def export_weights(self):
"""
Function to store TensorFlow weights back to into a dict for use with MXNet
Returns
-------
tf_export_params: Dictionary
Dictionary of weights from TensorFlow stored as {weight_name: weight_value}
"""
tf_export_params = {}
# collect all TF variables to include running_mean and running_variance
tvars = _tf.global_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if val.ndim == 1:
tf_export_params.update(
{var.name.replace(":0", ""): val})
elif val.ndim == 4:
tf_export_params.update(
{var.name.replace(":0", ""): _utils.convert_conv2d_tf_to_coreml(val)})
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(tf_export_params[layer_name])
return tf_export_params
def export_weights(self):
"""
Retrieve weights from the TF model, convert to the format Core ML
expects and store in a dictionary.
Returns
-------
net_params : dict
Dictionary of weights, where the key is the name of the
layer (e.g. `drawing_conv0_weight`) and the value is the
respective weight of type `numpy.ndarray`.
"""
net_params = {}
with self.dc_graph.as_default():
layer_names = _tf.trainable_variables()
layer_weights = self.sess.run(layer_names)
for var, val in zip(layer_names, layer_weights):
if "bias" in var.name:
net_params.update({var.name.replace(":0", ""): val})
else:
if "dense" in var.name:
if "drawing_dense0_weight" in var.name:
"""
To make output of TF pool3 (NHWC) compatible with CoreML (NCHW).
Decompose FC weights to NHWC. Transpose to NCHW. Reshape back to FC.
"""
tf_576_128 = val
tf_576_128 = _np.reshape(tf_576_128, (3, 3, 64, 128))
tf_576_128 = _np.transpose(tf_576_128, (2, 0, 1, 3))
tf_576_128 = _np.reshape(tf_576_128, (576, 128))
net_params.update(
{
var.name.replace(":0", ""): _np.transpose(
tf_576_128, (1, 0)
)
}
)
else:
net_params.update(
{var.name.replace(":0", ""): val.transpose(1, 0)}
)
else:
# np.transpose won't change the underlying memory layout
# but in turicreate we will force it.
net_params.update(
{
var.name.replace(
":0", ""
): _utils.convert_conv2d_tf_to_coreml(val)
}
)
return net_params
def export_weights(self):
tf_export_params = {}
tvars = _tf.trainable_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if 'weight' in var.name:
if 'conv' in var.name:
tf_export_params[var.name.split(':')[0]] = _utils.convert_conv2d_tf_to_coreml(val)
else:
tf_export_params[var.name.split(':')[0]] = _utils.convert_dense_tf_to_coreml(val)
else:
tf_export_params[var.name.split(':')[0]] = _np.array(val)
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(tf_export_params[layer_name])
return tf_export_params
def export_weights(self):
_tf = _lazy_import_tensorflow()
tf_export_params = {}
with self.st_graph.as_default():
tvars = _tf.trainable_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if "weight" in var.name:
if "conv" in var.name:
tf_export_params[var.name.split(
":")[0]] = _utils.convert_conv2d_tf_to_coreml(val)
else:
tf_export_params[var.name.split(
":")[0]] = _utils.convert_dense_tf_to_coreml(val)
else:
tf_export_params[var.name.split(":")[0]] = _np.array(val)
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(
tf_export_params[layer_name])
return tf_export_params
