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import torch
import torchvision.transforms as transforms
from PIL import Image
def print_examples(model, device, dataset):
transform = transforms.Compose(
[
transforms.Resize((299, 299)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
model.eval()
test_img1 = transform(Image.open("test_examples/dog.jpg").convert("RGB")).unsqueeze(
0
)
print("Example 1 CORRECT: Dog on a beach by the ocean")
print(
"Example 1 OUTPUT: "
+ " ".join(model.caption_image(test_img1.to(device), dataset.vocab))
)
test_img2 = transform(
Image.open("test_examples/child.jpg").convert("RGB")
).unsqueeze(0)
print("Example 2 CORRECT: Child holding red frisbee outdoors")
print(
"Example 2 OUTPUT: "
+ " ".join(model.caption_image(test_img2.to(device), dataset.vocab))
)
test_img3 = transform(Image.open("test_examples/bus.png").convert("RGB")).unsqueeze(
0
)
print("Example 3 CORRECT: Bus driving by parked cars")
print(
"Example 3 OUTPUT: "
+ " ".join(model.caption_image(test_img3.to(device), dataset.vocab))
)
test_img4 = transform(
Image.open("test_examples/boat.png").convert("RGB")
).unsqueeze(0)
print("Example 4 CORRECT: A small boat in the ocean")
print(
"Example 4 OUTPUT: "
+ " ".join(model.caption_image(test_img4.to(device), dataset.vocab))
)
test_img5 = transform(
Image.open("test_examples/horse.png").convert("RGB")
).unsqueeze(0)
print("Example 5 CORRECT: A cowboy riding a horse in the desert")
print(
"Example 5 OUTPUT: "
+ " ".join(model.caption_image(test_img5.to(device), dataset.vocab))
)
model.train()
def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
print("=> Saving checkpoint")
torch.save(state, filename)
def load_checkpoint(checkpoint, model, optimizer):
print("=> Loading checkpoint")
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
step = checkpoint["step"]
return step
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