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from caffe2.python import core, workspace
from hypothesis import assume, given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestReductionOps(serial.SerializedTestCase):
@serial.given(n=st.integers(5, 8), **hu.gcs)
def test_elementwise_sum(self, n, gc, dc):
X = np.random.rand(n).astype(np.float32)
def sum_op(X):
return [np.sum(X)]
op = core.CreateOperator(
"SumElements",
["X"],
["y"]
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X],
reference=sum_op,
)
self.assertGradientChecks(
device_option=gc,
op=op,
inputs=[X],
outputs_to_check=0,
outputs_with_grads=[0],
)
@given(n=st.integers(5, 8), **hu.gcs)
@settings(deadline=10000)
def test_elementwise_int_sum(self, n, gc, dc):
X = np.random.rand(n).astype(np.int32)
def sum_op(X):
return [np.sum(X)]
op = core.CreateOperator(
"SumElementsInt",
["X"],
["y"]
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X],
reference=sum_op,
)
@given(n=st.integers(1, 65536),
dtype=st.sampled_from([np.float32, np.float16]),
**hu.gcs)
@settings(deadline=10000)
def test_elementwise_sqrsum(self, n, dtype, gc, dc):
if dtype == np.float16:
# fp16 is only supported with CUDA/HIP
assume(gc.device_type == workspace.GpuDeviceType)
dc = [d for d in dc if d.device_type == workspace.GpuDeviceType]
X = np.random.rand(n).astype(dtype)
def sumsqr_op(X):
return [np.sum(X * X)]
op = core.CreateOperator(
"SumSqrElements",
["X"],
["y"]
)
threshold = 0.01 if dtype == np.float16 else 0.005
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X],
reference=sumsqr_op,
threshold=threshold,
)
@given(n=st.integers(5, 8), **hu.gcs)
def test_elementwise_avg(self, n, gc, dc):
X = np.random.rand(n).astype(np.float32)
def avg_op(X):
return [np.mean(X)]
op = core.CreateOperator(
"SumElements",
["X"],
["y"],
average=1
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X],
reference=avg_op,
)
self.assertGradientChecks(
device_option=gc,
op=op,
inputs=[X],
outputs_to_check=0,
outputs_with_grads=[0],
)
@serial.given(batch_size=st.integers(1, 3),
m=st.integers(1, 3),
n=st.integers(1, 4),
**hu.gcs)
def test_rowwise_max(self, batch_size, m, n, gc, dc):
X = np.random.rand(batch_size, m, n).astype(np.float32)
def rowwise_max(X):
return [np.max(X, axis=2)]
op = core.CreateOperator(
"RowwiseMax",
["x"],
["y"]
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X],
reference=rowwise_max,
)
@serial.given(batch_size=st.integers(1, 3),
m=st.integers(1, 3),
n=st.integers(1, 4),
**hu.gcs)
def test_columnwise_max(self, batch_size, m, n, gc, dc):
X = np.random.rand(batch_size, m, n).astype(np.float32)
def columnwise_max(X):
return [np.max(X, axis=1)]
op = core.CreateOperator(
"ColwiseMax",
["x"],
["y"]
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X],
reference=columnwise_max,
)
# Test shape inference logic
net = core.Net("test_shape_inference")
workspace.FeedBlob("x", X)
output = net.ColwiseMax(["x"], ["y"])
(shapes, types) = workspace.InferShapesAndTypes([net])
workspace.RunNetOnce(net)
self.assertEqual(shapes[output], list(workspace.blobs[output].shape))
self.assertEqual(shapes[output], [X.shape[0]] + [X.shape[2]])
self.assertEqual(types[output], core.DataType.FLOAT)
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