Undo lucidrains changes for new discriminator

This "new" code will live in the styledsr directory from now on.

codes/models/stylegan/stylegan2_lucidrains.py

@@ -317,9 +317,6 @@ class StyleGan2Augmentor(nn.Module):
 
         return self.D(images)
 
-    def network_loaded(self):
-        self.D.network_loaded()
-
 
 # stylegan2 classes
 
@@ -741,7 +738,6 @@ class StyleGan2GeneratorWithLatent(nn.Module):
 class DiscriminatorBlock(nn.Module):
     def __init__(self, input_channels, filters, downsample=True):
         super().__init__()
-        self.filters = filters
         self.conv_res = nn.Conv2d(input_channels, filters, 1, stride=(2 if downsample else 1))
 
         self.net = nn.Sequential(
@@ -767,7 +763,7 @@ class DiscriminatorBlock(nn.Module):
 
 class StyleGan2Discriminator(nn.Module):
     def __init__(self, image_size, network_capacity=16, fq_layers=[], fq_dict_size=256, attn_layers=[],
-                 transparent=False, fmap_max=512, input_filters=3, quantize=False, do_checkpointing=False, mlp=False):
+                 transparent=False, fmap_max=512, input_filters=3, quantize=False, do_checkpointing=False):
         super().__init__()
         num_layers = int(log2(image_size) - 1)
 
@@ -809,12 +805,7 @@ class StyleGan2Discriminator(nn.Module):
 
         self.final_conv = nn.Conv2d(chan_last, chan_last, 3, padding=1)
         self.flatten = Flatten()
-        if mlp:
+        self.to_logit = nn.Linear(latent_dim, 1)
-            self.to_logit = nn.Sequential(nn.Linear(latent_dim, 100),
-                                          leaky_relu(),
-                                          nn.Linear(100, 1))
-        else:
-            self.to_logit = nn.Linear(latent_dim, 1)
 
         self._init_weights()
 
@@ -849,38 +840,6 @@ class StyleGan2Discriminator(nn.Module):
             if type(m) in {nn.Conv2d, nn.Linear}:
                 nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
 
-    # Configures the network as partially pre-trained. This means:
-    # 1) The top (high-resolution) `num_blocks` will have their weights re-initialized.
-    # 2) The haed (linear layers) will also have their weights re-initialized
-    # 3) All intermediate blocks will be frozen until step `frozen_until_step`
-    # These settings will be applied after the weights have been loaded (network_loaded())
-    def configure_partial_training(self, bypass_blocks=0, num_blocks=2, frozen_until_step=0):
-        self.bypass_blocks = bypass_blocks
-        self.num_blocks = num_blocks
-        self.frozen_until_step = frozen_until_step
-
-    # Called after the network weights are loaded.
-    def network_loaded(self):
-        if not hasattr(self, 'frozen_until_step'):
-            return
-
-        if self.bypass_blocks > 0:
-            self.blocks = self.blocks[self.bypass_blocks:]
-            self.blocks[0] = DiscriminatorBlock(3, self.blocks[0].filters, downsample=True).to(next(self.parameters()).device)
-
-        reset_blocks = [self.to_logit]
-        for i in range(self.num_blocks):
-            reset_blocks.append(self.blocks[i])
-        for bl in reset_blocks:
-            for m in bl.modules():
-                if type(m) in {nn.Conv2d, nn.Linear}:
-                    nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
-                for p in m.parameters(recurse=True):
-                    p._NEW_BLOCK = True
-        for p in self.parameters():
-            if not hasattr(p, '_NEW_BLOCK'):
-                p.DO_NOT_TRAIN_UNTIL = self.frozen_until_step
-
 
 class StyleGan2DivergenceLoss(L.ConfigurableLoss):
     def __init__(self, opt, env):
@@ -957,8 +916,5 @@ def register_stylegan2_discriminator(opt_net, opt):
     attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
     disc = StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn,
                                   do_checkpointing=opt_get(opt_net, ['do_checkpointing'], False),
-                                  quantize=opt_get(opt_net, ['quantize'], False),
+                                  quantize=opt_get(opt_net, ['quantize'], False))
-                                  mlp=opt_get(opt_net, ['mlp_head'], True))
-    if 'use_partial_pretrained' in opt_net.keys():
-        disc.configure_partial_training(opt_net['bypass_blocks'], opt_net['partial_training_blocks'], opt_net['intermediate_blocks_frozen_until'])
     return StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])