mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
52ee7697f4
Summary: `brew_test.py` is just plain broken. `core_test.py` doesn't work with pytest. `apmeter_test.py` and `top_k_test.py` don't work for CUDA builds. Closes https://github.com/caffe2/caffe2/pull/765 Differential Revision: D5211817 Pulled By: Yangqing fbshipit-source-id: 78ec5af35a3fa870978e4c9590210ade9e3bc5ac
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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def calculate_ap(predictions, labels):
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N, D = predictions.shape
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ap = np.zeros(D)
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num_range = np.arange((N), dtype=np.float32) + 1
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for k in range(D):
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scores = predictions[:N, k]
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label = labels[:N, k]
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sortind = np.argsort(-scores, kind='mergesort')
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truth = label[sortind]
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precision = np.cumsum(truth) / num_range
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ap[k] = precision[truth.astype(np.bool)].sum() / max(1, truth.sum())
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return ap
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class TestAPMeterOps(hu.HypothesisTestCase):
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@given(predictions=hu.arrays(dims=[10, 3],
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elements=st.floats(allow_nan=False,
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allow_infinity=False,
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min_value=0.1,
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max_value=1)),
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labels=hu.arrays(dims=[10, 3],
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dtype=np.int32,
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elements=st.integers(min_value=0,
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max_value=1)),
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**hu.gcs_cpu_only)
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def test_average_precision(self, predictions, labels, gc, dc):
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op = core.CreateOperator(
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"APMeter",
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["predictions", "labels"],
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["AP"],
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buffer_size=10,
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)
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def op_ref(predictions, labels):
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ap = calculate_ap(predictions, labels)
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return (ap, )
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[predictions, labels],
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reference=op_ref)
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@given(predictions=hu.arrays(dims=[10, 3],
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elements=st.floats(allow_nan=False,
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allow_infinity=False,
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min_value=0.1,
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max_value=1)),
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labels=hu.arrays(dims=[10, 3],
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dtype=np.int32,
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elements=st.integers(min_value=0,
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max_value=1)),
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**hu.gcs_cpu_only)
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def test_average_precision_small_buffer(self, predictions, labels, gc, dc):
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op_small_buffer = core.CreateOperator(
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"APMeter",
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["predictions", "labels"],
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["AP"],
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buffer_size=5,
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)
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def op_ref(predictions, labels):
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# We can only hold the last 5 in the buffer
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ap = calculate_ap(predictions[5:], labels[5:])
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return (ap, )
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self.assertReferenceChecks(
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device_option=gc,
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op=op_small_buffer,
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inputs=[predictions, labels],
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reference=op_ref
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)
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