Update script.py

script.py

@@ -1,107 +1,3 @@
-import subprocess
 import sys
-import os, shutil
-import os, zipfile
-from torchvision import transforms
-from torchvision.datasets import ImageFolder
-from torchmetrics.image.fid import FrechetInceptionDistance
-from torch.utils.data import DataLoader
-from PIL import Image
-import torch
-from tqdm import tqdm
-import pandas as pd
-
-print("/tmp/data: ", ", ".join(os.listdir("/tmp/data")) if os.listdir("/tmp/data") else "The directory /tmp/data is empty.")
-print("/tmp/: ",", ".join(os.listdir("/tmp/")) if os.listdir("/tmp/") else "The directory /tmp/ is empty.")
-print("/tmp/model: ",", ".join(os.listdir("/tmp/model")) if os.listdir("/tmp/model") else "The directory /tmp/model is empty.")
-
-# print("/tmp/model/script.py: ", open('/tmp/model/script.py').read())
-print("/tmp/model/params.json: ", open('/tmp/model/params.json').read())
-
-
-
-os.makedirs('/tmp/data/hub/checkpoints/', exist_ok=True)
-os.environ['TORCH_HOME'] = '/tmp/data'
-shutil.move('/tmp/data/weights-inception-2015-12-05-6726825d.pth', '/tmp/data/hub/checkpoints/')
-
-print(f"Unzipping single")
-with zipfile.ZipFile("/tmp/data/real-images-single.zip", 'r') as zip_ref: zip_ref.extractall("/tmp/data")
-print(f"Unzipping multi")
-with zipfile.ZipFile("/tmp/data/real-images-multi.zip", 'r') as zip_ref: zip_ref.extractall("/tmp/data")
-
-# Directories with images
-fdir2 = "/tmp/model/generated"  # Directory with fake images
-
-# Real data directories
-
-real_dirs = {
-    "Single": "/tmp/data/real-images-single",
-    "Multiple": "/tmp/data/real-images-multi",
-    "Both": "/tmp/data/real-images-both"
-}
-## generate real-images-both directory
-print("Preparing test dataset. . .")
-target_directory= '/tmp/data/real-images-both/images'
-os.makedirs(target_directory, exist_ok=True)
-
-for directory in ["/tmp/data/real-images-single", "/tmp/data/real-images-multi"]:
-    print("for :"+ directory)
-    images_dir = os.path.join(directory, "images")
-    os.makedirs(images_dir, exist_ok=True)
-    subprocess.run(f"mv {directory}/*.* {images_dir}/", shell=True, check=True)
-    subprocess.run(f"ln {images_dir}/*.* {target_directory}/", shell=True, check=True)
-
-print("link generated images")
-# Organize directories for generated images
-dest_generated = "/tmp/model/generated/images"
-os.makedirs(dest_generated, exist_ok=True)
-subprocess.run(f"ln {fdir2}/*.* {dest_generated}/", shell=True, check=True)
-
-
-print("Init FID")
-# Initialize FID metric
-fid = FrechetInceptionDistance(feature=2048)
-
-# Define image transformations
-transform = transforms.Compose([
-    transforms.Resize((299, 299)),
-    transforms.ToTensor(),
-])
-
-# Prepare fake dataset and loader
-generated_dataset = ImageFolder(root=fdir2, transform=transform)
-generated_loader = DataLoader(generated_dataset, batch_size=50, shuffle=False)
-
-# Compute FID scores for each real data directory
-fid_scores = {}
-for key, fdir1 in real_dirs.items():
-    print("for set: ", fdir1)
-
-    # Organize real images
-    dest_real = os.path.join(fdir1, "images")
-    
-    # Create dataset and loader for real images
-    real_dataset = ImageFolder(root=fdir1, transform=transform)
-    real_loader = DataLoader(real_dataset, batch_size=50, shuffle=False)
-
-    # Reset FID metric for current folder
-    fid.reset()
-
-    # Process real images
-    for images, _ in tqdm(real_loader, desc=f"Processing real images: {key}"):
-        images = (images * 255).to(torch.uint8)
-        fid.update(images, real=True)
-
-    # Process generated images
-    for images, _ in tqdm(generated_loader, desc=f"Processing fake images: {key}"):
-        images = (images * 255).to(torch.uint8)
-        fid.update(images, real=False)
-
-    # Compute FID score
-    fid_score = fid.compute()
-    fid_scores[f"FID_Score_{key}"] = fid_score.item()
-    print(f"FID Score ({key}):", fid_score.item())
-
-# Save FID scores to CSV
-df = pd.DataFrame([fid_scores])
-df.to_csv("submission.csv", index=False)
+sys.path.insert(0, "/tmp/data")
+import script