def test_shape_and_type_inference(self):
with hu.temp_workspace("shape_type_inf_int64"):
net = core.Net('test_net')
net.ConstantFill(
[],
"values",
shape=[64],
dtype=core.DataType.INT64,
)
net.IndexHash(['values'], ['values_output'])
(shapes, types) = workspace.InferShapesAndTypes([net], {})
self.assertEqual(shapes["values_output"], [64])
self.assertEqual(types["values_output"], core.DataType.INT64)
with hu.temp_workspace("shape_type_inf_int32"):
net = core.Net('test_net')
net.ConstantFill(
[],
"values",
shape=[2, 32],
dtype=core.DataType.INT32,
)
net.IndexHash(['values'], ['values_output'])
(shapes, types) = workspace.InferShapesAndTypes([net], {})
self.assertEqual(shapes["values_output"], [2, 32])
self.assertEqual(types["values_output"], core.DataType.INT32)
def ref(data):
ret = data.astype(dtype=np.str)
# the string blob will be fetched as object, we feed and re-fetch
# to mimic this.
with hu.temp_workspace('tmp_ref_int_to_string'):
workspace.FeedBlob('tmp_blob', ret)
fetched_ret = workspace.FetchBlob('tmp_blob')
return (fetched_ret, )
def test_shape_and_type_inference(self):
with hu.temp_workspace("shape_type_inf_int32"):
net = core.Net("test_net")
net.ConstantFill([], "ranges", shape=[3, 5, 2], dtype=core.DataType.INT32)
net.ConstantFill([], "values", shape=[64], dtype=core.DataType.INT64)
net.GatherRanges(["values", "ranges"], ["values_output", "lengths_output"])
(shapes, types) = workspace.InferShapesAndTypes([net], {})
self.assertEqual(shapes["values_output"], [64])
self.assertEqual(types["values_output"], core.DataType.INT64)
self.assertEqual(shapes["lengths_output"], [3])
self.assertEqual(types["lengths_output"], core.DataType.INT32)
def test_shape_and_type_inference(self):
with hu.temp_workspace("shape_type_inf_int64"):
net = core.Net('test_net')
net.ConstantFill(
[], "values", shape=[64], dtype=core.DataType.INT64,
)
net.IndexHash(['values'], ['values_output'])
(shapes, types) = workspace.InferShapesAndTypes([net], {})
self.assertEqual(shapes["values_output"], [64])
self.assertEqual(types["values_output"], core.DataType.INT64)
with hu.temp_workspace("shape_type_inf_int32"):
net = core.Net('test_net')
net.ConstantFill(
[], "values", shape=[2, 32], dtype=core.DataType.INT32,
)
net.IndexHash(['values'], ['values_output'])
(shapes, types) = workspace.InferShapesAndTypes([net], {})
self.assertEqual(shapes["values_output"], [2, 32])
self.assertEqual(types["values_output"], core.DataType.INT32)
def test_shape_and_type_inference(self):
with hu.temp_workspace("shape_type_inf_int32"):
net = core.Net('test_net')
net.ConstantFill(
[], "ranges", shape=[3, 5, 2], dtype=core.DataType.INT32,
)
net.ConstantFill(
[], "values", shape=[64], dtype=core.DataType.INT64,
)
net.GatherRanges(['values', 'ranges'], ['values_output', 'lengths_output'])
(shapes, types) = workspace.InferShapesAndTypes([net], {})
self.assertEqual(shapes["values_output"], [64])
self.assertEqual(types["values_output"], core.DataType.INT64)
self.assertEqual(shapes["lengths_output"], [3])
self.assertEqual(types["lengths_output"], core.DataType.INT32)
def run_test(
size_tuple, means, stds, label_type, num_labels, dc, validator,
output1=None, output2_size=None):
# TODO: Does not test on GPU and does not test use_gpu_transform
# WARNING: Using ModelHelper automatically does NHWC to NCHW
# transformation if needed.
width, height, minsize, crop = size_tuple
means = [float(m) for m in means]
stds = [float(s) for s in stds]
out_dir = tempfile.mkdtemp()
count_images = 2 # One with bounding box and one without
expected_images = create_test(
out_dir,
width=width,
height=height,
default_bound=(3, 5, height - 3, width - 5),
minsize=minsize,
crop=crop,
means=means,
stds=stds,
count=count_images,
label_type=label_type,
num_labels=num_labels,
output1=output1,
output2_size=output2_size
)
for device_option in dc:
with hu.temp_workspace():
reader_net = core.Net('reader')
reader_net.CreateDB(
[],
'DB',
db=out_dir,
db_type="lmdb"
)
workspace.RunNetOnce(reader_net)
outputs = ['data', 'label']
output_sizes = []
if output1:
outputs.append('output1')
output_sizes.append(1)
if output2_size:
outputs.append('output2')
output_sizes.append(output2_size)
imageop = core.CreateOperator(
'ImageInput',
['DB'],
outputs,
batch_size=count_images,
color=3,
minsize=minsize,
crop=crop,
is_test=True,
bounding_ymin=3,
bounding_xmin=5,
bounding_height=height - 3,
bounding_width=width - 5,
mean_per_channel=means,
std_per_channel=stds,
use_gpu_transform=(device_option.device_type == 1),
label_type=label_type,
num_labels=num_labels,
output_sizes=output_sizes
)
imageop.device_option.CopyFrom(device_option)
main_net = core.Net('main')
main_net.Proto().op.extend([imageop])
workspace.RunNetOnce(main_net)
validator(expected_images, device_option, count_images)
# End for
# End with
# End for
shutil.rmtree(out_dir)
def test_basic_rnn(self, seed, seq_length, batch_size, input_size,
hidden_size, drop_states, sequence_lengths, gc, dc):
np.random.seed(seed)
seq_lengths_data = np.random.randint(1,
seq_length + 1,
size=(batch_size, )).astype(
np.int32)
input_blob_data = np.random.randn(seq_length, batch_size,
input_size).astype(np.float32)
initial_h_data = np.random.randn(batch_size,
hidden_size).astype(np.float32)
gates_t_w_data = np.random.randn(hidden_size,
hidden_size).astype(np.float32)
gates_t_b_data = np.random.randn(hidden_size).astype(np.float32)
i2h_w_data = np.random.randn(hidden_size,
input_size).astype(np.float32)
i2h_b_data = np.random.randn(hidden_size).astype(np.float32)
with core.DeviceScope(gc):
with hu.temp_workspace():
workspace.FeedBlob('input_blob',
input_blob_data,
device_option=gc)
workspace.FeedBlob('seq_lengths',
seq_lengths_data,
device_option=gc)
workspace.FeedBlob('initial_h',
initial_h_data,
device_option=gc)
workspace.FeedBlob('basic_rnn/gates_t_w',
gates_t_w_data,
device_option=gc)
workspace.FeedBlob('basic_rnn/gates_t_b',
gates_t_b_data,
device_option=gc)
workspace.FeedBlob('basic_rnn/i2h_w',
i2h_w_data,
device_option=gc)
workspace.FeedBlob('basic_rnn/i2h_b',
i2h_b_data,
device_option=gc)
model = ModelHelper(name='model')
hidden_t_all, _ = rnn_cell.BasicRNN(
model,
'input_blob',
'seq_lengths' if sequence_lengths else None, ['initial_h'],
input_size,
hidden_size,
"basic_rnn",
activation='tanh',
forward_only=True,
drop_states=drop_states)
workspace.RunNetOnce(model.net)
result = workspace.FetchBlob(hidden_t_all)
reference = basic_rnn_reference(
input_blob_data,
initial_h_data,
i2h_w_data,
i2h_b_data,
gates_t_w_data,
gates_t_b_data,
seq_lengths_data if sequence_lengths else None,
drop_states=drop_states,
use_sequence_lengths=sequence_lengths)
np.testing.assert_allclose(result, reference, atol=1e-4, rtol=1e-4)
def run_test(
size_tuple, means, stds, label_type, num_labels, is_test, scale_jitter_type,
color_jitter, color_lighting, dc, validator, output1=None, output2_size=None):
# TODO: Does not test on GPU and does not test use_gpu_transform
# WARNING: Using ModelHelper automatically does NHWC to NCHW
# transformation if needed.
width, height, minsize, crop = size_tuple
means = [float(m) for m in means]
stds = [float(s) for s in stds]
out_dir = tempfile.mkdtemp()
count_images = 2 # One with bounding box and one without
expected_images = create_test(
out_dir,
width=width,
height=height,
default_bound=(3, 5, height - 3, width - 5),
minsize=minsize,
crop=crop,
means=means,
stds=stds,
count=count_images,
label_type=label_type,
num_labels=num_labels,
output1=output1,
output2_size=output2_size
)
for device_option in dc:
with hu.temp_workspace():
reader_net = core.Net('reader')
reader_net.CreateDB(
[],
'DB',
db=out_dir,
db_type="lmdb"
)
workspace.RunNetOnce(reader_net)
outputs = ['data', 'label']
output_sizes = []
if output1:
outputs.append('output1')
output_sizes.append(1)
if output2_size:
outputs.append('output2')
output_sizes.append(output2_size)
imageop = core.CreateOperator(
'ImageInput',
['DB'],
outputs,
batch_size=count_images,
color=3,
minsize=minsize,
crop=crop,
is_test=is_test,
bounding_ymin=3,
bounding_xmin=5,
bounding_height=height - 3,
bounding_width=width - 5,
mean_per_channel=means,
std_per_channel=stds,
use_gpu_transform=(device_option.device_type == 1),
label_type=label_type,
num_labels=num_labels,
output_sizes=output_sizes,
scale_jitter_type=scale_jitter_type,
color_jitter=color_jitter,
color_lighting=color_lighting
)
imageop.device_option.CopyFrom(device_option)
main_net = core.Net('main')
main_net.Proto().op.extend([imageop])
workspace.RunNetOnce(main_net)
validator(expected_images, device_option, count_images)
# End for
# End with
# End for
shutil.rmtree(out_dir)
def test_basic_rnn(self, seed, seq_length, batch_size, input_size, hidden_size,
drop_states, sequence_lengths, gc, dc):
np.random.seed(seed)
seq_lengths_data = np.random.randint(
1, seq_length + 1, size=(batch_size,)).astype(np.int32)
input_blob_data = np.random.randn(
seq_length, batch_size, input_size).astype(np.float32)
initial_h_data = np.random.randn(
batch_size, hidden_size).astype(np.float32)
gates_t_w_data = np.random.randn(
hidden_size, hidden_size).astype(np.float32)
gates_t_b_data = np.random.randn(
hidden_size).astype(np.float32)
i2h_w_data = np.random.randn(
hidden_size, input_size).astype(np.float32)
i2h_b_data = np.random.randn(
hidden_size).astype(np.float32)
with core.DeviceScope(gc):
with hu.temp_workspace():
workspace.FeedBlob(
'input_blob', input_blob_data, device_option=gc)
workspace.FeedBlob(
'seq_lengths', seq_lengths_data, device_option=gc)
workspace.FeedBlob(
'initial_h', initial_h_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/gates_t_w', gates_t_w_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/gates_t_b', gates_t_b_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/i2h_w', i2h_w_data, device_option=gc)
workspace.FeedBlob(
'basic_rnn/i2h_b', i2h_b_data, device_option=gc)
model = ModelHelper(name='model')
hidden_t_all, _ = rnn_cell.BasicRNN(
model,
'input_blob',
'seq_lengths' if sequence_lengths else None,
['initial_h'],
input_size,
hidden_size,
"basic_rnn",
activation='tanh',
forward_only=True,
drop_states=drop_states)
workspace.RunNetOnce(model.net)
result = workspace.FetchBlob(hidden_t_all)
reference = basic_rnn_reference(
input_blob_data,
initial_h_data,
i2h_w_data,
i2h_b_data,
gates_t_w_data,
gates_t_b_data,
seq_lengths_data if sequence_lengths else None,
drop_states=drop_states,
use_sequence_lengths=sequence_lengths
)
np.testing.assert_allclose(result, reference, atol=1e-4, rtol=1e-4)
def test_imageinput(self, size_tuple, means, stds, gc, dc):
# TODO: Does not test on GPU and does not test use_gpu_transform
# WARNING: Using ModelHelper automatically does NHWC to NCHW
# transformation if needed.
width, height, minsize, crop = size_tuple
means = [float(m) for m in means]
stds = [float(s) for s in stds]
out_dir = tempfile.mkdtemp()
count_images = 2 # One with bounding box and one without
expected_images = create_test(out_dir,
width=width,
height=height,
default_bound=(3, 5, height - 3,
width - 5),
minsize=minsize,
crop=crop,
means=means,
stds=stds,
count=count_images)
for device_option in dc:
with hu.temp_workspace():
reader_net = core.Net('reader')
reader_net.CreateDB([], 'DB', db=out_dir, db_type="lmdb")
workspace.RunNetOnce(reader_net)
imageop = core.CreateOperator(
'ImageInput', ['DB'], ["data", "label"],
batch_size=count_images,
color=3,
minsize=minsize,
crop=crop,
is_test=True,
bounding_ymin=3,
bounding_xmin=5,
bounding_height=height - 3,
bounding_width=width - 5,
mean_per_channel=means,
std_per_channel=stds,
use_gpu_transform=(device_option.device_type == 1))
imageop.device_option.CopyFrom(device_option)
main_net = core.Net('main')
main_net.Proto().op.extend([imageop])
workspace.RunNetOnce(main_net)
l = workspace.FetchBlob('label')
result = workspace.FetchBlob('data').astype(np.int32)
# If we don't use_gpu_transform, the output is in NHWC
# Our reference output is CHW so we swap
if device_option.device_type != 1:
expected = [
img.swapaxes(0, 1).swapaxes(1, 2)
for img in expected_images
]
else:
expected = expected_images
for i in range(count_images):
self.assertEqual(l[i], i)
self.assertEqual((expected[i] - result[i] > 1).sum(), 0)
# End for
# End with
# End for
shutil.rmtree(out_dir)
