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Merge branch 'master' of https://github.com/onnx/onnx into neraoof/op…
…tional Signed-off-by: neginraoof <neginmr@utexas.edu> # Conflicts: # docs/Changelog.md # docs/TestCoverage.md # onnx/defs/operator_sets.h
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| # coding: utf-8 | ||
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| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
| from __future__ import unicode_literals | ||
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| import numpy as np # type: ignore | ||
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| import onnx | ||
| from onnx import TensorProto | ||
| from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE | ||
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| from ..base import Base | ||
| from . import expect | ||
| import sys | ||
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| class CastLike(Base): | ||
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| @staticmethod | ||
| def export(): # type: () -> None | ||
| shape = (3, 4) | ||
| test_cases = [ | ||
| ('FLOAT', 'FLOAT16'), | ||
| ('FLOAT', 'DOUBLE'), | ||
| ('FLOAT16', 'FLOAT'), | ||
| ('FLOAT16', 'DOUBLE'), | ||
| ('DOUBLE', 'FLOAT'), | ||
| ('DOUBLE', 'FLOAT16'), | ||
| ('FLOAT', 'STRING'), | ||
| ('STRING', 'FLOAT'), | ||
| ('FLOAT', 'BFLOAT16'), | ||
| ('BFLOAT16', 'FLOAT'), | ||
| ] | ||
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| for from_type, to_type in test_cases: | ||
| input_type_proto = None | ||
| output_type_proto = None | ||
| if 'BFLOAT16' == from_type or 'BFLOAT16' == to_type: | ||
| np_fp32 = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545', | ||
| u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038', | ||
| u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.float32) | ||
| little_endisan = sys.byteorder == 'little' | ||
| np_uint16_view = np_fp32.view(dtype=np.uint16) | ||
| np_bfp16 = np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2] | ||
| if 'BFLOAT16' == to_type: | ||
| assert from_type == 'FLOAT' | ||
| input = np_fp32.reshape([3, 4]) | ||
| output = np_bfp16.reshape([3, 4]) | ||
| input_type = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), None) | ||
| output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), None) | ||
| else: | ||
| assert to_type == 'FLOAT' | ||
| input = np_bfp16.reshape([3, 4]) | ||
| #convert bfloat to FLOAT | ||
| np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16) | ||
| if little_endisan: | ||
| np_fp32_zeros[1::2] = np_bfp16 | ||
| else: | ||
| np_fp32_zeros[0::2] = np_bfp16 | ||
| np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32) | ||
| output = np_fp32_from_bfloat.reshape([3, 4]) | ||
| input_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.BFLOAT16), None) | ||
| output_type_proto = onnx.helper.make_tensor_type_proto(int(TensorProto.FLOAT), None) | ||
| elif 'STRING' != from_type: | ||
| input = np.random.random_sample(shape).astype( | ||
| TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, from_type)]) | ||
| if ('STRING' == to_type): | ||
| # Converting input to str, then give it np.object dtype for generating script | ||
| ss = [] | ||
| for i in input.flatten(): | ||
| s = str(i).encode('utf-8') | ||
| su = s.decode('utf-8') | ||
| ss.append(su) | ||
|
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| output = np.array(ss).astype(np.object).reshape([3, 4]) | ||
| else: | ||
| output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)]) | ||
| else: | ||
| input = np.array([u'0.47892547', u'0.48033667', u'0.49968487', u'0.81910545', | ||
| u'0.47031248', u'0.816468', u'0.21087195', u'0.7229038', | ||
| u'NaN', u'INF', u'+INF', u'-INF'], dtype=np.dtype(np.object)).reshape([3, 4]) | ||
| output = input.astype(TENSOR_TYPE_TO_NP_TYPE[getattr(TensorProto, to_type)]) | ||
| like = output.flatten()[0:1] | ||
| node = onnx.helper.make_node( | ||
| 'CastLike', | ||
| inputs=['input', 'like'], | ||
| outputs=['output'], | ||
| ) | ||
| if input_type_proto and output_type_proto: | ||
| expect(node, inputs=[input, like], outputs=[output], | ||
| name='test_castlike_' + from_type + '_to_' + to_type, | ||
| input_type_protos=[input_type_proto, output_type_proto], | ||
| output_type_protos=[output_type_proto]) | ||
| else: | ||
| expect(node, inputs=[input, like], outputs=[output], | ||
| name='test_castlike_' + from_type + '_to_' + to_type) |
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