Merge branch 'dev' into improve-frontend-responsiveness

1 files changed, 12 insertions, 11 deletions

diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
index 1b52b0f5..2b9888ba 100644
--- a/modules/esrgan_model_arch.py
+++ b/modules/esrgan_model_arch.py
@@ -2,7 +2,6 @@
 

 from collections import OrderedDict

 import math

-import functools

 import torch

 import torch.nn as nn

 import torch.nn.functional as F

@@ -38,7 +37,7 @@ class RRDBNet(nn.Module):
         elif upsample_mode == 'pixelshuffle':

             upsample_block = pixelshuffle_block

         else:

-            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))

+            raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')

         if upscale == 3:

             upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)

         else:

@@ -106,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module):
     Modified options that can be used:

         - "Partial Convolution based Padding" arXiv:1811.11718

         - "Spectral normalization" arXiv:1802.05957

-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 

+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.

             {Rakotonirina} and A. {Rasoanaivo}

     """

 

@@ -171,7 +170,7 @@ class GaussianNoise(nn.Module):
             scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x

             sampled_noise = self.noise.repeat(*x.size()).normal_() * scale

             x = x + sampled_noise

-        return x 

+        return x

 

 def conv1x1(in_planes, out_planes, stride=1):

     return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

@@ -261,10 +260,10 @@ class Upsample(nn.Module):
 

     def extra_repr(self):

         if self.scale_factor is not None:

-            info = 'scale_factor=' + str(self.scale_factor)

+            info = f'scale_factor={self.scale_factor}'

         else:

-            info = 'size=' + str(self.size)

-        info += ', mode=' + self.mode

+            info = f'size={self.size}'

+        info += f', mode={self.mode}'

         return info

 

 

@@ -350,7 +349,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
     elif act_type == 'sigmoid':  # [0, 1] range output

         layer = nn.Sigmoid()

     else:

-        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))

+        raise NotImplementedError(f'activation layer [{act_type}] is not found')

     return layer

 

 

@@ -372,7 +371,7 @@ def norm(norm_type, nc):
     elif norm_type == 'none':

         def norm_layer(x): return Identity()

     else:

-        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))

+        raise NotImplementedError(f'normalization layer [{norm_type}] is not found')

     return layer

 

 

@@ -388,7 +387,7 @@ def pad(pad_type, padding):
     elif pad_type == 'zero':

         layer = nn.ZeroPad2d(padding)

     else:

-        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))

+        raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')

     return layer

 

 

@@ -432,15 +431,17 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
                pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',

                spectral_norm=False):

     """ Conv layer with padding, normalization, activation """

-    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)

+    assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'

     padding = get_valid_padding(kernel_size, dilation)

     p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None

     padding = padding if pad_type == 'zero' else 0

 

     if convtype=='PartialConv2D':

+        from torchvision.ops import PartialConv2d  # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer

         c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,

                dilation=dilation, bias=bias, groups=groups)

     elif convtype=='DeformConv2D':

+        from torchvision.ops import DeformConv2d  # not tested

         c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,

                dilation=dilation, bias=bias, groups=groups)

     elif convtype=='Conv3D':