# Copyright (c) 2018 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. import unittest from functools import reduce import numpy as np import paddle from paddle import base from paddle.base import core from paddle.base.framework import ( Program, convert_np_dtype_to_dtype_, default_main_program, ) paddle.enable_static() class TestVariable(unittest.TestCase): def setUp(self): np.random.seed(2022) def test_np_dtype_convert(self): DT = core.VarDesc.VarType convert = convert_np_dtype_to_dtype_ self.assertEqual(DT.FP32, convert(np.float32)) self.assertEqual(DT.FP16, convert("float16")) self.assertEqual(DT.FP64, convert("float64")) self.assertEqual(DT.INT32, convert("int32")) self.assertEqual(DT.INT16, convert("int16")) self.assertEqual(DT.INT64, convert("int64")) self.assertEqual(DT.BOOL, convert("bool")) self.assertEqual(DT.INT8, convert("int8")) self.assertEqual(DT.UINT8, convert("uint8")) def test_var(self): b = default_main_program().current_block() w = b.create_var( dtype="float64", shape=[784, 100], lod_level=0, name="fc.w" ) self.assertNotEqual(str(w), "") self.assertEqual(core.VarDesc.VarType.FP64, w.dtype) self.assertEqual((784, 100), w.shape) self.assertEqual("fc.w", w.name) self.assertEqual("fc.w@GRAD", w.grad_name) self.assertEqual(0, w.lod_level) w = b.create_var(name='fc.w') self.assertEqual(core.VarDesc.VarType.FP64, w.dtype) self.assertEqual((784, 100), w.shape) self.assertEqual("fc.w", w.name) self.assertEqual("fc.w@GRAD", w.grad_name) self.assertEqual(0, w.lod_level) self.assertRaises( ValueError, lambda: b.create_var(name="fc.w", shape=(24, 100)) ) w = b.create_var( dtype=paddle.base.core.VarDesc.VarType.STRINGS, shape=[1], name="str_var", ) self.assertEqual(None, w.lod_level) def test_element_size(self): with base.program_guard(Program(), Program()): x = paddle.static.data(name='x1', shape=[2], dtype='bool') self.assertEqual(x.element_size(), 1) x = paddle.static.data(name='x2', shape=[2], dtype='float16') self.assertEqual(x.element_size(), 2) x = paddle.static.data(name='x3', shape=[2], dtype='float32') self.assertEqual(x.element_size(), 4) x = paddle.static.data(name='x4', shape=[2], dtype='float64') self.assertEqual(x.element_size(), 8) x = paddle.static.data(name='x5', shape=[2], dtype='int8') self.assertEqual(x.element_size(), 1) x = paddle.static.data(name='x6', shape=[2], dtype='int16') self.assertEqual(x.element_size(), 2) x = paddle.static.data(name='x7', shape=[2], dtype='int32') self.assertEqual(x.element_size(), 4) x = paddle.static.data(name='x8', shape=[2], dtype='int64') self.assertEqual(x.element_size(), 8) x = paddle.static.data(name='x9', shape=[2], dtype='uint8') self.assertEqual(x.element_size(), 1) def test_step_scopes(self): prog = Program() b = prog.current_block() var = b.create_var( name='step_scopes', type=core.VarDesc.VarType.STEP_SCOPES ) self.assertEqual(core.VarDesc.VarType.STEP_SCOPES, var.type) def _test_slice(self, place): b = default_main_program().current_block() w = b.create_var(dtype="float64", shape=[784, 100, 100], lod_level=0) for i in range(3): nw = w[i] self.assertEqual((100, 100), nw.shape) nw = w[:] self.assertEqual((784, 100, 100), nw.shape) nw = w[:, :] self.assertEqual((784, 100, 100), nw.shape) nw = w[:, :, -1] self.assertEqual((784, 100), nw.shape) nw = w[1, 1, 1] self.assertEqual(len(nw.shape), 0) nw = w[:, :, :-1] self.assertEqual((784, 100, 99), nw.shape) self.assertEqual(0, nw.lod_level) main = base.Program() with base.program_guard(main): exe = base.Executor(place) tensor_array = np.array( [ [[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]], ] ).astype('float32') var = paddle.assign(tensor_array) var1 = var[0, 1, 1] var2 = var[1:] var3 = var[0:1] var4 = var[::-1] var5 = var[1, 1:, 1:] var_reshape = paddle.reshape(var, [3, -1, 3]) var6 = var_reshape[:, :, -1] var7 = var[:, :, :-1] var8 = var[:1, :1, :1] var9 = var[:-1, :-1, :-1] var10 = var[::-1, :1, :-1] var11 = var[:-1, ::-1, -1:] var12 = var[1:2, 2:, ::-1] var13 = var[2:10, 2:, -2:-1] var14 = var[1:-1, 0:2, ::-1] var15 = var[::-1, ::-1, ::-1] x = paddle.static.data(name='x', shape=[-1, 13], dtype='float32') y = paddle.static.nn.fc(x, size=1, activation=None) y_1 = y[:, 0] feeder = base.DataFeeder(place=place, feed_list=[x]) data = [] data.append(np.random.randint(10, size=[13]).astype('float32')) exe.run(base.default_startup_program()) local_out = exe.run( main, feed=feeder.feed([data]), fetch_list=[ var, var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, ], ) np.testing.assert_array_equal(local_out[1], tensor_array[0, 1, 1:2]) np.testing.assert_array_equal(local_out[2], tensor_array[1:]) np.testing.assert_array_equal(local_out[3], tensor_array[0:1]) np.testing.assert_array_equal(local_out[4], tensor_array[::-1]) np.testing.assert_array_equal(local_out[5], tensor_array[1, 1:, 1:]) np.testing.assert_array_equal( local_out[6], tensor_array.reshape((3, -1, 3))[:, :, -1] ) np.testing.assert_array_equal(local_out[7], tensor_array[:, :, :-1]) np.testing.assert_array_equal( local_out[8], tensor_array[:1, :1, :1] ) np.testing.assert_array_equal( local_out[9], tensor_array[:-1, :-1, :-1] ) np.testing.assert_array_equal( local_out[10], tensor_array[::-1, :1, :-1] ) np.testing.assert_array_equal( local_out[11], tensor_array[:-1, ::-1, -1:] ) np.testing.assert_array_equal( local_out[12], tensor_array[1:2, 2:, ::-1] ) np.testing.assert_array_equal( local_out[13], tensor_array[2:10, 2:, -2:-1] ) np.testing.assert_array_equal( local_out[14], tensor_array[1:-1, 0:2, ::-1] ) np.testing.assert_array_equal( local_out[15], tensor_array[::-1, ::-1, ::-1] ) def _test_slice_index_tensor(self, place): data = np.random.rand(2, 3).astype("float32") prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) idx0 = [1, 0] idx1 = [0, 1] idx2 = [0, 0] idx3 = [1, 1] out0 = x[paddle.assign(np.array(idx0))] out1 = x[paddle.assign(np.array(idx1))] out2 = x[paddle.assign(np.array(idx2))] out3 = x[paddle.assign(np.array(idx3))] exe = paddle.static.Executor(place) result = exe.run(prog, fetch_list=[out0, out1, out2, out3]) expected = [data[idx0], data[idx1], data[idx2], data[idx3]] self.assertTrue((result[0] == expected[0]).all()) self.assertTrue((result[1] == expected[1]).all()) self.assertTrue((result[2] == expected[2]).all()) self.assertTrue((result[3] == expected[3]).all()) def _test_slice_index_list(self, place): data = np.random.rand(2, 3).astype("float32") prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) idx0 = [1, 0] idx1 = [0, 1] idx2 = [0, 0] idx3 = [1, 1] out0 = x[idx0] out1 = x[idx1] out2 = x[idx2] out3 = x[idx3] exe = paddle.static.Executor(place) result = exe.run(prog, fetch_list=[out0, out1, out2, out3]) expected = [data[idx0], data[idx1], data[idx2], data[idx3]] self.assertTrue((result[0] == expected[0]).all()) self.assertTrue((result[1] == expected[1]).all()) self.assertTrue((result[2] == expected[2]).all()) self.assertTrue((result[3] == expected[3]).all()) def _test_slice_index_ellipsis(self, place): data = np.random.rand(2, 3, 4).astype("float32") prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) y = paddle.assign([1, 2, 3, 4]) out1 = x[0:, ..., 1:] out2 = x[0:, ...] out3 = x[..., 1:] out4 = x[...] out5 = x[[1, 0], [0, 0]] out6 = x[([1, 0], [0, 0])] out7 = y[..., 0] exe = paddle.static.Executor(place) result = exe.run( prog, fetch_list=[out1, out2, out3, out4, out5, out6, out7] ) expected = [ data[0:, ..., 1:], data[0:, ...], data[..., 1:], data[...], data[[1, 0], [0, 0]], data[([1, 0], [0, 0])], np.array([1]), ] self.assertTrue((result[0] == expected[0]).all()) self.assertTrue((result[1] == expected[1]).all()) self.assertTrue((result[2] == expected[2]).all()) self.assertTrue((result[3] == expected[3]).all()) self.assertTrue((result[4] == expected[4]).all()) self.assertTrue((result[5] == expected[5]).all()) self.assertTrue((result[6] == expected[6]).all()) def _test_slice_index_list_bool(self, place): data = np.random.rand(2, 3, 4).astype("float32") np_idx = np.array([[True, False, False], [True, False, True]]) prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) idx0 = [True, False] idx1 = [False, True] idx2 = [True, True] idx3 = [False, False, 1] idx4 = [True, False, 0] idx5 = paddle.assign(np_idx) out0 = x[idx0] out1 = x[idx1] out2 = x[idx2] out3 = x[idx3] out4 = x[idx4] out5 = x[idx5] out6 = x[x < 0.36] out7 = x[x > 0.6] exe = paddle.static.Executor(place) result = exe.run( prog, fetch_list=[out0, out1, out2, out3, out4, out5, out6, out7] ) expected = [ data[idx0], data[idx1], data[idx2], data[idx3], data[idx4], data[np_idx], data[data < 0.36], data[data > 0.6], ] self.assertTrue((result[0] == expected[0]).all()) self.assertTrue((result[1] == expected[1]).all()) self.assertTrue((result[2] == expected[2]).all()) self.assertTrue((result[3] == expected[3]).all()) self.assertTrue((result[4] == expected[4]).all()) self.assertTrue((result[5] == expected[5]).all()) self.assertTrue((result[6] == expected[6]).all()) self.assertTrue((result[7] == expected[7]).all()) with self.assertRaises(IndexError): res = x[[True, False, False]] def _test_slice_index_scalar_bool(self, place): data = np.random.rand(1, 3, 4).astype("float32") np_idx = np.array([True]) prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) idx = paddle.assign(np_idx) out = x[idx] exe = paddle.static.Executor(place) result = exe.run(prog, fetch_list=[out]) expected = [data[np_idx]] self.assertTrue((result[0] == expected[0]).all()) def test_slice(self): places = [base.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: self._test_slice(place) self._test_slice_index_tensor(place) self._test_slice_index_list(place) self._test_slice_index_ellipsis(place) self._test_slice_index_list_bool(place) self._test_slice_index_scalar_bool(place) def _tostring(self): b = default_main_program().current_block() w = b.create_var(dtype="float64", lod_level=0) self.assertTrue(isinstance(str(w), str)) if core.is_compiled_with_cuda(): wc = b.create_var(dtype="int", lod_level=0) self.assertTrue(isinstance(str(wc), str)) def test_tostring(self): with base.dygraph.guard(): self._tostring() with base.program_guard(default_main_program()): self._tostring() def test_fake_interface_only_api(self): b = default_main_program().current_block() var = b.create_var(dtype="float64", lod_level=0) with base.dygraph.guard(): self.assertRaises(AssertionError, var.numpy) self.assertRaises(AssertionError, var.backward) self.assertRaises(AssertionError, var.gradient) self.assertRaises(AssertionError, var.clear_gradient) def test_variable_in_dygraph_mode(self): b = default_main_program().current_block() var = b.create_var(dtype="float64", shape=[1, 1]) with base.dygraph.guard(): self.assertTrue(var.to_string(True).startswith('name:')) self.assertFalse(var.persistable) var.persistable = True self.assertTrue(var.persistable) self.assertFalse(var.stop_gradient) var.stop_gradient = True self.assertTrue(var.stop_gradient) self.assertTrue(var.name.startswith('_generated_var_')) self.assertEqual(var.shape, (1, 1)) self.assertEqual(var.dtype, base.core.VarDesc.VarType.FP64) self.assertEqual(var.type, base.core.VarDesc.VarType.LOD_TENSOR) def test_create_selected_rows(self): b = default_main_program().current_block() var = b.create_var( name="var", shape=[1, 1], dtype="float32", type=base.core.VarDesc.VarType.SELECTED_ROWS, persistable=True, ) def _test(): var.lod_level() self.assertRaises(Exception, _test) def test_size(self): prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(np.random.rand(2, 3, 4).astype("float32")) exe = paddle.static.Executor(base.CPUPlace()) exe.run(paddle.static.default_startup_program()) output = exe.run(prog, fetch_list=[x.size()]) self.assertEqual(output[0], [24]) def test_detach(self): b = default_main_program().current_block() x = b.create_var(shape=[2, 3, 5], dtype="float64", lod_level=0) detach_x = x.detach() self.assertEqual(x.persistable, detach_x.persistable) self.assertEqual(x.shape, detach_x.shape) self.assertEqual(x.dtype, detach_x.dtype) self.assertEqual(x.type, detach_x.type) self.assertTrue(detach_x.stop_gradient) xx = b.create_var(name='xx', type=core.VarDesc.VarType.STEP_SCOPES) self.assertRaises(AssertionError, xx.detach) startup = paddle.static.Program() main = paddle.static.Program() scope = base.core.Scope() with paddle.static.scope_guard(scope): with paddle.static.program_guard(main, startup): x = paddle.static.data( name='x', shape=[3, 2, 1], dtype='float32' ) x.persistable = True feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32) detach_x = x.detach() exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(startup) result = exe.run( main, feed={'x': feed_data}, fetch_list=[x, detach_x] ) self.assertTrue((result[1] == feed_data).all()) self.assertTrue((result[0] == result[1]).all()) modified_value = np.zeros(shape=[3, 2, 1], dtype=np.float32) detach_x.set_value(modified_value, scope) result = exe.run(main, fetch_list=[x, detach_x]) self.assertTrue((result[1] == modified_value).all()) self.assertTrue((result[0] == result[1]).all()) modified_value = np.random.uniform( -1, 1, size=[3, 2, 1] ).astype('float32') x.set_value(modified_value, scope) result = exe.run(main, fetch_list=[x, detach_x]) self.assertTrue((result[1] == modified_value).all()) self.assertTrue((result[0] == result[1]).all()) class TestVariableSlice(unittest.TestCase): def setUp(self): np.random.seed(2022) def _test_item_none(self, place): data = np.random.rand(2, 3, 4).astype("float32") prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) out0 = x[0:, None, 1:] out1 = x[0:, None] out2 = x[None, 1:] out3 = x[None] out4 = x[..., None, :, None] outs = [out0, out1, out2, out3, out4] exe = paddle.static.Executor(place) result = exe.run(prog, fetch_list=outs) expected = [ data[0:, None, 1:], data[0:, None], data[None, 1:], data[None], data[..., None, :, None], ] for i in range(len(outs)): self.assertEqual(outs[i].shape, expected[i].shape) self.assertTrue((result[i] == expected[i]).all()) def _test_item_none_and_decrease(self, place): data = np.random.rand(2, 3, 4).astype("float32") prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.assign(data) out0 = x[0, 1:, None] out1 = x[0, None] out2 = x[None, 1] out3 = x[None] out4 = x[0, 0, 0, None] out5 = x[None, 0, 0, 0, None] outs = [out0, out1, out2, out3, out4, out5] exe = paddle.static.Executor(place) result = exe.run(prog, fetch_list=outs) expected = [ data[0, 1:, None], data[0, None], data[None, 1], data[None], data[0, 0, 0, None], data[None, 0, 0, 0, None], ] for i in range(len(outs)): self.assertEqual(outs[i].shape, expected[i].shape) self.assertTrue((result[i] == expected[i]).all()) def test_slice(self): places = [base.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: self._test_item_none(place) self._test_item_none_and_decrease(place) class TestListIndex(unittest.TestCase): # note(chenjianye): # Non-tuple sequence for multidimensional indexing is supported in numpy < 1.23. # For List case, the outermost `[]` will be treated as tuple `()` in version less than 1.23, # which is used to wrap index elements for multiple axes. # And from 1.23, this will be treat as a whole and only works on one axis. # # e.g. x[[[0],[1]]] == x[([0],[1])] == x[[0],[1]] (in version < 1.23) # x[[[0],[1]]] == x[array([[0],[1]])] (in version >= 1.23) # # Here, we just modify the code to remove the impact of numpy version changes, # changing x[[[0],[1]]] to x[tuple([[0],[1]])] == x[([0],[1])] == x[[0],[1]]. # Whether the paddle behavior in this case will change is still up for debate. def setUp(self): np.random.seed(2022) def numel(self, shape): return reduce(lambda x, y: x * y, shape, 1) def test_static_graph_list_index(self): paddle.enable_static() inps_shape = [3, 4, 5, 2] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [3, 3, 2, 1] index = np.arange(self.numel(index_shape)).reshape(index_shape) for _ in range(3): program = paddle.static.Program() index_mod = (index % (array.shape[0])).tolist() with paddle.static.program_guard(program): x = paddle.static.data( name='x', shape=array.shape, dtype='float32' ) y = x[index_mod] place = ( paddle.base.CPUPlace() if not paddle.base.core.is_compiled_with_cuda() else paddle.base.CUDAPlace(0) ) prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [y.name] getitem_np = array[tuple(index_mod)] getitem_pp = exe.run( prog, feed={x.name: array}, fetch_list=fetch_list ) np.testing.assert_array_equal(getitem_np, getitem_pp[0]) array = array[0] index = index[0] def test_dygraph_list_index(self): paddle.disable_static() inps_shape = [3, 4, 5, 3] array = np.arange(self.numel(inps_shape)).reshape(inps_shape) index_shape = [2, 3, 4, 5, 6] index = np.arange(self.numel(index_shape)).reshape(index_shape) for _ in range(len(inps_shape) - 1): pt = paddle.to_tensor(array) index_mod = (index % (array.shape[-1])).tolist() try: getitem_np = array[tuple(index_mod)] except: with self.assertRaises(ValueError): getitem_pp = pt[index_mod] array = array[0] index = index[0] continue getitem_pp = pt[index_mod] np.testing.assert_array_equal(getitem_np, getitem_pp.numpy()) array = array[0] index = index[0] def test_static_graph_list_index_muti_dim(self): paddle.enable_static() inps_shape = [3, 4, 5] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [2, 2] index1 = np.arange(self.numel(index_shape)).reshape(index_shape) index2 = np.arange(self.numel(index_shape)).reshape(index_shape) + 2 value_shape = [3, 2, 2, 3] value_np = ( np.arange(self.numel(value_shape), dtype='float32').reshape( value_shape ) + 100 ) index_mod1 = (index1 % (min(array.shape))).tolist() index_mod2 = (index2 % (min(array.shape))).tolist() program = paddle.static.Program() with paddle.static.program_guard(program): x = paddle.static.data(name='x', shape=array.shape, dtype='float32') value = paddle.static.data( name='value', shape=value_np.shape, dtype='float32' ) index1 = paddle.static.data( name='index1', shape=index1.shape, dtype='int32' ) index2 = paddle.static.data( name='index2', shape=index2.shape, dtype='int32' ) y = x[index1, index2] place = ( paddle.base.CPUPlace() if not paddle.base.core.is_compiled_with_cuda() else paddle.base.CUDAPlace(0) ) prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [y.name] array2 = array.copy() y2 = array2[index_mod1, index_mod2] getitem_pp = exe.run( prog, feed={ x.name: array, index1.name: index_mod1, index2.name: index_mod2, }, fetch_list=fetch_list, ) np.testing.assert_array_equal( y2, getitem_pp[0], err_msg=f'\n numpy:{y2},\n paddle:{getitem_pp[0]}', ) def test_dygraph_list_index_muti_dim(self): paddle.disable_static() inps_shape = [3, 4, 5] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [2, 2] index1 = np.arange(self.numel(index_shape)).reshape(index_shape) index2 = np.arange(self.numel(index_shape)).reshape(index_shape) + 2 value_shape = [3, 2, 2, 3] value_np = ( np.arange(self.numel(value_shape), dtype='float32').reshape( value_shape ) + 100 ) index_mod1 = (index1 % (min(array.shape))).tolist() index_mod2 = (index2 % (min(array.shape))).tolist() x = paddle.to_tensor(array) index_t1 = paddle.to_tensor(index_mod1) index_t2 = paddle.to_tensor(index_mod2) y_np = array[index_t1, index_t2] y = x[index_t1, index_t2] np.testing.assert_array_equal(y.numpy(), y_np) def run_getitem_list_index(self, array, index): x = paddle.static.data(name='x', shape=array.shape, dtype='float32') y = x[index] place = paddle.base.CPUPlace() prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [y.name] array2 = array.copy() try: value_np = array2[index] except: with self.assertRaises(ValueError): getitem_pp = exe.run( prog, feed={x.name: array}, fetch_list=fetch_list ) return getitem_pp = exe.run(prog, feed={x.name: array}, fetch_list=fetch_list) np.testing.assert_allclose( value_np, getitem_pp[0], rtol=1e-5, atol=1e-8 ) def test_static_graph_getitem_bool_index(self): paddle.enable_static() # case 1: array = np.ones((4, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([True, False, False, False]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_getitem_list_index(array, index) # case 2: array = np.ones((4, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([False, True, False, False]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_getitem_list_index(array, index) # case 3: array = np.ones((4, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([True, True, True, True]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_getitem_list_index(array, index) def run_setitem_list_index(self, array, index, value_np): x = paddle.static.data(name='x', shape=array.shape, dtype='float32') value = paddle.static.data( name='value', shape=value_np.shape, dtype='float32' ) y = paddle.static.setitem(x, index, value) place = paddle.base.CPUPlace() prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [y.name] array2 = array.copy() try: index = ( tuple(index) if isinstance(index, list) and isinstance(index[0], list) else index ) array2[index] = value_np except: with self.assertRaises(ValueError): setitem_pp = exe.run( prog, feed={x.name: array, value.name: value_np}, fetch_list=fetch_list, ) return setitem_pp = exe.run( prog, feed={x.name: array, value.name: value_np}, fetch_list=fetch_list, ) np.testing.assert_allclose(array2, setitem_pp[0], rtol=1e-5, atol=1e-8) def test_static_graph_setitem_list_index(self): paddle.enable_static() # case 1: inps_shape = [3, 4, 5, 2, 3] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [3, 3, 1, 2] index = np.arange(self.numel(index_shape)).reshape(index_shape) value_shape = inps_shape[3:] value_np = ( np.arange(self.numel(value_shape), dtype='float32').reshape( value_shape ) + 100 ) for _ in range(3): program = paddle.static.Program() index_mod = (index % (min(array.shape))).tolist() with paddle.static.program_guard(program): self.run_setitem_list_index(array, index_mod, value_np) array = array[0] index = index[0] # case 2: inps_shape = [3, 4, 5, 4, 3] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [4, 3, 2, 2] index = np.arange(self.numel(index_shape)).reshape(index_shape) value_shape = [3] value_np = ( np.arange(self.numel(value_shape), dtype='float32').reshape( value_shape ) + 100 ) for _ in range(4): program = paddle.static.Program() index_mod = (index % (min(array.shape))).tolist() with paddle.static.program_guard(program): self.run_setitem_list_index(array, index_mod, value_np) array = array[0] index = index[0] # case 3: inps_shape = [3, 4, 5, 3, 3] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [4, 3, 2, 2] index = np.arange(self.numel(index_shape)).reshape(index_shape) value_shape = [3, 2, 2, 3] value_np = ( np.arange(self.numel(value_shape), dtype='float32').reshape( value_shape ) + 100 ) index_mod = (index % (min(array.shape))).tolist() self.run_setitem_list_index(array, index_mod, value_np) def test_static_graph_setitem_bool_index(self): paddle.enable_static() # case 1: array = np.ones((4, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([True, False, False, False]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_setitem_list_index(array, index, value_np) # case 2: array = np.ones((4, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([False, True, False, False]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_setitem_list_index(array, index, value_np) # case 3: array = np.ones((4, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([True, True, True, True]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_setitem_list_index(array, index, value_np) def test_static_graph_setitem_bool_scalar_index(self): paddle.enable_static() array = np.ones((1, 2, 3), dtype='float32') value_np = np.random.random((2, 3)).astype('float32') index = np.array([True]) program = paddle.static.Program() with paddle.static.program_guard(program): self.run_setitem_list_index(array, index, value_np) def test_static_graph_tensor_index_setitem_muti_dim(self): paddle.enable_static() inps_shape = [3, 4, 5, 4] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [2, 3, 4] index1 = np.arange(self.numel(index_shape), dtype='int32').reshape( index_shape ) index2 = ( np.arange(self.numel(index_shape), dtype='int32').reshape( index_shape ) + 2 ) value_shape = [4] value_np = ( np.arange(self.numel(value_shape), dtype='float32').reshape( value_shape ) + 100 ) for _ in range(3): index_mod1 = index1 % (min(array.shape)) index_mod2 = index2 % (min(array.shape)) array2 = array.copy() array2[index_mod1, index_mod2] = value_np array3 = array.copy() array3[index_mod1] = value_np program = paddle.static.Program() with paddle.static.program_guard(program): x1 = paddle.static.data( name='x1', shape=array.shape, dtype='float32' ) x2 = paddle.static.data( name='x2', shape=array.shape, dtype='float32' ) value = paddle.static.data( name='value', shape=value_np.shape, dtype='float32' ) index_1 = paddle.static.data( name='index_1', shape=index1.shape, dtype='int32' ) index_2 = paddle.static.data( name='index_2', shape=index2.shape, dtype='int32' ) x1_out = paddle.static.setitem(x1, (index_1, index_2), value) x2_out = paddle.static.setitem(x2, index_1, value) place = ( paddle.base.CPUPlace() if not paddle.base.core.is_compiled_with_cuda() else paddle.base.CUDAPlace(0) ) prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [x1_out.name, x2_out.name] setitem_pp = exe.run( prog, feed={ x1.name: array, x2.name: array, value.name: value_np, index_1.name: index_mod1, index_2.name: index_mod2, }, fetch_list=fetch_list, ) np.testing.assert_array_equal( array2, setitem_pp[0], err_msg='\n numpy:{},\n paddle:{}'.format( array2, setitem_pp[0] ), ) np.testing.assert_array_equal( array3, setitem_pp[1], err_msg='\n numpy:{},\n paddle:{}'.format( array3, setitem_pp[1] ), ) array = array[0] index1 = index1[0] index2 = index2[0] def test_static_graph_array_index_muti_dim(self): paddle.enable_static() inps_shape = [3, 4, 5, 4] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [2, 3, 4] index1 = np.arange(self.numel(index_shape), dtype='int32').reshape( index_shape ) index2 = ( np.arange(self.numel(index_shape), dtype='int32').reshape( index_shape ) + 2 ) for _ in range(3): index_mod1 = index1 % (min(array.shape)) index_mod2 = index2 % (min(array.shape)) array2 = array.copy() array2[index_mod1, index_mod2] = 1 y_np1 = array2[index_mod2, index_mod1] array3 = array.copy() array3[index_mod1] = 2.5 y_np2 = array3[index_mod2] program = paddle.static.Program() with paddle.static.program_guard(program): x1 = paddle.static.data( name='x1', shape=array.shape, dtype='float32' ) x2 = paddle.static.data( name='x2', shape=array.shape, dtype='float32' ) x1_out = paddle.static.setitem(x1, (index_mod1, index_mod2), 1) x2_out = paddle.static.setitem(x2, index_mod1, 2.5) y1 = x1_out[index_mod2, index_mod1] y2 = x2_out[index_mod2] place = ( paddle.base.CPUPlace() if not paddle.base.core.is_compiled_with_cuda() else paddle.base.CUDAPlace(0) ) prog = paddle.static.default_main_program() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) fetch_list = [x1_out.name, x2_out.name, y1.name, y2.name] setitem_pp = exe.run( prog, feed={x1.name: array, x2.name: array}, fetch_list=fetch_list, ) np.testing.assert_array_equal( array2, setitem_pp[0], err_msg='\n numpy:{},\n paddle:{}'.format( array2, setitem_pp[0] ), ) np.testing.assert_array_equal( array3, setitem_pp[1], err_msg='\n numpy:{},\n paddle:{}'.format( array3, setitem_pp[1] ), ) np.testing.assert_array_equal( y_np1, setitem_pp[2], err_msg='\n numpy:{},\n paddle:{}'.format( y_np1, setitem_pp[2] ), ) np.testing.assert_array_equal( y_np2, setitem_pp[3], err_msg='\n numpy:{},\n paddle:{}'.format( y_np2, setitem_pp[3] ), ) array = array[0] index1 = index1[0] index2 = index2[0] def test_dygraph_array_index_muti_dim(self): paddle.disable_static() inps_shape = [3, 4, 5, 4] array = np.arange(self.numel(inps_shape), dtype='float32').reshape( inps_shape ) index_shape = [2, 3, 4] index1 = np.arange(self.numel(index_shape), dtype='int32').reshape( index_shape ) index2 = ( np.arange(self.numel(index_shape), dtype='int32').reshape( index_shape ) + 2 ) for _ in range(3): index_mod1 = index1 % (min(array.shape)) index_mod2 = index2 % (min(array.shape)) index_mod_t1 = paddle.to_tensor(index_mod1) index_mod_t2 = paddle.to_tensor(index_mod2) # 2 dim getitem array1 = array.copy() y_np1 = array1[index_mod2, index_mod1] tensor1 = paddle.to_tensor(array) y_t1 = tensor1[index_mod_t2, index_mod_t1] np.testing.assert_array_equal( y_t1.numpy(), y_np1, err_msg=f'\n numpy:{y_np1},\n paddle:{y_t1.numpy()}', ) # 1 dim getitem array2 = array.copy() y_np2 = array2[index_mod2] tensor2 = paddle.to_tensor(array) y_t2 = tensor2[index_mod_t2] np.testing.assert_array_equal( y_t2.numpy(), y_np2, err_msg=f'\n numpy:{y_np2},\n paddle:{y_t2.numpy()}', ) # 2 dim setitem array1 = array.copy() array1[index_mod1, index_mod2] = 1 tensor1[index_mod_t1, index_mod_t2] = 1 np.testing.assert_array_equal( tensor1.numpy(), array1, err_msg='\n numpy:{},\n paddle:{}'.format( array1, tensor1.numpy() ), ) # 1 dim setitem array2 = array.copy() array2[index_mod1] = 2.5 tensor2[index_mod_t1] = 2.5 np.testing.assert_array_equal( tensor2.numpy(), array2, err_msg='\n numpy:{},\n paddle:{}'.format( array2, tensor2.numpy() ), ) array = array[0] index1 = index1[0] index2 = index2[0] if __name__ == '__main__': unittest.main()