94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from functools import partial
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import pytest
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import torch
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from mmcls.core import average_performance, mAP
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from mmcls.models.losses.accuracy import Accuracy, accuracy_numpy
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def test_mAP():
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target = torch.Tensor([[1, 1, 0, -1], [1, 1, 0, -1], [0, -1, 1, -1],
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[0, 1, 0, -1]])
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pred = torch.Tensor([[0.9, 0.8, 0.3, 0.2], [0.1, 0.2, 0.2, 0.1],
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[0.7, 0.5, 0.9, 0.3], [0.8, 0.1, 0.1, 0.2]])
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# target and pred should both be np.ndarray or torch.Tensor
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with pytest.raises(TypeError):
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target_list = target.tolist()
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_ = mAP(pred, target_list)
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# target and pred should be in the same shape
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with pytest.raises(AssertionError):
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target_shorter = target[:-1]
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_ = mAP(pred, target_shorter)
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assert mAP(pred, target) == pytest.approx(68.75, rel=1e-2)
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target_no_difficult = torch.Tensor([[1, 1, 0, 0], [0, 1, 0, 0],
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[0, 0, 1, 0], [1, 0, 0, 0]])
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assert mAP(pred, target_no_difficult) == pytest.approx(70.83, rel=1e-2)
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def test_average_performance():
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target = torch.Tensor([[1, 1, 0, -1], [1, 1, 0, -1], [0, -1, 1, -1],
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[0, 1, 0, -1], [0, 1, 0, -1]])
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pred = torch.Tensor([[0.9, 0.8, 0.3, 0.2], [0.1, 0.2, 0.2, 0.1],
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[0.7, 0.5, 0.9, 0.3], [0.8, 0.1, 0.1, 0.2],
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[0.8, 0.1, 0.1, 0.2]])
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# target and pred should both be np.ndarray or torch.Tensor
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with pytest.raises(TypeError):
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target_list = target.tolist()
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_ = average_performance(pred, target_list)
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# target and pred should be in the same shape
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with pytest.raises(AssertionError):
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target_shorter = target[:-1]
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_ = average_performance(pred, target_shorter)
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assert average_performance(pred, target) == average_performance(
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pred, target, thr=0.5)
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assert average_performance(pred, target, thr=0.5, k=2) \
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== average_performance(pred, target, thr=0.5)
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assert average_performance(
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pred, target, thr=0.3) == pytest.approx(
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(31.25, 43.75, 36.46, 33.33, 42.86, 37.50), rel=1e-2)
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assert average_performance(
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pred, target, k=2) == pytest.approx(
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(43.75, 50.00, 46.67, 40.00, 57.14, 47.06), rel=1e-2)
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def test_accuracy():
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pred_tensor = torch.tensor([[0.1, 0.2, 0.4], [0.2, 0.5, 0.3],
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[0.4, 0.3, 0.1], [0.8, 0.9, 0.0]])
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target_tensor = torch.tensor([2, 0, 0, 0])
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pred_array = pred_tensor.numpy()
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target_array = target_tensor.numpy()
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acc_top1 = 50.
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acc_top2 = 75.
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compute_acc = Accuracy(topk=1)
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assert compute_acc(pred_tensor, target_tensor) == acc_top1
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assert compute_acc(pred_array, target_array) == acc_top1
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compute_acc = Accuracy(topk=(1, ))
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assert compute_acc(pred_tensor, target_tensor)[0] == acc_top1
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assert compute_acc(pred_array, target_array)[0] == acc_top1
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compute_acc = Accuracy(topk=(1, 2))
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assert compute_acc(pred_tensor, target_array)[0] == acc_top1
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assert compute_acc(pred_tensor, target_tensor)[1] == acc_top2
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assert compute_acc(pred_array, target_array)[0] == acc_top1
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assert compute_acc(pred_array, target_array)[1] == acc_top2
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with pytest.raises(AssertionError):
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compute_acc(pred_tensor, 'other_type')
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# test accuracy_numpy
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compute_acc = partial(accuracy_numpy, topk=(1, 2))
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assert compute_acc(pred_array, target_array)[0] == acc_top1
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assert compute_acc(pred_array, target_array)[1] == acc_top2
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