divisible(v, divisor, min_value=None): |
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""" |
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This function is taken from the original tf repo. |
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It ensures that all layers have a channel number that is divisible by 8 |
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It can be seen here: |
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py |
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:param v: |
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:param divisor: |
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:param min_value: |
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:return: |
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""" |
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if min_value is None: |
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min_value = divisor |
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) |
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if new_v < 0.9 * v: |
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new_v += divisor |
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return new_v |
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def make_divisible(v, divisor=8, min_value=None, round_limit=0.9): |
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min_value = min_value or divisor |
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) |
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if new_v < round_limit * v: |
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new_v += divisor |
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return new_v |
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class SEModule(nn.Module): |
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"""SE Module as defined in original SE-Nets with a few additions |
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Additions include: |
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* divisor can be specified to keep channels % div == 0 (default: 8) |
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* reduction channels can be specified directly by arg (if rd_channels is set) |
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* reduction channels can be specified by float rd_ratio (default: 1/16) |
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* global max pooling can be added to the squeeze aggregation |
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* customizable activation, normalization, and gate layer |
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""" |
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def __init__( |
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self, |
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channels, |
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rd_ratio=1.0 / 16, |
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rd_channels=None, |
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rd_divisor=8, |
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act_layer=nn.ReLU, |
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): |
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super(SEModule, self).__init__() |
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if not rd_channels: |
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rd_channels = make_divisible(channels * rd_ratio, |
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rd_divisor, |
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round_limit=0.0) |
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self.fc1 = nn.Conv2d(channels, rd_channels, kernel_size=1, bias=True) |
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self.act = act_layer() |
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self.fc2 = nn.Conv2d(rd_channels, channels, kernel_size=1, bias=True) |
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def forward(self, x): |
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x_se = x.mean((2, 3), keepdim=True) |
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x_se = self.fc1(x_se) |
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x_se = self.act(x_se) |
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x_se = self.fc2(x_se) |
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return x * torch.sigmoid(x_se) |
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class Conv2D_BN(nn.Sequential): |
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def __init__( |
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self, |
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a, |
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b, |
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ks=1, |
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stride=1, |
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pad=0, |
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dilation=1, |
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groups=1, |
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bn_weight_init=1, |
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resolution=-10000, |
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): |
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super().__init__() |
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self.add_module( |
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'c', nn.Conv2d(a, b, ks, stride, pad, dilation, groups, |
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bias=False)) |
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self.add_module('bn', nn.BatchNorm2d(b)) |
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constant_(self.bn.weight, bn_weight_init) |
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constant_(self.bn.bias, 0) |
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@torch.no_grad() |
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def fuse(self): |
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c, bn = self._modules.values() |
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w = bn.weight / (bn.running_var + bn.eps)**0.5 |
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w = c.weight * w[:, None, None, None] |
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b = bn.bias - bn.running_mean * bn.weight / \ |
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(bn.running_var + bn.eps)**0.5 |
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m = nn.Conv2d(w.size(1) * self.c.groups, |
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w.size(0), |
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w.shape[2:], |
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stride=self.c.stride, |
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padding=self.c.padding, |
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dilation=self.c.dilation, |
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groups=self.c.groups, |
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device=c.weight.device) |
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m.weight.data.copy_(w) |
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m.bias.data.copy_(b) |
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return m |
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class Residual(torch.nn.Module): |
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def __init__(self, m, drop=0.): |
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super().__init__() |
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self.m = m |
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self.drop = drop |
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def forward(self, x): |
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if self.training and self.drop > 0: |
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return x + self.m(x) * torch.rand( |
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x.size(0), 1, 1, 1, device=x.device).ge_( |
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self.drop).div(1 - self.drop).detach() |
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else: |
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return x + self.m(x) |
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@torch.no_grad() |
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def fuse(self): |
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if isinstance(self.m, Conv2D_BN): |
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m = self.m.fuse() |
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assert (m.groups == m.in_channels) |
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identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1) |
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identity = nn.functional.pad(identity, [1, 1, 1, 1]) |
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m.weight += identity.to(m.weight.device) |
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return m |
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elif isinstance(self.m, nn.Conv2d): |
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m = self.m |
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assert (m.groups != m.in_channels) |
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identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1) |
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identity = nn.functional.pad(identity, [1, 1, 1, 1]) |
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m.weight += identity.to(m.weight.device) |
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return m |
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else: |
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return self |
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class RepVGGDW(nn.Module): |
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def __init__(self, ed) -> None: |
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super().__init__() |
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self.conv = Conv2D_BN(ed, ed, 3, 1, 1, groups=ed) |
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self.conv1 = nn.Conv2d(ed, ed, 1, 1, 0, groups=ed) |
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self.dim = ed |
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self.bn = nn.BatchNorm2d(ed) |
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def forward(self, x): |
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return self.bn((self.conv(x) + self.conv1(x)) + x) |
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@torch.no_grad() |
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def fuse(self): |
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conv = self.conv.fuse() |
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conv1 = self.conv1 |
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conv_w = conv.weight |
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conv_b = conv.bias |
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conv1_w = conv1.weight |
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conv1_b = conv1.bias |
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conv1_w = nn.functional.pad(conv1_w, [1, 1, 1, 1]) |
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identity = nn.functional.pad( |
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torch.ones(conv1_w.shape[0], |
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conv1_w.shape[1], |
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1, |
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1, |
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device=conv1_w.device), [1, 1, 1, 1]) |
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final_conv_w = conv_w + conv1_w + identity |
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final_conv_b = conv_b + conv1_b |
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conv.weight.data.copy_(final_conv_w) |
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conv.bias.data.copy_(final_conv_b) |
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bn = self.bn |
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w = bn.weight / (bn.running_var + bn.eps)**0.5 |
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w = conv.weight * w[:, None, None, None] |
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b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \ |
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(bn.running_var + bn.eps)**0.5 |
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conv.weight.data.copy_(w) |
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conv.bias.data.copy_(b) |
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return conv |
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class RepViTBlock(nn.Module): |
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def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, |
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use_hs): |
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super(RepViTBlock, self).__init__() |
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self.identity = stride == 1 and inp == oup |
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assert hidden_dim == 2 * inp |
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if stride != 1: |
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self.token_mixer = nn.Sequential( |
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Conv2D_BN(inp, |
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inp, |
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kernel_size, |
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stride, (kernel_size - 1) // 2, |
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groups=inp), |
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SEModule(inp, 0.25) if use_se else nn.Identity(), |
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Conv2D_BN(inp, oup, ks=1, stride=1, pad=0), |
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) |
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self.channel_mixer = Residual( |
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nn.Sequential( |
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Conv2D_BN(oup, 2 * oup, 1, 1, 0), |
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nn.GELU() if use_hs else nn.GELU(), |
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Conv2D_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0), |
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)) |
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else: |
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assert self.identity |
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self.token_mixer = nn.Sequential( |
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RepVGGDW(inp), |
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SEModule(inp, 0.25) if use_se else nn.Identity(), |
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) |
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self.channel_mixer = Residual( |
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nn.Sequential( |
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Conv2D_BN(inp, hidden_dim, 1, 1, 0), |
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nn.GELU() if use_hs else nn.GELU(), |
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Conv2D_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0), |
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)) |
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def forward(self, x): |
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return self.channel_mixer(self.token_mixer(x)) |
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class RepViT(nn.Module): |
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def __init__(self, cfgs, in_channels=3, out_indices=None): |
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super(RepViT, self).__init__() |
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self.cfgs = cfgs |
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input_channel = self.cfgs[0][2] |
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patch_embed = nn.Sequential( |
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Conv2D_BN(in_channels, input_channel // 2, 3, 2, 1), |
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nn.GELU(), |
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Conv2D_BN(input_channel // 2, input_channel, 3, 2, 1), |
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) |
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layers = [patch_embed] |
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block = RepViTBlock |
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for k, t, c, use_se, use_hs, s in self.cfgs: |
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output_channel = _make_divisible(c, 8) |
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exp_size = _make_divisible(input_channel * t, 8) |
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layers.append( |
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block(input_channel, exp_size, output_channel, k, s, use_se, |
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use_hs)) |
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input_channel = output_channel |
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self.features = nn.ModuleList(layers) |
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self.out_indices = out_indices |
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if out_indices is not None: |
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self.out_channels = [self.cfgs[ids - 1][2] for ids in out_indices] |
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else: |
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self.out_channels = self.cfgs[-1][2] |
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def forward(self, x): |
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if self.out_indices is not None: |
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return self.forward_det(x) |
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return self.forward_rec(x) |
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|
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def forward_det(self, x): |
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outs = [] |
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for i, f in enumerate(self.features): |
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x = f(x) |
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if i in self.out_indices: |
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outs.append(x) |
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return outs |
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def forward_rec(self, x): |
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for f in self.features: |
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x = f(x) |
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h = x.shape[2] |
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x = nn.functional.avg_pool2d(x, [h, 2]) |
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return x |
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def RepSVTR(in_channels=3): |
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""" |
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|
Constructs a MobileNetV3-Large model |
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|
""" |
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|
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cfgs = [ |
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[3, 2, 96, 1, 0, 1], |
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[3, 2, 96, 0, 0, 1], |
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[3, 2, 96, 0, 0, 1], |
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[3, 2, 192, 0, 1, (2, 1)], |
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[3, 2, 192, 1, 1, 1], |
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[3, 2, 192, 0, 1, 1], |
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[3, 2, 192, 1, 1, 1], |
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[3, 2, 192, 0, 1, 1], |
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[3, 2, 192, 1, 1, 1], |
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[3, 2, 192, 0, 1, 1], |
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[3, 2, 384, 0, 1, (2, 1)], |
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[3, 2, 384, 1, 1, 1], |
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[3, 2, 384, 0, 1, 1], |
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] |
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return RepViT(cfgs, in_channels=in_channels) |
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|
|
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|
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def RepSVTR_det(in_channels=3, out_indices=[2, 5, 10, 13]): |
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|
""" |
|
|
Constructs a MobileNetV3-Large model |
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|
""" |
|
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|
|
|
cfgs = [ |
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[3, 2, 48, 1, 0, 1], |
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[3, 2, 48, 0, 0, 1], |
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[3, 2, 96, 0, 0, 2], |
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[3, 2, 96, 1, 0, 1], |
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[3, 2, 96, 0, 0, 1], |
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[3, 2, 192, 0, 1, 2], |
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[3, 2, 192, 1, 1, 1], |
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[3, 2, 192, 0, 1, 1], |
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[3, 2, 192, 1, 1, 1], |
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[3, 2, 192, 0, 1, 1], |
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[3, 2, 384, 0, 1, 2], |
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[3, 2, 384, 1, 1, 1], |
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|
[3, 2, 384, 0, 1, 1], |
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] |
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return RepViT(cfgs, in_channels=in_channels, out_indices=out_indices) |
|
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|
