| # Copyright (c) 2016-present, Facebook, Inc. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| ############################################################################## |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| from hypothesis import given |
| import hypothesis.strategies as st |
| import numpy as np |
| |
| from caffe2.python.transformations import Transformer |
| from caffe2.python import core, workspace, test_util |
| |
| transformer = Transformer() |
| |
| |
| def str_compare(a, b, encoding="utf8"): |
| if isinstance(a, bytes): |
| a = a.decode(encoding) |
| if isinstance(b, bytes): |
| b = b.decode(encoding) |
| return a == b |
| |
| |
| class TestTransformations(test_util.TestCase): |
| def test_transformer_AddNNPACK(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y2"]) |
| transformer.AddNNPACK(net) |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| |
| def test_transformer_FuseNNPACKConvRelu(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y2"]) |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 1 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation"): |
| assert str_compare(arg.s, "Relu") |
| has_activation_arg = True |
| assert has_activation_arg |
| |
| def test_noFuseNNPACKConvRelu(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y2"]) |
| net.Relu(["Y"], ["Y3"]) |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 3 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation") and str_compare(arg.s, "Relu"): |
| has_activation_arg = True |
| assert not has_activation_arg |
| |
| def test_transformer_FuseNNPACKConvReluNoInplace(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["X"]) |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 1 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation"): |
| assert str_compare(arg.s, "Relu") |
| has_activation_arg = True |
| assert has_activation_arg |
| assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0] |
| |
| def test_transformer_FuseNNPACKConvReluInplaceRelu(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y"]) |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 1 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation"): |
| assert str_compare(arg.s, "Relu") |
| has_activation_arg = True |
| assert has_activation_arg |
| assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0] |
| |
| def test_transformer_FuseNNPACKConvReluPingPongNaming(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["X"]) |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 2 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation"): |
| assert str_compare(arg.s, "Relu") |
| has_activation_arg = True |
| assert has_activation_arg |
| assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0] |
| assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0] |
| |
| def test_transformer_FuseNNPACKConvReluFollowedByMultipleInputOp(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y2"]) |
| net.Conv(["Y2", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y2"]) |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 2 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation"): |
| assert str_compare(arg.s, "Relu") |
| has_activation_arg = True |
| assert has_activation_arg |
| assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0] |
| assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0] |
| |
| def test_transformer_FuseNNPACKConvReluInplaceFollowedByMultipleInputOp(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y"], ["Y"]) |
| net.Conv(["Y", "w", "b"], ["Y2"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.Relu(["Y2"], ["Y2"]) |
| transformer.AddNNPACK(net) # get the NNPACK engine |
| assert str_compare(net.Proto().op[0].engine, "NNPACK") |
| transformer.FuseNNPACKConvRelu(net) |
| assert len(net.Proto().op) == 2 |
| has_activation_arg = False |
| for arg in net.Proto().op[0].arg: |
| if str_compare(arg.name, "activation"): |
| assert str_compare(arg.s, "Relu") |
| has_activation_arg = True |
| assert has_activation_arg |
| assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0] |
| assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0] |
| |
| def test_transformer_SinkMaxPool(self): |
| net = core.Net("net") |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW") |
| net.MaxPool(["Y"], ["Y1"], kernel=3) |
| net.Relu(["Y1"], ["Y1"]) |
| transformer.SinkMaxPool(net) |
| assert str_compare(net.Proto().op[1].type, "Relu") |
| assert str_compare(net.Proto().op[2].type, "MaxPool") |
| |
| @given( |
| size=st.integers(7, 10), |
| input_channels=st.integers(1, 10), |
| seed=st.integers(0, 65535), |
| order=st.sampled_from(["NCHW", "NHWC"]), |
| epsilon=st.floats(min_value=1e-5, max_value=1e-2), |
| ) |
| def test_transformer_FuseConvBN(self, size, input_channels, seed, order, epsilon): |
| workspace.ResetWorkspace() |
| net = core.Net("net") |
| c = input_channels |
| h = size |
| w = size |
| k = 3 |
| net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=k, order=order) |
| net.SpatialBN( |
| ["Y", "scale", "bias", "mean", "var"], |
| ["Y2"], |
| is_test=True, |
| order=order, |
| epsilon=epsilon, |
| ) |
| |
| np.random.seed(seed) |
| if order == "NCHW": |
| workspace.FeedBlob("X", np.random.rand(1, c, h, w).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, c, k, k).astype(np.float32)) |
| else: |
| workspace.FeedBlob("X", np.random.rand(1, h, w, c).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, k, k, c).astype(np.float32)) |
| workspace.FeedBlob("b", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("bias", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32)) |
| # This is necessary because 1/sqrt(var) is used and if var is too small |
| # we get floating point artifacts that cause test failures |
| workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5) |
| workspace.RunNetOnce(net) |
| preTransformOutput = workspace.FetchBlob("Y2").flatten() |
| workspace.FeedBlob("Y2", np.zeros((1, 1))) |
| transformer.FuseConvBN(net) |
| |
| # Ensure fusion |
| assert len(net.Proto().op) == 1 |
| workspace.RunNetOnce(net) |
| postTransformOutput = workspace.FetchBlob("Y2").flatten() |
| # Check that there is no numerical difference |
| assert np.allclose( |
| preTransformOutput, |
| postTransformOutput, |
| rtol=5e-02, |
| atol=1e-03 |
| ) |
| |
| @given( |
| size=st.integers(7, 10), |
| input_channels=st.integers(1, 10), |
| seed=st.integers(0, 65535), |
| order=st.sampled_from(["NCHW", "NHWC"]), |
| epsilon=st.floats(min_value=1e-5, max_value=1e-2), |
| ) |
| def test_transformer_FuseConvBNNoConvBias(self, size, input_channels, seed, order, epsilon): |
| workspace.ResetWorkspace() |
| net = core.Net("net") |
| c = input_channels |
| h = size |
| w = size |
| k = 3 |
| net.Conv(["X", "w"], ["Y"], stride=1, pad=0, kernel=k, order=order) |
| net.SpatialBN( |
| ["Y", "scale", "bias", "mean", "var"], |
| ["Y2"], |
| is_test=True, |
| order=order, |
| epsilon=epsilon, |
| ) |
| |
| np.random.seed(seed) |
| if order == "NCHW": |
| workspace.FeedBlob("X", np.random.rand(1, c, h, w).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, c, k, k).astype(np.float32)) |
| else: |
| workspace.FeedBlob("X", np.random.rand(1, h, w, c).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, k, k, c).astype(np.float32)) |
| workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("bias", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32)) |
| # This is necessary because 1/sqrt(var) is used and if var is too small |
| # we get floating point artifacts that cause test failures |
| workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5) |
| workspace.RunNetOnce(net) |
| preTransformOutput = workspace.FetchBlob("Y2").flatten() |
| workspace.FeedBlob("Y2", np.zeros((1, 1))) |
| transformer.FuseConvBN(net) |
| |
| # Ensure fusion |
| assert len(net.Proto().op) == 1 |
| workspace.RunNetOnce(net) |
| postTransformOutput = workspace.FetchBlob("Y2").flatten() |
| # Check that there is no numerical difference |
| assert np.allclose( |
| preTransformOutput, |
| postTransformOutput, |
| rtol=5e-02, |
| atol=1e-03 |
| ) |
| |
| @given( |
| size=st.integers(7, 10), |
| input_channels=st.integers(1, 10), |
| seed=st.integers(0, 65535), |
| order=st.sampled_from(["NCHW", "NHWC"]), |
| epsilon=st.floats(min_value=1e-5, max_value=1e-2), |
| ) |
| def test_transformer_FuseConvBNNoConvBiasDuplicatedName(self, size, input_channels, seed, order, epsilon): |
| workspace.ResetWorkspace() |
| net = core.Net("net") |
| c = input_channels |
| h = size |
| w = size |
| k = 3 |
| net.Conv(["X", "w"], ["Y"], stride=1, pad=0, kernel=k, order=order) |
| net.SpatialBN( |
| ["Y", "scale", "_bias0", "mean", "var"], |
| ["Y2"], |
| is_test=True, |
| order=order, |
| epsilon=epsilon, |
| ) |
| |
| np.random.seed(seed) |
| if order == "NCHW": |
| workspace.FeedBlob("X", np.random.rand(1, c, h, w).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, c, k, k).astype(np.float32)) |
| else: |
| workspace.FeedBlob("X", np.random.rand(1, h, w, c).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, k, k, c).astype(np.float32)) |
| workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("_bias0", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32)) |
| # This is necessary because 1/sqrt(var) is used and if var is too small |
| # we get floating point artifacts that cause test failures |
| workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5) |
| workspace.RunNetOnce(net) |
| preTransformOutput = workspace.FetchBlob("Y2").flatten() |
| workspace.FeedBlob("Y2", np.zeros((1, 1))) |
| transformer.FuseConvBN(net) |
| |
| # Ensure fusion |
| assert len(net.Proto().op) == 1 |
| workspace.RunNetOnce(net) |
| postTransformOutput = workspace.FetchBlob("Y2").flatten() |
| print("pre") |
| print(preTransformOutput) |
| print("after") |
| print(postTransformOutput) |
| # Check that there is no numerical difference |
| assert np.allclose( |
| preTransformOutput, |
| postTransformOutput, |
| rtol=5e-02, |
| atol=1e-03 |
| ) |
| |
| @given( |
| size=st.integers(7, 10), |
| input_channels=st.integers(1, 10), |
| kt=st.integers(3, 5), |
| kh=st.integers(3, 5), |
| kw=st.integers(3, 5), |
| seed=st.integers(0, 65535), |
| epsilon=st.floats(min_value=1e-5, max_value=1e-2), |
| ) |
| def test_transformer_FuseConv3DBN( |
| self, size, input_channels, kt, kh, kw, seed, epsilon |
| ): |
| workspace.ResetWorkspace() |
| net = core.Net("net") |
| c = input_channels |
| t = size |
| h = size |
| w = size |
| net.Conv( |
| ["X", "w", "b"], |
| ["Y"], |
| kernels=[kt, kh, kw], |
| ) |
| net.SpatialBN( |
| ["Y", "scale", "bias", "mean", "var"], |
| ["Y2"], |
| is_test=True, |
| epsilon=epsilon, |
| ) |
| |
| np.random.seed(seed) |
| workspace.FeedBlob("X", np.random.rand(1, c, t, h, w).astype(np.float32)) |
| workspace.FeedBlob("w", np.random.rand(c, c, kt, kh, kw).astype(np.float32)) |
| workspace.FeedBlob("b", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("bias", np.random.rand(c).astype(np.float32)) |
| workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32)) |
| # This is necessary because 1/sqrt(var) is used and if var is too small |
| # we get floating point artifacts that cause test failures |
| workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5) |
| workspace.RunNetOnce(net) |
| preTransformOutput = workspace.FetchBlob("Y2").flatten() |
| workspace.FeedBlob("Y2", np.zeros((1, 1))) |
| transformer.FuseConvBN(net) |
| |
| # Ensure fusion |
| assert len(net.Proto().op) == 1 |
| workspace.RunNetOnce(net) |
| postTransformOutput = workspace.FetchBlob("Y2").flatten() |
| # Check that there is no numerical difference |
| assert np.allclose( |
| preTransformOutput, |
| postTransformOutput, |
| rtol=1e-02, |
| atol=1e-04 |
| ) |
| |
| def test_converterEnforceUnusedInputs(self): |
| net = core.Net("net") |
| net.Relu(["X"], ["Y"]) |
| net.Proto().external_input.extend(["fake"]) |
| with self.assertRaises(Exception): |
| transformer.AddNNPACK(net) # just testing the converter |
| |
| def test_converterEnforceUnusedOutputs(self): |
| net = core.Net("net") |
| net.Relu(["X"], ["Y"]) |
| net.Proto().external_output.extend(["fake"]) |
| with self.assertRaises(Exception): |
| transformer.AddNNPACK(net) # just testing the converter |
