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2024-08-08 nightly release (0d80848)
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import os | ||
import platform | ||
import statistics | ||
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import torch | ||
import torch.utils.benchmark as benchmark | ||
import torchvision | ||
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def print_machine_specs(): | ||
print("Processor:", platform.processor()) | ||
print("Platform:", platform.platform()) | ||
print("Logical CPUs:", os.cpu_count()) | ||
print(f"\nCUDA device: {torch.cuda.get_device_name()}") | ||
print(f"Total Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB") | ||
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def get_data(): | ||
transform = torchvision.transforms.Compose( | ||
[ | ||
torchvision.transforms.PILToTensor(), | ||
] | ||
) | ||
path = os.path.join(os.getcwd(), "data") | ||
testset = torchvision.datasets.Places365( | ||
root="./data", download=not os.path.exists(path), transform=transform, split="val" | ||
) | ||
testloader = torch.utils.data.DataLoader( | ||
testset, batch_size=1000, shuffle=False, num_workers=1, collate_fn=lambda batch: [r[0] for r in batch] | ||
) | ||
return next(iter(testloader)) | ||
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def run_encoding_benchmark(decoded_images): | ||
results = [] | ||
for device in ["cpu", "cuda"]: | ||
decoded_images_device = [t.to(device=device) for t in decoded_images] | ||
for size in [1, 100, 1000]: | ||
for num_threads in [1, 12, 24]: | ||
for stmt, strat in zip( | ||
[ | ||
"[torchvision.io.encode_jpeg(img) for img in decoded_images_device_trunc]", | ||
"torchvision.io.encode_jpeg(decoded_images_device_trunc)", | ||
], | ||
["unfused", "fused"], | ||
): | ||
decoded_images_device_trunc = decoded_images_device[:size] | ||
t = benchmark.Timer( | ||
stmt=stmt, | ||
setup="import torchvision", | ||
globals={"decoded_images_device_trunc": decoded_images_device_trunc}, | ||
label="Image Encoding", | ||
sub_label=f"{device.upper()} ({strat}): {stmt}", | ||
description=f"{size} images", | ||
num_threads=num_threads, | ||
) | ||
results.append(t.blocked_autorange()) | ||
compare = benchmark.Compare(results) | ||
compare.print() | ||
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def run_decoding_benchmark(encoded_images): | ||
results = [] | ||
for device in ["cpu", "cuda"]: | ||
for size in [1, 100, 1000]: | ||
for num_threads in [1, 12, 24]: | ||
for stmt, strat in zip( | ||
[ | ||
f"[torchvision.io.decode_jpeg(img, device='{device}') for img in encoded_images_trunc]", | ||
f"torchvision.io.decode_jpeg(encoded_images_trunc, device='{device}')", | ||
], | ||
["unfused", "fused"], | ||
): | ||
encoded_images_trunc = encoded_images[:size] | ||
t = benchmark.Timer( | ||
stmt=stmt, | ||
setup="import torchvision", | ||
globals={"encoded_images_trunc": encoded_images_trunc}, | ||
label="Image Decoding", | ||
sub_label=f"{device.upper()} ({strat}): {stmt}", | ||
description=f"{size} images", | ||
num_threads=num_threads, | ||
) | ||
results.append(t.blocked_autorange()) | ||
compare = benchmark.Compare(results) | ||
compare.print() | ||
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if __name__ == "__main__": | ||
print_machine_specs() | ||
decoded_images = get_data() | ||
mean_h, mean_w = statistics.mean(t.shape[-2] for t in decoded_images), statistics.mean( | ||
t.shape[-1] for t in decoded_images | ||
) | ||
print(f"\nMean image size: {int(mean_h)}x{int(mean_w)}") | ||
run_encoding_benchmark(decoded_images) | ||
encoded_images_cuda = torchvision.io.encode_jpeg([img.cuda() for img in decoded_images]) | ||
encoded_images_cpu = [img.cpu() for img in encoded_images_cuda] | ||
run_decoding_benchmark(encoded_images_cpu) |
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