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from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
class TestAffineChannelOp(serial.SerializedTestCase):
def affine_channel_nchw_ref(self, X, scale, bias):
dims = X.shape
N = dims[0]
C = dims[1]
X = X.reshape(N, C, -1)
scale = scale.reshape(C, 1)
bias = bias.reshape(C, 1)
Y = X * scale + bias
return [Y.reshape(dims)]
def affine_channel_nhwc_ref(self, X, scale, bias):
dims = X.shape
N = dims[0]
C = dims[-1]
X = X.reshape(N, -1, C)
Y = X * scale + bias
return [Y.reshape(dims)]
@serial.given(N=st.integers(1, 5), C=st.integers(1, 5),
H=st.integers(1, 5), W=st.integers(1, 5),
order=st.sampled_from(["NCHW", "NHWC"]), is_learnable=st.booleans(),
in_place=st.booleans(), **hu.gcs)
def test_affine_channel_2d(
self, N, C, H, W, order, is_learnable, in_place, gc, dc):
op = core.CreateOperator(
"AffineChannel",
["X", "scale", "bias"],
["X"] if in_place and not is_learnable else ["Y"],
order=order,
is_learnable=is_learnable,
)
if order == "NCHW":
X = np.random.randn(N, C, H, W).astype(np.float32)
else:
X = np.random.randn(N, H, W, C).astype(np.float32)
scale = np.random.randn(C).astype(np.float32)
bias = np.random.randn(C).astype(np.float32)
inputs = [X, scale, bias]
def ref_op(X, scale, bias):
if order == "NCHW":
return self.affine_channel_nchw_ref(X, scale, bias)
else:
return self.affine_channel_nhwc_ref(X, scale, bias)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=ref_op,
)
self.assertDeviceChecks(dc, op, inputs, [0])
num_grad = len(inputs) if is_learnable else 1
for i in range(num_grad):
self.assertGradientChecks(gc, op, inputs, i, [0])
@given(N=st.integers(1, 5), C=st.integers(1, 5), T=st.integers(1, 3),
H=st.integers(1, 3), W=st.integers(1, 3),
order=st.sampled_from(["NCHW", "NHWC"]), is_learnable=st.booleans(),
in_place=st.booleans(), **hu.gcs)
@settings(deadline=10000)
def test_affine_channel_3d(
self, N, C, T, H, W, order, is_learnable, in_place, gc, dc):
op = core.CreateOperator(
"AffineChannel",
["X", "scale", "bias"],
["X"] if in_place and not is_learnable else ["Y"],
order=order,
is_learnable=is_learnable,
)
if order == "NCHW":
X = np.random.randn(N, C, T, H, W).astype(np.float32)
else:
X = np.random.randn(N, T, H, W, C).astype(np.float32)
scale = np.random.randn(C).astype(np.float32)
bias = np.random.randn(C).astype(np.float32)
inputs = [X, scale, bias]
def ref_op(X, scale, bias):
if order == "NCHW":
return self.affine_channel_nchw_ref(X, scale, bias)
else:
return self.affine_channel_nhwc_ref(X, scale, bias)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=ref_op,
)
self.assertDeviceChecks(dc, op, inputs, [0])
num_grad = len(inputs) if is_learnable else 1
for i in range(num_grad):
self.assertGradientChecks(gc, op, inputs, i, [0])
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