mirror of
https://github.com/saymrwulf/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
152 lines
5 KiB
Python
152 lines
5 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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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 assume, 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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class TestReduceFrontSum(hu.HypothesisTestCase):
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@given(batch_size=st.integers(1, 3),
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stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(1, 5),
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dilation=st.integers(1, 3),
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size=st.integers(7, 10),
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channels=st.integers(1, 8),
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**hu.gcs)
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def test_im2col_layout(self, batch_size, stride, pad, kernel, dilation,
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size, channels, gc, dc):
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dkernel = (dilation * (kernel - 1) + 1)
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assume(size >= dkernel)
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NCHW_TO_NHWC = (0, 2, 3, 1)
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NHWC_TO_NCHW = (0, 3, 1, 2)
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COL_NHWC_TO_NCHW = (4, 2, 3, 0, 1)
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N = batch_size
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C = channels
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H = size
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W = size
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out_h = int((H + (2 * pad) - dkernel) / stride + 1)
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out_w = int((W + (2 * pad) - dkernel) / stride + 1)
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im_nchw = np.random.rand(N, C, H, W).astype(np.float32) - 0.5
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im_nhwc = im_nchw.transpose(NCHW_TO_NHWC)
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op_im2col_nchw = core.CreateOperator(
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"Im2Col",
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["im_nchw"], ["col_nchw"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order="NCHW",
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device_option=gc)
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op_im2col_nhwc = core.CreateOperator(
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"Im2Col",
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["im_nhwc"], ["col_nhwc"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order="NHWC",
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device_option=gc)
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self.ws.create_blob("im_nchw").feed(im_nchw, device_option=gc)
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self.ws.create_blob("im_nhwc").feed(im_nhwc, device_option=gc)
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self.ws.run(op_im2col_nchw)
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self.ws.run(op_im2col_nhwc)
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# there is probably a clever way to spell this in np
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col_nchw = self.ws.blobs["col_nchw"].fetch()
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col_nhwc = self.ws.blobs["col_nhwc"].fetch()
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col_nchw_ = col_nchw.reshape(N, C, kernel, kernel, out_h, out_w)
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col_nhwc_ = col_nhwc.reshape(N, out_h, out_w, kernel, kernel, C)
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for i in range(0, N):
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np.testing.assert_allclose(
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col_nchw_[i],
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col_nhwc_[i].transpose(COL_NHWC_TO_NCHW),
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atol=1e-4,
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rtol=1e-4)
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op_col2im_nchw = core.CreateOperator(
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"Col2Im",
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["col_nchw", "im_nchw"],
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["out_nchw"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order="NCHW",
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device_option=gc)
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op_col2im_nhwc = core.CreateOperator(
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"Col2Im",
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["col_nhwc", "im_nhwc"],
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["out_nhwc"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order="NHWC",
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device_option=gc)
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self.ws.run(op_col2im_nchw)
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self.ws.run(op_col2im_nhwc)
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out_nchw = self.ws.blobs["out_nchw"].fetch()
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out_nhwc = self.ws.blobs["out_nhwc"].fetch()
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np.testing.assert_allclose(
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out_nchw,
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out_nhwc.transpose(NHWC_TO_NCHW),
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atol=1e-4,
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rtol=1e-4)
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@given(batch_size=st.integers(1, 3),
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stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(1, 5),
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dilation=st.integers(1, 3),
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size=st.integers(7, 10),
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channels=st.integers(1, 8),
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order=st.sampled_from(["NCHW"]),
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**hu.gcs)
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def test_col2im_gradients(self, batch_size, stride, pad, kernel,
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dilation, size, channels, order, gc, dc):
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assume(size >= dilation * (kernel - 1) + 1)
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op = core.CreateOperator(
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"Im2Col",
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["X"], ["Y"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order=order,
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device_option=gc)
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X = np.random.rand(batch_size, channels, size, size).astype(np.float32)
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self.assertGradientChecks(gc, op, [X], 0, [0])
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return
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