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| # copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| This code is refer from: | |
| https://github.com/LBH1024/CAN/models/can.py | |
| https://github.com/LBH1024/CAN/models/counting.py | |
| https://github.com/LBH1024/CAN/models/decoder.py | |
| https://github.com/LBH1024/CAN/models/attention.py | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import paddle.nn as nn | |
| import paddle | |
| import math | |
| ''' | |
| Counting Module | |
| ''' | |
| class ChannelAtt(nn.Layer): | |
| def __init__(self, channel, reduction): | |
| super(ChannelAtt, self).__init__() | |
| self.avg_pool = nn.AdaptiveAvgPool2D(1) | |
| self.fc = nn.Sequential( | |
| nn.Linear(channel, channel // reduction), | |
| nn.ReLU(), nn.Linear(channel // reduction, channel), nn.Sigmoid()) | |
| def forward(self, x): | |
| b, c, _, _ = x.shape | |
| y = paddle.reshape(self.avg_pool(x), [b, c]) | |
| y = paddle.reshape(self.fc(y), [b, c, 1, 1]) | |
| return x * y | |
| class CountingDecoder(nn.Layer): | |
| def __init__(self, in_channel, out_channel, kernel_size): | |
| super(CountingDecoder, self).__init__() | |
| self.in_channel = in_channel | |
| self.out_channel = out_channel | |
| self.trans_layer = nn.Sequential( | |
| nn.Conv2D( | |
| self.in_channel, | |
| 512, | |
| kernel_size=kernel_size, | |
| padding=kernel_size // 2, | |
| bias_attr=False), | |
| nn.BatchNorm2D(512)) | |
| self.channel_att = ChannelAtt(512, 16) | |
| self.pred_layer = nn.Sequential( | |
| nn.Conv2D( | |
| 512, self.out_channel, kernel_size=1, bias_attr=False), | |
| nn.Sigmoid()) | |
| def forward(self, x, mask): | |
| b, _, h, w = x.shape | |
| x = self.trans_layer(x) | |
| x = self.channel_att(x) | |
| x = self.pred_layer(x) | |
| if mask is not None: | |
| x = x * mask | |
| x = paddle.reshape(x, [b, self.out_channel, -1]) | |
| x1 = paddle.sum(x, axis=-1) | |
| return x1, paddle.reshape(x, [b, self.out_channel, h, w]) | |
| ''' | |
| Attention Decoder | |
| ''' | |
| class PositionEmbeddingSine(nn.Layer): | |
| def __init__(self, | |
| num_pos_feats=64, | |
| temperature=10000, | |
| normalize=False, | |
| scale=None): | |
| super().__init__() | |
| self.num_pos_feats = num_pos_feats | |
| self.temperature = temperature | |
| self.normalize = normalize | |
| if scale is not None and normalize is False: | |
| raise ValueError("normalize should be True if scale is passed") | |
| if scale is None: | |
| scale = 2 * math.pi | |
| self.scale = scale | |
| def forward(self, x, mask): | |
| y_embed = paddle.cumsum(mask, 1, dtype='float32') | |
| x_embed = paddle.cumsum(mask, 2, dtype='float32') | |
| if self.normalize: | |
| eps = 1e-6 | |
| y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale | |
| x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale | |
| dim_t = paddle.arange(self.num_pos_feats, dtype='float32') | |
| dim_d = paddle.expand(paddle.to_tensor(2), dim_t.shape) | |
| dim_t = self.temperature**(2 * (dim_t / dim_d).astype('int64') / | |
| self.num_pos_feats) | |
| pos_x = paddle.unsqueeze(x_embed, [3]) / dim_t | |
| pos_y = paddle.unsqueeze(y_embed, [3]) / dim_t | |
| pos_x = paddle.flatten( | |
| paddle.stack( | |
| [ | |
| paddle.sin(pos_x[:, :, :, 0::2]), | |
| paddle.cos(pos_x[:, :, :, 1::2]) | |
| ], | |
| axis=4), | |
| 3) | |
| pos_y = paddle.flatten( | |
| paddle.stack( | |
| [ | |
| paddle.sin(pos_y[:, :, :, 0::2]), | |
| paddle.cos(pos_y[:, :, :, 1::2]) | |
| ], | |
| axis=4), | |
| 3) | |
| pos = paddle.transpose( | |
| paddle.concat( | |
| [pos_y, pos_x], axis=3), [0, 3, 1, 2]) | |
| return pos | |
| class AttDecoder(nn.Layer): | |
| def __init__(self, ratio, is_train, input_size, hidden_size, | |
| encoder_out_channel, dropout, dropout_ratio, word_num, | |
| counting_decoder_out_channel, attention): | |
| super(AttDecoder, self).__init__() | |
| self.input_size = input_size | |
| self.hidden_size = hidden_size | |
| self.out_channel = encoder_out_channel | |
| self.attention_dim = attention['attention_dim'] | |
| self.dropout_prob = dropout | |
| self.ratio = ratio | |
| self.word_num = word_num | |
| self.counting_num = counting_decoder_out_channel | |
| self.is_train = is_train | |
| self.init_weight = nn.Linear(self.out_channel, self.hidden_size) | |
| self.embedding = nn.Embedding(self.word_num, self.input_size) | |
| self.word_input_gru = nn.GRUCell(self.input_size, self.hidden_size) | |
| self.word_attention = Attention(hidden_size, attention['attention_dim']) | |
| self.encoder_feature_conv = nn.Conv2D( | |
| self.out_channel, | |
| self.attention_dim, | |
| kernel_size=attention['word_conv_kernel'], | |
| padding=attention['word_conv_kernel'] // 2) | |
| self.word_state_weight = nn.Linear(self.hidden_size, self.hidden_size) | |
| self.word_embedding_weight = nn.Linear(self.input_size, | |
| self.hidden_size) | |
| self.word_context_weight = nn.Linear(self.out_channel, self.hidden_size) | |
| self.counting_context_weight = nn.Linear(self.counting_num, | |
| self.hidden_size) | |
| self.word_convert = nn.Linear(self.hidden_size, self.word_num) | |
| if dropout: | |
| self.dropout = nn.Dropout(dropout_ratio) | |
| def forward(self, cnn_features, labels, counting_preds, images_mask): | |
| if self.is_train: | |
| _, num_steps = labels.shape | |
| else: | |
| num_steps = 36 | |
| batch_size, _, height, width = cnn_features.shape | |
| images_mask = images_mask[:, :, ::self.ratio, ::self.ratio] | |
| word_probs = paddle.zeros((batch_size, num_steps, self.word_num)) | |
| word_alpha_sum = paddle.zeros((batch_size, 1, height, width)) | |
| hidden = self.init_hidden(cnn_features, images_mask) | |
| counting_context_weighted = self.counting_context_weight(counting_preds) | |
| cnn_features_trans = self.encoder_feature_conv(cnn_features) | |
| position_embedding = PositionEmbeddingSine(256, normalize=True) | |
| pos = position_embedding(cnn_features_trans, images_mask[:, 0, :, :]) | |
| cnn_features_trans = cnn_features_trans + pos | |
| word = paddle.ones([batch_size, 1], dtype='int64') # init word as sos | |
| word = word.squeeze(axis=1) | |
| for i in range(num_steps): | |
| word_embedding = self.embedding(word) | |
| _, hidden = self.word_input_gru(word_embedding, hidden) | |
| word_context_vec, _, word_alpha_sum = self.word_attention( | |
| cnn_features, cnn_features_trans, hidden, word_alpha_sum, | |
| images_mask) | |
| current_state = self.word_state_weight(hidden) | |
| word_weighted_embedding = self.word_embedding_weight(word_embedding) | |
| word_context_weighted = self.word_context_weight(word_context_vec) | |
| if self.dropout_prob: | |
| word_out_state = self.dropout( | |
| current_state + word_weighted_embedding + | |
| word_context_weighted + counting_context_weighted) | |
| else: | |
| word_out_state = current_state + word_weighted_embedding + word_context_weighted + counting_context_weighted | |
| word_prob = self.word_convert(word_out_state) | |
| word_probs[:, i] = word_prob | |
| if self.is_train: | |
| word = labels[:, i] | |
| else: | |
| word = word_prob.argmax(1) | |
| word = paddle.multiply( | |
| word, labels[:, i] | |
| ) # labels are oneslike tensor in infer/predict mode | |
| return word_probs | |
| def init_hidden(self, features, feature_mask): | |
| average = paddle.sum(paddle.sum(features * feature_mask, axis=-1), | |
| axis=-1) / paddle.sum( | |
| (paddle.sum(feature_mask, axis=-1)), axis=-1) | |
| average = self.init_weight(average) | |
| return paddle.tanh(average) | |
| ''' | |
| Attention Module | |
| ''' | |
| class Attention(nn.Layer): | |
| def __init__(self, hidden_size, attention_dim): | |
| super(Attention, self).__init__() | |
| self.hidden = hidden_size | |
| self.attention_dim = attention_dim | |
| self.hidden_weight = nn.Linear(self.hidden, self.attention_dim) | |
| self.attention_conv = nn.Conv2D( | |
| 1, 512, kernel_size=11, padding=5, bias_attr=False) | |
| self.attention_weight = nn.Linear( | |
| 512, self.attention_dim, bias_attr=False) | |
| self.alpha_convert = nn.Linear(self.attention_dim, 1) | |
| def forward(self, | |
| cnn_features, | |
| cnn_features_trans, | |
| hidden, | |
| alpha_sum, | |
| image_mask=None): | |
| query = self.hidden_weight(hidden) | |
| alpha_sum_trans = self.attention_conv(alpha_sum) | |
| coverage_alpha = self.attention_weight( | |
| paddle.transpose(alpha_sum_trans, [0, 2, 3, 1])) | |
| alpha_score = paddle.tanh( | |
| paddle.unsqueeze(query, [1, 2]) + coverage_alpha + paddle.transpose( | |
| cnn_features_trans, [0, 2, 3, 1])) | |
| energy = self.alpha_convert(alpha_score) | |
| energy = energy - energy.max() | |
| energy_exp = paddle.exp(paddle.squeeze(energy, -1)) | |
| if image_mask is not None: | |
| energy_exp = energy_exp * paddle.squeeze(image_mask, 1) | |
| alpha = energy_exp / (paddle.unsqueeze( | |
| paddle.sum(paddle.sum(energy_exp, -1), -1), [1, 2]) + 1e-10) | |
| alpha_sum = paddle.unsqueeze(alpha, 1) + alpha_sum | |
| context_vector = paddle.sum( | |
| paddle.sum((paddle.unsqueeze(alpha, 1) * cnn_features), -1), -1) | |
| return context_vector, alpha, alpha_sum | |
| class CANHead(nn.Layer): | |
| def __init__(self, in_channel, out_channel, ratio, attdecoder, **kwargs): | |
| super(CANHead, self).__init__() | |
| self.in_channel = in_channel | |
| self.out_channel = out_channel | |
| self.counting_decoder1 = CountingDecoder(self.in_channel, | |
| self.out_channel, 3) # mscm | |
| self.counting_decoder2 = CountingDecoder(self.in_channel, | |
| self.out_channel, 5) | |
| self.decoder = AttDecoder(ratio, **attdecoder) | |
| self.ratio = ratio | |
| def forward(self, inputs, targets=None): | |
| cnn_features, images_mask, labels = inputs | |
| counting_mask = images_mask[:, :, ::self.ratio, ::self.ratio] | |
| counting_preds1, _ = self.counting_decoder1(cnn_features, counting_mask) | |
| counting_preds2, _ = self.counting_decoder2(cnn_features, counting_mask) | |
| counting_preds = (counting_preds1 + counting_preds2) / 2 | |
| word_probs = self.decoder(cnn_features, labels, counting_preds, | |
| images_mask) | |
| return word_probs, counting_preds, counting_preds1, counting_preds2 | |