Upload app.py

app.py

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+from options.test_options import TestOptions
+from models import create_model
+import torch
+import numpy as np
+import gradio as gr
+from einops import rearrange
+import torchvision
+import torchvision.transforms as transforms
+
+def tensor2im(input_image, imtype=np.uint8):
+    if not isinstance(input_image, np.ndarray):
+        if isinstance(input_image, torch.Tensor):  # get the data from a variable
+            image_tensor = input_image.data
+        else:
+            return input_image
+        image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array
+        if image_numpy.shape[0] == 1:  # grayscale to RGB
+            image_numpy = np.tile(image_numpy, (3, 1, 1))
+        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling
+    else:  # if it is a numpy array, do nothing
+        image_numpy = input_image
+    return image_numpy.astype(imtype)
+
+def get_model(translation):
+    if translation == 'Orange to Apple':
+        return 'orange2apple'
+    elif translation == 'Horse to Zebra':
+        return 'horse2zebra'
+    elif translation == 'Image to Van Gogh':
+        return 'style_vangogh'
+    elif translation == 'Image to Monet':
+        return 'style_monet'
+
+def unpaired_img2img(translation, image):
+    opt = TestOptions().parse()
+    m_name = get_model(translation)
+    opt.name = m_name + '_pretrained'
+    opt.model = 'test'
+    opt.no_dropout = True
+    opt.num_threads = 0
+    opt.batch_size = 1
+    opt.no_flip = True
+    model = create_model(opt)
+    model.setup(opt)
+    model.eval()
+
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    image = torch.from_numpy(image) # Convert image from numpy to PyTorch tensor
+    image = rearrange(image, "h w c -> c h w") # Since PyTorch is channel first
+    
+    # Perform necessary image transforms
+    image = transforms.Resize(256)(image)
+    image = transforms.CenterCrop(256)(image).float()/255.
+    image = normalize(image)
+    
+    image = rearrange(image, "c h w -> 1 c h w") # Insert batch size of 1 (as required by our model)
+
+    model.set_input(image)
+    model.test()
+    visuals = model.get_current_visuals()  # get image results
+    for i in visuals.values():
+        im_data = i
+    im = tensor2im(im_data)
+    return im
+
+gr.Interface(fn=unpaired_img2img,
+            inputs=[gr.inputs.Dropdown(['Horse to Zebra', 'Orange to Apple', 'Image to Van Gogh', 'Image to Monet']), 
+                    gr.inputs.Image(shape=(256,256))],
+            outputs=gr.outputs.Image(type="numpy"),
+            title="Unpaired Image to Image Translation",
+            examples=[['Horse to Zebra',"data/horse1.jpg"], 
+            ['Horse to Zebra',"data/horse3.jpg"],
+            ['Orange to Apple', "data/orange1.jpg"],
+            ['Orange to Apple', "data/orange2.jpg"],
+            ['Image to Van Gogh', "data/img1.jpg"],
+            ['Image to Van Gogh', "data/img2.jpg"],
+            ['Image to Monet', "data/img1.jpg"],
+            ['Image to Monet', "data/img2.jpg"]],
+            description="This is a PyTorch implementation of the unpaired image-to-image translation using a pretrained CycleGAN model. Kindly select first the type of translation you wish to see using the dropdown menu. Then, upload the image you wish to translate and click on the 'Submit' button.",
+            article="To know more about Unpaired Image to Image Translation and CycleGAN, you may access their <a href = https://paperswithcode.com/paper/unpaired-image-to-image-translation-using>Papers with Code</a> page.",
+            allow_flagging="never").launch(inbrowser=True)