545 lines
23 KiB
Python
545 lines
23 KiB
Python
# ---------------------------------------------------------------
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# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
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#
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# This work is licensed under the NVIDIA Source Code License
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# ---------------------------------------------------------------
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import math
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import warnings
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import numpy as np
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from functools import partial
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import torch
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import torch.nn as nn
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
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"The distribution of values may be incorrect.",
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stacklevel=2)
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with torch.no_grad():
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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tensor.clamp_(min=a, max=b)
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return tensor
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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r"""
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Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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Examples:
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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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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class OverlapPatchEmbed(nn.Module):
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def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
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super().__init__()
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patch_size = (patch_size, patch_size)
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
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padding=(patch_size[0] // 2, patch_size[1] // 2))
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self.norm = nn.LayerNorm(embed_dim)
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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.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x):
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x = self.proj(x)
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_, _, H, W = x.shape
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x = x.flatten(2).transpose(1, 2)
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x = self.norm(x)
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return x, H, W
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#--------------------------------------------------------------------------------------------------------------------#
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# Attention机制
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# 将输入的特征qkv特征进行划分，首先生成query, key, value。query是查询向量、key是键向量、v是值向量。
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# 然后利用 查询向量query 叉乘 转置后的键向量key，这一步可以通俗的理解为，利用查询向量去查询序列的特征，获得序列每个部分的重要程度score。
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# 然后利用 score 叉乘 value，这一步可以通俗的理解为，将序列每个部分的重要程度重新施加到序列的值上去。
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#
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# 在segformer中，为了减少计算量，首先对特征图进行了浓缩，所有特征层都压缩到原图的1/32。
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# 当输入图片为512, 512时，Block1的特征图为128, 128，此时就先将特征层压缩为16, 16。
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# 在Block1的Attention模块中，相当于将8x8个特征点进行特征浓缩，浓缩为一个特征点。
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# 然后利用128x128个查询向量对16x16个键向量与值向量进行查询。尽管键向量与值向量的数量较少，但因为查询向量的不同，依然可以获得不同的输出。
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#--------------------------------------------------------------------------------------------------------------------#
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class Attention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
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super().__init__()
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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self.dim = dim
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Linear(dim, dim, bias=qkv_bias)
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self.sr_ratio = sr_ratio
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if sr_ratio > 1:
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self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
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self.norm = nn.LayerNorm(dim)
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self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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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.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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B, N, C = x.shape
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# bs, 16384, 32 => bs, 16384, 32 => bs, 16384, 8, 4 => bs, 8, 16384, 4
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q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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if self.sr_ratio > 1:
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# bs, 16384, 32 => bs, 32, 128, 128
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x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
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# bs, 32, 128, 128 => bs, 32, 16, 16 => bs, 256, 32
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x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
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x_ = self.norm(x_)
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# bs, 256, 32 => bs, 256, 64 => bs, 256, 2, 8, 4 => 2, bs, 8, 256, 4
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kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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else:
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kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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k, v = kv[0], kv[1]
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# bs, 8, 16384, 4 @ bs, 8, 4, 256 => bs, 8, 16384, 256
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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# bs, 8, 16384, 256 @ bs, 8, 256, 4 => bs, 8, 16384, 4 => bs, 16384, 32
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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# bs, 16384, 32 => bs, 16384, 32
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
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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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This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
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changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
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'survival rate' as the argument.
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"""
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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) # work with diff dim tensors, not just 2D ConvNets
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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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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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class DWConv(nn.Module):
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def __init__(self, dim=768):
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super(DWConv, self).__init__()
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self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
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def forward(self, x, H, W):
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B, N, C = x.shape
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x = x.transpose(1, 2).view(B, C, H, W)
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x = self.dwconv(x)
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x = x.flatten(2).transpose(1, 2)
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return x
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.dwconv = DWConv(hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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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.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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x = self.fc1(x)
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x = self.dwconv(x, H, W)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio
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)
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self.norm2 = norm_layer(dim)
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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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.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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x = x + self.drop_path(self.attn(self.norm1(x), H, W))
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x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
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return x
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class MixVisionTransformer(nn.Module):
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def __init__(self, in_chans=3, num_classes=1000, embed_dims=[32, 64, 160, 256],
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num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
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attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
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depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
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super().__init__()
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self.num_classes = num_classes
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self.depths = depths
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#----------------------------------#
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# Transformer模块，共有四个部分
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#----------------------------------#
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
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#----------------------------------#
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# block1
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#----------------------------------#
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#-----------------------------------------------#
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# 对输入图像进行分区，并下采样
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# 512, 512, 3 => 128, 128, 32 => 16384, 32
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#-----------------------------------------------#
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self.patch_embed1 = OverlapPatchEmbed(patch_size=7, stride=4, in_chans=in_chans, embed_dim=embed_dims[0])
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#-----------------------------------------------#
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# 利用transformer模块进行特征提取
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# 16384, 32 => 16384, 32
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#-----------------------------------------------#
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cur = 0
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self.block1 = nn.ModuleList(
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[
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Block(
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dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[0]
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)
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for i in range(depths[0])
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]
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)
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self.norm1 = norm_layer(embed_dims[0])
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#----------------------------------#
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# block2
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#----------------------------------#
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#-----------------------------------------------#
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# 对输入图像进行分区，并下采样
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# 128, 128, 32 => 64, 64, 64 => 4096, 64
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#-----------------------------------------------#
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self.patch_embed2 = OverlapPatchEmbed(patch_size=3, stride=2, in_chans=embed_dims[0], embed_dim=embed_dims[1])
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#-----------------------------------------------#
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# 利用transformer模块进行特征提取
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# 4096, 64 => 4096, 64
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#-----------------------------------------------#
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cur += depths[0]
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self.block2 = nn.ModuleList(
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[
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Block(
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dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[1]
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)
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for i in range(depths[1])
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]
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)
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self.norm2 = norm_layer(embed_dims[1])
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#----------------------------------#
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# block3
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#----------------------------------#
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#-----------------------------------------------#
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# 对输入图像进行分区，并下采样
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# 64, 64, 64 => 32, 32, 160 => 1024, 160
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#-----------------------------------------------#
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self.patch_embed3 = OverlapPatchEmbed(patch_size=3, stride=2, in_chans=embed_dims[1], embed_dim=embed_dims[2])
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#-----------------------------------------------#
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# 利用transformer模块进行特征提取
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# 1024, 160 => 1024, 160
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#-----------------------------------------------#
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cur += depths[1]
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self.block3 = nn.ModuleList(
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[
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Block(
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dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[2]
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)
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for i in range(depths[2])
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]
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)
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self.norm3 = norm_layer(embed_dims[2])
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#----------------------------------#
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# block4
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#----------------------------------#
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#-----------------------------------------------#
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# 对输入图像进行分区，并下采样
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# 32, 32, 160 => 16, 16, 256 => 256, 256
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#-----------------------------------------------#
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self.patch_embed4 = OverlapPatchEmbed(patch_size=3, stride=2, in_chans=embed_dims[2], embed_dim=embed_dims[3])
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#-----------------------------------------------#
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# 利用transformer模块进行特征提取
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# 256, 256 => 256, 256
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#-----------------------------------------------#
|
||
cur += depths[2]
|
||
self.block4 = nn.ModuleList(
|
||
[
|
||
Block(
|
||
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[3]
|
||
)
|
||
for i in range(depths[3])
|
||
]
|
||
)
|
||
self.norm4 = norm_layer(embed_dims[3])
|
||
|
||
self.apply(self._init_weights)
|
||
|
||
def _init_weights(self, m):
|
||
if isinstance(m, nn.Linear):
|
||
trunc_normal_(m.weight, std=.02)
|
||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||
nn.init.constant_(m.bias, 0)
|
||
elif isinstance(m, nn.LayerNorm):
|
||
nn.init.constant_(m.bias, 0)
|
||
nn.init.constant_(m.weight, 1.0)
|
||
elif isinstance(m, nn.Conv2d):
|
||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||
fan_out //= m.groups
|
||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||
if m.bias is not None:
|
||
m.bias.data.zero_()
|
||
|
||
def forward(self, x):
|
||
B = x.shape[0]
|
||
outs = []
|
||
|
||
#----------------------------------#
|
||
# block1
|
||
#----------------------------------#
|
||
x, H, W = self.patch_embed1.forward(x)
|
||
for i, blk in enumerate(self.block1):
|
||
x = blk.forward(x, H, W)
|
||
x = self.norm1(x)
|
||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||
outs.append(x)
|
||
|
||
#----------------------------------#
|
||
# block2
|
||
#----------------------------------#
|
||
x, H, W = self.patch_embed2.forward(x)
|
||
for i, blk in enumerate(self.block2):
|
||
x = blk.forward(x, H, W)
|
||
x = self.norm2(x)
|
||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||
outs.append(x)
|
||
|
||
#----------------------------------#
|
||
# block3
|
||
#----------------------------------#
|
||
x, H, W = self.patch_embed3.forward(x)
|
||
for i, blk in enumerate(self.block3):
|
||
x = blk.forward(x, H, W)
|
||
x = self.norm3(x)
|
||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||
outs.append(x)
|
||
|
||
#----------------------------------#
|
||
# block4
|
||
#----------------------------------#
|
||
x, H, W = self.patch_embed4.forward(x)
|
||
for i, blk in enumerate(self.block4):
|
||
x = blk.forward(x, H, W)
|
||
x = self.norm4(x)
|
||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||
outs.append(x)
|
||
|
||
return outs
|
||
|
||
class mit_b0(MixVisionTransformer):
|
||
def __init__(self, pretrained = False):
|
||
super(mit_b0, self).__init__(
|
||
embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||
drop_rate=0.0, drop_path_rate=0.1)
|
||
if pretrained:
|
||
print("Load backbone weights")
|
||
self.load_state_dict(torch.load("model_data/segformer_b0_backbone_weights.pth"), strict=False)
|
||
|
||
class mit_b1(MixVisionTransformer):
|
||
def __init__(self, pretrained = False):
|
||
super(mit_b1, self).__init__(
|
||
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||
drop_rate=0.0, drop_path_rate=0.1)
|
||
if pretrained:
|
||
print("Load backbone weights")
|
||
self.load_state_dict(torch.load("model_data/segformer_b1_backbone_weights.pth"), strict=False)
|
||
|
||
class mit_b2(MixVisionTransformer):
|
||
def __init__(self, pretrained = False):
|
||
super(mit_b2, self).__init__(
|
||
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
|
||
drop_rate=0.0, drop_path_rate=0.1)
|
||
if pretrained:
|
||
print("Load backbone weights")
|
||
self.load_state_dict(torch.load("model_data/segformer_b2_backbone_weights.pth"), strict=False)
|
||
|
||
class mit_b3(MixVisionTransformer):
|
||
def __init__(self, pretrained = False):
|
||
super(mit_b3, self).__init__(
|
||
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
|
||
drop_rate=0.0, drop_path_rate=0.1)
|
||
if pretrained:
|
||
print("Load backbone weights")
|
||
self.load_state_dict(torch.load("model_data/segformer_b3_backbone_weights.pth"), strict=False)
|
||
|
||
class mit_b4(MixVisionTransformer):
|
||
def __init__(self, pretrained = False):
|
||
super(mit_b4, self).__init__(
|
||
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
|
||
drop_rate=0.0, drop_path_rate=0.1)
|
||
if pretrained:
|
||
print("Load backbone weights")
|
||
self.load_state_dict(torch.load("model_data/segformer_b4_backbone_weights.pth"), strict=False)
|
||
|
||
class mit_b5(MixVisionTransformer):
|
||
def __init__(self, pretrained = False):
|
||
super(mit_b5, self).__init__(
|
||
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
|
||
drop_rate=0.0, drop_path_rate=0.1)
|
||
if pretrained:
|
||
print("Load backbone weights")
|
||
self.load_state_dict(torch.load("model_data/segformer_b5_backbone_weights.pth"), strict=False)
|