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https://github.com/zebrajr/pytorch.git
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26ddefbda1
* Add operators based-on IDEEP interfaces Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Enable IDEEP as a caffe2 device Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Add test cases for IDEEP ops Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Add IDEEP as a caffe2 submodule Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Skip test cases if no IDEEP support Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Correct cmake options for IDEEP Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Add dependences on ideep libraries Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Fix issues in IDEEP conv ops and etc. Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Move ideep from caffe2/ideep to caffe2/contrib/ideep Signed-off-by: Gu Jinghui <jinghui.gu@intel.com> * Update IDEEP to fix cmake issue Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Fix cmake issue caused by USE_MKL option Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com> * Correct comments in MKL cmake file Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>
248 lines
8.6 KiB
Python
248 lines
8.6 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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from __future__ import unicode_literals
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given, settings, unlimited
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import copy
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import numpy as np
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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_ideep, "No IDEEP support.")
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class ConvFusionTest(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(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(1, 16),
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output_channels=st.integers(1, 16),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(1, 1),
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**mu.gcs)
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@settings(deadline=None, timeout=unlimited)
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def test_convolution_relu_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, gc, dc):
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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relu = core.CreateOperator(
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"Relu",
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["Y0"],
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["Y0"],
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device_option=dc[0]
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)
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conv_fusion = core.CreateOperator(
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"ConvFusion",
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["X1", "w1", "b1"] if use_bias else ["X1", "w1"],
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["Y1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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fusion_type = 1,
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device_option=dc[1]
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)
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('w0', w, dc[0])
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workspace.FeedBlob('b0', b, dc[0])
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workspace.RunOperatorOnce(conv)
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workspace.RunOperatorOnce(relu)
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Y0 = workspace.FetchBlob('Y0')
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workspace.ResetWorkspace()
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workspace.FeedBlob('X1', X, dc[1])
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workspace.FeedBlob('w1', w, dc[1])
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workspace.FeedBlob('b1', b, dc[1])
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workspace.RunOperatorOnce(conv_fusion)
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Y1 = workspace.FetchBlob('Y1')
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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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(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(1, 16),
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output_channels=st.integers(1, 16),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(1, 1),
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**mu.gcs)
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@settings(deadline=None, timeout=unlimited)
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def test_convolution_sum_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, gc, dc):
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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sum = core.CreateOperator(
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"Sum",
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["S0", "Y0"],
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["S0"],
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device_option=dc[0]
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)
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conv_fusion = core.CreateOperator(
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"ConvFusion",
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["X1", "w1", "b1", "S1"] if use_bias else ["X1", "w1", "S1"],
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["S1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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fusion_type = 2,
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device_option=dc[1]
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)
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('w0', w, dc[0])
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workspace.FeedBlob('b0', b, dc[0])
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workspace.RunOperatorOnce(conv)
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Y0 = workspace.FetchBlob('Y0')
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S = np.random.rand(*Y0.shape).astype(np.float32) - 0.5
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workspace.FeedBlob('S0', S, dc[0])
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workspace.RunOperatorOnce(sum)
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S0 = workspace.FetchBlob('S0')
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workspace.ResetWorkspace()
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workspace.FeedBlob('X1', X, dc[1])
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workspace.FeedBlob('w1', w, dc[1])
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workspace.FeedBlob('b1', b, dc[1])
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workspace.FeedBlob('S1', S, dc[1])
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workspace.RunOperatorOnce(conv_fusion)
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S1 = workspace.FetchBlob('S1')
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if not np.allclose(S0, S1, atol=0.01, rtol=0.01):
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print(S1.flatten())
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print(S0.flatten())
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print(np.max(np.abs(S1 - S0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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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(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(1, 16),
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output_channels=st.integers(1, 16),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(1, 1),
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**mu.gcs)
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@settings(deadline=None, timeout=unlimited)
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def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, gc, dc):
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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sum = core.CreateOperator(
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"Sum",
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["S0", "Y0"],
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["S0"],
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device_option=dc[0]
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)
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relu = core.CreateOperator(
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"Relu",
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["S0"],
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["S0"],
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device_option=dc[0]
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)
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conv_fusion = core.CreateOperator(
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"ConvFusion",
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["X1", "w1", "b1", "S1"] if use_bias else ["X1", "w1", "S1"],
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["S1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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fusion_type = 3,
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device_option=dc[1]
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)
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('w0', w, dc[0])
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workspace.FeedBlob('b0', b, dc[0])
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workspace.RunOperatorOnce(conv)
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Y0 = workspace.FetchBlob('Y0')
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S = np.random.rand(*Y0.shape).astype(np.float32) - 0.5
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workspace.FeedBlob('S0', S, dc[0])
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workspace.RunOperatorOnce(sum)
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workspace.RunOperatorOnce(relu)
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S0 = workspace.FetchBlob('S0')
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workspace.ResetWorkspace()
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workspace.FeedBlob('X1', X, dc[1])
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workspace.FeedBlob('w1', w, dc[1])
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workspace.FeedBlob('b1', b, dc[1])
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workspace.FeedBlob('S1', S, dc[1])
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workspace.RunOperatorOnce(conv_fusion)
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S1 = workspace.FetchBlob('S1')
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if not np.allclose(S0, S1, atol=0.01, rtol=0.01):
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print(S1.flatten())
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print(S0.flatten())
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print(np.max(np.abs(S1 - S0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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if __name__ == "__main__":
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unittest.main()
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