132 lines
5.1 KiB
Python
132 lines
5.1 KiB
Python
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# 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 numpy as np
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import math
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ppdet.core.workspace import register
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from .. import ops
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@register
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class AnchorGenerator(nn.Layer):
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"""
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Generate anchors according to the feature maps
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Args:
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anchor_sizes (list[float] | list[list[float]]): The anchor sizes at
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each feature point. list[float] means all feature levels share the
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same sizes. list[list[float]] means the anchor sizes for
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each level. The sizes stand for the scale of input size.
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aspect_ratios (list[float] | list[list[float]]): The aspect ratios at
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each feature point. list[float] means all feature levels share the
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same ratios. list[list[float]] means the aspect ratios for
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each level.
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strides (list[float]): The strides of feature maps which generate
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anchors
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offset (float): The offset of the coordinate of anchors, default 0.
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"""
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def __init__(self,
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anchor_sizes=[32, 64, 128, 256, 512],
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aspect_ratios=[0.5, 1.0, 2.0],
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strides=[16.0],
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variance=[1.0, 1.0, 1.0, 1.0],
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offset=0.):
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super(AnchorGenerator, self).__init__()
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self.anchor_sizes = anchor_sizes
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self.aspect_ratios = aspect_ratios
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self.strides = strides
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self.variance = variance
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self.cell_anchors = self._calculate_anchors(len(strides))
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self.offset = offset
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def _broadcast_params(self, params, num_features):
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if not isinstance(params[0], (list, tuple)): # list[float]
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return [params] * num_features
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if len(params) == 1:
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return list(params) * num_features
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return params
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def generate_cell_anchors(self, sizes, aspect_ratios):
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anchors = []
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for size in sizes:
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area = size**2.0
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for aspect_ratio in aspect_ratios:
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w = math.sqrt(area / aspect_ratio)
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h = aspect_ratio * w
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x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0
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anchors.append([x0, y0, x1, y1])
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return paddle.to_tensor(anchors, dtype='float32')
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def _calculate_anchors(self, num_features):
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sizes = self._broadcast_params(self.anchor_sizes, num_features)
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aspect_ratios = self._broadcast_params(self.aspect_ratios, num_features)
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cell_anchors = [
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self.generate_cell_anchors(s, a)
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for s, a in zip(sizes, aspect_ratios)
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]
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[
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self.register_buffer(
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t.name, t, persistable=False) for t in cell_anchors
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]
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return cell_anchors
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def _create_grid_offsets(self, size, stride, offset):
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grid_height, grid_width = size[0], size[1]
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shifts_x = paddle.arange(
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offset * stride, grid_width * stride, step=stride, dtype='float32')
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shifts_y = paddle.arange(
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offset * stride, grid_height * stride, step=stride, dtype='float32')
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shift_y, shift_x = paddle.meshgrid(shifts_y, shifts_x)
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shift_x = paddle.reshape(shift_x, [-1])
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shift_y = paddle.reshape(shift_y, [-1])
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return shift_x, shift_y
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def _grid_anchors(self, grid_sizes):
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anchors = []
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for size, stride, base_anchors in zip(grid_sizes, self.strides,
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self.cell_anchors):
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shift_x, shift_y = self._create_grid_offsets(size, stride,
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self.offset)
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shifts = paddle.stack((shift_x, shift_y, shift_x, shift_y), axis=1)
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shifts = paddle.reshape(shifts, [-1, 1, 4])
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base_anchors = paddle.reshape(base_anchors, [1, -1, 4])
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anchors.append(paddle.reshape(shifts + base_anchors, [-1, 4]))
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return anchors
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def forward(self, input):
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grid_sizes = [paddle.shape(feature_map)[-2:] for feature_map in input]
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anchors_over_all_feature_maps = self._grid_anchors(grid_sizes)
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return anchors_over_all_feature_maps
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@property
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def num_anchors(self):
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"""
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Returns:
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int: number of anchors at every pixel
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location, on that feature map.
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For example, if at every pixel we use anchors of 3 aspect
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ratios and 5 sizes, the number of anchors is 15.
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For FPN models, `num_anchors` on every feature map is the same.
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"""
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return len(self.cell_anchors[0])
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