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未验证 提交 ca09b195 编写于 作者: jm_12138's avatar jm_12138 提交者： GitHub
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update deoldify (#1992) 

* update deoldify

* add clean func

* update README

* update format
上级 ae6fcc6a
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modules/image/Image_editing/colorization/deoldify/README.md
浏览文件 @ ca09b195
# deoldify
|模型名称|deoldify|
| :--- | :---: |
| :--- | :---: |
|类别|图像-图像编辑|
|网络|NoGAN|
|数据集|ILSVRC 2012|
......@@ -14,7 +14,7 @@
## 一、模型基本信息
- ### 应用效果展示
- 样例结果示例(左为原图，右为效果图)：
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/130886749-668dfa38-42ed-4a09-8d4a-b18af0475375.jpg" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130886685-76221736-839a-46a2-8415-e5e0dd3b345e.png" width = "450" height = "300" hspace='10'/>
......@@ -45,7 +45,7 @@
- ```shell
$ hub install deoldify
```
- 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)
......@@ -59,7 +59,9 @@
import paddlehub as hub
model = hub.Module(name='deoldify')
model.predict('/PATH/TO/IMAGE/OR/VIDEO')
model.predict('/PATH/TO/IMAGE')
# model.predict('/PATH/TO/VIDEO')
```
- ### 2、API
......@@ -170,3 +172,11 @@
* 1.0.1
适配paddlehub2.0版本
* 1.1.0
移除 Fluid API
```shell
$ hub install deoldify == 1.1.0
```
modules/image/Image_editing/colorization/deoldify/README_en.md
浏览文件 @ ca09b195
# deoldify
| Module Name |deoldify|
| :--- | :---: |
| :--- | :---: |
|Category|Image editing|
|Network |NoGAN|
|Dataset|ILSVRC 2012|
......@@ -11,10 +11,10 @@
|Latest update date |2021-04-13|
## I. Basic Information
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/130886749-668dfa38-42ed-4a09-8d4a-b18af0475375.jpg" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130886685-76221736-839a-46a2-8415-e5e0dd3b345e.png" width = "450" height = "300" hspace='10'/>
......@@ -45,7 +45,7 @@
- ```shell
$ hub install deoldify
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
......@@ -58,7 +58,9 @@
import paddlehub as hub
model = hub.Module(name='deoldify')
model.predict('/PATH/TO/IMAGE/OR/VIDEO')
model.predict('/PATH/TO/IMAGE')
# model.predict('/PATH/TO/VIDEO')
```
- ### 2、API
......@@ -169,3 +171,11 @@
- 1.0.1
Adapt to paddlehub2.0
* 1.1.0
Remove Fluid API
```shell
$ hub install deoldify == 1.1.0
```
modules/image/Image_editing/colorization/deoldify/base_module.py
浏览文件 @ ca09b195
import paddle
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.vision.models import resnet101
import deoldify.utils as U
from . import utils as U
class SequentialEx(nn.Layer):
......@@ -39,6 +39,7 @@ class SequentialEx(nn.Layer):
class Deoldify(SequentialEx):
def __init__(self,
encoder,
n_classes,
......@@ -76,17 +77,16 @@ class Deoldify(SequentialEx):
n_out = nf if not_final else nf // 2
unet_block = UnetBlockWide(
up_in_c,
x_in_c,
n_out,
self.sfs[i],
final_div=not_final,
blur=blur,
self_attention=sa,
norm_type=norm_type,
extra_bn=extra_bn,
**kwargs)
unet_block = UnetBlockWide(up_in_c,
x_in_c,
n_out,
self.sfs[i],
final_div=not_final,
blur=blur,
self_attention=sa,
norm_type=norm_type,
extra_bn=extra_bn,
**kwargs)
unet_block.eval()
layers.append(unet_block)
x = unet_block(x)
......@@ -288,7 +288,7 @@ class CustomPixelShuffle_ICNR(nn.Layer):
self.shuf = PixelShuffle(scale)
self.pad = ReplicationPad2d([1, 0, 1, 0])
self.blur = paddle.nn.AvgPool2D(2, stride=1)
self.blur = nn.AvgPool2D(2, stride=1)
self.relu = nn.LeakyReLU(leaky) if leaky is not None else nn.ReLU() # relu(True, leaky=leaky)
def forward(self, x):
......@@ -315,9 +315,8 @@ def res_block(nf, dense: bool = False, norm_type='Batch', bottle: bool = False,
norm2 = norm_type
if not dense and (norm_type == 'Batch'): norm2 = 'BatchZero'
nf_inner = nf // 2 if bottle else nf
return SequentialEx(
conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs), MergeLayer(dense))
return SequentialEx(conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs), MergeLayer(dense))
class SigmoidRange(nn.Layer):
......@@ -337,6 +336,7 @@ def sigmoid_range(x, low, high):
class PixelShuffle(nn.Layer):
def __init__(self, upscale_factor):
super(PixelShuffle, self).__init__()
self.upscale_factor = upscale_factor
......@@ -346,6 +346,7 @@ class PixelShuffle(nn.Layer):
class ReplicationPad2d(nn.Layer):
def __init__(self, size):
super(ReplicationPad2d, self).__init__()
self.size = size
......
modules/image/Image_editing/colorization/deoldify/module.py
浏览文件 @ ca09b195
......@@ -12,32 +12,32 @@
# 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 glob
import os
import cv2
import numpy as np
import paddle
import paddle.nn as nn
import numpy as np
from PIL import Image
from tqdm import tqdm
import deoldify.utils as U
from paddlehub.module.module import moduleinfo, serving, Module
from deoldify.base_module import build_model
@moduleinfo(
name="deoldify",
type="CV/image_editing",
author="paddlepaddle",
author_email="",
summary="Deoldify is a colorizaton model",
version="1.0.0")
class DeOldifyPredictor(Module):
def _initialize(self, render_factor: int = 32, output_path: int = 'result', load_checkpoint: str = None):
#super(DeOldifyPredictor, self).__init__()
from . import utils as U
from .base_module import build_model
from paddlehub.module.module import moduleinfo
from paddlehub.module.module import serving
@moduleinfo(name="deoldify",
type="CV/image_editing",
author="paddlepaddle",
author_email="",
summary="Deoldify is a colorizaton model",
version="1.1.0")
class DeOldifyPredictor(nn.Layer):
def __init__(self, render_factor: int = 32, output_path: int = 'output', load_checkpoint: str = None):
super(DeOldifyPredictor, self).__init__()
self.model = build_model()
self.render_factor = render_factor
self.output = os.path.join(output_path, 'DeOldify')
......@@ -50,6 +50,8 @@ class DeOldifyPredictor(Module):
else:
checkpoint = os.path.join(self.directory, 'DeOldify_stable.pdparams')
if not os.path.exists(checkpoint):
os.system('wget https://paddlegan.bj.bcebos.com/applications/DeOldify_stable.pdparams -O ' + checkpoint)
state_dict = paddle.load(checkpoint)
self.model.load_dict(state_dict)
print("load pretrained checkpoint success")
......@@ -140,8 +142,6 @@ class DeOldifyPredictor(Module):
return frame_pattern_combined, vid_out_path
def predict(self, input):
if not os.path.exists(self.output):
os.makedirs(self.output)
if not U.is_image(input):
return self.run_video(input)
......
modules/image/Image_editing/colorization/deoldify/resnet.py 已删除 100644 → 0
浏览文件 @ ae6fcc6a
# Copyright (c) 2020 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.
from __future__ import division
from __future__ import print_function
import math
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.container import Sequential
from paddle.utils.download import get_weights_path_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
'resnet18': ('https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams', '0ba53eea9bc970962d0ef96f7b94057e'),
'resnet34': ('https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams', '46bc9f7c3dd2e55b7866285bee91eff3'),
'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams', '5ce890a9ad386df17cf7fe2313dca0a1'),
'resnet101': ('https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams', 'fb07a451df331e4b0bb861ed97c3a9b9'),
'resnet152': ('https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams', 'f9c700f26d3644bb76ad2226ed5f5713'),
}
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm = BatchNorm(num_filters, act=act)
def forward(self, inputs):
x = self._conv(inputs)
x = self._batch_norm(x)
return x
class BasicBlock(fluid.dygraph.Layer):
"""residual block of resnet18 and resnet34
"""
expansion = 1
def __init__(self, num_channels, num_filters, stride, shortcut=True):
super(BasicBlock, self).__init__()
self.conv0 = ConvBNLayer(num_channels=num_channels, num_filters=num_filters, filter_size=3, act='relu')
self.conv1 = ConvBNLayer(
num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act='relu')
if not shortcut:
self.short = ConvBNLayer(num_channels=num_channels, num_filters=num_filters, filter_size=1, stride=stride)
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = short + conv1
return fluid.layers.relu(y)
class BottleneckBlock(fluid.dygraph.Layer):
"""residual block of resnet50, resnet101 amd resnet152
"""
expansion = 4
def __init__(self, num_channels, num_filters, stride, shortcut=True):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(num_channels=num_channels, num_filters=num_filters, filter_size=1, act='relu')
self.conv1 = ConvBNLayer(
num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act='relu')
self.conv2 = ConvBNLayer(
num_channels=num_filters, num_filters=num_filters * self.expansion, filter_size=1, act=None)
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels, num_filters=num_filters * self.expansion, filter_size=1, stride=stride)
self.shortcut = shortcut
self._num_channels_out = num_filters * self.expansion
def forward(self, inputs):
x = self.conv0(inputs)
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
x = fluid.layers.elementwise_add(x=short, y=conv2)
return fluid.layers.relu(x)
class ResNet(fluid.dygraph.Layer):
"""ResNet model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
Block (BasicBlock|BottleneckBlock): block module of model.
depth (int): layers of resnet, default: 50.
num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer
will not be defined. Default: 1000.
with_pool (bool): use pool before the last fc layer or not. Default: True.
classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
Examples:
.. code-block:: python
from paddle.vision.models import ResNet
from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
resnet50 = ResNet(BottleneckBlock, 50)
resnet18 = ResNet(BasicBlock, 18)
"""
def __init__(self, Block, depth=50, num_classes=1000, with_pool=True, classifier_activation='softmax'):
super(ResNet, self).__init__()
self.num_classes = num_classes
self.with_pool = with_pool
layer_config = {
18: [2, 2, 2, 2],
34: [3, 4, 6, 3],
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
}
assert depth in layer_config.keys(), \
"supported depth are {} but input layer is {}".format(
layer_config.keys(), depth)
layers = layer_config[depth]
in_channels = 64
out_channels = [64, 128, 256, 512]
self.conv = ConvBNLayer(num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.pool = Pool2D(pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.layers = []
for idx, num_blocks in enumerate(layers):
blocks = []
shortcut = False
for b in range(num_blocks):
if b == 1:
in_channels = out_channels[idx] * Block.expansion
block = Block(
num_channels=in_channels,
num_filters=out_channels[idx],
stride=2 if b == 0 and idx != 0 else 1,
shortcut=shortcut)
blocks.append(block)
shortcut = True
layer = self.add_sublayer("layer_{}".format(idx), Sequential(*blocks))
self.layers.append(layer)
if with_pool:
self.global_pool = Pool2D(pool_size=7, pool_type='avg', global_pooling=True)
if num_classes > 0:
stdv = 1.0 / math.sqrt(out_channels[-1] * Block.expansion * 1.0)
self.fc_input_dim = out_channels[-1] * Block.expansion * 1 * 1
self.fc = Linear(
self.fc_input_dim,
num_classes,
act=classifier_activation,
param_attr=fluid.param_attr.ParamAttr(initializer=fluid.initializer.Uniform(-stdv, stdv)))
def forward(self, inputs):
x = self.conv(inputs)
x = self.pool(x)
for layer in self.layers:
x = layer(x)
if self.with_pool:
x = self.global_pool(x)
if self.num_classes > -1:
x = fluid.layers.reshape(x, shape=[-1, self.fc_input_dim])
x = self.fc(x)
return x
def _resnet(arch, Block, depth, pretrained, **kwargs):
model = ResNet(Block, depth, **kwargs)
if pretrained:
assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
arch)
weight_path = get_weights_path_from_url(model_urls[arch][0], model_urls[arch][1])
assert weight_path.endswith('.pdparams'), "suffix of weight must be .pdparams"
param, _ = fluid.load_dygraph(weight_path)
model.set_dict(param)
return model
def resnet18(pretrained=False, **kwargs):
"""ResNet 18-layer model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
from paddle.vision.models import resnet18
# build model
model = resnet18()
# build model and load imagenet pretrained weight
# model = resnet18(pretrained=True)
"""
return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
def resnet34(pretrained=False, **kwargs):
"""ResNet 34-layer model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
from paddle.vision.models import resnet34
# build model
model = resnet34()
# build model and load imagenet pretrained weight
# model = resnet34(pretrained=True)
"""
return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
def resnet50(pretrained=False, **kwargs):
"""ResNet 50-layer model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
from paddle.vision.models import resnet50
# build model
model = resnet50()
# build model and load imagenet pretrained weight
# model = resnet50(pretrained=True)
"""
return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
def resnet101(pretrained=False, **kwargs):
"""ResNet 101-layer model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
from paddle.vision.models import resnet101
# build model
model = resnet101()
# build model and load imagenet pretrained weight
# model = resnet101(pretrained=True)
"""
return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
def resnet152(pretrained=False, **kwargs):
"""ResNet 152-layer model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
from paddle.vision.models import resnet152
# build model
model = resnet152()
# build model and load imagenet pretrained weight
# model = resnet152(pretrained=True)
"""
return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
modules/image/Image_editing/colorization/deoldify/test.py 0 → 100644
浏览文件 @ ca09b195
import os
import shutil
import unittest
import cv2
import numpy as np
import requests
import paddlehub as hub
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
class TestHubModule(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
img_url = 'https://unsplash.com/photos/1sLIu1XKQrY/download?ixid=MnwxMjA3fDB8MXxhbGx8MTJ8fHx8fHwyfHwxNjYyMzQxNDUx&force=true&w=640'
if not os.path.exists('tests'):
os.makedirs('tests')
response = requests.get(img_url)
assert response.status_code == 200, 'Network Error.'
with open('tests/test.jpg', 'wb') as f:
f.write(response.content)
cls.module = hub.Module(name="deoldify")
@classmethod
def tearDownClass(cls) -> None:
shutil.rmtree('tests')
shutil.rmtree('output')
def test_run_image1(self):
results = self.module.run_image(img='tests/test.jpg')
self.assertIsInstance(results, np.ndarray)
def test_run_image2(self):
results = self.module.run_image(img=cv2.imread('tests/test.jpg'))
self.assertIsInstance(results, np.ndarray)
def test_run_image3(self):
self.assertRaises(FileNotFoundError, self.module.run_image, img='no.jpg')
def test_predict1(self):
pred_img, out_path = self.module.predict(input='tests/test.jpg')
self.assertIsInstance(pred_img, np.ndarray)
self.assertIsInstance(out_path, str)
def test_predict2(self):
self.assertRaises(RuntimeError, self.module.predict, input='no.jpg')
if __name__ == "__main__":
unittest.main()
modules/image/Image_editing/colorization/deoldify/utils.py
浏览文件 @ ca09b195
import base64
import os
import sys
import base64
import cv2
import numpy as np
......@@ -91,7 +91,7 @@ class Hooks():
def _hook_inner(m, i, o):
return o if isinstance(o, paddle.fluid.framework.Variable) else o if is_listy(o) else list(o)
return o if isinstance(o, paddle.Tensor) else o if is_listy(o) else list(o)
def hook_output(module, detach=True, grad=False):
......@@ -124,6 +124,7 @@ def dummy_batch(size=(64, 64), ch_in=3):
class _SpectralNorm(nn.SpectralNorm):
def __init__(self, weight_shape, dim=0, power_iters=1, eps=1e-12, dtype='float32'):
super(_SpectralNorm, self).__init__(weight_shape, dim, power_iters, eps, dtype)
......@@ -131,22 +132,22 @@ class _SpectralNorm(nn.SpectralNorm):
inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_v}
out = self._helper.create_variable_for_type_inference(self._dtype)
_power_iters = self._power_iters if self.training else 0
self._helper.append_op(
type="spectral_norm",
inputs=inputs,
outputs={
"Out": out,
},
attrs={
"dim": self._dim,
"power_iters": _power_iters,
"eps": self._eps,
})
self._helper.append_op(type="spectral_norm",
inputs=inputs,
outputs={
"Out": out,
},
attrs={
"dim": self._dim,
"power_iters": _power_iters,
"eps": self._eps,
})
return out
class Spectralnorm(paddle.nn.Layer):
def __init__(self, layer, dim=0, power_iters=1, eps=1e-12, dtype='float32'):
super(Spectralnorm, self).__init__()
self.spectral_norm = _SpectralNorm(layer.weight.shape, dim, power_iters, eps, dtype)
......@@ -167,6 +168,7 @@ class Spectralnorm(paddle.nn.Layer):
def video2frames(video_path, outpath, **kargs):
def _dict2str(kargs):
cmd_str = ''
for k, v in kargs.items():
......@@ -196,12 +198,8 @@ def video2frames(video_path, outpath, **kargs):
def frames2video(frame_path, video_path, r):
ffmpeg = ['ffmpeg ', ' -y -loglevel ', ' error ']
cmd = ffmpeg + [
' -r ', r, ' -f ', ' image2 ', ' -i ', frame_path, ' -vcodec ', ' libx264 ', ' -pix_fmt ', ' yuv420p ',
' -crf ', ' 16 ', video_path
]
cmd = ffmpeg + [' -r ', r, ' -f ', ' image2 ', ' -i ', frame_path, ' -pix_fmt ', ' yuv420p ', video_path]
cmd = ''.join(cmd)
if os.system(cmd) != 0:
......
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