/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/upsample_op_test.py (7308B)
# 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 caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
import unittest
class TestUpSample(serial.SerializedTestCase):
@given(height_scale=st.floats(1.0, 4.0) | st.just(2.0),
width_scale=st.floats(1.0, 4.0) | st.just(2.0),
height=st.integers(4, 32),
width=st.integers(4, 32),
num_channels=st.integers(1, 4),
batch_size=st.integers(1, 4),
seed=st.integers(0, 65535),
**hu.gcs)
@settings(max_examples=50, deadline=None)
def test_upsample(self, height_scale, width_scale, height, width,
num_channels, batch_size, seed,
gc, dc):
np.random.seed(seed)
X = np.random.rand(
batch_size, num_channels, height, width).astype(np.float32)
scales = np.array([height_scale, width_scale]).astype(np.float32)
ops = [
(
core.CreateOperator(
"UpsampleBilinear",
["X"],
["Y"],
width_scale=width_scale,
height_scale=height_scale,
),
[X],
),
(
core.CreateOperator(
"UpsampleBilinear",
["X", "scales"],
["Y"],
),
[X, scales],
),
]
for op, inputs in ops:
def ref(X, scales=None):
output_height = np.int32(height * height_scale)
output_width = np.int32(width * width_scale)
Y = np.random.rand(
batch_size, num_channels, output_height,
output_width).astype(np.float32)
rheight = ((height - 1) / (output_height - 1)
if output_height > 1
else float(0))
rwidth = ((width - 1) / (output_width - 1)
if output_width > 1
else float(0))
for i in range(output_height):
h1r = rheight * i
h1 = int(h1r)
h1p = 1 if h1 < height - 1 else 0
h1lambda = h1r - h1
h0lambda = float(1) - h1lambda
for j in range(output_width):
w1r = rwidth * j
w1 = int(w1r)
w1p = 1 if w1 < width - 1 else 0
w1lambda = w1r - w1
w0lambda = float(1) - w1lambda
Y[:, :, i, j] = (h0lambda * (
w0lambda * X[:, :, h1, w1] +
w1lambda * X[:, :, h1, w1 + w1p]) +
h1lambda * (w0lambda * X[:, :, h1 + h1p, w1] +
w1lambda * X[:, :, h1 + h1p, w1 + w1p]))
return Y,
self.assertReferenceChecks(gc, op, inputs, ref)
self.assertDeviceChecks(dc, op, inputs, [0])
self.assertGradientChecks(gc, op, inputs, 0, [0], stepsize=0.1,
threshold=1e-2)
@given(height_scale=st.floats(1.0, 4.0) | st.just(2.0),
width_scale=st.floats(1.0, 4.0) | st.just(2.0),
height=st.integers(4, 32),
width=st.integers(4, 32),
num_channels=st.integers(1, 4),
batch_size=st.integers(1, 4),
seed=st.integers(0, 65535),
**hu.gcs)
@settings(deadline=10000)
def test_upsample_grad(self, height_scale, width_scale, height, width,
num_channels, batch_size, seed, gc, dc):
np.random.seed(seed)
output_height = np.int32(height * height_scale)
output_width = np.int32(width * width_scale)
X = np.random.rand(batch_size,
num_channels,
height,
width).astype(np.float32)
dY = np.random.rand(batch_size,
num_channels,
output_height,
output_width).astype(np.float32)
scales = np.array([height_scale, width_scale]).astype(np.float32)
ops = [
(
core.CreateOperator(
"UpsampleBilinearGradient",
["dY", "X"],
["dX"],
width_scale=width_scale,
height_scale=height_scale,
),
[dY, X],
),
(
core.CreateOperator(
"UpsampleBilinearGradient",
["dY", "X", "scales"],
["dX"],
),
[dY, X, scales],
),
]
for op, inputs in ops:
def ref(dY, X, scales=None):
dX = np.zeros_like(X)
rheight = ((height - 1) / (output_height - 1)
if output_height > 1
else float(0))
rwidth = ((width - 1) / (output_width - 1)
if output_width > 1
else float(0))
for i in range(output_height):
h1r = rheight * i
h1 = int(h1r)
h1p = 1 if h1 < height - 1 else 0
h1lambda = h1r - h1
h0lambda = float(1) - h1lambda
for j in range(output_width):
w1r = rwidth * j
w1 = int(w1r)
w1p = 1 if w1 < width - 1 else 0
w1lambda = w1r - w1
w0lambda = float(1) - w1lambda
dX[:, :, h1, w1] += (
h0lambda * w0lambda * dY[:, :, i, j])
dX[:, :, h1, w1 + w1p] += (
h0lambda * w1lambda * dY[:, :, i, j])
dX[:, :, h1 + h1p, w1] += (
h1lambda * w0lambda * dY[:, :, i, j])
dX[:, :, h1 + h1p, w1 + w1p] += (
h1lambda * w1lambda * dY[:, :, i, j])
return dX,
self.assertDeviceChecks(dc, op, inputs, [0])
self.assertReferenceChecks(gc, op, inputs, ref)
if __name__ == "__main__":
unittest.main()