wehub-resource-sync
367 lines
10 KiB
Python
367 lines
10 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 unittest
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from unittest import TestCase
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import numpy as np
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from op_test import get_places
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import paddle
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import paddle.base.dygraph as dg
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import paddle.nn.functional as F
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from paddle import base
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class TestFunctionalConv2DError(TestCase):
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batch_size = 4
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spatial_shape = (16, 16)
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dtype = "float32"
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = "not_valid"
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.data_format = "NHWC"
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def test_exception(self):
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self.prepare()
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with self.assertRaises(ValueError):
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self.static_graph_case()
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def prepare(self):
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if isinstance(self.filter_shape, int):
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filter_shape = (self.filter_shape,) * 2
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else:
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filter_shape = tuple(self.filter_shape)
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self.weight_shape = (
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self.out_channels,
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self.in_channels // self.groups,
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*filter_shape,
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)
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self.bias_shape = (self.out_channels,)
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def static_graph_case(self):
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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self.channel_last = self.data_format == "NHWC"
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if self.channel_last:
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x = x = paddle.static.data(
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"input",
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(-1, -1, -1, self.in_channels),
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dtype=self.dtype,
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)
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else:
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x = paddle.static.data(
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"input",
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(-1, self.in_channels, -1, -1),
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dtype=self.dtype,
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)
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weight = paddle.static.data(
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"weight", self.weight_shape, dtype=self.dtype
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)
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if not self.no_bias:
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bias = paddle.static.data(
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"bias", self.bias_shape, dtype=self.dtype
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)
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y = F.conv2d(
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x,
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weight,
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None if self.no_bias else bias,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format,
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)
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class TestFunctionalConv2DErrorCase2(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = [[0, 0], [1, 2], [3, 4], [5, 6]]
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = False
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self.data_format = "NCHW"
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class TestFunctionalConv2DErrorCase3(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 4
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self.filter_shape = 3
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self.padding = "same"
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = False
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self.data_format = "not_valid"
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class TestFunctionalConv2DErrorCase4(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 4
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self.out_channels = 3
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self.filter_shape = 3
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self.padding = "same"
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = False
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self.data_format = "NCHW"
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class TestFunctionalConv2DErrorCase7(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = "same"
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = True
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self.data_format = "not_valid"
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class TestFunctionalConv2DErrorCase8(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = [1, 2, 1, 2, 1]
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = True
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self.data_format = "NCHW"
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class TestFunctionalConv2DErrorCase9(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = -5
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = [[0, 0], [0, 0], [3, 2], [1, 2]]
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = False
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self.data_format = "NCHW"
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class TestFunctionalConv2DErrorCase10(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 4
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self.filter_shape = 3
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self.padding = "same"
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self.stride = 1
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self.dilation = 1
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self.groups = 2
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = False
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self.data_format = "NHWC"
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class TestFunctionalConv2DErrorCase11(TestFunctionalConv2DError):
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def setUp(self):
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self.in_channels = 3
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self.out_channels = 5
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self.filter_shape = 3
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.no_bias = False
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self.act = "sigmoid"
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self.use_cudnn = False
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self.data_format = "NHCW"
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class TestFunctionalConv2DErrorCase12(TestCase):
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def setUp(self):
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self.input = np.random.randn(1, 3, 3, 3)
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self.filter = np.random.randn(3, 3, 1, 1)
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self.num_filters = 3
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self.filter_size = 1
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self.bias = None
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 0
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self.data_format = "NCHW"
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def dygraph_case(self):
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with dg.guard():
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x = paddle.to_tensor(self.input, dtype=paddle.float32)
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w = paddle.to_tensor(self.filter, dtype=paddle.float32)
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b = (
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None
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if self.bias is None
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else paddle.to_tensor(self.bias, dtype=paddle.float32)
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)
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y = F.conv2d(
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x,
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w,
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b,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format,
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)
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def test_dygraph_exception(self):
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with self.assertRaises(ValueError):
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self.dygraph_case()
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class TestFunctionalConv2D_ZeroSize(TestCase):
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def init_data(self):
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self.input = np.random.random([0, 3, 4, 4])
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self.filter = np.random.random([2, 3, 3, 3])
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self.np_out = np.zeros([0, 2, 2, 2])
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def setUp(self):
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self.init_data()
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self.bias = None
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.data_format = "NCHW"
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self.places = get_places()
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def test_dygraph(self):
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for place in self.places:
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with dg.guard(place):
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input = paddle.to_tensor(self.input)
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input.stop_gradient = False
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filter = paddle.to_tensor(self.filter)
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y = F.conv2d(
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input,
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filter,
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self.bias,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format,
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)
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np.testing.assert_allclose(y.numpy(), self.np_out)
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loss = y.sum()
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loss.backward()
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np.testing.assert_allclose(input.grad.shape, input.shape)
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class TestFunctionalConv2D_ZeroSize2(TestFunctionalConv2D_ZeroSize):
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def init_data(self):
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self.input = np.random.random([0, 0, 4, 4])
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self.filter = np.random.random([2, 0, 3, 3])
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self.np_out = np.zeros([0, 0, 2, 2])
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class TestFunctionalConv2D_ZeroKernelError(TestCase):
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"""kernel_size=0 in any spatial dim should raise InvalidArgument."""
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def _assert_raises(self, x_shape, w_shape, **kwargs):
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places = get_places()
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for place in places:
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with dg.guard(place):
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x = paddle.randn(x_shape)
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w = paddle.to_tensor(
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np.random.randn(*w_shape).astype('float32')
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)
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with self.assertRaises(ValueError):
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F.conv2d(x, w, **kwargs)
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def test_depthwise_zero_kH(self):
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# depthwise, kernel_height=0
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self._assert_raises(
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[16, 3, 260, 260],
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[3, 1, 0, 5],
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groups=3,
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)
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def test_depthwise_zero_kW(self):
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# depthwise, kernel_width=0
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self._assert_raises(
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[16, 3, 260, 260],
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[3, 1, 5, 0],
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groups=3,
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)
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def test_depthwise_large_zero_kH(self):
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self._assert_raises(
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[16, 3, 268, 268],
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[3, 1, 0, 13],
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groups=3,
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)
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def test_depthwise_large_zero_kW(self):
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self._assert_raises(
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[16, 3, 268, 268],
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[3, 1, 13, 0],
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groups=3,
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)
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def test_regular_conv_zero_kH(self):
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# regular conv2d (groups=1), kernel_height=0
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self._assert_raises(
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[2, 3, 8, 8],
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[6, 3, 0, 3],
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groups=1,
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)
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def test_regular_conv_zero_kW(self):
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# regular conv2d (groups=1), kernel_width=0
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self._assert_raises(
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[2, 3, 8, 8],
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[6, 3, 3, 0],
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groups=1,
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)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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