wehub-resource-sync
575 lines
15 KiB
Python
575 lines
15 KiB
Python
# Copyright (c) 2020 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 os
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import tempfile
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import unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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enable_to_static_guard,
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)
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import paddle
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from paddle.static import InputSpec
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# 0. for in range var.numpy()[0]
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def for_in_range(x):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x = paddle.to_tensor(x)
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for i in range(x.numpy().item()):
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z = z + i
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return z
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# 1. for iter list
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def for_iter_list(x_array):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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for x in x_array:
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z = z + x
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return z
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# 2. for enumerate list
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def for_enumerate_list(x_array):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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for i, x in enumerate(x_array):
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z = z + x + i
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return z
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# 3. for iter var.numpy()
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def for_iter_var_numpy(x_array):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for x in x_array.numpy():
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z = z + x
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return z
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# 4. for enumerate var.numpy()
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def for_enumerate_var_numpy(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array.numpy()):
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y = y + i
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z = z + x
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return y, z
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# 5. for enumerate var.numpy() with start
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def for_enumerate_var_numpy_with_start(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array.numpy(), 1):
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y = y + i
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z = z + x
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return y, z
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# 6. for in range with break
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def for_in_range_with_break(x):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x = paddle.to_tensor(x)
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for i in range(x.numpy()[0]):
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z = z + i
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if i > 2:
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break
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return z
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# 7. for enumerate var.numpy() with break
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def for_enumerate_var_numpy_with_break(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array.numpy()):
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y = y + i
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z = z + x
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if i > 2:
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break
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return y, z
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# 8. for enumerate var.numpy() with continue
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def for_enumerate_var_numpy_with_continue(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array.numpy()):
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y = y + i
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if i > 2:
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continue
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z = z + x
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return y, z
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# 9. for enumerate var.numpy() with start & break
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def for_enumerate_var_numpy_with_start_break(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array.numpy(), 1):
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y = y + i
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z = z + x
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if i > 2:
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break
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return y, z
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# 10. for enumerate var.numpy() with start & continue
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def for_enumerate_var_numpy_with_start_continue(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array.numpy(), 1):
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y = y + i
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if i > 2:
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continue
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z = z + x
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return y, z
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# 11. for iter var
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def for_iter_var(x_array):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for x in x_array:
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z = z + x
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return z
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# 12. for enumerate var
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def for_enumerate_var(x_array):
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, x in enumerate(x_array):
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y = y + i
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z = z + x
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return y, z
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# 13. for iter list[var]
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def for_iter_var_list(x):
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# 1. prepare data, ref test_list.py
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x = paddle.to_tensor(x)
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iter_num = paddle.tensor.fill_constant(shape=[1], value=5, dtype="int32")
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a = []
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for i in range(iter_num):
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a.append(x + i)
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# 2. iter list[var]
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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for x in a:
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y = y + x.astype('int32')
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return y
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# 14. for enumerate list[var]
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def for_enumerate_var_list(x):
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# 1. prepare data, ref test_list.py
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x = paddle.to_tensor(x)
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iter_num = paddle.tensor.fill_constant(shape=[1], value=5, dtype="int32")
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a = []
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for i in range(iter_num):
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a.append(x + i)
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# 2. iter list[var]
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y = paddle.tensor.fill_constant([1], 'int32', 0)
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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for i, x in enumerate(a):
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y = y + i
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z = z + x.astype('int32')
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return y, z
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# 15. for enumerate list[var] with a nested for range
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def for_enumerate_var_with_nested_range(x_array):
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x = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for i, num in enumerate(x_array):
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for idx in range(num):
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x = x + num
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return x
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# 16. for iter var[idx]
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def for_iter_var_idx(x_array):
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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x_array = paddle.to_tensor(x_array)
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for x in x_array[0:]:
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z = z + x
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return z
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# 17. for a,b,c in z: (a, b, c) is a tuple
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def for_tuple_as_iter_var(x_array):
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x = paddle.to_tensor(x_array)
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z = paddle.to_tensor(np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]))
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a_result = paddle.zeros([3])
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b_result = paddle.zeros([3])
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c_result = paddle.zeros([3])
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for a, b, c in z:
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a_result += a
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b_result += b
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c_result += c
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return a_result, b_result, c_result
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# 18. for t in enumerate(collection): t is tuple of (idx, element)
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def for_tuple_as_enumerate_iter(x_array):
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x = paddle.to_tensor(x_array)
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x_list = [x, x, x]
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a_result = paddle.zeros([5])
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for t in enumerate(x_list):
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a_result += t[1].astype('float32')
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return a_result
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# 19. for i, (a, b, c, d, e) in enumerate(collection): (a, b, c, d, e) is a tuple
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def for_tuple_as_enumerate_value(x_array):
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x = paddle.to_tensor(x_array)
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x_list = [x, x, x]
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a_result = paddle.zeros([1])
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b_result = paddle.zeros([1])
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c_result = paddle.zeros([1])
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d_result = paddle.zeros([1])
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e_result = paddle.zeros([1])
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for i, (a, b, c, d, e) in enumerate(x_list):
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a_result += a
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b_result += b
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c_result += c
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d_result += d
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e_result += e
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return a_result
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# 20. test for function in a class
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class ForwardContainsForLayer(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.high = 5
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self.low = 3
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def forward(self, x):
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# just for test case, x is useless in this method
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y = paddle.zeros([10, 2, 3])
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z = []
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for i in range(self.high - self.low):
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z.append(y[i].clone())
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return z
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# 21. for original list
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def for_original_list():
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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for x in [1, 2, 3]:
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z = z + x
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return z
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# 22. for original tuple
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def for_original_tuple():
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z = paddle.tensor.fill_constant([1], 'int32', 0)
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for x in (1, 2, 3):
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z = z + x
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return z
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# 23. for zip error
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def for_zip_error(x, y):
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for i, j in zip(x, y):
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a = i + j
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return x + y
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# 24. for zip
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def for_zip(x, y):
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for i, j in zip(x, y):
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a = i + j
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return x + y
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def tensor_array_slice_in_enumerate():
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feats = {}
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feats['key'] = []
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feats_idx = paddle.arange(0, 10)
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for i, idx in enumerate(feats_idx):
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if i > 1:
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feat_n2 = feats['key'][-2]
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feats['key'].append(idx)
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return feat_n2
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class TestTransformBase(Dy2StTestBase):
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def setUp(self):
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self.place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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self.set_input()
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def set_input(self):
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self.input = [1, 2, 3]
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def set_test_func(self):
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raise NotImplementedError(
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"For Enumerate test should implement set_test_func"
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)
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def _run(self):
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self.dygraph_func = paddle.jit.to_static(self.dygraph_func)
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return self.dygraph_func(self.input)
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def get_dygraph_output(self):
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with enable_to_static_guard(False):
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return self._run()
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def get_static_output(self):
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with enable_to_static_guard(True):
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return self._run()
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class TestTransform(TestTransformBase):
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def transformed_result_compare(self):
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with enable_to_static_guard(False):
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dy_outs = self.get_dygraph_output()
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if not isinstance(dy_outs, (tuple, list)):
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dy_outs = (dy_outs,)
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with enable_to_static_guard(True):
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self.dygraph_func.eval()
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st_outs = self.get_static_output()
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if not isinstance(st_outs, (tuple, list)):
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st_outs = (st_outs,)
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for x, y in zip(dy_outs, st_outs):
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np.testing.assert_allclose(x.numpy(), y.numpy(), rtol=1e-05)
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class TestTransformForOriginalList(TestTransform):
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def _run(self):
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self.dygraph_func = paddle.jit.to_static(self.dygraph_func)
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return self.dygraph_func()
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class TestTransformError(TestTransformBase):
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def transformed_error(self, etype):
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with self.assertRaises(etype):
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dy_out = self.get_dygraph_output()
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st_out = self.get_static_output()
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class TestForInRangeConfig(TestTransform):
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def set_input(self):
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self.input = np.array([5]).astype("int32")
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def set_test_func(self):
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self.dygraph_func = for_in_range
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class TestForInRange(TestForInRangeConfig):
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def test_transformed_result_compare(self):
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self.set_test_func()
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self.transformed_result_compare()
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class TestForIterList(TestTransform):
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def set_test_func(self):
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self.dygraph_func = for_iter_list
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def test_transformed_result_compare(self):
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self.set_test_func()
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self.transformed_result_compare()
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class TestForEnumerateSimple(TestForIterList):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_list
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class TestForInRangeWithBreak(TestForInRange):
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def set_test_func(self):
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self.dygraph_func = for_in_range_with_break
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class TestForIterVarNumpy(TestTransform):
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def set_input(self):
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self.input = np.array([1, 2, 3, 4, 5])
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def set_test_func(self):
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self.dygraph_func = for_iter_var_numpy
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def test_transformed_result_compare(self):
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self.set_test_func()
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self.transformed_result_compare()
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class TestForEnumerateVarNumpy(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_numpy
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class TestForEnumerateVarNumpyWithStart(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_numpy_with_start
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class TestForEnumerateVarNumpyWithBreak(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_numpy_with_break
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class TestForEnumerateVarNumpyWithContinue(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_numpy_with_continue
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class TestForEnumerateVarNumpyWithStartAndBreak(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_numpy_with_start_break
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class TestForEnumerateVarNumpyWithStartAndContinue(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_numpy_with_start_continue
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class TestForIterVar(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_iter_var
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class TestForIterVarIdx(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_iter_var_idx
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class TestForEnumerateVar(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var
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class TestForEnumerateVarWithNestedRange(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_with_nested_range
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class TestForIterVarList(TestForInRangeConfig):
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def set_test_func(self):
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self.dygraph_func = for_iter_var_list
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def test_transformed_result_compare(self):
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self.set_test_func()
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self.transformed_result_compare()
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class TestForEnumerateVarList(TestForInRangeConfig):
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def set_test_func(self):
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self.dygraph_func = for_enumerate_var_list
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def test_transformed_result_compare(self):
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self.set_test_func()
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self.transformed_result_compare()
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class TestForTupleAsIterVar(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_tuple_as_iter_var
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class TestForTupleAsEnumerateIter(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_tuple_as_enumerate_iter
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class TestForTupleAsEnumerateValue(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = for_tuple_as_enumerate_value
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class TestForwardContainsForLayer(TestForIterVarNumpy):
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def set_test_func(self):
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self.dygraph_func = ForwardContainsForLayer()
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class TestForOriginalList(TestTransformForOriginalList):
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def set_test_func(self):
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self.dygraph_func = for_original_list
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def test_transformed_result_compare(self):
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self.set_test_func()
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self.transformed_result_compare()
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class TestForOriginalTuple(TestForOriginalList):
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def set_test_func(self):
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self.dygraph_func = for_original_tuple
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|
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class TestSliceTensorArrayInEnumerate(TestForOriginalList):
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def set_test_func(self):
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self.dygraph_func = tensor_array_slice_in_enumerate
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|
|
|
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class TestForZip(Dy2StTestBase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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|
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_for_zip_error(self):
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with self.assertRaises(RuntimeError):
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|
model_path = os.path.join(self.temp_dir.name, 'for_zip_error')
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|
paddle.jit.save(
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|
paddle.jit.to_static(
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|
function=for_zip_error,
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|
input_spec=[
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|
InputSpec(shape=[None, 10]),
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|
InputSpec(shape=[None, 10]),
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|
],
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|
),
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|
model_path,
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|
)
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|
|
|
def test_for_zip(self):
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|
model_path = os.path.join(self.temp_dir.name, 'for_zip')
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|
paddle.jit.save(
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|
paddle.jit.to_static(
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|
function=for_zip,
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|
input_spec=[InputSpec(shape=[2, 10]), InputSpec(shape=[2, 10])],
|
|
),
|
|
model_path,
|
|
)
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|
|
|
|
|
if __name__ == '__main__':
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|
unittest.main()
|