mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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320 lines
12 KiB
Python
320 lines
12 KiB
Python
# copyright (c) 2019 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/LBH1024/CAN/models/can.py
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https://github.com/LBH1024/CAN/models/counting.py
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https://github.com/LBH1024/CAN/models/decoder.py
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https://github.com/LBH1024/CAN/models/attention.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle.nn as nn
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import paddle
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import math
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'''
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Counting Module
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'''
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class ChannelAtt(nn.Layer):
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def __init__(self, channel, reduction):
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super(ChannelAtt, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2D(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction),
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nn.ReLU(), nn.Linear(channel // reduction, channel), nn.Sigmoid())
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def forward(self, x):
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b, c, _, _ = x.shape
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y = paddle.reshape(self.avg_pool(x), [b, c])
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y = paddle.reshape(self.fc(y), [b, c, 1, 1])
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return x * y
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class CountingDecoder(nn.Layer):
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def __init__(self, in_channel, out_channel, kernel_size):
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super(CountingDecoder, self).__init__()
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self.in_channel = in_channel
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self.out_channel = out_channel
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self.trans_layer = nn.Sequential(
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nn.Conv2D(
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self.in_channel,
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512,
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kernel_size=kernel_size,
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padding=kernel_size // 2,
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bias_attr=False),
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nn.BatchNorm2D(512))
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self.channel_att = ChannelAtt(512, 16)
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self.pred_layer = nn.Sequential(
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nn.Conv2D(
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512, self.out_channel, kernel_size=1, bias_attr=False),
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nn.Sigmoid())
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def forward(self, x, mask):
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b, _, h, w = x.shape
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x = self.trans_layer(x)
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x = self.channel_att(x)
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x = self.pred_layer(x)
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if mask is not None:
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x = x * mask
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x = paddle.reshape(x, [b, self.out_channel, -1])
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x1 = paddle.sum(x, axis=-1)
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return x1, paddle.reshape(x, [b, self.out_channel, h, w])
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'''
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Attention Decoder
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'''
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class PositionEmbeddingSine(nn.Layer):
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def __init__(self,
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num_pos_feats=64,
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temperature=10000,
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normalize=False,
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scale=None):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, x, mask):
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y_embed = paddle.cumsum(mask, 1, dtype='float32')
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x_embed = paddle.cumsum(mask, 2, dtype='float32')
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if self.normalize:
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eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = paddle.arange(self.num_pos_feats, dtype='float32')
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dim_d = paddle.expand(paddle.to_tensor(2), dim_t.shape)
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dim_t = self.temperature**(2 * (dim_t / dim_d).astype('int64') /
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self.num_pos_feats)
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pos_x = paddle.unsqueeze(x_embed, [3]) / dim_t
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pos_y = paddle.unsqueeze(y_embed, [3]) / dim_t
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pos_x = paddle.flatten(
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paddle.stack(
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[
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paddle.sin(pos_x[:, :, :, 0::2]),
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paddle.cos(pos_x[:, :, :, 1::2])
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],
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axis=4),
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3)
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pos_y = paddle.flatten(
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paddle.stack(
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[
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paddle.sin(pos_y[:, :, :, 0::2]),
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paddle.cos(pos_y[:, :, :, 1::2])
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],
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axis=4),
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3)
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pos = paddle.transpose(
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paddle.concat(
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[pos_y, pos_x], axis=3), [0, 3, 1, 2])
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return pos
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class AttDecoder(nn.Layer):
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def __init__(self, ratio, is_train, input_size, hidden_size,
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encoder_out_channel, dropout, dropout_ratio, word_num,
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counting_decoder_out_channel, attention):
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super(AttDecoder, self).__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.out_channel = encoder_out_channel
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self.attention_dim = attention['attention_dim']
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self.dropout_prob = dropout
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self.ratio = ratio
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self.word_num = word_num
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self.counting_num = counting_decoder_out_channel
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self.is_train = is_train
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self.init_weight = nn.Linear(self.out_channel, self.hidden_size)
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self.embedding = nn.Embedding(self.word_num, self.input_size)
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self.word_input_gru = nn.GRUCell(self.input_size, self.hidden_size)
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self.word_attention = Attention(hidden_size, attention['attention_dim'])
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self.encoder_feature_conv = nn.Conv2D(
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self.out_channel,
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self.attention_dim,
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kernel_size=attention['word_conv_kernel'],
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padding=attention['word_conv_kernel'] // 2)
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self.word_state_weight = nn.Linear(self.hidden_size, self.hidden_size)
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self.word_embedding_weight = nn.Linear(self.input_size,
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self.hidden_size)
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self.word_context_weight = nn.Linear(self.out_channel, self.hidden_size)
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self.counting_context_weight = nn.Linear(self.counting_num,
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self.hidden_size)
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self.word_convert = nn.Linear(self.hidden_size, self.word_num)
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if dropout:
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self.dropout = nn.Dropout(dropout_ratio)
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def forward(self, cnn_features, labels, counting_preds, images_mask):
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if self.is_train:
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_, num_steps = labels.shape
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else:
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num_steps = 36
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batch_size, _, height, width = cnn_features.shape
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images_mask = images_mask[:, :, ::self.ratio, ::self.ratio]
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word_probs = paddle.zeros((batch_size, num_steps, self.word_num))
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word_alpha_sum = paddle.zeros((batch_size, 1, height, width))
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hidden = self.init_hidden(cnn_features, images_mask)
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counting_context_weighted = self.counting_context_weight(counting_preds)
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cnn_features_trans = self.encoder_feature_conv(cnn_features)
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position_embedding = PositionEmbeddingSine(256, normalize=True)
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pos = position_embedding(cnn_features_trans, images_mask[:, 0, :, :])
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cnn_features_trans = cnn_features_trans + pos
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word = paddle.ones([batch_size, 1], dtype='int64') # init word as sos
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word = word.squeeze(axis=1)
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for i in range(num_steps):
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word_embedding = self.embedding(word)
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_, hidden = self.word_input_gru(word_embedding, hidden)
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word_context_vec, _, word_alpha_sum = self.word_attention(
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cnn_features, cnn_features_trans, hidden, word_alpha_sum,
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images_mask)
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current_state = self.word_state_weight(hidden)
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word_weighted_embedding = self.word_embedding_weight(word_embedding)
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word_context_weighted = self.word_context_weight(word_context_vec)
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if self.dropout_prob:
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word_out_state = self.dropout(
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current_state + word_weighted_embedding +
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word_context_weighted + counting_context_weighted)
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else:
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word_out_state = current_state + word_weighted_embedding + word_context_weighted + counting_context_weighted
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word_prob = self.word_convert(word_out_state)
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word_probs[:, i] = word_prob
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if self.is_train:
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word = labels[:, i]
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else:
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word = word_prob.argmax(1)
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word = paddle.multiply(
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word, labels[:, i]
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) # labels are oneslike tensor in infer/predict mode
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return word_probs
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def init_hidden(self, features, feature_mask):
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average = paddle.sum(paddle.sum(features * feature_mask, axis=-1),
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axis=-1) / paddle.sum(
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(paddle.sum(feature_mask, axis=-1)), axis=-1)
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average = self.init_weight(average)
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return paddle.tanh(average)
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'''
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Attention Module
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'''
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class Attention(nn.Layer):
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def __init__(self, hidden_size, attention_dim):
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super(Attention, self).__init__()
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self.hidden = hidden_size
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self.attention_dim = attention_dim
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self.hidden_weight = nn.Linear(self.hidden, self.attention_dim)
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self.attention_conv = nn.Conv2D(
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1, 512, kernel_size=11, padding=5, bias_attr=False)
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self.attention_weight = nn.Linear(
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512, self.attention_dim, bias_attr=False)
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self.alpha_convert = nn.Linear(self.attention_dim, 1)
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def forward(self,
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cnn_features,
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cnn_features_trans,
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hidden,
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alpha_sum,
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image_mask=None):
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query = self.hidden_weight(hidden)
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alpha_sum_trans = self.attention_conv(alpha_sum)
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coverage_alpha = self.attention_weight(
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paddle.transpose(alpha_sum_trans, [0, 2, 3, 1]))
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alpha_score = paddle.tanh(
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paddle.unsqueeze(query, [1, 2]) + coverage_alpha + paddle.transpose(
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cnn_features_trans, [0, 2, 3, 1]))
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energy = self.alpha_convert(alpha_score)
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energy = energy - energy.max()
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energy_exp = paddle.exp(paddle.squeeze(energy, -1))
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if image_mask is not None:
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energy_exp = energy_exp * paddle.squeeze(image_mask, 1)
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alpha = energy_exp / (paddle.unsqueeze(
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paddle.sum(paddle.sum(energy_exp, -1), -1), [1, 2]) + 1e-10)
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alpha_sum = paddle.unsqueeze(alpha, 1) + alpha_sum
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context_vector = paddle.sum(
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paddle.sum((paddle.unsqueeze(alpha, 1) * cnn_features), -1), -1)
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return context_vector, alpha, alpha_sum
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class CANHead(nn.Layer):
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def __init__(self, in_channel, out_channel, ratio, attdecoder, **kwargs):
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super(CANHead, self).__init__()
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self.in_channel = in_channel
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self.out_channel = out_channel
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self.counting_decoder1 = CountingDecoder(self.in_channel,
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self.out_channel, 3) # mscm
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self.counting_decoder2 = CountingDecoder(self.in_channel,
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self.out_channel, 5)
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self.decoder = AttDecoder(ratio, **attdecoder)
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self.ratio = ratio
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def forward(self, inputs, targets=None):
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cnn_features, images_mask, labels = inputs
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counting_mask = images_mask[:, :, ::self.ratio, ::self.ratio]
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counting_preds1, _ = self.counting_decoder1(cnn_features, counting_mask)
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counting_preds2, _ = self.counting_decoder2(cnn_features, counting_mask)
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counting_preds = (counting_preds1 + counting_preds2) / 2
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word_probs = self.decoder(cnn_features, labels, counting_preds,
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images_mask)
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return word_probs, counting_preds, counting_preds1, counting_preds2
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