def flatten(x):
return utils.flatten(x) if isinstance(x, (list, tuple)) else x
def _test(self,
fun,
cmp=True,
custom_tf_to_nnef_converters="",
custom_nnef_to_tf_converters="",
test_module="nnef_tests.conversion.tf_py_layer_test_cases",
atol=1e-5):
activation_testing = int(os.environ.get('NNEF_ACTIVATION_TESTING',
'1'))
print("Activation testing is", "ON" if activation_testing else "OFF")
out_dir = os.path.join("out", fun.__name__)
try:
tf.reset_default_graph()
tf.set_random_seed(0)
network_outputs = fun()
feed_dict = get_feed_dict()
old_names = [
placeholder.name for placeholder in get_placeholders()
]
checkpoint_path = os.path.join("out", fun.__name__,
"orig_checkpoint",
fun.__name__ + ".ckpt")
checkpoint_path = save_random_checkpoint(network_outputs,
checkpoint_path,
feed_dict)
tf.reset_default_graph()
tf.set_random_seed(0)
compress_nnef = False
command = """
./nnef_tools/convert.py --input-format tensorflow-py \\
--output-format nnef \\
--input-model {module}.{network} {checkpoint} \\
--output-model out/{network}/{network}.nnef{tgz} \\
--custom-converters {custom} \\
--permissive \\
--io-transformation SMART_TF_NHWC_TO_NCHW \\
{compress}
""".format(checkpoint=checkpoint_path if checkpoint_path else "",
network=fun.__name__,
custom=" ".join(custom_tf_to_nnef_converters),
compress="--compress" if compress_nnef else "",
module=test_module,
tgz=".tgz" if compress_nnef else "")
convert.convert_using_command(command)
if activation_testing:
tf.reset_default_graph()
tf.set_random_seed(0)
network_outputs = fun()
network_output_list = []
utils.recursive_visit(network_outputs,
lambda t: network_output_list.append(t))
# Flatten is needed because of MaxPoolWithArgMax objects
outputs = utils.flatten(
self._run_tfpy(network_output_list, feed_dict,
checkpoint_path))
else:
outputs = None
prefer_nhwc_options = [True]
if tf_has_cuda_gpu():
prefer_nhwc_options += [False]
for prefer_nhwc in prefer_nhwc_options:
print("Converting to TensorFlow {}".format(
"NHWC" if prefer_nhwc else "NCHW"))
data_format_str = ("nhwc" if prefer_nhwc else "nchw")
tf_output_path = os.path.join(
"out", fun.__name__,
fun.__name__ + '_' + data_format_str + '.py')
command = """
./nnef_tools/convert.py --input-format nnef \\
--output-format tensorflow-py \\
--input-model out/{network}/{network}.nnef{tgz} \\
--output-model {output} \\
--io-transformation SMART_NCHW_TO_TF_NHWC \\
--custom-converters {custom} \\
--permissive
""".format(network=fun.__name__,
custom=" ".join(custom_nnef_to_tf_converters),
tgz=".nnef.tgz" if compress_nnef else "",
output=tf_output_path)
convert.convert_using_command(command)
with open(os.path.join(tf_output_path), 'r') as f:
tf_src = f.read()
# noinspection PyProtectedMember
new_net_fun = tf_py_io._tfsource_to_function(
tf_src, fun.__name__)
tf.reset_default_graph()
tf.set_random_seed(0)
if activation_testing:
tf.reset_default_graph()
tf.set_random_seed(0)
network_outputs = new_net_fun()
network_output_list = []
utils.recursive_visit(
network_outputs,
lambda t: network_output_list.append(t))
feed_dict2 = {
placeholder.name: feed_dict[old_names[i]]
for i, placeholder in enumerate(get_placeholders())
}
outputs2 = utils.flatten(
self._run_tfpy(
network_output_list, feed_dict2,
(os.path.join(tf_output_path + ".checkpoint")
if checkpoint_path else None)))
if cmp:
self.assertTrue(len(outputs) == len(outputs2))
for a, b in zip(outputs, outputs2):
if a.dtype == np.bool:
self.assertTrue(np.all(a == b))
else:
print('Max diff:', np.max(np.abs(a - b)))
self.assertTrue(np.all(np.isfinite(a)))
self.assertTrue(np.all(np.isfinite(b)))
self.assertTrue(np.allclose(a, b, atol=atol))
finally:
if self.delete_dats_and_checkpoints:
dat_files = utils.recursive_glob(out_dir, "*.dat")
checkpoints = utils.recursive_glob(out_dir, "*ckpt*")
for file_name in set(dat_files + checkpoints):
os.remove(file_name)