wehub-resource-sync
269 lines
8.1 KiB
Python
269 lines
8.1 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.base import core
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class TestUnfoldOp(OpTest):
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"""
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This is for test on unfold Op
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"""
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def init_data(self):
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self.batch_size = 3
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self.input_channels = 3
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self.input_height = 20
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self.input_width = 20
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self.kernel_sizes = [3, 3]
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self.strides = [1, 1]
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self.paddings = [1, 1, 1, 1]
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self.dilations = [1, 1]
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input_shape = [
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self.batch_size,
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self.input_channels,
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self.input_height,
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self.input_width,
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]
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if self.dtype == np.uint16:
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as_type = self.np_dtype
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else:
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as_type = self.dtype
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self.x = np.random.rand(*input_shape).astype(as_type)
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def calc_unfold(self):
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output_shape = [0] * 3
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output_shape[0] = self.batch_size
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output_shape[1] = (
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self.input_channels * self.kernel_sizes[0] * self.kernel_sizes[1]
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)
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dkernel_h = self.dilations[0] * (self.kernel_sizes[0] - 1) + 1
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dkernel_w = self.dilations[1] * (self.kernel_sizes[1] - 1) + 1
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out_height = (
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int(
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(
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self.input_height
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+ self.paddings[0]
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+ self.paddings[2]
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- dkernel_h
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)
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/ self.strides[0]
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)
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+ 1
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)
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out_width = (
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int(
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(
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self.input_width
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+ self.paddings[1]
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+ self.paddings[3]
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- dkernel_w
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)
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/ self.strides[1]
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)
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+ 1
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)
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output_shape[2] = out_height * out_width
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if self.dtype == np.uint16:
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as_type = self.np_dtype
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else:
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as_type = self.dtype
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output = np.zeros(output_shape).astype(as_type)
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# ------------ calculate output -------------- #
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for i in range(output_shape[0]):
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for j in range(output_shape[1]):
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for k in range(output_shape[2]):
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h_out = int(k / out_width)
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w_out = k % out_width
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w_offset = j % self.kernel_sizes[1]
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h_offset = (
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int(j / self.kernel_sizes[1]) % self.kernel_sizes[0]
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)
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c_in = int(
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j / (self.kernel_sizes[0] * self.kernel_sizes[1])
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)
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h_in = (
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h_offset * self.dilations[0]
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+ h_out * self.strides[0]
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- self.paddings[0]
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)
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w_in = (
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w_offset * self.dilations[1]
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+ w_out * self.strides[1]
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- self.paddings[1]
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)
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if (h_in >= 0 and h_in < self.input_height) and (
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w_in >= 0 and w_in < self.input_width
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):
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output[i, j, k] = self.x[i, c_in, h_in, w_in]
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self.outputs = output
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def set_data(self):
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self.init_data()
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self.calc_unfold()
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self.inputs = {'X': self.x}
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self.attrs = {
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'kernel_sizes': self.kernel_sizes,
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'paddings': self.paddings,
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'dilations': self.dilations,
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'strides': self.strides,
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}
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self.outputs = {'Y': self.outputs}
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def setUp(self):
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self.op_type = 'unfold'
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self.init_dtype()
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self.python_api = paddle.nn.functional.unfold
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self.set_data()
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def init_dtype(self):
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self.dtype = np.float64
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Y', check_pir=True)
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def test_support_tuple(self):
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paddle.disable_static()
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x = paddle.randn((10, 3, 64, 64))
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paddle.nn.functional.unfold(x, 3, (1, 1), 1, 1)
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paddle.nn.functional.unfold(x, 3, 1, (1, 1), 1)
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paddle.nn.functional.unfold(x, 3, 1, 1, (1, 1))
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out1 = paddle.nn.functional.unfold(x, 3, (1, 1), (1, 1), (1, 1))
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out2 = paddle.nn.functional.unfold(x, (3, 3), (1, 1), (1, 1), (1, 1))
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np.testing.assert_allclose(out1.numpy(), out2.numpy())
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paddle.enable_static()
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class TestUnfoldFP16Op(TestUnfoldOp):
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def init_dtype(self):
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self.dtype = np.float16
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class TestUnfoldZeroSize(TestUnfoldOp):
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"""
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This is for test on unfold Op with zero size input
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"""
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def init_data(self):
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self.batch_size = 3
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self.input_channels = 0
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self.input_height = 20
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self.input_width = 20
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self.kernel_sizes = [3, 3]
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self.strides = [1, 1]
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self.paddings = [1, 1, 1, 1]
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self.dilations = [1, 1]
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input_shape = [
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self.batch_size,
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self.input_channels,
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self.input_height,
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self.input_width,
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]
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if self.dtype == np.uint16:
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as_type = self.np_dtype
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else:
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as_type = self.dtype
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self.x = np.random.rand(*input_shape).astype(as_type)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestUnfoldBF16Op(TestUnfoldOp):
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# Notice: The test is time consuming, may cause timeout, modify the parameters to reduce the time
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def init_data(self):
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self.batch_size = 3
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self.input_channels = 3
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self.input_height = 5
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self.input_width = 5
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self.kernel_sizes = [3, 3]
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self.strides = [1, 1]
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self.paddings = [1, 1, 1, 1]
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self.dilations = [1, 1]
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input_shape = [
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self.batch_size,
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self.input_channels,
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self.input_height,
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self.input_width,
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]
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self.x = np.random.rand(*input_shape).astype(self.np_dtype)
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def init_dtype(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def setUp(self):
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self.op_type = 'unfold'
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self.init_dtype()
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self.python_api = paddle.nn.functional.unfold
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self.set_data()
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.outputs['Y'] = convert_float_to_uint16(self.outputs['Y'])
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self.place = get_device_place()
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def test_check_output(self):
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self.check_output_with_place(self.place, check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ['X'], 'Y', check_pir=True)
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class TestUnfoldAPI(TestUnfoldOp):
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"""
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This is for test on paddle.nn.Unfold
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"""
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def setUp(self):
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self.op_type = 'unfold'
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self.python_api = paddle.nn.functional.unfold
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self.set_data()
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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 base.dygraph.guard(place):
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input = paddle.to_tensor(self.inputs['X'])
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m = paddle.nn.Unfold(**self.attrs)
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m.eval()
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result = m(input)
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np.testing.assert_allclose(
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result.numpy(), self.outputs['Y'], rtol=1e-05
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)
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def test_info(self):
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str(paddle.nn.Unfold(**self.attrs))
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if __name__ == '__main__':
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unittest.main()
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