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7f08eb6b3f
* avoid numpy version check when xtcocotools can be imported Link: https://code.alibaba-inc.com/pai-vision/EasyCV/codereview/10377599 * * move thirdparty into easycv * fix code style Link: https://code.alibaba-inc.com/pai-vision/EasyCV/codereview/10395748 * move thirdparty into easycv * fix missing thirdparty/deformable_attention/src when build package * optimize ci_test * update version to 0.6.3.8 Link: https://code.alibaba-inc.com/pai-vision/EasyCV/codereview/10412059 * update version to 0.6.3.8 * fix face keypoints bugs in FT * update version to 0.6.3.9 Link: https://code.alibaba-inc.com/pai-vision/EasyCV/codereview/10443200 * update version to 0.6.3.9 * fix import thirdparty * fix unittest * fix unittest Co-authored-by: wenmeng.zwm <wenmeng.zwm@alibaba-inc.com> Co-authored-by: shouzhou.bx <shouzhou.bx@alibaba-inc.com>
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
import copy
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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from easycv.models.builder import LOSSES
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CONSTANT_CONTOUR = 66
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CONSTANT_EYEBROW = 18
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CONSTANT_EYE = 18
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CONSTANT_NOSE = 30
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CONSTANT_LIPS = 40
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CONSTANT_EYE_CENTER = 4
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@LOSSES.register_module()
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class WingLossWithPose(nn.Module):
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def __init__(self,
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num_points=106,
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left_eye_left_corner_index=66,
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right_eye_right_corner_index=79,
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points_weight=1.0,
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contour_weight=1.5,
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eyebrow_weight=1.5,
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eye_weight=1.7,
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nose_weight=1.3,
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lip_weight=1.7,
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omega=10,
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epsilon=2):
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super(WingLossWithPose, self).__init__()
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self.omega = omega
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self.epsilon = epsilon
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self.num_points = num_points
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self.left_eye_left_corner_index = left_eye_left_corner_index
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self.right_eye_right_corner_index = right_eye_right_corner_index
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self.points_weight = points_weight
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contour_weight = np.full(CONSTANT_CONTOUR, contour_weight)
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eyebrow_left_weight = np.full(CONSTANT_EYEBROW, eyebrow_weight)
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eyebrow_right_weight = np.full(CONSTANT_EYEBROW, eyebrow_weight)
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nose_weight = np.full(CONSTANT_NOSE, nose_weight)
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eye_left_weight = np.full(CONSTANT_EYE, eye_weight)
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eye_right_weight = np.full(CONSTANT_EYE, eye_weight)
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lips_weight = np.full(CONSTANT_LIPS, lip_weight)
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eye_center_weight = np.full(CONSTANT_EYE_CENTER, eye_weight)
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part_weight = np.concatenate(
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(contour_weight, eyebrow_left_weight, eyebrow_right_weight,
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nose_weight, eye_left_weight, eye_right_weight, lips_weight,
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eye_center_weight),
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axis=0)
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self.part_weight = None
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if part_weight is not None:
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self.part_weight = torch.from_numpy(part_weight)
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def forward(self, pred, target, pose):
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weight = 5.0 * (1.0 - torch.cos(pose * np.pi / 180.0)) + 1.0
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weight = torch.sum(weight, dim=1) / 3.0
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weight = weight.view((weight.shape[0], 1))
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self.part_weight = self.part_weight.to(weight.device)
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if self.part_weight is not None:
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weight = weight * self.part_weight
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y = target
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y_hat = pred
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delta_y = (y - y_hat).abs() * weight
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delta_y1 = delta_y[delta_y < self.omega]
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delta_y2 = delta_y[delta_y >= self.omega]
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loss1 = self.omega * torch.log(1 + delta_y1 / self.epsilon)
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C = self.omega - self.omega * math.log(1 + self.omega / self.epsilon)
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loss = delta_y2 - C
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result = self.points_weight * (loss1.sum() + loss.sum()) / (
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len(loss1) + len(loss))
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return result
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@LOSSES.register_module()
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class FacePoseLoss(nn.Module):
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def __init__(self, pose_weight=1.0):
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super(FacePoseLoss, self).__init__()
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self.criterion = nn.MSELoss()
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self.pose_weight = pose_weight
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def forward(self, pred, target):
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result = self.pose_weight * self.criterion(pred, target)
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return result
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