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ppocr/losses/det_fce_loss.py
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ppocr/losses/det_fce_loss.py
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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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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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"""
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This code is refer from:
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https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/losses/fce_loss.py
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"""
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import numpy as np
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from paddle import nn
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import paddle
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import paddle.nn.functional as F
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from functools import partial
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def multi_apply(func, *args, **kwargs):
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pfunc = partial(func, **kwargs) if kwargs else func
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map_results = map(pfunc, *args)
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return tuple(map(list, zip(*map_results)))
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class FCELoss(nn.Layer):
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"""The class for implementing FCENet loss
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FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped
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Text Detection
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[https://arxiv.org/abs/2104.10442]
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Args:
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fourier_degree (int) : The maximum Fourier transform degree k.
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num_sample (int) : The sampling points number of regression
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loss. If it is too small, fcenet tends to be overfitting.
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ohem_ratio (float): the negative/positive ratio in OHEM.
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"""
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def __init__(self, fourier_degree, num_sample, ohem_ratio=3.0):
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super().__init__()
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self.fourier_degree = fourier_degree
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self.num_sample = num_sample
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self.ohem_ratio = ohem_ratio
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def forward(self, preds, labels):
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assert isinstance(preds, dict)
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preds = preds["levels"]
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p3_maps, p4_maps, p5_maps = labels[1:]
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assert (
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p3_maps[0].shape[0] == 4 * self.fourier_degree + 5
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), "fourier degree not equal in FCEhead and FCEtarget"
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# to tensor
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gts = [p3_maps, p4_maps, p5_maps]
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for idx, maps in enumerate(gts):
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gts[idx] = paddle.to_tensor(np.stack(maps))
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losses = multi_apply(self.forward_single, preds, gts)
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loss_tr = paddle.to_tensor(0.0).astype("float32")
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loss_tcl = paddle.to_tensor(0.0).astype("float32")
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loss_reg_x = paddle.to_tensor(0.0).astype("float32")
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loss_reg_y = paddle.to_tensor(0.0).astype("float32")
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loss_all = paddle.to_tensor(0.0).astype("float32")
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for idx, loss in enumerate(losses):
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loss_all += sum(loss)
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if idx == 0:
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loss_tr += sum(loss)
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elif idx == 1:
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loss_tcl += sum(loss)
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elif idx == 2:
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loss_reg_x += sum(loss)
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else:
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loss_reg_y += sum(loss)
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results = dict(
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loss=loss_all,
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loss_text=loss_tr,
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loss_center=loss_tcl,
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loss_reg_x=loss_reg_x,
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loss_reg_y=loss_reg_y,
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)
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return results
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def forward_single(self, pred, gt):
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cls_pred = paddle.transpose(pred[0], (0, 2, 3, 1))
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reg_pred = paddle.transpose(pred[1], (0, 2, 3, 1))
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gt = paddle.transpose(gt, (0, 2, 3, 1))
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k = 2 * self.fourier_degree + 1
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tr_pred = paddle.reshape(cls_pred[:, :, :, :2], (-1, 2))
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tcl_pred = paddle.reshape(cls_pred[:, :, :, 2:], (-1, 2))
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x_pred = paddle.reshape(reg_pred[:, :, :, 0:k], (-1, k))
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y_pred = paddle.reshape(reg_pred[:, :, :, k : 2 * k], (-1, k))
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tr_mask = gt[:, :, :, :1].reshape([-1])
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tcl_mask = gt[:, :, :, 1:2].reshape([-1])
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train_mask = gt[:, :, :, 2:3].reshape([-1])
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x_map = paddle.reshape(gt[:, :, :, 3 : 3 + k], (-1, k))
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y_map = paddle.reshape(gt[:, :, :, 3 + k :], (-1, k))
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tr_train_mask = (train_mask * tr_mask).astype("bool")
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tr_train_mask2 = paddle.concat(
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[tr_train_mask.unsqueeze(1), tr_train_mask.unsqueeze(1)], axis=1
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)
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# tr loss
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loss_tr = self.ohem(tr_pred, tr_mask, train_mask)
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# tcl loss
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loss_tcl = paddle.to_tensor(0.0).astype("float32")
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tr_neg_mask = tr_train_mask.logical_not()
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tr_neg_mask2 = paddle.concat(
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[tr_neg_mask.unsqueeze(1), tr_neg_mask.unsqueeze(1)], axis=1
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)
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if tr_train_mask.sum().item() > 0:
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loss_tcl_pos = F.cross_entropy(
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tcl_pred.masked_select(tr_train_mask2).reshape([-1, 2]),
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tcl_mask.masked_select(tr_train_mask).astype("int64"),
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)
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loss_tcl_neg = F.cross_entropy(
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tcl_pred.masked_select(tr_neg_mask2).reshape([-1, 2]),
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tcl_mask.masked_select(tr_neg_mask).astype("int64"),
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)
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loss_tcl = loss_tcl_pos + 0.5 * loss_tcl_neg
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# regression loss
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loss_reg_x = paddle.to_tensor(0.0).astype("float32")
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loss_reg_y = paddle.to_tensor(0.0).astype("float32")
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if tr_train_mask.sum().item() > 0:
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weight = (
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tr_mask.masked_select(tr_train_mask.astype("bool")).astype("float32")
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+ tcl_mask.masked_select(tr_train_mask.astype("bool")).astype("float32")
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) / 2
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weight = weight.reshape([-1, 1])
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ft_x, ft_y = self.fourier2poly(x_map, y_map)
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ft_x_pre, ft_y_pre = self.fourier2poly(x_pred, y_pred)
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dim = ft_x.shape[1]
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tr_train_mask3 = paddle.concat(
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[tr_train_mask.unsqueeze(1) for i in range(dim)], axis=1
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)
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loss_reg_x = paddle.mean(
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weight
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* F.smooth_l1_loss(
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ft_x_pre.masked_select(tr_train_mask3).reshape([-1, dim]),
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ft_x.masked_select(tr_train_mask3).reshape([-1, dim]),
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reduction="none",
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)
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)
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loss_reg_y = paddle.mean(
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weight
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* F.smooth_l1_loss(
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ft_y_pre.masked_select(tr_train_mask3).reshape([-1, dim]),
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ft_y.masked_select(tr_train_mask3).reshape([-1, dim]),
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reduction="none",
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)
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)
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return loss_tr, loss_tcl, loss_reg_x, loss_reg_y
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def ohem(self, predict, target, train_mask):
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pos = (target * train_mask).astype("bool")
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neg = ((1 - target) * train_mask).astype("bool")
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pos2 = paddle.concat([pos.unsqueeze(1), pos.unsqueeze(1)], axis=1)
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neg2 = paddle.concat([neg.unsqueeze(1), neg.unsqueeze(1)], axis=1)
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n_pos = pos.astype("float32").sum()
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if n_pos.item() > 0:
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loss_pos = F.cross_entropy(
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predict.masked_select(pos2).reshape([-1, 2]),
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target.masked_select(pos).astype("int64"),
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reduction="sum",
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)
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loss_neg = F.cross_entropy(
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predict.masked_select(neg2).reshape([-1, 2]),
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target.masked_select(neg).astype("int64"),
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reduction="none",
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)
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n_neg = min(
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int(neg.astype("float32").sum().item()),
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int(self.ohem_ratio * n_pos.astype("float32")),
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)
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else:
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loss_pos = paddle.to_tensor(0.0)
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loss_neg = F.cross_entropy(
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predict.masked_select(neg2).reshape([-1, 2]),
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target.masked_select(neg).astype("int64"),
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reduction="none",
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)
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n_neg = 100
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if len(loss_neg) > n_neg:
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loss_neg, _ = paddle.topk(loss_neg, n_neg)
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return (loss_pos + loss_neg.sum()) / (n_pos + n_neg).astype("float32")
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def fourier2poly(self, real_maps, imag_maps):
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"""Transform Fourier coefficient maps to polygon maps.
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Args:
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real_maps (tensor): A map composed of the real parts of the
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Fourier coefficients, whose shape is (-1, 2k+1)
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imag_maps (tensor):A map composed of the imag parts of the
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Fourier coefficients, whose shape is (-1, 2k+1)
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Returns
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x_maps (tensor): A map composed of the x value of the polygon
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represented by n sample points (xn, yn), whose shape is (-1, n)
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y_maps (tensor): A map composed of the y value of the polygon
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represented by n sample points (xn, yn), whose shape is (-1, n)
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"""
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k_vect = paddle.arange(
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-self.fourier_degree, self.fourier_degree + 1, dtype="float32"
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).reshape([-1, 1])
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i_vect = paddle.arange(0, self.num_sample, dtype="float32").reshape([1, -1])
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transform_matrix = 2 * np.pi / self.num_sample * paddle.matmul(k_vect, i_vect)
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x1 = paddle.einsum("ak, kn-> an", real_maps, paddle.cos(transform_matrix))
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x2 = paddle.einsum("ak, kn-> an", imag_maps, paddle.sin(transform_matrix))
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y1 = paddle.einsum("ak, kn-> an", real_maps, paddle.sin(transform_matrix))
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y2 = paddle.einsum("ak, kn-> an", imag_maps, paddle.cos(transform_matrix))
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x_maps = x1 - x2
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y_maps = y1 + y2
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return x_maps, y_maps
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