271 lines
8.0 KiB
Python
271 lines
8.0 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import os
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import sys
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import unittest
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import onnx.backend.base
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import onnx.backend.test
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from onnx.backend.base import Device, DeviceType
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import subprocess
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import test_config
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VERBOSE = bool(os.environ.get("VERBOSE"))
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CXX = test_config.CXX_PATH
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ONNF = os.path.join(test_config.ONNF_BUILD_PATH, "bin/onnf")
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LLC = os.path.join(test_config.LLVM_PROJ_BUILD_PATH, "bin/llc")
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# Make lib folder under build directory visible in PYTHONPATH
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doc_check_base_dir = os.path.dirname(os.path.realpath(__file__))
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RUNTIME_DIR = os.path.join(test_config.ONNF_BUILD_PATH, "lib")
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sys.path.append(RUNTIME_DIR)
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from pyruntime import ExecutionSession
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def execute_commands(cmds):
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if (VERBOSE):
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print(" ".join(cmds))
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subprocess.run(cmds, stdout=subprocess.PIPE)
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class DummyBackend(onnx.backend.base.Backend):
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@classmethod
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def prepare(cls, model, device='CPU', **kwargs):
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super(DummyBackend, cls).prepare(model, device, **kwargs)
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# Save model to disk as temp_model.onnx.
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onnx.save(model, "temp_model.onnx")
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# Call frontend to process temp_model.onnx, bit code will be generated.
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execute_commands([ONNF, "temp_model.onnx"])
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# Call llc to generate object file from bitcode.
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execute_commands(
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[LLC, "-filetype=obj", "-relocation-model=pic", "model.bc"])
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# Generate shared library from object file, linking with c runtime.
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execute_commands([
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CXX, "-shared", "-fPIC", "model.o", "-o", "model.so",
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"-L" + RUNTIME_DIR, "-lcruntime"
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])
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return ExecutionSession("./model.so", "_dyn_entry_point_main_graph")
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@classmethod
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def supports_device(cls, device):
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d = Device(device)
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if d.type == DeviceType.CPU:
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return True
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return False
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backend_test = onnx.backend.test.BackendTest(DummyBackend, __name__)
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# Test directories:
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# https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node
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test_to_enable = [
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# Add Op:
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"test_add_cpu",
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"test_add_bcast_cpu",
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# And Op:
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# Sub Op:
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"test_sub_cpu",
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"test_sub_bcast_cpu",
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"test_sub_example_cpu",
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# Cosh Op:
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"test_cosh_cpu",
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"test_cosh_example_cpu",
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# Div Op:
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"test_div_cpu",
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"test_div_bcast_cpu",
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"test_div_example_cpu",
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# Elu Op:
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"test_elu_cpu",
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"test_elu_default_cpu",
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"test_elu_example_cpu",
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# Exp Op:
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"test_exp_cpu",
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"test_exp_example_cpu",
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# Gemm Op:
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"test_gemm_all_attributes_cpu",
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"test_gemm_alpha_cpu",
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"test_gemm_beta_cpu",
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"test_gemm_default_matrix_bias_cpu",
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# "test_gemm_default_no_bias_cpu", <- error, need support for optional operands
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# "test_gemm_default_scalar_bias_cpu", <- error, shapes mismatch, why?
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"test_gemm_default_single_elem_vector_bias_cpu",
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"test_gemm_default_vector_bias_cpu",
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"test_gemm_default_zero_bias_cpu",
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"test_gemm_transposeA_cpu",
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"test_gemm_transposeB_cpu",
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# Hard Sigmoid Op:
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"test_hardsigmoid_cpu",
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"test_hardsigmoid_default_cpu",
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"test_hardsigmoid_example_cpu",
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# Leaky Relu Op:
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"test_leakyrelu_cpu",
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"test_leakyrelu_default_cpu",
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"test_leakyrelu_example_cpu",
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# Max Op:
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"test_max_example_cpu",
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"test_max_one_input_cpu",
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"test_max_two_inputs_cpu",
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# Min Op:
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"test_min_example_cpu",
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"test_min_one_input_cpu",
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"test_min_two_inputs_cpu",
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# Mul Op:
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"test_mul_cpu",
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"test_mul_bcast_cpu",
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"test_mul_example_cpu",
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# Relu Op:
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"test_relu_cpu",
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# ReduceMax Op:
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"test_reduce_max_default_axes_keepdim_example_cpu",
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"test_reduce_max_default_axes_keepdims_random_cpu",
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"test_reduce_max_do_not_keepdims_example_cpu",
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"test_reduce_max_do_not_keepdims_random_cpu",
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"test_reduce_max_keepdims_example_cpu",
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"test_reduce_max_keepdims_random_cpu",
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"test_reduce_max_negative_axes_keepdims_example_cpu",
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"test_reduce_max_negative_axes_keepdims_random_cpu",
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# ReduceMin Op:
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"test_reduce_min_default_axes_keepdims_example_cpu",
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"test_reduce_min_default_axes_keepdims_random_cpu",
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"test_reduce_min_do_not_keepdims_example_cpu",
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"test_reduce_min_do_not_keepdims_random_cpu",
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"test_reduce_min_keepdims_example_cpu",
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"test_reduce_min_keepdims_random_cpu",
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"test_reduce_min_negative_axes_keepdims_example_cpu",
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"test_reduce_min_negative_axes_keepdims_random_cpu",
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# ReduceProd Op:
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"test_reduce_prod_default_axes_keepdims_example_cpu",
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"test_reduce_prod_default_axes_keepdims_random_cpu",
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"test_reduce_prod_do_not_keepdims_example_cpu",
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"test_reduce_prod_do_not_keepdims_random_cpu",
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"test_reduce_prod_keepdims_example_cpu",
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"test_reduce_prod_keepdims_random_cpu",
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"test_reduce_prod_negative_axes_keepdims_example_cpu",
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"test_reduce_prod_negative_axes_keepdims_random_cpu",
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# ReduceSum Op:
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"test_reduce_sum_default_axes_keepdims_example_cpu",
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"test_reduce_sum_default_axes_keepdims_random_cpu",
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"test_reduce_sum_do_not_keepdims_example_cpu",
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"test_reduce_sum_do_not_keepdims_random_cpu",
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"test_reduce_sum_keepdims_example_cpu",
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"test_reduce_sum_keepdims_random_cpu",
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"test_reduce_sum_negative_axes_keepdims_example_cpu",
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"test_reduce_sum_negative_axes_keepdims_random_cpu",
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# Selu Op:
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"test_selu_cpu",
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"test_selu_default_cpu",
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"test_selu_example_cpu",
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# Sigmoid Op:
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"test_sigmoid_cpu",
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"test_sigmoid_example_cpu",
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# Softmax Op:
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"test_softmax_axis_0_cpu",
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"test_softmax_axis_1_cpu",
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"test_softmax_axis_2_cpu",
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"test_softmax_default_axis_cpu",
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"test_softmax_example_cpu",
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"test_softmax_large_number_cpu",
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# Sqrt Op:
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"test_sqrt_cpu",
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"test_sqrt_example_cpu",
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# Sum Op:
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"test_sum_example_cpu",
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"test_sum_one_input_cpu",
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"test_sum_two_inputs_cpu",
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# Unsqueeze Op:
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"test_unsqueeze_axis_0_cpu",
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"test_unsqueeze_axis_1_cpu",
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"test_unsqueeze_axis_2_cpu",
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"test_unsqueeze_axis_3_cpu",
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"test_unsqueeze_negative_axes_cpu",
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"test_unsqueeze_three_axes_cpu",
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"test_unsqueeze_two_axes_cpu",
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# "test_unsqueeze_unsorted_axes_cpu",
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# Reciprocal Op:
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"test_reciprocal_cpu",
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"test_reciprocal_example_cpu",
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# SoftplusOp:
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"test_softplus_cpu",
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"test_softplus_example_cpu",
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# SoftsignOp:
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"test_softsign_cpu",
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"test_softsign_example_cpu",
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# ReshapeOp:
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"test_reshape_extended_dims_cpu",
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#"test_reshape_negative_dim_cpu", <- handle nagative dim
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#"test_reshape_negative_extended_dims_cpu", <- handle nagative dim
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"test_reshape_one_dim_cpu",
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"test_reshape_reduced_dims_cpu",
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"test_reshape_reordered_all_dims_cpu",
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"test_reshape_reordered_last_dims_cpu",
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#"test_reshape_zero_and_negative_dim_cpu", <- handle nagative dim
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"test_reshape_zero_dim_cpu",
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# Transpose
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"test_transpose_default_cpu",
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"test_transpose_all_permutations_0_cpu",
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"test_transpose_all_permutations_1_cpu",
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"test_transpose_all_permutations_2_cpu",
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"test_transpose_all_permutations_3_cpu",
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"test_transpose_all_permutations_4_cpu",
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"test_transpose_all_permutations_5_cpu",
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# Conv
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"test_basic_conv_without_padding_cpu",
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# Sign Op:
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"test_sign_cpu",
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]
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# Extract name of all test cases.
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import inspect
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all_tests = inspect.getmembers(
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backend_test.test_cases["OnnxBackendNodeModelTest"])
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all_test_names = list(map(lambda x: x[0], all_tests))
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# Ensure that test names specified in test_to_enable actually exist.
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for test_name in test_to_enable:
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assert test_name in all_test_names, """test name {} not found, it is likely
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that you may have misspelled the test name or the specified test does not
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exist in the version of onnx package you installed.""".format(
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test_name)
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backend_test.include(r"^{}$".format(test_name))
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# import all test cases at global scope to make them visible to python.unittest
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globals().update(backend_test.test_cases)
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if __name__ == '__main__':
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unittest.main()
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