Initial commit

app.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+from PIL import Image
+from tqdm import tqdm
+import gradio as gr
+
+model = torch.hub.load('pytorch/vision:v0.10.0', 'vgg19', pretrained=True)
+
+feature_layers = [0,5,10,19,28]
+
+class StyleTransfer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = model
+        self.feature_layers = feature_layers
+        self.avg_pool = nn.AvgPool2d(kernel_size=2,stride=2,padding=0,ceil_mode=False)
+    def forward(self,x):
+        style_features = []
+        for i,layer in enumerate(self.model.features[:29]):
+            if isinstance(layer,nn.MaxPool2d):
+                x = self.avg_pool(x)
+                continue
+            x = layer(x)
+            if i in self.feature_layers:
+                style_features.append(x)
+            if i == 23:
+                content_features = x
+            
+        return style_features,content_features
+    
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def image_merger(content, style,beta=10,device=device):
+    size = 400
+    alpha = 1
+    beta *= 1000
+    content = Image.fromarray(content)
+    style = Image.fromarray(style)
+    t = transforms.Compose(
+        [
+            transforms.Resize((size,size)),
+            transforms.ToTensor(),
+        ]
+    )
+    style = t(style).unsqueeze(0).to(device)
+    content = t(content).unsqueeze(0).to(device)
+    generated = content.clone().to(device).requires_grad_(True)
+    generator = StyleTransfer().to(device).eval()
+    opt = torch.optim.Adam([generated],lr=0.06)
+    scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=5, gamma=0.9)  # Learning rate scheduler
+    num_epochs = 30 if device == "cpu" else 100
+    style_features,_ = generator(style)
+    _,content_features = generator(content)
+    loop = tqdm(range(num_epochs),leave=False)
+    for i in loop:
+        content_loss = 0
+        style_loss = 0
+        generated_style_features,generated_content_features = generator(generated)
+        content_loss = 0.5 * torch.mean((content_features - generated_content_features) ** 2)
+        for style_feature,generated_style_feature in zip(style_features,generated_style_features):
+            b,c,h,w = style_feature.shape
+            s1 = style_feature.view(c,h*w) @ style_feature.view(c,h*w).T
+            s2 = generated_style_feature.view(c,h*w) @ generated_style_feature.view(c,h*w).T
+
+            layer_style_loss = torch.mean((s2 - s1)**2)/(4 *(c) * (h*w))
+            style_loss += layer_style_loss
+        total_loss = alpha * content_loss + beta * style_loss
+        loop.set_postfix(loss=total_loss.item())
+        opt.zero_grad()
+        total_loss.backward(retain_graph=True)
+        opt.step()
+        scheduler.step()
+        if total_loss < 200 and device=='cpu':
+            break
+    print(total_loss.item())
+    img = np.array(generated.cpu().detach().squeeze(0).permute(1,2,0)) 
+    img = np.clip(img,0,1) * 255
+    img = Image.fromarray(img.astype(np.uint8))
+    return img
+
+iface = gr.Interface(
+    fn=image_merger,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Image(label="Style Image"),
+        gr.Slider(label="Style strength", minimum=10, maximum=100, step=10),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Neural Style Transfer",
+    description="Upload your desired input image and style image. Adjust the 'Style strength' slider to control the intensity of the style transfer. The generated image will showcase your input content with the stylistic elements of the chosen style image. Generation can take upto two minutes",
+)
+
+iface.launch()

license.txt

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+MIT License
+
+Copyright (c) 2023 Om Alve
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

requirements.txt

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+pytorch
+gradio
+PIL
+tqdm