* conv1d onnx
* [Work in progress] conv1d + enforcing full padding tuple length
* make ONNX padding reorder not hardcoded, works for 1D and 3D convs now
* conv2d interprets padding based on the input tensor dimensions
* use tensor dtype for zeros_like()
* add tests for zeros_like dtype
* iterate over dtypes
* remove space
* remove print
* fix test, iterate over a list
* feat: int8 support
* feat: uint8 support
* feat: int8 tests
* fix: fix uint8 on clang
* feat: test casting between int8/uint8/float16/float32
* clean: way cleaner dtype tests
* feat: preprocess_imagenet using the correct dtype
* feat: add test for overflow between uint8 and int8
* 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
* add retinanet with resnet backbone
* adds resnext to support loading retinanet pretrained on openimages
* object detection post processing with numpy
* data is downloaded and converted to coco format with fiftyone
* data loading and mAP evaluation with pycocotools
* remove fiftyone dep
* * eval freq
* fix model timing
* del jit for last batch
* faster accumulate
* feat: add mlperf bert model
* feat: switch to nn.Embedding
* clean+fix: fix formatting
* feat: add simple downloader
* feat: metrics
* feat: don't actually need exact match
* feat: doing a run
* feat: set eps on the layernorms
* clean+fix: cleaner impl + hopefully fixed
* feat: move dataset initialization into iterate
* feat: move tokenizer out of iterate
* clean+fix: cleaner + working
* clean: cleanup
* fix: fix metrics
* feat: need to use original bert gelu + download vocab
* feat: make directory if it doesn't exist yet
* feat: jit go brrr
* lr schedulers + test
* lr scheduler test moved + integration test
* integration test for all lr scheduler
* lr scheduler test now deterministic
* changed optimizer + parameters for lr sched test
* optimizations in symbolic.py
* fix infinite recursion when expanding sums
* add test case to make sure NumNodes are hoisted up in cases where MulNodes cancel eachother out
* Don't collapse dimensions during batched matmul (FIX#799)
* Avoid reshaping tensor to the same shape
* Skip batched matrix multiply when IMAGE is set
* feat: promote Embedding to nn
* fix: fix failing test
* feat: add test with jit
* feat: rewrite embedding to no longer need stacked for loops
* clean+fix: don't know how that happened
