pytorch/caffe2/python/operator_test/upsample_op_test.py

# 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 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
import hypothesis.strategies as st
import numpy as np
import unittest


class TestUpSample(serial.SerializedTestCase):
    @serial.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)
    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)

    @serial.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)
    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()