fix copyright (PaddlePaddle#461)

* fix copyright * fix copyright 2 * fix copyright 3
AnnaTrainingG · Nov 5, 2021 · bc27b74 · bc27b74
1 parent 3e6fb64
commit bc27b74
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diff --git a/ppgan/apps/styleganv2fitting_predictor.py b/ppgan/apps/styleganv2fitting_predictor.py
@@ -38,14 +38,12 @@ def make_image(tensor):
 
 
 class StyleGANv2FittingPredictor(StyleGANv2Predictor):
-    def run(
-            self,
+    def run(self,
             image,
             need_align=False,
             start_lr=0.1,
             final_lr=0.025,
-            latent_level=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
-                          11],  # for ffhq (0~17)
+            latent_level=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
             step=100,
             mse_weight=1,
             pre_latent=None):

diff --git a/ppgan/datasets/animeganv2_dataset.py b/ppgan/datasets/animeganv2_dataset.py
@@ -36,9 +36,9 @@ def __init__(self,
         """Initialize this dataset class.
 
         Args:
-            cfg (dict) -- stores all the experiment flags
+            dataroot (dict): Directory of dataset.
+
         """
-        # self.cfg = cfg
         self.root = dataroot
         self.style = style
 

diff --git a/ppgan/datasets/firstorder_dataset.py b/ppgan/datasets/firstorder_dataset.py
@@ -239,8 +239,7 @@ def __getitem__(self, idx):
                 out['driving'] = out['source']
                 out['source'] = buf
         else:
-            video = np.stack(video_array, axis=0).astype(
-                    np.float32) / 255.0
+            video = np.stack(video_array, axis=0).astype(np.float32) / 255.0
             out['video'] = video.transpose(3, 0, 1, 2)
         out['name'] = video_name
         return out

diff --git a/ppgan/datasets/mpr_dataset.py b/ppgan/datasets/mpr_dataset.py
@@ -1,16 +1,6 @@
-# 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/swz30/MPRNet
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/swz30/MPRNet/blob/main/LICENSE.md
 
 import os
 import random

diff --git a/ppgan/datasets/starganv2_dataset.py b/ppgan/datasets/starganv2_dataset.py
@@ -1,3 +1,6 @@
+# code was heavily based on https://github.com/clovaai/stargan-v2
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/clovaai/stargan-v2#license
 
 import paddle
 from .base_dataset import BaseDataset
@@ -14,8 +17,11 @@
 
 
 def listdir(dname):
-    fnames = list(chain(*[list(Path(dname).rglob('*.' + ext))
-                          for ext in ['png', 'jpg', 'jpeg', 'JPG']]))
+    fnames = list(
+        chain(*[
+            list(Path(dname).rglob('*.' + ext))
+            for ext in ['png', 'jpg', 'jpeg', 'JPG']
+        ]))
     return fnames
 
 
@@ -97,7 +103,6 @@ def __len__(self):
         return len(self.targets)
 
 
-
 @DATASETS.register()
 class StarGANv2Dataset(BaseDataset):
     """
@@ -120,23 +125,24 @@ def __init__(self, dataroot, is_train, preprocess, test_count=0):
         else:
             files = os.listdir(self.dataroot)
             if 'src' in files and 'ref' in files:
-                self.src_loader = ImageFolder(os.path.join(self.dataroot, 'src'))
-                self.ref_loader = ImageFolder(os.path.join(self.dataroot, 'ref'))
+                self.src_loader = ImageFolder(os.path.join(
+                    self.dataroot, 'src'))
+                self.ref_loader = ImageFolder(os.path.join(
+                    self.dataroot, 'ref'))
             else:
                 self.src_loader = ImageFolder(self.dataroot)
                 self.ref_loader = ImageFolder(self.dataroot)
             self.counts = min(test_count, len(self.src_loader))
             self.counts = min(self.counts, len(self.ref_loader))
 
-
     def _fetch_inputs(self):
         try:
             x, y = next(self.iter_src)
         except (AttributeError, StopIteration):
             self.iter_src = iter(self.src_loader)
             x, y = next(self.iter_src)
         return x, y
-    
+
     def _fetch_refs(self):
         try:
             x, x2, y = next(self.iter_ref)
@@ -165,14 +171,14 @@ def __getitem__(self, idx):
                 'ref_path': x_ref,
                 'ref_cls': y_ref,
             }
-        
+
         if hasattr(self, 'preprocess') and self.preprocess:
             datas = self.preprocess(datas)
 
         return datas
 
     def __len__(self):
         return self.counts
-    
+
     def prepare_data_infos(self, dataroot):
         pass
diff --git a/ppgan/models/animeganv2_model.py b/ppgan/models/animeganv2_model.py
@@ -27,6 +27,8 @@
 
 @MODELS.register()
 class AnimeGANV2Model(BaseModel):
+    """ This class implements the AnimeGANV2 model.
+    """
     def __init__(self,
                  generator,
                  discriminator=None,
@@ -40,8 +42,10 @@ def __init__(self,
                  tv_weight=1.):
         """Initialize the AnimeGANV2 class.
 
-        Parameters:
-            opt (config dict)-- stores all the experiment flags; needs to be a subclass of Dict
+        Args:
+            generator (dict): config of generator.
+            discriminator (dict): config of discriminator.
+            gan_criterion (dict): config of gan criterion.
         """
         super(AnimeGANV2Model, self).__init__()
         self.g_adv_weight = g_adv_weight
@@ -54,7 +58,7 @@ def __init__(self,
         self.nets['netG'] = build_generator(generator)
         init_weights(self.nets['netG'])
 
-        # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
+        # define a discriminator
         if self.is_train:
             self.nets['netD'] = build_discriminator(discriminator)
             init_weights(self.nets['netD'])
@@ -87,14 +91,14 @@ def setup_input(self, input):
 
     def forward(self):
         """Run forward pass; called by both functions <optimize_parameters> and <test>."""
-        self.fake = self.nets['netG'](self.real)  # G(A)
+        self.fake = self.nets['netG'](self.real)
 
         # put items to visual dict
         self.visual_items['real'] = self.real
         self.visual_items['fake'] = self.fake
 
     def test(self):
-        self.fake = self.nets['netG'](self.real)  # G(A)
+        self.fake = self.nets['netG'](self.real)
 
         # put items to visual dict
         self.visual_items['real'] = self.real

diff --git a/ppgan/models/discriminators/dcdiscriminator.py b/ppgan/models/discriminators/dcdiscriminator.py
@@ -12,6 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# code was heavily based on https://github.com/aidotse/Team-Haste
+# MIT License
+# Copyright (c) 2020 AI Sweden
+
 import paddle
 import functools
 import numpy as np
@@ -31,15 +35,14 @@ def __init__(self, input_nc, ndf=64, norm_type='instance'):
         """Construct a DCGAN discriminator
 
         Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            norm_type (str)      -- normalization layer type
+            input_nc (int): the number of channels in input images
+            ndf (int): the number of filters in the last conv layer
+            norm_type (str): normalization layer type
         """
         super(DCDiscriminator, self).__init__()
         norm_layer = build_norm_layer(norm_type)
-        if type(
-                norm_layer
-        ) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
+        if type(norm_layer) == functools.partial:
+            # no need to use bias as BatchNorm2d has affine parameters
             use_bias = norm_layer.func == nn.BatchNorm2D
         else:
             use_bias = norm_layer == nn.BatchNorm2D
@@ -48,29 +51,30 @@ def __init__(self, input_nc, ndf=64, norm_type='instance'):
         padw = 1
 
         sequence = [
-                nn.Conv2D(input_nc,
-                          ndf,
-                          kernel_size=kw,
-                          stride=2,
-                          padding=padw,
-                          bias_attr=use_bias),
-                nn.LeakyReLU(0.2)
-            ]
+            nn.Conv2D(input_nc,
+                      ndf,
+                      kernel_size=kw,
+                      stride=2,
+                      padding=padw,
+                      bias_attr=use_bias),
+            nn.LeakyReLU(0.2)
+        ]
 
         nf_mult = 1
         nf_mult_prev = 1
         n_downsampling = 4
 
-        for n in range(1, n_downsampling):  # gradually increase the number of filters
+        # gradually increase the number of filters
+        for n in range(1, n_downsampling):
             nf_mult_prev = nf_mult
             nf_mult = min(2**n, 8)
             if norm_type == 'batch':
                 sequence += [
                     nn.Conv2D(ndf * nf_mult_prev,
-                                  ndf * nf_mult,
-                                  kernel_size=kw,
-                                  stride=2,
-                                  padding=padw),
+                              ndf * nf_mult,
+                              kernel_size=kw,
+                              stride=2,
+                              padding=padw),
                     BatchNorm2D(ndf * nf_mult),
                     nn.LeakyReLU(0.2)
                 ]
@@ -88,13 +92,14 @@ def __init__(self, input_nc, ndf=64, norm_type='instance'):
 
         nf_mult_prev = nf_mult
 
+        # output 1 channel prediction map
         sequence += [
-                nn.Conv2D(ndf * nf_mult_prev,
-                          1,
-                          kernel_size=kw,
-                          stride=1,
-                          padding=0)
-            ]  # output 1 channel prediction map
+            nn.Conv2D(ndf * nf_mult_prev,
+                      1,
+                      kernel_size=kw,
+                      stride=1,
+                      padding=0)
+        ]
 
         self.model = nn.Sequential(*sequence)
 

diff --git a/ppgan/models/discriminators/discriminator_animegan.py b/ppgan/models/discriminators/discriminator_animegan.py
@@ -1,16 +1,6 @@
-#  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/TachibanaYoshino/AnimeGANv2
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/TachibanaYoshino/AnimeGANv2#license
 
 import paddle.nn as nn
 import paddle.nn.functional as F

diff --git a/ppgan/models/discriminators/discriminator_starganv2.py b/ppgan/models/discriminators/discriminator_starganv2.py
@@ -1,3 +1,6 @@
+# code was heavily based on https://github.com/clovaai/stargan-v2
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/clovaai/stargan-v2#license
 
 import paddle.nn as nn
 import paddle
@@ -18,7 +21,7 @@ def __init__(self, img_size=256, num_domains=2, max_conv_dim=512):
 
         repeat_num = int(np.log2(img_size)) - 2
         for _ in range(repeat_num):
-            dim_out = min(dim_in*2, max_conv_dim)
+            dim_out = min(dim_in * 2, max_conv_dim)
             blocks += [ResBlk(dim_in, dim_out, downsample=True)]
             dim_in = dim_out
 

diff --git a/ppgan/models/discriminators/discriminator_styleganv2.py b/ppgan/models/discriminators/discriminator_styleganv2.py
@@ -12,6 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# code was heavily based on https://github.com/rosinality/stylegan2-pytorch
+# MIT License
+# Copyright (c) 2019 Kim Seonghyeon
+
 import math
 import paddle
 import paddle.nn as nn

diff --git a/ppgan/models/firstorder_model.py b/ppgan/models/firstorder_model.py
@@ -13,6 +13,8 @@
 # limitations under the License.
 
 # code was heavily based on https://github.com/AliaksandrSiarohin/first-order-model
+# Users should be careful about adopting these functions in any commercial matters.
+# https://github.com/AliaksandrSiarohin/first-order-model/blob/master/LICENSE.md
 
 import paddle