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from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
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
def _fill_diagonal(shape, value):
result = np.zeros(shape)
np.fill_diagonal(result, value)
return (result,)
class TestFillerOperator(serial.SerializedTestCase):
@given(**hu.gcs)
@settings(deadline=10000)
def test_shape_error(self, gc, dc):
op = core.CreateOperator(
'GaussianFill',
[],
'out',
shape=32, # illegal parameter
mean=0.0,
std=1.0,
)
exception = False
try:
workspace.RunOperatorOnce(op)
except Exception:
exception = True
self.assertTrue(exception, "Did not throw exception on illegal shape")
op = core.CreateOperator(
'ConstantFill',
[],
'out',
shape=[], # scalar
value=2.0,
)
exception = False
self.assertTrue(workspace.RunOperatorOnce(op))
self.assertEqual(workspace.FetchBlob('out'), [2.0])
@given(**hu.gcs)
@settings(deadline=10000)
def test_int64_shape(self, gc, dc):
large_dim = 2 ** 31 + 1
net = core.Net("test_shape_net")
net.UniformFill(
[],
'out',
shape=[0, large_dim],
min=0.0,
max=1.0,
)
self.assertTrue(workspace.CreateNet(net))
self.assertTrue(workspace.RunNet(net.Name()))
self.assertEqual(workspace.blobs['out'].shape, (0, large_dim))
@given(
shape=hu.dims().flatmap(
lambda dims: hu.arrays(
[dims], dtype=np.int64,
elements=st.integers(min_value=0, max_value=20)
)
),
a=st.integers(min_value=0, max_value=100),
b=st.integers(min_value=0, max_value=100),
**hu.gcs
)
@settings(deadline=10000)
def test_uniform_int_fill_op_blob_input(self, shape, a, b, gc, dc):
net = core.Net('test_net')
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
shape_blob = net.Const(shape, dtype=np.int64)
a_blob = net.Const(a, dtype=np.int32)
b_blob = net.Const(b, dtype=np.int32)
uniform_fill = net.UniformIntFill([shape_blob, a_blob, b_blob],
1, input_as_shape=1)
workspace.RunNetOnce(net)
blob_out = workspace.FetchBlob(uniform_fill)
if b < a:
new_shape = shape[:]
new_shape[0] = 0
np.testing.assert_array_equal(new_shape, blob_out.shape)
else:
np.testing.assert_array_equal(shape, blob_out.shape)
self.assertTrue((blob_out >= a).all())
self.assertTrue((blob_out <= b).all())
@given(
**hu.gcs
)
def test_uniform_fill_using_arg(self, gc, dc):
net = core.Net('test_net')
shape = [2**3, 5]
# uncomment this to test filling large blob
# shape = [2**30, 5]
min_v = -100
max_v = 100
output_blob = net.UniformIntFill(
[],
['output_blob'],
shape=shape,
min=min_v,
max=max_v,
)
workspace.RunNetOnce(net)
output_data = workspace.FetchBlob(output_blob)
np.testing.assert_array_equal(shape, output_data.shape)
min_data = np.min(output_data)
max_data = np.max(output_data)
self.assertGreaterEqual(min_data, min_v)
self.assertLessEqual(max_data, max_v)
self.assertNotEqual(min_data, max_data)
@serial.given(
shape=st.sampled_from(
[
[3, 3],
[5, 5, 5],
[7, 7, 7, 7],
]
),
**hu.gcs
)
def test_diagonal_fill_op_float(self, shape, gc, dc):
value = 2.5
op = core.CreateOperator(
'DiagonalFill',
[],
'out',
shape=shape, # scalar
value=value,
)
for device_option in dc:
op.device_option.CopyFrom(device_option)
# Check against numpy reference
self.assertReferenceChecks(gc, op, [shape, value], _fill_diagonal)
@given(**hu.gcs)
def test_diagonal_fill_op_int(self, gc, dc):
value = 2
shape = [3, 3]
op = core.CreateOperator(
'DiagonalFill',
[],
'out',
shape=shape,
dtype=core.DataType.INT32,
value=value,
)
# Check against numpy reference
self.assertReferenceChecks(gc, op, [shape, value], _fill_diagonal)
@serial.given(lengths=st.lists(st.integers(min_value=0, max_value=10),
min_size=0,
max_size=10),
**hu.gcs)
def test_lengths_range_fill(self, lengths, gc, dc):
op = core.CreateOperator(
"LengthsRangeFill",
["lengths"],
["increasing_seq"])
def _len_range_fill(lengths):
sids = []
for _, l in enumerate(lengths):
sids.extend(list(range(l)))
return (np.array(sids, dtype=np.int32), )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[np.array(lengths, dtype=np.int32)],
reference=_len_range_fill)
@given(**hu.gcs)
def test_gaussian_fill_op(self, gc, dc):
op = core.CreateOperator(
'GaussianFill',
[],
'out',
shape=[17, 3, 3], # sample odd dimensions
mean=0.0,
std=1.0,
)
for device_option in dc:
op.device_option.CopyFrom(device_option)
assert workspace.RunOperatorOnce(op), "GaussianFill op did not run "
"successfully"
blob_out = workspace.FetchBlob('out')
assert np.count_nonzero(blob_out) > 0, "All generated elements are "
"zeros. Is the random generator functioning correctly?"
@given(**hu.gcs)
def test_msra_fill_op(self, gc, dc):
op = core.CreateOperator(
'MSRAFill',
[],
'out',
shape=[15, 5, 3], # sample odd dimensions
)
for device_option in dc:
op.device_option.CopyFrom(device_option)
assert workspace.RunOperatorOnce(op), "MSRAFill op did not run "
"successfully"
blob_out = workspace.FetchBlob('out')
assert np.count_nonzero(blob_out) > 0, "All generated elements are "
"zeros. Is the random generator functioning correctly?"
@given(
min=st.integers(min_value=0, max_value=5),
range=st.integers(min_value=1, max_value=10),
emb_size=st.sampled_from((10000, 20000, 30000)),
dim_size=st.sampled_from((16, 32, 64)),
**hu.gcs)
@settings(deadline=None)
def test_fp16_uniformfill_op(self, min, range, emb_size, dim_size, gc, dc):
op = core.CreateOperator(
'Float16UniformFill',
[],
'out',
shape=[emb_size, dim_size],
min=float(min),
max=float(min + range),
)
for device_option in dc:
op.device_option.CopyFrom(device_option)
assert workspace.RunOperatorOnce(op), "Float16UniformFill op did not run successfully"
self.assertEqual(workspace.blobs['out'].shape, (emb_size, dim_size))
blob_out = workspace.FetchBlob('out')
expected_type = "float16"
expected_mean = min + range / 2.0
expected_var = range * range / 12.0
expected_min = min
expected_max = min + range
self.assertEqual(blob_out.dtype.name, expected_type)
self.assertAlmostEqual(np.mean(blob_out, dtype=np.float32), expected_mean, delta=0.1)
self.assertAlmostEqual(np.var(blob_out, dtype=np.float32), expected_var, delta=0.1)
self.assertGreaterEqual(np.min(blob_out), expected_min)
self.assertLessEqual(np.max(blob_out), expected_max)
if __name__ == "__main__":
import unittest
unittest.main()
|
