wehub-resource-sync
633 lines
18 KiB
Python
633 lines
18 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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class TestFlattenOp(OpTest):
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def setUp(self):
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self.python_api = paddle.flatten
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self.public_python_api = paddle.flatten
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self.python_out_sig = ["Out"]
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self.op_type = "flatten_contiguous_range"
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self.prim_op_type = "comp"
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self.start_axis = 0
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self.stop_axis = -1
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self.if_enable_cinn()
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self.init_test_case()
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self.init_test_dtype()
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self.init_input_data()
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self.init_attrs()
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.new_shape),
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"XShape": np.random.random(self.in_shape).astype("float32"),
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}
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def if_enable_cinn(self):
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pass
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def test_check_output(self):
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if str(self.dtype) in {"float16", "uint16"}:
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self.check_output_with_place(
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get_device_place(),
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no_check_set=["XShape"],
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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else:
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self.check_output(
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no_check_set=["XShape"],
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_grad(self):
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if str(self.dtype) in {"float16", "uint16"}:
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self.check_grad_with_place(
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get_device_place(),
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["X"],
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"Out",
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check_prim=True,
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check_pir=True,
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)
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else:
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self.check_grad(["X"], "Out", check_prim=True, check_pir=True)
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = -1
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self.new_shape = 120
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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def init_test_dtype(self):
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self.dtype = "float64"
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def init_input_data(self):
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if str(self.dtype) != "uint16":
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x = np.random.random(self.in_shape).astype(self.dtype)
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else:
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x = np.random.random(self.in_shape).astype("float32")
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x = convert_float_to_uint16(x)
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self.inputs = {"X": x}
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class TestFlattenFP32Op(TestFlattenOp):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op(TestFlattenOp):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16Op(TestFlattenOp):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlattenOp_1(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 1
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self.stop_axis = 2
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self.new_shape = (3, 10, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32Op_1(TestFlattenOp_1):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op_1(TestFlattenOp_1):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16Op_1(TestFlattenOp_1):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlattenOp_2(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 1
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self.new_shape = (6, 5, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32Op_2(TestFlattenOp_2):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op_2(TestFlattenOp_2):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16Op_2(TestFlattenOp_2):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlattenOp_3(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 2
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self.new_shape = (30, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32Op_3(TestFlattenOp_3):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op_3(TestFlattenOp_3):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16Op_3(TestFlattenOp_3):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlattenOp_4(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = -2
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self.stop_axis = -1
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self.new_shape = (3, 2, 20)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32Op_4(TestFlattenOp_4):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op_4(TestFlattenOp_4):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16Op_4(TestFlattenOp_4):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlattenOp_5(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 2
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self.stop_axis = 2
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self.new_shape = (3, 2, 5, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32Op_5(TestFlattenOp_5):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op_5(TestFlattenOp_5):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16Op_5(TestFlattenOp_5):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlattenOp_ZeroDim(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = ()
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self.start_axis = 0
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self.stop_axis = -1
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self.new_shape = (1,)
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def if_enable_cinn(self):
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self.enable_cinn = False
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32Op_ZeroDim(TestFlattenOp_ZeroDim):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16Op_ZeroDim(TestFlattenOp_ZeroDim):
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def init_test_dtype(self):
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self.dtype = "float16"
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class TestFlattenOpSixDims(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 3, 2, 4, 4)
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self.start_axis = 3
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self.stop_axis = 5
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self.new_shape = (3, 2, 3, 32)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenFP32OpSixDims(TestFlattenOpSixDims):
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def init_test_dtype(self):
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self.dtype = "float32"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlattenFP16OpSixDims(TestFlattenOpSixDims):
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def init_test_dtype(self):
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self.dtype = "float16"
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestFlattenBF16OpSixDims(TestFlattenOpSixDims):
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def if_enable_cinn(self):
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pass
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def init_test_dtype(self):
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self.dtype = "uint16"
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class TestFlatten2OpError(unittest.TestCase):
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def test_errors(self):
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image_shape = (2, 3, 4, 4)
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x = (
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np.arange(
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image_shape[0]
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* image_shape[1]
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* image_shape[2]
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* image_shape[3]
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).reshape(image_shape)
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/ 100.0
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)
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x = x.astype('float32')
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def test_ValueError1():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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out = paddle.flatten(x_var, start_axis=3, stop_axis=1)
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self.assertRaises(ValueError, test_ValueError1)
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def test_ValueError2():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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paddle.flatten(x_var, start_axis=10, stop_axis=1)
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self.assertRaises(ValueError, test_ValueError2)
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def test_ValueError3():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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paddle.flatten(x_var, start_axis=2, stop_axis=10)
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self.assertRaises(ValueError, test_ValueError3)
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def test_ValueError4():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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paddle.flatten(x_var, start_axis=2.0, stop_axis=10)
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self.assertRaises(ValueError, test_ValueError4)
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def test_ValueError5():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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paddle.flatten(x_var, start_axis=2, stop_axis=10.0)
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self.assertRaises(ValueError, test_ValueError5)
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def test_InputError():
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out = paddle.flatten(x)
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self.assertRaises(ValueError, test_InputError)
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class TestStaticFlattenPythonAPI(unittest.TestCase):
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def execute_api(self, x, start_axis=0, stop_axis=-1):
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return paddle.flatten(x, start_axis, stop_axis)
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def test_static_api(self):
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paddle.enable_static()
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np_x = np.random.rand(2, 3, 4, 4).astype('float32')
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, paddle.static.Program()):
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x = paddle.static.data(
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name="x", shape=[2, 3, 4, 4], dtype='float32'
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)
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out = self.execute_api(x, start_axis=-2, stop_axis=-1)
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exe = paddle.static.Executor(place=paddle.CPUPlace())
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fetch_out = exe.run(main_prog, feed={"x": np_x}, fetch_list=[out])
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self.assertTrue((2, 3, 16) == fetch_out[0].shape)
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class TestStaticFlattenInferShapePythonAPI(unittest.TestCase):
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def execute_api(self, x, start_axis=0, stop_axis=-1):
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return paddle.flatten(x, start_axis, stop_axis)
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def test_static_api(self):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, paddle.static.Program()):
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x = paddle.static.data(
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name="x", shape=[-1, 3, -1, -1], dtype='float32'
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)
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out = self.execute_api(x, start_axis=2, stop_axis=3)
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self.assertTrue((-1, 3, -1) == tuple(out.shape))
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class TestStaticInplaceFlattenPythonAPI(TestStaticFlattenPythonAPI):
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def execute_api(self, x, start_axis=0, stop_axis=-1):
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return x.flatten_(start_axis, stop_axis)
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class TestFlattenPython(unittest.TestCase):
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def test_python_api(self):
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image_shape = (2, 3, 4, 4)
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x = (
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np.arange(
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image_shape[0]
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* image_shape[1]
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* image_shape[2]
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* image_shape[3]
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).reshape(image_shape)
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/ 100.0
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)
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x = x.astype('float32')
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def test_InputError():
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out = paddle.flatten(x)
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self.assertRaises(ValueError, test_InputError)
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def test_Negative():
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paddle.disable_static()
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|
img = paddle.to_tensor(x)
|
|
out = paddle.flatten(img, start_axis=-2, stop_axis=-1)
|
|
return out.numpy().shape
|
|
|
|
res_shape = test_Negative()
|
|
self.assertTrue((2, 3, 16) == res_shape)
|
|
|
|
|
|
class TestDygraphInplaceFlattenPython(unittest.TestCase):
|
|
def test_python_api(self):
|
|
image_shape = (2, 3, 4, 4)
|
|
x = (
|
|
np.arange(
|
|
image_shape[0]
|
|
* image_shape[1]
|
|
* image_shape[2]
|
|
* image_shape[3]
|
|
).reshape(image_shape)
|
|
/ 100.0
|
|
)
|
|
x = x.astype('float32')
|
|
|
|
def test_Negative():
|
|
paddle.disable_static()
|
|
img = paddle.to_tensor(x)
|
|
out = img.flatten_(start_axis=-2, stop_axis=-1)
|
|
return out.numpy().shape
|
|
|
|
res_shape = test_Negative()
|
|
self.assertTrue((2, 3, 16) == res_shape)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestFlatten0DTensorOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
image_shape = ()
|
|
x = np.random.uniform(-1.0, 1.0, []).astype('float32')
|
|
|
|
def test_ValueError1():
|
|
x_var = paddle.static.data(
|
|
name="x", shape=image_shape, dtype='float32'
|
|
)
|
|
out = paddle.flatten(x_var, start_axis=10, stop_axis=0)
|
|
|
|
self.assertRaises(ValueError, test_ValueError1)
|
|
|
|
def test_ValueError2():
|
|
x_var = paddle.static.data(
|
|
name="x", shape=image_shape, dtype='float32'
|
|
)
|
|
out = paddle.flatten(x_var, start_axis=0, stop_axis=10)
|
|
|
|
self.assertRaises(ValueError, test_ValueError2)
|
|
|
|
|
|
class TestFlattenZeroSizedTensorAPI(unittest.TestCase):
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
data = np.random.randn(2, 3, 0)
|
|
x = paddle.to_tensor(data)
|
|
out = paddle.flatten(x)
|
|
out_np = data.flatten()
|
|
np.testing.assert_equal(out.numpy(), out_np)
|
|
|
|
def test_static(self):
|
|
paddle.enable_static()
|
|
data = np.random.randn(2, 3, 0)
|
|
main_prog = paddle.static.Program()
|
|
with paddle.static.program_guard(main_prog, paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[2, 3, 0], dtype='float64')
|
|
out = paddle.flatten(x)
|
|
|
|
exe = paddle.static.Executor(place=paddle.CPUPlace())
|
|
fetch_out = exe.run(main_prog, feed={"x": data}, fetch_list=[out])[0]
|
|
out_np = data.flatten()
|
|
np.testing.assert_equal(fetch_out, out_np)
|
|
|
|
|
|
class TestFlattenAPI_Compatible(unittest.TestCase):
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
data = np.random.randn(2, 3, 5)
|
|
x = paddle.to_tensor(data)
|
|
out = paddle.flatten(input=x, start_dim=0, end_dim=-1)
|
|
out_np = data.flatten()
|
|
np.testing.assert_equal(out.numpy(), out_np)
|
|
|
|
def test_static(self):
|
|
paddle.enable_static()
|
|
data = np.random.randn(2, 3, 5)
|
|
main_prog = paddle.static.Program()
|
|
with paddle.static.program_guard(main_prog, paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[2, 3, 5], dtype='float64')
|
|
out = paddle.flatten(input=x, start_dim=0, end_dim=-1)
|
|
|
|
exe = paddle.static.Executor(place=paddle.CPUPlace())
|
|
fetch_out = exe.run(main_prog, feed={"x": data}, fetch_list=[out])[0]
|
|
out_np = data.flatten()
|
|
np.testing.assert_equal(fetch_out, out_np)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|