update deoldify (#1992)

* update deoldify

* add clean func

* update README

* update format

modules/image/Image_editing/colorization/deoldify/README.md

 # 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

 # 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

-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

@@ -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

-# 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

+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

+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: