mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
5eb836880d
Summary:
Needed by oss.
This is done by running the following line:
find . -name "*_test.py" -exec sed -i '$ a \\nif __name__ == "__main__":\n import unittest\n unittest.main()' {} \;
Reviewed By: ajtulloch
Differential Revision: D4223848
fbshipit-source-id: ef4696e9701d45962134841165c53e76a2e19233
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
from hypothesis import assume, given, settings
|
|
import hypothesis.strategies as st
|
|
import collections
|
|
|
|
from caffe2.python import core, workspace
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
|
|
|
|
class TestPooling(hu.HypothesisTestCase):
|
|
# CUDNN does NOT support different padding values and we skip it
|
|
@given(stride_h=st.integers(1, 3),
|
|
stride_w=st.integers(1, 3),
|
|
pad_t=st.integers(0, 3),
|
|
pad_l=st.integers(0, 3),
|
|
pad_b=st.integers(0, 3),
|
|
pad_r=st.integers(0, 3),
|
|
kernel=st.integers(3, 5),
|
|
size=st.integers(7, 9),
|
|
input_channels=st.integers(1, 3),
|
|
batch_size=st.integers(1, 3),
|
|
order=st.sampled_from(["NCHW", "NHWC"]),
|
|
method=st.sampled_from(["MaxPool", "AveragePool", "LpPool"]),
|
|
**hu.gcs)
|
|
def test_pooling_separate_stride_pad(self, stride_h, stride_w,
|
|
pad_t, pad_l, pad_b,
|
|
pad_r, kernel, size,
|
|
input_channels,
|
|
batch_size, order,
|
|
method,
|
|
gc, dc):
|
|
assume(np.max([pad_t, pad_l, pad_b, pad_r]) < kernel)
|
|
op = core.CreateOperator(
|
|
method,
|
|
["X"],
|
|
["Y"],
|
|
stride_h=stride_h,
|
|
stride_w=stride_w,
|
|
pad_t=pad_t,
|
|
pad_l=pad_l,
|
|
pad_b=pad_b,
|
|
pad_r=pad_r,
|
|
kernel=kernel,
|
|
order=order,
|
|
)
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32)
|
|
|
|
if order == "NCHW":
|
|
X = X.transpose((0, 3, 1, 2))
|
|
self.assertDeviceChecks(dc, op, [X], [0])
|
|
if method not in ('MaxPool'):
|
|
self.assertGradientChecks(gc, op, [X], 0, [0])
|
|
|
|
@given(stride=st.integers(1, 3),
|
|
pad=st.integers(0, 3),
|
|
kernel=st.integers(1, 5),
|
|
size=st.integers(7, 9),
|
|
input_channels=st.integers(1, 3),
|
|
batch_size=st.integers(1, 3),
|
|
order=st.sampled_from(["NCHW", "NHWC"]),
|
|
method=st.sampled_from(["MaxPool", "AveragePool", "LpPool"]),
|
|
engine=st.sampled_from(["", "CUDNN"]),
|
|
**hu.gcs)
|
|
def test_pooling(self, stride, pad, kernel, size,
|
|
input_channels, batch_size,
|
|
order, method, engine, gc, dc):
|
|
assume(pad < kernel)
|
|
op = core.CreateOperator(
|
|
method,
|
|
["X"],
|
|
["Y"],
|
|
stride=stride,
|
|
kernel=kernel,
|
|
pad=pad,
|
|
order=order,
|
|
engine=engine,
|
|
)
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32)
|
|
if order == "NCHW":
|
|
X = X.transpose((0, 3, 1, 2))
|
|
|
|
self.assertDeviceChecks(dc, op, [X], [0])
|
|
if method not in ('MaxPool'):
|
|
self.assertGradientChecks(gc, op, [X], 0, [0])
|
|
|
|
@given(size=st.integers(7, 9),
|
|
input_channels=st.integers(1, 3),
|
|
batch_size=st.integers(1, 3),
|
|
order=st.sampled_from(["NCHW", "NHWC"]),
|
|
method=st.sampled_from(["MaxPool", "AveragePool", "LpPool"]),
|
|
engine=st.sampled_from(["", "CUDNN"]),
|
|
**hu.gcs)
|
|
def test_global_pooling(self, size, input_channels, batch_size,
|
|
order, method, engine, gc, dc):
|
|
op = core.CreateOperator(
|
|
method,
|
|
["X"],
|
|
["Y"],
|
|
order=order,
|
|
engine=engine,
|
|
global_pooling=True,
|
|
)
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32)
|
|
if order == "NCHW":
|
|
X = X.transpose((0, 3, 1, 2))
|
|
|
|
self.assertDeviceChecks(dc, op, [X], [0])
|
|
if method not in ('MaxPool'):
|
|
self.assertGradientChecks(gc, op, [X], 0, [0])
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|