* add int64 as supported dtype from numpy
Without this, examples/transformer.py didn't run. With this change it runs successfully.
* Update helpers.py
* Update transformer.py
* Update training.py
* Refactor getenv into helpers
* Remove unused os
* Fix default value
* Fix more defaults for CI
* Fix bracket
* Revert changes to openpilot/compile.py
* Use getenv from helpers when possible
* added resnets
* fix minor
* fix minor
* resnet in models
* added resnet test
* added resnet train test
* added linear, conv2d nn tests
* fix minor in extra/training
* resnet in models
* fix minor
* fix tolerance for linear in nn test
* fix eval, this causes cpu and gpu UT failing
* revert transformer test
* fix minor for CPU test
* improved model get_params for sequential layer
* fix minor for params counting
* commented broken ops tests
* improved train for resnet
* Split tests
Split tests into "Test CPU" and "Test GPU".
Add test flag "TEST_DEVICES" which is a comma separated list of devices:
CPU,GPU,ANE
* Run tests based on provided TEST_DEVICES flag
By default will run all "CPU,GPU,ANE"
* fix bad quote
* Revert changes and use GPU=1
This is done through setting the default Tensor Device to Device.CPU of
GPU=1 is set.
Run GPU tests: GPU=1 pytest -s -v
* 2serious
* load/save
* fixing GPU
* added DEBUG
* needs BatchNorm or doesn't learn anything
* old file not needed
* added conv biases
* added extra/training.py and checkpoint
* assert in test only
* save
* padding
* num_classes
* checkpoint
* checkpoints for padding
* training was broken
* merge
* rotation augmentation
* more aug
* needs testing
* streamline augment, augment is fast thus bicubic
* tidying up
* transformer eval
* axis=-1
* transpose
* test for permutation using torch.movedims
* another test
* line
* 2serious
* load/save
* fixing GPU
* added DEBUG
* needs BatchNorm or doesn't learn anything
* old file not needed
* added conv biases
* added extra/training.py and checkpoint
* assert in test only
* save
* padding
* num_classes
* checkpoint
* checkpoints for padding
* training was broken
* merge
* rotation augmentation
* more aug
* needs testing
* streamline augment, augment is fast thus bicubic
* tidying up
* transformer eval
* Update all devices to be tested
ANE, CPU and OCL all now support all tests.
However tests are not currently passing on GPU and I cannot test on CPU.
Failing GPU test are not an issue caused by this update. Tests have not
been passing due to a missing "six" required installation.
OpenCL Tests have not been run since commit: 1a1c63a08b
devices have 3 types and are handle by a new DeviceTypes enum. (The goal
is to revert to Tensor.<type>, but this current setup allows for keyword
argument defaults: `device=DeviceType.CPU`)
All references to Tensor.GPU/CPU/ANE as been converted to the
corresponding `DeviceTypes` enum.
Refactor of the conversion code to allow for any device to any device
conversion.
* Add six dependency in requirements.txt
* Resolve failure to run tests
Move six into gpu required installs. Remove six from standard
installation.
* Remove repeated data conversion
* Refactor method names
Also reduce code with .to and .to_
* Dynamic device handlers
* Refactor DeviceTypes -> Device
* Add mem copy profiling back
* test_backward_pass_diamond_model passing
* Resolve Sum issue on GPU
* Revert batchnorm2d tests
* Update README with upadated API
* ANE testing with
* Last minute line gains
* 2serious
* load/save
* fixing GPU
* added DEBUG
* needs BatchNorm or doesn't learn anything
* old file not needed
* added conv biases
* added extra/training.py and checkpoint
* assert in test only
* save
* padding
* num_classes
* checkpoint
* checkpoints for padding
* training was broken
* merge
* rotation augmentation
* more aug
* needs testing
* streamline augment, augment is fast thus bicubic
* tidying up
