mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
##############################################################################
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from caffe2.python import core
|
|
from hypothesis import given
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
import hypothesis.strategies as st
|
|
import numpy as np
|
|
|
|
|
|
def calculate_ap(predictions, labels):
|
|
N, D = predictions.shape
|
|
ap = np.zeros(D)
|
|
num_range = np.arange((N), dtype=np.float32) + 1
|
|
for k in range(D):
|
|
scores = predictions[:N, k]
|
|
label = labels[:N, k]
|
|
sortind = np.argsort(-scores, kind='mergesort')
|
|
truth = label[sortind]
|
|
precision = np.cumsum(truth) / num_range
|
|
ap[k] = precision[truth.astype(np.bool)].sum() / max(1, truth.sum())
|
|
return ap
|
|
|
|
|
|
class TestAPMeterOps(hu.HypothesisTestCase):
|
|
@given(predictions=hu.arrays(dims=[10, 3],
|
|
elements=st.floats(allow_nan=False,
|
|
allow_infinity=False,
|
|
min_value=0.1,
|
|
max_value=1)),
|
|
labels=hu.arrays(dims=[10, 3],
|
|
dtype=np.int32,
|
|
elements=st.integers(min_value=0,
|
|
max_value=1)),
|
|
**hu.gcs_cpu_only)
|
|
def test_average_precision(self, predictions, labels, gc, dc):
|
|
op = core.CreateOperator(
|
|
"APMeter",
|
|
["predictions", "labels"],
|
|
["AP"],
|
|
buffer_size=10,
|
|
)
|
|
|
|
def op_ref(predictions, labels):
|
|
ap = calculate_ap(predictions, labels)
|
|
return (ap, )
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[predictions, labels],
|
|
reference=op_ref)
|
|
|
|
@given(predictions=hu.arrays(dims=[10, 3],
|
|
elements=st.floats(allow_nan=False,
|
|
allow_infinity=False,
|
|
min_value=0.1,
|
|
max_value=1)),
|
|
labels=hu.arrays(dims=[10, 3],
|
|
dtype=np.int32,
|
|
elements=st.integers(min_value=0,
|
|
max_value=1)),
|
|
**hu.gcs_cpu_only)
|
|
def test_average_precision_small_buffer(self, predictions, labels, gc, dc):
|
|
op_small_buffer = core.CreateOperator(
|
|
"APMeter",
|
|
["predictions", "labels"],
|
|
["AP"],
|
|
buffer_size=5,
|
|
)
|
|
|
|
def op_ref(predictions, labels):
|
|
# We can only hold the last 5 in the buffer
|
|
ap = calculate_ap(predictions[5:], labels[5:])
|
|
return (ap, )
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op_small_buffer,
|
|
inputs=[predictions, labels],
|
|
reference=op_ref
|
|
)
|