mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
552 lines
21 KiB
Python
552 lines
21 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 collections
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import functools
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import numpy as np
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from hypothesis import assume, given
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import hypothesis.strategies as st
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from caffe2.proto import caffe2_pb2
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from caffe2.python import brew, core, workspace
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import caffe2.python.hypothesis_test_util as hu
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from caffe2.python.model_helper import ModelHelper
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def _cudnn_supports(
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dilation=False,
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nhwc=False,
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backward=False,
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):
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"""Return True if cuDNN supports this configuration."""
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v = workspace.GetCuDNNVersion()
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if backward:
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if nhwc:
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# nhwc isn't supported in backward ops.
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return False
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else:
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# Forward mode.
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if dilation and v < 6000:
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# Dilation not supported until v6
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return False
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if dilation and nhwc:
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# Dilation and NHWC not supported together
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return False
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return True
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class TestConvolution(hu.HypothesisTestCase):
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# CUDNN does NOT support different padding values and we skip it
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@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
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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(3, 5),
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size=st.integers(1, 8),
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input_channels=st.integers(1, 3),
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output_channels=st.integers(1, 3),
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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(["", "EIGEN"]),
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shared_buffer=st.booleans(),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_convolution_separate_stride_pad_gradients(self, op_type,
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stride_h, stride_w,
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pad_t, pad_l, pad_b,
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pad_r, kernel, size,
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input_channels,
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output_channels,
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batch_size, order,
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engine, shared_buffer,
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use_bias,
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gc, dc):
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op = core.CreateOperator(
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op_type,
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["X", "w", "b"] if use_bias else ["X", "w"],
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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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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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kernel=kernel,
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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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)
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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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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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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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inputs = [X, w, b] if use_bias else [X, w]
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# Error handling path.
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if size + pad_r + pad_l < kernel or size + pad_t + pad_b < kernel:
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with self.assertRaises(RuntimeError):
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self.assertDeviceChecks(dc, op, inputs, [0])
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return
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self.assertDeviceChecks(dc, op, inputs, [0])
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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# CUDNN does NOT support different padding values and we skip it
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@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
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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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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(["", "EIGEN"]),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_convolution_separate_stride_pad_layout(self, op_type,
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stride_h, stride_w,
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pad_t, pad_l, pad_b, pad_r,
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kernel, size,
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input_channels,
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output_channels, batch_size,
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engine, use_bias, gc, dc):
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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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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 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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op_type,
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["X", "w", "b"] if use_bias else ["X", "w"],
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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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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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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(op_type=st.sampled_from(["Conv", "Conv2D"]),
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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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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", "MKLDNN"]),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_convolution_gradients(self, op_type, stride, pad, kernel, dilation,
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size, input_channels, output_channels,
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batch_size, order, engine, use_bias, gc, dc):
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dkernel = dilation * (kernel - 1) + 1
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if engine == 'CUDNN':
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assume(_cudnn_supports(dilation=(dilation > 1),
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nhwc=(order == 'NHWC'),
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backward=True))
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assume(engine != "MKLDNN" or use_bias is True)
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op = core.CreateOperator(
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op_type,
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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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dilation=dilation,
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pad=pad,
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order=order,
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engine=engine,
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)
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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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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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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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inputs = [X, w, b] if use_bias else [X, w]
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# Error handling path.
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if size + pad + pad < dkernel or size + pad + pad < dkernel:
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with self.assertRaises(RuntimeError):
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self.assertDeviceChecks(dc, op, inputs, [0])
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return
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self.assertDeviceChecks(dc, op, inputs, [0])
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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def _nd_convolution_nchw(self, n, input_channels, output_channels,
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batch_size, stride, size, kernel, dilation, pad,
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use_bias, gc, dc):
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dkernel = dilation * (kernel - 1) + 1
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for op_type in ["Conv", "Conv" + str(n) + "D"]:
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op = core.CreateOperator(
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op_type,
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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strides=[stride] * n,
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kernels=[kernel] * n,
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dilations=[dilation] * n,
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pads=[pad] * n * 2,
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order="NCHW",
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engine="",
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)
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input_dims = [batch_size, input_channels]
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input_dims.extend([size] * n)
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filter_dims = [output_channels, input_channels]
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filter_dims.extend([kernel] * n)
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X = np.random.rand(*input_dims).astype(np.float32) - 0.5
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w = np.random.rand(*filter_dims).astype(np.float32) - 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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inputs = [X, w, b] if use_bias else [X, w]
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if size + pad + pad < dkernel or size + pad + pad < dkernel:
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with self.assertRaises(RuntimeError):
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self.assertDeviceChecks(dc, op, inputs, [0])
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return
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self.assertDeviceChecks(dc, op, inputs, [0])
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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@given(input_channels=st.integers(1, 3),
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output_channels=st.integers(1, 2),
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batch_size=st.integers(1, 3),
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stride=st.integers(1, 3),
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size=st.integers(7, 10),
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kernel=st.integers(1, 2),
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dilation=st.integers(1, 3),
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pad=st.integers(0, 3),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_1d_convolution_nchw(self, input_channels, output_channels,
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batch_size, stride, size, kernel, dilation,
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pad, use_bias, gc, dc):
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self._nd_convolution_nchw(
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1, input_channels, output_channels, batch_size, stride, size,
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kernel, dilation, pad, use_bias, gc, dc
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)
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@given(input_channels=st.integers(1, 2),
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output_channels=st.integers(1, 2),
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batch_size=st.integers(1, 2),
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stride=st.integers(1, 2),
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size=st.integers(4, 5),
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kernel=st.integers(1, 2),
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dilation=st.integers(1, 2),
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pad=st.integers(0, 2),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_3d_convolution_nchw(self, input_channels, output_channels,
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batch_size, stride, size, kernel, dilation,
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pad, use_bias, gc, dc):
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self._nd_convolution_nchw(
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3, input_channels, output_channels, batch_size, stride, size,
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kernel, dilation, pad, use_bias, gc, dc
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)
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@given(op_type=st.sampled_from(["Conv", "Conv3D"]),
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batch_size=st.integers(1, 2),
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stride=st.integers(1, 2),
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size=st.integers(3, 5),
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kernel=st.integers(1, 2),
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dilation=st.integers(1, 2),
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pad=st.integers(0, 2),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_3d_convolution_cudnn_nchw(self, op_type, batch_size, stride, size,
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kernel, dilation, pad, use_bias, gc, dc):
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input_channels = 1
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output_channels = 1
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n = 3
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dkernel = dilation * (kernel - 1) + 1
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order = "NCHW"
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op = core.CreateOperator(
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op_type,
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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strides=[stride] * n,
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kernels=[kernel] * n,
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dilations=[dilation] * n,
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pads=[pad] * n * 2,
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order=order,
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engine="CUDNN",
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)
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input_dims = [batch_size, input_channels]
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input_dims.extend([size] * n)
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filter_dims = [output_channels, input_channels]
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filter_dims.extend([kernel] * n)
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X = np.random.rand(*input_dims).astype(np.float32) - 0.5
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w = np.random.rand(*filter_dims).astype(np.float32) - 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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inputs = [X, w, b] if use_bias else [X, w]
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if size + pad + pad < dkernel or size + pad + pad < dkernel:
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with self.assertRaises(RuntimeError):
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self.assertDeviceChecks(dc, op, inputs, [0])
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return
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self.assertDeviceChecks(dc, op, inputs, [0])
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
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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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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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**hu.gcs)
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def test_convolution_layout(self, op_type, stride, pad, kernel, dilation,
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size, input_channels, output_channels,
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batch_size, use_bias, gc, dc):
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assume(size >= dilation * (kernel - 1) + 1)
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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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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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Output = collections.namedtuple("Output", ["Y", "engine", "order"])
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outputs = []
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for order in ["NCHW", "NHWC"]:
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engine_list = ['']
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if _cudnn_supports(dilation=(dilation > 1), nhwc=(order == 'NHWC')):
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engine_list.append('CUDNN')
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for engine in engine_list:
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op = core.CreateOperator(
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op_type,
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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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dilation=dilation,
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pad=pad,
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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.assertDeviceChecks(
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dc,
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op,
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[X_f, w_f, b] if use_bias else [X_f, w_f],
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[0])
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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.append(Output(
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Y=self.ws.blobs["Y"].fetch(), engine=engine, order=order))
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def canonical(o):
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if o.order == "NHWC":
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return o.Y.transpose((0, 3, 1, 2))
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else:
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return o.Y
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for o in outputs:
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np.testing.assert_allclose(
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canonical(outputs[0]),
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canonical(o),
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atol=1e-4,
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rtol=1e-4)
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@given(num_workers=st.integers(1, 4),
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net_type=st.sampled_from(
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["simple", "dag"] +
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(["async_dag"] if workspace.has_gpu_support else [])),
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do=st.sampled_from(hu.device_options),
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engine=st.sampled_from(["CUDNN", ""]))
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def test_convolution_sync(self, net_type, num_workers, do, engine):
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m = ModelHelper(name="test_model")
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n = 1
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d = 2
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depth = 3
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iters = 5
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h = 5
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w = 5
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workspace.ResetWorkspace()
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use_cudnn = (engine == 'CUDNN')
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np.random.seed(1701)
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# Build a binary tree of conv layers, summing at each node.
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for i in reversed(range(depth)):
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for j in range(2 ** i):
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bottom_1 = "{}_{}".format(i + 1, 2 * j)
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bottom_2 = "{}_{}".format(i + 1, 2 * j + 1)
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mid_1 = "{}_{}_m".format(i + 1, 2 * j)
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mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
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top = "{}_{}".format(i, j)
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w1, b1, w2, b2 = np.random.randn(4).tolist()
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brew.conv(
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m, bottom_1, mid_1,
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dim_in=d, dim_out=d,
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kernel=3,
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weight_init=('ConstantFill', dict(value=w1)),
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bias_init=('ConstantFill', dict(value=b1)),
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cudnn_state=np.random.randint(0, 3),
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stride=1,
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pad=1,
|
|
deterministic=1,
|
|
use_cudnn=use_cudnn,
|
|
engine=engine)
|
|
brew.conv(
|
|
m, bottom_2, mid_2,
|
|
dim_in=d, dim_out=d,
|
|
kernel=3,
|
|
stride=1,
|
|
pad=1,
|
|
weight_init=('ConstantFill', dict(value=w2)),
|
|
bias_init=('ConstantFill', dict(value=b2)),
|
|
deterministic=1,
|
|
cudnn_state=np.random.randint(0, 3),
|
|
use_cudnn=use_cudnn,
|
|
engine=engine)
|
|
m.net.Sum([mid_1, mid_2], top)
|
|
|
|
m.net.Flatten(["0_0"], ["0_0_flat"])
|
|
m.net.SquaredL2Distance(["0_0_flat", "label"], "xent")
|
|
m.net.AveragedLoss("xent", "loss")
|
|
input_to_grad = m.AddGradientOperators(["loss"])
|
|
m.Proto().device_option.CopyFrom(do)
|
|
m.param_init_net.Proto().device_option.CopyFrom(do)
|
|
m.Proto().type = net_type
|
|
m.Proto().num_workers = num_workers
|
|
self.ws.run(m.param_init_net)
|
|
|
|
def run():
|
|
import numpy as np
|
|
np.random.seed(1701)
|
|
input_blobs = ["{}_{}".format(depth, j) for j in range(2 ** depth)]
|
|
for input_blob in input_blobs:
|
|
self.ws.create_blob(input_blob).feed(
|
|
np.random.randn(n, d, h, w).astype(np.float32),
|
|
device_option=do)
|
|
self.ws.create_blob("label").feed(
|
|
np.random.randn(n, d * h * w).astype(np.float32),
|
|
device_option=do)
|
|
self.ws.run(m.net)
|
|
gradients = [
|
|
self.ws.blobs[str(input_to_grad[input_blob])].fetch()
|
|
for input_blob in input_blobs]
|
|
return gradients
|
|
|
|
outputs = [run() for _ in range(iters)]
|
|
for output in outputs[1:]:
|
|
np.testing.assert_array_equal(outputs[0], output)
|
|
np.testing.assert_allclose(
|
|
np.sum(np.square(output)),
|
|
1763719461732352.0,
|
|
rtol=1e-5)
|
|
|
|
def test_use_cudnn_engine_interactions(self):
|
|
"""Make sure the use_cudnn and engine kwargs work as expected."""
|
|
for model_default in [None, True, False]:
|
|
arg_scope = {}
|
|
if model_default is not None:
|
|
arg_scope['use_cudnn'] = model_default
|
|
else:
|
|
model_default = True # the default
|
|
|
|
model = ModelHelper(arg_scope=arg_scope)
|
|
self.assertEqual(model.arg_scope['use_cudnn'], model_default)
|
|
f = functools.partial(brew.conv, model,
|
|
'conv_in', 'conv_out', 10, 10, 5)
|
|
|
|
for op_cudnn in [None, True, False]:
|
|
for op_engine in [None, '', 'CUDNN']:
|
|
kwargs = {}
|
|
if op_cudnn is not None:
|
|
kwargs['use_cudnn'] = op_cudnn
|
|
else:
|
|
op_cudnn = False # the default
|
|
if op_engine is not None:
|
|
kwargs['engine'] = op_engine
|
|
|
|
calculated_cudnn = kwargs.get('use_cudnn', model_default)
|
|
expected_engine = kwargs.get(
|
|
'engine',
|
|
'CUDNN' if calculated_cudnn else '')
|
|
|
|
if ((calculated_cudnn is True and op_engine == '') or
|
|
(calculated_cudnn is False and op_engine == 'CUDNN')):
|
|
with self.assertRaises(ValueError):
|
|
f(**kwargs)
|
|
else:
|
|
f(**kwargs)
|
|
self.assertEqual(model.Proto().op[-1].engine,
|
|
expected_engine)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|