Transfer learning using Niftynet pretrained weights.

[AkhilaPerumalla123]

I want to use niftynet pretrained segmentation model for segmenting custom data. I downloaded the pre trained weights and and modified model_dir path to downloaded one.
However when I run, I am getting the error below.
Caused by op 'save/Assign_17', defined at: File "net_segment.py", line 8, in <module> sys.exit(main()) File "/home/NiftyNet/niftynet/__init__.py", line 142, in main app_driver.run(app_driver.app) File "/home/NiftyNet/niftynet/engine/application_driver.py", line 197, in run SESS_STARTED.send(application, iter_msg=None) File "/usr/local/lib/python3.5/dist-packages/blinker/base.py", line 267, in send for receiver in self.receivers_for(sender)] File "/usr/local/lib/python3.5/dist-packages/blinker/base.py", line 267, in <listcomp> for receiver in self.receivers_for(sender)] File "/home/NiftyNet/niftynet/engine/handler_model.py", line 109, in restore_model var_list=to_restore, save_relative_paths=True) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1102, in __init__ self.build() File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1114, in build self._build(self._filename, build_save=True, build_restore=True) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1151, in _build build_save=build_save, build_restore=build_restore) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 795, in _build_internal restore_sequentially, reshape) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 428, in _AddRestoreOps assign_ops.append(saveable.restore(saveable_tensors, shapes)) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 119, in restore self.op.get_shape().is_fully_defined()) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/state_ops.py", line 221, in assign validate_shape=validate_shape) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 61, in assign use_locking=use_locking, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1770, in __init__ self._traceback = tf_stack.extract_stack() InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error: Assign requires shapes of both tensors to match. lhs shape= [3,3,61,256] rhs shape= [3,3,3,61,9] [[node save/Assign_17 (defined at /home/NiftyNet/niftynet/engine/handler_model.py:109) = Assign[T=DT_FLOAT, _class=["loc:@DenseVNet/conv/conv_/w"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](DenseVNet/conv/conv_/w, save/RestoreV2/_35)

Below is my config file details
`[promise12]
path_to_search = /home/Container_data/Nifti/Images_nii
filename_contains = nii
spatial_window_size = (64, 64, 1)
interp_order = 3
axcodes=(A, R, S)

[label]
path_to_search = /home/Container_data/Nifti/Annotations_colored_nii
filename_contains = nii
spatial_window_size = (64, 64, 1)
interp_order = 0
axcodes=(A, R, S)

############################## system configuration sections
[SYSTEM]
cuda_devices = ""
num_threads = 2
num_gpus = 4
model_dir = ./dense_vnet_abdominal_ct

[NETWORK]
name = dense_vnet
activation_function = prelu
batch_size = 1

volume level preprocessing

volume_padding_size = 0

histogram normalisation

histogram_ref_file = standardisation_models.txt
norm_type = percentile
cutoff = (0.01, 0.99)
normalisation = True
whitening = True
normalise_foreground_only=True
foreground_type = otsu_plus
multimod_foreground_type = and
window_sampling = resize

queue_length = 8

[TRAINING]
sample_per_volume = 4
#rotation_angle = (-10.0, 10.0)
#scaling_percentage = (-10.0, 10.0)
#random_flipping_axes= 1
lr = 0.0002
loss_type = Dice
starting_iter = -1
save_every_n = 1250
max_iter = 25000
max_checkpoints = 20

############################ custom configuration sections
[SEGMENTATION]
image = promise12
label = label
output_prob = False
num_classes = 256
label_normalisation = True
min_numb_labels = 2
min_sampling_ratio = 0.0001
`

[ericspod]

This appears to be the same issue as mentioned in #37. This appears to be caused by creating a network based on data with a different dimensionality than the one being loaded from the checkpoint. If you want to train on a 2D dataset the weights of some convolution would have a 4D dimension (ie. shape= [3,3,61,256]) whereas those being loaded from the zoo would have 5D (ie. shape= [3,3,3,61,9]).