# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from paddle.fluid.param_attr import ParamAttr from paddle.fluid.initializer import NumpyArrayInitializer from paddle import fluid from ppdet.core.workspace import register, serializable __all__ = ['IouLoss'] @register @serializable class IouLoss(object): """ iou loss, see https://arxiv.org/abs/1908.03851 loss = 1.0 - iou * iou Args: loss_weight (float): iou loss weight, default is 2.5 max_height (int): max height of input to support random shape input max_width (int): max width of input to support random shape input """ def __init__(self, loss_weight=2.5, max_height=608, max_width=608): self._loss_weight = loss_weight self._MAX_HI = max_height self._MAX_WI = max_width def __call__(self, x, y, w, h, tx, ty, tw, th, anchors, downsample_ratio, batch_size, eps=1.e-10): ''' Args: x | y | w | h ([Variables]): the output of yolov3 for encoded x|y|w|h tx |ty |tw |th ([Variables]): the target of yolov3 for encoded x|y|w|h anchors ([float]): list of anchors for current output layer downsample_ratio (float): the downsample ratio for current output layer batch_size (int): training batch size eps (float): the decimal to prevent the denominator eqaul zero ''' x1, y1, x2, y2 = self._bbox_transform(x, y, w, h, anchors, downsample_ratio, batch_size, False) x1g, y1g, x2g, y2g = self._bbox_transform(tx, ty, tw, th, anchors, downsample_ratio, batch_size, True) x2 = fluid.layers.elementwise_max(x1, x2) y2 = fluid.layers.elementwise_max(y1, y2) xkis1 = fluid.layers.elementwise_max(x1, x1g) ykis1 = fluid.layers.elementwise_max(y1, y1g) xkis2 = fluid.layers.elementwise_min(x2, x2g) ykis2 = fluid.layers.elementwise_min(y2, y2g) xc1 = fluid.layers.elementwise_min(x1, x1g) yc1 = fluid.layers.elementwise_min(y1, y1g) xc2 = fluid.layers.elementwise_max(x2, x2g) yc2 = fluid.layers.elementwise_max(y2, y2g) intsctk = (xkis2 - xkis1) * (ykis2 - ykis1) intsctk = intsctk * fluid.layers.greater_than( xkis2, xkis1) * fluid.layers.greater_than(ykis2, ykis1) unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk + eps iouk = intsctk / unionk loss_iou = 1. - iouk * iouk loss_iou = loss_iou * self._loss_weight return loss_iou def _bbox_transform(self, dcx, dcy, dw, dh, anchors, downsample_ratio, batch_size, is_gt): grid_x = int(self._MAX_WI / downsample_ratio) grid_y = int(self._MAX_HI / downsample_ratio) an_num = len(anchors) // 2 shape_fmp = fluid.layers.shape(dcx) shape_fmp.stop_gradient = True # generate the grid_w x grid_h center of feature map idx_i = np.array([[i for i in range(grid_x)]]) idx_j = np.array([[j for j in range(grid_y)]]).transpose() gi_np = np.repeat(idx_i, grid_y, axis=0) gi_np = np.reshape(gi_np, newshape=[1, 1, grid_y, grid_x]) gi_np = np.tile(gi_np, reps=[batch_size, an_num, 1, 1]) gj_np = np.repeat(idx_j, grid_x, axis=1) gj_np = np.reshape(gj_np, newshape=[1, 1, grid_y, grid_x]) gj_np = np.tile(gj_np, reps=[batch_size, an_num, 1, 1]) gi_max = self._create_tensor_from_numpy(gi_np.astype(np.float32)) gi = fluid.layers.crop(x=gi_max, shape=dcx) gi.stop_gradient = True gj_max = self._create_tensor_from_numpy(gj_np.astype(np.float32)) gj = fluid.layers.crop(x=gj_max, shape=dcx) gj.stop_gradient = True grid_x_act = fluid.layers.cast(shape_fmp[3], dtype="float32") grid_x_act.stop_gradient = True grid_y_act = fluid.layers.cast(shape_fmp[2], dtype="float32") grid_y_act.stop_gradient = True if is_gt: cx = fluid.layers.elementwise_add(dcx, gi) / grid_x_act cx.gradient = True cy = fluid.layers.elementwise_add(dcy, gj) / grid_y_act cy.gradient = True else: dcx_sig = fluid.layers.sigmoid(dcx) cx = fluid.layers.elementwise_add(dcx_sig, gi) / grid_x_act dcy_sig = fluid.layers.sigmoid(dcy) cy = fluid.layers.elementwise_add(dcy_sig, gj) / grid_y_act anchor_w_ = [anchors[i] for i in range(0, len(anchors)) if i % 2 == 0] anchor_w_np = np.array(anchor_w_) anchor_w_np = np.reshape(anchor_w_np, newshape=[1, an_num, 1, 1]) anchor_w_np = np.tile(anchor_w_np, reps=[batch_size, 1, grid_y, grid_x]) anchor_w_max = self._create_tensor_from_numpy(anchor_w_np.astype(np.float32)) anchor_w = fluid.layers.crop(x=anchor_w_max, shape=dcx) anchor_w.stop_gradient = True anchor_h_ = [anchors[i] for i in range(0, len(anchors)) if i % 2 == 1] anchor_h_np = np.array(anchor_h_) anchor_h_np = np.reshape(anchor_h_np, newshape=[1, an_num, 1, 1]) anchor_h_np = np.tile(anchor_h_np, reps=[batch_size, 1, grid_y, grid_x]) anchor_h_max = self._create_tensor_from_numpy(anchor_h_np.astype(np.float32)) anchor_h = fluid.layers.crop(x=anchor_h_max, shape=dcx) anchor_h.stop_gradient = True # e^tw e^th exp_dw = fluid.layers.exp(dw) exp_dh = fluid.layers.exp(dh) pw = fluid.layers.elementwise_mul(exp_dw, anchor_w) / \ (grid_x_act * downsample_ratio) ph = fluid.layers.elementwise_mul(exp_dh, anchor_h) / \ (grid_y_act * downsample_ratio) if is_gt: exp_dw.stop_gradient = True exp_dh.stop_gradient = True pw.stop_gradient = True ph.stop_gradient = True x1 = cx - 0.5 * pw y1 = cy - 0.5 * ph x2 = cx + 0.5 * pw y2 = cy + 0.5 * ph if is_gt: x1.stop_gradient = True y1.stop_gradient = True x2.stop_gradient = True y2.stop_gradient = True return x1, y1, x2, y2 def _create_tensor_from_numpy(self, numpy_array): paddle_array = fluid.layers.create_parameter( attr=ParamAttr(), shape=numpy_array.shape, dtype=numpy_array.dtype, default_initializer=NumpyArrayInitializer(numpy_array)) paddle_array.stop_gradient = True return paddle_array