mirror of
https://github.com/zebrajr/pytorch.git
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7ee82d48a8
Summary: …en fixed in v0.18 Pull Request resolved: https://github.com/pytorch/pytorch/pull/22184 Differential Revision: D15982627 Pulled By: bddppq fbshipit-source-id: 8725d5b5e5b68e029ffb08af12b416bd310c9638
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
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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import unittest
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import numpy as np
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from hypothesis import assume, given, settings
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import hypothesis.strategies as st
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class ConvTransposeTest(hu.HypothesisTestCase):
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@given(stride=st.integers(1, 2),
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pad=st.integers(0, 3),
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kernel=st.integers(1, 5),
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adj=st.integers(0, 2),
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size=st.integers(7, 10),
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input_channels=st.integers(1, 8),
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output_channels=st.integers(1, 8),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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training_mode=st.booleans(),
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compute_dX=st.booleans(),
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**mu.gcs)
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@settings(max_examples=2, timeout=100)
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def test_convolution_transpose_gradients(self, stride, pad, kernel, adj,
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size, input_channels,
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output_channels, batch_size,
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use_bias, training_mode,
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compute_dX, gc, dc):
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training = 1 if training_mode else 0
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assume(adj < stride)
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X = np.random.rand(
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batch_size, input_channels, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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input_channels, output_channels, kernel, kernel)\
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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op = core.CreateOperator(
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"ConvTranspose",
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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stride=stride,
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kernel=kernel,
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pad=pad,
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adj=adj,
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training_mode=training,
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no_gradient_to_input=not compute_dX,
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)
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inputs = [X, w, b] if use_bias else [X, w]
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self.assertDeviceChecks(dc, op, inputs, [0], threshold=0.001)
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if training_mode:
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if use_bias and compute_dX:
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# w, b, X
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outputs_to_check = [1, 2, 0]
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elif use_bias:
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# w, b
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outputs_to_check = [1, 2]
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elif compute_dX:
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# w, X
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outputs_to_check = [1, 0]
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else:
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# w
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outputs_to_check = [1]
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for i in outputs_to_check:
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self.assertGradientChecks(gc, op, inputs, i, [0])
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if __name__ == "__main__":
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unittest.main()
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