No attribute of `acceleration` in h5 file of singlecoil_val about fastmri HOT 4 CLOSED

I think that's expected. Examples in *_train and *_val are fully-sampled and don't have the acceleration parameter, while examples in *_test have undersampled k-space and the acceleration parameter specifies the degree of undersampling (either 4 or 8). All examples have the acquisition parameter, which specifies whether the image is fat-suppressed or not.

I'd have to ask @anuroopsriram, but it looks to me like the evaluate.py script is not intended to be run by participants of the fastMRI challenge. Rather it is included to show how submissions are evaluated on the server.

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Thank you for your response.

it looks to me like the evaluate.py script is not intended to be run by participants of the fastMRI challenge.

If so, it is better to delete description related with evaluate.py from Github.
For example, in https://github.com/facebookresearch/fastMRI/tree/master/models/unet, I find the following description.

The outputs will be saved to reconstructions_val. To evaluate the results, run:

python common/evaluate.py --target-path TARGET_DATA --predictions-path reconstructions_val --challenge CHALLENGE.

According to the description, I think that evaluate.py is intended to be run by participants of the fastMRI challenge.

By the way, I would like to reproduce the results of paper (https://arxiv.org/abs/1811.08839). Without evaluate.py and the attribute of acceleration, I think that Tables 4-7 cannot be reproduced by participants of the fastMRI challenge.

How do I reproduce the Tables 4-7 by using resources of the fastMRI challenge?

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You can run the evaluate.py script on validation data without the --acceleration argument. I have removed that argument from the code to remove the confusion.
For the tables in the paper, I used 4x or 8x sampling on the full validation data to get better estimates of the metrics. So, when you run run_model.py, use --accelerations 4 --center-fractions 0.08 for 4x or --accelerations 8 --center-fractions 0.04 for 8x. Then run evaluate.py on each of the outputs.

from fastmri.

from fastmri.