/usr/local/lib64/python3.6/site-packages/caffe2/python
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/transformations_test.py (11960B)
# 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 hypothesis import given
import hypothesis.strategies as st
import numpy as np
from caffe2.python.transformations import Transformer
from caffe2.python import core, workspace
from caffe2.python import test_util as tu
transformer = Transformer()
class TestTransformations(tu.TestCase):
def _base_test_net(self):
net = core.Net("net")
net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
return net
def _add_nnpack(self, net):
transformer.AddNNPACK(net)
assert tu.str_compare(net.Proto().op[0].engine, "NNPACK")
def _fuse_nnpack_convrelu(self, net, expected_result_num_ops,
expected_activation_arg=True):
self._add_nnpack(net)
transformer.FuseNNPACKConvRelu(net)
self.assertEquals(tu.numOps(net), expected_result_num_ops)
has_activation_arg = False
for arg in net.Proto().op[0].arg:
if tu.str_compare(arg.name, "activation"):
assert tu.str_compare(arg.s, "Relu")
has_activation_arg = True
if expected_activation_arg:
assert has_activation_arg
else:
assert not has_activation_arg
def test_transformer_AddNNPACK(self):
net = self._base_test_net()
net.Relu(["Y"], ["Y2"])
self._add_nnpack(net)
def test_transformer_FuseNNPACKConvRelu(self):
net = self._base_test_net()
net.Relu(["Y"], ["Y2"])
self._fuse_nnpack_convrelu(net, 1)
def test_noFuseNNPACKConvRelu(self):
net = self._base_test_net()
net.Relu(["Y"], ["Y2"])
net.Relu(["Y"], ["Y3"])
self._fuse_nnpack_convrelu(net, 3, expected_activation_arg=False)
def test_transformer_FuseNNPACKConvReluNoInplace(self):
net = self._base_test_net()
net.Relu(["Y"], ["X"])
self._fuse_nnpack_convrelu(net, 1)
assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
def test_transformer_FuseNNPACKConvReluInplaceRelu(self):
net = self._base_test_net()
net.Relu(["Y"], ["Y"])
self._fuse_nnpack_convrelu(net, 1)
assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
def test_transformer_FuseNNPACKConvReluPingPongNaming(self):
net = self._base_test_net()
net.Relu(["Y"], ["X"])
net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
self._fuse_nnpack_convrelu(net, 2)
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 = self._base_test_net()
net.Relu(["Y"], ["Y2"])
net.Conv(["Y2", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
net.Relu(["Y"], ["Y2"])
self._fuse_nnpack_convrelu(net, 2)
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 = self._base_test_net()
net.Relu(["Y"], ["Y"])
net.Conv(["Y", "w", "b"], ["Y2"], stride=1, pad=0, kernel=3, order="NCHW")
net.Relu(["Y2"], ["Y2"])
self._fuse_nnpack_convrelu(net, 2)
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]
@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":
tu.randBlobFloat32("X", 1, c, h, w)
tu.randBlobFloat32("w", c, c, k, k)
else:
tu.randBlobFloat32("X", 1, h, w, c)
tu.randBlobFloat32("w", c, k, k, c)
tu.randBlobsFloat32(["b", "scale", "bias", "mean"], c)
# This is necessary because 1/sqrt(var) is used and if var is too small
# we get floating point artifacts that cause test failures
tu.randBlobFloat32("var", c, offset=0.5)
workspace.RunNetOnce(net)
preTransformOutput = workspace.FetchBlob("Y2").flatten()
workspace.FeedBlob("Y2", np.zeros((1, 1)))
transformer.FuseConvBN(net)
# Ensure fusion
assert tu.numOps(net) == 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":
tu.randBlobFloat32("X", 1, c, h, w)
tu.randBlobFloat32("w", c, c, k, k)
else:
tu.randBlobFloat32("X", 1, h, w, c)
tu.randBlobFloat32("w", c, k, k, c)
tu.randBlobsFloat32(["scale", "bias", "mean"], c)
# This is necessary because 1/sqrt(var) is used and if var is too small
# we get floating point artifacts that cause test failures
tu.randBlobFloat32("var", c, offset=0.5)
workspace.RunNetOnce(net)
preTransformOutput = workspace.FetchBlob("Y2").flatten()
workspace.FeedBlob("Y2", np.zeros((1, 1)))
transformer.FuseConvBN(net)
# Ensure fusion
assert tu.numOps(net) == 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":
tu.randBlobFloat32("X", 1, c, h, w)
tu.randBlobFloat32("w", c, c, k, k)
else:
tu.randBlobFloat32("X", 1, h, w, c)
tu.randBlobFloat32("w", c, k, k, c)
tu.randBlobsFloat32(["scale", "_bias0", "mean"], c)
# This is necessary because 1/sqrt(var) is used and if var is too small
# we get floating point artifacts that cause test failures
tu.randBlobFloat32("var", c, offset=0.5)
workspace.RunNetOnce(net)
preTransformOutput = workspace.FetchBlob("Y2").flatten()
workspace.FeedBlob("Y2", np.zeros((1, 1)))
transformer.FuseConvBN(net)
# Ensure fusion
assert tu.numOps(net) == 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)
tu.randBlobFloat32("X", 1, c, t, h, w)
tu.randBlobFloat32("w", c, c, kt, kh, kw)
tu.randBlobsFloat32(["b", "scale", "bias", "mean"], c)
# This is necessary because 1/sqrt(var) is used and if var is too small
# we get floating point artifacts that cause test failures
tu.randBlobFloat32("var", c, offset=0.5)
workspace.RunNetOnce(net)
preTransformOutput = workspace.FetchBlob("Y2").flatten()
workspace.FeedBlob("Y2", np.zeros((1, 1)))
transformer.FuseConvBN(net)
# Ensure fusion
assert tu.numOps(net) == 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_converterDontEnforceUnusedInputs(self):
net = core.Net("net")
net.Relu(["X"], ["Y"])
net.Proto().external_input.extend(["fake"])
# This should now work
transformer.AddNNPACK(net) # just testing the converter
def test_converterDontEnforceUnusedOutputs(self):
net = core.Net("net")
net.Relu(["X"], ["Y"])
net.Proto().external_output.extend(["fake"])
transformer.AddNNPACK(net) # just testing the converter