From 552e70a032fb8f0e8b00d222ff780c037d233e33 Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Sun, 18 Oct 2020 10:09:37 -0600
Subject: [PATCH] Get rid of excessive checkpointed disc params

---
 codes/models/archs/discriminator_vgg_arch.py | 58 ++++++++++----------
 codes/models/steps/progressive_zoom.py       |  4 +-
 2 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/codes/models/archs/discriminator_vgg_arch.py b/codes/models/archs/discriminator_vgg_arch.py
index 7d56e889..3de79f0d 100644
--- a/codes/models/archs/discriminator_vgg_arch.py
+++ b/codes/models/archs/discriminator_vgg_arch.py
@@ -143,44 +143,44 @@ class Discriminator_VGG_128_GN_Checkpointed(nn.Module):
     def __init__(self, in_nc, nf, input_img_factor=1):
         super(Discriminator_VGG_128_GN_Checkpointed, self).__init__()
         # [64, 128, 128]
-        self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
-        self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
+        conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
+        bn0_1 = nn.GroupNorm(8, nf, affine=True)
         # [64, 64, 64]
-        self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
-        self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
-        self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
-        self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
+        conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
+        bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
+        conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
+        bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
         # [128, 32, 32]
-        self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
-        self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
-        self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
-        self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
+        conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
+        bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
+        conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
+        bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
         # [256, 16, 16]
-        self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
-        self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
-        self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
-        self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
+        conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
+        bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
+        conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
+        bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
         # [512, 8, 8]
-        self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
-        self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
-        self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
-        self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
+        conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
+        bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
+        conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
+        bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
         final_nf = nf * 8
 
         # activation function
         self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
 
-        self.body = nn.Sequential(self.conv0_0, self.lrelu,
-                                  self.conv0_1, self.bn0_1, self.lrelu,
-                                  self.conv1_0, self.bn1_0, self.lrelu,
-                                  self.conv1_1, self.bn1_1, self.lrelu,
-                                  self.conv2_0, self.bn2_0, self.lrelu,
-                                  self.conv2_1, self.bn2_1, self.lrelu,
-                                  self.conv3_0, self.bn3_0, self.lrelu,
-                                  self.conv3_1, self.bn3_1, self.lrelu,
-                                  self.conv4_0, self.bn4_0, self.lrelu,
-                                  self.conv4_1, self.bn4_1, self.lrelu)
+        self.body = nn.Sequential(conv0_0, self.lrelu,
+                                  conv0_1, bn0_1, self.lrelu,
+                                  conv1_0, bn1_0, self.lrelu,
+                                  conv1_1, bn1_1, self.lrelu,
+                                  conv2_0, bn2_0, self.lrelu,
+                                  conv2_1, bn2_1, self.lrelu,
+                                  conv3_0, bn3_0, self.lrelu,
+                                  conv3_1, bn3_1, self.lrelu,
+                                  conv4_0, bn4_0, self.lrelu,
+                                  conv4_1, bn4_1, self.lrelu)
 
         self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100)
         self.linear2 = nn.Linear(100, 1)
diff --git a/codes/models/steps/progressive_zoom.py b/codes/models/steps/progressive_zoom.py
index 98d7af7f..a648e235 100644
--- a/codes/models/steps/progressive_zoom.py
+++ b/codes/models/steps/progressive_zoom.py
@@ -67,10 +67,10 @@ class ProgressiveGeneratorInjector(Injector):
         if not isinstance(inputs, list):
             inputs = [inputs]
         if not isinstance(self.output, list):
-            self.output = [self.output]
+            output = [self.output]
         results = {}   # A list of outputs produced by feeding each progressive lq input into the generator.
         results_hq = []
-        for out_key in self.output:
+        for out_key in output:
             results[out_key] = []
 
         b, f, h, w = lq_inputs[:, 0].shape