def test_reshape():
fname = os.path.join(this_file_dir,
'runtime_analysis/{}/general/reshape.txt'.format(DIM))
if os.path.exists(fname):
os.remove(fname)
for lib, call in [
(l, c) for l, c in helpers.calls
if c not in [helpers.tf_graph_call, helpers.mx_graph_call]
]:
time_lib = LIB_DICT[lib]
append_to_file(fname, '{}'.format(lib))
x0 = ivy_gen.array([random.uniform(0, 1) for _ in range(DIM)], f=lib)
ivy_gen.reshape(x0, [1, DIM], f=lib)
ivy_gen_w_time.reshape(x0, [1, DIM], f=time_lib)
TIMES_DICT.clear()
for _ in range(100):
log_time(fname, 'tb0')
ivy_gen_w_time.reshape(x0, [1, DIM], f=time_lib)
log_time(fname, 'tb4', time_at_start=True)
log_time(fname, 'tt0')
ivy_gen.reshape(x0, [1, DIM], f=lib)
log_time(fname, 'tt1', time_at_start=True)
write_times()
append_to_file(fname, 'end of analysis')
def concat(containers, dim, f=None):
"""
Concatenate containers together along the specified dimension.
:param containers: containers to _concatenate
:type containers: sequence of Container objects
:param dim: dimension along which to _concatenate
:type dim: int
:param f: Machine learning framework. Inferred from inputs if None.
:type f: ml_framework, optional
:return: Concatenated containers
"""
container0 = containers[0]
if isinstance(container0, dict):
return_dict = dict()
for key in container0.keys():
return_dict[key] = Container.concat([container[key] for container in containers], dim)
return Container(return_dict)
else:
f = _get_framework(container0, f=f)
# noinspection PyBroadException
try:
if len(containers[0].shape) == 0:
return _ivy_gen.concatenate([_ivy_gen.reshape(item, [1]*(dim+1)) for item in containers], dim, f=f)
else:
return _ivy_gen.concatenate(containers, dim, f=f)
except Exception as e:
raise Exception(str(e) + '\nContainer concat operation only valid for containers of arrays')
def test_conv3d_transpose():
fname = os.path.join(this_file_dir, 'runtime_analysis/{}/layers/conv3d_transpose.txt'.format(DIM))
if os.path.exists(fname):
os.remove(fname)
for lib, call in [(l, c) for l, c in helpers.calls if c not in [helpers.tf_graph_call, helpers.mx_graph_call]]:
time_lib = LIB_DICT[lib]
if call in [helpers.np_call, helpers.jnp_call, helpers.mx_call, helpers.mx_graph_call]:
# numpy and jax do not yet support 3d convolutions, and mxnet only supports with CUDNN
continue
x0 = ivy_gen.array([[[[[random.uniform(0, 1)], [random.uniform(0, 1)]],
[[random.uniform(0, 1)], [random.uniform(0, 1)]]],
[[[random.uniform(0, 1)], [random.uniform(0, 1)]],
[[random.uniform(0, 1)], [random.uniform(0, 1)]]]] for _ in range(DIM)], f=lib)
if call in [helpers.tf_call, helpers.tf_graph_call]:
data_format = "NDHWC"
else:
x0 = ivy_gen.reshape(x0, (DIM, 1, 2, 2, 2))
data_format = "NCDHW"
append_to_file(fname, '{}'.format(lib))
filters = ivy_gen.array([[[[[0.]], [[0.]]], [[[1.]], [[1.]]]],
[[[[1.]], [[1.]]], [[[0.]], [[0.]]]]], f=lib)
ivy_layers.conv3d_transpose(x0, filters, 1, "SAME", (DIM, 2, 2, 2, 1), data_format, filter_shape=[2, 2, 2],
num_filters=1, f=lib)
ivy_layers_w_time.conv3d_transpose(x0, filters, 1, "SAME", (DIM, 2, 2, 2, 1), data_format,
filter_shape=[2, 2, 2], num_filters=1, f=time_lib)
TIMES_DICT.clear()
for _ in range(100):
log_time(fname, 'tb0')
ivy_layers_w_time.conv3d_transpose(x0, filters, 1, "SAME", (DIM, 2, 2, 2, 1), data_format,
filter_shape=[2, 2, 2], num_filters=1, f=time_lib)
log_time(fname, 'tb4', time_at_start=True)
log_time(fname, 'tt0')
ivy_layers.conv3d_transpose(x0, filters, 1, "SAME", (DIM, 2, 2, 2, 1), data_format, filter_shape=[2, 2, 2],
num_filters=1, f=lib)
log_time(fname, 'tt1', time_at_start=True)
write_times()
append_to_file(fname, 'end of analysis')