forked from PulseFocusPlatform/PulseFocusPlatform
233 lines
9.1 KiB
Python
233 lines
9.1 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from paddle import fluid
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from ppdet.core.workspace import register, serializable
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__all__ = ['IouLoss']
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@register
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@serializable
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class IouLoss(object):
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"""
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iou loss, see https://arxiv.org/abs/1908.03851
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loss = 1.0 - iou * iou
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Args:
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loss_weight (float): iou loss weight, default is 2.5
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max_height (int): max height of input to support random shape input
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max_width (int): max width of input to support random shape input
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ciou_term (bool): whether to add ciou_term
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loss_square (bool): whether to square the iou term
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"""
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def __init__(self,
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loss_weight=2.5,
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max_height=608,
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max_width=608,
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ciou_term=False,
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loss_square=True):
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self._loss_weight = loss_weight
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self._MAX_HI = max_height
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self._MAX_WI = max_width
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self.ciou_term = ciou_term
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self.loss_square = loss_square
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def __call__(self,
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x,
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y,
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w,
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h,
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tx,
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ty,
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tw,
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th,
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anchors,
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downsample_ratio,
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batch_size,
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scale_x_y=1.,
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ioup=None,
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eps=1.e-10):
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'''
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Args:
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x | y | w | h ([Variables]): the output of yolov3 for encoded x|y|w|h
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tx |ty |tw |th ([Variables]): the target of yolov3 for encoded x|y|w|h
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anchors ([float]): list of anchors for current output layer
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downsample_ratio (float): the downsample ratio for current output layer
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batch_size (int): training batch size
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eps (float): the decimal to prevent the denominator eqaul zero
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'''
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pred = self._bbox_transform(x, y, w, h, anchors, downsample_ratio,
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batch_size, False, scale_x_y, eps)
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gt = self._bbox_transform(tx, ty, tw, th, anchors, downsample_ratio,
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batch_size, True, scale_x_y, eps)
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iouk = self._iou(pred, gt, ioup, eps)
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if self.loss_square:
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loss_iou = 1. - iouk * iouk
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else:
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loss_iou = 1. - iouk
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loss_iou = loss_iou * self._loss_weight
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return loss_iou
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def _iou(self, pred, gt, ioup=None, eps=1.e-10):
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x1, y1, x2, y2 = pred
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x1g, y1g, x2g, y2g = gt
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x2 = fluid.layers.elementwise_max(x1, x2)
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y2 = fluid.layers.elementwise_max(y1, y2)
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xkis1 = fluid.layers.elementwise_max(x1, x1g)
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ykis1 = fluid.layers.elementwise_max(y1, y1g)
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xkis2 = fluid.layers.elementwise_min(x2, x2g)
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ykis2 = fluid.layers.elementwise_min(y2, y2g)
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intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
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intsctk = intsctk * fluid.layers.greater_than(
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xkis2, xkis1) * fluid.layers.greater_than(ykis2, ykis1)
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unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
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) - intsctk + eps
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iouk = intsctk / unionk
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if self.ciou_term:
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ciou = self.get_ciou_term(pred, gt, iouk, eps)
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iouk = iouk - ciou
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return iouk
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def get_ciou_term(self, pred, gt, iouk, eps):
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x1, y1, x2, y2 = pred
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x1g, y1g, x2g, y2g = gt
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cx = (x1 + x2) / 2
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cy = (y1 + y2) / 2
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w = (x2 - x1) + fluid.layers.cast((x2 - x1) == 0, 'float32')
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h = (y2 - y1) + fluid.layers.cast((y2 - y1) == 0, 'float32')
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cxg = (x1g + x2g) / 2
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cyg = (y1g + y2g) / 2
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wg = x2g - x1g
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hg = y2g - y1g
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# A or B
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xc1 = fluid.layers.elementwise_min(x1, x1g)
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yc1 = fluid.layers.elementwise_min(y1, y1g)
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xc2 = fluid.layers.elementwise_max(x2, x2g)
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yc2 = fluid.layers.elementwise_max(y2, y2g)
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# DIOU term
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dist_intersection = (cx - cxg) * (cx - cxg) + (cy - cyg) * (cy - cyg)
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dist_union = (xc2 - xc1) * (xc2 - xc1) + (yc2 - yc1) * (yc2 - yc1)
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diou_term = (dist_intersection + eps) / (dist_union + eps)
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# CIOU term
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ciou_term = 0
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ar_gt = wg / hg
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ar_pred = w / h
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arctan = fluid.layers.atan(ar_gt) - fluid.layers.atan(ar_pred)
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ar_loss = 4. / np.pi / np.pi * arctan * arctan
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alpha = ar_loss / (1 - iouk + ar_loss + eps)
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alpha.stop_gradient = True
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ciou_term = alpha * ar_loss
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return diou_term + ciou_term
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def _bbox_transform(self, dcx, dcy, dw, dh, anchors, downsample_ratio,
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batch_size, is_gt, scale_x_y, eps):
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grid_x = int(self._MAX_WI / downsample_ratio)
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grid_y = int(self._MAX_HI / downsample_ratio)
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an_num = len(anchors) // 2
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shape_fmp = fluid.layers.shape(dcx)
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shape_fmp.stop_gradient = True
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# generate the grid_w x grid_h center of feature map
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idx_i = np.array([[i for i in range(grid_x)]])
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idx_j = np.array([[j for j in range(grid_y)]]).transpose()
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gi_np = np.repeat(idx_i, grid_y, axis=0)
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gi_np = np.reshape(gi_np, newshape=[1, 1, grid_y, grid_x])
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gi_np = np.tile(gi_np, reps=[batch_size, an_num, 1, 1])
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gj_np = np.repeat(idx_j, grid_x, axis=1)
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gj_np = np.reshape(gj_np, newshape=[1, 1, grid_y, grid_x])
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gj_np = np.tile(gj_np, reps=[batch_size, an_num, 1, 1])
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gi_max = self._create_tensor_from_numpy(gi_np.astype(np.float32))
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gi = fluid.layers.crop(x=gi_max, shape=dcx)
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gi.stop_gradient = True
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gj_max = self._create_tensor_from_numpy(gj_np.astype(np.float32))
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gj = fluid.layers.crop(x=gj_max, shape=dcx)
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gj.stop_gradient = True
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grid_x_act = fluid.layers.cast(shape_fmp[3], dtype="float32")
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grid_x_act.stop_gradient = True
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grid_y_act = fluid.layers.cast(shape_fmp[2], dtype="float32")
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grid_y_act.stop_gradient = True
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if is_gt:
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cx = fluid.layers.elementwise_add(dcx, gi) / grid_x_act
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cx.gradient = True
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cy = fluid.layers.elementwise_add(dcy, gj) / grid_y_act
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cy.gradient = True
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else:
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dcx_sig = fluid.layers.sigmoid(dcx)
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dcy_sig = fluid.layers.sigmoid(dcy)
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if (abs(scale_x_y - 1.0) > eps):
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dcx_sig = scale_x_y * dcx_sig - 0.5 * (scale_x_y - 1)
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dcy_sig = scale_x_y * dcy_sig - 0.5 * (scale_x_y - 1)
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cx = fluid.layers.elementwise_add(dcx_sig, gi) / grid_x_act
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cy = fluid.layers.elementwise_add(dcy_sig, gj) / grid_y_act
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anchor_w_ = [anchors[i] for i in range(0, len(anchors)) if i % 2 == 0]
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anchor_w_np = np.array(anchor_w_)
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anchor_w_np = np.reshape(anchor_w_np, newshape=[1, an_num, 1, 1])
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anchor_w_np = np.tile(anchor_w_np, reps=[batch_size, 1, grid_y, grid_x])
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anchor_w_max = self._create_tensor_from_numpy(
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anchor_w_np.astype(np.float32))
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anchor_w = fluid.layers.crop(x=anchor_w_max, shape=dcx)
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anchor_w.stop_gradient = True
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anchor_h_ = [anchors[i] for i in range(0, len(anchors)) if i % 2 == 1]
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anchor_h_np = np.array(anchor_h_)
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anchor_h_np = np.reshape(anchor_h_np, newshape=[1, an_num, 1, 1])
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anchor_h_np = np.tile(anchor_h_np, reps=[batch_size, 1, grid_y, grid_x])
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anchor_h_max = self._create_tensor_from_numpy(
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anchor_h_np.astype(np.float32))
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anchor_h = fluid.layers.crop(x=anchor_h_max, shape=dcx)
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anchor_h.stop_gradient = True
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# e^tw e^th
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exp_dw = fluid.layers.exp(dw)
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exp_dh = fluid.layers.exp(dh)
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pw = fluid.layers.elementwise_mul(exp_dw, anchor_w) / \
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(grid_x_act * downsample_ratio)
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ph = fluid.layers.elementwise_mul(exp_dh, anchor_h) / \
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(grid_y_act * downsample_ratio)
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if is_gt:
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exp_dw.stop_gradient = True
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exp_dh.stop_gradient = True
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pw.stop_gradient = True
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ph.stop_gradient = True
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x1 = cx - 0.5 * pw
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y1 = cy - 0.5 * ph
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x2 = cx + 0.5 * pw
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y2 = cy + 0.5 * ph
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if is_gt:
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x1.stop_gradient = True
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y1.stop_gradient = True
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x2.stop_gradient = True
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y2.stop_gradient = True
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return x1, y1, x2, y2
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def _create_tensor_from_numpy(self, numpy_array):
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paddle_array = fluid.layers.create_global_var(
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shape=numpy_array.shape, value=0., dtype=numpy_array.dtype)
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fluid.layers.assign(numpy_array, paddle_array)
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return paddle_array
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