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import torch
import torch.nn as nn
import torch.nn.init as init
from transformers import SwinForImageClassification
from huggingface_hub import hf_hub_download
from PIL import Image
import json
import os
import random
from torchvision import transforms
# Load labels
with open("labels.json", "r") as f:
class_names = json.load(f)
print("class_names:", class_names)
MODEL_NAME = "microsoft/swin-large-patch4-window7-224"
class SwinCustom(nn.Module):
def __init__(self, model_name=MODEL_NAME, num_classes=40):
super(SwinCustom, self).__init__()
self.model = SwinForImageClassification.from_pretrained(model_name, num_labels=num_classes, ignore_mismatched_sizes=True)
in_features = self.model.classifier.in_features
self.model.classifier = nn.Sequential(
nn.Linear(in_features, in_features),
nn.LeakyReLU(),
nn.Dropout(0.3),
nn.Linear(in_features, num_classes)
)
# Weight initialization
for m in self.model.classifier:
if isinstance(m, nn.Linear):
init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
def forward(self, images):
outputs = self.model(images)
return outputs.logits
model_path = hf_hub_download(repo_id="surateakash/scene_classification_swin_large", filename="large_swin_best_model.pth")
print("Model path:", model_path)
model = SwinCustom(model_name=MODEL_NAME, num_classes=40)
state_dict = torch.load(model_path, map_location="cpu")
if "model_state_dict" in state_dict:
state_dict = state_dict["model_state_dict"]
model.load_state_dict(state_dict, strict=False)
model.eval()
# Preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def predict(image_path):
image = Image.open(image_path).convert("RGB")
x = transform(image).unsqueeze(0)
with torch.no_grad():
outputs = model(x)
print("Logits:", outputs)
probs = torch.nn.functional.softmax(outputs, dim=1)[0]
print("Probs:", probs)
print("Sum of probs:", probs.sum())
top5 = torch.topk(probs, k=5)
top1_idx = int(top5.indices[0])
top1_label = class_names[top1_idx]
# Select a random image from the class subfolder
class_folder = f"sample_images/{str(top1_label).replace(' ', '_')}"
reference_image = None
if os.path.isdir(class_folder):
image_files = [f for f in os.listdir(class_folder) if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp", ".gif", ".webp"))]
if image_files:
chosen_file = random.choice(image_files)
ref_path = os.path.join(class_folder, chosen_file)
print(f"[DEBUG] Randomly selected reference image: {ref_path}")
reference_image = Image.open(ref_path).convert("RGB")
else:
print(f"[DEBUG] No images found in {class_folder}")
else:
print(f"[DEBUG] Class folder does not exist: {class_folder}")
top5_probs = {class_names[int(idx)]: float(score) for idx, score in zip(top5.indices, top5.values)}
print(f"image path: {image_path}")
print(f"top1_label: {top1_label}")
print(f"[DEBUG] Top-5 indices: {top5.indices}")
print(f"[DEBUG] Top-5 labels: {[class_names[int(idx)] for idx in top5.indices]}")
print(f"[DEBUG] Top-5 probs: {top5_probs}")
return image, reference_image, top5_probs |