Add samples

app.py

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+
+import os
+
+from PIL import Image
+from torchvision import transforms as T
+from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
+from torchvision.utils import make_grid
+from torch.utils.data import DataLoader
+from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
+import torch.nn as nn
+import torch
+import gradio as gr
+
+from collections import OrderedDict
+import glob
+
+
+
+
+def pred_pipeline(img, transforms):
+        orig_shape = img.shape
+        input = transforms(img)
+        input = input.unsqueeze(0)
+        output_syn = real2sim(input)
+        output_real = sim2real(output_syn)
+        out_img_syn = make_grid(output_syn,
+                           nrow=1, normalize=True)  
+        out_img_real = make_grid(output_real,
+                           nrow=1, normalize=True)          
+
+
+
+        out_transform = Compose([
+            T.Resize(orig_shape[:2]),
+            T.ToPILImage()
+        ])
+        return out_transform(out_img_syn), out_transform(out_img_real)
+
+
+
+
+n_channels = 3
+image_size = 512
+input_shape = (image_size, image_size)
+
+transform = Compose([
+     T.ToPILImage(),
+        T.Resize(input_shape),
+        ToTensor(),
+        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ])
+
+
+sim2real = GeneratorResNet.from_pretrained('Chris1/sim2real-512', input_shape=(n_channels, image_size, image_size), 
+                num_residual_blocks=9)
+real2sim = GeneratorResNet.from_pretrained('Chris1/real2sim-512', input_shape=(n_channels, image_size, image_size), 
+                num_residual_blocks=9)
+
+gr.Interface(lambda image: pred_pipeline(image, transform), 
+    inputs=gr.inputs.Image( label='input synthetic image'), 
+    outputs=[
+            gr.outputs.Image( type="pil",label='GAN real2sim prediction: style transfer of the input to the synthetic world '),
+            gr.outputs.Image( type="pil",label='GAN sim2real prediction: translation to real of the above prediction')
+            ],#plot,
+    title = "Cityscapes (real) to GTA5(simulated) translation",
+    examples = [
+                [example] for example in glob.glob('./samples/*.png')
+                ])\
+    .launch()
+
+

requirements.txt

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+huggingface-hub
+numpy
+torch
+transformers
+git+https://github.com/huggingface/community-events@main