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from typing import Union, List, Tuple |
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import numpy as np |
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import torch |
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from .utils import benchmark |
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
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def random_benchmark( |
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model: torch.nn.Module, |
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batch_size: Union[int, List[int]] = 1, |
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image_size: Union[Tuple[int], List[Tuple[int]]] = (3, 1024, 1024), |
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): |
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""" |
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Calculate the FPS of a given model using randomly generated tensors. |
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Args: |
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model: instance of a model (e.g. SegFormer) |
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batch_size: the batch size(s) at which to calculate the FPS (e.g. 1 or [1, 2, 4]) |
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image_size: the size of the images to use (e.g. (3, 1024, 1024)) |
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Returns: the FPS values calculated for all image sizes and batch sizes in the form of a dictionary |
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""" |
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if isinstance(batch_size, int): |
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batch_size = [batch_size] |
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if isinstance(image_size, tuple): |
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image_size = [image_size] |
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values = {} |
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throughput_values = [] |
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for i in image_size: |
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fps = [] |
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for b in batch_size: |
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for _ in range(4): |
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if i[1] >= 1024: |
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r = 16 |
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else: |
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r = 32 |
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baseline_throughput = benchmark( |
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model.to(device), |
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device=device, |
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verbose=True, |
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runs=r, |
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batch_size=b, |
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input_size=i |
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) |
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throughput_values.append(baseline_throughput) |
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throughput_values = np.asarray(throughput_values) |
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throughput = np.around(np.mean(throughput_values), decimals=2) |
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print('Im_size:', i, 'Batch_size:', b, 'Mean:', throughput, 'Std:', |
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np.around(np.std(throughput_values), decimals=2)) |
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throughput_values = [] |
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fps.append({b: throughput}) |
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values[i] = fps |
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return values |
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