polish code (#457)

ppgan/datasets/wav2lip_dataset.py

-# Copyright (c) 2020 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.
+# code was heavily based on https://github.com/Rudrabha/Wav2Lip
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/Rudrabha/Wav2Lip#license-and-citation
 
 import cv2
 import random

ppgan/models/criterions/perceptual_loss.py

+# Copyright (c) MMEditing Authors.
 import paddle
 import paddle.nn as nn
 import paddle.vision.models.vgg as vgg

ppgan/models/generators/basicvsr.py

-#   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.
+# Copyright (c) MMEditing Authors.
 
 import paddle
 

ppgan/models/generators/deoldify.py

-#   Copyright (c) 2020 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.
+# MIT License
+# Copyright (c) 2018 Jason Antic
 
 import numpy as np
 import paddle

ppgan/models/ugatit_model.py

@@ -48,8 +48,18 @@ class UGATITModel(BaseModel):
                  cam_weight=1000.0):
         """Initialize the CycleGAN class.
 
-        Parameters:
-            opt (config)-- stores all the experiment flags; needs to be a subclass of Dict
+        Args:
+            generator (dict): config of generator.
+            discriminator_g (dict): config of discriminator_g.
+            discriminator_l (dict): config of discriminator_l.
+            l1_criterion (dict): config of l1_criterion.
+            mse_criterion (dict): config of mse_criterion.
+            bce_criterion (dict): config of bce_criterion.
+            direction (str): direction of dataset, default: 'a2b'.
+            adv_weight (float): adversial loss weight, default: 1.0.
+            cycle_weight (float): cycle loss weight, default: 10.0.
+            identity_weight (float): identity loss weight, default: 10.0.
+            cam_weight (float): cam loss weight, default: 1000.0.
         """
         super(UGATITModel, self).__init__()
         self.adv_weight = adv_weight

ppgan/utils/animate.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# code was heavily based on https://github.com/AliaksandrSiarohin/first-order-model
+
 import numpy as np
 from scipy.spatial import ConvexHull
 

ppgan/utils/audio.py

+# code was heavily based on https://github.com/Rudrabha/Wav2Lip
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/Rudrabha/Wav2Lip#license-and-citation
+
 import numpy as np
 from scipy import signal
 from scipy.io import wavfile

ppgan/utils/audio_config.py

-from easydict import EasyDict as edict
+from .config import AttrDict
 
-_C = edict()
+_audio_cfg = AttrDict()
 
-_C.num_mels = 80
-_C.rescale = True
-_C.rescaling_max = 0.9
-_C.use_lws = False
-_C.n_fft = 800
-_C.hop_size = 200
-_C.win_size = 800
-_C.sample_rate = 16000
-_C.frame_shift_ms = None
-_C.signal_normalization = True
-_C.allow_clipping_in_normalization = True
-_C.symmetric_mels = True
-_C.max_abs_value = 4.
-_C.preemphasize = True
-_C.preemphasis = 0.97
-_C.min_level_db = -100
-_C.ref_level_db = 20
-_C.fmin = 55
-_C.fmax = 7600
-_C.fps = 25
+_audio_cfg.num_mels = 80
+_audio_cfg.rescale = True
+_audio_cfg.rescaling_max = 0.9
+_audio_cfg.use_lws = False
+_audio_cfg.n_fft = 800
+_audio_cfg.hop_size = 200
+_audio_cfg.win_size = 800
+_audio_cfg.sample_rate = 16000
+_audio_cfg.frame_shift_ms = None
+_audio_cfg.signal_normalization = True
+_audio_cfg.allow_clipping_in_normalization = True
+_audio_cfg.symmetric_mels = True
+_audio_cfg.max_abs_value = 4.
+_audio_cfg.preemphasize = True
+_audio_cfg.preemphasis = 0.97
+_audio_cfg.min_level_db = -100
+_audio_cfg.ref_level_db = 20
+_audio_cfg.fmin = 55
+_audio_cfg.fmax = 7600
+_audio_cfg.fps = 25
 
 
 def get_audio_config():
-    return _C
+    return _audio_cfg

ppgan/utils/config.py

@@ -19,7 +19,6 @@ __all__ = ['get_config']
 
 class AttrDict(dict):
     def __getattr__(self, key):
-        # return self[key]
         try:
             return self[key]
         except KeyError:

ppgan/utils/image_pool.py

@@ -21,51 +21,48 @@ class ImagePool():
 
     This buffer enables us to update discriminators using a history of generated images
     rather than the ones produced by the latest generators.
-    """
-    def __init__(self, pool_size):
-        """Initialize the ImagePool class
 
-        Parameters:
-            pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
-        """
+    Args:
+        pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
+    """
+    def __init__(self, pool_size, prob=0.5):
         self.pool_size = pool_size
-        if self.pool_size > 0:  # create an empty pool
+        self.prob = prob
+
+        if self.pool_size > 0:
             self.num_imgs = 0
             self.images = []
 
     def query(self, images):
         """Return an image from the pool.
 
-        Parameters:
-            images: the latest generated images from the generator
+        Args:
+            images (paddle.Tensor): the latest generated images from the generator
 
         Returns images from the buffer.
-
-        By 50/100, the buffer will return input images.
-        By 50/100, the buffer will return images previously stored in the buffer,
-        and insert the current images to the buffer.
         """
-        if self.pool_size == 0:  # if the buffer size is 0, do nothing
+        # if the buffer size is 0, do nothing
+        if self.pool_size == 0:
             return images
         return_images = []
         for image in images:
             image = paddle.unsqueeze(image, 0)
-            if self.num_imgs < self.pool_size:  # if the buffer is not full; keep inserting current images to the buffer
+            # if the buffer is not full; keep inserting current images to the buffer
+            if self.num_imgs < self.pool_size:
                 self.num_imgs = self.num_imgs + 1
                 self.images.append(image)
                 return_images.append(image)
             else:
                 p = random.uniform(0, 1)
-                if p > 0.5:  # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
-                    random_id = random.randint(0, self.pool_size -
-                                               1)  # randint is inclusive
-                    # FIXME: clone
-                    # tmp = (self.images[random_id]).detach() #.clone()
-                    tmp = self.images[random_id]  #.clone()
+                # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
+                if p > self.prob:
+                    random_id = random.randint(0, self.pool_size - 1)
+                    tmp = self.images[random_id].clone()
                     self.images[random_id] = image
                     return_images.append(tmp)
-                else:  # by another 50% chance, the buffer will return the current image
+                else:
+                    # by another 50% chance, the buffer will return the current image
                     return_images.append(image)
-        return_images = paddle.concat(return_images,
-                                      0)  # collect all the images and return
+        # collect all the images and return
+        return_images = paddle.concat(return_images, 0)
         return return_images

ppgan/utils/logger.py

@@ -61,10 +61,9 @@ def setup_logger(output=None, name="ppgan"):
         if local_rank > 0:
             filename = filename + ".rank{}".format(local_rank)
 
-        # PathManager.mkdirs(os.path.dirname(filename))
+        # make dir if path not exist
         os.makedirs(os.path.dirname(filename), exist_ok=True)
 
-        # fh = logging.StreamHandler(_cached_log_stream(filename)
         fh = logging.FileHandler(filename, mode='a')
         fh.setLevel(logging.DEBUG)
         fh.setFormatter(plain_formatter)

ppgan/utils/registry.py

@@ -77,6 +77,8 @@ class Registry(object):
         return ret
 
 
+# code was based on mmcv
+# Copyright (c) Copyright (c) OpenMMLab.
 def build_from_config(cfg, registry, default_args=None):
     """Build a class from config dict.