259 lines
8.4 KiB
Python
259 lines
8.4 KiB
Python
# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle import ParamAttr
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from paddle.nn.initializer import KaimingNormal
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import numpy as np
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import paddle
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import paddle.nn as nn
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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trunc_normal_ = TruncatedNormal(std=.02)
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normal_ = Normal
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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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 = paddle.to_tensor(1 - drop_prob)
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shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
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random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
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random_tensor = paddle.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 DropPath(nn.Layer):
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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.Layer):
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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.Layer):
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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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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 Attention(nn.Layer):
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def __init__(self,
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dim,
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num_heads=8,
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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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self.dim = dim
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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_attr=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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def forward(self, x):
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qkv = paddle.reshape(self.qkv(x), (0, -1, 3, self.num_heads, self.dim //
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self.num_heads)).transpose((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.transpose((0, 1, 3, 2))))
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attn = nn.functional.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((0, -1, self.dim))
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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.Layer):
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def __init__(self,
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dim,
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num_heads,
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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, epsilon=epsilon)
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else:
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self.norm1 = norm_layer(dim)
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self.mixer = Attention(
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dim,
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num_heads=num_heads,
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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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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, epsilon=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 ViT(nn.Layer):
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def __init__(
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self,
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img_size=[32, 128],
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patch_size=[4,4],
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in_channels=3,
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embed_dim=384,
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depth=12,
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num_heads=6,
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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_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.1,
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norm_layer='nn.LayerNorm',
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epsilon=1e-6,
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act='nn.GELU',
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prenorm=False,
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**kwargs):
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super().__init__()
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self.embed_dim = embed_dim
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self.out_channels = embed_dim
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self.prenorm = prenorm
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self.patch_embed = nn.Conv2D(in_channels, embed_dim, patch_size, patch_size, padding=(0, 0))
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self.pos_embed = self.create_parameter(
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shape=[1, 257, embed_dim], default_initializer=zeros_)
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self.add_parameter("pos_embed", self.pos_embed)
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = np.linspace(0, drop_path_rate, depth)
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self.blocks1 = nn.LayerList([
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Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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act_layer=eval(act),
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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epsilon=epsilon,
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prenorm=prenorm) for i in range(depth)
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])
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if not prenorm:
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self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
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self.avg_pool = nn.AdaptiveAvgPool2D([1, 25])
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self.last_conv = nn.Conv2D(
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in_channels=embed_dim,
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out_channels=self.out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias_attr=False)
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self.hardswish = nn.Hardswish()
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self.dropout = nn.Dropout(p=0.1, mode="downscale_in_infer")
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trunc_normal_(self.pos_embed)
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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)
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if isinstance(m, nn.Linear) and m.bias is not None:
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zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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zeros_(m.bias)
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ones_(m.weight)
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def forward(self, x):
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x = self.patch_embed(x).flatten(2).transpose((0, 2, 1))
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x = x + self.pos_embed[:, 1:, :] #[:, :paddle.shape(x)[1], :]
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x = self.pos_drop(x)
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for blk in self.blocks1:
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x = blk(x)
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if not self.prenorm:
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x = self.norm(x)
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x = self.avg_pool(x.transpose([0, 2, 1]).reshape(
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[0, self.embed_dim, -1, 25]))
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x = self.last_conv(x)
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x = self.hardswish(x)
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x = self.dropout(x)
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return x
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