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import numpy as np
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, utils
from hypothesis import given, settings
import hypothesis.strategies as st
class Depthwise3x3ConvOpsTest(hu.HypothesisTestCase):
@given(pad=st.integers(0, 1),
kernel=st.integers(3, 3),
size=st.integers(4, 8),
channels=st.integers(2, 4),
batch_size=st.integers(1, 1),
order=st.sampled_from(["NCHW"]),
engine=st.sampled_from(["DEPTHWISE_3x3"]),
use_bias=st.booleans(),
**hu.gcs)
@settings(deadline=10000)
def test_convolution_gradients(self, pad, kernel, size,
channels, batch_size,
order, engine, use_bias, gc, dc):
op = core.CreateOperator(
"Conv",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
kernel=kernel,
pad=pad,
group=channels,
order=order,
engine=engine,
)
X = np.random.rand(
batch_size, size, size, channels).astype(np.float32) - 0.5
w = np.random.rand(
channels, kernel, kernel, 1).astype(np.float32)\
- 0.5
b = np.random.rand(channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, w, b] if use_bias else [X, w]
# Error handling path.
if size + pad + pad < kernel or size + pad + pad < kernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
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