# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Test set_value op in static mode from __future__ import print_function import unittest import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.layer_helper import LayerHelper from functools import reduce from paddle.fluid.framework import _test_eager_guard, _in_legacy_dygraph class TestSetValueBase(unittest.TestCase): def setUp(self): paddle.enable_static() self.set_dtype() self.set_value() self.set_shape() self.data = np.ones(self.shape).astype(self.dtype) self.program = paddle.static.Program() def set_shape(self): self.shape = [2, 3, 4] def set_value(self): self.value = 6 def set_dtype(self): self.dtype = "float32" def _call_setitem(self, x): x[0, 0] = self.value def _get_answer(self): self.data[0, 0] = self.value class TestSetValueApi(TestSetValueBase): def _run_static(self): paddle.enable_static() with paddle.static.program_guard(self.program): x = paddle.ones(shape=self.shape, dtype=self.dtype) self._call_setitem(x) exe = paddle.static.Executor(paddle.CPUPlace()) out = exe.run(self.program, fetch_list=[x]) paddle.disable_static() return out def _run_dynamic(self): paddle.disable_static() x = paddle.ones(shape=self.shape, dtype=self.dtype) self._call_setitem(x) out = x.numpy() paddle.enable_static() return out def func_test_api(self): static_out = self._run_static() dynamic_out = self._run_dynamic() self._get_answer() error_msg = "\nIn {} mode: \nExpected res = \n{}, \n\nbut received : \n{}" self.assertTrue((self.data == static_out).all(), msg=error_msg.format("static", self.data, static_out)) self.assertTrue((self.data == dynamic_out).all(), msg=error_msg.format("dynamic", self.data, dynamic_out)) def test_api(self): with _test_eager_guard(): self.func_test_api() self.func_test_api() # 1. Test different type of item: int, Python slice, Paddle Tensor # 1.1 item is int class TestSetValueItemInt(TestSetValueApi): def _call_setitem(self, x): x[0] = self.value def _get_answer(self): self.data[0] = self.value # 1.2 item is slice # 1.2.1 step is 1 class TestSetValueItemSlice(TestSetValueApi): def _call_setitem(self, x): x[0:2] = self.value def _get_answer(self): self.data[0:2] = self.value class TestSetValueItemSlice2(TestSetValueApi): def _call_setitem(self, x): x[0:-1] = self.value def _get_answer(self): self.data[0:-1] = self.value class TestSetValueItemSlice3(TestSetValueApi): def _call_setitem(self, x): x[0:-1, 0:2] = self.value def _get_answer(self): self.data[0:-1, 0:2] = self.value class TestSetValueItemSlice4(TestSetValueApi): def _call_setitem(self, x): x[0:, 1:2, :] = self.value def _get_answer(self): self.data[0:, 1:2, :] = self.value class TestSetValueItemSlice5(TestSetValueApi): def _call_setitem(self, x): x[0:, 1:1, :] = self.value def _get_answer(self): self.data[0:, 1:1, :] = self.value class TestSetValueItemSliceInWhile(TestSetValueApi): def _call_setitem(self, x): def cond(i, x): return i < 1 def body(i, x): x[i] = self.value i = i + 1 return i, x i = paddle.zeros(shape=(1, ), dtype='int32') i, x = paddle.fluid.layers.while_loop(cond, body, [i, x]) def _get_answer(self): self.data[0] = self.value # 1.2.2 step > 1 class TestSetValueItemSliceStep(TestSetValueApi): def set_shape(self): self.shape = [5, 5, 5] def _call_setitem(self, x): x[0:2:2] = self.value def _get_answer(self): self.data[0:2:2] = self.value class TestSetValueItemSliceStep2(TestSetValueApi): def set_shape(self): self.shape = [7, 5, 5] def _call_setitem(self, x): x[0:-1:3] = self.value def _get_answer(self): self.data[0:-1:3] = self.value class TestSetValueItemSliceStep3(TestSetValueApi): def _call_setitem(self, x): x[0:-1, 0:2, ::2] = self.value def _get_answer(self): self.data[0:-1, 0:2, ::2] = self.value class TestSetValueItemSliceStep4(TestSetValueApi): def _call_setitem(self, x): x[0:, 1:2:2, :] = self.value def _get_answer(self): self.data[0:, 1:2:2, :] = self.value # 1.2.3 step < 0 class TestSetValueItemSliceNegetiveStep(TestSetValueApi): def set_shape(self): self.shape = [5, 2] def set_value(self): self.value = np.array([3, 4]) def _call_setitem(self, x): x[5:2:-1] = self.value def _get_answer(self): self.data[5:2:-1] = self.value class TestSetValueItemSliceNegetiveStep2(TestSetValueApi): def set_shape(self): self.shape = [5] def set_value(self): self.value = np.array([3, 4]) def _call_setitem(self, x): x[1::-1] = self.value def _get_answer(self): self.data[1::-1] = self.value class TestSetValueItemSliceNegetiveStep3(TestSetValueApi): def set_shape(self): self.shape = [3] def set_value(self): self.value = np.array([3, 4, 5]) def _call_setitem(self, x): x[::-1] = self.value def _get_answer(self): self.data[::-1] = self.value class TestSetValueItemSliceNegetiveStep4(TestSetValueApi): def set_shape(self): self.shape = [3, 4, 5] def _call_setitem(self, x): x[2:0:-1, 0:2, ::-1] = self.value def _get_answer(self): self.data[2:0:-1, 0:2, ::-1] = self.value # 1.3 item is Ellipsis class TestSetValueItemEllipsis1(TestSetValueApi): def _call_setitem(self, x): x[0:, ..., 1:] = self.value def _get_answer(self): self.data[0:, ..., 1:] = self.value class TestSetValueItemEllipsis2(TestSetValueApi): def _call_setitem(self, x): x[0:, ...] = self.value def _get_answer(self): self.data[0:, ...] = self.value class TestSetValueItemEllipsis3(TestSetValueApi): def _call_setitem(self, x): x[..., 1:] = self.value def _get_answer(self): self.data[..., 1:] = self.value class TestSetValueItemEllipsis4(TestSetValueApi): def _call_setitem(self, x): x[...] = self.value def _get_answer(self): self.data[...] = self.value # 1.4 item is Paddle Tensor class TestSetValueItemTensor(TestSetValueApi): def _call_setitem(self, x): zero = paddle.full([1], 0, dtype="int32") x[zero] = self.value def _get_answer(self): self.data[0] = self.value class TestSetValueItemTensor2(TestSetValueApi): def _call_setitem(self, x): zero = paddle.full([1], 0, dtype="int32") two = paddle.full([1], 2, dtype="int64") x[zero:two] = self.value def _get_answer(self): self.data[0:2] = self.value class TestSetValueItemTensor3(TestSetValueApi): def _call_setitem(self, x): zero = paddle.full([1], 0, dtype="int32") two = paddle.full([1], 2, dtype="int64") x[zero:-1, 0:two] = self.value def _get_answer(self): self.data[0:-1, 0:2] = self.value class TestSetValueItemTensor4(TestSetValueApi): def _call_setitem(self, x): zero = paddle.full([1], 0, dtype="int32") two = paddle.full([1], 2, dtype="int64") x[0:-1, zero:2, 0:6:two] = self.value def _get_answer(self): self.data[0:-1, 0:2, ::2] = self.value class TestSetValueItemTensor5(TestSetValueApi): def _call_setitem(self, x): zero = paddle.full([1], 0, dtype="int32") two = paddle.full([1], 2, dtype="int64") x[zero:, 1:2:two, :] = self.value def _get_answer(self): self.data[0:, 1:2:2, :] = self.value class TestSetValueItemTensor6(TestSetValueApi): def set_shape(self): self.shape = [3, 4, 5] def _call_setitem(self, x): minus1 = paddle.full([1], -1, dtype="int32") zero = paddle.full([1], 0, dtype="int32") x[2:zero:minus1, 0:2, 10:-6:minus1] = self.value def _get_answer(self): self.data[2:0:-1, 0:2, ::-1] = self.value # 1.5 item is None class TestSetValueItemNone1(TestSetValueApi): def _call_setitem(self, x): x[None] = self.value def _get_answer(self): self.data[None] = self.value class TestSetValueItemNone2(TestSetValueApi): def _call_setitem(self, x): x[0, None, 1] = self.value def _get_answer(self): self.data[0, None, 1] = self.value class TestSetValueItemNone3(TestSetValueApi): def _call_setitem(self, x): x[:, None, None, 1] = self.value def _get_answer(self): self.data[:, None, None, 1] = self.value class TestSetValueItemNone4(TestSetValueApi): def _call_setitem(self, x): x[0, 0, None, 1] = self.value def _get_answer(self): self.data[0, 0, None, 1] = self.value class TestSetValueItemNone5(TestSetValueApi): def _call_setitem(self, x): x[0, None, 0, None, 1] = self.value def _get_answer(self): self.data[0, None, 0, None, 1] = self.value class TestSetValueItemNone6(TestSetValueApi): def _call_setitem(self, x): x[None, 0, 0, None, 0] = self.value def _get_answer(self): self.data[None, 0, 0, None, 0] = self.value class TestSetValueItemNone7(TestSetValueApi): def _call_setitem(self, x): x[:, None, 1] = np.zeros(self.shape)[:, None, 0] def _get_answer(self): self.data[:, None, 1] = np.zeros(self.shape)[:, None, 0] class TestSetValueItemNone8(TestSetValueApi): def _call_setitem(self, x): x[:, 1, None] = np.zeros(self.shape)[:, 0, None] def _get_answer(self): self.data[:, 1, None] = np.zeros(self.shape)[:, 0, None] class TestSetValueItemNone9(TestSetValueApi): def _call_setitem(self, x): x[None, :, 1, ..., None] = np.zeros(self.shape)[0, 0, :, None] def _get_answer(self): self.data[None, :, 1, ..., None] = np.zeros(self.shape)[0, 0, :, None] class TestSetValueItemNone10(TestSetValueApi): def _call_setitem(self, x): x[..., None, :, None] = np.zeros(self.shape)[..., None, :, None] def _get_answer(self): self.data[..., None, :, None] = np.zeros(self.shape)[..., None, :, None] # 1.5 item is list or Tensor of bol class TestSetValueItemBool1(TestSetValueApi): def _call_setitem(self, x): x[[True, False]] = self.value def _get_answer(self): self.data[[True, False]] = self.value class TestSetValueItemBool2(TestSetValueApi): def _call_setitem(self, x): x[[False, False]] = self.value def _get_answer(self): self.data[[False, False]] = self.value class TestSetValueItemBool3(TestSetValueApi): def _call_setitem(self, x): x[[False, True]] = np.zeros(self.shape[2]) def _get_answer(self): self.data[[False, True]] = np.zeros(self.shape[2]) class TestSetValueItemBool4(TestSetValueApi): def _call_setitem(self, x): idx = paddle.assign(np.array([False, True])) x[idx] = np.zeros(self.shape[2]) def _get_answer(self): self.data[np.array([False, True])] = np.zeros(self.shape[2]) class TestSetValueItemBool5(TestSetValueApi): def _call_setitem(self, x): idx = paddle.assign( np.array([[False, True, False], [True, True, False]])) x[idx] = self.value def _get_answer(self): self.data[np.array([[False, True, False], [True, True, False]])] = self.value class TestSetValueItemBool6(TestSetValueApi): def _call_setitem(self, x): x[0, ...] = 0 x[x > 0] = self.value def _get_answer(self): self.data[0, ...] = 0 self.data[self.data > 0] = self.value # 2. Test different type of value: int, float, numpy.ndarray, Tensor # 2.1 value is int32, int64, float32, float64, bool def create_test_value_int32(parent): class TestValueInt(parent): def set_value(self): self.value = 7 def set_dtype(self): self.dtype = "int32" cls_name = "{0}_{1}".format(parent.__name__, "ValueInt32") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_int32(TestSetValueItemInt) create_test_value_int32(TestSetValueItemSlice) create_test_value_int32(TestSetValueItemSlice2) create_test_value_int32(TestSetValueItemSlice3) create_test_value_int32(TestSetValueItemSlice4) def create_test_value_int64(parent): class TestValueInt(parent): def set_value(self): self.value = 7 def set_dtype(self): self.dtype = "int64" cls_name = "{0}_{1}".format(parent.__name__, "ValueInt64") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_int64(TestSetValueItemInt) create_test_value_int64(TestSetValueItemSlice) create_test_value_int64(TestSetValueItemSlice2) create_test_value_int64(TestSetValueItemSlice3) create_test_value_int64(TestSetValueItemSlice4) def create_test_value_fp16(parent): class TestValueInt(parent): def set_value(self): self.value = 3.7 def set_dtype(self): self.dtype = "float16" cls_name = "{0}_{1}".format(parent.__name__, "Valuefp16") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_fp16(TestSetValueItemInt) create_test_value_fp16(TestSetValueItemSlice) create_test_value_fp16(TestSetValueItemSlice2) create_test_value_fp16(TestSetValueItemSlice3) create_test_value_fp16(TestSetValueItemSlice4) def create_test_value_fp32(parent): class TestValueInt(parent): def set_value(self): self.value = 3.3 def set_dtype(self): self.dtype = "float32" cls_name = "{0}_{1}".format(parent.__name__, "ValueFp32") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_fp32(TestSetValueItemInt) create_test_value_fp32(TestSetValueItemSlice) create_test_value_fp32(TestSetValueItemSlice2) create_test_value_fp32(TestSetValueItemSlice3) create_test_value_fp32(TestSetValueItemSlice4) def create_test_value_fp64(parent): class TestValueInt(parent): def set_value(self): self.value = 2.0**127 # float32:[-2^128, 2^128) def set_dtype(self): self.dtype = "float64" cls_name = "{0}_{1}".format(parent.__name__, "ValueFp64") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_fp64(TestSetValueItemInt) create_test_value_fp64(TestSetValueItemSlice) create_test_value_fp64(TestSetValueItemSlice2) create_test_value_fp64(TestSetValueItemSlice3) create_test_value_fp64(TestSetValueItemSlice4) def create_test_value_bool(parent): class TestValueInt(parent): def set_value(self): self.value = 0 def set_dtype(self): self.dtype = "bool" cls_name = "{0}_{1}".format(parent.__name__, "ValueBool") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_bool(TestSetValueItemInt) create_test_value_bool(TestSetValueItemSlice) create_test_value_bool(TestSetValueItemSlice2) create_test_value_bool(TestSetValueItemSlice3) create_test_value_bool(TestSetValueItemSlice4) # 2.2 value is numpy.array (int32, int64, float32, float64, bool) def create_test_value_numpy_int32(parent): class TestValueInt(parent): def set_value(self): self.value = np.array([5]) def set_dtype(self): self.dtype = "int32" cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyInt32") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_numpy_int32(TestSetValueItemInt) create_test_value_numpy_int32(TestSetValueItemSlice) create_test_value_numpy_int32(TestSetValueItemSlice2) create_test_value_numpy_int32(TestSetValueItemSlice3) create_test_value_numpy_int32(TestSetValueItemSlice4) def create_test_value_numpy_int64(parent): class TestValueInt(parent): def set_value(self): self.value = np.array([1]) def set_dtype(self): self.dtype = "int64" cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyInt64") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_numpy_int64(TestSetValueItemInt) create_test_value_numpy_int64(TestSetValueItemSlice) create_test_value_numpy_int64(TestSetValueItemSlice2) create_test_value_numpy_int64(TestSetValueItemSlice3) create_test_value_numpy_int64(TestSetValueItemSlice4) def create_test_value_numpy_fp32(parent): class TestValueInt(parent): def set_value(self): self.value = np.array([1]) def set_dtype(self): self.dtype = "float32" cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyFp32") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_numpy_fp32(TestSetValueItemInt) create_test_value_numpy_fp32(TestSetValueItemSlice) create_test_value_numpy_fp32(TestSetValueItemSlice2) create_test_value_numpy_fp32(TestSetValueItemSlice3) create_test_value_numpy_fp32(TestSetValueItemSlice4) def create_test_value_numpy_fp64(parent): class TestValueInt(parent): def set_value(self): self.value = np.array([2**127]).astype("float64") def set_dtype(self): self.dtype = "float64" cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyFp64") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_numpy_fp64(TestSetValueItemInt) create_test_value_numpy_fp64(TestSetValueItemSlice) create_test_value_numpy_fp64(TestSetValueItemSlice2) create_test_value_numpy_fp64(TestSetValueItemSlice3) create_test_value_numpy_fp64(TestSetValueItemSlice4) def create_test_value_numpy_bool(parent): class TestValueInt(parent): def set_value(self): self.value = np.array([0]) def set_dtype(self): self.dtype = "bool" cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyBool") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_numpy_bool(TestSetValueItemInt) create_test_value_numpy_bool(TestSetValueItemSlice) create_test_value_numpy_bool(TestSetValueItemSlice2) create_test_value_numpy_bool(TestSetValueItemSlice3) create_test_value_numpy_bool(TestSetValueItemSlice4) # 2.3 value is a Paddle Tensor (int32, int64, float32, float64, bool) def create_test_value_tensor_int32(parent): class TestValueInt(parent): def set_dtype(self): self.dtype = "int32" def _call_setitem(self, x): value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype) x[0, 1] = value def _get_answer(self): self.data[0, 1] = 3 cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorInt32") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_tensor_int32(TestSetValueItemInt) create_test_value_tensor_int32(TestSetValueItemSlice) create_test_value_tensor_int32(TestSetValueItemSlice2) create_test_value_tensor_int32(TestSetValueItemSlice3) create_test_value_tensor_int32(TestSetValueItemSlice4) def create_test_value_tensor_int64(parent): class TestValueInt(parent): def set_dtype(self): self.dtype = "int64" def _call_setitem(self, x): value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype) x[0, 1] = value def _get_answer(self): self.data[0, 1] = 3 cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorInt64") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_tensor_int64(TestSetValueItemInt) create_test_value_tensor_int64(TestSetValueItemSlice) create_test_value_tensor_int64(TestSetValueItemSlice2) create_test_value_tensor_int64(TestSetValueItemSlice3) create_test_value_tensor_int64(TestSetValueItemSlice4) def create_test_value_tensor_fp32(parent): class TestValueInt(parent): def set_dtype(self): self.dtype = "float32" def _call_setitem(self, x): value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype) x[0, 1] = value def _get_answer(self): self.data[0, 1] = 3 cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorFp32") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_tensor_fp32(TestSetValueItemInt) create_test_value_tensor_fp32(TestSetValueItemSlice) create_test_value_tensor_fp32(TestSetValueItemSlice2) create_test_value_tensor_fp32(TestSetValueItemSlice3) create_test_value_tensor_fp32(TestSetValueItemSlice4) def create_test_value_tensor_fp64(parent): class TestValueInt(parent): def set_dtype(self): self.dtype = "float64" def _call_setitem(self, x): value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype) x[0, 1] = value def _get_answer(self): self.data[0, 1] = 3 cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorFp64") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_tensor_fp64(TestSetValueItemInt) create_test_value_tensor_fp64(TestSetValueItemSlice) create_test_value_tensor_fp64(TestSetValueItemSlice2) create_test_value_tensor_fp64(TestSetValueItemSlice3) create_test_value_tensor_fp64(TestSetValueItemSlice4) def create_test_value_tensor_bool(parent): class TestValueInt(parent): def set_dtype(self): self.dtype = "bool" def _call_setitem(self, x): value = paddle.full(shape=[1], fill_value=False, dtype=self.dtype) x[0, 1] = value def _get_answer(self): self.data[0, 1] = False cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorBool") TestValueInt.__name__ = cls_name globals()[cls_name] = TestValueInt create_test_value_tensor_bool(TestSetValueItemInt) create_test_value_tensor_bool(TestSetValueItemSlice) create_test_value_tensor_bool(TestSetValueItemSlice2) create_test_value_tensor_bool(TestSetValueItemSlice3) create_test_value_tensor_bool(TestSetValueItemSlice4) # 3. Test different shape of value class TestSetValueValueShape1(TestSetValueApi): def set_value(self): self.value = np.array([3, 4, 5, 6]) # shape is (4,) def _call_setitem(self, x): x[0] = self.value def _get_answer(self): self.data[0] = self.value class TestSetValueValueShape2(TestSetValueApi): def set_value(self): self.value = np.array([[3, 4, 5, 6]]) # shape is (1,4) def _call_setitem(self, x): x[0:1] = self.value def _get_answer(self): self.data[0:1] = self.value class TestSetValueValueShape3(TestSetValueApi): def set_value(self): self.value = np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]) # shape is (3,4) def _call_setitem(self, x): x[0] = self.value def _get_answer(self): self.data[0] = self.value class TestSetValueValueShape4(TestSetValueApi): def set_value(self): self.value = np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]).astype(self.dtype) # shape is (3,4) def _call_setitem(self, x): x[0] = paddle.assign(self.value) # x is Paddle.Tensor def _get_answer(self): self.data[0] = self.value class TestSetValueValueShape5(TestSetValueApi): def set_value(self): self.value = np.array([3, 3, 3]).astype(self.dtype) def set_shape(self): self.shape = [3, 4] def _call_setitem(self, x): x[:, 0] = paddle.assign(self.value) # x is Paddle.Tensor def _get_answer(self): self.data[:, 0] = self.value # 4. Test error class TestError(TestSetValueBase): def _value_type_error(self): with self.assertRaisesRegexp( TypeError, "Only support to assign an integer, float, numpy.ndarray or paddle.Tensor" ): x = paddle.ones(shape=self.shape, dtype=self.dtype) value = [1] x[0] = value def _dtype_error(self): with self.assertRaisesRegexp( TypeError, "When assign a numpy.ndarray, integer or float to a paddle.Tensor, " ): y = paddle.ones(shape=self.shape, dtype="float16") y[0] = 1 def _step_error(self): with self.assertRaisesRegexp(ValueError, "step can not be 0"): x = paddle.ones(shape=self.shape, dtype=self.dtype) x[0:1:0] = self.value def _ellipsis_error(self): with self.assertRaisesRegexp( IndexError, "An index can only have a single ellipsis"): x = paddle.ones(shape=self.shape, dtype=self.dtype) x[..., ...] = self.value with self.assertRaisesRegexp(ValueError, "the start or end is None"): x = paddle.ones(shape=self.shape, dtype=self.dtype) one = paddle.ones([1]) x[::one] = self.value def _bool_list_error(self): with self.assertRaises(TypeError): x = paddle.ones(shape=self.shape, dtype=self.dtype) x[[True, False, 0]] = 0 with self.assertRaises(IndexError): x = paddle.ones(shape=self.shape, dtype=self.dtype) x[[True, False], [True, False]] = 0 def _bool_tensor_error(self): with self.assertRaises(IndexError): x = paddle.ones(shape=self.shape, dtype=self.dtype) idx = paddle.assign([True, False, True]) x[idx] = 0 def _broadcast_mismatch(self): program = paddle.static.Program() with paddle.static.program_guard(program): x = paddle.ones(shape=self.shape, dtype=self.dtype) value = np.array([3, 4, 5, 6, 7]) x[0] = value exe = paddle.static.Executor(paddle.CPUPlace()) with self.assertRaises(ValueError): exe.run(program) def test_error(self): paddle.enable_static() with paddle.static.program_guard(self.program): self._value_type_error() self._step_error() self._bool_list_error() self._bool_tensor_error() self._broadcast_mismatch() # 5. Test backward class Model(paddle.nn.Layer): def __init__(self): super(Model, self).__init__() self.conv = paddle.nn.Conv2D(12, 12, 3) def forward(self, x, y): x = self.conv(x) y = self.conv(y) var = y.flatten() x[0, :, 0, 0] = var loss = paddle.mean(x) return loss, var, x class TestBackward(unittest.TestCase): def test_static(self): paddle.enable_static() main_program = paddle.static.Program() startup_program = paddle.static.Program() x_np = np.random.random(size=(4, 4)).astype('float32') y_np = np.random.random(size=(4, 4)).astype('float32') label_np = np.random.randint(2, size=(4, 1)).astype('int64') with paddle.static.program_guard(main_program, startup_program): x = paddle.static.data(name="x", shape=[4, 4], dtype='float32') y = paddle.static.data(name="y", shape=[4, 4], dtype='float32') label = paddle.static.data(name="label", shape=[4, 1], dtype='int64') z = paddle.add(x, y) var = y[0, :] z[0, :] = var prediction = paddle.static.nn.fc(x=z, size=2, activation='softmax') cost = paddle.nn.functional.cross_entropy(input=prediction, label=label) loss = paddle.mean(cost) sgd = paddle.optimizer.SGD(learning_rate=0.01) sgd.minimize(loss) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(startup_program) var_grad, z_grad = exe.run( main_program, feed={ "x": x_np, "y": y_np, "label": label_np }, fetch_list=[var.name + "@GRAD", z.name + "@GRAD"]) self.assertTrue((var_grad == z_grad[0, :]).all()) paddle.disable_static() def func_test_dynamic(self): model = Model() x = paddle.ones([1, 12, 3, 3]).astype("float32") y = paddle.ones([1, 12, 3, 3]).astype("float32") loss, var, x = model(x, y) loss.backward() self.assertTrue(var.grad.shape == x.grad[0, :, 0, 0].shape) self.assertTrue((0 == x.grad[0, :, 0, 0]).all()) def test_dynamic(self): fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True}) with _test_eager_guard(): self.func_test_dynamic() self.func_test_dynamic() fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) class TestGradientTruncated(unittest.TestCase): def func_test_consistent_with_competitor(self): paddle.disable_static() def set_value(t, value): a = t * t a[0, 1] = value y = a * a return y.sum() # case 1 array = np.arange(1, 1 + 2 * 3 * 4, dtype="float32").reshape([1, 2, 1, 3, 1, 4]) value = np.arange(100, 104, dtype="float32").reshape(1, 4) inps = paddle.to_tensor(array, stop_gradient=False) value = paddle.to_tensor(value, stop_gradient=False) loss = set_value(inps, value) loss.backward() value_grad = np.array([[600., 606., 612., 618.]]) input_grad = np.array([[[[[[4., 32., 108., 256.]], [[500., 864., 1372., 2048.]], [[2916., 4000., 5324., 6912.]]]], [[[[0., 0., 0., 0.]], [[0., 0., 0., 0.]], [[0., 0., 0., 0.]]]]]]) np.testing.assert_array_equal( inps.grad.numpy(), input_grad, err_msg='The gradient of value should be \n{},\n but reveived {}'. format(input_grad, inps.grad.numpy())) np.testing.assert_array_equal( value.grad.numpy(), value_grad, err_msg='The gradient of input should be \n{},\n but reveived {}'. format(value_grad, value.grad.numpy())) # case 2 array = np.arange(1, 2 * 3 * 4 + 1, dtype="float32").reshape([4, 2, 3]) value = np.arange(100, 100 + 1, dtype="float32") inps2 = paddle.to_tensor(array, stop_gradient=False) value2 = paddle.to_tensor(value, stop_gradient=False) loss = set_value(inps2, value2) loss.backward() value_grad2 = np.array([600.]) input_grad2 = np.array([[[4., 32., 108.], [0., 0., 0.]], [[1372., 2048., 2916.], [4000., 5324., 6912.]], [[8788., 10976., 13500.], [16384., 19652., 23328.]], [[27436., 32000., 37044.], [42592., 48668., 55296.]]]) np.testing.assert_array_equal( inps2.grad.numpy(), input_grad2, err_msg='The gradient of value should be \n{},\n but reveived {}'. format(input_grad, inps2.grad.numpy())) np.testing.assert_array_equal( value2.grad.numpy(), value_grad2, err_msg='The gradient of input should be \n{},\n but reveived {}'. format(value_grad, value2.grad.numpy())) # case 3 def set_value3(t, value): a = t * t a[0, :, 0, :] = value y = a * a return y.sum() array = np.arange(1, 1 + 2 * 3 * 4, dtype="float32").reshape([4, 3, 1, 1, 2, 1]) value = np.arange(100, 100 + 2, dtype="float32").reshape(1, 2, 1) inps = paddle.to_tensor(array, stop_gradient=False) value = paddle.to_tensor(value, stop_gradient=False) loss = set_value3(inps, value) loss.backward() value_grad = np.array([[[600.], [606.]]]) input_grad = np.array([[[[[[0.], [0.]]]], [[[[0.], [0.]]]], [[[[0.], [0.]]]]], [[[[[1372.], [2048.]]]], [[[[2916.], [4000.]]]], [[[[5324.], [6912.]]]]], [[[[[8788.], [10976.]]]], [[[[13500.], [16384.]]]], [[[[19652.], [23328.]]]]], [[[[[27436.], [32000.]]]], [[[[37044.], [42592.]]]], [[[[48668.], [55296.]]]]]]) np.testing.assert_array_equal( inps.grad.numpy(), input_grad, err_msg='The gradient of value should be \n{},\n but reveived {}'. format(input_grad, inps.grad.numpy())) np.testing.assert_array_equal( value.grad.numpy(), value_grad, err_msg='The gradient of input should be \n{},\n but reveived {}'. format(value_grad, value.grad.numpy())) #case 4: step >0 def set_value4(t, value): a = t * t a[0, :, 0, ::3] = value y = a * a return y.sum() array = np.arange(1, 1 + 2 * 3 * 4, dtype="float32").reshape([2, 3, 1, 4, 1]) value = np.arange(100, 100 + 2, dtype="float32").reshape(1, 2, 1) inps = paddle.to_tensor(array, stop_gradient=False) value = paddle.to_tensor(value, stop_gradient=False) loss = set_value4(inps, value) loss.backward() value_grad = np.array([[[600.], [606.]]]) input_grad = np.array([[[[[0.], [32.], [108.], [0.]]], [[[0.], [864.], [1372.], [0.]]], [[[0.], [4000.], [5324.], [0.]]]], [[[[8788.], [10976.], [13500.], [16384.]]], [[[19652.], [23328.], [27436.], [32000.]]], [[[37044.], [42592.], [48668.], [55296.]]]]]) np.testing.assert_array_equal( inps.grad.numpy(), input_grad, err_msg='The gradient of value should be \n{},\n but reveived {}'. format(input_grad, inps.grad.numpy())) np.testing.assert_array_equal( value.grad.numpy(), value_grad, err_msg='The gradient of input should be \n{},\n but reveived {}'. format(value_grad, value.grad.numpy())) # case 5:a[0].shape==value.shape def set_value5(t, value): a = t * t a[0] = value y = a * a return y.sum() array = np.arange(1, 1 + 2 * 3 * 4, dtype="float32").reshape([2, 3, 4]) value = np.arange(100, 100 + 12, dtype="float32").reshape(3, 4) inps = paddle.to_tensor(array, stop_gradient=False) value = paddle.to_tensor(value, stop_gradient=False) loss = set_value5(inps, value) loss.backward() value_grad = np.array([[200., 202., 204., 206.], [208., 210., 212., 214.], [216., 218., 220., 222.]]) input_grad = np.array([[[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]], [[8788., 10976., 13500., 16384.], [19652., 23328., 27436., 32000.], [37044., 42592., 48668., 55296.]]]) np.testing.assert_array_equal( inps.grad.numpy(), input_grad, err_msg='The gradient of value should be \n{},\n but reveived {}'. format(input_grad, inps.grad.numpy())) np.testing.assert_array_equal( value.grad.numpy(), value_grad, err_msg='The gradient of input should be \n{},\n but reveived {}'. format(value_grad, value.grad.numpy())) # case 6: pass stop_gradient from value to x x = paddle.zeros([8, 8], dtype='float32') value = paddle.to_tensor([10], dtype='float32', stop_gradient=False) self.assertTrue(x.stop_gradient) self.assertTrue(x.is_leaf) x[0, :] = value self.assertTrue(not x.stop_gradient) self.assertTrue(not x.is_leaf) def test_consistent_with_competitor(self): with _test_eager_guard(): self.func_test_consistent_with_competitor() self.func_test_consistent_with_competitor() def test_static_graph(self): paddle.enable_static() to_string = lambda x, i, : x + '_' + str(i) numel = lambda input_shape: reduce(lambda x, y: x * y, input_shape) def op1(x): value = paddle.fluid.layers.fill_constant([1], "float32", 1) # test stop_gradient value.stop_gradient = True x.stop_gradient = False start = paddle.fluid.layers.fill_constant([1], "int32", 5, force_cpu=True) end = paddle.fluid.layers.fill_constant([1], "int32", 0, force_cpu=True) step = paddle.fluid.layers.fill_constant([1], "int32", -2, force_cpu=True) inputs = { 'Input': x, 'ValueTensor': value, 'StartsTensorList': [ start, ], 'EndsTensorList': [ end, ], 'StepsTensorList': [ step, ] } helper = LayerHelper("set_value") y = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op(type="set_value", inputs=inputs, outputs={'Out': y}, attrs={'axes': [0]}) return y, value def op2(x): value = paddle.fluid.layers.fill_constant([1, 3, 2], "float32", 1) # test stop_gradient value.stop_gradient = False x.stop_gradient = False attrs = { 'axes': [0], 'starts': [6], 'ends': [0], 'steps': [-4], 'decrease_axes': [], 'none_axes': [], 'dtype': paddle.float32 } inputs = {'Input': x, 'ValueTensor': value} helper = LayerHelper("set_value") y = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op(type="set_value", inputs=inputs, outputs={'Out': y}, attrs=attrs) return y, value def op3(x): value = paddle.fluid.layers.fill_constant([1], "float32", 1) x.stop_gradient = True value.stop_gradient = False start = paddle.fluid.layers.fill_constant([1], "int32", 0, force_cpu=True) end = paddle.fluid.layers.fill_constant([1], "int32", 5, force_cpu=True) step = paddle.fluid.layers.fill_constant([1], "int32", 3, force_cpu=True) inputs = { 'Input': x, 'ValueTensor': value, 'StartsTensorList': [ start, ], 'EndsTensorList': [ end, ], 'StepsTensorList': [ step, ] } helper = LayerHelper("set_value") y = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op(type="set_value", inputs=inputs, outputs={'Out': y}, attrs={'axes': [0]}) return y, value def set_value(array, i, op): name_x = to_string('x', i) x = paddle.static.data(name=name_x, shape=array.shape, dtype='float32') # set_value_op in __get/setitem__ is an inplace operation. # When `input.stop_gradient = True` and `value.stop_gradient = False`, # set_value_grad_op will not be run during backward. y, value = op(x) y2 = y + 1 loss = paddle.fluid.layers.reduce_sum(y2) sgd = paddle.optimizer.Adam() sgd.minimize(loss) place = paddle.fluid.CPUPlace( ) if not paddle.fluid.core.is_compiled_with_cuda( ) else paddle.fluid.CUDAPlace(0) prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [] if not x.stop_gradient: fetch_list.append(x.grad_name) if not value.stop_gradient: fetch_list.append(value.grad_name) out = exe.run(prog, feed={x.name: array}, fetch_list=fetch_list) return out input_shape = [7, 6, 5, 4, 3, 2] array = np.arange(0, numel(input_shape), dtype="float32").reshape(input_shape) for i in range(len(input_shape)): program = paddle.static.Program() with paddle.static.program_guard(program): out1 = set_value(array, i, op1) self.assertTrue((out1[0][5:0:-2] == 0).all()) if len(array.shape) > 2: program2 = paddle.static.Program() with paddle.static.program_guard(program2): out2 = set_value(array, i, op2) self.assertTrue((out2[0][6:0:-4] == 0).all()) program3 = paddle.static.Program() with paddle.static.program_guard(program3): out3 = set_value(array, i, op3) self.assertTrue((numel(out1[0][0:5:3].shape) == out3[0]).all()) array = array[0] paddle.disable_static() class TestSetValueInplace(unittest.TestCase): def test_inplace(self): paddle.disable_static() with paddle.fluid.dygraph.guard(): paddle.seed(100) a = paddle.rand(shape=[1, 4]) a.stop_gradient = False b = a[:] c = b b[paddle.to_tensor(0)] = 1.0 self.assertTrue(id(b) == id(c)) np.testing.assert_array_equal(b.numpy(), c.numpy()) self.assertEqual(b.inplace_version, 1) paddle.enable_static() class TestSetValueInplaceLeafVar(unittest.TestCase): def test_inplace_var_become_leaf_var(self): paddle.disable_static() a_grad_1, b_grad_1, a_grad_2, b_grad_2 = 0, 1, 2, 3 with paddle.fluid.dygraph.guard(): paddle.seed(100) a = paddle.rand(shape=[1, 4]) b = paddle.rand(shape=[1, 4]) a.stop_gradient = False b.stop_gradient = False c = a / b c.sum().backward() a_grad_1 = a.grad.numpy() b_grad_1 = b.grad.numpy() with paddle.fluid.dygraph.guard(): paddle.seed(100) a = paddle.rand(shape=[1, 4]) b = paddle.rand(shape=[1, 4]) a.stop_gradient = False b.stop_gradient = False c = a / b d = paddle.zeros((4, 4)) self.assertTrue(d.stop_gradient) d[0, :] = c self.assertFalse(d.stop_gradient) d[0, :].sum().backward() a_grad_2 = a.grad.numpy() b_grad_2 = b.grad.numpy() np.testing.assert_array_equal(a_grad_1, a_grad_2) np.testing.assert_array_equal(b_grad_1, b_grad_2) paddle.enable_static() if __name__ == '__main__': unittest.main()