300 lines
10 KiB
Python
300 lines
10 KiB
Python
# copyright (c) 2021 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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"""
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This code is refer from:
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https://github.com/FangShancheng/ABINet/tree/main/modules
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"""
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import math
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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from paddle.nn import LayerList
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from ppocr.modeling.heads.rec_nrtr_head import TransformerBlock, PositionalEncoding
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class BCNLanguage(nn.Layer):
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def __init__(self,
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d_model=512,
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nhead=8,
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num_layers=4,
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dim_feedforward=2048,
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dropout=0.,
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max_length=25,
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detach=True,
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num_classes=37):
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super().__init__()
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self.d_model = d_model
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self.detach = detach
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self.max_length = max_length + 1 # additional stop token
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self.proj = nn.Linear(num_classes, d_model, bias_attr=False)
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self.token_encoder = PositionalEncoding(
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dropout=0.1, dim=d_model, max_len=self.max_length)
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self.pos_encoder = PositionalEncoding(
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dropout=0, dim=d_model, max_len=self.max_length)
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self.decoder = nn.LayerList([
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TransformerBlock(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=dim_feedforward,
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attention_dropout_rate=dropout,
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residual_dropout_rate=dropout,
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with_self_attn=False,
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with_cross_attn=True) for i in range(num_layers)
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])
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self.cls = nn.Linear(d_model, num_classes)
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def forward(self, tokens, lengths):
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"""
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Args:
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tokens: (B, N, C) where N is length, B is batch size and C is classes number
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lengths: (B,)
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"""
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if self.detach: tokens = tokens.detach()
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embed = self.proj(tokens) # (B, N, C)
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embed = self.token_encoder(embed) # (B, N, C)
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padding_mask = _get_mask(lengths, self.max_length)
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zeros = paddle.zeros_like(embed) # (B, N, C)
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qeury = self.pos_encoder(zeros)
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for decoder_layer in self.decoder:
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qeury = decoder_layer(qeury, embed, cross_mask=padding_mask)
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output = qeury # (B, N, C)
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logits = self.cls(output) # (B, N, C)
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return output, logits
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def encoder_layer(in_c, out_c, k=3, s=2, p=1):
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return nn.Sequential(
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nn.Conv2D(in_c, out_c, k, s, p), nn.BatchNorm2D(out_c), nn.ReLU())
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def decoder_layer(in_c,
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out_c,
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k=3,
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s=1,
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p=1,
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mode='nearest',
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scale_factor=None,
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size=None):
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align_corners = False if mode == 'nearest' else True
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return nn.Sequential(
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nn.Upsample(
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size=size,
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scale_factor=scale_factor,
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mode=mode,
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align_corners=align_corners),
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nn.Conv2D(in_c, out_c, k, s, p),
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nn.BatchNorm2D(out_c),
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nn.ReLU())
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class PositionAttention(nn.Layer):
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def __init__(self,
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max_length,
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in_channels=512,
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num_channels=64,
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h=8,
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w=32,
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mode='nearest',
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**kwargs):
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super().__init__()
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self.max_length = max_length
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self.k_encoder = nn.Sequential(
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encoder_layer(
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in_channels, num_channels, s=(1, 2)),
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encoder_layer(
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num_channels, num_channels, s=(2, 2)),
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encoder_layer(
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num_channels, num_channels, s=(2, 2)),
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encoder_layer(
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num_channels, num_channels, s=(2, 2)))
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self.k_decoder = nn.Sequential(
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decoder_layer(
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num_channels, num_channels, scale_factor=2, mode=mode),
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decoder_layer(
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num_channels, num_channels, scale_factor=2, mode=mode),
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decoder_layer(
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num_channels, num_channels, scale_factor=2, mode=mode),
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decoder_layer(
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num_channels, in_channels, size=(h, w), mode=mode))
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self.pos_encoder = PositionalEncoding(
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dropout=0, dim=in_channels, max_len=max_length)
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self.project = nn.Linear(in_channels, in_channels)
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def forward(self, x):
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B, C, H, W = x.shape
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k, v = x, x
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# calculate key vector
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features = []
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for i in range(0, len(self.k_encoder)):
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k = self.k_encoder[i](k)
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features.append(k)
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for i in range(0, len(self.k_decoder) - 1):
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k = self.k_decoder[i](k)
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# print(k.shape, features[len(self.k_decoder) - 2 - i].shape)
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k = k + features[len(self.k_decoder) - 2 - i]
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k = self.k_decoder[-1](k)
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# calculate query vector
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# TODO q=f(q,k)
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zeros = paddle.zeros(
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(B, self.max_length, C), dtype=x.dtype) # (T, N, C)
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q = self.pos_encoder(zeros) # (B, N, C)
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q = self.project(q) # (B, N, C)
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# calculate attention
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attn_scores = q @k.flatten(2) # (B, N, (H*W))
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attn_scores = attn_scores / (C**0.5)
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attn_scores = F.softmax(attn_scores, axis=-1)
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v = v.flatten(2).transpose([0, 2, 1]) # (B, (H*W), C)
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attn_vecs = attn_scores @v # (B, N, C)
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return attn_vecs, attn_scores.reshape([0, self.max_length, H, W])
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class ABINetHead(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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d_model=512,
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nhead=8,
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num_layers=3,
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dim_feedforward=2048,
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dropout=0.1,
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max_length=25,
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use_lang=False,
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iter_size=1,
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image_size=(32, 128)):
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super().__init__()
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self.max_length = max_length + 1
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h, w = image_size[0] // 4, image_size[1] // 4
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self.pos_encoder = PositionalEncoding(
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dropout=0.1, dim=d_model, max_len=h * w)
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self.encoder = nn.LayerList([
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TransformerBlock(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=dim_feedforward,
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attention_dropout_rate=dropout,
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residual_dropout_rate=dropout,
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with_self_attn=True,
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with_cross_attn=False) for i in range(num_layers)
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])
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self.decoder = PositionAttention(
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max_length=max_length + 1, # additional stop token
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mode='nearest', h=h, w=w)
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self.out_channels = out_channels
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self.cls = nn.Linear(d_model, self.out_channels)
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self.use_lang = use_lang
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if use_lang:
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self.iter_size = iter_size
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self.language = BCNLanguage(
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d_model=d_model,
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nhead=nhead,
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num_layers=4,
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dim_feedforward=dim_feedforward,
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dropout=dropout,
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max_length=max_length,
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num_classes=self.out_channels)
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# alignment
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self.w_att_align = nn.Linear(2 * d_model, d_model)
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self.cls_align = nn.Linear(d_model, self.out_channels)
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def forward(self, x, targets=None):
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x = x.transpose([0, 2, 3, 1])
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_, H, W, C = x.shape
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feature = x.flatten(1, 2)
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feature = self.pos_encoder(feature)
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for encoder_layer in self.encoder:
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feature = encoder_layer(feature)
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feature = feature.reshape([0, H, W, C]).transpose([0, 3, 1, 2])
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v_feature, attn_scores = self.decoder(
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feature) # (B, N, C), (B, C, H, W)
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vis_logits = self.cls(v_feature) # (B, N, C)
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logits = vis_logits
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vis_lengths = _get_length(vis_logits)
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if self.use_lang:
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align_logits = vis_logits
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align_lengths = vis_lengths
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all_l_res, all_a_res = [], []
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for i in range(self.iter_size):
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tokens = F.softmax(align_logits, axis=-1)
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lengths = align_lengths
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lengths = paddle.clip(
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lengths, 2, self.max_length) # TODO:move to langauge model
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l_feature, l_logits = self.language(tokens, lengths)
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# alignment
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all_l_res.append(l_logits)
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fuse = paddle.concat((l_feature, v_feature), -1)
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f_att = F.sigmoid(self.w_att_align(fuse))
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output = f_att * v_feature + (1 - f_att) * l_feature
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align_logits = self.cls_align(output) # (B, N, C)
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align_lengths = _get_length(align_logits)
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all_a_res.append(align_logits)
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if self.training:
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return {
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'align': all_a_res,
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'lang': all_l_res,
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'vision': vis_logits
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}
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else:
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logits = align_logits
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if self.training:
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return logits
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else:
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return F.softmax(logits, -1)
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def _get_length(logit):
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""" Greed decoder to obtain length from logit"""
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out = (logit.argmax(-1) == 0)
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abn = out.any(-1)
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out_int = out.cast('int32')
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out = (out_int.cumsum(-1) == 1) & out
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out = out.cast('int32')
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out = out.argmax(-1)
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out = out + 1
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len_seq = paddle.zeros_like(out) + logit.shape[1]
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out = paddle.where(abn, out, len_seq)
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return out
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def _get_mask(length, max_length):
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"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
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Unmasked positions are filled with float(0.0).
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"""
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length = length.unsqueeze(-1)
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B = paddle.shape(length)[0]
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grid = paddle.arange(0, max_length).unsqueeze(0).tile([B, 1])
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zero_mask = paddle.zeros([B, max_length], dtype='float32')
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inf_mask = paddle.full([B, max_length], '-inf', dtype='float32')
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diag_mask = paddle.diag(
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paddle.full(
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[max_length], '-inf', dtype=paddle.float32),
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offset=0,
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name=None)
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mask = paddle.where(grid >= length, inf_mask, zero_mask)
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mask = mask.unsqueeze(1) + diag_mask
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return mask.unsqueeze(1)
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