wehub-resource-sync
277 lines
8.4 KiB
Python
277 lines
8.4 KiB
Python
# Copyright (c) 2018 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 OpTest, get_places
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import paddle
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def AffineGrid4D(theta, size, align_corners):
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n = size[0]
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w = size[3]
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h = size[2]
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h_factor = w_factor = 1
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if not align_corners:
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h_factor = (h - 1) / float(h)
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w_factor = (w - 1) / float(w)
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h_idx = (
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np.repeat(np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[
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:, :, np.newaxis
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]
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* h_factor
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)
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w_idx = (
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np.repeat(np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[
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:, :, np.newaxis
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]
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* w_factor
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)
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grid = np.concatenate(
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[w_idx, h_idx, np.ones([h, w, 1])], axis=2
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) # h * w * 3
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grid = np.repeat(grid[np.newaxis, :], size[0], axis=0) # n * h * w *3
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ret = np.zeros([n, h * w, 2])
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theta = theta.transpose([0, 2, 1])
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for i in range(len(theta)):
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ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i])
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return ret.reshape([n, h, w, 2]).astype("float32")
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def AffineGrid5D(theta, size, align_corners):
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n = size[0]
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d = size[2]
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h = size[3]
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w = size[4]
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d_factor = h_factor = w_factor = 1
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if not align_corners:
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d_factor = (d - 1) / float(d)
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h_factor = (h - 1) / float(h)
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w_factor = (w - 1) / float(w)
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d_idx = (
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np.repeat(
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np.repeat(
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np.linspace(-1, 1, d)[:, np.newaxis, np.newaxis], h, axis=1
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),
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w,
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axis=2,
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)[:, :, :, np.newaxis]
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* d_factor
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)
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h_idx = (
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np.repeat(
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np.repeat(
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np.linspace(-1, 1, h)[np.newaxis, :, np.newaxis], w, axis=2
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),
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d,
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axis=0,
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)[:, :, :, np.newaxis]
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* h_factor
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)
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w_idx = (
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np.repeat(
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np.repeat(
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np.linspace(-1, 1, w)[np.newaxis, np.newaxis, :], h, axis=1
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),
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d,
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axis=0,
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)[:, :, :, np.newaxis]
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* w_factor
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)
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grid = np.concatenate(
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[w_idx, h_idx, d_idx, np.ones([d, h, w, 1])], axis=3
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) # d * h * w * 4
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grid = np.repeat(grid[np.newaxis, :], size[0], axis=0) # n * d * h * w * 4
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ret = np.zeros([n, d * h * w, 3])
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theta = theta.transpose([0, 2, 1])
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for i in range(len(theta)):
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ret[i] = np.dot(grid[i].reshape([d * h * w, 4]), theta[i])
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return ret.reshape([n, d, h, w, 3]).astype("float32")
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class TestAffineGridOp(OpTest):
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def setUp(self):
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self.initTestCase()
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self.op_type = "affine_grid"
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self.python_api = paddle.nn.functional.vision.affine_grid
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theta = np.random.randint(1, 3, self.theta_shape).astype("float32")
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self.inputs = {'Theta': theta}
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self.attrs = {
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"use_cudnn": self.use_cudnn,
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"align_corners": self.align_corners,
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}
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if self.dynamic_shape:
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self.inputs['OutputShape'] = self.output_shape
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else:
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self.attrs['output_shape'] = self.output_shape
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if self.theta_shape[1] == 2 and self.theta_shape[2] == 3:
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self.outputs = {
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'Output': AffineGrid4D(
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theta, self.output_shape, self.align_corners
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)
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}
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else:
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self.outputs = {
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'Output': AffineGrid5D(
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theta, self.output_shape, self.align_corners
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)
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}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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self.check_grad(
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['Theta'], 'Output', no_grad_set=['OutputShape'], check_pir=True
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)
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def initTestCase(self):
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self.theta_shape = (17, 2, 3)
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self.output_shape = np.array([17, 2, 5, 7]).astype("int32")
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self.dynamic_shape = False
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self.use_cudnn = False
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self.align_corners = True
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class TestAffineGridOpCase1(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (20, 2, 3)
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self.output_shape = np.array([20, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = True
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if paddle.base.core.is_compiled_with_rocm():
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self.use_cudnn = (
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False # ROCM platform do not have MIOPEN kernel for affine_grid
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)
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self.align_corners = True
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class TestAffineGridOpCase2(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (20, 2, 3)
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self.output_shape = np.array([20, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = True
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class TestAffineGridOpCase3(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (20, 2, 3)
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self.output_shape = np.array([20, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = False
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class TestAffineGridOpCase4(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (25, 2, 3)
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self.output_shape = np.array([25, 2, 5, 6]).astype("int32")
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self.dynamic_shape = False
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self.use_cudnn = False
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self.align_corners = False
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class TestAffineGridOp5DCase1(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (20, 3, 4)
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self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = False
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class TestAffineGridOp5DCase2(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (20, 3, 4)
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self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = True
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class TestAffineGridOp5DCase3(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (20, 3, 4)
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self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = False
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class TestAffineGridOp5DCase4(TestAffineGridOp):
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def initTestCase(self):
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self.theta_shape = (25, 3, 4)
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self.output_shape = np.array([25, 1, 2, 5, 6]).astype("int32")
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self.dynamic_shape = False
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self.use_cudnn = False
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self.align_corners = False
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class TestAffineGridAPI_ZeroSize(unittest.TestCase):
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def init_dtype(self):
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self.dtype = 'float32'
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def setUp(self):
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self.init_dtype()
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self.place = get_places()
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self.theta_shape = (17, 2, 3)
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self.output_shape = np.random.random([0]).astype("int32")
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def test_dygraph_api(self):
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def run(place):
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paddle.disable_static(place)
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theta_np = np.random.randint(1, 3, self.theta_shape).astype(
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self.dtype
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)
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theta = paddle.to_tensor(theta_np)
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with self.assertRaises(ValueError):
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paddle.nn.functional.vision.affine_grid(
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theta, paddle.to_tensor(self.output_shape)
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)
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paddle.enable_static()
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for place in self.place:
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run(place)
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class TestAffineGridOpTorchCompatible1(TestAffineGridOp):
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def initTestCase(self):
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paddle.set_flags({'FLAGS_use_accuracy_compatible_kernel': 1})
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self.theta_shape = (20, 2, 3)
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self.output_shape = np.array([20, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = True
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class TestAffineGridOpTorchCompatible2(TestAffineGridOp):
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def initTestCase(self):
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paddle.set_flags({'FLAGS_use_accuracy_compatible_kernel': 1})
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self.theta_shape = (20, 3, 4)
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self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32")
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self.dynamic_shape = True
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self.use_cudnn = False
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self.align_corners = False
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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