Comments (11)
Hi !
Can you show me the code snippet you used to run the PDNet?
I think this error rings a bell, but I would need to see what you did exactly to be sure.
I did reach the challenge result using this repository indeed, but not the PDNet, and rather the XPDNet.
from fastmri-reproducible-benchmark.
I guess the error comes from this line, where we try to determine whether we are using mixed precision in order to not use norm clipping (I think I saw at some point it wasn't compatible but can't track down a specific issue).
I currently don't have the error you are mentioning even re-running the PDNet.
I guess there is a version problem: which version of TensorFlow are you using?
It should work for TensorFlow 2.2 and 2.3.
from fastmri-reproducible-benchmark.
Hello,
Sorry, I saw your message late. Yes, it is tensorflow error. But Changing the compile function according to Tensorflow 2.2.0 gives another errors about attributes.
My tensorflow version is 2.2.0.
from fastmri-reproducible-benchmark.
I am not sure I understand what you are saying.
Can you show me what code you ran to have the error?
Also can you paste the entire stack trace?
from fastmri-reproducible-benchmark.
Hello,
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-16-0fe99a9645e2> in <module>()
----> 1 get_ipython().run_cell_magic('time', '', '\nfor net_params in all_net_params:\n save_figure_for_params(**net_params)\n ')
6 frames
/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
2115 magic_arg_s = self.var_expand(line, stack_depth)
2116 with self.builtin_trap:
-> 2117 result = fn(magic_arg_s, cell)
2118 return result
2119
<decorator-gen-60> in time(self, line, cell, local_ns)
/usr/local/lib/python3.6/dist-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
/usr/local/lib/python3.6/dist-packages/IPython/core/magics/execution.py in time(self, line, cell, local_ns)
1191 else:
1192 st = clock2()
-> 1193 exec(code, glob, local_ns)
1194 end = clock2()
1195 out = None
<timed exec> in <module>()
<ipython-input-15-e4cc7e338f3a> in save_figure_for_params(reco_function, test_gen, name, **net_params)
30 def save_figure_for_params(reco_function=None, test_gen=None, name=None, **net_params):
31
---> 32 model = unpack_model(**net_params)
33 for image_index in range((len(test_gen_scaled))):
34 im_recos= reco_function(*test_gen[image_index], model)
<ipython-input-15-e4cc7e338f3a> in unpack_model(init_function, run_params, run_id, epoch, **dummy_kwargs)
20
21 def unpack_model(init_function=None, run_params=None, run_id=None, epoch=300, **dummy_kwargs):
---> 22 model = init_function ( **run_params )
23 chkpt_path = f'/content/drive/My Drive/fastmri_master/checkpoints/{run_id}-{epoch}.hdf5'
24 model.load_weights ( chkpt_path )
/content/drive/My Drive/fastmri_master/fastmri_recon/models/functional_models/pdnet.py in pdnet(input_size, n_filters, lr, n_primal, n_dual, n_iter, primal_only, fastmri, activation)
85 image_res = Lambda(tf.math.abs)(image_res)
86 model = Model(inputs=[kspace_input, mask], outputs=image_res)
---> 87 default_model_compile(model, lr)
88
89
/content/drive/My Drive/fastmri_master/fastmri_recon/models/training/compile.py in default_model_compile(model, lr, loss)
12 opt_kwargs = {}
13 precision_policy = mixed_precision.global_policy()
---> 14 if precision_policy.loss_scale is None:
15 opt_kwargs['clipnorm'] = 1.
16 if loss == 'compound_mssim':
AttributeError: 'Policy' object has no attribute 'loss_scale'
I have changed mixed_precision.global_policy
as mixed_precision.experimental.Policy()
in compile
function. But I have faced another problem.
Best,
from fastmri-reproducible-benchmark.
Hi,
You can find here a colab notebook where in TensorFlow v2.2, the loss_scale
is available in the Policy
object.
I think you are using a different TensorFlow version. Can you try to verify that using:
import tensorflow as tf
print(tf.__version__)
Please note that I have edited your comments and initial issue, in order to use the GitHub markdown code formatting. This allows an easier read of the code (you can even have color coding for python code).
You can find some examples for code here.
from fastmri-reproducible-benchmark.
Hi @GeraldGore , do you have any updates on this?
from fastmri-reproducible-benchmark.
Hello,
Sorry for seeing late. There are 2 folder about fastmri-reproducible-benchmark. For the old version, I did not face that contains this error. But the latest folder, I have faced lots of times.
Best,
from fastmri-reproducible-benchmark.
Hi,
I am not sure which folders you are referring to.
Maybe if you have different versions try pulling master in order to have the latest one git pull master
.
from fastmri-reproducible-benchmark.
Hi,
It works for old version. Thank you. Also I want to ask you a question. Does these networks have the transfer learning property? Can I use your checkpoint for this?
Best,
from fastmri-reproducible-benchmark.
Again, I really am not sure what you are refering to when you talk about "old version". Do you mean the old unet implementation?
Or the functional models as opposed to the subclassed models?
In any case I am surprised you got the mixed_precision
error. But I am happy that you managed to get over it, and therefore I am closing this issue.
Regarding your next question, can you please open a new issue for this? This way we can keep things tidy.
from fastmri-reproducible-benchmark.
Related Issues (20)
- maybe remove or update np_metrics HOT 4
- need to specify the scikit-image version in the requirements.txt HOT 2
- Update the introduction notebook
- Add HuggingFace Hub integration to store the models HOT 1
- Regarding sessions of OASIS3 dataset to be downloaded HOT 5
- Error in training PD-net for OASIS3 dataset HOT 14
- Unpin TF version number
- Correct bias setting in MWCNN
- PSNR and SSIM metrics from skimage HOT 1
- Nufft and its adj operator with cropped 320x320 image HOT 8
- Error in training XPDNet HOT 3
- GPU->CPU Memcpy failed HOT 3
- Move to using tensorflow-nufft HOT 3
- Difficulty in running inference for NCPDNet HOT 2
- Getting NaN values when running nc_train.py HOT 8
- question about mask HOT 1
- Time per epoch increasing HOT 1
- Getting error when I run postBuild HOT 1
- How to predict using the XPDNet-brain-af4 model HOT 2
- error
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