Upload app.py

app.py

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+import streamlit as st
+import torch.nn as nn
+import torchvision.transforms as T
+from torchvision.utils import make_grid
+import torch
+from IPython.display import display
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+@torch.inference_mode()
+def inference_gan():
+    generator = torch.jit.load("models/mnist-G-torchscript.pt").to(device)
+    x = torch.randn(30, 256, device='cuda')
+    y = generator(x)
+    y = y.view(-1, 1, 28, 28)  # reshape y to have 1 channel
+    grid = make_grid(y.cpu().detach(), nrow=8)
+    img = T.functional.to_pil_image(grid)
+    return img
+
+@torch.inference_mode()
+def inference_dcgan():
+    generator = torch.jit.load("models/animefacedataset-G2-torchscript.pt").to(device)
+    def denorm(img_tensors):
+        stats = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
+        return img_tensors * stats[1][0] + stats[0][0]
+    x = torch.randn(64, 128, 1, 1, device='cuda')
+    y = generator(x)
+    y = y.view(-1, 3, 64, 64)  # reshape y to have 3 channels
+    grid = make_grid(denorm(y.cpu().detach()), nrow=8)
+    img = T.functional.to_pil_image(grid)
+    return img
+def inference_both():
+    inference_gan()
+    inference_dcgan()
+
+st.markdown("# Image Generation with GANs and DCGANs")
+st.button("Generate Images", on_click=inference_both)
+st.image(inference_dcgan(), caption="", use_column_width=True)
+st.image(inference_gan(), caption="", use_column_width=True)