[Feat] Add Accuracy #6
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zhouzaida
reviewed
Sep 16, 2022
zhouzaida
reviewed
Sep 16, 2022
zhouzaida
reviewed
Sep 16, 2022
zhouzaida
reviewed
Sep 16, 2022
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mzr1996
reviewed
Oct 8, 2022
mmeval/classification/accuracy.py
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| for pred, label in zip(predictions, labels): | ||
| self._results.append((pred, label)) |
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Better to only reserve the first top-k results only in the intermediate results.
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The intermediate results can be very large if the number of classes is large.
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Updated, use corrects as the intermediate results.
mzr1996
reviewed
Oct 9, 2022
mmeval/classification/accuracy.py
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| for pred, label in zip(predictions, labels): | ||
| corrects = self.compute_correct(pred, label) | ||
| self._results.append(corrects) |
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Why calculate corrects one by one instead of a batch?
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Updated, calculate corrects in batch now.
zhouzaida
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Oct 9, 2022
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| if torch is not None: | ||
| values, indices = _torch_topk(torch.from_numpy(inputs), k, dim=axis) | ||
| return values.numpy(), indices.numpy() |
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How fast does the pytorch compared with numpy?
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This depends on the size of the input. The larger the size of the input, the greater the speedup.
The following is a simple benchmark.
import torch
import numpy as np
import time
def numpy_topk(inputs, k, axis=None, use_torch=False):
if use_torch:
values, indices = torch.from_numpy(inputs).topk(k, dim=axis)
return values.numpy(), indices.numpy()
indices = np.argsort(inputs, axis=axis)
indices = np.take(indices, np.arange(k), axis=axis)
values = np.take_along_axis(inputs, indices, axis=axis)
return values, indices
def test(shape, k, axis):
print('Test setting: ', shape, k, axis)
arr = np.random.rand(*shape)
t1 = time.time()
numpy_topk(arr, k, axis, use_torch=False)
t2 = time.time()
numpy_topk(arr, k, axis, use_torch=True)
t3 = time.time()
print(f'custom impl numpy topk cost: {t2-t1}')
print(f'torch impl numpy topk cost: {t3-t2}')
if __name__ == "__main__":
test((100, 100), k=4, axis=1)
test((100, 1000), k=4, axis=1)
test((100, 1000), k=10, axis=1)
test((1000, 1000), k=4, axis=1)
test((10000, 1000), k=4, axis=1)Got the following outputs:
Test setting: (100, 100) 4 1
custom impl numpy topk cost: 0.0005044937133789062
torch impl numpy topk cost: 0.0046596527099609375
Test setting: (100, 1000) 4 1
custom impl numpy topk cost: 0.005578756332397461
torch impl numpy topk cost: 0.004395961761474609
Test setting: (100, 1000) 10 1
custom impl numpy topk cost: 0.005606651306152344
torch impl numpy topk cost: 0.0003619194030761719
Test setting: (1000, 1000) 4 1
custom impl numpy topk cost: 0.05330657958984375
torch impl numpy topk cost: 0.001957416534423828
Test setting: (10000, 1000) 4 1
custom impl numpy topk cost: 0.5171260833740234
torch impl numpy topk cost: 0.0032396316528320312
zhouzaida
reviewed
Oct 9, 2022
zhouzaida
reviewed
Oct 9, 2022
zhouzaida
reviewed
Oct 9, 2022
zhouzaida
approved these changes
Oct 10, 2022
mzr1996
requested changes
Oct 10, 2022
mmeval/classification/accuracy.py
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|
||
| @overload # type: ignore | ||
| @dispatch | ||
| def compute_corrects( |
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Maybe a private method if the method won't be used by users.
mzr1996
approved these changes
Oct 10, 2022
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Motivation
Adding accuracy metric and test case.
Modification
MMCls