!pip install -q datasets

from datasets import load_dataset

cifar10 = load_dataset("cifar10")
label_map = {0: 0, 2: 1}
class_names = ['airplane', 'bird']
cifar2_train = [(example['img'], label_map[example['label']]) for example in cifar10['train'] if example['label'] in [0, 2]]
cifar2_val = [(example['img'], label_map[example['label']]) for example in cifar10['test'] if example['label'] in [0, 2]]

example = cifar2_val[0]
img, label = example

import torch
from torchvision.transforms import v2

tfms = v2.Compose([
    v2.ToImage(),
    v2.ToDtype(torch.float32, scale=True),
    v2.Normalize(mean=[0.4915, 0.4823, 0.4468], std=[0.2470, 0.2435, 0.2616])
])
img = tfms(img)
batch = img.unsqueeze(0)

import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin

class Net(nn.Module, PyTorchModelHubMixin):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1, stride=1)
        self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1, stride=1)
        self.fc1 = nn.Linear(8 * 8 * 8, 32)
        self.fc2 = nn.Linear(32, 2)

    def forward(self, x):
        out = F.max_pool2d(torch.tanh(self.conv1(x)), kernel_size=2, stride=2) # Output shape: (batch_size, 16, 16, 16)
        out = F.max_pool2d(torch.tanh(self.conv2(out)), kernel_size=2, stride=2) # Output shape: (batch_size, 8, 8, 8)
        out = out.view(-1, 8 * 8 * 8) # Output shape: (batch_size, 512)
        out = torch.tanh(self.fc1(out)) # Output shape: (batch_size, 32)
        out = self.fc2(out) # Output shape: (batch_size, 2)
        return out

model = Net.from_pretrained("sadhaklal/custom-cnn-cifar2")
model.eval()

with torch.no_grad():
    logits = model(batch)
    pred = logits[0].argmax().item()
    proba = torch.softmax(logits, dim=1)

print(f"Predicted class: {class_names[pred]}")
print(f"Predicted class probabilities ('airplane' vs. 'bird'): {proba[0].tolist()}")