Initial
This commit is contained in:
249
network/ConvNext.py
Normal file
249
network/ConvNext.py
Normal file
@@ -0,0 +1,249 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
|
||||
if drop_prob == 0. or not training:
|
||||
return x
|
||||
keep_prob = 1 - drop_prob
|
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
|
||||
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
||||
if keep_prob > 0.0 and scale_by_keep:
|
||||
random_tensor.div_(keep_prob)
|
||||
return x * random_tensor
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""
|
||||
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
"""
|
||||
def __init__(self, drop_prob=None, scale_by_keep=True):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
self.scale_by_keep = scale_by_keep
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
|
||||
|
||||
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
||||
def norm_cdf(x):
|
||||
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||
|
||||
with torch.no_grad():
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
tensor.erfinv_()
|
||||
|
||||
tensor.mul_(std * math.sqrt(2.))
|
||||
tensor.add_(mean)
|
||||
|
||||
tensor.clamp_(min=a, max=b)
|
||||
return tensor
|
||||
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||
|
||||
#--------------------------------------#
|
||||
# Gelu激活函数的实现
|
||||
# 利用近似的数学公式
|
||||
#--------------------------------------#
|
||||
class GELU(nn.Module):
|
||||
def __init__(self):
|
||||
super(GELU, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x,3))))
|
||||
|
||||
#---------------------------------------------------------------------------------#
|
||||
# LayerNorm 支持两种形式channels_last (default) or channels_first.
|
||||
# channels_last 对应具有形状的输入(batch_size, height, width, channels)
|
||||
# channels_first 对应具有形状的输入(batch_size, channels, height, width).
|
||||
#---------------------------------------------------------------------------------#
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(normalized_shape))
|
||||
self.bias = nn.Parameter(torch.zeros(normalized_shape))
|
||||
self.eps = eps
|
||||
self.data_format = data_format
|
||||
if self.data_format not in ["channels_last", "channels_first"]:
|
||||
raise NotImplementedError
|
||||
self.normalized_shape = (normalized_shape, )
|
||||
|
||||
def forward(self, x):
|
||||
if self.data_format == "channels_last":
|
||||
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
|
||||
elif self.data_format == "channels_first":
|
||||
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
|
||||
|
||||
#--------------------------------------------------------------------------------------------------------------#
|
||||
# ConvNeXt Block有两种等效的实现:
|
||||
# (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
|
||||
# (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
|
||||
# 代码中使用（2），因为这个在PyTorch中稍微快一点
|
||||
#--------------------------------------------------------------------------------------------------------------#
|
||||
class Block(nn.Module):
|
||||
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
|
||||
super().__init__()
|
||||
#--------------------------#
|
||||
# 7x7的逐层卷积
|
||||
#--------------------------#
|
||||
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
|
||||
self.norm = LayerNorm(dim, eps=1e-6)
|
||||
#--------------------------#
|
||||
# 利用全连接层代替1x1卷积
|
||||
#--------------------------#
|
||||
self.pwconv1 = nn.Linear(dim, 4 * dim)
|
||||
self.act = GELU()
|
||||
#--------------------------#
|
||||
# 利用全连接层代替1x1卷积
|
||||
#--------------------------#
|
||||
self.pwconv2 = nn.Linear(4 * dim, dim)
|
||||
#--------------------------#
|
||||
# 加入缩放系数
|
||||
#--------------------------#
|
||||
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) if layer_scale_init_value > 0 else None
|
||||
#--------------------------#
|
||||
# 加入Drop_path正则化
|
||||
#--------------------------#
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
input = x
|
||||
#--------------------------#
|
||||
# 7x7的逐层卷积
|
||||
#--------------------------#
|
||||
x = self.dwconv(x)
|
||||
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
|
||||
x = self.norm(x)
|
||||
#--------------------------#
|
||||
# 利用全连接层代替1x1卷积
|
||||
#--------------------------#
|
||||
x = self.pwconv1(x)
|
||||
x = self.act(x)
|
||||
#--------------------------#
|
||||
# 利用全连接层代替1x1卷积
|
||||
#--------------------------#
|
||||
x = self.pwconv2(x)
|
||||
#--------------------------#
|
||||
# 加入缩放系数
|
||||
#--------------------------#
|
||||
if self.gamma is not None:
|
||||
x = self.gamma * x
|
||||
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
|
||||
#--------------------------#
|
||||
# 加入Drop_path正则化
|
||||
#--------------------------#
|
||||
x = input + self.drop_path(x)
|
||||
return x
|
||||
|
||||
#-----------------------------------------------------#
|
||||
# ConvNeXt
|
||||
# A PyTorch impl of : `A ConvNet for the 2020s`
|
||||
# https://arxiv.org/pdf/2201.03545.pdf
|
||||
#-----------------------------------------------------#
|
||||
class ConvNeXt(nn.Module):
|
||||
def __init__(
|
||||
self, in_chans=3, num_classes=1000, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
|
||||
drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., **kwargs
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.downsample_layers = nn.ModuleList()
|
||||
#--------------------------------------------------#
|
||||
# bs, 3, 224, 224 -> bs, 96, 56, 56
|
||||
#--------------------------------------------------#
|
||||
stem = nn.Sequential(
|
||||
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
|
||||
LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
|
||||
)
|
||||
self.downsample_layers.append(stem)
|
||||
|
||||
#--------------------------------------------------#
|
||||
# 定义三次下采样的过程
|
||||
# 利用步长为2x2，卷积核大小为2x2的卷积进行下采样
|
||||
#--------------------------------------------------#
|
||||
for i in range(3):
|
||||
downsample_layer = nn.Sequential(
|
||||
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
|
||||
nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
|
||||
)
|
||||
self.downsample_layers.append(downsample_layer)
|
||||
|
||||
#--------------------------------------------------#
|
||||
# 根据深度的不同，定义不同的drop率
|
||||
#--------------------------------------------------#
|
||||
self.stages = nn.ModuleList()
|
||||
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
|
||||
cur = 0
|
||||
#--------------------------------------------------#
|
||||
# 整个ConvNeXt除了Stem外，存在四个Stage
|
||||
# 每个Stage里面是多个ConvNeXt Block的堆叠。
|
||||
#--------------------------------------------------#
|
||||
for i in range(4):
|
||||
stage = nn.Sequential(
|
||||
*[Block(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
|
||||
)
|
||||
self.stages.append(stage)
|
||||
cur += depths[i]
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, (nn.Conv2d, nn.Linear)):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def forward(self, x):
|
||||
outs = []
|
||||
for i in range(4):
|
||||
x = self.downsample_layers[i](x)
|
||||
x = self.stages[i](x)
|
||||
if i != 0:
|
||||
outs.append(x)
|
||||
return outs
|
||||
|
||||
model_urls = {
|
||||
"convnext_tiny_1k" : "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/convnext_tiny_1k_224_ema_no_jit.pth",
|
||||
"convnext_small_1k" : "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/convnext_small_1k_224_ema_no_jit.pth",
|
||||
}
|
||||
|
||||
#------------------------------------------------------#
|
||||
# Tiny约等于Cspdarknet-L的尺寸
|
||||
#------------------------------------------------------#
|
||||
def ConvNeXt_Tiny(pretrained=False, **kwargs):
|
||||
model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
|
||||
if pretrained:
|
||||
url = model_urls['convnext_tiny_1k']
|
||||
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
|
||||
model.load_state_dict(checkpoint, strict=False)
|
||||
print("Load weights from ", url.split('/')[-1])
|
||||
return model
|
||||
|
||||
#------------------------------------------------------#
|
||||
# Tiny约等于Cspdarknet-X的尺寸
|
||||
#------------------------------------------------------#
|
||||
def ConvNeXt_Small(pretrained=False, **kwargs):
|
||||
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
|
||||
if pretrained:
|
||||
url = model_urls['convnext_small_1k']
|
||||
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
|
||||
model.load_state_dict(checkpoint, strict=False)
|
||||
print("Load weights from ", url.split('/')[-1])
|
||||
return model
|
||||
Reference in New Issue
Block a user