Self-Correction-Human-Parsing/utils/criterion.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-

"""
@Author  :   Peike Li
@Contact :   [email protected]
@File    :   criterion.py
@Time    :   8/30/19 8:59 PM
@Desc    :
@License :   This source code is licensed under the license found in the
             LICENSE file in the root directory of this source tree.
"""

import torch.nn as nn
import torch
import numpy as np
from torch.nn import functional as F
from .lovasz_softmax import LovaszSoftmax
from .kl_loss import KLDivergenceLoss
from .consistency_loss import ConsistencyLoss

NUM_CLASSES = 20


class CriterionAll(nn.Module):
    def __init__(self, use_class_weight=False, ignore_index=255, lambda_1=1, lambda_2=1, lambda_3=1,
                 num_classes=20):
        super(CriterionAll, self).__init__()
        self.ignore_index = ignore_index
        self.use_class_weight = use_class_weight
        self.criterion = torch.nn.CrossEntropyLoss(ignore_index=ignore_index)
        self.lovasz = LovaszSoftmax(ignore_index=ignore_index)
        self.kldiv = KLDivergenceLoss(ignore_index=ignore_index)
        self.reg = ConsistencyLoss(ignore_index=ignore_index)
        self.lamda_1 = lambda_1
        self.lamda_2 = lambda_2
        self.lamda_3 = lambda_3
        self.num_classes = num_classes

    def parsing_loss(self, preds, target, cycle_n=None):
        """
        Loss function definition.

        Args:
            preds: [[parsing result1, parsing result2],[edge result]]
            target: [parsing label, egde label]
            soft_preds: [[parsing result1, parsing result2],[edge result]]
        Returns:
            Calculated Loss.
        """
        h, w = target[0].size(1), target[0].size(2)

        pos_num = torch.sum(target[1] == 1, dtype=torch.float)
        neg_num = torch.sum(target[1] == 0, dtype=torch.float)

        weight_pos = neg_num / (pos_num + neg_num)
        weight_neg = pos_num / (pos_num + neg_num)
        weights = torch.tensor([weight_neg, weight_pos])  # edge loss weight

        loss = 0

        # loss for segmentation
        preds_parsing = preds[0]
        for pred_parsing in preds_parsing:
            scale_pred = F.interpolate(input=pred_parsing, size=(h, w),
                                       mode='bilinear', align_corners=True)

            loss += 0.5 * self.lamda_1 * self.lovasz(scale_pred, target[0])
            if target[2] is None:
                loss += 0.5 * self.lamda_1 * self.criterion(scale_pred, target[0])
            else:
                soft_scale_pred = F.interpolate(input=target[2], size=(h, w),
                                                mode='bilinear', align_corners=True)
                soft_scale_pred = moving_average(soft_scale_pred, to_one_hot(target[0], num_cls=self.num_classes),
                                                 1.0 / (cycle_n + 1.0))
                loss += 0.5 * self.lamda_1 * self.kldiv(scale_pred, soft_scale_pred, target[0])

        # loss for edge
        preds_edge = preds[1]
        for pred_edge in preds_edge:
            scale_pred = F.interpolate(input=pred_edge, size=(h, w),
                                       mode='bilinear', align_corners=True)
            if target[3] is None:
                loss += self.lamda_2 * F.cross_entropy(scale_pred, target[1],
                                                       weights.cuda(), ignore_index=self.ignore_index)
            else:
                soft_scale_edge = F.interpolate(input=target[3], size=(h, w),
                                                mode='bilinear', align_corners=True)
                soft_scale_edge = moving_average(soft_scale_edge, to_one_hot(target[1], num_cls=2),
                                                 1.0 / (cycle_n + 1.0))
                loss += self.lamda_2 * self.kldiv(scale_pred, soft_scale_edge, target[0])

        # consistency regularization
        preds_parsing = preds[0]
        preds_edge = preds[1]
        for pred_parsing in preds_parsing:
            scale_pred = F.interpolate(input=pred_parsing, size=(h, w),
                                       mode='bilinear', align_corners=True)
            scale_edge = F.interpolate(input=preds_edge[0], size=(h, w),
                                       mode='bilinear', align_corners=True)
            loss += self.lamda_3 * self.reg(scale_pred, scale_edge, target[0])

        return loss

    def forward(self, preds, target, cycle_n=None):
        loss = self.parsing_loss(preds, target, cycle_n)
        return loss

    def _generate_weights(self, masks, num_classes):
        """
        masks: torch.Tensor with shape [B, H, W]
        """
        masks_label = masks.data.cpu().numpy().astype(np.int64)
        pixel_nums = []
        tot_pixels = 0
        for i in range(num_classes):
            pixel_num_of_cls_i = np.sum(masks_label == i).astype(np.float)
            pixel_nums.append(pixel_num_of_cls_i)
            tot_pixels += pixel_num_of_cls_i
        weights = []
        for i in range(num_classes):
            weights.append(
                (tot_pixels - pixel_nums[i]) / tot_pixels / (num_classes - 1)
            )
        weights = np.array(weights, dtype=np.float)
        # weights = torch.from_numpy(weights).float().to(masks.device)
        return weights


def moving_average(target1, target2, alpha=1.0):
    target = 0
    target += (1.0 - alpha) * target1
    target += target2 * alpha
    return target


def to_one_hot(tensor, num_cls, dim=1, ignore_index=255):
    b, h, w = tensor.shape
    tensor[tensor == ignore_index] = 0
    onehot_tensor = torch.zeros(b, num_cls, h, w).cuda()
    onehot_tensor.scatter_(dim, tensor.unsqueeze(dim), 1)
    return onehot_tensor