wehub-resource-sync
271 lines
8.7 KiB
Python
271 lines
8.7 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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import numpy as np
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import paddle
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class TestInterpolateParam(unittest.TestCase):
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def setUp(self):
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self.input_data = paddle.randn(shape=(2, 3, 6, 10)).astype(
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paddle.float32
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)
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def test_alias_input_for_x(self):
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"""test parameter alias input/x"""
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out_with_input = paddle.nn.functional.interpolate(
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input=self.input_data, scale_factor=[2, 1], mode="bilinear"
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)
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out_with_x = paddle.nn.functional.interpolate(
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x=self.input_data, scale_factor=[2, 1], mode="bilinear"
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)
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np.testing.assert_array_equal(
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out_with_input.numpy(), out_with_x.numpy()
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)
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def test_params_consistency(self):
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"""test both paddle and torch formats works."""
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out_torch = paddle.nn.functional.interpolate(
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self.input_data, # input
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None, # size
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[2, 1], # scale_factor
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'bilinear', # mode
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True, # align_corners
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True, # recompute_scale_factor
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False, # antialias
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)
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out_paddle = paddle.nn.functional.interpolate(
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x=self.input_data,
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size=None,
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scale_factor=[2, 1],
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mode='bilinear',
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align_corners=True,
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recompute_scale_factor=True,
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)
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np.testing.assert_array_equal(out_torch.numpy(), out_paddle.numpy())
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def test_params_1(self):
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"""test all args with torch format"""
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try:
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out_torch = paddle.nn.functional.interpolate(
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self.input_data, # input
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None, # size
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[2, 1], # scale_factor
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'bilinear', # mode
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True, # align_corners
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True, # recompute_scale_factor
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False, # antialias
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)
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self.assertTrue(True, "Function call succeeded without error")
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except Exception as e:
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self.fail(f"Function raised an unexpected exception: {e}")
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def test_params_2(self):
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"""test all kwargs with torch format"""
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try:
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out_torch = paddle.nn.functional.interpolate(
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input=self.input_data,
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size=None,
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scale_factor=[2, 1],
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mode='bilinear',
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align_corners=True,
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recompute_scale_factor=True,
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antialias=False,
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)
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self.assertTrue(True, "Function call succeeded without error")
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except Exception as e:
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self.fail(f"Function raised an unexpected exception: {e}")
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def test_params_3(self):
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"""test of passing both args and kwargs parameters"""
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try:
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out1 = paddle.nn.functional.interpolate(
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input=self.input_data,
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size=None,
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scale_factor=[2, 1],
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mode='bilinear',
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align_corners=True,
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recompute_scale_factor=True,
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antialias=False,
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)
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out2 = paddle.nn.functional.interpolate(
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self.input_data,
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None,
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[2, 1],
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mode='bilinear',
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align_corners=True,
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recompute_scale_factor=True,
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antialias=False,
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)
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self.assertTrue(True, "Function call succeeded without error")
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except Exception as e:
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self.fail(f"Function raised an unexpected exception: {e}")
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def test_params_4(self):
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"""test duplicate parameters"""
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with self.assertRaises(TypeError):
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out1 = paddle.nn.functional.interpolate(
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x=self.input_data,
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input=self.input_data,
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size=[12, 12],
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)
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with self.assertRaises(TypeError):
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out1 = paddle.nn.functional.interpolate(
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self.input_data,
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input=self.input_data,
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size=[12, 12],
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)
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def test_unsupported_antialias(self):
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"""test unsupported antialias"""
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with self.assertRaises(TypeError):
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out1 = paddle.nn.functional.interpolate(
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input=self.input_data,
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size=[12, 12],
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antialias="True",
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)
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with self.assertRaises(ValueError):
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out1 = paddle.nn.functional.interpolate(
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input=self.input_data,
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size=[12, 12],
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mode="nearest",
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antialias=True,
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)
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class TestInterpolateAntialias(unittest.TestCase):
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def setUp(self):
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self.input_shape = (1, 1, 8, 8)
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self.input_data = paddle.arange(64, dtype="float32").reshape(
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self.input_shape
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)
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# A pattern that has high frequency components
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self.input_data[0, 0, ::2, ::2] = 100.0
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def test_bilinear_antialias(self):
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if not paddle.is_compiled_with_cuda():
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return
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# Downsample by 0.5
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scale = 0.5
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out_aa = paddle.nn.functional.interpolate(
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self.input_data,
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scale_factor=scale,
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mode='bilinear',
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align_corners=False,
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antialias=True,
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)
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# Compare with CPU non-antialias result (since GPU non-antialias might crash)
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x_cpu = self.input_data.cpu()
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out_no_aa_cpu = paddle.nn.functional.interpolate(
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x_cpu,
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scale_factor=scale,
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mode='bilinear',
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align_corners=False,
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antialias=False,
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)
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# Results should be different
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self.assertFalse(
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np.allclose(out_no_aa_cpu.numpy(), out_aa.cpu().numpy()),
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"Bilinear: Antialias=True should differ from False",
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)
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def test_bicubic_antialias(self):
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if not paddle.is_compiled_with_cuda():
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return
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# Downsample by 0.5
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scale = 0.5
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out_aa = paddle.nn.functional.interpolate(
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self.input_data,
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scale_factor=scale,
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mode='bicubic',
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align_corners=False,
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antialias=True,
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)
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x_cpu = self.input_data.cpu()
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out_no_aa_cpu = paddle.nn.functional.interpolate(
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x_cpu,
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scale_factor=scale,
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mode='bicubic',
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align_corners=False,
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antialias=False,
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)
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# Results should be different
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self.assertFalse(
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np.allclose(out_no_aa_cpu.numpy(), out_aa.cpu().numpy()),
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"Bicubic: Antialias=True should differ from False",
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)
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def test_error_on_other_modes(self):
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with self.assertRaises(ValueError):
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paddle.nn.functional.interpolate(
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self.input_data,
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scale_factor=0.5,
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mode='nearest',
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antialias=True,
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)
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with self.assertRaises(ValueError):
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paddle.nn.functional.interpolate(
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self.input_data, scale_factor=0.5, mode='linear', antialias=True
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)
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def test_bilinear_antialias_grad(self):
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if not paddle.is_compiled_with_cuda():
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return
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x = paddle.to_tensor(self.input_data, stop_gradient=False)
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scale = 0.5
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out = paddle.nn.functional.interpolate(
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x,
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scale_factor=scale,
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mode='bilinear',
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align_corners=False,
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antialias=True,
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)
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loss = out.mean()
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loss.backward()
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self.assertIsNotNone(x.grad)
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# Check if grad is not all zeros (it shouldn't be)
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self.assertTrue(np.any(x.grad.numpy() != 0))
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def test_bicubic_antialias_grad(self):
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if not paddle.is_compiled_with_cuda():
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return
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x = paddle.to_tensor(self.input_data, stop_gradient=False)
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scale = 0.5
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out = paddle.nn.functional.interpolate(
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x,
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scale_factor=scale,
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mode='bicubic',
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align_corners=False,
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antialias=True,
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)
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loss = out.mean()
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loss.backward()
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self.assertIsNotNone(x.grad)
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self.assertTrue(np.any(x.grad.numpy() != 0))
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if __name__ == '__main__':
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unittest.main()
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