updated README and python requirements

AFRVSRTrain.py

@@ -72,8 +72,6 @@ def printCUDAStats():
 optimizerG = optim.Adam(netG.parameters(), lr=lr)
 optimizerD = optim.Adam(netD.parameters(), lr=lr)
 
-results = {'DLoss': [], 'GLoss': [], 'DScore': [], 'GScore': [], 'PSNR': [], 'SSIM': []}
-
 def trainModel(epoch):
     trainBar = tqdm(trainLoader)
     runningResults = {'batchSize': 0, 'DLoss': 0, 'GLoss': 0, 'DScore': 0, 'GScore': 0}
@@ -199,6 +197,8 @@ def validateModel():
         return validationResults
 
 def saveModelParams(epoch, runningResults, validationResults={}):
+    results = {'DLoss': [], 'GLoss': [], 'DScore': [], 'GScore': [], 'PSNR': [], 'SSIM': []}
+
     # Save model parameters
     torch.save(netG.state_dict(), 'epochs/netG_epoch_%d_%d.pth' % (UPSCALE_FACTOR, epoch))
     torch.save(netD.state_dict(), 'epochs/netD_epoch_%d_%d.pth' % (UPSCALE_FACTOR, epoch))

DatasetLoader.py

@@ -61,7 +61,7 @@ def get_from_set(dir, frame_set):
             file_dir_frames = os.path.join(dir, frames)
             imgs_path = os.listdir(file_dir_frames)
             imgs_path.sort()
-            #import pdb; pdb.set_trace()
+
             i = 0
             for img in imgs_path:
                 final_path = file_dir_frames + "/" + img

README.md

@@ -7,11 +7,12 @@ Project for Stanford CS230: Deep Learning
 ## Required Packages
 
 ```
-pytorch
-numpy
-scikit-image
-opencv-python
-tqdm
+torch==1.3.0.post2
+pytorch-ssim==0.1
+numpy==1.16.4
+opencv-python==4.1.1.26
+scikit-image==0.15.0
+tqdm==4.37.0
 ```
 
 To load,
@@ -55,7 +56,7 @@ Train the model using (takes roughly an hour per epoch on an NVIDIA Tesla V100):
 
 To use the pre-trained model and test on a random video from within the dataset:
 
-```python AFRVSRTest.py`````
+```python AFRVSRTest.py```
 
 ## Acknowledgements
 

requirements.txt

@@ -1,5 +1,6 @@
-pytorch
-numpy
-scikit-image
-opencv-python
-tqdm
+torch==1.3.0.post2
+pytorch-ssim==0.1
+numpy==1.16.4
+opencv-python==4.1.1.26
+scikit-image==0.15.0
+tqdm==4.37.0