qertrent.blogg.se - Cropit demo

#CROPIT DEMO PDF#
#CROPIT DEMO PC#

This is unrelatd to the max dim in each dimension. so in the example it resamples to 1x1x1 mm^3. Scale by resolution change the resolution on the image, it is not normalizing.

#CROPIT DEMO PC#

Or is this in the PC’s RAM, and this is a sort of “paging”-like setup where the API shifts data from PC ram to the GPU’s memory (the “cache”).

In the GPU’s memory? Is this an abstraction over the GPU’s limited memory?.

Stores the result of deterministically transformed data.

Whereas for Smart Cache, it sounds like it

Are these overlapping chunks? If so, what is the overlap “increment”? 1x1x1 voxel?ĮDIT: Actually this slide seems to mention loading to memory too? Do Smart Cache and Batch by Transform both concern themselves with loading data from disk?.

I.e., “process the image as a set of 64圆4圆4 chunks”.

instead it just changes how the data is processed.

Just to make sure I understand, it looks like FastPosNegRatioCropROI does not crop the entire 3d image to.

Must have one of the batching transformations:.

Sets Batched_bt_transform= true, ignores output_batch_size.

Take multiple crops from same data volume as your batch.

To quote the screenshot (converted to MD): BATCH BY TRANSFORMĬopy data to memory, crop, discard Batch by transform

#CROPIT DEMO PDF#

What slide deck is this from, can I download the PDF of that? It’s slide 11 of… something.

It looks like the notebook uses a screenshot of a slide for “batch by transform”, maybe that explains why grep and Google didn’t find this. I ran through one of the clara-train-examples repository notebooks, but not the two you mentioned, oops.

Clara train V3.1 is closed source, we have listened to this feedback and therefore we are currently moving the back end to use MONAI which is open source.

it means the input size to the model, for batch and batch by transform and other parameters please refer to this notebook

SegmentationImagePipeline is specifying the pipeline so the crop is a bad name I agree with you.

FastPosNegRatioCropROI will crop 64圆4圆4 pixels around the foreground sampled point and another for background according to the pos, neg ratio.scale by resolution change the resolution on the image, it is not normalizing.real example on spleen segmentation is at clara-train-examples/PerformanceSpleen.ipynb at 7b522adcf1fab9380cd77fbbbe0cc958fa197a0e.concepts of acceleration including caching etc is explained here.I think some concepts are not clear and is making some confusion, sorry about that we are working on improving our documentation. prefetch_size: What is being prefetched? What does it mean to prefetch something? Is this loading images into the GPU’s memory? I’d guess I should set this to some number N, where * N ~=, is that correct?Īlso one more semi-related thing, is the Python source available for some of these transforms? It would be interesting to review it and I think it would help with debugging.num_workers: This is mostly just a curiosity, but since most of this happens on the GPU, what are the worker “data transformation” threads used for? Do some transforms execute on the CPU? How do I know what to set this to?.batched_by_transforms: What does it mean to “batch” something / what is being batched? Does a transform do the batching, or is it something like “group batches by transforms”?.It sounds like it has something to do with Smart Cache. output_batch_size: What is this? The documentation doesn’t say much.This same setting is in the validation too but it doesn’t seem like the inferred images are in dimensions. output_crop_size: Why are we cropping again?.It would be nice to have a few more details on the meaning of these parameters "rotation": false, "rotation_degree": 20, Does this transform always crop from (0,0,0)? (meaning, just crop the image in the range ?).What is the 3d point of origin of this crop? (e.g., the center of the rectangular prism region).Didn’t we just normalize the image? Wouldn’t 64.0 be far outside the bounds of the image ((1.0, 1.0, 1.0])?.So I’d guess that this makes the voxel coordinate space įrom the documentation, it looks like this crops the image and label to a box in dimensions, but I’m a bit confused about the coordinate space involved in this.It looks like this transform effectively normalizes the coordinate space of the image.In the spleen demo’s pre_transforms in trn_base.json, ScaleByResolution