mirror of
https://github.com/pytorch/pytorch.git
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e43ffb0148
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/12101 clean up some duplicate test code Reviewed By: ZolotukhinM Differential Revision: D10051914 fbshipit-source-id: 698ff144a85e8c70572116c5ddb415cd2396b4e3
344 lines
12 KiB
Python
344 lines
12 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 hypothesis import given
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python.transformations import Transformer
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from caffe2.python import core, workspace
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from caffe2.python import test_util as tu
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transformer = Transformer()
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class TestTransformations(tu.TestCase):
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def _base_test_net(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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return net
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def _add_nnpack(self, net):
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transformer.AddNNPACK(net)
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assert tu.str_compare(net.Proto().op[0].engine, "NNPACK")
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def _fuse_nnpack_convrelu(self, net, expected_result_num_ops,
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expected_activation_arg=True):
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self._add_nnpack(net)
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transformer.FuseNNPACKConvRelu(net)
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self.assertEquals(tu.numOps(net), expected_result_num_ops)
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if tu.str_compare(arg.name, "activation"):
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assert tu.str_compare(arg.s, "Relu")
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has_activation_arg = True
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if expected_activation_arg:
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assert has_activation_arg
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else:
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assert not has_activation_arg
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def test_transformer_AddNNPACK(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["Y2"])
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self._add_nnpack(net)
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def test_transformer_FuseNNPACKConvRelu(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["Y2"])
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self._fuse_nnpack_convrelu(net, 1)
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def test_noFuseNNPACKConvRelu(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["Y2"])
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net.Relu(["Y"], ["Y3"])
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self._fuse_nnpack_convrelu(net, 3, expected_activation_arg=False)
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def test_transformer_FuseNNPACKConvReluNoInplace(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["X"])
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self._fuse_nnpack_convrelu(net, 1)
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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def test_transformer_FuseNNPACKConvReluInplaceRelu(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["Y"])
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self._fuse_nnpack_convrelu(net, 1)
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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def test_transformer_FuseNNPACKConvReluPingPongNaming(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["X"])
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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self._fuse_nnpack_convrelu(net, 2)
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0]
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def test_transformer_FuseNNPACKConvReluFollowedByMultipleInputOp(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["Y2"])
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net.Conv(["Y2", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y2"])
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self._fuse_nnpack_convrelu(net, 2)
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0]
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def test_transformer_FuseNNPACKConvReluInplaceFollowedByMultipleInputOp(self):
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net = self._base_test_net()
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net.Relu(["Y"], ["Y"])
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net.Conv(["Y", "w", "b"], ["Y2"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y2"], ["Y2"])
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self._fuse_nnpack_convrelu(net, 2)
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0]
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def test_transformer_SinkMaxPool(self):
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net = self._base_test_net()
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net.MaxPool(["Y"], ["Y1"], kernel=3)
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net.Relu(["Y1"], ["Y1"])
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transformer.SinkMaxPool(net)
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assert tu.str_compare(net.Proto().op[1].type, "Relu")
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assert tu.str_compare(net.Proto().op[2].type, "MaxPool")
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW", "NHWC"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConvBN(self, size, input_channels, seed, order, epsilon):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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h = size
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w = size
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k = 3
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=k, order=order)
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net.SpatialBN(
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["Y", "scale", "bias", "mean", "var"],
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["Y2"],
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is_test=True,
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order=order,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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if order == "NCHW":
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tu.randBlobFloat32("X", 1, c, h, w)
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tu.randBlobFloat32("w", c, c, k, k)
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else:
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tu.randBlobFloat32("X", 1, h, w, c)
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tu.randBlobFloat32("w", c, k, k, c)
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tu.randBlobsFloat32(["b", "scale", "bias", "mean"], c)
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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tu.randBlobFloat32("var", c, offset=0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert tu.numOps(net) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=5e-02,
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atol=1e-03
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)
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW", "NHWC"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConvBNNoConvBias(self, size, input_channels, seed, order, epsilon):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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h = size
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w = size
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k = 3
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net.Conv(["X", "w"], ["Y"], stride=1, pad=0, kernel=k, order=order)
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net.SpatialBN(
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["Y", "scale", "bias", "mean", "var"],
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["Y2"],
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is_test=True,
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order=order,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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if order == "NCHW":
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tu.randBlobFloat32("X", 1, c, h, w)
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tu.randBlobFloat32("w", c, c, k, k)
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else:
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tu.randBlobFloat32("X", 1, h, w, c)
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tu.randBlobFloat32("w", c, k, k, c)
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tu.randBlobsFloat32(["scale", "bias", "mean"], c)
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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tu.randBlobFloat32("var", c, offset=0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert tu.numOps(net) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=5e-02,
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atol=1e-03
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)
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW", "NHWC"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConvBNNoConvBiasDuplicatedName(self, size, input_channels, seed, order, epsilon):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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h = size
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w = size
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k = 3
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net.Conv(["X", "w"], ["Y"], stride=1, pad=0, kernel=k, order=order)
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net.SpatialBN(
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["Y", "scale", "_bias0", "mean", "var"],
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["Y2"],
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is_test=True,
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order=order,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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if order == "NCHW":
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tu.randBlobFloat32("X", 1, c, h, w)
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tu.randBlobFloat32("w", c, c, k, k)
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else:
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tu.randBlobFloat32("X", 1, h, w, c)
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tu.randBlobFloat32("w", c, k, k, c)
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tu.randBlobsFloat32(["scale", "_bias0", "mean"], c)
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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tu.randBlobFloat32("var", c, offset=0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert tu.numOps(net) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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print("pre")
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print(preTransformOutput)
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print("after")
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print(postTransformOutput)
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=5e-02,
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atol=1e-03
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)
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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kt=st.integers(3, 5),
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kh=st.integers(3, 5),
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kw=st.integers(3, 5),
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seed=st.integers(0, 65535),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConv3DBN(
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self, size, input_channels, kt, kh, kw, seed, epsilon
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):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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t = size
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h = size
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w = size
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net.Conv(
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["X", "w", "b"],
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["Y"],
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kernels=[kt, kh, kw],
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)
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net.SpatialBN(
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["Y", "scale", "bias", "mean", "var"],
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["Y2"],
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is_test=True,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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tu.randBlobFloat32("X", 1, c, t, h, w)
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tu.randBlobFloat32("w", c, c, kt, kh, kw)
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tu.randBlobsFloat32(["b", "scale", "bias", "mean"], c)
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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tu.randBlobFloat32("var", c, offset=0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert tu.numOps(net) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=1e-02,
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atol=1e-04
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)
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def test_converterEnforceUnusedInputs(self):
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net = core.Net("net")
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net.Relu(["X"], ["Y"])
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net.Proto().external_input.extend(["fake"])
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# This should now work
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transformer.AddNNPACK(net) # just testing the converter
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def test_converterEnforceUnusedOutputs(self):
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net = core.Net("net")
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net.Relu(["X"], ["Y"])
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net.Proto().external_output.extend(["fake"])
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with self.assertRaises(Exception):
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transformer.AddNNPACK(net) # just testing the converter
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