joaoluiscarreira
commented
5 years ago Hi,
this is old tensorflow code (2017), but hopefully still helpful:
def assign_from_checkpoint_2d_to_3d_scale(model_path, var_list): """Creates an operation to assign specific variables from a checkpoint.
Args:
model_path: The full path to the model checkpoint. To get latest checkpoint
use model_path = tf.train.latest_checkpoint(checkpoint_dir)
var_list: A list of Variable objects or a dictionary mapping names in the
checkpoint to the corresponding variables to initialize. If empty or
None, it would return no_op(), None.
Returns: the restore_op and the feed_dict that need to be run to restore var_list.
Raises:
ValueError: If the checkpoint specified at model_path is missing one of
the variables in var_list.
"""
reader = pywrap_tensorflow.NewCheckpointReader(model_path)
if isinstance(var_list, (tuple, list)): var_list = {var.op.name: var for var in var_list}
feed_dict = {} assign_ops = []
for checkpoint_var_name in var_list: var = var_list[checkpoint_var_name] if not reader.has_tensor(checkpoint_var_name): raise ValueError( 'Checkpoint is missing variable [%s]' % checkpoint_var_name)
var_value = reader.get_tensor(checkpoint_var_name)
placeholder_name = 'placeholder/' + var.op.name
placeholder_value = tf.placeholder(
dtype=var.dtype.base_dtype,
shape=var.get_shape(),
name=placeholder_name)
assign_ops.append(var.assign(placeholder_value))
if var.get_shape() != var_value.shape:
n = var_value.shape[0]
feed_dict[placeholder_value] = np.tile(np.expand_dims(var_value/n,0), [n,1,1,1,1])
else:
feed_dict[placeholder_value] = var_value.reshape(var.get_shape())assign_op = tf.group(*assign_ops) return assign_op, feed_dict
emergencyd
Could you please provide the code which loads 2D parameters (pretrained on imagenet) to I3D model？ (especially the processing of BN/GN).
Thank you very much!