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152 lines
6.3 KiB
152 lines
6.3 KiB
# 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 math
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import numpy as np
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class PyPrRoIPool(object):
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def __init__(self):
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pass
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def _PrRoIPoolingGetData(self, data, h, w, height, width):
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overflow = (h < 0) or (w < 0) or (h >= height) or (w >= width)
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if overflow:
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return 0.0
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else:
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return data[h][w]
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def _PrRoIPoolingMatCalculation(self, this_data, s_h, s_w, e_h, e_w, y0, x0,
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y1, x1, h0, w0):
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sum_out = 0.0
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alpha = x0 - float(s_w)
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beta = y0 - float(s_h)
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lim_alpha = x1 - float(s_w)
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lim_beta = y1 - float(s_h)
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tmp = (
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lim_alpha - 0.5 * lim_alpha * lim_alpha - alpha + 0.5 * alpha *
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alpha) * (
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lim_beta - 0.5 * lim_beta * lim_beta - beta + 0.5 * beta * beta)
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sum_out += self._PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp
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alpha = float(e_w) - x1
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lim_alpha = float(e_w) - x0
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tmp = (
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lim_alpha - 0.5 * lim_alpha * lim_alpha - alpha + 0.5 * alpha *
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alpha) * (
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lim_beta - 0.5 * lim_beta * lim_beta - beta + 0.5 * beta * beta)
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sum_out += self._PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp
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alpha = x0 - float(s_w)
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beta = float(e_h) - y1
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lim_alpha = x1 - float(s_w)
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lim_beta = float(e_h) - y0
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tmp = (
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lim_alpha - 0.5 * lim_alpha * lim_alpha - alpha + 0.5 * alpha *
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alpha) * (
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lim_beta - 0.5 * lim_beta * lim_beta - beta + 0.5 * beta * beta)
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sum_out += self._PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp
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alpha = float(e_w) - x1
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lim_alpha = float(e_w) - x0
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tmp = (
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lim_alpha - 0.5 * lim_alpha * lim_alpha - alpha + 0.5 * alpha *
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alpha) * (
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lim_beta - 0.5 * lim_beta * lim_beta - beta + 0.5 * beta * beta)
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sum_out += self._PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp
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return sum_out
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def compute(self,
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x,
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rois,
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output_channels,
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spatial_scale=0.1,
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pooled_height=1,
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pooled_width=1):
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'''
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calculate the precise roi pooling values
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Note: This function is implements as pure python without any paddle concept involved
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:param x (array): array[N, C, H, W]
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:param rois (array): ROIs[id, x1, y1, x2, y2] (Regions of Interest) to pool over.
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:param output_channels (Integer): Expected output channels
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:param spatial_scale (float): spatial scale, default = 0.1
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:param pooled_height (Integer): Expected output height, default = 1
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:param pooled_width (Integer): Expected output width, default = 1
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:return: array[len(rois), output_channels, pooled_height, pooled_width]
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'''
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if not isinstance(output_channels, int):
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raise TypeError("output_channels must be int type")
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if not isinstance(spatial_scale, float):
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raise TypeError("spatial_scale must be float type")
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if not isinstance(pooled_height, int):
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raise TypeError("pooled_height must be int type")
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if not isinstance(pooled_width, int):
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raise TypeError("pooled_width must be int type")
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(batch_size, channels, height, width) = np.array(x).shape
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rois_num = len(rois)
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output_shape = (rois_num, output_channels, pooled_height, pooled_width)
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out_data = np.zeros(output_shape)
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for i in range(rois_num):
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roi = rois[i]
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roi_batch_id = int(roi[0])
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roi_start_w = roi[1] * spatial_scale
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roi_start_h = roi[2] * spatial_scale
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roi_end_w = roi[3] * spatial_scale
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roi_end_h = roi[4] * spatial_scale
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roi_width = max(roi_end_w - roi_start_w, 0.0)
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roi_height = max(roi_end_h - roi_start_h, 0.0)
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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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win_start_w = roi_start_w + bin_size_w * pw
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win_start_h = roi_start_h + bin_size_h * ph
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win_end_w = win_start_w + bin_size_w
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win_end_h = win_start_h + bin_size_h
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win_size = max(0.0, bin_size_w * bin_size_h)
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if win_size == 0.0:
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out_data[i, c, ph, pw] = 0.0
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else:
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sum_out = 0
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s_w = math.floor(win_start_w)
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e_w = math.ceil(win_end_w)
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s_h = math.floor(win_start_h)
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e_h = math.ceil(win_end_h)
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c_in = (c * pooled_height + ph) * pooled_width + pw
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for w_iter in range(int(s_w), int(e_w)):
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for h_iter in range(int(s_h), int(e_h)):
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sum_out += self._PrRoIPoolingMatCalculation(
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x_i[c_in], h_iter, w_iter, h_iter + 1,
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w_iter + 1,
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max(win_start_h, float(h_iter)),
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max(win_start_w, float(w_iter)),
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min(win_end_h, float(h_iter) + 1.0),
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min(win_end_w, float(w_iter + 1.0)),
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height, width)
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out_data[i, c, ph, pw] = sum_out / win_size
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return out_data
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