mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-02-16 17:45:38 -05:00
* 1
* 83 failed
* learning how git works
* lol idk
* zero shape aaaa
* space lol
* aaa
* test check
* haha
* fixed gather
* 73 failing
* 71 failing
* 68 failing
* added some debug
* fking resize
* lol
* 62 failing
* 58 failling fucking did nearest resize hell yeah
* clean up
* 56 failing
* janitor duty
* lol
* 53 failing
* hi mom
* 50 failing
* added linear interp, but coord_trans is wrong
* did lin interpolation woohoo
* 43 failing
* 40 failing
* temporary Gather fix
* 39 failing
* fixed slice onnxver<10
* 37 failing
* 35 failing
* excluded tests that use float64
* 32 failing with hacks
* added _batchnorm() for 3D 5D batchnorm, 29 failing
* changed ALLOWED_KERNEL_COUNT from 199 to 207
* added improved Gather op, reverted ALLOWED_KERNEL_COUNT commit
* support Round op
* added storage_order/indices maxpool, 27 failing
* support maxunpool, 25 failures
* support Gradient, 23 failures
* merged new where
* added Adam
* cleanups
* added Momentum and Nesterov Momentum
* added Adagrad
* support sequence_type, 20 failing
* ugh git
* I give up on cubic interp :D, 9 failing
* sexy 1 liner gather, much improved, wow
* polished gather to make it shine bright like a diamond
* clean 1 liner for gather
* improved readability of gather
* uhh
* clean up
* more clean up
* WHITEspace
* implemented SoftmaxCrossEntropyLoss op
* added comments and cleaned up if statements
* update
* thank based wozeparrot for pow and new GatherElements
* CPU and TORCH all pass | cast float64 -> float32 for all fromCPU()
* _nearest_gather() failing on yolo
* reverted ops_cpu change and added assert in Resize
* added comments for resize for multiple channels
* oops
* merge
* test
* switched np.pad to Tensor.pad for constant padding
* gah
* gah2
* sexy reflect pad with movementops -> add
* delete commented out lines
* edge mode pad sexy as well
* trying out model_benchmark
* revert gitignore change lol
* init
* Revert "init"
This reverts commit 682bf2073a.
* wrote cast workaround for CPU, CPU and TORCH all pass
* wrote cast workaround for CPU, CPU and TORCH all pass
* skipped tests w/ 0 shape for METAL and GPU
* excluded tests for CLANG, CPU, TORCH, CLANG pass
* fixed hacky ConvTranspose
* gotta figure out autopad
* UOps.STORE support cast bool -> float
* small fix for fast gather
* reverted 0 shape skipped tests
* oops missed a file
* added comment
* fixed slice op hack
* First commit to pr
* More trig ops
* More trig ops
* format
* isinf support
* More ops
* changed onnx_ops to use our new gather :D
* Det op bug fix
* rebase
* fixed some tests
* det broken and slow
* fixed compress to use new gather
* implemented argmax argmin
* support variable types in type_proto
* support Upsample and Identity sequence
* we support float64 now and tinygrad support automatic broadcasting
* added EyeLike op
* resize does support multiple channels now actually
* yolov8 onnx runs successfully
* added batch size 1
* oops
* finally fixed type_proto I think
* fixed some llvm bugs
* del whitespaces
* added ZenginU Format PR
* test
* oops
* added float64 exclude tests back
* more skipped tests
* try
* ok openpilot pass
* flake8 pass
* woooooohooo
* revert external_model_benchmark changes
* perf tested gather
* removed promote types from ops_cpu
* numerical errors from 1681 is fixed
---------
Co-authored-by: ZenginU <umutzengin00@gmail.com>
236 lines
9.6 KiB
Python
236 lines
9.6 KiB
Python
import unittest
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from onnx.backend.base import Backend, BackendRep
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import onnx.backend.test
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import numpy as np
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from tinygrad.tensor import Tensor
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from typing import Any, Tuple
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from tinygrad.helpers import getenv
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# pip3 install tabulate
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pytest_plugins = 'onnx.backend.test.report',
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from extra.onnx import get_run_onnx
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class TinygradModel(BackendRep):
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def __init__(self, run_onnx, input_names):
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super().__init__()
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self.fxn = run_onnx
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self.input_names = input_names
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def run(self, inputs: Any, **kwargs: Any) -> Tuple[Any, ...]:
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real_inputs = {k:v for k,v in zip(self.input_names, inputs)}
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ret = self.fxn(real_inputs, debug=True)
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return tuple(x.numpy() if isinstance(x, Tensor) else [i.numpy() for i in x] if isinstance(x, list) else np.array(x) for x in ret.values())
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class TinygradBackend(Backend):
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@classmethod
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def prepare(cls, model, device):
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input_all = [x.name for x in model.graph.input]
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input_initializer = [x.name for x in model.graph.initializer]
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net_feed_input = [x for x in input_all if x not in input_initializer]
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print("prepare", cls, device, net_feed_input)
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run_onnx = get_run_onnx(model)
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return TinygradModel(run_onnx, net_feed_input)
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@classmethod
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def supports_device(cls, device: str) -> bool:
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return device == "CPU"
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backend_test = onnx.backend.test.BackendTest(TinygradBackend, __name__)
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# no support for reduce with multiply (needs llop)
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backend_test.exclude('test_reduce_prod_*')
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# TODO figure out why it's returning wrong values, geohotstan's uneducated guess is it's due to imprecision from float64 (double) -> float32
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# see Type Constraints: https://onnx.ai/onnx/operators/onnx_aionnxpreviewtraining_Adam.html#type-constraints
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backend_test.exclude('test_adam_multiple_cpu')
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backend_test.exclude('test_nesterov_momentum_cpu')
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# we only support float32
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backend_test.exclude('uint8')
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backend_test.exclude('uint16')
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backend_test.exclude('uint32')
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backend_test.exclude('uint64')
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backend_test.exclude('int8')
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backend_test.exclude('int16')
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backend_test.exclude('float64')
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backend_test.exclude('string')
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backend_test.exclude('test_pow_types_int*')
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backend_test.exclude('test_cast_*')
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backend_test.exclude('test_castlike_*')
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backend_test.exclude('test_convinteger_*')
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backend_test.exclude('test_matmulinteger_*')
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backend_test.exclude('test_reduce_log_sum_exp*') # dependent on actual float64 implementation for backends
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backend_test.exclude('test_operator_add*') # dependent on float64 math. Without it values default to 0 or inf
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# we don't support indexes
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# backend_test.exclude('test_argmax_*') # Needs more work #select_last_index
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# backend_test.exclude('test_argmin_*') # Needs more work #select_last_index
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backend_test.exclude('test_nonzero_*')
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# no support for mod
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backend_test.exclude('test_mod_*')
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# no boolean ops (2d, 3d, 4d)
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backend_test.exclude('test_bitshift_*')
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# no scatternd gathernd
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backend_test.exclude('test_gathernd_*')
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backend_test.exclude('test_scatternd_*')
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# no quantize
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backend_test.exclude('test_dynamicquantizelinear_*')
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backend_test.exclude('test_qlinearmatmul_*')
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backend_test.exclude('test_qlinearconv_*')
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backend_test.exclude('test_quantizelinear_*')
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# no rnn
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backend_test.exclude('test_gru_*')
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backend_test.exclude('test_rnn_*')
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backend_test.exclude('test_lstm_*')
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backend_test.exclude('test_simple_rnn_*')
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# no control flow
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backend_test.exclude('test_if_*')
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backend_test.exclude('test_loop*')
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backend_test.exclude('test_range_float_type_positive_delta_expanded_cpu') # requires loop
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# unsupported (strange) ops
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backend_test.exclude('test_bitwise_*')
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backend_test.exclude('test_blackmanwindow_*')
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backend_test.exclude('test_bernoulli_*')
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backend_test.exclude('test_cumsum_*')
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backend_test.exclude('test_det_*')
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backend_test.exclude('test_tril_zero_cpu') # TODO: zero array support
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backend_test.exclude('test_triu_zero_cpu') # TODO: zero array support
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backend_test.exclude('test_col2im_*')
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backend_test.exclude('test_hammingwindow_*')
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backend_test.exclude('test_hannwindow_*')
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backend_test.exclude('test_hardmax_*')
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backend_test.exclude('test_gridsample_*')
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backend_test.exclude('test_dft_*')
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backend_test.exclude('test_einsum_*')
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backend_test.exclude('test_strnorm_*')
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backend_test.exclude('test_unique_*')
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backend_test.exclude('test_sequence_*')
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backend_test.exclude('test_nonmaxsuppression_*')
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backend_test.exclude('test_reversesequence_*')
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backend_test.exclude('test_roialign_*')
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backend_test.exclude('test_top_k_*')
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backend_test.exclude('test_tfidfvectorizer_*')
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backend_test.exclude('test_stft_*')
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backend_test.exclude('test_melweightmatrix_*')
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# more strange ops
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backend_test.exclude('test_basic_deform_conv_*')
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backend_test.exclude('test_deform_conv_*')
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backend_test.exclude('test_lppool_*')
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backend_test.exclude('test_depthtospace_*')
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backend_test.exclude('test_spacetodepth_*')
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backend_test.exclude('test_scan*')
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backend_test.exclude('test_split_to_sequence_*')
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backend_test.exclude('test_resize_downsample_scales_cubic_*') # unsure how to implement cubic
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backend_test.exclude('test_resize_downsample_sizes_cubic_*') # unsure how to implement cubic
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backend_test.exclude('test_resize_upsample_scales_cubic_*') # unsure how to implement cubic
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backend_test.exclude('test_resize_upsample_sizes_cubic_*') # unsure how to implement cubic
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# rest of the failing tests
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backend_test.exclude('test_averagepool_2d_dilations_cpu') # dilations != 1 not supported for avgpool
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backend_test.exclude('test_convtranspose_autopad_same_cpu') # TODO geohotstan has no idea how this is done, autopad requires output_shape but output_shape requires pads from autopad
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backend_test.exclude('test_optional_has_element_empty_optional_input_cpu') # Attempts to create Tensor from None
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backend_test.exclude('test_range_int32_type_negative_delta_expanded_cpu') # AttributeProto.GRAPH not implemented
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backend_test.exclude('test_reshape_allowzero_reordered_cpu') # reshaping to 0 shape
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backend_test.exclude('test_resize_downsample_scales_linear_antialias_cpu') # antialias not implemented
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backend_test.exclude('test_resize_downsample_sizes_linear_antialias_cpu') # antialias not implemented
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backend_test.exclude('test_resize_tf_crop_and_resize_cpu') # unsure about fill value after clip
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backend_test.exclude('test_operator_addconstant_cpu') # bad data type
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# 1556
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backend_test.exclude('test_isinf_cpu')
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backend_test.exclude('test_isinf_negative_cpu')
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backend_test.exclude('test_isinf_positive_cpu')
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backend_test.exclude('test_isnan_cpu')
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if getenv("CPU") or getenv("ARM64"):
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# not too sure
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backend_test.exclude('test_dequantizelinear_axis_cpu')
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backend_test.exclude('test_dequantizelinear_cpu')
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if getenv("TORCH"): # 1562
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backend_test.exclude('test_and2d_cpu')
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backend_test.exclude('test_and3d_cpu')
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backend_test.exclude('test_and4d_cpu')
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backend_test.exclude('test_and_bcast3v1d_cpu')
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backend_test.exclude('test_and_bcast3v2d_cpu')
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backend_test.exclude('test_and_bcast4v2d_cpu')
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backend_test.exclude('test_and_bcast4v3d_cpu')
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backend_test.exclude('test_and_bcast4v4d_cpu')
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backend_test.exclude('test_dequantizelinear_axis_cpu')
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backend_test.exclude('test_dequantizelinear_cpu')
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backend_test.exclude('test_greater_equal_bcast_expanded_cpu')
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backend_test.exclude('test_greater_equal_expanded_cpu')
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backend_test.exclude('test_isinf_cpu')
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backend_test.exclude('test_isinf_negative_cpu')
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backend_test.exclude('test_isinf_positive_cpu')
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backend_test.exclude('test_isnan_cpu')
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backend_test.exclude('test_less_equal_bcast_expanded_cpu')
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backend_test.exclude('test_less_equal_expanded_cpu')
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backend_test.exclude('test_or2d_cpu')
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backend_test.exclude('test_or3d_cpu')
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backend_test.exclude('test_or4d_cpu')
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backend_test.exclude('test_or_bcast3v1d_cpu')
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backend_test.exclude('test_or_bcast3v2d_cpu')
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backend_test.exclude('test_or_bcast4v2d_cpu')
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backend_test.exclude('test_or_bcast4v3d_cpu')
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backend_test.exclude('test_or_bcast4v4d_cpu')
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backend_test.exclude('test_xor2d_cpu')
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backend_test.exclude('test_xor3d_cpu')
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backend_test.exclude('test_xor4d_cpu')
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backend_test.exclude('test_xor_bcast3v1d_cpu')
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backend_test.exclude('test_xor_bcast3v2d_cpu')
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backend_test.exclude('test_xor_bcast4v2d_cpu')
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backend_test.exclude('test_xor_bcast4v3d_cpu')
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backend_test.exclude('test_xor_bcast4v4d_cpu')
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if getenv('LLVM') or getenv('GPU') or getenv('CLANG') or getenv('METAL') or getenv('MPS'):
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# compiled backends cannot reshape to 0 or from 0
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backend_test.exclude('test_slice_start_out_of_bounds_cpu')
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backend_test.exclude('test_constantofshape_int_shape_zero_cpu')
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if getenv('GPU') or getenv('METAL') or getenv('MPS'):
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backend_test.exclude('test_mish_cpu') # weird inaccuracy
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backend_test.exclude('test_mish_expanded_cpu') # weird inaccuracy
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backend_test.exclude('test_eyelike_with_dtype_cpu') # I'm not sure about this...
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if getenv('METAL') or getenv('MPS'):
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# (((Tensor([0,1,2,3,4,5])+0.5)/3.5 - 0.5)) Try this with METAL and LLVM, weird weird inaccuracy
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backend_test.exclude('test_resize_upsample_sizes_nearest_axes_2_3_cpu')
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backend_test.exclude('test_resize_upsample_sizes_nearest_axes_3_2_cpu')
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backend_test.exclude('test_resize_upsample_sizes_nearest_cpu')
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# disable model tests for now since they are slow
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if not getenv("MODELTESTS"):
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for x in backend_test.test_suite:
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if 'OnnxBackendRealModelTest' in str(type(x)):
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backend_test.exclude(str(x).split(" ")[0])
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else:
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# model tests all pass!
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backend_test.include('test_resnet50')
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backend_test.include('test_inception_v1')
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backend_test.include('test_inception_v2')
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backend_test.include('test_densenet121')
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backend_test.include('test_shufflenet')
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backend_test.include('test_squeezenet')
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backend_test.include('test_bvlc_alexnet')
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backend_test.include('test_zfnet512')
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backend_test.include('test_vgg19')
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globals().update(backend_test.enable_report().test_cases)
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if __name__ == '__main__':
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unittest.main()
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