def testGetExpectedImageSizeFromImageModuleInfo(self):
with tf.Graph().as_default():
spec = native_module.create_module_spec(image_module_fn_with_info)
self.assertAllEqual(image_util.get_expected_image_size(spec),
[2, 4])
m = module.Module(spec)
self.assertAllEqual(image_util.get_expected_image_size(m), [2, 4])
def _check_module_is_image_embedding(module_spec, check_image_size):
"""Raises ValueError if `module_spec` is not usable as image embedding.
Args:
module_spec: A `_ModuleSpec` to test.
check_image_size: Whether to check for compatibility with
get_expected_image_size.
Raises:
ValueError: if `module_spec` default signature is not compatible with
mappingan "images" input to a Tensor(float32, shape=(_,K)).
"""
issues = []
# Find issues with "default" signature inputs. The common signatures for
# image models prescribe a specific name; we trust it if we find it
# and if we can do the necessary inference of input shapes from it.
input_info_dict = module_spec.get_input_info_dict()
if (list(input_info_dict.keys()) != ["images"]
or input_info_dict["images"].dtype != tf.float32):
issues.append(
"Module 'default' signature must require a single input, "
"which must have type float32 and name 'images'.")
else:
try:
if check_image_size:
image_util.get_expected_image_size(module_spec)
except ValueError as e:
issues.append(
"Module does not support hub.get_expected_image_size(); "
"original error was:\n" + str(e)) # Raised again below.
# Find issues with "default" signature outputs. We test that the dtype and
# shape is appropriate for use in input_layer().
output_info_dict = module_spec.get_output_info_dict()
if "default" not in output_info_dict:
issues.append(
"Module 'default' signature must have a 'default' output.")
else:
output_type = output_info_dict["default"].dtype
output_shape = output_info_dict["default"].get_shape()
if not (output_type == tf.float32 and output_shape.ndims == 2
and output_shape.dims[1].value):
issues.append(
"Module 'default' signature must have a 'default' output "
"of tf.Tensor(shape=(_,K), dtype=float32).")
if issues:
raise ValueError("Module is not usable as image embedding: %r" %
issues)
def parse_example_spec(self):
"""Returns a `tf.Example` parsing spec as dict."""
if self.image_size:
height, width = self.image_size
else:
height, width = image_util.get_expected_image_size(self.module_spec)
input_shape = [height, width, 3]
return {self.key: tf.compat.v1.FixedLenFeature(input_shape, tf.float32)}
def _check_module_is_image_embedding(module_spec):
"""Raises ValueError if `module_spec` is not usable as image embedding.
Args:
module_spec: A `_ModuleSpec` to test.
Raises:
ValueError: if `module_spec` default signature is not compatible with
mappingan "images" input to a Tensor(float32, shape=(_,K)).
"""
issues = []
# Find issues with "default" signature inputs. The common signatures for
# image models prescribe a specific name; we trust it if we find it
# and if we can do the necessary inference of input shapes from it.
input_info_dict = module_spec.get_input_info_dict()
if (list(input_info_dict.keys()) != ["images"] or
input_info_dict["images"].dtype != tf.float32):
issues.append("Module 'default' signature must require a single input, "
"which must have type float32 and name 'images'.")
else:
try:
image_util.get_expected_image_size(module_spec)
except ValueError as e:
issues.append("Module does not support hub.get_expected_image_size(); "
"original error was:\n" + str(e)) # Raised again below.
# Find issues with "default" signature outputs. We test that the dtype and
# shape is appropriate for use in input_layer().
output_info_dict = module_spec.get_output_info_dict()
if "default" not in output_info_dict:
issues.append("Module 'default' signature must have a 'default' output.")
else:
output_type = output_info_dict["default"].dtype
output_shape = output_info_dict["default"].get_shape()
if not (output_type == tf.float32 and output_shape.ndims == 2 and
output_shape.dims[1].value):
issues.append("Module 'default' signature must have a 'default' output "
"of tf.Tensor(shape=(_,K), dtype=float32).")
if issues:
raise ValueError("Module is not usable as image embedding: %r" % issues)
def _parse_example_spec(self):
"""Returns a `tf.Example` parsing spec as dict."""
height, width = image_util.get_expected_image_size(self.module_spec)
input_shape = [height, width, 3]
return {self.key: tf.FixedLenFeature(input_shape, tf.float32)}
def testGetExpectedImageSizeFromShape(self):
spec = native_module.create_module_spec(image_module_fn)
self.assertAllEqual(image_util.get_expected_image_size(spec), [2, 4])
m = module.Module(spec)
self.assertAllEqual(image_util.get_expected_image_size(m), [2, 4])
def testGetExpectedImageSizeFromShape(self): spec = native_module.create_module_spec(image_module_fn) self.assertAllEqual(image_util.get_expected_image_size(spec), [2, 4]) m = module.Module(spec) self.assertAllEqual(image_util.get_expected_image_size(m), [2, 4])