wehub-resource-sync
443 lines
16 KiB
Python
443 lines
16 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 math
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import unittest
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import numpy as np
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from op_test import OpTest, get_devices, get_places
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import paddle
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def calc_psroi_pool(
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x,
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rois,
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rois_num_per_img,
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output_channels,
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spatial_scale,
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pooled_height,
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pooled_width,
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):
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"""
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Psroi_pool implemented by Numpy.
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x: 4-D as (N, C, H, W),
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rois: 2-D as [[x1, y1, x2, y2], ...],
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rois_num_per_img: 1-D as [nums_of_batch_0, nums_of_batch_1, ...]
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"""
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output_shape = (len(rois), output_channels, pooled_height, pooled_width)
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out_data = np.zeros(output_shape)
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batch_id = 0
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rois_num_id = 0
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rois_num_left = rois_num_per_img[rois_num_id]
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for i in range(len(rois)):
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roi = rois[i]
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roi_batch_id = batch_id
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rois_num_left -= 1
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if rois_num_left == 0:
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rois_num_id += 1
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if rois_num_id < len(rois_num_per_img):
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rois_num_left = rois_num_per_img[rois_num_id]
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batch_id += 1
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roi_start_w = round(roi[0]) * spatial_scale
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roi_start_h = round(roi[1]) * spatial_scale
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roi_end_w = (round(roi[2]) + 1.0) * spatial_scale
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roi_end_h = (round(roi[3]) + 1.0) * spatial_scale
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roi_height = max(roi_end_h - roi_start_h, 0.1)
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roi_width = max(roi_end_w - roi_start_w, 0.1)
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bin_size_h = roi_height / float(pooled_height)
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bin_size_w = roi_width / float(pooled_width)
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x_i = x[roi_batch_id]
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for c in range(output_channels):
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for ph in range(pooled_height):
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for pw in range(pooled_width):
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hstart = int(
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math.floor(float(ph) * bin_size_h + roi_start_h)
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)
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wstart = int(
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math.floor(float(pw) * bin_size_w + roi_start_w)
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)
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hend = int(
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math.ceil(float(ph + 1) * bin_size_h + roi_start_h)
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)
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wend = int(
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math.ceil(float(pw + 1) * bin_size_w + roi_start_w)
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)
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hstart = min(max(hstart, 0), x.shape[2])
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hend = min(max(hend, 0), x.shape[2])
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wstart = min(max(wstart, 0), x.shape[3])
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wend = min(max(wend, 0), x.shape[3])
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c_in = (c * pooled_height + ph) * pooled_width + pw
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is_empty = (hend <= hstart) or (wend <= wstart)
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out_sum = 0.0
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for ih in range(hstart, hend):
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for iw in range(wstart, wend):
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out_sum += x_i[c_in, ih, iw]
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bin_area = (hend - hstart) * (wend - wstart)
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out_data[i, c, ph, pw] = (
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0.0 if is_empty else (out_sum / float(bin_area))
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)
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return out_data
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class TestPSROIPoolOp(OpTest):
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def set_data(self):
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paddle.enable_static()
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self.init_test_case()
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self.make_rois()
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self.outs = calc_psroi_pool(
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self.x,
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self.boxes,
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self.boxes_num,
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self.output_channels,
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self.spatial_scale,
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self.pooled_height,
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self.pooled_width,
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).astype('float64')
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self.inputs = {
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'X': self.x,
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'ROIs': (self.rois_with_batch_id[:, 1:5], self.rois_lod),
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'RoisNum': self.boxes_num,
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}
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self.attrs = {
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'output_channels': self.output_channels,
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'spatial_scale': self.spatial_scale,
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'pooled_height': self.pooled_height,
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'pooled_width': self.pooled_width,
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}
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self.outputs = {'Out': self.outs}
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def init_test_case(self):
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self.batch_size = 3
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self.channels = 3 * 2 * 2
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self.height = 6
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self.width = 4
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self.x_dim = [self.batch_size, self.channels, self.height, self.width]
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self.spatial_scale = 1.0 / 4.0
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self.output_channels = 3
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self.pooled_height = 2
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self.pooled_width = 2
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self.x = np.random.random(self.x_dim).astype('float64')
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def make_rois(self):
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rois = []
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self.rois_lod = [[]]
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for bno in range(self.batch_size):
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self.rois_lod[0].append(bno + 1)
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for i in range(bno + 1):
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x1 = np.random.random_integers(
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0, self.width // self.spatial_scale - self.pooled_width
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)
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y1 = np.random.random_integers(
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0, self.height // self.spatial_scale - self.pooled_height
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)
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x2 = np.random.random_integers(
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x1 + self.pooled_width, self.width // self.spatial_scale
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)
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y2 = np.random.random_integers(
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y1 + self.pooled_height, self.height // self.spatial_scale
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)
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roi = [bno, x1, y1, x2, y2]
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rois.append(roi)
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self.rois_num = len(rois)
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self.rois_with_batch_id = np.array(rois).astype('float64')
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self.boxes = self.rois_with_batch_id[:, 1:]
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self.boxes_num = np.array(
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[bno + 1 for bno in range(self.batch_size)]
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).astype('int32')
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def setUp(self):
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self.op_type = 'psroi_pool'
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self.python_api = (
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lambda x, boxes, boxes_num, pooled_height, pooled_width, output_channels, spatial_scale: (
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paddle.vision.ops.psroi_pool(
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x,
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boxes,
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boxes_num,
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(pooled_height, pooled_width),
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spatial_scale,
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)
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)
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)
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self.set_data()
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestPSROIPoolDynamicFunctionAPI(unittest.TestCase):
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def setUp(self):
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self.x = np.random.random([2, 490, 28, 28]).astype(np.float32)
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self.boxes = np.array(
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[[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]]
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).astype(np.float32)
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self.boxes_num = np.array([1, 2]).astype(np.int32)
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def test_output_size(self):
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def test_output_size_is_int():
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output_size = 7
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out = paddle.vision.ops.psroi_pool(
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paddle.to_tensor(self.x),
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paddle.to_tensor(self.boxes),
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paddle.to_tensor(self.boxes_num),
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output_size,
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).numpy()
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 10, 1.0, 7, 7
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)
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np.testing.assert_allclose(out, expect_out, rtol=1e-05)
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def test_output_size_is_tuple():
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output_size = (7, 7)
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out = paddle.vision.ops.psroi_pool(
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paddle.to_tensor(self.x),
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paddle.to_tensor(self.boxes),
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paddle.to_tensor(self.boxes_num),
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output_size,
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).numpy()
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 10, 1.0, 7, 7
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)
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np.testing.assert_allclose(out, expect_out, rtol=1e-05)
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def test_dytype_is_float64():
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output_size = (7, 7)
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out = paddle.vision.ops.psroi_pool(
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paddle.to_tensor(self.x, 'float64'),
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paddle.to_tensor(self.boxes, 'float64'),
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paddle.to_tensor(self.boxes_num, 'int32'),
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output_size,
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).numpy()
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 10, 1.0, 7, 7
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)
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np.testing.assert_allclose(out, expect_out, rtol=1e-05)
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places = get_devices()
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for place in places:
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paddle.set_device(place)
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test_output_size_is_int()
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test_output_size_is_tuple()
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test_dytype_is_float64()
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class TestPSROIPoolDynamicClassAPI(unittest.TestCase):
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def setUp(self):
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self.x = np.random.random([2, 128, 32, 32]).astype(np.float32)
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self.boxes = np.array(
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[[3, 5, 6, 13], [7, 4, 22, 18], [4, 5, 7, 10], [5, 3, 25, 21]]
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).astype(np.float32)
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self.boxes_num = np.array([2, 2]).astype(np.int32)
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def test_output_size(self):
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def test_output_size_is_int():
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psroi_module = paddle.vision.ops.PSRoIPool(8, 1.1)
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out = psroi_module(
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paddle.to_tensor(self.x),
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paddle.to_tensor(self.boxes),
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paddle.to_tensor(self.boxes_num),
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).numpy()
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 2, 1.1, 8, 8
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)
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np.testing.assert_allclose(out, expect_out, rtol=1e-05)
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def test_output_size_is_tuple():
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psroi_pool_module = paddle.vision.ops.PSRoIPool(8, 1.1)
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out = psroi_pool_module(
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paddle.to_tensor(self.x),
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paddle.to_tensor(self.boxes),
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paddle.to_tensor(self.boxes_num),
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).numpy()
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 2, 1.1, 8, 8
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)
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np.testing.assert_allclose(out, expect_out, rtol=1e-05)
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def test_dytype_is_float64():
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psroi_pool_module = paddle.vision.ops.PSRoIPool(8, 1.1)
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out = psroi_pool_module(
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paddle.to_tensor(self.x, 'float64'),
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paddle.to_tensor(self.boxes, 'float64'),
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paddle.to_tensor(self.boxes_num, 'int32'),
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).numpy()
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 2, 1.1, 8, 8
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)
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np.testing.assert_allclose(out, expect_out, rtol=1e-05)
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paddle.disable_static()
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places = get_devices()
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for place in places:
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paddle.set_device(place)
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test_output_size_is_int()
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test_output_size_is_tuple()
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test_dytype_is_float64()
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class TestPSROIPoolBoxesNumError(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x = paddle.uniform([2, 490, 28, 28], dtype='float32')
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self.boxes = paddle.to_tensor(
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[[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], 'float32'
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)
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def test_errors(self):
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def test_boxes_num_nums_error():
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boxes_num = paddle.to_tensor([1, 5], 'int32')
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out = paddle.vision.ops.psroi_pool(
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self.x, self.boxes, boxes_num, output_size=7
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)
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self.assertRaises(ValueError, test_boxes_num_nums_error)
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def test_boxes_num_length_error():
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boxes_num = paddle.to_tensor([1, 1, 1], 'int32')
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out = paddle.vision.ops.psroi_pool(
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self.x, self.boxes, boxes_num, output_size=7
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)
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self.assertRaises(ValueError, test_boxes_num_length_error)
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class TestPSROIPoolChannelError(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x = paddle.uniform([2, 490, 28, 28], dtype='float32')
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self.boxes = paddle.to_tensor(
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[[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], 'float32'
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)
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self.output_size = 4
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def test_errors(self):
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def test_channel_error():
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boxes_num = paddle.to_tensor([2, 1], 'int32')
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out = paddle.vision.ops.psroi_pool(
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self.x, self.boxes, boxes_num, self.output_size
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)
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self.assertRaises(ValueError, test_channel_error)
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class TestPSROIPoolZeroDivError(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x = paddle.uniform([2, 490, 28, 28], dtype='float32')
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self.boxes = paddle.to_tensor(
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[[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], dtype='float32'
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)
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self.boxes_num = paddle.to_tensor([1, 2], dtype='int32')
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def test_errors(self):
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def test_zero_div_error():
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paddle.vision.ops.psroi_pool(self.x, self.boxes, self.boxes_num, 0)
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self.assertRaises(ValueError, test_zero_div_error)
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class TestPSROIPoolStaticAPI(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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self.x_placeholder = paddle.static.data(
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name='x', shape=[2, 490, 28, 28]
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)
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self.x = np.random.random([2, 490, 28, 28]).astype(np.float32)
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self.boxes_placeholder = paddle.static.data(name='boxes', shape=[3, 4])
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self.boxes = np.array(
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[[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]]
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).astype(np.float32)
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self.boxes_num = np.array([1, 2]).astype(np.int32)
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def test_function_in_static(self):
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output_size = 7
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out = paddle.vision.ops.psroi_pool(
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self.x_placeholder,
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self.boxes_placeholder,
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paddle.to_tensor(self.boxes_num),
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output_size,
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)
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 10, 1.0, 7, 7
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)
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places = get_places()
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for place in places:
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exe = paddle.static.Executor(place)
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boxes_lod_data = paddle.base.create_lod_tensor(
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self.boxes, [[1, 2]], place
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)
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(out_res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': self.x, 'boxes': boxes_lod_data},
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fetch_list=[out],
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)
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np.testing.assert_allclose(out_res, expect_out, rtol=1e-05)
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class TestPSROIPoolStaticAPI_NOLOD(unittest.TestCase):
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def setUp(self):
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self.x = np.random.random([2, 490, 28, 28]).astype(np.float32)
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self.boxes = np.array(
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[[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]]
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).astype(np.float32)
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self.boxes_num = np.array([1, 2]).astype(np.int32)
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def test_function_in_pir(self):
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with (
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paddle.pir_utils.IrGuard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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output_size = 7
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x_placeholder = paddle.static.data(name='x', shape=[2, 490, 28, 28])
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boxes_placeholder = paddle.static.data(
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name='boxes_nolod', shape=[3, 4]
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)
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boxes_num = paddle.to_tensor(self.boxes_num, 'int32')
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out = paddle.vision.ops.psroi_pool(
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x_placeholder,
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boxes_placeholder,
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boxes_num,
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output_size,
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)
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expect_out = calc_psroi_pool(
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self.x, self.boxes, self.boxes_num, 10, 1.0, 7, 7
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)
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places = get_places()
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for place in places:
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exe = paddle.static.Executor(place)
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(out_res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': self.x, 'boxes_nolod': self.boxes},
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fetch_list=[out],
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)
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np.testing.assert_allclose(out_res, expect_out, rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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