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# copyright (c) 2021 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
import numpy as np
import cv2
__all__ = ["Kie_backbone"]
class Encoder(nn.Layer):
def __init__(self, num_channels, num_filters):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2D(
num_channels,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn1 = nn.BatchNorm(num_filters, act='relu')
self.conv2 = nn.Conv2D(
num_filters,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn2 = nn.BatchNorm(num_filters, act='relu')
self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
def forward(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.conv2(x)
x = self.bn2(x)
x_pooled = self.pool(x)
return x, x_pooled
class Decoder(nn.Layer):
def __init__(self, num_channels, num_filters):
super(Decoder, self).__init__()
self.conv1 = nn.Conv2D(
num_channels,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn1 = nn.BatchNorm(num_filters, act='relu')
self.conv2 = nn.Conv2D(
num_filters,
num_filters,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn2 = nn.BatchNorm(num_filters, act='relu')
self.conv0 = nn.Conv2D(
num_channels,
num_filters,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False)
self.bn0 = nn.BatchNorm(num_filters, act='relu')
def forward(self, inputs_prev, inputs):
x = self.conv0(inputs)
x = self.bn0(x)
x = paddle.nn.functional.interpolate(
x, scale_factor=2, mode='bilinear', align_corners=False)
x = paddle.concat([inputs_prev, x], axis=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.conv2(x)
x = self.bn2(x)
return x
class UNet(nn.Layer):
def __init__(self):
super(UNet, self).__init__()
self.down1 = Encoder(num_channels=3, num_filters=16)
self.down2 = Encoder(num_channels=16, num_filters=32)
self.down3 = Encoder(num_channels=32, num_filters=64)
self.down4 = Encoder(num_channels=64, num_filters=128)
self.down5 = Encoder(num_channels=128, num_filters=256)
self.up1 = Decoder(32, 16)
self.up2 = Decoder(64, 32)
self.up3 = Decoder(128, 64)
self.up4 = Decoder(256, 128)
self.out_channels = 16
def forward(self, inputs):
x1, _ = self.down1(inputs)
_, x2 = self.down2(x1)
_, x3 = self.down3(x2)
_, x4 = self.down4(x3)
_, x5 = self.down5(x4)
x = self.up4(x4, x5)
x = self.up3(x3, x)
x = self.up2(x2, x)
x = self.up1(x1, x)
return x
class Kie_backbone(nn.Layer):
def __init__(self, in_channels, **kwargs):
super(Kie_backbone, self).__init__()
self.out_channels = 16
self.img_feat = UNet()
self.maxpool = nn.MaxPool2D(kernel_size=7)
def bbox2roi(self, bbox_list):
rois_list = []
rois_num = []
for img_id, bboxes in enumerate(bbox_list):
rois_num.append(bboxes.shape[0])
rois_list.append(bboxes)
rois = paddle.concat(rois_list, 0)
rois_num = paddle.to_tensor(rois_num, dtype='int32')
return rois, rois_num
def pre_process(self, img, relations, texts, gt_bboxes, tag, img_size):
img, relations, texts, gt_bboxes, tag, img_size = img.numpy(
), relations.numpy(), texts.numpy(), gt_bboxes.numpy(), tag.numpy(
).tolist(), img_size.numpy()
temp_relations, temp_texts, temp_gt_bboxes = [], [], []
h, w = int(np.max(img_size[:, 0])), int(np.max(img_size[:, 1]))
img = paddle.to_tensor(img[:, :, :h, :w])
batch = len(tag)
for i in range(batch):
num, recoder_len = tag[i][0], tag[i][1]
temp_relations.append(
paddle.to_tensor(
relations[i, :num, :num, :], dtype='float32'))
temp_texts.append(
paddle.to_tensor(
texts[i, :num, :recoder_len], dtype='float32'))
temp_gt_bboxes.append(
paddle.to_tensor(
gt_bboxes[i, :num, ...], dtype='float32'))
return img, temp_relations, temp_texts, temp_gt_bboxes
def forward(self, inputs):
img = inputs[0]
relations, texts, gt_bboxes, tag, img_size = inputs[1], inputs[
2], inputs[3], inputs[5], inputs[-1]
img, relations, texts, gt_bboxes = self.pre_process(
img, relations, texts, gt_bboxes, tag, img_size)
x = self.img_feat(img)
boxes, rois_num = self.bbox2roi(gt_bboxes)
feats = paddle.vision.ops.roi_align(
x, boxes, spatial_scale=1.0, output_size=7, boxes_num=rois_num)
feats = self.maxpool(feats).squeeze(-1).squeeze(-1)
return [relations, texts, feats]
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