mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-29 03:00:14 -04:00
5d212864b5
* Add ResNet inference test and cannon * Test with ResNet50 * test_car works with resnet fix * Add KiTS19 dataset * KiTS19: Implement iterate * No batch load for this dataset * Save results on iterate * Implement dice score * Add data prep and eval functions * Resolve shape issue * Conversion works but wrong values * Segfaults when load_from_pretrained is called * Fix segfault and assign properly * Final result generated, though very slow * Store and load final result to save time * Fix typo in finalize * Score computes * More bug fixes, dice score is very low * Working broken code * Assign output values to result * Getting a much higher score now * Fix dataset preprocessing * Mean DICE score of 88.5 * Ugh, typo * Attempt to reimplement model * Rename layers * Tiny model works, kinda * Accuracy? gone * Implement InstanceNorm and match torch * Test instance norm 2d and 3d * Combined input block with downsample block * Tiny model works, support strided convtranspose * Commands to download dataset * Clean up a bit * unet3d_v2 -> unet3d * Remove duplicated code * Oops, put tests back
Each model should be a clean single file. They are imported from the top level `models` directory It should be capable of loading weights from the reference imp. We will focus on these 5 models: # Resnet50-v1.5 (classic) -- 8.2 GOPS/input # Retinanet # 3D UNET (upconvs) # RNNT # BERT-large (transformer) They are used in both the training and inference benchmark: https://mlcommons.org/en/training-normal-21/ https://mlcommons.org/en/inference-edge-30/ And we will submit to both. NOTE: we are Edge since we don't have ECC RAM