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b759a14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import torch
import torch.nn as nn
import numpy as np
from torch.utils.data import DataLoader, TensorDataset
from torchvision.models import resnet18, resnet34, resnet50
# the dataset is provided as a .npz file (compressed numpy archive)
# it contains two arrays:
# images: uint8 array of shape (N, 3, 32, 32), values in [0, 255]
# labels: integer class labels in range [0, 8]
# we divide images by 255.0 to get float values in [0, 1]
data = np.load("train.npz")
images = torch.from_numpy(data["images"]).float() / 255.0
labels = torch.from_numpy(data["labels"]).long()
dataset = TensorDataset(images, labels)
loader = DataLoader(dataset, batch_size=256, shuffle=True)
print("Dataset size:", len(dataset))
print("Image shape:", images.shape)
print("Label range:", labels.min().item(), "to", labels.max().item())
NUM_CLASSES = 9
# pick one of: resnet18, resnet34, resnet50
model = resnet18(weights=None)
model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
# resnet34 example
# model = resnet34(weights=None)
# model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
# resnet50 example
# model = resnet50(weights=None)
# model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
# sanity check -- output shape must be (1, 9)
model.eval()
with torch.no_grad():
out = model(torch.randn(1, 3, 32, 32))
print("Output shape:", out.shape)
# save only the state dict, not the full model instance
torch.save(model.state_dict(), "model.pt") |