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Unverified Commit 25e8cf0b authored by Ted Themistokleous's avatar Ted Themistokleous Committed by GitHub
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Merge branch 'develop' into test_onnx_zoo

parents a313a68e 635502be
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test/onnx/gemm_test.onnx
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test/onnx/gen_onnx.py 100755 → 100644
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......@@ -23,33 +23,42 @@
#####################################################################################
# This script generates onnx files for MIGraphX onnx operator tests.
# To generate an individual onnx file, you can use the following
# command: python -c "import gen_onnx; gen_onnx.{test_name}_test()"
# command: python3 -c "import gen_onnx; gen_onnx.{test_name}_test()"
import numpy as np
import onnx
from onnx import helper
from onnx import TensorProto
def onnx_test(op_test):
def run_test():
op_info = op_test()
if len(op_info) > 3:
graph_def = helper.make_graph(op_info[0],
op_test.__name__,
op_info[1],
op_info[2],
initializer=op_info[3])
else:
graph_def = helper.make_graph(op_info[0], op_test.__name__,
op_info[1], op_info[2])
model_def = helper.make_model(graph_def,
producer_name=op_test.__name__)
onnx.save(model_def, '{}.onnx'.format(op_test.__name__))
return run_test
@onnx_test
from onnx.numpy_helper import from_array
def onnx_test(external_data=False):
def create_onnx_test(op_test):
def run_test():
op_info = op_test()
if len(op_info) > 3:
graph_def = helper.make_graph(op_info[0],
op_test.__name__,
op_info[1],
op_info[2],
initializer=op_info[3])
else:
graph_def = helper.make_graph(op_info[0], op_test.__name__,
op_info[1], op_info[2])
model_def = helper.make_model(graph_def,
producer_name=op_test.__name__)
onnx.save_model(model_def,
'{}.onnx'.format(op_test.__name__),
save_as_external_data=external_data,
location='{}.weight'.format(op_test.__name__),
size_threshold=0,
convert_attribute=True)
return run_test
return create_onnx_test
@onnx_test()
def acos_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -63,7 +72,7 @@ def acos_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def acosh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -77,7 +86,7 @@ def acosh_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def add_bcast_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
......@@ -92,7 +101,7 @@ def add_bcast_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def add_fp16_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1])
......@@ -115,7 +124,7 @@ def add_fp16_test():
])
@onnx_test
@onnx_test()
def add_scalar_test():
x = helper.make_tensor_value_info('0', TensorProto.UINT8, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.UINT8, [])
......@@ -126,7 +135,7 @@ def add_scalar_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def argmax_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
......@@ -140,7 +149,21 @@ def argmax_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def argmax_dyn_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 4, 6])
node = onnx.helper.make_node('ArgMax',
inputs=['x'],
outputs=['y'],
axis=2,
keepdims=0)
return ([node], [x], [y])
@onnx_test()
def argmin_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])
......@@ -154,7 +177,7 @@ def argmin_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def asin_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -168,7 +191,7 @@ def asin_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def asinh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -182,7 +205,7 @@ def asinh_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def atan_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -196,7 +219,7 @@ def atan_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def atanh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -210,7 +233,7 @@ def atanh_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def averagepool_1d_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5])
out = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3])
......@@ -223,7 +246,7 @@ def averagepool_1d_test():
return ([node], [x], [out])
@onnx_test
@onnx_test()
def averagepool_3d_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5])
out = helper.make_tensor_value_info('1', TensorProto.FLOAT,
......@@ -237,7 +260,65 @@ def averagepool_3d_test():
return ([node], [x], [out])
@onnx_test
@onnx_test()
def averagepool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 5, 5, 5])
out = helper.make_tensor_value_info('1', TensorProto.FLOAT,
[None, 3, 3, 3, 3])
node = onnx.helper.make_node('AveragePool',
inputs=['0'],
outputs=['1'],
kernel_shape=[3, 3, 3])
return ([node], [x], [out])
@onnx_test()
def averagepool_dyn_autopad_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1, 5, 5])
node = onnx.helper.make_node('AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
auto_pad='SAME_LOWER')
return ([node], [x], [y])
@onnx_test()
def averagepool_dyn_asym_padding_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1, 3, 3])
node = onnx.helper.make_node('AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
strides=[2, 2],
pads=[0, 0, 1, 1])
return ([node], [x], [y])
@onnx_test()
def averagepool_dyn_cip_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1, 1, 1])
node = onnx.helper.make_node('AveragePool',
inputs=['x'],
outputs=['y'],
kernel_shape=[2, 2],
count_include_pad=1)
return ([node], [x], [y])
@onnx_test()
def averagepool_notset_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1])
......@@ -253,7 +334,7 @@ def averagepool_notset_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def averagepool_nt_cip_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1])
......@@ -270,7 +351,7 @@ def averagepool_nt_cip_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def averagepool_same_lower_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])
......@@ -284,7 +365,7 @@ def averagepool_same_lower_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def averagepool_sl_cip_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])
......@@ -299,7 +380,7 @@ def averagepool_sl_cip_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def averagepool_same_upper_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])
......@@ -313,7 +394,7 @@ def averagepool_same_upper_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def batch_norm_flat_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [1])
......@@ -331,7 +412,7 @@ def batch_norm_flat_test():
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
@onnx_test()
def batch_norm_rank_2_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [5])
......@@ -349,7 +430,7 @@ def batch_norm_rank_2_test():
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
@onnx_test()
def batch_norm_1d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [2, 3, 4])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [3])
......@@ -366,7 +447,7 @@ def batch_norm_1d_test():
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
@onnx_test()
def batch_norm_2d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4, 4])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [3])
......@@ -383,7 +464,7 @@ def batch_norm_2d_test():
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
@onnx_test()
def batch_norm_3d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16,
[2, 2, 2, 2, 2])
......@@ -403,7 +484,7 @@ def batch_norm_3d_test():
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
@onnx_test()
def batch_norm_invalid_bias_rank_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4, 4])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [3])
......@@ -420,7 +501,7 @@ def batch_norm_invalid_bias_rank_test():
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
@onnx_test()
def binary_dyn_brcst_prelu_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 4, 5])
......@@ -437,7 +518,7 @@ def binary_dyn_brcst_prelu_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def binary_dyn_brcst_add_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT,
......@@ -454,7 +535,7 @@ def binary_dyn_brcst_add_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def binary_dyn_brcst_attr_error_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT,
......@@ -471,7 +552,7 @@ def binary_dyn_brcst_attr_error_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def binary_dyn_brcst_mul_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 4, 5])
......@@ -488,7 +569,7 @@ def binary_dyn_brcst_mul_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def cast_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -498,7 +579,7 @@ def cast_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def ceil_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -512,7 +593,7 @@ def ceil_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def celu_alpha_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3])
......@@ -525,7 +606,7 @@ def celu_alpha_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def celu_default_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3])
......@@ -535,7 +616,7 @@ def celu_default_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def celu_verify_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3])
......@@ -548,7 +629,7 @@ def celu_verify_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def celu_wrong_type_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [2, 3])
......@@ -558,7 +639,7 @@ def celu_wrong_type_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def celu_zero_alpha_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3])
......@@ -571,7 +652,7 @@ def celu_zero_alpha_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def clip_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -585,7 +666,7 @@ def clip_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def clip_test_op11():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -600,7 +681,7 @@ def clip_test_op11():
return ([node], [x], [y], [min_val, max_val])
@onnx_test
@onnx_test()
def clip_test_op11_max_only():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -614,7 +695,7 @@ def clip_test_op11_max_only():
return ([node], [x], [y], [max_val])
@onnx_test
@onnx_test()
def clip_test_op11_min_only():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -626,7 +707,7 @@ def clip_test_op11_min_only():
return ([node], [x], [y], [min_val])
@onnx_test
@onnx_test()
def clip_test_op11_no_args():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -636,7 +717,7 @@ def clip_test_op11_no_args():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def clip_test_op11_no_args1():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -646,7 +727,7 @@ def clip_test_op11_no_args1():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def clip_test_args_type_mismatch():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 3])
......@@ -662,7 +743,7 @@ def clip_test_args_type_mismatch():
return ([node], [x], [y], [min_val, max_val])
@onnx_test
@onnx_test()
def concat_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 4, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7, 4, 3])
......@@ -678,7 +759,7 @@ def concat_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def constant_test():
x = np.array([0, 1, 2])
y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
......@@ -698,7 +779,7 @@ def constant_test():
return ([node], [], [y])
@onnx_test
@onnx_test()
def constant_fill_test():
value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])
......@@ -715,7 +796,7 @@ def constant_fill_test():
return ([node], [], [value])
@onnx_test
@onnx_test()
def constant_fill_input_as_shape_test():
np_shape = np.array([2, 3])
value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])
......@@ -744,7 +825,7 @@ def constant_fill_input_as_shape_test():
return ([const_shape_node, node], [], [value])
@onnx_test
@onnx_test()
def constant_scalar_test():
x = np.array([1])
y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1])
......@@ -764,7 +845,7 @@ def constant_scalar_test():
return ([node], [], [y])
@onnx_test
@onnx_test()
def constant_empty_scalar_int64_test():
x = np.array([]).astype(np.int64)
y = helper.make_tensor_value_info('0', TensorProto.INT64, [0])
......@@ -784,7 +865,7 @@ def constant_empty_scalar_int64_test():
return ([node], [], [y])
@onnx_test
@onnx_test()
def constant_one_val_int64_test():
x = np.array([1]).astype(np.int64)
y = helper.make_tensor_value_info('0', TensorProto.INT64, [0])
......@@ -804,7 +885,7 @@ def constant_one_val_int64_test():
return ([node], [], [y])
@onnx_test
@onnx_test()
def const_of_shape_empty_input_test():
tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
[10])
......@@ -831,7 +912,7 @@ def const_of_shape_empty_input_test():
return ([shape_const, node], [], [y])
@onnx_test
@onnx_test()
def const_of_shape_float_test():
tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.FLOAT, [1],
[10])
......@@ -858,7 +939,7 @@ def const_of_shape_float_test():
return ([shape_const, node], [], [y])
@onnx_test
@onnx_test()
def const_of_shape_int64_test():
tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
[10])
......@@ -883,7 +964,7 @@ def const_of_shape_int64_test():
return ([shape_const, node], [], [y])
@onnx_test
@onnx_test()
def const_of_shape_no_value_attr_test():
shape_val = np.array([2, 3, 4]).astype(np.int64)
shape_ts = helper.make_tensor(name='shape_tensor',
......@@ -907,7 +988,7 @@ def const_of_shape_no_value_attr_test():
return ([shape_const, node], [], [y])
@onnx_test
@onnx_test()
def conv_1d_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3])
......@@ -918,7 +999,7 @@ def conv_1d_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_3d_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3, 3])
......@@ -930,7 +1011,7 @@ def conv_3d_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_attr_fail_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3])
......@@ -944,7 +1025,7 @@ def conv_attr_fail_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_autopad_fail_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
......@@ -961,7 +1042,7 @@ def conv_autopad_fail_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_autopad_same_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
......@@ -977,7 +1058,7 @@ def conv_autopad_same_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_bias_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
......@@ -993,7 +1074,7 @@ def conv_bias_test():
return ([node], [x, y, z], [out])
@onnx_test
@onnx_test()
def conv_bn_relu_maxpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
......@@ -1029,7 +1110,7 @@ def conv_bn_relu_maxpool_test():
return ([node0, node1, node2, node3], [x, y, z, m, n, k, l], [out])
@onnx_test
@onnx_test()
def conv_dynamic_batch_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 5, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
......@@ -1040,7 +1121,25 @@ def conv_dynamic_batch_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_dynamic_bias_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
out = helper.make_tensor_value_info('3', TensorProto.FLOAT,
[None, 2, 28, 28])
node = onnx.helper.make_node('Conv',
inputs=['0', '1', '2'],
outputs=['3'],
dilations=[1, 1],
strides=[1, 1])
return ([node], [x, y, z], [out])
@onnx_test()
def conv_dynamic_img_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, 3, None, None])
......@@ -1052,7 +1151,7 @@ def conv_dynamic_img_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_dynamic_weights_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT,
......@@ -1064,7 +1163,7 @@ def conv_dynamic_weights_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_dynamic_img_and_weights_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, 3, None, None])
......@@ -1077,7 +1176,7 @@ def conv_dynamic_img_and_weights_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_dynamic_batch_same_upper_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 5, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
......@@ -1090,7 +1189,7 @@ def conv_dynamic_batch_same_upper_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_dynamic_img_same_upper_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, 3, None, None])
......@@ -1105,7 +1204,7 @@ def conv_dynamic_img_same_upper_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_dynamic_kernel_same_lower_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT,
......@@ -1119,7 +1218,7 @@ def conv_dynamic_kernel_same_lower_test():
return ([node], [x, y], [out])
@onnx_test
@onnx_test()
def conv_relu_maxpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
......@@ -1145,7 +1244,7 @@ def conv_relu_maxpool_test():
return ([node1, node2, node3], [x, y, z], [out])
@onnx_test
@onnx_test()
def conv_relu_maxpool_x2_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5, 3, 5, 5])
......@@ -1189,7 +1288,7 @@ def conv_relu_maxpool_x2_test():
return ([node1, node2, node3, node4, node5, node6], [x, y, z, m, n], [out])
@onnx_test
@onnx_test()
def convinteger_bias_test():
x = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.INT8, [1, 3, 5, 5])
......@@ -1205,7 +1304,7 @@ def convinteger_bias_test():
return ([node], [x, y, z], [out])
@onnx_test
@onnx_test()
def cos_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -1219,7 +1318,7 @@ def cos_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def cosh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
......@@ -1233,7 +1332,7 @@ def cosh_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def deconv_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 1, 3, 3])
......@@ -1247,7 +1346,7 @@ def deconv_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_bias_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 1, 3, 3])
......@@ -1262,7 +1361,7 @@ def deconv_bias_test():
return ([node], [x, w, b], [y])
@onnx_test
@onnx_test()
def deconv_input_pads_strides_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
......@@ -1277,7 +1376,7 @@ def deconv_input_pads_strides_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_input_pads_asymm_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
......@@ -1292,7 +1391,7 @@ def deconv_input_pads_asymm_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_input_pads_asymm_1d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3])
......@@ -1308,7 +1407,7 @@ def deconv_input_pads_asymm_1d_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_output_padding_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
......@@ -1323,7 +1422,7 @@ def deconv_output_padding_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_output_padding_3d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3, 3])
......@@ -1338,7 +1437,7 @@ def deconv_output_padding_3d_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_output_shape_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
......@@ -1353,7 +1452,7 @@ def deconv_output_shape_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_output_shape_3d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3, 3])
......@@ -1368,7 +1467,7 @@ def deconv_output_shape_3d_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def deconv_stride_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
......@@ -1382,7 +1481,7 @@ def deconv_stride_test():
return ([node], [x, w], [y])
@onnx_test
@onnx_test()
def depthtospace_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 8, 5, 5])
......@@ -1397,7 +1496,7 @@ def depthtospace_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def depthtospace_simple_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 8, 2, 3])
......@@ -1412,7 +1511,7 @@ def depthtospace_simple_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def depthtospace_crd_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 8, 5, 5])
......@@ -1427,7 +1526,7 @@ def depthtospace_crd_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def spacetodepth_test():
x = helper.make_tensor_value_info('x', TensorProto.float, [2, 2, 10, 10])
......@@ -1441,7 +1540,7 @@ def spacetodepth_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def spacetodepth_simple_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 2, 4, 6])
......@@ -1455,7 +1554,7 @@ def spacetodepth_simple_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def spacetodepth_invalid_blocksize_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 2, 4, 6])
......@@ -1469,7 +1568,7 @@ def spacetodepth_invalid_blocksize_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def spacetodepth_nondivisibility_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 2, 5, 5])
......@@ -1483,7 +1582,7 @@ def spacetodepth_nondivisibility_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def dequantizelinear_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.INT8, [5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1])
......@@ -1498,7 +1597,7 @@ def dequantizelinear_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def dequantizelinear_zero_point_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.INT8, [5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1])
......@@ -1529,17 +1628,17 @@ def make_dequantizelinear_axis_graph(axis):
return ([node], [arg0, arg1, arg2], [arg_out])
@onnx_test
@onnx_test()
def dequantizelinear_axis_test():
return make_dequantizelinear_axis_graph(2)
@onnx_test
@onnx_test()
def dequantizelinear_neg_axis_test():
return make_dequantizelinear_axis_graph(-2)
@onnx_test
@onnx_test()
def dropout_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
......@@ -1553,7 +1652,7 @@ def dropout_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def elu_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -1566,7 +1665,7 @@ def elu_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def embedding_bag_test():
index_val = np.array([1, 0, 2])
......@@ -1619,7 +1718,7 @@ def embedding_bag_test():
return ([index, offset, node1, node2, node3], [weight], [y1, y2, y3])
@onnx_test
@onnx_test()
def embedding_bag_offset_test():
index_val = np.array([1, 0])
......@@ -1658,7 +1757,7 @@ def embedding_bag_offset_test():
return ([index, offset, node], [weight], [y])
@onnx_test
@onnx_test()
def equal_test():
ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
x1 = helper.make_tensor("x1",
......@@ -1678,7 +1777,7 @@ def equal_test():
return ([node], [x2], [y], [x1])
@onnx_test
@onnx_test()
def equal_bool_test():
x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [2, 3])
......@@ -1696,7 +1795,7 @@ def equal_bool_test():
return ([node1, node2], [x1, x2], [y])
@onnx_test
@onnx_test()
def erf_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])
......@@ -1710,7 +1809,7 @@ def erf_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def exp_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -1724,7 +1823,7 @@ def exp_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def expand_test():
shape_val = np.array([2, 3, 4, 5]).astype(np.int64)
shape_ts = helper.make_tensor(name='shape_tensor',
......@@ -1747,7 +1846,23 @@ def expand_test():
return ([shape_const, node], [x], [y])
@onnx_test
@onnx_test(True)
def external_constant_test():
x = np.array([0, 1, 2])
y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
tensor = from_array(x)
tensor.name = 'const_tensor'
node = onnx.helper.make_node('Constant',
inputs=[],
outputs=['0'],
value=tensor)
return ([node], [], [y])
@onnx_test()
def eyelike_default_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [3, 4])
......@@ -1760,7 +1875,7 @@ def eyelike_default_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_double_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.DOUBLE, [6, 15])
T2 = helper.make_tensor_value_info('T2', TensorProto.DOUBLE, [6, 15])
......@@ -1773,7 +1888,7 @@ def eyelike_double_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_half_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT16, [8, 8])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT16, [8, 8])
......@@ -1786,7 +1901,7 @@ def eyelike_half_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_k_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [3, 4])
......@@ -1794,7 +1909,7 @@ def eyelike_k_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_k_outofbounds_neg_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [2, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [2, 4])
......@@ -1805,7 +1920,7 @@ def eyelike_k_outofbounds_neg_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_k_outofbounds_pos_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [3, 4])
......@@ -1813,7 +1928,7 @@ def eyelike_k_outofbounds_pos_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_not_rank2_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4, 2])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [3, 4])
......@@ -1825,7 +1940,7 @@ def eyelike_not_rank2_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_verify_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [3, 4])
......@@ -1833,7 +1948,7 @@ def eyelike_verify_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_verify_negk_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.FLOAT, [3, 4])
......@@ -1844,7 +1959,7 @@ def eyelike_verify_negk_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def eyelike_set_dtype_test():
T1 = helper.make_tensor_value_info('T1', TensorProto.FLOAT, [3, 4])
T2 = helper.make_tensor_value_info('T2', TensorProto.DOUBLE, [3, 4])
......@@ -1855,7 +1970,7 @@ def eyelike_set_dtype_test():
return ([node], [T1], [T2])
@onnx_test
@onnx_test()
def flatten_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20])
......@@ -1871,7 +1986,7 @@ def flatten_test():
return ([node, node2], [x], [y, y2])
@onnx_test
@onnx_test()
def flatten_nonstd_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 5, 4])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20])
......@@ -1894,7 +2009,20 @@ def flatten_nonstd_test():
return ([trans, node, node2], [x], [y, y2])
@onnx_test
@onnx_test()
def flatten_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 4, 5])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [None, 20])
node = onnx.helper.make_node('Flatten',
inputs=['0'],
axis=2,
outputs=['2'])
return ([node], [x], [y])
@onnx_test()
def floor_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -1908,7 +2036,7 @@ def floor_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def gather_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32,
......@@ -1925,7 +2053,7 @@ def gather_test():
return ([node], [x, i], [y])
@onnx_test
@onnx_test()
def gather_elements_axis0_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4])
i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3])
......@@ -1941,7 +2069,7 @@ def gather_elements_axis0_test():
return ([node], [x, i], [y])
@onnx_test
@onnx_test()
def gather_elements_axis1_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4])
i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3])
......@@ -1957,7 +2085,7 @@ def gather_elements_axis1_test():
return ([node], [x, i], [y])
@onnx_test
@onnx_test()
def gathernd_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2])
i = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 2])
......@@ -1970,7 +2098,7 @@ def gathernd_test():
return ([node], [x, i], [y])
@onnx_test
@onnx_test()
def gathernd_batch_dims_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
i = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 1])
......@@ -1986,76 +2114,141 @@ def gathernd_batch_dims_test():
return ([node], [x, i], [y])
@onnx_test
@onnx_test()
def gemm_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [])
a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11])
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [8, 6])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [8, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [6, 7])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [6, 7])
node = onnx.helper.make_node('Gemm',
inputs=['0', '1', '2'],
outputs=['3'],
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=0.5,
beta=0.8,
transA=1)
return ([node], [A, B, C], [Y])
@onnx_test()
def gemm_no_C_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [5, 7])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [11, 5])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [7, 11])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=2.0,
beta=2.0,
transA=1,
transB=1)
return ([node], [x, y, z], [a])
return ([node], [A, B, C], [Y])
@onnx_test
def gemm_ex_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 8, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 8, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])
@onnx_test()
def gemm_brcst_C_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [5, 6])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [5, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [6, 1])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [6, 7])
node = onnx.helper.make_node('Gemm',
inputs=['1', '2', '3'],
outputs=['y'],
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=0.5,
beta=0.8,
transA=1)
return ([node], [m1, m2, m3], [y])
return ([node], [A, B, C], [Y])
@onnx_test
def gemm_ex_brcst_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])
@onnx_test()
def gemm_half_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT16, [8, 6])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT16, [8, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT16, [6, 1])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT16, [6, 7])
node = onnx.helper.make_node('Gemm',
inputs=['1', '2', '3'],
outputs=['y'],
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=0.5,
beta=0.8,
transA=1)
return ([node], [m1, m2, m3], [y])
return ([node], [A, B, C], [Y])
@onnx_test
def gemm_half_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 1, 8, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1, 1, 8, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT16, [1, 1, 6, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [1, 1, 6, 7])
@onnx_test()
def gemm_dyn_inner_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [None, 6])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [None, 7])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [6, 7])
node = onnx.helper.make_node('Gemm',
inputs=['1', '2', '3'],
outputs=['y'],
inputs=['A', 'B'],
outputs=['Y'],
alpha=0.5,
transA=1)
return ([node], [A, B], [Y])
@onnx_test()
def gemm_dyn_outer_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [5, None])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [11, 5])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, 11])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B'],
outputs=['Y'],
alpha=2.0,
transA=1,
transB=1)
return ([node], [A, B], [Y])
@onnx_test()
def gemm_dyn_bias_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [8, None])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [8, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 7])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, 7])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=1.0,
beta=1.0,
transA=1)
return ([node], [A, B, C], [Y])
@onnx_test()
def gemm_rank_error():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [4, 1, 8, 6])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [4, 1, 8, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [6, 7])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4, 1, 6, 7])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=0.5,
beta=0.8,
transA=1)
return ([node], [m1, m2, m3], [y])
return ([node], [A, B, C], [Y])
@onnx_test
@onnx_test()
def globalavgpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
......@@ -2069,7 +2262,22 @@ def globalavgpool_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def globalavgpool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 1, 1])
node = onnx.helper.make_node(
'GlobalAveragePool',
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test()
def globallppool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
......@@ -2083,7 +2291,22 @@ def globallppool_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def globallppool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, 3, None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
node = onnx.helper.make_node(
'GlobalLpPool',
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test()
def globalmaxpool_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
......@@ -2097,7 +2320,22 @@ def globalmaxpool_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def globalmaxpool_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 1, 1])
node = onnx.helper.make_node(
'GlobalMaxPool',
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test()
def greater_test():
ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
x1 = helper.make_tensor("x1",
......@@ -2117,7 +2355,7 @@ def greater_test():
return ([node], [x2], [y], [x1])
@onnx_test
@onnx_test()
def greater_bool_test():
x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [2, 3])
......@@ -2135,7 +2373,7 @@ def greater_bool_test():
return ([node1, node2], [x1, x2], [y])
@onnx_test
@onnx_test()
def greaterorequal_test():
x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [3])
......@@ -2151,7 +2389,7 @@ def greaterorequal_test():
return ([node], [x1, x2], [y])
@onnx_test
@onnx_test()
def group_conv_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 4, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1, 3, 3])
......@@ -2167,7 +2405,7 @@ def group_conv_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def hardsigmoid_default_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 3, 4, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 4, 5])
......@@ -2177,7 +2415,7 @@ def hardsigmoid_default_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def hardsigmoid_double_test():
x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [1, 3, 4, 5])
y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [1, 3, 4, 5])
......@@ -2191,7 +2429,7 @@ def hardsigmoid_double_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def hardsigmoid_half_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [1, 3, 4, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [1, 3, 4, 5])
......@@ -2201,7 +2439,7 @@ def hardsigmoid_half_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def hardsigmoid_verify_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 5])
......@@ -2211,7 +2449,7 @@ def hardsigmoid_verify_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def hardswish_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 5])
......@@ -2221,7 +2459,7 @@ def hardswish_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def if_else_test():
x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3])
y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
......@@ -2275,7 +2513,7 @@ def if_else_test():
return ([node], [x, y], [res], [cond_tensor, xt_tensor, yt_tensor])
@onnx_test
@onnx_test()
def if_literal_test():
then_out = onnx.helper.make_tensor_value_info('then_out',
onnx.TensorProto.FLOAT, [5])
......@@ -2323,7 +2561,7 @@ def if_literal_test():
return ([node], [cond_input], [ret])
@onnx_test
@onnx_test()
def if_param_excp_test():
then_out = onnx.helper.make_tensor_value_info('then_out',
onnx.TensorProto.FLOAT,
......@@ -2375,7 +2613,7 @@ def if_param_excp_test():
return ([node], [cond_input, x, y], [ret])
@onnx_test
@onnx_test()
def if_param_excp1_test():
then_out = onnx.helper.make_tensor_value_info('sub_out',
onnx.TensorProto.FLOAT,
......@@ -2410,7 +2648,7 @@ def if_param_excp1_test():
return ([node], [cond_input, x], [ret])
@onnx_test
@onnx_test()
def if_param_test():
then_out = onnx.helper.make_tensor_value_info('then_out',
onnx.TensorProto.FLOAT,
......@@ -2462,7 +2700,7 @@ def if_param_test():
return ([node], [cond_input, x, y], [ret])
@onnx_test
@onnx_test()
def if_pl_test():
out_x = onnx.helper.make_tensor_value_info('out_x', onnx.TensorProto.FLOAT,
[2, 3])
......@@ -2530,7 +2768,7 @@ def if_pl_test():
return ([node], [cond_input, x, y], [ret], [xt_tensor, yt_tensor])
@onnx_test
@onnx_test()
def if_then_test():
x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3])
y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
......@@ -2584,7 +2822,7 @@ def if_then_test():
return ([node], [x, y], [res], [cond_tensor, xt_tensor, yt_tensor])
@onnx_test
@onnx_test()
def if_tuple_test():
x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [1, 4])
y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [3, 4])
......@@ -2655,7 +2893,7 @@ def if_tuple_test():
y], [res0, res1], [one_tensor, two_tensor, three_tensor])
@onnx_test
@onnx_test()
def imagescaler_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 16, 16])
......@@ -2669,7 +2907,7 @@ def imagescaler_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def imagescaler_half_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 3, 16, 16])
......@@ -2683,7 +2921,7 @@ def imagescaler_half_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def implicit_add_bcast_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
......@@ -2698,7 +2936,7 @@ def implicit_add_bcast_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def implicit_pow_bcast_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
......@@ -2714,7 +2952,7 @@ def implicit_pow_bcast_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def implicit_sub_bcast_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.UINT64, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.UINT64, [4, 5])
......@@ -2730,7 +2968,7 @@ def implicit_sub_bcast_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def initializer_not_an_input():
values = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
w = helper.make_tensor(name='w',
......@@ -2750,7 +2988,7 @@ def initializer_not_an_input():
return ([node], [x], [y], [w])
@onnx_test
@onnx_test()
def instance_norm_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2])
......@@ -2764,7 +3002,7 @@ def instance_norm_test():
return ([node], [x, scale, bias], [y])
@onnx_test
@onnx_test()
def instance_norm_half_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1, 2, 3, 3])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [2])
......@@ -2778,7 +3016,7 @@ def instance_norm_half_test():
return ([node], [x, scale, bias], [y])
@onnx_test
@onnx_test()
def instance_norm_type_mismatch_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [2])
......@@ -2792,7 +3030,7 @@ def instance_norm_type_mismatch_test():
return ([node], [x, scale, bias], [y])
@onnx_test
@onnx_test()
def instance_norm_invalid_type_test():
x = helper.make_tensor_value_info('0', TensorProto.INT32, [1, 2, 3, 3])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2])
......@@ -2806,7 +3044,7 @@ def instance_norm_invalid_type_test():
return ([node], [x, scale, bias], [y])
@onnx_test
@onnx_test()
def instance_norm_nonbroadcastable_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4])
......@@ -2820,7 +3058,7 @@ def instance_norm_nonbroadcastable_test():
return ([node], [x, scale, bias], [y])
@onnx_test
@onnx_test()
def instance_norm_val_test():
x = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]]])
......@@ -2850,7 +3088,7 @@ def instance_norm_val_test():
return ([node], [], [y], [x_tensor, scale_tensor, bias_tensor])
@onnx_test
@onnx_test()
def instance_norm_val_3d_test():
x = np.array([[[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],
[[[0, 1], [2, 3]], [[4, 5], [6, 7]]]]])
......@@ -2880,7 +3118,7 @@ def instance_norm_val_3d_test():
return ([node], [], [y], [x_tensor, scale_tensor, bias_tensor])
@onnx_test
@onnx_test()
def isnan_float_test():
t1 = helper.make_tensor_value_info('t1', TensorProto.FLOAT, [2, 3])
t2 = helper.make_tensor_value_info('t2', TensorProto.FLOAT, [2, 3])
......@@ -2893,7 +3131,7 @@ def isnan_float_test():
return ([node], [t1], [t2])
@onnx_test
@onnx_test()
def isnan_half_test():
t1 = helper.make_tensor_value_info('t1', TensorProto.FLOAT16, [2, 3])
t2 = helper.make_tensor_value_info('t2', TensorProto.FLOAT16, [2, 3])
......@@ -2906,7 +3144,7 @@ def isnan_half_test():
return ([node], [t1], [t2])
@onnx_test
@onnx_test()
def layernorm_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 1, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5])
......@@ -2969,7 +3207,7 @@ def layernorm_test():
bias_add], [x, scale, bias], [y], [pow_tensor, epsilon_tensor])
@onnx_test
@onnx_test()
def leaky_relu_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -2982,7 +3220,7 @@ def leaky_relu_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def less_test():
ax1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
x1 = helper.make_tensor("x1",
......@@ -3002,7 +3240,7 @@ def less_test():
return ([node], [x2], [y], [x1])
@onnx_test
@onnx_test()
def less_bool_test():
x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [2, 3])
......@@ -3020,7 +3258,7 @@ def less_bool_test():
return ([node1, node2], [x1, x2], [y])
@onnx_test
@onnx_test()
def lessorequal_test():
x1 = helper.make_tensor_value_info('x1', TensorProto.FLOAT, [3])
......@@ -3036,7 +3274,7 @@ def lessorequal_test():
return ([node], [x1, x2], [y])
@onnx_test
@onnx_test()
def log_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -3050,7 +3288,7 @@ def log_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def logical_and_bcast_test():
x = helper.make_tensor_value_info('0', TensorProto.BOOL, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.BOOL, [4, 5])
......@@ -3061,7 +3299,7 @@ def logical_and_bcast_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def logical_or_test():
x = helper.make_tensor_value_info('0', TensorProto.BOOL, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.BOOL, [2, 3, 4, 5])
......@@ -3072,7 +3310,7 @@ def logical_or_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def logical_xor_bcast_test():
x = helper.make_tensor_value_info('0', TensorProto.BOOL, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.BOOL, [4, 1])
......@@ -3083,7 +3321,7 @@ def logical_xor_bcast_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def logsoftmax_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6])
......@@ -3096,7 +3334,7 @@ def logsoftmax_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def logsoftmax_nonstd_input_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [6, 9])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 4])
......@@ -3116,7 +3354,7 @@ def logsoftmax_nonstd_input_test():
return ([node0, node1], [x], [y])
@onnx_test
@onnx_test()
def loop_default_test():
body = helper.make_graph([
helper.make_node("Add", ["a", "b_in"], ["my_local"]),
......@@ -3153,7 +3391,7 @@ def loop_default_test():
return ([node], [a, b], [b_loop, uout])
@onnx_test
@onnx_test()
def loop_test():
body = helper.make_graph([
helper.make_node("Add", ["a", "b_in"], ["my_local"]),
......@@ -3194,7 +3432,7 @@ def loop_test():
return ([node], [iter, cond, a, b], [b_loop, uout])
@onnx_test
@onnx_test()
def lpnormalization_axis_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3])
......@@ -3206,7 +3444,7 @@ def lpnormalization_axis_error_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lpnormalization_default_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
......@@ -3220,7 +3458,7 @@ def lpnormalization_default_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lpnormalization_l1_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
......@@ -3234,7 +3472,7 @@ def lpnormalization_l1_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lpnormalization_l2_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
......@@ -3246,7 +3484,7 @@ def lpnormalization_l2_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lpnormalization_p_error_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3])
......@@ -3258,7 +3496,7 @@ def lpnormalization_p_error_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lppool_l1_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 3, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 3])
......@@ -3271,7 +3509,7 @@ def lppool_l1_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lppool_l2_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 3, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 3])
......@@ -3284,7 +3522,7 @@ def lppool_l2_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def lrn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 28, 24, 24])
......@@ -3300,7 +3538,7 @@ def lrn_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def matmul_bmbm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 2, 1, 7, 8])
......@@ -3315,7 +3553,7 @@ def matmul_bmbm_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmul_bmv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
......@@ -3330,7 +3568,7 @@ def matmul_bmv_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmul_mv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [6, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
......@@ -3345,7 +3583,7 @@ def matmul_mv_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmul_vbm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, 8])
......@@ -3360,7 +3598,7 @@ def matmul_vbm_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmul_vm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, 8])
......@@ -3375,7 +3613,7 @@ def matmul_vm_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmul_vv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
......@@ -3390,7 +3628,82 @@ def matmul_vv_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmul_dyn_mm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, None])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_mv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_vm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, None])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_vv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_broadcast_error():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, None])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmulinteger_test():
m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16])
m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [3, 16, 8])
......@@ -3405,7 +3718,22 @@ def matmulinteger_test():
return ([node], [m1, m2], [y])
@onnx_test
@onnx_test()
def matmulinteger_dyn_error():
m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [None, 6, 16])
m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [None, 16, 8])
y = helper.make_tensor_value_info('y', TensorProto.INT32, [None, 6, 8])
node = onnx.helper.make_node(
'MatMulInteger',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def max_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -3421,7 +3749,7 @@ def max_test():
return ([node], [a, b, c], [y])
@onnx_test
@onnx_test()
def maxpool_notset_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1])
......@@ -3437,7 +3765,7 @@ def maxpool_notset_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def maxpool_same_upper_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])
......@@ -3451,7 +3779,7 @@ def maxpool_same_upper_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def mean_broadcast_test():
data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 4])
data_1 = helper.make_tensor_value_info('1', TensorProto.FLOAT,
......@@ -3470,7 +3798,7 @@ def mean_broadcast_test():
return ([node], [data_0, data_1, data_2, data_3, data_4], [mean])
@onnx_test
@onnx_test()
def mean_fp16_test():
data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1, 2, 3])
data_1 = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 2, 3])
......@@ -3486,7 +3814,7 @@ def mean_fp16_test():
return ([node], [data_0, data_1, data_2], [mean])
@onnx_test
@onnx_test()
def mean_invalid_broadcast_test():
data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3])
data_1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2, 3])
......@@ -3501,7 +3829,7 @@ def mean_invalid_broadcast_test():
return ([node], [data_0, data_1, data_2], [mean])
@onnx_test
@onnx_test()
def mean_single_input_test():
data_0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [1, 2, 3])
......@@ -3511,7 +3839,7 @@ def mean_single_input_test():
return ([node], [data_0], [mean])
@onnx_test
@onnx_test()
def mean_test():
data = [
helper.make_tensor_value_info(str(i), TensorProto.DOUBLE, [2, 2, 2])
......@@ -3525,7 +3853,7 @@ def mean_test():
return ([node], data, [mean])
@onnx_test
@onnx_test()
def mean_integral_test():
data = [
helper.make_tensor_value_info(str(i), TensorProto.INT32, [2, 2, 2])
......@@ -3539,7 +3867,7 @@ def mean_integral_test():
return ([node], data, [mean])
@onnx_test
@onnx_test()
def min_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -3555,7 +3883,7 @@ def min_test():
return ([node], [a, b, c], [y])
@onnx_test
@onnx_test()
def mod_test():
a = helper.make_tensor_value_info('0', TensorProto.INT32, [3, 3, 3])
b = helper.make_tensor_value_info('1', TensorProto.INT32, [3, 3, 3])
......@@ -3566,7 +3894,7 @@ def mod_test():
return ([node], [a, b], [y])
@onnx_test
@onnx_test()
def mod_test_half():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [3, 3, 3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [3, 3, 3])
......@@ -3577,7 +3905,7 @@ def mod_test_half():
return ([node], [a, b], [y])
@onnx_test
@onnx_test()
def mod_test_different_dtypes():
a = helper.make_tensor_value_info('0', TensorProto.INT16, [3, 3, 3])
b = helper.make_tensor_value_info('1', TensorProto.INT32, [3, 3, 3])
......@@ -3592,7 +3920,7 @@ def mod_test_different_dtypes():
return ([node], [a, b], [y])
@onnx_test
@onnx_test()
def mod_test_fmod():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 3, 3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 3, 3])
......@@ -3608,7 +3936,7 @@ def mod_test_fmod():
return ([node], [a, b], [y])
@onnx_test
@onnx_test()
def mod_test_fmod_half():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [3, 3, 3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [3, 3, 3])
......@@ -3622,7 +3950,7 @@ def mod_test_fmod_half():
return ([node], [a, b], [y])
@onnx_test
@onnx_test()
def mod_test_fmod_different_dtypes():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 3, 3])
b = helper.make_tensor_value_info('1', TensorProto.INT32, [3, 3, 3])
......@@ -3638,7 +3966,7 @@ def mod_test_fmod_different_dtypes():
return ([node], [a, b], [y])
@onnx_test
@onnx_test()
def multinomial_test():
sample_size = 10
seed = 0.0
......@@ -3655,7 +3983,7 @@ def multinomial_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def multinomial_generated_seed_test():
sample_size = 10
input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
......@@ -3670,7 +3998,7 @@ def multinomial_generated_seed_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def multinomial_dtype_error_test():
sample_size = 10
dtype = 0
......@@ -3687,7 +4015,7 @@ def multinomial_dtype_error_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def multinomial_int64_test():
sample_size = 10
dtype = 7
......@@ -3706,7 +4034,7 @@ def multinomial_int64_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def neg_test():
x = helper.make_tensor_value_info('0', TensorProto.INT64, [2, 3])
y = helper.make_tensor_value_info('1', TensorProto.INT64, [2, 3])
......@@ -3716,7 +4044,7 @@ def neg_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def neg_dynamic_test():
x = helper.make_tensor_value_info('0', TensorProto.INT64, [None, 3])
y = helper.make_tensor_value_info('1', TensorProto.INT64, [None, 3])
......@@ -3726,7 +4054,7 @@ def neg_dynamic_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def nms_test():
b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4])
s = helper.make_tensor_value_info('scores', TensorProto.FLOAT, [1, 1, 6])
......@@ -3751,7 +4079,7 @@ def nms_test():
return ([node], [b, s, mo, iou, st], [out])
@onnx_test
@onnx_test()
def nms_use_dyn_output_false_test():
b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4])
s = helper.make_tensor_value_info('scores', TensorProto.FLOAT, [1, 1, 6])
......@@ -3776,7 +4104,7 @@ def nms_use_dyn_output_false_test():
return ([node], [b, s, mo, iou, st], [out])
@onnx_test
@onnx_test()
def nms_dynamic_batch_test():
b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [None, 6, 4])
s = helper.make_tensor_value_info('scores', TensorProto.FLOAT,
......@@ -3803,7 +4131,7 @@ def nms_dynamic_batch_test():
return ([node], [b, s, mo, iou, st], [out])
@onnx_test
@onnx_test()
def nms_dynamic_boxes_test():
b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, None, 4])
s = helper.make_tensor_value_info('scores', TensorProto.FLOAT,
......@@ -3828,7 +4156,7 @@ def nms_dynamic_boxes_test():
return ([node], [b, s, mo, iou, st], [out])
@onnx_test
@onnx_test()
def nms_dynamic_classes_test():
b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4])
s = helper.make_tensor_value_info('scores', TensorProto.FLOAT,
......@@ -3853,7 +4181,7 @@ def nms_dynamic_classes_test():
return ([node], [b, s, mo, iou, st], [out])
@onnx_test
@onnx_test()
def not_test():
x = helper.make_tensor_value_info('0', TensorProto.INT32, [4])
y = helper.make_tensor_value_info('1', TensorProto.INT32, [4])
......@@ -3863,7 +4191,7 @@ def not_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def not_bool_test():
x = helper.make_tensor_value_info('0', TensorProto.BOOL, [4])
y = helper.make_tensor_value_info('1', TensorProto.BOOL, [4])
......@@ -3873,7 +4201,7 @@ def not_bool_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def no_pad_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 2])
......@@ -3886,7 +4214,7 @@ def no_pad_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def nonzero_dynamic_test():
x = helper.make_tensor_value_info('data', TensorProto.BOOL, [2, 2])
y = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 3])
......@@ -3898,7 +4226,7 @@ def nonzero_dynamic_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def nonzero_test():
data1 = np.array([[1., 0.], [1., 1.]])
data = helper.make_tensor(name='data',
......@@ -3914,7 +4242,7 @@ def nonzero_test():
return ([node], [], [y], [data])
@onnx_test
@onnx_test()
def nonzero_int_test():
data1 = np.array([[1, 1, 0], [1, 0, 1]])
data = helper.make_tensor(name='data',
......@@ -3930,7 +4258,7 @@ def nonzero_int_test():
return ([node], [], [y], [data])
@onnx_test
@onnx_test()
def onehot_test():
axis_value = 0
depth = np.array([3])
......@@ -3952,7 +4280,7 @@ def onehot_test():
return ([node], [indices, values], [y], [depth_tensor])
@onnx_test
@onnx_test()
def pad_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 4])
......@@ -3965,7 +4293,7 @@ def pad_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def pad_3arg_test():
values = np.array([1])
val_tensor = helper.make_tensor(name='val',
......@@ -3997,7 +4325,7 @@ def pad_3arg_test():
return ([arg_val, arg_pad, node], [x], [y])
@onnx_test
@onnx_test()
def pad_reflect_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 5])
......@@ -4020,7 +4348,7 @@ def pad_reflect_test():
return ([arg_pad, node], [x], [y])
@onnx_test
@onnx_test()
def pad_reflect_multiaxis_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
......@@ -4037,13 +4365,60 @@ def pad_reflect_multiaxis_test():
node = onnx.helper.make_node('Pad',
mode='reflect',
inputs=['0', 'arg_pad'],
inputs=['0', 'arg_pad'],
outputs=['1'])
return ([arg_pad, node], [x], [y])
@onnx_test()
def pad_attr_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
node = onnx.helper.make_node('Pad',
inputs=['0'],
pads=[1, 1, 1, 1],
outputs=['1'])
return ([node], [x], [y])
@onnx_test()
def pad_cnst_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
sizes = np.array([0, 2, 0, 1])
pad_tensor = helper.make_tensor(name='pad_size',
data_type=TensorProto.INT32,
dims=sizes.shape,
vals=sizes.astype(int))
arg_pad = onnx.helper.make_node('Constant',
inputs=[],
outputs=['arg_pad'],
value=pad_tensor)
node = onnx.helper.make_node('Pad', inputs=['0', 'arg_pad'], outputs=['1'])
return ([arg_pad, node], [x], [y])
@onnx_test()
def pad_dyn_reflect_error():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
node = onnx.helper.make_node('Pad',
mode='reflect',
inputs=['0'],
pads=[0, 2, 0, 1],
outputs=['1'])
return ([arg_pad, node], [x], [y])
return ([node], [x], [y])
@onnx_test
@onnx_test()
def pow_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
......@@ -4059,7 +4434,7 @@ def pow_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def pow_fp32_i64_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.INT64, [2, 3, 4, 5])
......@@ -4075,7 +4450,7 @@ def pow_fp32_i64_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def pow_i64_fp32_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.INT64, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
......@@ -4091,7 +4466,7 @@ def pow_i64_fp32_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def prefix_scan_sum_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 2])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 2])
......@@ -4108,7 +4483,7 @@ def prefix_scan_sum_test():
return ([node], [x], [y], [axis_tensor])
@onnx_test
@onnx_test()
def prelu_brcst_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
......@@ -4124,7 +4499,7 @@ def prelu_brcst_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def quantizelinear_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1])
......@@ -4139,7 +4514,7 @@ def quantizelinear_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def quantizelinear_int32_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.INT32, [5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1])
......@@ -4154,7 +4529,7 @@ def quantizelinear_int32_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def quantizelinear_zero_point_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1])
......@@ -4185,17 +4560,17 @@ def make_quantizelinear_axis_graph(axis):
return ([node], [arg0, arg1, arg2], [arg_out])
@onnx_test
@onnx_test()
def quantizelinear_axis_test():
return make_quantizelinear_axis_graph(2)
@onnx_test
@onnx_test()
def quantizelinear_neg_axis_test():
return make_quantizelinear_axis_graph(-2)
@onnx_test
@onnx_test()
def randomnormal_test():
dtype = 11
mean = 10.0
......@@ -4217,7 +4592,7 @@ def randomnormal_test():
return ([node], [], [output])
@onnx_test
@onnx_test()
def randomnormal_dtype_error_test():
dtype = 6
shape = [2, 3, 4]
......@@ -4233,7 +4608,7 @@ def randomnormal_dtype_error_test():
return ([node], [], [output])
@onnx_test
@onnx_test()
def randomnormal_generated_seed_test():
sample_size = 10
input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
......@@ -4248,7 +4623,7 @@ def randomnormal_generated_seed_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def randomnormal_shape_error_test():
dtype = 1
output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
......@@ -4262,7 +4637,7 @@ def randomnormal_shape_error_test():
return ([node], [], [output])
@onnx_test
@onnx_test()
def randomnormallike_test():
dtype = 10
mean = 10.0
......@@ -4284,7 +4659,7 @@ def randomnormallike_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def randomnormallike_type_error_test():
seed = 0
input = helper.make_tensor_value_info('input', TensorProto.INT32,
......@@ -4300,7 +4675,7 @@ def randomnormallike_type_error_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def randomuniform_test():
dtype = 11
high = 1.0
......@@ -4322,7 +4697,7 @@ def randomuniform_test():
return ([node], [], [output])
@onnx_test
@onnx_test()
def randomuniform_dtype_error_test():
dtype = 6
shape = [2, 3, 4]
......@@ -4338,7 +4713,7 @@ def randomuniform_dtype_error_test():
return ([node], [], [output])
@onnx_test
@onnx_test()
def randomuniform_generated_seed_test():
sample_size = 10
input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
......@@ -4353,7 +4728,7 @@ def randomuniform_generated_seed_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def randomuniform_shape_error_test():
dtype = 1
output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
......@@ -4367,7 +4742,7 @@ def randomuniform_shape_error_test():
return ([node], [], [output])
@onnx_test
@onnx_test()
def randomuniformlike_test():
dtype = 10
high = 10.0
......@@ -4389,7 +4764,7 @@ def randomuniformlike_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def randomuniformlike_type_error_test():
seed = 0
input = helper.make_tensor_value_info('input', TensorProto.INT32,
......@@ -4405,7 +4780,7 @@ def randomuniformlike_type_error_test():
return ([node], [input], [output])
@onnx_test
@onnx_test()
def range_test():
start_val = np.array([10])
......@@ -4448,7 +4823,7 @@ def range_test():
return ([start, limit, delta, node], [], [y])
@onnx_test
@onnx_test()
def range_float_test():
start_val = np.array([2])
......@@ -4491,7 +4866,7 @@ def range_float_test():
return ([start, limit, delta, node], [], [y])
@onnx_test
@onnx_test()
def recip_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3])
......@@ -4505,7 +4880,7 @@ def recip_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducel1_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
......@@ -4521,6 +4896,34 @@ def reducel1_test():
@onnx_test
def reducel1_dyn_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
axes = [-2]
node = onnx.helper.make_node('ReduceL1',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims=0)
return ([node], [x], [y])
@onnx_test
def reducel1_dyn_noaxes_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
node = onnx.helper.make_node('ReduceL1',
inputs=['x'],
outputs=['y'],
keepdims=0)
return ([node], [x], [y])
@onnx_test()
def reducel2_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])
......@@ -4535,7 +4938,7 @@ def reducel2_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reduce_log_sum_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 1, 5, 6])
......@@ -4550,7 +4953,7 @@ def reduce_log_sum_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reduce_log_sum_exp_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 5, 6])
......@@ -4565,10 +4968,11 @@ def reduce_log_sum_exp_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducemax_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
axes = [2]
node = onnx.helper.make_node('ReduceMax',
......@@ -4581,6 +4985,21 @@ def reducemax_test():
@onnx_test
def reducemax_dyn_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 4, 6])
axes = [2]
node = onnx.helper.make_node('ReduceMax',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims=0)
return ([node], [x], [y])
@onnx_test()
def reducemean_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
......@@ -4595,7 +5014,7 @@ def reducemean_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducemean_keepdims_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
......@@ -4610,7 +5029,7 @@ def reducemean_keepdims_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducemin_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 1, 5, 1])
......@@ -4625,7 +5044,7 @@ def reducemin_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reduceprod_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
......@@ -4640,7 +5059,7 @@ def reduceprod_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducesum_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
......@@ -4655,7 +5074,7 @@ def reducesum_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducesum_empty_axes_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
......@@ -4674,7 +5093,7 @@ def reducesum_empty_axes_test():
return ([node], [x], [y], [axes_tensor])
@onnx_test
@onnx_test()
def reducesum_noop_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
......@@ -4693,7 +5112,7 @@ def reducesum_noop_test():
return ([node], [x], [y], [axes_tensor])
@onnx_test
@onnx_test()
def reducesum_keepdims_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
......@@ -4708,7 +5127,7 @@ def reducesum_keepdims_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducesum_multiaxis_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
......@@ -4723,7 +5142,7 @@ def reducesum_multiaxis_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reducesum_square_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
......@@ -4738,7 +5157,7 @@ def reducesum_square_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reshape_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [4, 2, 3])
x_shape = helper.make_tensor_value_info('1', TensorProto.INT64, [2])
......@@ -4757,7 +5176,7 @@ def reshape_test():
[helper.make_tensor('1', TensorProto.INT64, [2], [3, 8])])
@onnx_test
@onnx_test()
def reshape_non_standard_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 3, 2])
......@@ -4777,7 +5196,7 @@ def reshape_non_standard_test():
return ([trans, res], [x], [y])
@onnx_test
@onnx_test()
def resize_downsample_f_test():
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -4799,7 +5218,7 @@ def resize_downsample_f_test():
return ([node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def resize_downsample_c_test():
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -4820,7 +5239,7 @@ def resize_downsample_c_test():
return ([node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def resize_downsample_linear_test():
scales = np.array([1.0, 1.0, 0.6, 0.5], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -4839,7 +5258,7 @@ def resize_downsample_linear_test():
return ([node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def resize_nonstd_input_test():
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -4865,7 +5284,7 @@ def resize_nonstd_input_test():
return ([trn, node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def resize_outsize_test():
out_lens = np.array([1, 1, 4, 6], dtype=np.int64)
out_lens_tensor = helper.make_tensor(name='out_lens',
......@@ -4888,7 +5307,7 @@ def resize_outsize_test():
return ([node], [X], [Y], [out_lens_tensor])
@onnx_test
@onnx_test()
def resize_upsample_linear_ac_test():
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
scales_tensor = helper.make_tensor(name='scales',
......@@ -4909,7 +5328,7 @@ def resize_upsample_linear_ac_test():
return ([node], [X], [Y], [scales_tensor])
@onnx_test
@onnx_test()
def resize_upsample_linear_test():
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
scales_tensor = helper.make_tensor(name='scales',
......@@ -4928,7 +5347,7 @@ def resize_upsample_linear_test():
return ([node], [X], [Y], [scales_tensor])
@onnx_test
@onnx_test()
def resize_upsample_pf_test():
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -4947,7 +5366,7 @@ def resize_upsample_pf_test():
return ([node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def resize_upsample_pc_test():
scales = np.array([1.0, 1.0, 2.0, 1.5], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -4970,7 +5389,7 @@ def resize_upsample_pc_test():
return ([node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def reversesequence_4D_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 2, 2])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 2, 2])
......@@ -4986,7 +5405,7 @@ def reversesequence_4D_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_batch_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4, 4])
seq_lens = np.array([1, 2, 3, 4])
......@@ -5014,7 +5433,7 @@ def reversesequence_batch_test():
return ([arg_seq_lens, node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_batch_axis_err_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4, 4, 2])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 4, 2])
......@@ -5030,7 +5449,7 @@ def reversesequence_batch_axis_err_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_rank_err_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4])
......@@ -5044,7 +5463,7 @@ def reversesequence_rank_err_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_sequence_lens_shape_err_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 4])
......@@ -5058,7 +5477,7 @@ def reversesequence_sequence_lens_shape_err_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_same_axis_err_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 4])
......@@ -5074,7 +5493,7 @@ def reversesequence_same_axis_err_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_time_axis_err_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4, 4, 2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 4, 2, 3])
......@@ -5090,7 +5509,7 @@ def reversesequence_time_axis_err_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def reversesequence_time_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [4, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 4])
......@@ -5106,7 +5525,7 @@ def reversesequence_time_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def roialign_default_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 4, 7, 8])
roi = helper.make_tensor_value_info('rois', TensorProto.FLOAT, [8, 4])
......@@ -5120,7 +5539,7 @@ def roialign_default_test():
return ([node], [x, roi, bi], [y])
@onnx_test
@onnx_test()
def roialign_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 5, 4, 7])
roi = helper.make_tensor_value_info('rois', TensorProto.FLOAT, [8, 4])
......@@ -5141,7 +5560,7 @@ def roialign_test():
return ([node], [x, roi, bi], [y])
@onnx_test
@onnx_test()
def scatter_add_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32,
......@@ -5161,7 +5580,7 @@ def scatter_add_test():
return ([node], [x, i, u], [y])
@onnx_test
@onnx_test()
def scatter_mul_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32,
......@@ -5181,7 +5600,7 @@ def scatter_mul_test():
return ([node], [x, i, u], [y])
@onnx_test
@onnx_test()
def scatter_none_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32,
......@@ -5201,7 +5620,7 @@ def scatter_none_test():
return ([node], [x, i, u], [y])
@onnx_test
@onnx_test()
def scatternd_add_test():
data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
......@@ -5219,7 +5638,7 @@ def scatternd_add_test():
return ([node], [data, indices, updates], [output])
@onnx_test
@onnx_test()
def scatternd_mul_test():
data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
......@@ -5237,7 +5656,7 @@ def scatternd_mul_test():
return ([node], [data, indices, updates], [output])
@onnx_test
@onnx_test()
def scatternd_test():
data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
......@@ -5254,7 +5673,7 @@ def scatternd_test():
return ([node], [data, indices, updates], [output])
@onnx_test
@onnx_test()
def selu_test():
x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [2, 3])
y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [2, 3])
......@@ -5268,7 +5687,7 @@ def selu_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def shape_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [4])
......@@ -5282,7 +5701,7 @@ def shape_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def shape_gather_test():
values = np.array([1])
# value = helper.make_tensor_value_info('value', TensorProto.INT32, [1])
......@@ -5317,7 +5736,7 @@ def shape_gather_test():
return ([node_const, node_shape, node_gather], [x], [z])
@onnx_test
@onnx_test()
def sign_test():
x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [10, 5])
y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [10, 5])
......@@ -5331,7 +5750,7 @@ def sign_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def sin_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -5345,7 +5764,7 @@ def sin_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def sinh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -5359,7 +5778,7 @@ def sinh_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def sinh_dynamic_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
......@@ -5373,7 +5792,7 @@ def sinh_dynamic_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def size_float_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [1])
......@@ -5385,7 +5804,7 @@ def size_float_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def size_half_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [3, 1])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [1])
......@@ -5397,7 +5816,7 @@ def size_half_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def size_int_test():
x = helper.make_tensor_value_info('x', TensorProto.INT32, [8, 2, 3])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [1])
......@@ -5409,7 +5828,7 @@ def size_int_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def size_verify_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5, 3])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [1])
......@@ -5421,7 +5840,7 @@ def size_verify_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def slice_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2])
......@@ -5436,7 +5855,7 @@ def slice_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def slice_3arg_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 5])
......@@ -5468,7 +5887,7 @@ def slice_3arg_test():
return ([arg_start, arg_end, node], [x], [y])
@onnx_test
@onnx_test()
def slice_5arg_test():
step = np.array([1, 1])
step_tensor = helper.make_tensor(name="step",
......@@ -5521,7 +5940,7 @@ def slice_5arg_test():
return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y])
@onnx_test
@onnx_test()
def slice_5arg_reverse_test():
step = np.array([-1, 1])
step_tensor = helper.make_tensor(name="step",
......@@ -5574,7 +5993,7 @@ def slice_5arg_reverse_test():
return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y])
@onnx_test
@onnx_test()
def slice_5arg_step_test():
step = np.array([-2, 2])
step_tensor = helper.make_tensor(name="step",
......@@ -5627,7 +6046,7 @@ def slice_5arg_step_test():
return ([arg_step, arg_axis, arg_end, arg_start, node], [x], [y])
@onnx_test
@onnx_test()
def slice_max_end_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [10, 20])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [9, 17])
......@@ -5642,7 +6061,7 @@ def slice_max_end_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def softmax_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3])
......@@ -5652,7 +6071,7 @@ def softmax_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def softmax_nonstd_input_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [6, 8])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 4])
......@@ -5669,7 +6088,17 @@ def softmax_nonstd_input_test():
return ([node0, node1], [x], [y])
@onnx_test
@onnx_test()
def softmax_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 4, 4])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 4, 4])
node = onnx.helper.make_node('Softmax', inputs=['0'], outputs=['1'])
return ([node], [x], [y])
@onnx_test()
def softsign_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5])
......@@ -5688,7 +6117,7 @@ def softplus_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def softsign_nd_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [3, 4, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [3, 4, 5])
......@@ -5707,7 +6136,7 @@ def softplus_nd_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def split_minus_axis_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 5])
......@@ -5724,7 +6153,7 @@ def split_minus_axis_test():
return ([node], [x], [y1, y2, y3])
@onnx_test
@onnx_test()
def split_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 7])
......@@ -5740,7 +6169,7 @@ def split_test():
return ([node], [x], [y1, y2, y3])
@onnx_test
@onnx_test()
def split_test_default():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [5, 15])
......@@ -5755,7 +6184,7 @@ def split_test_default():
return ([node], [x], [y1, y2])
@onnx_test
@onnx_test()
def split_test_no_attribute():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [300, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [75, 15])
......@@ -5782,7 +6211,7 @@ def split_test_no_attribute():
return ([const_node, node], [x], [y1, y2, y3, y4])
@onnx_test
@onnx_test()
def split_test_no_attribute_invalid_split():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [300, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [75, 15])
......@@ -5809,7 +6238,7 @@ def split_test_no_attribute_invalid_split():
return ([const_node, node], [x], [y1, y2, y3, y4])
@onnx_test
@onnx_test()
def split_test_invalid_split():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 7])
......@@ -5825,7 +6254,7 @@ def split_test_invalid_split():
return ([node], [x], [y1, y2, y3])
@onnx_test
@onnx_test()
def split_test_no_attribute_invalid_input_split():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 7])
......@@ -5841,7 +6270,7 @@ def split_test_no_attribute_invalid_input_split():
return ([node], [x], [y1, y2, y3])
@onnx_test
@onnx_test()
def sqrt_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])
......@@ -5855,7 +6284,7 @@ def sqrt_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def squeeze_axes_input_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 5, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 5])
......@@ -5872,7 +6301,7 @@ def squeeze_axes_input_test():
return ([node], [x], [y], [axes_tensor])
@onnx_test
@onnx_test()
def squeeze_empty_axes_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 5, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 5])
......@@ -5889,7 +6318,7 @@ def squeeze_empty_axes_test():
return ([node], [x], [y], [axes_tensor])
@onnx_test
@onnx_test()
def squeeze_unsqueeze_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, 3, 1, 1, 2, 1])
......@@ -5909,7 +6338,27 @@ def squeeze_unsqueeze_test():
return ([node, node2], [x], [y])
@onnx_test
@onnx_test()
def squeeze_unsqueeze_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, None, 1, 1, None, 1])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT,
[1, 1, None, 1, None, 1])
node = onnx.helper.make_node('Squeeze',
inputs=['0'],
axes=[0, 2, 3, 5],
outputs=['1'])
node2 = onnx.helper.make_node('Unsqueeze',
inputs=['1'],
axes=[0, 1, 3, 5],
outputs=['2'])
return ([node, node2], [x], [y])
@onnx_test()
def sub_bcast_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
......@@ -5927,7 +6376,7 @@ def sub_bcast_test():
return ([node], [arg0, arg1], [arg_out])
@onnx_test
@onnx_test()
def sub_scalar_test():
values = np.array([1])
arg_node = helper.make_tensor_value_info('0', TensorProto.FLOAT,
......@@ -5956,7 +6405,7 @@ def sub_scalar_test():
return ([arg_const, node], [arg_node], [arg_out])
@onnx_test
@onnx_test()
def sum_int_test():
a = helper.make_tensor_value_info('0', TensorProto.INT16, [3])
b = helper.make_tensor_value_info('1', TensorProto.UINT16, [3])
......@@ -5976,7 +6425,7 @@ def sum_int_test():
return ([cnode1, cnode2, node], [a, b, c], [y])
@onnx_test
@onnx_test()
def sum_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
......@@ -5992,7 +6441,7 @@ def sum_test():
return ([node], [a, b, c], [y])
@onnx_test
@onnx_test()
def sum_type_test():
valb = np.array([1, 0])
t_bool = helper.make_tensor(name="bool",
......@@ -6086,7 +6535,7 @@ def sum_type_test():
])
@onnx_test
@onnx_test()
def tan_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
......@@ -6100,7 +6549,7 @@ def tan_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def tanh_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
......@@ -6114,7 +6563,7 @@ def tanh_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def thresholdedrelu_default_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
......@@ -6126,7 +6575,7 @@ def thresholdedrelu_default_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def thresholdedrelu_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
......@@ -6140,7 +6589,7 @@ def thresholdedrelu_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def thresholdedrelu_int_test():
x = helper.make_tensor_value_info('x', TensorProto.INT32, [2, 2, 3])
y = helper.make_tensor_value_info('y', TensorProto.INT32, [2, 2, 3])
......@@ -6154,7 +6603,7 @@ def thresholdedrelu_int_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def tile_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [2])
......@@ -6166,7 +6615,7 @@ def tile_test():
[helper.make_tensor('y', TensorProto.INT64, [2], [1, 2])])
@onnx_test
@onnx_test()
def tile_test_3x2():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2])
y = helper.make_tensor_value_info('y', TensorProto.INT64, [2])
......@@ -6178,7 +6627,7 @@ def tile_test_3x2():
[helper.make_tensor('y', TensorProto.INT64, [2], [3, 2])])
@onnx_test
@onnx_test()
def topk_attrk_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 5, 3, 2])
val = helper.make_tensor_value_info('val', TensorProto.FLOAT, [2, 2, 3, 2])
......@@ -6192,7 +6641,7 @@ def topk_attrk_test():
return ([node], [x], [val, ind])
@onnx_test
@onnx_test()
def topk_neg_axis_test():
k = np.array([3])
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
......@@ -6213,7 +6662,7 @@ def topk_neg_axis_test():
return ([node], [x], [val, ind], [k_tensor])
@onnx_test
@onnx_test()
def topk_test():
k = np.array([4])
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 5, 3, 2])
......@@ -6247,7 +6696,7 @@ def transpose_default_perm_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def transpose_invalid_perm_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 4, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
......@@ -6262,7 +6711,7 @@ def transpose_invalid_perm_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def transpose_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 2, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
......@@ -6277,6 +6726,21 @@ def transpose_test():
return ([node], [x], [y])
@onnx_test()
def transpose_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 2, 2, 3])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 3, 2, 2])
node = onnx.helper.make_node(
'Transpose',
perm=[0, 3, 1, 2],
inputs=['0'],
outputs=['1'],
)
return ([node], [x], [y])
@onnx_test
def transpose_gather_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 5, 4, 6])
......@@ -6307,7 +6771,93 @@ def transpose_gather_test():
return ([td, ti, node], [x, i], [y])
@onnx_test
@onnx_test()
def trilu_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
)
return ([node], [x], [y])
@onnx_test()
def trilu_batch_diff_k_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
k = np.array([2])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
return ([node], [x], [y], [k_tensor])
@onnx_test()
def trilu_lower_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
node = onnx.helper.make_node('Trilu', inputs=['x'], outputs=['y'], upper=0)
return ([node], [x], [y])
@onnx_test()
def trilu_neg_k_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
k = np.array([-1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node('Trilu', inputs=['x', 'k'], outputs=['y'])
return ([node], [x], [y], [k_tensor])
@onnx_test()
def trilu_out_k_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
k = np.array([5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node('Trilu', inputs=['x', 'k'], outputs=['y'])
return ([node], [x], [y], [k_tensor])
@onnx_test()
def trilu_row_one_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4])
k = np.array([1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
return ([node], [x], [y], [k_tensor])
@onnx_test()
def undefined_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
......@@ -6317,7 +6867,7 @@ def undefined_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def unknown_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
......@@ -6333,7 +6883,7 @@ def unknown_test():
return ([node, node2], [x, y], [a])
@onnx_test
@onnx_test()
def unknown_aten_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
......@@ -6350,7 +6900,7 @@ def unknown_aten_test():
return ([node], [x, y], [a])
@onnx_test
@onnx_test()
def upsample_linear_test():
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
scales_tensor = helper.make_tensor(name='scales',
......@@ -6369,7 +6919,7 @@ def upsample_linear_test():
return ([node], [X], [Y], [scales_tensor])
@onnx_test
@onnx_test()
def upsample_test():
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
scale_tensor = helper.make_tensor(name='scales',
......@@ -6390,7 +6940,7 @@ def upsample_test():
return ([node], [X], [Y], [scale_tensor])
@onnx_test
@onnx_test()
def variable_batch_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 16, 16])
......@@ -6402,7 +6952,7 @@ def variable_batch_test():
return ([node], [x], [y])
@onnx_test
@onnx_test()
def variable_batch_leq_zero_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [0, 3, 16, 16])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [-1, 3, 16, 16])
......@@ -6413,7 +6963,7 @@ def variable_batch_leq_zero_test():
return ([node], [x, y], [z])
@onnx_test
@onnx_test()
def where_test():
c = helper.make_tensor_value_info('c', TensorProto.BOOL, [2])
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 2])
......
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