591 lines
19 KiB
Python
591 lines
19 KiB
Python
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import torch
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import torch.nn as nn
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import numpy as np
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from torch.nn.init import trunc_normal_, zeros_, ones_
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from torch.nn import functional
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def drop_path(x, drop_prob=0., training=False):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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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 ...
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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 = torch.tensor(1 - drop_prob)
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shape = (x.size()[0], ) + (1, ) * (x.ndim - 1)
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random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype)
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random_tensor = torch.floor(random_tensor) # binarize
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output = x.divide(keep_prob) * random_tensor
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return output
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class Swish(nn.Module):
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def __int__(self):
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super(Swish, self).__int__()
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def forward(self,x):
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return x*torch.sigmoid(x)
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class ConvBNLayer(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=0,
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bias_attr=False,
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groups=1,
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act=nn.GELU):
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super().__init__()
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self.conv = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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# weight_attr=paddle.ParamAttr(initializer=nn.initializer.KaimingUniform()),
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bias=bias_attr)
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self.norm = nn.BatchNorm2d(out_channels)
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self.act = act()
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def forward(self, inputs):
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out = self.conv(inputs)
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out = self.norm(out)
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out = self.act(out)
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return out
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class DropPath(nn.Module):
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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):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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class Identity(nn.Module):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, input):
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return input
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class Mlp(nn.Module):
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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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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if isinstance(act_layer, str):
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self.act = Swish()
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else:
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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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def forward(self, x):
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x = self.fc1(x)
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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 ConvMixer(nn.Module):
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def __init__(
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self,
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dim,
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num_heads=8,
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HW=(8, 25),
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local_k=(3, 3), ):
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super().__init__()
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self.HW = HW
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self.dim = dim
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self.local_mixer = nn.Conv2d(
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dim,
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dim,
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local_k,
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1, (local_k[0] // 2, local_k[1] // 2),
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groups=num_heads,
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# weight_attr=ParamAttr(initializer=KaimingNormal())
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)
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def forward(self, x):
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h = self.HW[0]
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w = self.HW[1]
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x = x.transpose([0, 2, 1]).reshape([0, self.dim, h, w])
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x = self.local_mixer(x)
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x = x.flatten(2).transpose([0, 2, 1])
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return x
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class Attention(nn.Module):
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def __init__(self,
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dim,
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num_heads=8,
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mixer='Global',
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HW=(8, 25),
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local_k=(7, 11),
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.):
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super().__init__()
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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.qkv = nn.Linear(dim, dim * 3, 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.HW = HW
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if HW is not None:
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H = HW[0]
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W = HW[1]
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self.N = H * W
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self.C = dim
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if mixer == 'Local' and HW is not None:
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hk = local_k[0]
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wk = local_k[1]
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mask = torch.ones([H * W, H + hk - 1, W + wk - 1])
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for h in range(0, H):
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for w in range(0, W):
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mask[h * W + w, h:h + hk, w:w + wk] = 0.
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mask_paddle = mask[:, hk // 2:H + hk // 2, wk // 2:W + wk //
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2].flatten(1)
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mask_inf = torch.full([H * W, H * W],fill_value=float('-inf'))
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mask = torch.where(mask_paddle < 1, mask_paddle, mask_inf)
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self.mask = mask[None,None,:]
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# self.mask = mask.unsqueeze([0, 1])
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self.mixer = mixer
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def forward(self, x):
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if self.HW is not None:
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N = self.N
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C = self.C
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else:
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_, N, C = x.shape
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qkv = self.qkv(x).reshape((-1, N, 3, self.num_heads, C //self.num_heads)).permute((2, 0, 3, 1, 4))
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q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
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attn = (q.matmul(k.permute((0, 1, 3, 2))))
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if self.mixer == 'Local':
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attn += self.mask
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attn = functional.softmax(attn, dim=-1)
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attn = self.attn_drop(attn)
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x = (attn.matmul(v)).permute((0, 2, 1, 3)).reshape((-1, N, C))
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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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class Block(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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mixer='Global',
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local_mixer=(7, 11),
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HW=(8, 25),
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer='nn.LayerNorm',
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epsilon=1e-6,
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prenorm=True):
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super().__init__()
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if isinstance(norm_layer, str):
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self.norm1 = eval(norm_layer)(dim, eps=epsilon)
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else:
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self.norm1 = norm_layer(dim)
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if mixer == 'Global' or mixer == 'Local':
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self.mixer = Attention(
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dim,
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num_heads=num_heads,
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mixer=mixer,
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HW=HW,
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local_k=local_mixer,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop)
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elif mixer == 'Conv':
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self.mixer = ConvMixer(
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dim, num_heads=num_heads, HW=HW, local_k=local_mixer)
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else:
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raise TypeError("The mixer must be one of [Global, Local, Conv]")
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self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
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if isinstance(norm_layer, str):
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self.norm2 = eval(norm_layer)(dim, eps=epsilon)
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else:
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp_ratio = mlp_ratio
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self.mlp = Mlp(in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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self.prenorm = prenorm
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def forward(self, x):
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if self.prenorm:
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x = self.norm1(x + self.drop_path(self.mixer(x)))
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x = self.norm2(x + self.drop_path(self.mlp(x)))
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else:
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x = x + self.drop_path(self.mixer(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class PatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self,
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img_size=(32, 100),
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in_channels=3,
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embed_dim=768,
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sub_num=2):
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super().__init__()
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num_patches = (img_size[1] // (2 ** sub_num)) * \
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(img_size[0] // (2 ** sub_num))
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self.img_size = img_size
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self.num_patches = num_patches
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self.embed_dim = embed_dim
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self.norm = None
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if sub_num == 2:
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self.proj = nn.Sequential(
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ConvBNLayer(
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in_channels=in_channels,
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out_channels=embed_dim // 2,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=False),
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ConvBNLayer(
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in_channels=embed_dim // 2,
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out_channels=embed_dim,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=False))
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if sub_num == 3:
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self.proj = nn.Sequential(
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ConvBNLayer(
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in_channels=in_channels,
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out_channels=embed_dim // 4,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=False),
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ConvBNLayer(
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in_channels=embed_dim // 4,
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out_channels=embed_dim // 2,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=False),
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ConvBNLayer(
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in_channels=embed_dim // 2,
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out_channels=embed_dim,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=False))
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x).flatten(2).permute(0, 2, 1)
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return x
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class SubSample(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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types='Pool',
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stride=(2, 1),
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sub_norm='nn.LayerNorm',
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act=None):
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super().__init__()
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self.types = types
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if types == 'Pool':
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self.avgpool = nn.AvgPool2d(
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kernel_size=(3, 5), stride=stride, padding=(1, 2))
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self.maxpool = nn.MaxPool2d(
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kernel_size=(3, 5), stride=stride, padding=(1, 2))
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self.proj = nn.Linear(in_channels, out_channels)
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else:
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self.conv = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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# weight_attr=ParamAttr(initializer=KaimingNormal())
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)
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self.norm = eval(sub_norm)(out_channels)
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if act is not None:
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self.act = act()
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else:
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self.act = None
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def forward(self, x):
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if self.types == 'Pool':
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x1 = self.avgpool(x)
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x2 = self.maxpool(x)
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x = (x1 + x2) * 0.5
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out = self.proj(x.flatten(2).permute((0, 2, 1)))
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else:
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x = self.conv(x)
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out = x.flatten(2).permute((0, 2, 1))
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out = self.norm(out)
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if self.act is not None:
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out = self.act(out)
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|
return out
|
||
|
|
|
||
|
|
|
||
|
|
class SVTRNet(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
img_size=[48, 100],
|
||
|
|
in_channels=3,
|
||
|
|
embed_dim=[64, 128, 256],
|
||
|
|
depth=[3, 6, 3],
|
||
|
|
num_heads=[2, 4, 8],
|
||
|
|
mixer=['Local'] * 6 + ['Global'] *
|
||
|
|
6, # Local atten, Global atten, Conv
|
||
|
|
local_mixer=[[7, 11], [7, 11], [7, 11]],
|
||
|
|
patch_merging='Conv', # Conv, Pool, None
|
||
|
|
mlp_ratio=4,
|
||
|
|
qkv_bias=True,
|
||
|
|
qk_scale=None,
|
||
|
|
drop_rate=0.,
|
||
|
|
last_drop=0.1,
|
||
|
|
attn_drop_rate=0.,
|
||
|
|
drop_path_rate=0.1,
|
||
|
|
norm_layer='nn.LayerNorm',
|
||
|
|
sub_norm='nn.LayerNorm',
|
||
|
|
epsilon=1e-6,
|
||
|
|
out_channels=192,
|
||
|
|
out_char_num=25,
|
||
|
|
block_unit='Block',
|
||
|
|
act='nn.GELU',
|
||
|
|
last_stage=True,
|
||
|
|
sub_num=2,
|
||
|
|
prenorm=True,
|
||
|
|
use_lenhead=False,
|
||
|
|
**kwargs):
|
||
|
|
super().__init__()
|
||
|
|
self.img_size = img_size
|
||
|
|
self.embed_dim = embed_dim
|
||
|
|
self.out_channels = out_channels
|
||
|
|
self.prenorm = prenorm
|
||
|
|
patch_merging = None if patch_merging != 'Conv' and patch_merging != 'Pool' else patch_merging
|
||
|
|
self.patch_embed = PatchEmbed(
|
||
|
|
img_size=img_size,
|
||
|
|
in_channels=in_channels,
|
||
|
|
embed_dim=embed_dim[0],
|
||
|
|
sub_num=sub_num)
|
||
|
|
num_patches = self.patch_embed.num_patches
|
||
|
|
self.HW = [img_size[0] // (2**sub_num), img_size[1] // (2**sub_num)]
|
||
|
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim[0]))
|
||
|
|
# self.pos_embed = self.create_parameter(
|
||
|
|
# shape=[1, num_patches, embed_dim[0]], default_initializer=zeros_)
|
||
|
|
|
||
|
|
# self.add_parameter("pos_embed", self.pos_embed)
|
||
|
|
|
||
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
||
|
|
Block_unit = eval(block_unit)
|
||
|
|
|
||
|
|
dpr = np.linspace(0, drop_path_rate, sum(depth))
|
||
|
|
self.blocks1 = nn.ModuleList(
|
||
|
|
[
|
||
|
|
Block_unit(
|
||
|
|
dim=embed_dim[0],
|
||
|
|
num_heads=num_heads[0],
|
||
|
|
mixer=mixer[0:depth[0]][i],
|
||
|
|
HW=self.HW,
|
||
|
|
local_mixer=local_mixer[0],
|
||
|
|
mlp_ratio=mlp_ratio,
|
||
|
|
qkv_bias=qkv_bias,
|
||
|
|
qk_scale=qk_scale,
|
||
|
|
drop=drop_rate,
|
||
|
|
act_layer=eval(act),
|
||
|
|
attn_drop=attn_drop_rate,
|
||
|
|
drop_path=dpr[0:depth[0]][i],
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
epsilon=epsilon,
|
||
|
|
prenorm=prenorm) for i in range(depth[0])
|
||
|
|
]
|
||
|
|
)
|
||
|
|
if patch_merging is not None:
|
||
|
|
self.sub_sample1 = SubSample(
|
||
|
|
embed_dim[0],
|
||
|
|
embed_dim[1],
|
||
|
|
sub_norm=sub_norm,
|
||
|
|
stride=[2, 1],
|
||
|
|
types=patch_merging)
|
||
|
|
HW = [self.HW[0] // 2, self.HW[1]]
|
||
|
|
else:
|
||
|
|
HW = self.HW
|
||
|
|
self.patch_merging = patch_merging
|
||
|
|
self.blocks2 = nn.ModuleList([
|
||
|
|
Block_unit(
|
||
|
|
dim=embed_dim[1],
|
||
|
|
num_heads=num_heads[1],
|
||
|
|
mixer=mixer[depth[0]:depth[0] + depth[1]][i],
|
||
|
|
HW=HW,
|
||
|
|
local_mixer=local_mixer[1],
|
||
|
|
mlp_ratio=mlp_ratio,
|
||
|
|
qkv_bias=qkv_bias,
|
||
|
|
qk_scale=qk_scale,
|
||
|
|
drop=drop_rate,
|
||
|
|
act_layer=eval(act),
|
||
|
|
attn_drop=attn_drop_rate,
|
||
|
|
drop_path=dpr[depth[0]:depth[0] + depth[1]][i],
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
epsilon=epsilon,
|
||
|
|
prenorm=prenorm) for i in range(depth[1])
|
||
|
|
])
|
||
|
|
if patch_merging is not None:
|
||
|
|
self.sub_sample2 = SubSample(
|
||
|
|
embed_dim[1],
|
||
|
|
embed_dim[2],
|
||
|
|
sub_norm=sub_norm,
|
||
|
|
stride=[2, 1],
|
||
|
|
types=patch_merging)
|
||
|
|
HW = [self.HW[0] // 4, self.HW[1]]
|
||
|
|
else:
|
||
|
|
HW = self.HW
|
||
|
|
self.blocks3 = nn.ModuleList([
|
||
|
|
Block_unit(
|
||
|
|
dim=embed_dim[2],
|
||
|
|
num_heads=num_heads[2],
|
||
|
|
mixer=mixer[depth[0] + depth[1]:][i],
|
||
|
|
HW=HW,
|
||
|
|
local_mixer=local_mixer[2],
|
||
|
|
mlp_ratio=mlp_ratio,
|
||
|
|
qkv_bias=qkv_bias,
|
||
|
|
qk_scale=qk_scale,
|
||
|
|
drop=drop_rate,
|
||
|
|
act_layer=eval(act),
|
||
|
|
attn_drop=attn_drop_rate,
|
||
|
|
drop_path=dpr[depth[0] + depth[1]:][i],
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
epsilon=epsilon,
|
||
|
|
prenorm=prenorm) for i in range(depth[2])
|
||
|
|
])
|
||
|
|
self.last_stage = last_stage
|
||
|
|
if last_stage:
|
||
|
|
self.avg_pool = nn.AdaptiveAvgPool2d((1, out_char_num))
|
||
|
|
self.last_conv = nn.Conv2d(
|
||
|
|
in_channels=embed_dim[2],
|
||
|
|
out_channels=self.out_channels,
|
||
|
|
kernel_size=1,
|
||
|
|
stride=1,
|
||
|
|
padding=0,
|
||
|
|
bias=False)
|
||
|
|
self.hardswish = nn.Hardswish()
|
||
|
|
self.dropout = nn.Dropout(p=last_drop)
|
||
|
|
if not prenorm:
|
||
|
|
self.norm = eval(norm_layer)(embed_dim[-1], epsilon=epsilon)
|
||
|
|
self.use_lenhead = use_lenhead
|
||
|
|
if use_lenhead:
|
||
|
|
self.len_conv = nn.Linear(embed_dim[2], self.out_channels)
|
||
|
|
self.hardswish_len = nn.Hardswish()
|
||
|
|
self.dropout_len = nn.Dropout(
|
||
|
|
p=last_drop)
|
||
|
|
|
||
|
|
trunc_normal_(self.pos_embed,std=.02)
|
||
|
|
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:
|
||
|
|
zeros_(m.bias)
|
||
|
|
elif isinstance(m, nn.LayerNorm):
|
||
|
|
zeros_(m.bias)
|
||
|
|
ones_(m.weight)
|
||
|
|
|
||
|
|
def forward_features(self, x):
|
||
|
|
x = self.patch_embed(x)
|
||
|
|
x = x + self.pos_embed
|
||
|
|
x = self.pos_drop(x)
|
||
|
|
for blk in self.blocks1:
|
||
|
|
x = blk(x)
|
||
|
|
if self.patch_merging is not None:
|
||
|
|
x = self.sub_sample1(
|
||
|
|
x.permute([0, 2, 1]).reshape(
|
||
|
|
[-1, self.embed_dim[0], self.HW[0], self.HW[1]]))
|
||
|
|
for blk in self.blocks2:
|
||
|
|
x = blk(x)
|
||
|
|
if self.patch_merging is not None:
|
||
|
|
x = self.sub_sample2(
|
||
|
|
x.permute([0, 2, 1]).reshape(
|
||
|
|
[-1, self.embed_dim[1], self.HW[0] // 2, self.HW[1]]))
|
||
|
|
for blk in self.blocks3:
|
||
|
|
x = blk(x)
|
||
|
|
if not self.prenorm:
|
||
|
|
x = self.norm(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.forward_features(x)
|
||
|
|
if self.use_lenhead:
|
||
|
|
len_x = self.len_conv(x.mean(1))
|
||
|
|
len_x = self.dropout_len(self.hardswish_len(len_x))
|
||
|
|
if self.last_stage:
|
||
|
|
if self.patch_merging is not None:
|
||
|
|
h = self.HW[0] // 4
|
||
|
|
else:
|
||
|
|
h = self.HW[0]
|
||
|
|
x = self.avg_pool(
|
||
|
|
x.permute([0, 2, 1]).reshape(
|
||
|
|
[-1, self.embed_dim[2], h, self.HW[1]]))
|
||
|
|
x = self.last_conv(x)
|
||
|
|
x = self.hardswish(x)
|
||
|
|
x = self.dropout(x)
|
||
|
|
if self.use_lenhead:
|
||
|
|
return x, len_x
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
if __name__=="__main__":
|
||
|
|
a = torch.rand(1,3,48,100)
|
||
|
|
svtr = SVTRNet()
|
||
|
|
|
||
|
|
out = svtr(a)
|
||
|
|
print(svtr)
|
||
|
|
print(out.size())
|