mirror of
https://github.com/saymrwulf/pytorch.git
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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
236 lines
8.9 KiB
Python
236 lines
8.9 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 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
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import caffe2.python.hypothesis_test_util as hu
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class TestConvolutionTranspose(hu.HypothesisTestCase):
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@given(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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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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engine=st.sampled_from(["", "CUDNN", "BLOCK"]),
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shared_buffer=st.booleans(),
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**hu.gcs)
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def test_convolution_transpose_layout(self, stride, pad, kernel, adj,
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size, input_channels,
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output_channels, batch_size,
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engine, shared_buffer, gc, dc):
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assume(adj < stride)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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input_channels, kernel, kernel, output_channels)\
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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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outputs = {}
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for order in ["NCHW", "NHWC"]:
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op = core.CreateOperator(
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"ConvTranspose",
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["X", "w", "b"],
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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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order=order,
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engine=engine,
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shared_buffer=int(shared_buffer),
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device_option=gc,
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)
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if order == "NCHW":
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X_f = X.transpose((0, 3, 1, 2))
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w_f = w.transpose((0, 3, 1, 2))
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else:
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X_f = X
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w_f = w
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self.ws.create_blob("X").feed(X_f, device_option=gc)
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self.ws.create_blob("w").feed(w_f, device_option=gc)
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self.ws.create_blob("b").feed(b, device_option=gc)
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self.ws.run(op)
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outputs[order] = self.ws.blobs["Y"].fetch()
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output_size = (size - 1) * stride + kernel + adj - 2 * pad
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self.assertEqual(
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outputs["NCHW"].shape,
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(batch_size, output_channels, output_size, output_size))
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np.testing.assert_allclose(
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outputs["NCHW"],
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outputs["NHWC"].transpose((0, 3, 1, 2)),
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atol=1e-4,
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rtol=1e-4)
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# CUDNN does not support separate stride and pad so we skip it.
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@given(stride_h=st.integers(1, 3),
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stride_w=st.integers(1, 3),
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pad_t=st.integers(0, 3),
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pad_l=st.integers(0, 3),
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pad_b=st.integers(0, 3),
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pad_r=st.integers(0, 3),
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kernel=st.integers(1, 5),
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adj_h=st.integers(0, 2),
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adj_w=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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engine=st.sampled_from(["", "BLOCK"]), **hu.gcs)
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def test_convolution_transpose_separate_stride_pad_adj_layout(
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self, stride_h, stride_w, pad_t, pad_l, pad_b, pad_r, kernel,
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adj_h, adj_w, size, input_channels, output_channels, batch_size,
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engine, gc, dc):
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assume(adj_h < stride_h)
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assume(adj_w < stride_w)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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input_channels, kernel, kernel, output_channels)\
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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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outputs = {}
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for order in ["NCHW", "NHWC"]:
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op = core.CreateOperator(
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"ConvTranspose",
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["X", "w", "b"],
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["Y"],
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stride_h=stride_h,
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stride_w=stride_w,
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kernel=kernel,
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pad_t=pad_t,
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pad_l=pad_l,
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pad_b=pad_b,
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pad_r=pad_r,
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adj_h=adj_h,
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adj_w=adj_w,
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order=order,
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engine=engine,
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device_option=gc,
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)
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if order == "NCHW":
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X_f = X.transpose((0, 3, 1, 2))
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w_f = w.transpose((0, 3, 1, 2))
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else:
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X_f = X
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w_f = w
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self.ws.create_blob("X").feed(X_f, device_option=gc)
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self.ws.create_blob("w").feed(w_f, device_option=gc)
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self.ws.create_blob("b").feed(b, device_option=gc)
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self.ws.run(op)
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outputs[order] = self.ws.blobs["Y"].fetch()
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output_h = (size - 1) * stride_h + kernel + adj_h - pad_t - pad_b
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output_w = (size - 1) * stride_w + kernel + adj_w - pad_l - pad_r
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self.assertEqual(
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outputs["NCHW"].shape,
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(batch_size, output_channels, output_h, output_w))
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np.testing.assert_allclose(
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outputs["NCHW"],
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outputs["NHWC"].transpose((0, 3, 1, 2)),
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atol=1e-4,
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rtol=1e-4)
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@given(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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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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order=st.sampled_from(["NCHW", "NHWC"]),
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engine=st.sampled_from(["", "CUDNN", "BLOCK"]), **hu.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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order, engine, gc, dc):
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assume(adj < stride)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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input_channels, kernel, kernel, output_channels)\
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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"],
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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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order=order,
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engine=engine,
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)
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if order == "NCHW":
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X = X.transpose((0, 3, 1, 2))
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w = w.transpose((0, 3, 1, 2))
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self.assertDeviceChecks(dc, op, [X, w, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, w, b], i, [0])
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# CUDNN does not support separate stride and pad so we skip it.
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@given(stride_h=st.integers(1, 3),
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stride_w=st.integers(1, 3),
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pad_t=st.integers(0, 3),
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pad_l=st.integers(0, 3),
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pad_b=st.integers(0, 3),
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pad_r=st.integers(0, 3),
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kernel=st.integers(1, 5),
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adj_h=st.integers(0, 2),
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adj_w=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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order=st.sampled_from(["NCHW", "NHWC"]),
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engine=st.sampled_from(["", "BLOCK"]), **hu.gcs)
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@settings(max_examples=2, timeout=100)
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def test_convolution_transpose_separate_stride_pad_adj_gradient(
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self, stride_h, stride_w, pad_t, pad_l, pad_b, pad_r, kernel,
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adj_h, adj_w, size, input_channels, output_channels, batch_size,
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order, engine, gc, dc):
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assume(adj_h < stride_h)
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assume(adj_w < stride_w)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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input_channels, kernel, kernel, output_channels)\
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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"],
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["Y"],
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stride_h=stride_h,
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stride_w=stride_w,
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kernel=kernel,
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pad_t=pad_t,
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pad_l=pad_l,
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pad_b=pad_b,
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pad_r=pad_r,
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adj_h=adj_h,
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adj_w=adj_w,
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order=order,
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engine=engine,
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)
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if order == "NCHW":
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X = X.transpose((0, 3, 1, 2))
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w = w.transpose((0, 3, 1, 2))
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self.assertDeviceChecks(dc, op, [X, w, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, w, b], i, [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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