39 lines
1.0 KiB
Python
39 lines
1.0 KiB
Python
import os
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import cv2
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import torch
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import numpy as np
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from torch.utils.data import DataLoader, TensorDataset
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from torch import nn, optim
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from BN import CatDogClassifier
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model = torch.load("super_model.pth")
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model.eval()
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model.to("cuda")
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IMG_SIZE = 128
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def predict_image(image_path):
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img = cv2.imread(image_path)
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img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) / 255.0
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img = np.transpose(img, (2, 0, 1)) # Convert to (C, H, W)
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img_tensor = torch.tensor(img, dtype=torch.float32).unsqueeze(0).to("cuda") # Add batch dimension
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model.eval() # Set model to evaluation mode
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with torch.no_grad():
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output = model(img_tensor)
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predicted = torch.argmax(output, dim=1).item()
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return "Dog" if predicted == 1 else "Cat"
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preds = []
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for filename in os.listdir("dataset/test_set/cats/"):
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img_path = os.path.join("dataset/test_set/cats/", filename)
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prediction = predict_image(img_path)
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preds.append(prediction)
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print(preds.count("Cat"))
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print(preds.count("Dog"))
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print(preds.count("Cat") / 1000)
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