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import torch.nn as nn |
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__all__ = ['repvit_m1'] |
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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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from timm.models.layers import SqueezeExcite |
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import torch |
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class LayerNorm2d(nn.Module): |
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def __init__(self, num_channels: int, eps: float = 1e-6) -> None: |
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super().__init__() |
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self.weight = nn.Parameter(torch.ones(num_channels)) |
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self.bias = nn.Parameter(torch.zeros(num_channels)) |
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self.eps = eps |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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u = x.mean(1, keepdim=True) |
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s = (x - u).pow(2).mean(1, keepdim=True) |
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x = (x - u) / torch.sqrt(s + self.eps) |
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x = self.weight[:, None, None] * x + self.bias[:, None, None] |
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return x |
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class Conv2d_BN(torch.nn.Sequential): |
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def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, |
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groups=1, bn_weight_init=1, resolution=-10000): |
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super().__init__() |
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self.add_module('c', torch.nn.Conv2d( |
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a, b, ks, stride, pad, dilation, groups, bias=False)) |
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self.add_module('bn', torch.nn.BatchNorm2d(b)) |
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torch.nn.init.constant_(self.bn.weight, bn_weight_init) |
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torch.nn.init.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 = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size( |
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0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, 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(x.size(0), 1, 1, 1, |
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device=x.device).ge_(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 = torch.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, torch.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 = torch.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(torch.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 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed) |
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self.dim = ed |
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self.bn = torch.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 = torch.nn.functional.pad(conv1_w, [1,1,1,1]) |
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identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, 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, use_hs): |
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super(RepViTBlock, self).__init__() |
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assert stride in [1, 2] |
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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 == 2: |
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self.token_mixer = nn.Sequential( |
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Conv2d_BN(inp, inp, kernel_size, stride if inp != 320 else 1, (kernel_size - 1) // 2, groups=inp), |
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SqueezeExcite(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(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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self.token_mixer = nn.Sequential( |
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RepVGGDW(inp), |
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SqueezeExcite(inp, 0.25) if use_se else nn.Identity(), |
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) |
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if self.identity: |
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self.channel_mixer = Residual(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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else: |
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self.channel_mixer = 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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from timm.models.vision_transformer import trunc_normal_ |
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class BN_Linear(torch.nn.Sequential): |
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def __init__(self, a, b, bias=True, std=0.02): |
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super().__init__() |
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self.add_module('bn', torch.nn.BatchNorm1d(a)) |
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self.add_module('l', torch.nn.Linear(a, b, bias=bias)) |
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trunc_normal_(self.l.weight, std=std) |
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if bias: |
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torch.nn.init.constant_(self.l.bias, 0) |
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@torch.no_grad() |
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def fuse(self): |
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bn, l = self._modules.values() |
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w = bn.weight / (bn.running_var + bn.eps)**0.5 |
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b = bn.bias - self.bn.running_mean * \ |
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self.bn.weight / (bn.running_var + bn.eps)**0.5 |
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w = l.weight * w[None, :] |
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if l.bias is None: |
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b = b @ self.l.weight.T |
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else: |
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b = (l.weight @ b[:, None]).view(-1) + self.l.bias |
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m = torch.nn.Linear(w.size(1), w.size(0), device=l.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 Classfier(nn.Module): |
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def __init__(self, dim, num_classes, distillation=True): |
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super().__init__() |
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self.classifier = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity() |
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self.distillation = distillation |
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if distillation: |
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self.classifier_dist = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity() |
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def forward(self, x): |
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if self.distillation: |
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x = self.classifier(x), self.classifier_dist(x) |
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if not self.training: |
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x = (x[0] + x[1]) / 2 |
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else: |
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x = self.classifier(x) |
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return x |
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@torch.no_grad() |
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def fuse(self): |
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classifier = self.classifier.fuse() |
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if self.distillation: |
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classifier_dist = self.classifier_dist.fuse() |
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classifier.weight += classifier_dist.weight |
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classifier.bias += classifier_dist.bias |
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classifier.weight /= 2 |
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classifier.bias /= 2 |
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return classifier |
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else: |
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return classifier |
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class RepViT(nn.Module): |
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def __init__(self, cfgs, num_classes=1000, distillation=False, img_size=1024): |
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super(RepViT, self).__init__() |
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self.cfgs = cfgs |
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self.img_size = img_size |
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input_channel = self.cfgs[0][2] |
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patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(), |
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Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1)) |
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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(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs)) |
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input_channel = output_channel |
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self.features = nn.ModuleList(layers) |
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self.neck = nn.Sequential( |
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nn.Conv2d( |
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output_channel, |
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256, |
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kernel_size=1, |
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bias=False, |
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), |
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LayerNorm2d(256), |
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nn.Conv2d( |
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256, |
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256, |
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kernel_size=3, |
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padding=1, |
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bias=False, |
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), |
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LayerNorm2d(256), |
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) |
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def forward(self, x): |
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for f in self.features: |
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x = f(x) |
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x = self.neck(x) |
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return x, None |
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from timm.models import register_model |
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@register_model |
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def repvit(pretrained=False, num_classes = 1000, distillation=False, **kwargs): |
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""" |
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Constructs a MobileNetV3-Large model |
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""" |
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cfgs = [ |
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[3, 2, 80, 1, 0, 1], |
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[3, 2, 80, 0, 0, 1], |
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[3, 2, 80, 1, 0, 1], |
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[3, 2, 80, 0, 0, 1], |
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[3, 2, 80, 1, 0, 1], |
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[3, 2, 80, 0, 0, 1], |
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[3, 2, 80, 0, 0, 1], |
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[3, 2, 160, 0, 0, 2], |
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[3, 2, 160, 1, 0, 1], |
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[3, 2, 160, 0, 0, 1], |
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[3, 2, 160, 1, 0, 1], |
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[3, 2, 160, 0, 0, 1], |
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[3, 2, 160, 1, 0, 1], |
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[3, 2, 160, 0, 0, 1], |
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[3, 2, 160, 0, 0, 1], |
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[3, 2, 320, 0, 1, 2], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 1, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 320, 0, 1, 1], |
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[3, 2, 640, 0, 1, 2], |
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[3, 2, 640, 1, 1, 1], |
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[3, 2, 640, 0, 1, 1], |
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] |
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return RepViT(cfgs, num_classes=num_classes, distillation=distillation) |
