md

jdit/trainer/instances/fashingGenerateGan.py

@@ -39,10 +39,10 @@ def forward(self, x):
 class Generator(nn.Module):
     def __init__(self, input_nc=256, output_nc=1, depth=64):
         super(Generator, self).__init__()
-        self.latent_to_features = nn.Sequential(
+        self.encoder = nn.Sequential(
                 nn.ConvTranspose2d(input_nc, 4 * depth, 4, 1, 0),  # 256,1,1 =>  256,4,4
                 nn.ReLU())
-        self.features_to_image = nn.Sequential(
+        self.decoder = nn.Sequential(
                 nn.ConvTranspose2d(4 * depth, 4 * depth, 4, 2, 1),  # 256,4,4 =>  256,8,8
                 nn.ReLU(),
                 nn.BatchNorm2d(4 * depth),
@@ -56,13 +56,13 @@ def __init__(self, input_nc=256, output_nc=1, depth=64):
                 )
 
     def forward(self, input_data):
-        out = self.latent_to_features(input_data)
-        out = self.features_to_image(out)
+        out = self.encoder(input_data)
+        out = self.decoder(out)
         return out
 
 
 class FashingGenerateGenerateGanTrainer(GenerateGanTrainer):
-    d_turn = 5
+    d_turn = 1
     def __init__(self, logdir, nepochs, gpu_ids_abs, netG, netD, optG, optD, dataset, latent_shape):
         super(FashingGenerateGenerateGanTrainer, self).__init__(logdir, nepochs, gpu_ids_abs, netG, netD, optG, optD,
                                                                 dataset,

jdit/trainer/super.py

@@ -295,7 +295,7 @@ def _record_configs(self, configs_names=None):
         if (configs_names is None) or "dataset" in configs_names:
             _datasets = super(SupTrainer, self).__getattribute__("_datasets")
             for name, dataset in _datasets.items():
-                self.loger.regist_config(dataset, self.current_epoch, self.step, config_filename=name)
+                self.loger.regist_config(dataset, config_filename=name)
         if (configs_names is None) or "optimizer" in configs_names:
             _opts = super(SupTrainer, self).__getattribute__("_opts")
             for name, opt in _opts.items():