From bb7cad19f85c805e33c101b3d662b16a0061a344 Mon Sep 17 00:00:00 2001 From: chenjian Date: Thu, 4 Nov 2021 19:16:25 +0800 Subject: [PATCH] add EnlightenGAN module --- .../image_processing/EnlightenGAN/README.md | 83 +++++++ .../enlighten_inference/__init__.py | 37 +++ .../pd_model/x2paddle_code.py | 219 ++++++++++++++++++ .../image_processing/EnlightenGAN/module.py | 96 ++++++++ .../EnlightenGAN/requirements.txt | 3 + .../EnlightenGAN/x2paddle_code.py | 219 ++++++++++++++++++ 6 files changed, 657 insertions(+) create mode 100644 modules/image/image_processing/EnlightenGAN/README.md create mode 100755 modules/image/image_processing/EnlightenGAN/enlighten_inference/__init__.py create mode 100755 modules/image/image_processing/EnlightenGAN/enlighten_inference/pd_model/x2paddle_code.py create mode 100644 modules/image/image_processing/EnlightenGAN/module.py create mode 100644 modules/image/image_processing/EnlightenGAN/requirements.txt create mode 100755 modules/image/image_processing/EnlightenGAN/x2paddle_code.py diff --git a/modules/image/image_processing/EnlightenGAN/README.md b/modules/image/image_processing/EnlightenGAN/README.md new file mode 100644 index 00000000..6191b716 --- /dev/null +++ b/modules/image/image_processing/EnlightenGAN/README.md @@ -0,0 +1,83 @@ +# EnlightenGAN + +|模型名称|EnlightenGAN| +| :--- | :---: | +|类别|图像 - 暗光增强| +|网络|EnlightenGAN| +|数据集|| +|是否支持Fine-tuning|否| +|模型大小|83MB| +|最新更新日期|2021-11-04| +|数据指标|-| + + +## 一、模型基本信息 + +- ### 模型介绍 + + - EnlightenGAN使用非成对的数据进行训练，通过设计自特征保留损失函数和自约束注意力机制，训练的网络可以应用到多种场景下的暗光增强中。 + + - 更多详情参考：[EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://arxiv.org/abs/1906.06972) + + + +## 二、安装 + +- ### 1、环境依赖 + - + - pillow + +- ### 2、安装 + + - ```shell + $ hub install EnlightenGAN + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + - 在windows下安装，由于paddledet package会依赖cython-bbox以及pycocotools, 这两个包需要windows用户提前装好，可参考[cython-bbox安装](https://blog.csdn.net/qq_24739717/article/details/105588729)和[pycocotools安装](https://github.com/PaddlePaddle/PaddleX/blob/release/1.3/docs/install.md#pycocotools安装问题) +## 三、模型API预测 + +- ### 1、命令行预测 + + - ```shell + # Read from a file + $ hub run EnlightenGAN --input_path "/PATH/TO/IMAGE" + ``` + - 通过命令行方式实现暗光增强模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst) + +- ### 2、代码示例 + + - ```python + import paddlehub as hub + + enlightener = hub.Module(name="EnlightenGAN") + input_path = "/PATH/TO/IMAGE" + # Read from a raw file + enlightener.enlightening(input_path, output_path='./enlightening_result.png', use_gpu=True) + ``` + +- ### 3、API + + - ```python + def enlightening(input_path, output_path='./enlightening_result.png', use_gpu=False) + ``` + - 。 + + - **参数** + + - input\_path (str): 输入图像文件的路径;
+ - output\_path (str): 结果保存的路径, 需要指定输出文件名；
+ - use\_gpu (bool): 是否使用 GPU；
+ + + + +## 四、更新历史 + +* 1.0.0 + + 初始发布 + + - ```shell + $ hub install EnlightenGAN==1.0.0 + ``` diff --git a/modules/image/image_processing/EnlightenGAN/enlighten_inference/__init__.py b/modules/image/image_processing/EnlightenGAN/enlighten_inference/__init__.py new file mode 100755 index 00000000..b9ebee0f --- /dev/null +++ b/modules/image/image_processing/EnlightenGAN/enlighten_inference/__init__.py @@ -0,0 +1,37 @@ +import os +from typing import Union + +import numpy as np +from onnxruntime import InferenceSession + + +def get_relative_path(root, *args): + return os.path.join(os.path.dirname(root), *args) + + +class EnlightenOnnxModel: + def __init__(self, model: Union[bytes, str, None] = None): + self.graph = InferenceSession(model or get_relative_path(__file__, 'enlighten.onnx')) + + def __repr__(self): + return f'' + + def _pad(self, img): + h, w, _ = img.shape + block_size = 16 + min_height = (h // block_size + 1) * block_size + min_width = (w // block_size + 1) * block_size + img = np.pad(img, ((0, min_height - h), (0, min_width - w), (0, 0)), mode='constant', constant_values=0) + return img, (h, w) + + def _preprocess(self, img): + if len(img.shape) != 3: + raise ValueError(f'Incorrect shape: expected 3, got {len(img.shape)}') + return np.expand_dims(np.transpose(img, (2, 0, 1)).astype(np.float32) / 255., 0) + + def predict(self, img): + padded, (h, w) = self._pad(img) + image_numpy, = self.graph.run(['output'], {'input': self._preprocess(padded)}) + image_numpy = (np.transpose(image_numpy[0], (1, 2, 0)) + 1) / 2.0 * 255.0 + image_numpy = np.clip(image_numpy, 0, 255) + return image_numpy.astype('uint8')[:h, :w, :] diff --git a/modules/image/image_processing/EnlightenGAN/enlighten_inference/pd_model/x2paddle_code.py b/modules/image/image_processing/EnlightenGAN/enlighten_inference/pd_model/x2paddle_code.py new file mode 100755 index 00000000..92da60a1 --- /dev/null +++ b/modules/image/image_processing/EnlightenGAN/enlighten_inference/pd_model/x2paddle_code.py @@ -0,0 +1,219 @@ +import paddle +import math +from x2paddle.op_mapper.onnx2paddle import onnx_custom_layer as x2paddle_nn + + +class ONNXModel(paddle.nn.Layer): + def __init__(self): + super(ONNXModel, self).__init__() + self.conv0 = paddle.nn.Conv2D(in_channels=3, out_channels=3, kernel_size=[1, 1], groups=3) + self.pool0 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.pool1 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv1 = paddle.nn.Conv2D(in_channels=4, out_channels=32, kernel_size=[3, 3], padding=1) + self.pool2 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.leakyrelu0 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.pool3 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.batchnorm0 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu1 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm1 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool4 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv3 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu2 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm2 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv4 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu3 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm3 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool5 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv5 = paddle.nn.Conv2D(in_channels=64, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu4 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm4 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv6 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu5 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm5 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool6 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv7 = paddle.nn.Conv2D(in_channels=128, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu6 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm6 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv8 = paddle.nn.Conv2D(in_channels=256, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu7 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm7 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool7 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv9 = paddle.nn.Conv2D(in_channels=256, out_channels=512, kernel_size=[3, 3], padding=1) + self.leakyrelu8 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm8 = paddle.nn.BatchNorm( + num_channels=512, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv10 = paddle.nn.Conv2D(in_channels=512, out_channels=512, kernel_size=[3, 3], padding=1) + self.leakyrelu9 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm9 = paddle.nn.BatchNorm( + num_channels=512, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv11 = paddle.nn.Conv2D(in_channels=512, out_channels=256, kernel_size=[3, 3], padding=1) + self.conv12 = paddle.nn.Conv2D(in_channels=512, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu10 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm10 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv13 = paddle.nn.Conv2D(in_channels=256, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu11 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm11 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv14 = paddle.nn.Conv2D(in_channels=256, out_channels=128, kernel_size=[3, 3], padding=1) + self.conv15 = paddle.nn.Conv2D(in_channels=256, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu12 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm12 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv16 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu13 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm13 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv17 = paddle.nn.Conv2D(in_channels=128, out_channels=64, kernel_size=[3, 3], padding=1) + self.conv18 = paddle.nn.Conv2D(in_channels=128, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu14 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm14 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv19 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu15 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm15 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv20 = paddle.nn.Conv2D(in_channels=64, out_channels=32, kernel_size=[3, 3], padding=1) + self.conv21 = paddle.nn.Conv2D(in_channels=64, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu16 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm16 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv22 = paddle.nn.Conv2D(in_channels=32, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu17 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.conv23 = paddle.nn.Conv2D(in_channels=32, out_channels=3, kernel_size=[1, 1]) + + def forward(self, x2paddle_input): + x2paddle_137 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_145 = paddle.full(dtype='float32', shape=[1], fill_value=0.29899999499320984) + x2paddle_147 = paddle.full(dtype='float32', shape=[1], fill_value=0.5870000123977661) + x2paddle_150 = paddle.full(dtype='float32', shape=[1], fill_value=0.11400000005960464) + x2paddle_153 = paddle.full(dtype='float32', shape=[1], fill_value=2.0) + x2paddle_155 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_256 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_134 = self.conv0(x2paddle_input) + x2paddle_135, = paddle.split(x=x2paddle_134, num_or_sections=[1]) + x2paddle_257 = paddle.multiply(x=x2paddle_134, y=x2paddle_256) + x2paddle_136 = paddle.squeeze(x=x2paddle_135, axis=[0]) + x2paddle_138 = paddle.add(x=x2paddle_136, y=x2paddle_137) + x2paddle_139_p0, x2paddle_139_p1, x2paddle_139_p2 = paddle.split(x=x2paddle_138, num_or_sections=[1, 1, 1]) + x2paddle_142 = paddle.squeeze(x=x2paddle_139_p0, axis=[0]) + x2paddle_143 = paddle.squeeze(x=x2paddle_139_p1, axis=[0]) + x2paddle_144 = paddle.squeeze(x=x2paddle_139_p2, axis=[0]) + x2paddle_146 = paddle.multiply(x=x2paddle_142, y=x2paddle_145) + x2paddle_148 = paddle.multiply(x=x2paddle_143, y=x2paddle_147) + x2paddle_151 = paddle.multiply(x=x2paddle_144, y=x2paddle_150) + x2paddle_149 = paddle.add(x=x2paddle_146, y=x2paddle_148) + x2paddle_152 = paddle.add(x=x2paddle_149, y=x2paddle_151) + x2paddle_154 = paddle.divide(x=x2paddle_152, y=x2paddle_153) + x2paddle_156 = paddle.subtract(x=x2paddle_155, y=x2paddle_154) + x2paddle_157 = paddle.unsqueeze(x=x2paddle_156, axis=[0]) + x2paddle_158 = paddle.unsqueeze(x=x2paddle_157, axis=[0]) + x2paddle_159 = self.pool0(x2paddle_158) + x2paddle_163 = paddle.concat(x=[x2paddle_134, x2paddle_158], axis=1) + x2paddle_160 = self.pool1(x2paddle_159) + x2paddle_164 = self.conv1(x2paddle_163) + x2paddle_161 = self.pool2(x2paddle_160) + x2paddle_165 = self.leakyrelu0(x2paddle_164) + x2paddle_162 = self.pool3(x2paddle_161) + x2paddle_166 = self.batchnorm0(x2paddle_165) + x2paddle_167 = self.conv2(x2paddle_166) + x2paddle_168 = self.leakyrelu1(x2paddle_167) + x2paddle_169 = self.batchnorm1(x2paddle_168) + x2paddle_170 = self.pool4(x2paddle_169) + x2paddle_246 = paddle.multiply(x=x2paddle_169, y=x2paddle_158) + x2paddle_171 = self.conv3(x2paddle_170) + x2paddle_172 = self.leakyrelu2(x2paddle_171) + x2paddle_173 = self.batchnorm2(x2paddle_172) + x2paddle_174 = self.conv4(x2paddle_173) + x2paddle_175 = self.leakyrelu3(x2paddle_174) + x2paddle_176 = self.batchnorm3(x2paddle_175) + x2paddle_177 = self.pool5(x2paddle_176) + x2paddle_232 = paddle.multiply(x=x2paddle_176, y=x2paddle_159) + x2paddle_178 = self.conv5(x2paddle_177) + x2paddle_179 = self.leakyrelu4(x2paddle_178) + x2paddle_180 = self.batchnorm4(x2paddle_179) + x2paddle_181 = self.conv6(x2paddle_180) + x2paddle_182 = self.leakyrelu5(x2paddle_181) + x2paddle_183 = self.batchnorm5(x2paddle_182) + x2paddle_184 = self.pool6(x2paddle_183) + x2paddle_218 = paddle.multiply(x=x2paddle_183, y=x2paddle_160) + x2paddle_185 = self.conv7(x2paddle_184) + x2paddle_186 = self.leakyrelu6(x2paddle_185) + x2paddle_187 = self.batchnorm6(x2paddle_186) + x2paddle_188 = self.conv8(x2paddle_187) + x2paddle_189 = self.leakyrelu7(x2paddle_188) + x2paddle_190 = self.batchnorm7(x2paddle_189) + x2paddle_191 = self.pool7(x2paddle_190) + x2paddle_204 = paddle.multiply(x=x2paddle_190, y=x2paddle_161) + x2paddle_192 = self.conv9(x2paddle_191) + x2paddle_193 = self.leakyrelu8(x2paddle_192) + x2paddle_194 = self.batchnorm8(x2paddle_193) + x2paddle_195 = paddle.multiply(x=x2paddle_194, y=x2paddle_162) + x2paddle_196 = self.conv10(x2paddle_195) + x2paddle_197 = self.leakyrelu9(x2paddle_196) + x2paddle_198 = self.batchnorm9(x2paddle_197) + x2paddle_203 = paddle.nn.functional.interpolate(x=x2paddle_198, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_205 = self.conv11(x2paddle_203) + x2paddle_206 = paddle.concat(x=[x2paddle_205, x2paddle_204], axis=1) + x2paddle_207 = self.conv12(x2paddle_206) + x2paddle_208 = self.leakyrelu10(x2paddle_207) + x2paddle_209 = self.batchnorm10(x2paddle_208) + x2paddle_210 = self.conv13(x2paddle_209) + x2paddle_211 = self.leakyrelu11(x2paddle_210) + x2paddle_212 = self.batchnorm11(x2paddle_211) + x2paddle_217 = paddle.nn.functional.interpolate(x=x2paddle_212, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_219 = self.conv14(x2paddle_217) + x2paddle_220 = paddle.concat(x=[x2paddle_219, x2paddle_218], axis=1) + x2paddle_221 = self.conv15(x2paddle_220) + x2paddle_222 = self.leakyrelu12(x2paddle_221) + x2paddle_223 = self.batchnorm12(x2paddle_222) + x2paddle_224 = self.conv16(x2paddle_223) + x2paddle_225 = self.leakyrelu13(x2paddle_224) + x2paddle_226 = self.batchnorm13(x2paddle_225) + x2paddle_231 = paddle.nn.functional.interpolate(x=x2paddle_226, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_233 = self.conv17(x2paddle_231) + x2paddle_234 = paddle.concat(x=[x2paddle_233, x2paddle_232], axis=1) + x2paddle_235 = self.conv18(x2paddle_234) + x2paddle_236 = self.leakyrelu14(x2paddle_235) + x2paddle_237 = self.batchnorm14(x2paddle_236) + x2paddle_238 = self.conv19(x2paddle_237) + x2paddle_239 = self.leakyrelu15(x2paddle_238) + x2paddle_240 = self.batchnorm15(x2paddle_239) + x2paddle_245 = paddle.nn.functional.interpolate(x=x2paddle_240, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_247 = self.conv20(x2paddle_245) + x2paddle_248 = paddle.concat(x=[x2paddle_247, x2paddle_246], axis=1) + x2paddle_249 = self.conv21(x2paddle_248) + x2paddle_250 = self.leakyrelu16(x2paddle_249) + x2paddle_251 = self.batchnorm16(x2paddle_250) + x2paddle_252 = self.conv22(x2paddle_251) + x2paddle_253 = self.leakyrelu17(x2paddle_252) + x2paddle_254 = self.conv23(x2paddle_253) + x2paddle_255 = paddle.multiply(x=x2paddle_254, y=x2paddle_158) + x2paddle_output = paddle.add(x=x2paddle_255, y=x2paddle_257) + return x2paddle_output, x2paddle_255 + + +def main(x2paddle_input): + # There are 1 inputs. + # x2paddle_input: shape-[-1, 3, 512, 512], type-float32. + paddle.disable_static() + params = paddle.load('/work/ToTransferInHub/EnlightenGAN-inference/enlighten_inference/pd_model/model.pdparams') + model = ONNXModel() + model.set_dict(params, use_structured_name=True) + model.eval() + out = model(x2paddle_input) + return out + + +if __name__ == '__main__': + inputtensor = paddle.randn([1, 3, 512, 512]) + main(inputtensor) diff --git a/modules/image/image_processing/EnlightenGAN/module.py b/modules/image/image_processing/EnlightenGAN/module.py new file mode 100644 index 00000000..fc99c7ca --- /dev/null +++ b/modules/image/image_processing/EnlightenGAN/module.py @@ -0,0 +1,96 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +import os +import argparse + +import paddle +import paddlehub as hub +from paddlehub.module.module import moduleinfo, runnable +from PIL import Image +import numpy as np +from .enlighten_inference import EnlightenOnnxModel +from .enlighten_inference.pd_model.x2paddle_code import ONNXModel + + +@moduleinfo( + name="EnlightenGAN", type="CV/enlighten", author="paddlepaddle", author_email="", summary="", version="1.0.0") +class EnlightenGAN: + def __init__(self): + self.pretrained_model = os.path.join(self.directory, "enlighten_inference/pd_model") + + def enlightening(self, input_path, output_path='./enlightening_result.png', use_gpu=False): + ''' + enlighten a image in the low-light scene. + + input_path: the image path + output_path: the path to save the results + use_gpu: if True, use gpu to perform the computation, otherwise cpu. + ''' + paddle.disable_static() + img = np.array(Image.open(input_path)) + img = np.expand_dims(np.transpose(img, (2, 0, 1)).astype(np.float32) / 255., 0) + inputtensor = paddle.to_tensor(img) + params = paddle.load(os.path.join(self.pretrained_model, 'model.pdparams')) + model = ONNXModel() + model.set_dict(params, use_structured_name=True) + model.eval() + out, out1 = model(inputtensor) + out = out.numpy()[0] + out = (np.transpose(out, (1, 2, 0)) + 1) / 2.0 * 255.0 + out = np.clip(out, 0, 255) + out = out.astype('uint8') + + print('enlighten Over.') + try: + Image.fromarray(out).save(os.path.join(output_path)) + print('Image saved in {}'.format(output_path)) + except: + print('Save image failed. Please check the output_path, should\ + be image format ext, e.g. png. current output path {}'.format(output_path)) + return out + + @runnable + def run_cmd(self, argvs: list): + """ + Run as a command. + """ + self.parser = argparse.ArgumentParser( + description="Run the {} module.".format(self.name), + prog='hub run {}'.format(self.name), + usage='%(prog)s', + add_help=True) + + self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required") + self.arg_config_group = self.parser.add_argument_group( + title="Config options", description="Run configuration for controlling module behavior, not required.") + self.add_module_config_arg() + self.add_module_input_arg() + self.args = self.parser.parse_args(argvs) + self.enlightening(input_path=self.args.input_path, output_path=self.args.output_path, use_gpu=self.args.use_gpu) + + def add_module_config_arg(self): + """ + Add the command config options. + """ + self.arg_config_group.add_argument('--use_gpu', action='store_true', help="use GPU or not") + + self.arg_config_group.add_argument( + '--output_path', type=str, default='enlightening_result.png', help='output path for saving result.') + + def add_module_input_arg(self): + """ + Add the command input options. + """ + self.arg_input_group.add_argument('--input_path', type=str, help="path to input image.") diff --git a/modules/image/image_processing/EnlightenGAN/requirements.txt b/modules/image/image_processing/EnlightenGAN/requirements.txt new file mode 100644 index 00000000..dfd19eb3 --- /dev/null +++ b/modules/image/image_processing/EnlightenGAN/requirements.txt @@ -0,0 +1,3 @@ +pillow +onnxruntime +x2paddle diff --git a/modules/image/image_processing/EnlightenGAN/x2paddle_code.py b/modules/image/image_processing/EnlightenGAN/x2paddle_code.py new file mode 100755 index 00000000..92da60a1 --- /dev/null +++ b/modules/image/image_processing/EnlightenGAN/x2paddle_code.py @@ -0,0 +1,219 @@ +import paddle +import math +from x2paddle.op_mapper.onnx2paddle import onnx_custom_layer as x2paddle_nn + + +class ONNXModel(paddle.nn.Layer): + def __init__(self): + super(ONNXModel, self).__init__() + self.conv0 = paddle.nn.Conv2D(in_channels=3, out_channels=3, kernel_size=[1, 1], groups=3) + self.pool0 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.pool1 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv1 = paddle.nn.Conv2D(in_channels=4, out_channels=32, kernel_size=[3, 3], padding=1) + self.pool2 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.leakyrelu0 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.pool3 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.batchnorm0 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu1 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm1 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool4 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv3 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu2 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm2 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv4 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu3 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm3 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool5 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv5 = paddle.nn.Conv2D(in_channels=64, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu4 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm4 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv6 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu5 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm5 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool6 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv7 = paddle.nn.Conv2D(in_channels=128, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu6 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm6 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv8 = paddle.nn.Conv2D(in_channels=256, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu7 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm7 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.pool7 = paddle.nn.MaxPool2D(kernel_size=[2, 2], stride=2) + self.conv9 = paddle.nn.Conv2D(in_channels=256, out_channels=512, kernel_size=[3, 3], padding=1) + self.leakyrelu8 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm8 = paddle.nn.BatchNorm( + num_channels=512, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv10 = paddle.nn.Conv2D(in_channels=512, out_channels=512, kernel_size=[3, 3], padding=1) + self.leakyrelu9 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm9 = paddle.nn.BatchNorm( + num_channels=512, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv11 = paddle.nn.Conv2D(in_channels=512, out_channels=256, kernel_size=[3, 3], padding=1) + self.conv12 = paddle.nn.Conv2D(in_channels=512, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu10 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm10 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv13 = paddle.nn.Conv2D(in_channels=256, out_channels=256, kernel_size=[3, 3], padding=1) + self.leakyrelu11 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm11 = paddle.nn.BatchNorm( + num_channels=256, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv14 = paddle.nn.Conv2D(in_channels=256, out_channels=128, kernel_size=[3, 3], padding=1) + self.conv15 = paddle.nn.Conv2D(in_channels=256, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu12 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm12 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv16 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=[3, 3], padding=1) + self.leakyrelu13 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm13 = paddle.nn.BatchNorm( + num_channels=128, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv17 = paddle.nn.Conv2D(in_channels=128, out_channels=64, kernel_size=[3, 3], padding=1) + self.conv18 = paddle.nn.Conv2D(in_channels=128, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu14 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm14 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv19 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=[3, 3], padding=1) + self.leakyrelu15 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm15 = paddle.nn.BatchNorm( + num_channels=64, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv20 = paddle.nn.Conv2D(in_channels=64, out_channels=32, kernel_size=[3, 3], padding=1) + self.conv21 = paddle.nn.Conv2D(in_channels=64, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu16 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.batchnorm16 = paddle.nn.BatchNorm( + num_channels=32, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, is_test=True) + self.conv22 = paddle.nn.Conv2D(in_channels=32, out_channels=32, kernel_size=[3, 3], padding=1) + self.leakyrelu17 = paddle.nn.LeakyReLU(negative_slope=0.20000000298023224) + self.conv23 = paddle.nn.Conv2D(in_channels=32, out_channels=3, kernel_size=[1, 1]) + + def forward(self, x2paddle_input): + x2paddle_137 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_145 = paddle.full(dtype='float32', shape=[1], fill_value=0.29899999499320984) + x2paddle_147 = paddle.full(dtype='float32', shape=[1], fill_value=0.5870000123977661) + x2paddle_150 = paddle.full(dtype='float32', shape=[1], fill_value=0.11400000005960464) + x2paddle_153 = paddle.full(dtype='float32', shape=[1], fill_value=2.0) + x2paddle_155 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_256 = paddle.full(dtype='float32', shape=[1], fill_value=1.0) + x2paddle_134 = self.conv0(x2paddle_input) + x2paddle_135, = paddle.split(x=x2paddle_134, num_or_sections=[1]) + x2paddle_257 = paddle.multiply(x=x2paddle_134, y=x2paddle_256) + x2paddle_136 = paddle.squeeze(x=x2paddle_135, axis=[0]) + x2paddle_138 = paddle.add(x=x2paddle_136, y=x2paddle_137) + x2paddle_139_p0, x2paddle_139_p1, x2paddle_139_p2 = paddle.split(x=x2paddle_138, num_or_sections=[1, 1, 1]) + x2paddle_142 = paddle.squeeze(x=x2paddle_139_p0, axis=[0]) + x2paddle_143 = paddle.squeeze(x=x2paddle_139_p1, axis=[0]) + x2paddle_144 = paddle.squeeze(x=x2paddle_139_p2, axis=[0]) + x2paddle_146 = paddle.multiply(x=x2paddle_142, y=x2paddle_145) + x2paddle_148 = paddle.multiply(x=x2paddle_143, y=x2paddle_147) + x2paddle_151 = paddle.multiply(x=x2paddle_144, y=x2paddle_150) + x2paddle_149 = paddle.add(x=x2paddle_146, y=x2paddle_148) + x2paddle_152 = paddle.add(x=x2paddle_149, y=x2paddle_151) + x2paddle_154 = paddle.divide(x=x2paddle_152, y=x2paddle_153) + x2paddle_156 = paddle.subtract(x=x2paddle_155, y=x2paddle_154) + x2paddle_157 = paddle.unsqueeze(x=x2paddle_156, axis=[0]) + x2paddle_158 = paddle.unsqueeze(x=x2paddle_157, axis=[0]) + x2paddle_159 = self.pool0(x2paddle_158) + x2paddle_163 = paddle.concat(x=[x2paddle_134, x2paddle_158], axis=1) + x2paddle_160 = self.pool1(x2paddle_159) + x2paddle_164 = self.conv1(x2paddle_163) + x2paddle_161 = self.pool2(x2paddle_160) + x2paddle_165 = self.leakyrelu0(x2paddle_164) + x2paddle_162 = self.pool3(x2paddle_161) + x2paddle_166 = self.batchnorm0(x2paddle_165) + x2paddle_167 = self.conv2(x2paddle_166) + x2paddle_168 = self.leakyrelu1(x2paddle_167) + x2paddle_169 = self.batchnorm1(x2paddle_168) + x2paddle_170 = self.pool4(x2paddle_169) + x2paddle_246 = paddle.multiply(x=x2paddle_169, y=x2paddle_158) + x2paddle_171 = self.conv3(x2paddle_170) + x2paddle_172 = self.leakyrelu2(x2paddle_171) + x2paddle_173 = self.batchnorm2(x2paddle_172) + x2paddle_174 = self.conv4(x2paddle_173) + x2paddle_175 = self.leakyrelu3(x2paddle_174) + x2paddle_176 = self.batchnorm3(x2paddle_175) + x2paddle_177 = self.pool5(x2paddle_176) + x2paddle_232 = paddle.multiply(x=x2paddle_176, y=x2paddle_159) + x2paddle_178 = self.conv5(x2paddle_177) + x2paddle_179 = self.leakyrelu4(x2paddle_178) + x2paddle_180 = self.batchnorm4(x2paddle_179) + x2paddle_181 = self.conv6(x2paddle_180) + x2paddle_182 = self.leakyrelu5(x2paddle_181) + x2paddle_183 = self.batchnorm5(x2paddle_182) + x2paddle_184 = self.pool6(x2paddle_183) + x2paddle_218 = paddle.multiply(x=x2paddle_183, y=x2paddle_160) + x2paddle_185 = self.conv7(x2paddle_184) + x2paddle_186 = self.leakyrelu6(x2paddle_185) + x2paddle_187 = self.batchnorm6(x2paddle_186) + x2paddle_188 = self.conv8(x2paddle_187) + x2paddle_189 = self.leakyrelu7(x2paddle_188) + x2paddle_190 = self.batchnorm7(x2paddle_189) + x2paddle_191 = self.pool7(x2paddle_190) + x2paddle_204 = paddle.multiply(x=x2paddle_190, y=x2paddle_161) + x2paddle_192 = self.conv9(x2paddle_191) + x2paddle_193 = self.leakyrelu8(x2paddle_192) + x2paddle_194 = self.batchnorm8(x2paddle_193) + x2paddle_195 = paddle.multiply(x=x2paddle_194, y=x2paddle_162) + x2paddle_196 = self.conv10(x2paddle_195) + x2paddle_197 = self.leakyrelu9(x2paddle_196) + x2paddle_198 = self.batchnorm9(x2paddle_197) + x2paddle_203 = paddle.nn.functional.interpolate(x=x2paddle_198, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_205 = self.conv11(x2paddle_203) + x2paddle_206 = paddle.concat(x=[x2paddle_205, x2paddle_204], axis=1) + x2paddle_207 = self.conv12(x2paddle_206) + x2paddle_208 = self.leakyrelu10(x2paddle_207) + x2paddle_209 = self.batchnorm10(x2paddle_208) + x2paddle_210 = self.conv13(x2paddle_209) + x2paddle_211 = self.leakyrelu11(x2paddle_210) + x2paddle_212 = self.batchnorm11(x2paddle_211) + x2paddle_217 = paddle.nn.functional.interpolate(x=x2paddle_212, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_219 = self.conv14(x2paddle_217) + x2paddle_220 = paddle.concat(x=[x2paddle_219, x2paddle_218], axis=1) + x2paddle_221 = self.conv15(x2paddle_220) + x2paddle_222 = self.leakyrelu12(x2paddle_221) + x2paddle_223 = self.batchnorm12(x2paddle_222) + x2paddle_224 = self.conv16(x2paddle_223) + x2paddle_225 = self.leakyrelu13(x2paddle_224) + x2paddle_226 = self.batchnorm13(x2paddle_225) + x2paddle_231 = paddle.nn.functional.interpolate(x=x2paddle_226, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_233 = self.conv17(x2paddle_231) + x2paddle_234 = paddle.concat(x=[x2paddle_233, x2paddle_232], axis=1) + x2paddle_235 = self.conv18(x2paddle_234) + x2paddle_236 = self.leakyrelu14(x2paddle_235) + x2paddle_237 = self.batchnorm14(x2paddle_236) + x2paddle_238 = self.conv19(x2paddle_237) + x2paddle_239 = self.leakyrelu15(x2paddle_238) + x2paddle_240 = self.batchnorm15(x2paddle_239) + x2paddle_245 = paddle.nn.functional.interpolate(x=x2paddle_240, scale_factor=[2.0, 2.0], mode='bilinear') + x2paddle_247 = self.conv20(x2paddle_245) + x2paddle_248 = paddle.concat(x=[x2paddle_247, x2paddle_246], axis=1) + x2paddle_249 = self.conv21(x2paddle_248) + x2paddle_250 = self.leakyrelu16(x2paddle_249) + x2paddle_251 = self.batchnorm16(x2paddle_250) + x2paddle_252 = self.conv22(x2paddle_251) + x2paddle_253 = self.leakyrelu17(x2paddle_252) + x2paddle_254 = self.conv23(x2paddle_253) + x2paddle_255 = paddle.multiply(x=x2paddle_254, y=x2paddle_158) + x2paddle_output = paddle.add(x=x2paddle_255, y=x2paddle_257) + return x2paddle_output, x2paddle_255 + + +def main(x2paddle_input): + # There are 1 inputs. + # x2paddle_input: shape-[-1, 3, 512, 512], type-float32. + paddle.disable_static() + params = paddle.load('/work/ToTransferInHub/EnlightenGAN-inference/enlighten_inference/pd_model/model.pdparams') + model = ONNXModel() + model.set_dict(params, use_structured_name=True) + model.eval() + out = model(x2paddle_input) + return out + + +if __name__ == '__main__': + inputtensor = paddle.randn([1, 3, 512, 512]) + main(inputtensor) -- GitLab