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# 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.
##############################################################################
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given, settings
class TestComputeEqualizationScaleOp(hu.HypothesisTestCase):
@settings(max_examples=10)
@given(
m=st.integers(1, 50),
n=st.integers(1, 50),
k=st.integers(1, 50),
rnd_seed=st.integers(1, 5),
**hu.gcs_cpu_only
)
def test_compute_equalization_scale(self, m, n, k, rnd_seed, gc, dc):
np.random.seed(rnd_seed)
W = np.random.rand(n, k).astype(np.float32) - 0.5
X = np.random.rand(m, k).astype(np.float32) - 0.5
def ref_compute_equalization_scale(X, W):
S = np.ones([X.shape[1]])
for j in range(W.shape[1]):
WcolMax = np.absolute(W[:, j]).max()
XcolMax = np.absolute(X[:, j]).max()
if WcolMax and XcolMax:
S[j] = np.sqrt(WcolMax / XcolMax)
return S
net = core.Net("test")
ComputeEqualizationScaleOp = core.CreateOperator(
"ComputeEqualizationScale", ["X", "W"], ["S"]
)
net.Proto().op.extend([ComputeEqualizationScaleOp])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.run(net)
S = self.ws.blobs["S"].fetch()
S_ref = ref_compute_equalization_scale(X, W)
np.testing.assert_allclose(S, S_ref, atol=1e-3, rtol=1e-3)
def test_compute_equalization_scale_shape_inference(self):
X = np.array([[1, 2], [2, 4], [6, 7]]).astype(np.float32)
W = np.array([[2, 3], [5, 4], [8, 2]]).astype(np.float32)
ComputeEqualizationScaleOp = core.CreateOperator(
"ComputeEqualizationScale", ["X", "W"], ["S"]
)
workspace.FeedBlob("X", X)
workspace.FeedBlob("W", W)
net = core.Net("test_shape_inference")
net.Proto().op.extend([ComputeEqualizationScaleOp])
shapes, types = workspace.InferShapesAndTypes(
[net],
blob_dimensions={"X": X.shape, "W": W.shape},
blob_types={"X": core.DataType.FLOAT, "W": core.DataType.FLOAT},
)
assert (
"S" in shapes and "S" in types
), "Failed to infer the shape or type of output"
self.assertEqual(shapes["S"], [1, 2])
self.assertEqual(types["S"], core.DataType.FLOAT)
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