# 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