VDSR added

.gitattributes

@@ -37,3 +37,4 @@ models/HAT/hat-for-image-sr-2.ipynb filter=lfs diff=lfs merge=lfs -text
 model/RCAN/rcan-for-image-sr.ipynb filter=lfs diff=lfs merge=lfs -text
 models/RCAN/rcan-for-image-sr.ipynb filter=lfs diff=lfs merge=lfs -text
 models/SRGAN/srgan-dl-prj.ipynb filter=lfs diff=lfs merge=lfs -text
+models/VDSR/vdsr.ipynb filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,216 +1,241 @@
-import time
-import streamlit as st
-import subprocess
-import numpy as np
-from PIL import Image
-from io import BytesIO
-from models.HAT.hat import *
-from models.RCAN.rcan import *
-from models.SRGAN.srgan import *
-from models.Interpolation.nearest_neighbor import NearestNeighbor_for_deployment
-from models.Interpolation.bilinear import Bilinear_for_deployment
-from models.Interpolation.bicubic import Bicubic_for_deployment
-
-subprocess.call('pip install natsort', shell=True)
-from models.SRFlow.srflow import * 
-
-# Initialize session state for enhanced images
-if 'nearest_enhanced_image' not in st.session_state:
-    st.session_state['nearest_enhanced_image'] = None
-if 'bilinear_enhanced_image' not in st.session_state:
-    st.session_state['bilinear_enhanced_image'] = None
-if 'bicubic_enhanced_image' not in st.session_state:
-    st.session_state['bicubic_enhanced_image'] = None
-if 'hat_enhanced_image' not in st.session_state:
-    st.session_state['hat_enhanced_image'] = None
-if 'rcan_enhanced_image' not in st.session_state:
-    st.session_state['rcan_enhanced_image'] = None
-if 'srgan_enhanced_image' not in st.session_state:
-    st.session_state['srgan_enhanced_image'] = None
-if 'srflow_enhanced_image' not in st.session_state:
-    st.session_state['srflow_enhanced_image'] = None
-
-# Initialize session state for button clicks
-if 'nearest_clicked' not in st.session_state:
-    st.session_state['nearest_clicked'] = False
-if 'bilinear_clicked' not in st.session_state:
-    st.session_state['bilinear_clicked'] = False
-if 'bicubic_clicked' not in st.session_state:
-    st.session_state['bicubic_clicked'] = False
-if 'hat_clicked' not in st.session_state:
-    st.session_state['hat_clicked'] = False
-if 'rcan_clicked' not in st.session_state:
-    st.session_state['rcan_clicked'] = False
-if 'srgan_clicked' not in st.session_state:
-    st.session_state['srgan_clicked'] = False 
-if 'srflow_clicked' not in st.session_state:
-    st.session_state['srflow_clicked'] = False
-
-st.markdown("<h1 style='text-align: center'>Image Super Resolution</h1>", unsafe_allow_html=True)
-
-# Sidebar for navigation
-st.sidebar.title("Options")
-app_mode = st.sidebar.selectbox("Choose the input source", ["Upload image", "Take a photo"])
-
-# Depending on the choice, show the uploader widget or webcam capture
-if app_mode == "Upload image":
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"], on_change=lambda: reset_states())
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file).convert("RGB")
-elif app_mode == "Take a photo":
-    camera_input = st.camera_input("Take a picture", on_change=lambda: reset_states())
-    if camera_input is not None:
-        image = Image.open(camera_input).convert("RGB")
-        
-def reset_states():
-    st.session_state['hat_enhanced_image'] = None
-    st.session_state['rcan_enhanced_image'] = None
-    st.session_state['srgan_enhanced_image'] = None
-    st.session_state['srflow_enhanced_image'] = None
-    st.session_state['bicubic_enhanced_image'] = None
-    st.session_state['bilinear_enhanced_image'] = None
-    st.session_state['nearest_enhanced_image'] = None
-    st.session_state['hat_clicked'] = False
-    st.session_state['rcan_clicked'] = False
-    st.session_state['srgan_clicked'] = False
-    st.session_state['srflow_clicked'] = False
-    st.session_state['bicubic_clicked'] = False
-    st.session_state['bilinear_clicked'] = False
-    st.session_state['nearest_clicked'] = False
-    
-def get_image_download_link(img, filename):
-    """Generates a link allowing the PIL image to be downloaded"""
-    # Convert the PIL image to Bytes
-    buffered = BytesIO()
-    img.save(buffered, format="PNG")
-    return st.download_button(
-        label="Download Image",
-        data=buffered.getvalue(),
-        file_name=filename,
-        mime="image/png"
-    )
-    
-if 'image' in locals():
-    # st.image(image, caption='Uploaded Image', use_column_width=True)
-    st.write("")
-    # ------------------------ Nearest Neighbor ------------------------ #
-    if st.button('Enhance with Nearest Neighbor'):
-        with st.spinner('Processing using Nearest Neighbor...'):
-            enhanced_image = NearestNeighbor_for_deployment(image)
-            st.session_state['nearest_enhanced_image'] = enhanced_image
-            st.session_state['nearest_clicked'] = True
-            st.success('Done!')
-    if st.session_state['nearest_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['nearest_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['nearest_enhanced_image'], 'nearest_enhanced.jpg')
-
-    # ------------------------ Bilinear ------------------------ #
-    if st.button('Enhance with Bilinear'):
-        with st.spinner('Processing using Bilinear...'):
-            enhanced_image = Bilinear_for_deployment(image)
-            st.session_state['bilinear_enhanced_image'] = enhanced_image
-            st.session_state['bilinear_clicked'] = True
-            st.success('Done!')
-    if st.session_state['bilinear_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['bilinear_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['bilinear_enhanced_image'], 'bilinear_enhanced.jpg')
-    
-    # ------------------------ Bicubic ------------------------ #
-    if st.button('Enhance with Bicubic'):
-        with st.spinner('Processing using Bicubic...'):
-            enhanced_image = Bicubic_for_deployment(image)
-            st.session_state['bicubic_enhanced_image'] = enhanced_image
-            st.session_state['bicubic_clicked'] = True
-            st.success('Done!')
-    if st.session_state['bicubic_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['bicubic_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['bicubic_enhanced_image'], 'bicubic_enhanced.jpg')
-
-    # ------------------------ HAT ------------------------ #
-    if st.button('Enhance with HAT'):
-        with st.spinner('Processing using HAT...'):                             
-            with st.spinner('Wait for it... the model is processing the image'):                                                  
-                enhanced_image = HAT_for_deployment(image)
-                st.session_state['hat_enhanced_image'] = enhanced_image
-            st.session_state['hat_clicked'] = True
-            st.success('Done!')
-    if st.session_state['hat_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['hat_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['hat_enhanced_image'], 'hat_enhanced.jpg')
-    
-    # ------------------------ RCAN ------------------------ #
-    if st.button('Enhance with RCAN'):
-        with st.spinner('Processing using RCAN...'):
-            with st.spinner('Wait for it... the model is processing the image'):
-                rcan_model = RCAN()
-                device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda')
-                rcan_model.load_state_dict(torch.load('models/RCAN/rcan_checkpoint.pth', map_location=device))
-                enhanced_image = rcan_model.inference(image)
-                st.session_state['rcan_enhanced_image'] = enhanced_image
-            st.session_state['rcan_clicked'] = True 
-            st.success('Done!')
-    if st.session_state['rcan_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['rcan_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['rcan_enhanced_image'], 'rcan_enhanced.jpg')
-    
-    # --------------------------SRGAN-------------------------- #
-    if st.button('Enhance with SRGAN'):
-        with st.spinner('Processing using SRGAN...'):
-            with st.spinner('Wait for it... the model is processing the image'):
-                srgan_model = GeneratorResnet()
-                device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda')
-                srgan_model = torch.load('models/SRGAN/srgan_checkpoint.pth', map_location=device)
-                enhanced_image = srgan_model.inference(image)
-                st.session_state['srgan_enhanced_image'] = enhanced_image
-            st.session_state['srgan_clicked'] = True 
-            st.success('Done!')
-    if st.session_state['srgan_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['srgan_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['srgan_enhanced_image'], 'srgan_enhanced.jpg')
-    
-    # ------------------------ SRFlow ------------------------ #
-    if st.button('Enhance with SRFlow'):
-        with st.spinner('Processing using SRFlow...'):
-            with st.spinner('Wait for it... the model is processing the image'):
-                enhanced_image = return_SRFlow_result(image)
-                st.session_state['srflow_enhanced_image'] = enhanced_image        
-            st.session_state['srflow_clicked'] = True 
-            st.success('Done!')
-    if st.session_state['srflow_enhanced_image'] is not None:
-        col1, col2 = st.columns(2)
-        col1.header("Original")
-        col1.image(image, use_column_width=True)
-        col2.header("Enhanced")
-        col2.image(st.session_state['srflow_enhanced_image'], use_column_width=True)
-        with col2:
-            get_image_download_link(st.session_state['srflow_enhanced_image'], 'srflow_enhanced.jpg')
+import time
+import streamlit as st
+import subprocess
+import numpy as np
+from PIL import Image
+from io import BytesIO
+from models.HAT.hat import *
+from models.RCAN.rcan import *
+from models.SRGAN.srgan import *
+from models.VDSR.vdsr import *
+from models.Interpolation.nearest_neighbor import NearestNeighbor_for_deployment
+from models.Interpolation.bilinear import Bilinear_for_deployment
+from models.Interpolation.bicubic import Bicubic_for_deployment
+
+subprocess.call('pip install natsort', shell=True)
+from models.SRFlow.srflow import * 
+
+# Initialize session state for enhanced images
+if 'nearest_enhanced_image' not in st.session_state:
+    st.session_state['nearest_enhanced_image'] = None
+if 'bilinear_enhanced_image' not in st.session_state:
+    st.session_state['bilinear_enhanced_image'] = None
+if 'bicubic_enhanced_image' not in st.session_state:
+    st.session_state['bicubic_enhanced_image'] = None
+if 'hat_enhanced_image' not in st.session_state:
+    st.session_state['hat_enhanced_image'] = None
+if 'rcan_enhanced_image' not in st.session_state:
+    st.session_state['rcan_enhanced_image'] = None
+if 'srgan_enhanced_image' not in st.session_state:
+    st.session_state['srgan_enhanced_image'] = None
+if 'srflow_enhanced_image' not in st.session_state:
+    st.session_state['srflow_enhanced_image'] = None
+if 'vdsr_enhanced_image' not in st.session_state:
+    st.session_state['vdsr_enhanced_image'] = None
+
+# Initialize session state for button clicks
+if 'nearest_clicked' not in st.session_state:
+    st.session_state['nearest_clicked'] = False
+if 'bilinear_clicked' not in st.session_state:
+    st.session_state['bilinear_clicked'] = False
+if 'bicubic_clicked' not in st.session_state:
+    st.session_state['bicubic_clicked'] = False
+if 'hat_clicked' not in st.session_state:
+    st.session_state['hat_clicked'] = False
+if 'rcan_clicked' not in st.session_state:
+    st.session_state['rcan_clicked'] = False
+if 'srgan_clicked' not in st.session_state:
+    st.session_state['srgan_clicked'] = False 
+if 'srflow_clicked' not in st.session_state:
+    st.session_state['srflow_clicked'] = False
+if 'vdsr_clicked' not in st.session_state:
+    st.session_state['vdsr_clicked'] = False
+
+st.markdown("<h1 style='text-align: center'>Image Super Resolution</h1>", unsafe_allow_html=True)
+
+# Sidebar for navigation
+st.sidebar.title("Options")
+app_mode = st.sidebar.selectbox("Choose the input source", ["Upload image", "Take a photo"])
+
+# Depending on the choice, show the uploader widget or webcam capture
+if app_mode == "Upload image":
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"], on_change=lambda: reset_states())
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("RGB")
+elif app_mode == "Take a photo":
+    camera_input = st.camera_input("Take a picture", on_change=lambda: reset_states())
+    if camera_input is not None:
+        image = Image.open(camera_input).convert("RGB")
+        
+def reset_states():
+    st.session_state['hat_enhanced_image'] = None
+    st.session_state['rcan_enhanced_image'] = None
+    st.session_state['srgan_enhanced_image'] = None
+    st.session_state['srflow_enhanced_image'] = None
+    st.session_state['bicubic_enhanced_image'] = None
+    st.session_state['bilinear_enhanced_image'] = None
+    st.session_state['nearest_enhanced_image'] = None
+    st.session_state['vdsr_enhanced_image'] = None
+    st.session_state['hat_clicked'] = False
+    st.session_state['rcan_clicked'] = False
+    st.session_state['srgan_clicked'] = False
+    st.session_state['srflow_clicked'] = False
+    st.session_state['bicubic_clicked'] = False
+    st.session_state['bilinear_clicked'] = False
+    st.session_state['nearest_clicked'] = False
+    st.session_state['vdsr_clicked'] = False
+    
+def get_image_download_link(img, filename):
+    """Generates a link allowing the PIL image to be downloaded"""
+    # Convert the PIL image to Bytes
+    buffered = BytesIO()
+    img.save(buffered, format="PNG")
+    return st.download_button(
+        label="Download Image",
+        data=buffered.getvalue(),
+        file_name=filename,
+        mime="image/png"
+    )
+    
+if 'image' in locals():
+    # st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    # ------------------------ Nearest Neighbor ------------------------ #
+    if st.button('Enhance with Nearest Neighbor'):
+        with st.spinner('Processing using Nearest Neighbor...'):
+            enhanced_image = NearestNeighbor_for_deployment(image)
+            st.session_state['nearest_enhanced_image'] = enhanced_image
+            st.session_state['nearest_clicked'] = True
+            st.success('Done!')
+    if st.session_state['nearest_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['nearest_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['nearest_enhanced_image'], 'nearest_enhanced.jpg')
+
+    # ------------------------ Bilinear ------------------------ #
+    if st.button('Enhance with Bilinear'):
+        with st.spinner('Processing using Bilinear...'):
+            enhanced_image = Bilinear_for_deployment(image)
+            st.session_state['bilinear_enhanced_image'] = enhanced_image
+            st.session_state['bilinear_clicked'] = True
+            st.success('Done!')
+    if st.session_state['bilinear_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['bilinear_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['bilinear_enhanced_image'], 'bilinear_enhanced.jpg')
+    
+    # ------------------------ Bicubic ------------------------ #
+    if st.button('Enhance with Bicubic'):
+        with st.spinner('Processing using Bicubic...'):
+            enhanced_image = Bicubic_for_deployment(image)
+            st.session_state['bicubic_enhanced_image'] = enhanced_image
+            st.session_state['bicubic_clicked'] = True
+            st.success('Done!')
+    if st.session_state['bicubic_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['bicubic_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['bicubic_enhanced_image'], 'bicubic_enhanced.jpg')
+
+    # ------------------------ HAT ------------------------ #
+    if st.button('Enhance with HAT'):
+        with st.spinner('Processing using HAT...'):                             
+            with st.spinner('Wait for it... the model is processing the image'):                                                  
+                enhanced_image = HAT_for_deployment(image)
+                st.session_state['hat_enhanced_image'] = enhanced_image
+            st.session_state['hat_clicked'] = True
+            st.success('Done!')
+    if st.session_state['hat_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['hat_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['hat_enhanced_image'], 'hat_enhanced.jpg')
+    
+    # ------------------------ RCAN ------------------------ #
+    if st.button('Enhance with RCAN'):
+        with st.spinner('Processing using RCAN...'):
+            with st.spinner('Wait for it... the model is processing the image'):
+                rcan_model = RCAN()
+                device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda')
+                rcan_model.load_state_dict(torch.load('models/RCAN/rcan_checkpoint.pth', map_location=device))
+                enhanced_image = rcan_model.inference(image)
+                st.session_state['rcan_enhanced_image'] = enhanced_image
+            st.session_state['rcan_clicked'] = True 
+            st.success('Done!')
+    if st.session_state['rcan_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['rcan_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['rcan_enhanced_image'], 'rcan_enhanced.jpg')
+    
+    # --------------------------SRGAN-------------------------- #
+    if st.button('Enhance with SRGAN'):
+        with st.spinner('Processing using SRGAN...'):
+            with st.spinner('Wait for it... the model is processing the image'):
+                srgan_model = GeneratorResnet()
+                device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda')
+                srgan_model = torch.load('models/SRGAN/srgan_checkpoint.pth', map_location=device)
+                enhanced_image = srgan_model.inference(image)
+                st.session_state['srgan_enhanced_image'] = enhanced_image
+            st.session_state['srgan_clicked'] = True 
+            st.success('Done!')
+    if st.session_state['srgan_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['srgan_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['srgan_enhanced_image'], 'srgan_enhanced.jpg')
+    
+    # ------------------------ SRFlow ------------------------ #
+    if st.button('Enhance with SRFlow'):
+        with st.spinner('Processing using SRFlow...'):
+            with st.spinner('Wait for it... the model is processing the image'):
+                enhanced_image = return_SRFlow_result(image)
+                st.session_state['srflow_enhanced_image'] = enhanced_image        
+            st.session_state['srflow_clicked'] = True 
+            st.success('Done!')
+    if st.session_state['srflow_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['srflow_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['srflow_enhanced_image'], 'srflow_enhanced.jpg')
+
+    # ------------------------ VDSR ------------------------ #
+    if st.button('Enhance with VDSR'):
+        with st.spinner('Processing using VDSR...'):
+            # Load the VDSR model
+            vdsr_model = torch.load('models/VDSR/vdsr_checkpoint.pth', map_location=torch.device('cpu'))
+            enhanced_image = vdsr_model.inference(image)
+            st.session_state['vdsr_enhanced_image'] = enhanced_image
+            st.session_state['vdsr_clicked'] = True
+            st.success('Done!')
+    if st.session_state['vdsr_enhanced_image'] is not None:
+        col1, col2 = st.columns(2)
+        col1.header("Original")
+        col1.image(image, use_column_width=True)
+        col2.header("Enhanced")
+        col2.image(st.session_state['vdsr_enhanced_image'], use_column_width=True)
+        with col2:
+            get_image_download_link(st.session_state['vdsr_enhanced_image'], 'vdsr_enhanced.jpg')

models/VDSR/vdsr.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:acef3257ccc3ca0d7071b66a57f326b921e5696b917fe75c773afc08d14debcb
+size 11173590

models/VDSR/vdsr.py

@@ -0,0 +1,66 @@
+import torch
+import torch.nn as nn
+from torchvision.transforms import ToTensor
+from PIL import Image
+import os
+from math import sqrt
+import torch.nn.functional as F
+
+#define class Block contain conv and relu layer
+class Block(nn.Module):
+    def __init__(self):
+        super(Block, self).__init__()
+        self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.relu = nn.ReLU(inplace=True)
+        
+    def forward(self, x):
+        return self.relu(self.conv(x))
+
+class VDSR(nn.Module):
+    def __init__(self, in_channels=3, out_channels=3, num_blocks=18):
+        super(VDSR, self).__init__()
+        self.residual_layer = self.make_layer(Block, num_blocks)
+        self.input = nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.output = nn.Conv2d(in_channels=64, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.relu = nn.ReLU(inplace=True)
+        
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, sqrt(2. / n))
+                          
+    def make_layer(self, block, num_layers):
+        layers=[]
+        for _ in range(num_layers):
+            layers.append(block())
+        return nn.Sequential(*layers)
+    
+    def forward(self, x):
+        residual = x
+        out = self.relu(self.input(x))
+        out = self.residual_layer(out)
+        out = self.output(out)
+        out = torch.add(residual, out)
+        return out
+    
+    def inference(self, x):
+        """
+        x is a PIL image
+        """
+        self.eval()
+        with torch.no_grad():
+            x = ToTensor()(x).unsqueeze(0)
+            x = F.interpolate(x, scale_factor=4, mode='bicubic', align_corners=False).clamp(0, 1)
+            x = self.forward(x).clamp(0, 1)
+            x = Image.fromarray((x.squeeze(0).permute(1, 2, 0).detach().numpy() * 255).astype('uint8'))
+        return x
+
+if __name__ == '__main__':
+    current_dir = os.path.dirname(os.path.realpath(__file__))
+
+    model = torch.load(current_dir + '/vdsr_checkpoint.pth', map_location=torch.device('cpu'))
+    model.eval()
+    with torch.no_grad():
+        input_image = Image.open('images/demo.png')
+        output_image = model.inference(input_image) 
+    print(input_image.size, output_image.size)

models/VDSR/vdsr_checkpoint.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:839903f0373cbbd60ee00c4367436a718dd8689c8fda1d901471aa0f570e54be
+size 2689946