| | import torch.nn as nn |
| |
|
| |
|
| | __all__ = ['repvit_m1'] |
| |
|
| |
|
| | def _make_divisible(v, divisor, min_value=None): |
| | """ |
| | This function is taken from the original tf repo. |
| | It ensures that all layers have a channel number that is divisible by 8 |
| | It can be seen here: |
| | https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py |
| | :param v: |
| | :param divisor: |
| | :param min_value: |
| | :return: |
| | """ |
| | if min_value is None: |
| | min_value = divisor |
| | new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) |
| | |
| | if new_v < 0.9 * v: |
| | new_v += divisor |
| | return new_v |
| |
|
| | from timm.models.layers import SqueezeExcite |
| |
|
| | import torch |
| |
|
| | |
| | |
| | class LayerNorm2d(nn.Module): |
| | def __init__(self, num_channels: int, eps: float = 1e-6) -> None: |
| | super().__init__() |
| | self.weight = nn.Parameter(torch.ones(num_channels)) |
| | self.bias = nn.Parameter(torch.zeros(num_channels)) |
| | self.eps = eps |
| |
|
| | def forward(self, x: torch.Tensor) -> torch.Tensor: |
| | u = x.mean(1, keepdim=True) |
| | s = (x - u).pow(2).mean(1, keepdim=True) |
| | x = (x - u) / torch.sqrt(s + self.eps) |
| | x = self.weight[:, None, None] * x + self.bias[:, None, None] |
| | return x |
| | |
| | class Conv2d_BN(torch.nn.Sequential): |
| | def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, |
| | groups=1, bn_weight_init=1, resolution=-10000): |
| | super().__init__() |
| | self.add_module('c', torch.nn.Conv2d( |
| | a, b, ks, stride, pad, dilation, groups, bias=False)) |
| | self.add_module('bn', torch.nn.BatchNorm2d(b)) |
| | torch.nn.init.constant_(self.bn.weight, bn_weight_init) |
| | torch.nn.init.constant_(self.bn.bias, 0) |
| |
|
| | @torch.no_grad() |
| | def fuse(self): |
| | c, bn = self._modules.values() |
| | w = bn.weight / (bn.running_var + bn.eps)**0.5 |
| | w = c.weight * w[:, None, None, None] |
| | b = bn.bias - bn.running_mean * bn.weight / \ |
| | (bn.running_var + bn.eps)**0.5 |
| | m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size( |
| | 0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups, |
| | device=c.weight.device) |
| | m.weight.data.copy_(w) |
| | m.bias.data.copy_(b) |
| | return m |
| |
|
| | class Residual(torch.nn.Module): |
| | def __init__(self, m, drop=0.): |
| | super().__init__() |
| | self.m = m |
| | self.drop = drop |
| |
|
| | def forward(self, x): |
| | if self.training and self.drop > 0: |
| | return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1, |
| | device=x.device).ge_(self.drop).div(1 - self.drop).detach() |
| | else: |
| | return x + self.m(x) |
| | |
| | @torch.no_grad() |
| | def fuse(self): |
| | if isinstance(self.m, Conv2d_BN): |
| | m = self.m.fuse() |
| | assert(m.groups == m.in_channels) |
| | identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1) |
| | identity = torch.nn.functional.pad(identity, [1,1,1,1]) |
| | m.weight += identity.to(m.weight.device) |
| | return m |
| | elif isinstance(self.m, torch.nn.Conv2d): |
| | m = self.m |
| | assert(m.groups != m.in_channels) |
| | identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1) |
| | identity = torch.nn.functional.pad(identity, [1,1,1,1]) |
| | m.weight += identity.to(m.weight.device) |
| | return m |
| | else: |
| | return self |
| |
|
| |
|
| | class RepVGGDW(torch.nn.Module): |
| | def __init__(self, ed) -> None: |
| | super().__init__() |
| | self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed) |
| | self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed) |
| | self.dim = ed |
| | self.bn = torch.nn.BatchNorm2d(ed) |
| | |
| | def forward(self, x): |
| | return self.bn((self.conv(x) + self.conv1(x)) + x) |
| | |
| | @torch.no_grad() |
| | def fuse(self): |
| | conv = self.conv.fuse() |
| | conv1 = self.conv1 |
| | |
| | conv_w = conv.weight |
| | conv_b = conv.bias |
| | conv1_w = conv1.weight |
| | conv1_b = conv1.bias |
| | |
| | conv1_w = torch.nn.functional.pad(conv1_w, [1,1,1,1]) |
| |
|
| | 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]) |
| |
|
| | final_conv_w = conv_w + conv1_w + identity |
| | final_conv_b = conv_b + conv1_b |
| |
|
| | conv.weight.data.copy_(final_conv_w) |
| | conv.bias.data.copy_(final_conv_b) |
| |
|
| | bn = self.bn |
| | w = bn.weight / (bn.running_var + bn.eps)**0.5 |
| | w = conv.weight * w[:, None, None, None] |
| | b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \ |
| | (bn.running_var + bn.eps)**0.5 |
| | conv.weight.data.copy_(w) |
| | conv.bias.data.copy_(b) |
| | return conv |
| |
|
| |
|
| | class RepViTBlock(nn.Module): |
| | def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs): |
| | super(RepViTBlock, self).__init__() |
| | assert stride in [1, 2] |
| |
|
| | self.identity = stride == 1 and inp == oup |
| | assert(hidden_dim == 2 * inp) |
| |
|
| | if stride == 2: |
| | self.token_mixer = nn.Sequential( |
| | Conv2d_BN(inp, inp, kernel_size, stride if inp != 320 else 1, (kernel_size - 1) // 2, groups=inp), |
| | SqueezeExcite(inp, 0.25) if use_se else nn.Identity(), |
| | Conv2d_BN(inp, oup, ks=1, stride=1, pad=0) |
| | ) |
| | self.channel_mixer = Residual(nn.Sequential( |
| | |
| | Conv2d_BN(oup, 2 * oup, 1, 1, 0), |
| | nn.GELU() if use_hs else nn.GELU(), |
| | |
| | Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0), |
| | )) |
| | else: |
| | |
| | self.token_mixer = nn.Sequential( |
| | RepVGGDW(inp), |
| | SqueezeExcite(inp, 0.25) if use_se else nn.Identity(), |
| | ) |
| | if self.identity: |
| | self.channel_mixer = Residual(nn.Sequential( |
| | |
| | Conv2d_BN(inp, hidden_dim, 1, 1, 0), |
| | nn.GELU() if use_hs else nn.GELU(), |
| | |
| | Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0), |
| | )) |
| | else: |
| | self.channel_mixer = nn.Sequential( |
| | |
| | Conv2d_BN(inp, hidden_dim, 1, 1, 0), |
| | nn.GELU() if use_hs else nn.GELU(), |
| | |
| | Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0), |
| | ) |
| |
|
| | def forward(self, x): |
| | return self.channel_mixer(self.token_mixer(x)) |
| |
|
| | from timm.models.vision_transformer import trunc_normal_ |
| | class BN_Linear(torch.nn.Sequential): |
| | def __init__(self, a, b, bias=True, std=0.02): |
| | super().__init__() |
| | self.add_module('bn', torch.nn.BatchNorm1d(a)) |
| | self.add_module('l', torch.nn.Linear(a, b, bias=bias)) |
| | trunc_normal_(self.l.weight, std=std) |
| | if bias: |
| | torch.nn.init.constant_(self.l.bias, 0) |
| |
|
| | @torch.no_grad() |
| | def fuse(self): |
| | bn, l = self._modules.values() |
| | w = bn.weight / (bn.running_var + bn.eps)**0.5 |
| | b = bn.bias - self.bn.running_mean * \ |
| | self.bn.weight / (bn.running_var + bn.eps)**0.5 |
| | w = l.weight * w[None, :] |
| | if l.bias is None: |
| | b = b @ self.l.weight.T |
| | else: |
| | b = (l.weight @ b[:, None]).view(-1) + self.l.bias |
| | m = torch.nn.Linear(w.size(1), w.size(0), device=l.weight.device) |
| | m.weight.data.copy_(w) |
| | m.bias.data.copy_(b) |
| | return m |
| |
|
| | class Classfier(nn.Module): |
| | def __init__(self, dim, num_classes, distillation=True): |
| | super().__init__() |
| | self.classifier = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity() |
| | self.distillation = distillation |
| | if distillation: |
| | self.classifier_dist = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity() |
| |
|
| | def forward(self, x): |
| | if self.distillation: |
| | x = self.classifier(x), self.classifier_dist(x) |
| | if not self.training: |
| | x = (x[0] + x[1]) / 2 |
| | else: |
| | x = self.classifier(x) |
| | return x |
| |
|
| | @torch.no_grad() |
| | def fuse(self): |
| | classifier = self.classifier.fuse() |
| | if self.distillation: |
| | classifier_dist = self.classifier_dist.fuse() |
| | classifier.weight += classifier_dist.weight |
| | classifier.bias += classifier_dist.bias |
| | classifier.weight /= 2 |
| | classifier.bias /= 2 |
| | return classifier |
| | else: |
| | return classifier |
| |
|
| | class RepViT(nn.Module): |
| | def __init__(self, cfgs, num_classes=1000, distillation=False, img_size=1024): |
| | super(RepViT, self).__init__() |
| | |
| | self.cfgs = cfgs |
| |
|
| | self.img_size = img_size |
| |
|
| | |
| | input_channel = self.cfgs[0][2] |
| | patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(), |
| | Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1)) |
| | layers = [patch_embed] |
| | |
| | block = RepViTBlock |
| | for k, t, c, use_se, use_hs, s in self.cfgs: |
| | output_channel = _make_divisible(c, 8) |
| | exp_size = _make_divisible(input_channel * t, 8) |
| | layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs)) |
| | input_channel = output_channel |
| | self.features = nn.ModuleList(layers) |
| | |
| | |
| | self.neck = nn.Sequential( |
| | nn.Conv2d( |
| | output_channel, |
| | 256, |
| | kernel_size=1, |
| | bias=False, |
| | ), |
| | LayerNorm2d(256), |
| | nn.Conv2d( |
| | 256, |
| | 256, |
| | kernel_size=3, |
| | padding=1, |
| | bias=False, |
| | ), |
| | LayerNorm2d(256), |
| | ) |
| |
|
| | def forward(self, x): |
| | |
| | for f in self.features: |
| | x = f(x) |
| | |
| | x = self.neck(x) |
| | return x, None |
| |
|
| | from timm.models import register_model |
| |
|
| | @register_model |
| | def repvit(pretrained=False, num_classes = 1000, distillation=False, **kwargs): |
| | """ |
| | Constructs a MobileNetV3-Large model |
| | """ |
| | cfgs = [ |
| | |
| | [3, 2, 80, 1, 0, 1], |
| | [3, 2, 80, 0, 0, 1], |
| | [3, 2, 80, 1, 0, 1], |
| | [3, 2, 80, 0, 0, 1], |
| | [3, 2, 80, 1, 0, 1], |
| | [3, 2, 80, 0, 0, 1], |
| | [3, 2, 80, 0, 0, 1], |
| | [3, 2, 160, 0, 0, 2], |
| | [3, 2, 160, 1, 0, 1], |
| | [3, 2, 160, 0, 0, 1], |
| | [3, 2, 160, 1, 0, 1], |
| | [3, 2, 160, 0, 0, 1], |
| | [3, 2, 160, 1, 0, 1], |
| | [3, 2, 160, 0, 0, 1], |
| | [3, 2, 160, 0, 0, 1], |
| | [3, 2, 320, 0, 1, 2], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 320, 1, 1, 1], |
| | [3, 2, 320, 0, 1, 1], |
| | |
| | |
| | [3, 2, 320, 0, 1, 1], |
| | [3, 2, 640, 0, 1, 2], |
| | [3, 2, 640, 1, 1, 1], |
| | [3, 2, 640, 0, 1, 1], |
| | |
| | |
| | ] |
| | return RepViT(cfgs, num_classes=num_classes, distillation=distillation) |
