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from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestChannelBackpropStats(serial.SerializedTestCase):
@given(
size=st.integers(7, 10),
inputChannels=st.integers(1, 10),
batchSize=st.integers(1, 3),
**hu.gcs
)
@settings(deadline=10000)
def testChannelBackpropStats(self, size, inputChannels, batchSize, gc, dc):
op = core.CreateOperator(
"ChannelBackpropStats",
["X", "mean", "invStdDev", "outputGrad"],
["scaleGrad", "biasGrad"],
)
def referenceChannelBackpropStatsTest(X, mean, invStdDev, outputGrad):
scaleGrad = np.zeros(inputChannels)
biasGrad = np.zeros(inputChannels)
for n in range(batchSize):
for c in range(inputChannels):
for h in range(size):
for w in range(size):
biasGrad[c] += outputGrad[n, c, h, w]
scaleGrad[c] += (
X[n, c, h, w] - mean[c]
) * invStdDev[c] * outputGrad[n, c, h, w]
return scaleGrad, biasGrad
X = np.random.rand(batchSize, inputChannels, size, size)\
.astype(np.float32) - 0.5
sums = np.sum(X, axis=(0, 2, 3), keepdims=False)
numPixels = size * size * batchSize
mean = sums / numPixels
sumsq = np.sum(X**2, axis=(0, 2, 3), keepdims=False)
var = ((sumsq -
(sums * sums) / numPixels) / numPixels).astype(np.float32)
invStdDev = 1 / np.sqrt(var)
outputGrad = np.random.rand(batchSize, inputChannels, size, size)\
.astype(np.float32) - 0.5
self.assertReferenceChecks(
gc, op, [X, mean, invStdDev, outputGrad],
referenceChannelBackpropStatsTest
)
if __name__ == "__main__":
unittest.main()
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