250 lines
10 KiB
Python
250 lines
10 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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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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import torch.nn.functional as F
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def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
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if drop_prob == 0. or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1)
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random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
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if keep_prob > 0.0 and scale_by_keep:
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random_tensor.div_(keep_prob)
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return x * random_tensor
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class DropPath(nn.Module):
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None, scale_by_keep=True):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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self.scale_by_keep = scale_by_keep
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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def norm_cdf(x):
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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with torch.no_grad():
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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tensor.erfinv_()
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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tensor.clamp_(min=a, max=b)
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return tensor
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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#--------------------------------------#
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# Gelu激活函数的实现
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# 利用近似的数学公式
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#--------------------------------------#
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class GELU(nn.Module):
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def __init__(self):
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super(GELU, self).__init__()
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def forward(self, x):
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return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x,3))))
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#---------------------------------------------------------------------------------#
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# LayerNorm 支持两种形式channels_last (default) or channels_first.
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# channels_last 对应具有形状的输入(batch_size, height, width, channels)
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# channels_first 对应具有形状的输入(batch_size, channels, height, width).
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#---------------------------------------------------------------------------------#
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class LayerNorm(nn.Module):
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def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.eps = eps
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self.data_format = data_format
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if self.data_format not in ["channels_last", "channels_first"]:
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raise NotImplementedError
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self.normalized_shape = (normalized_shape, )
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def forward(self, x):
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if self.data_format == "channels_last":
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return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
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elif self.data_format == "channels_first":
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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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#--------------------------------------------------------------------------------------------------------------#
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# ConvNeXt Block有两种等效的实现:
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# (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
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# (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
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# 代码中使用（2），因为这个在PyTorch中稍微快一点
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#--------------------------------------------------------------------------------------------------------------#
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class Block(nn.Module):
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def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
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super().__init__()
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#--------------------------#
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# 7x7的逐层卷积
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#--------------------------#
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self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
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self.norm = LayerNorm(dim, eps=1e-6)
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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self.pwconv1 = nn.Linear(dim, 4 * dim)
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self.act = GELU()
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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self.pwconv2 = nn.Linear(4 * dim, dim)
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#--------------------------#
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# 加入缩放系数
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#--------------------------#
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self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) if layer_scale_init_value > 0 else None
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#--------------------------#
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# 加入Drop_path正则化
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#--------------------------#
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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input = x
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#--------------------------#
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# 7x7的逐层卷积
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#--------------------------#
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x = self.dwconv(x)
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x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
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x = self.norm(x)
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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x = self.pwconv1(x)
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x = self.act(x)
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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x = self.pwconv2(x)
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#--------------------------#
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# 加入缩放系数
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#--------------------------#
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if self.gamma is not None:
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x = self.gamma * x
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x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
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#--------------------------#
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# 加入Drop_path正则化
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#--------------------------#
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x = input + self.drop_path(x)
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return x
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#-----------------------------------------------------#
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# ConvNeXt
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# A PyTorch impl of : `A ConvNet for the 2020s`
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# https://arxiv.org/pdf/2201.03545.pdf
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#-----------------------------------------------------#
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class ConvNeXt(nn.Module):
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def __init__(
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self, in_chans=3, num_classes=1000, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., **kwargs
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):
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super().__init__()
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self.downsample_layers = nn.ModuleList()
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#--------------------------------------------------#
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# bs, 3, 224, 224 -> bs, 96, 56, 56
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#--------------------------------------------------#
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stem = nn.Sequential(
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nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
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LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
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)
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self.downsample_layers.append(stem)
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#--------------------------------------------------#
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# 定义三次下采样的过程
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# 利用步长为2x2，卷积核大小为2x2的卷积进行下采样
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#--------------------------------------------------#
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for i in range(3):
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downsample_layer = nn.Sequential(
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LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
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nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
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)
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self.downsample_layers.append(downsample_layer)
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#--------------------------------------------------#
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# 根据深度的不同，定义不同的drop率
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#--------------------------------------------------#
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self.stages = nn.ModuleList()
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dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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#--------------------------------------------------#
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# 整个ConvNeXt除了Stem外，存在四个Stage
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# 每个Stage里面是多个ConvNeXt Block的堆叠。
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#--------------------------------------------------#
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for i in range(4):
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stage = nn.Sequential(
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*[Block(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2d, nn.Linear)):
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trunc_normal_(m.weight, std=.02)
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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outs = []
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for i in range(4):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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if i != 0:
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outs.append(x)
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return outs
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model_urls = {
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"convnext_tiny_1k" : "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/convnext_tiny_1k_224_ema_no_jit.pth",
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"convnext_small_1k" : "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/convnext_small_1k_224_ema_no_jit.pth",
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}
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#------------------------------------------------------#
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# Tiny约等于Cspdarknet-L的尺寸
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#------------------------------------------------------#
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def ConvNeXt_Tiny(pretrained=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
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if pretrained:
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url = model_urls['convnext_tiny_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
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model.load_state_dict(checkpoint, strict=False)
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print("Load weights from ", url.split('/')[-1])
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return model
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#------------------------------------------------------#
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# Tiny约等于Cspdarknet-X的尺寸
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#------------------------------------------------------#
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def ConvNeXt_Small(pretrained=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
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if pretrained:
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url = model_urls['convnext_small_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
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model.load_state_dict(checkpoint, strict=False)
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print("Load weights from ", url.split('/')[-1])
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return model
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