Commit @c3d88f8b15bd0c0d9de156d60fe1e421b403541f

윤영준 2023-07-05

corrected the inference output

@c3d88f8b15bd0c0d9de156d60fe1e421b403541f

e6ed062

c3d88f8

model/Autoencoder.py

--- model/Autoencoder.py

+++ model/Autoencoder.py


 
         skip_output_1 = self.skip_output1(relu12)
         skip_output_2 = self.skip_output2(relu14)
-        skip_output_3 = torch.tanh(self.skip_output3(relu16))
+        output = self.skip_output3(relu16)
+        skip_output_3 = torch.tanh(output)
 
         ret = {
             'skip_1': skip_output_1,
             'skip_2': skip_output_2,
             'skip_3': skip_output_3,
+            'output': output
         }
 
         return ret

e6ed062

c3d88f8

train.py

--- train.py

+++ train.py


         vis.line(Y=np.array([discriminator_loss.item()]), X=np.array([epoch * epoch_num + i]), win=Discriminator_loss_window,
                  update='append')
         vis.image(generator_attention_map[-1][0,0,:,:], win=Attention_map_visualizer, opts=dict(title="Attention Map"))
-        vis.image(generator_result[-1], win=Generator_output_visualizer, opts=dict(title="Generator Output"))
+        vis.image(generator_result[-1]*255, win=Generator_output_visualizer, opts=dict(title="Generator Output"))
     day = strftime("%Y-%m-%d %H:%M:%S", gmtime())
     if epoch % save_interval == 0 and epoch != 0:
         torch.save(generator.attentiveRNN.state_dict(), f"weight/Attention_RNN_{epoch}_{day}.pt")

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...	...	@@ -83,12 +83,14 @@
83	83
84	84	skip_output_1 = self.skip_output1(relu12)
85	85	skip_output_2 = self.skip_output2(relu14)
86		- skip_output_3 = torch.tanh(self.skip_output3(relu16))
	86	+ output = self.skip_output3(relu16)
	87	+ skip_output_3 = torch.tanh(output)
87	88
88	89	ret = {
89	90	'skip_1': skip_output_1,
90	91	'skip_2': skip_output_2,
91	92	'skip_3': skip_output_3,
	93	+ 'output': output
92	94	}
93	95
94	96	return ret

...	...	@@ -154,7 +154,7 @@
154	154	vis.line(Y=np.array([discriminator_loss.item()]), X=np.array([epoch * epoch_num + i]), win=Discriminator_loss_window,
155	155	update='append')
156	156	vis.image(generator_attention_map[-1][0,0,:,:], win=Attention_map_visualizer, opts=dict(title="Attention Map"))
157		- vis.image(generator_result[-1], win=Generator_output_visualizer, opts=dict(title="Generator Output"))
	157	+ vis.image(generator_result[-1]*255, win=Generator_output_visualizer, opts=dict(title="Generator Output"))
158	158	day = strftime("%Y-%m-%d %H:%M:%S", gmtime())
159	159	if epoch % save_interval == 0 and epoch != 0:
160	160	torch.save(generator.attentiveRNN.state_dict(), f"weight/Attention_RNN_{epoch}_{day}.pt")

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