Add art model of deoldify (#71)

* add art model of deoldify

applications/tools/video-enhance.py

@@ -67,6 +67,10 @@ parser.add_argument('--mindim',
                     default=360,
                     help='Length of minimum image edges')
 # DeOldify args
+parser.add_argument('--artistic',
+                    action='store_true',
+                    default=False,
+                    help='whether to use artistic DeOldify Model')
 parser.add_argument('--render_factor',
                     type=int,
                     default=32,
@@ -107,6 +111,7 @@ if __name__ == "__main__":
         elif order == 'DeOldify':
             predictor = DeOldifyPredictor(args.output,
                                           weight_path=args.DeOldify_weight,
+                                          artistic=args.artistic,
                                           render_factor=args.render_factor)
             frames_path, temp_video_path = predictor.run(temp_video_path)
         elif order == 'RealSR':

docs/en_US/tutorials/video_restore.md

+../../zh_CN/tutorials/video_restore.md
\ No newline at end of file

docs/zh_CN/apis/apps.md

@@ -39,6 +39,7 @@ ppgan.apps.DeOldifyPredictor(output='output', weight_path=None, render_factor=32
 >
 > > - output (str):  设置输出图片的保存路径，默认是output。注意，保存路径为设置output/DeOldify。
 > > - weight_path (str): 指定模型路径，默认是None，则会自动下载内置的已经训练好的模型。
+> > - artistic (bool): 是否使用偏"艺术性"的模型。"艺术性"的模型有可能产生一些有趣的颜色，但是毛刺比较多。
 > > - render_factor (int): 图片渲染上色时的缩放因子，图片会缩放到边长为16xrender_factor的正方形， 再上色，例如render_factor默认值为32，输入图片先缩放到(16x32=512) 512x512大小的图片。通常来说，render_factor越小，计算速度越快，颜色看起来也更鲜活。较旧和较低质量的图像通常会因降低渲染因子而受益。渲染因子越高，图像质量越好，但颜色可能会稍微褪色。
 
 ### run

docs/zh_CN/tutorials/video_restore.md

@@ -63,6 +63,7 @@ ppgan.apps.DeOldifyPredictor(output='output', weight_path=None, render_factor=32
 
 - `output (str，可选的)`: 输出的文件夹路径，默认值：`output`.
 - `weight_path (None，可选的)`: 载入的权重路径，如果没有设置，则从云端下载默认的权重到本地。默认值：`None`。
+- `artistic (bool)`: 是否使用偏"艺术性"的模型。"艺术性"的模型有可能产生一些有趣的颜色，但是毛刺比较多。
 - `render_factor (int)`: 会将该参数乘以16后作为输入帧的resize的值，如果该值设置为32，
                          则输入帧会resize到(32 * 16, 32 * 16)的尺寸再输入到网络中。
 

ppgan/apps/base_predictor.py

@@ -14,6 +14,7 @@
 
 import os
 import cv2
+import numpy as np
 from PIL import Image
 import paddle
 
@@ -64,9 +65,16 @@ class BasePredictor(object):
 
     def is_image(self, input):
         try:
-            img = Image.open(input)
-            _ = img.size
-            return True
+            if isinstance(input, (np.ndarray, Image.Image)):
+                return True
+            elif isinstance(input, str):
+                if not os.path.isfile(input):
+                    raise ValueError('input must be a file')
+                img = Image.open(input)
+                _ = img.size
+                return True
+            else:
+                return False
         except:
             return False
 

ppgan/apps/deoldify_predictor.py

@@ -26,17 +26,27 @@ from ppgan.models.generators.deoldify import build_model
 
 from .base_predictor import BasePredictor
 
-DEOLDIFY_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DeOldify_stable.pdparams'
+DEOLDIFY_STABLE_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DeOldify_stable.pdparams'
+DEOLDIFY_ART_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DeOldify_art.pdparams'
 
 
 class DeOldifyPredictor(BasePredictor):
-    def __init__(self, output='output', weight_path=None, render_factor=32):
-        # self.input = input
+    def __init__(self,
+                 output='output',
+                 weight_path=None,
+                 artistic=False,
+                 render_factor=32):
         self.output = os.path.join(output, 'DeOldify')
+        if not os.path.exists(self.output):
+            os.makedirs(self.output)
         self.render_factor = render_factor
-        self.model = build_model()
+        self.model = build_model(
+            model_type='artistic' if artistic else 'stable')
         if weight_path is None:
-            weight_path = get_path_from_url(DEOLDIFY_WEIGHT_URL)
+            if artistic:
+                weight_path = get_path_from_url(DEOLDIFY_ART_WEIGHT_URL)
+            else:
+                weight_path = get_path_from_url(DEOLDIFY_STABLE_WEIGHT_URL)
 
         state_dict = paddle.load(weight_path)
         self.model.load_dict(state_dict)
@@ -134,7 +144,10 @@ class DeOldifyPredictor(BasePredictor):
 
             out_path = None
             if self.output:
-                base_name = os.path.splitext(os.path.basename(input))[0]
+                try:
+                    base_name = os.path.splitext(os.path.basename(input))[0]
+                except:
+                    base_name = 'result'
                 out_path = os.path.join(self.output, base_name + '.png')
                 pred_img.save(out_path)
 

ppgan/apps/realsr_predictor.py

@@ -107,7 +107,10 @@ class RealSRPredictor(BasePredictor):
 
             out_path = None
             if self.output:
-                base_name = os.path.splitext(os.path.basename(input))[0]
+                try:
+                    base_name = os.path.splitext(os.path.basename(input))[0]
+                except:
+                    base_name = 'result'
                 out_path = os.path.join(self.output, base_name + '.png')
                 pred_img.save(out_path)
 

ppgan/models/generators/deoldify.py

@@ -16,7 +16,7 @@ import numpy as np
 import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
-from paddle.vision.models import resnet101
+from paddle.vision.models import resnet34, resnet101
 
 from .hook import hook_outputs, model_sizes, dummy_eval
 from ...modules.nn import Spectralnorm
@@ -57,6 +57,7 @@ class Deoldify(SequentialEx):
     def __init__(self,
                  encoder,
                  n_classes,
+                 model_type='stable',
                  blur=False,
                  blur_final=True,
                  self_attention=False,
@@ -95,18 +96,34 @@ class Deoldify(SequentialEx):
             do_blur = blur and (not_final or blur_final)
             sa = self_attention and (i == len(sfs_idxs) - 3)
 
-            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)
+            if model_type == 'stable':
+                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)
+            elif model_type == 'artistic':
+                unet_block = UnetBlockDeep(up_in_c,
+                                           x_in_c,
+                                           self.sfs[i],
+                                           final_div=not_final,
+                                           blur=blur,
+                                           self_attention=sa,
+                                           norm_type=norm_type,
+                                           extra_bn=extra_bn,
+                                           nf_factor=nf_factor,
+                                           **kwargs)
+            else:
+                raise ValueError(
+                    'Expected model_type in [stable, artistic], but got {}'.
+                    format(model_type))
+
             unet_block.eval()
             layers.append(unet_block)
             x = unet_block(x)
@@ -151,7 +168,7 @@ def custom_conv_layer(ni: int,
     bn = norm_type in ('Batch', 'Batchzero') or extra_bn == True
     if bias is None:
         bias = not bn
-    conv_func = nn.Conv2DTranspose if transpose else nn.Conv1d if is_1d else nn.Conv2D
+    conv_func = nn.Conv2DTranspose if transpose else nn.Conv1D if is_1d else nn.Conv2D
 
     conv = conv_func(ni,
                      nf,
@@ -222,19 +239,18 @@ class UnetBlockWide(nn.Layer):
 class UnetBlockDeep(nn.Layer):
     "A quasi-UNet block, using `PixelShuffle_ICNR upsampling`."
 
-    def __init__(
-            self,
-            up_in_c: int,
-            x_in_c: int,
-            # hook: Hook,
-            final_div: bool = True,
-            blur: bool = False,
-            leaky: float = None,
-            self_attention: bool = False,
-            nf_factor: float = 1.0,
-            **kwargs):
+    def __init__(self,
+                 up_in_c: int,
+                 x_in_c: int,
+                 hook,
+                 final_div: bool = True,
+                 blur: bool = False,
+                 leaky: float = None,
+                 self_attention: bool = False,
+                 nf_factor: float = 1.0,
+                 **kwargs):
         super().__init__()
-
+        self.hook = hook
         self.shuf = CustomPixelShuffle_ICNR(up_in_c,
                                             up_in_c // 2,
                                             blur=blur,
@@ -312,7 +328,7 @@ def conv_layer(ni: int,
     if padding is None: padding = (ks - 1) // 2 if not transpose else 0
     bn = norm_type in ('Batch', 'BatchZero')
     if bias is None: bias = not bn
-    conv_func = nn.Conv2DTranspose if transpose else nn.Conv1d if is_1d else nn.Conv2D
+    conv_func = nn.Conv2DTranspose if transpose else nn.Conv1D if is_1d else nn.Conv2D
 
     conv = conv_func(ni,
                      nf,
@@ -472,16 +488,27 @@ def _get_sfs_idxs(sizes):
     return sfs_idxs
 
 
-def build_model():
-    backbone = resnet101()
+def build_model(model_type='stable'):
+    if model_type == 'stable':
+        backbone = resnet101()
+        nf_factor = 2
+    elif model_type == 'artistic':
+        backbone = resnet34()
+        nf_factor = 1.5
+    else:
+        raise ValueError(
+            'Expected model_type in [stable, artistic], but got {}'.format(
+                model_type))
+
     cut = -2
     encoder = nn.Sequential(*list(backbone.children())[:cut])
 
     model = Deoldify(encoder,
                      3,
+                     model_type=model_type,
                      blur=True,
                      y_range=(-3, 3),
                      norm_type='Spectral',
                      self_attention=True,
-                     nf_factor=2)
+                     nf_factor=nf_factor)
     return model

ppgan/models/pix2pix_model.py

@@ -80,11 +80,8 @@ class Pix2PixModel(BaseModel):
 
         AtoB = self.cfg.dataset.train.direction == 'AtoB'
 
-        # TODO: replace to_varialbe with to_tensor
-        self.real_A = paddle.fluid.dygraph.to_variable(
-            input['A' if AtoB else 'B'])
-        self.real_B = paddle.fluid.dygraph.to_variable(
-            input['B' if AtoB else 'A'])
+        self.real_A = paddle.to_tensor(input['A' if AtoB else 'B'])
+        self.real_B = paddle.to_tensor(input['B' if AtoB else 'A'])
 
         self.image_paths = input['A_paths' if AtoB else 'B_paths']