<reponame>banana1024/PIPNet
import os, cv2
import numpy as np
from PIL import Image, ImageFilter
import logging
import torch
import torch.nn as nn
import random
import time

def get_label(data_name, label_file, task_type=None):
    label_path = os.path.join('data', data_name, label_file)
    with open(label_path, 'r') as f:
        labels = f.readlines()
    labels = [x.strip().split() for x in labels]
    if len(labels[0])==1:
        return labels

    labels_new = []
    for label in labels:
        image_name = label[0]
        target = label[1:]
        target = np.array([float(x) for x in target])
        if task_type is None:
            labels_new.append([image_name, target])
        else:
            labels_new.append([image_name, task_type, target])
    return labels_new

def get_meanface(meanface_file, num_nb):
    with open(meanface_file) as f:
        meanface = f.readlines()[0]
        
    meanface = meanface.strip().split()
    meanface = [float(x) for x in meanface]
    meanface = np.array(meanface).reshape(-1, 2)
    # each landmark predicts num_nb neighbors
    meanface_indices = []
    for i in range(meanface.shape[0]):
        pt = meanface[i,:]
        dists = np.sum(np.power(pt-meanface, 2), axis=1)
        indices = np.argsort(dists)
        meanface_indices.append(indices[1:1+num_nb])
    
    # each landmark predicted by X neighbors, X varies
    meanface_indices_reversed = {}
    for i in range(meanface.shape[0]):
        meanface_indices_reversed[i] = [[],[]]
    for i in range(meanface.shape[0]):
        for j in range(num_nb):
            meanface_indices_reversed[meanface_indices[i][j]][0].append(i)
            meanface_indices_reversed[meanface_indices[i][j]][1].append(j)
    
    max_len = 0
    for i in range(meanface.shape[0]):
        tmp_len = len(meanface_indices_reversed[i][0])
        if tmp_len > max_len:
            max_len = tmp_len
    
    # tricks, make them have equal length for efficient computation
    for i in range(meanface.shape[0]):
        tmp_len = len(meanface_indices_reversed[i][0])
        meanface_indices_reversed[i][0] += meanface_indices_reversed[i][0]*10
        meanface_indices_reversed[i][1] += meanface_indices_reversed[i][1]*10
        meanface_indices_reversed[i][0] = meanface_indices_reversed[i][0][:max_len]
        meanface_indices_reversed[i][1] = meanface_indices_reversed[i][1][:max_len]

    # make the indices 1-dim
    reverse_index1 = []
    reverse_index2 = []
    for i in range(meanface.shape[0]):
        reverse_index1 += meanface_indices_reversed[i][0]
        reverse_index2 += meanface_indices_reversed[i][1]
    return meanface_indices, reverse_index1, reverse_index2, max_len

def compute_loss_pip(outputs_map, outputs_local_x, outputs_local_y, outputs_nb_x, outputs_nb_y, labels_map, labels_local_x, labels_local_y, labels_nb_x, labels_nb_y,  criterion_cls, criterion_reg, num_nb):

    tmp_batch, tmp_channel, tmp_height, tmp_width = outputs_map.size()
    labels_map = labels_map.view(tmp_batch*tmp_channel, -1)
    labels_max_ids = torch.argmax(labels_map, 1)
    labels_max_ids = labels_max_ids.view(-1, 1)
    labels_max_ids_nb = labels_max_ids.repeat(1, num_nb).view(-1, 1)

    outputs_local_x = outputs_local_x.view(tmp_batch*tmp_channel, -1)
    outputs_local_x_select = torch.gather(outputs_local_x, 1, labels_max_ids)
    outputs_local_y = outputs_local_y.view(tmp_batch*tmp_channel, -1)
    outputs_local_y_select = torch.gather(outputs_local_y, 1, labels_max_ids)
    outputs_nb_x = outputs_nb_x.view(tmp_batch*num_nb*tmp_channel, -1)
    outputs_nb_x_select = torch.gather(outputs_nb_x, 1, labels_max_ids_nb)
    outputs_nb_y = outputs_nb_y.view(tmp_batch*num_nb*tmp_channel, -1)
    outputs_nb_y_select = torch.gather(outputs_nb_y, 1, labels_max_ids_nb)

    labels_local_x = labels_local_x.view(tmp_batch*tmp_channel, -1)
    labels_local_x_select = torch.gather(labels_local_x, 1, labels_max_ids)
    labels_local_y = labels_local_y.view(tmp_batch*tmp_channel, -1)
    labels_local_y_select = torch.gather(labels_local_y, 1, labels_max_ids)
    labels_nb_x = labels_nb_x.view(tmp_batch*num_nb*tmp_channel, -1)
    labels_nb_x_select = torch.gather(labels_nb_x, 1, labels_max_ids_nb)
    labels_nb_y = labels_nb_y.view(tmp_batch*num_nb*tmp_channel, -1)
    labels_nb_y_select = torch.gather(labels_nb_y, 1, labels_max_ids_nb)

    labels_map = labels_map.view(tmp_batch, tmp_channel, tmp_height, tmp_width)
    loss_map = criterion_cls(outputs_map, labels_map)
    loss_x = criterion_reg(outputs_local_x_select, labels_local_x_select)
    loss_y = criterion_reg(outputs_local_y_select, labels_local_y_select)
    loss_nb_x = criterion_reg(outputs_nb_x_select, labels_nb_x_select)
    loss_nb_y = criterion_reg(outputs_nb_y_select, labels_nb_y_select)
    return loss_map, loss_x, loss_y, loss_nb_x, loss_nb_y

def train_model(det_head, net, train_loader, criterion_cls, criterion_reg, cls_loss_weight, reg_loss_weight, num_nb, optimizer, num_epochs, scheduler, save_dir, save_interval, device):
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        logging.info('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)
        logging.info('-' * 10)
        net.train()
        epoch_loss = 0.0

        for i, data in enumerate(train_loader):
            if det_head == 'pip':
                inputs, labels_map, labels_x, labels_y, labels_nb_x, labels_nb_y = data
                inputs = inputs.to(device)
                labels_map = labels_map.to(device)
                labels_x = labels_x.to(device)
                labels_y = labels_y.to(device)
                labels_nb_x = labels_nb_x.to(device)
                labels_nb_y = labels_nb_y.to(device)
                outputs_map, outputs_x, outputs_y, outputs_nb_x, outputs_nb_y = net(inputs)
                loss_map, loss_x, loss_y, loss_nb_x, loss_nb_y = compute_loss_pip(outputs_map, outputs_x, outputs_y, outputs_nb_x, outputs_nb_y, labels_map, labels_x, labels_y, labels_nb_x, labels_nb_y, criterion_cls, criterion_reg, num_nb)
                loss = cls_loss_weight*loss_map + reg_loss_weight*loss_x + reg_loss_weight*loss_y + reg_loss_weight*loss_nb_x + reg_loss_weight*loss_nb_y
            else:
                print('No such head:', det_head)
                exit(0)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if i%10 == 0:
                if det_head == 'pip':
                    print('[Epoch {:d}/{:d}, Batch {:d}/{:d}] <Total loss: {:.6f}> <map loss: {:.6f}> <x loss: {:.6f}> <y loss: {:.6f}> <nbx loss: {:.6f}> <nby loss: {:.6f}>'.format(
                        epoch, num_epochs-1, i, len(train_loader)-1, loss.item(), cls_loss_weight*loss_map.item(), reg_loss_weight*loss_x.item(), reg_loss_weight*loss_y.item(), reg_loss_weight*loss_nb_x.item(), reg_loss_weight*loss_nb_y.item()))
                    logging.info('[Epoch {:d}/{:d}, Batch {:d}/{:d}] <Total loss: {:.6f}> <map loss: {:.6f}> <x loss: {:.6f}> <y loss: {:.6f}> <nbx loss: {:.6f}> <nby loss: {:.6f}>'.format(
                        epoch, num_epochs-1, i, len(train_loader)-1, loss.item(), cls_loss_weight*loss_map.item(), reg_loss_weight*loss_x.item(), reg_loss_weight*loss_y.item(), reg_loss_weight*loss_nb_x.item(), reg_loss_weight*loss_nb_y.item()))
                else:
                    print('No such head:', det_head)
                    exit(0)
            epoch_loss += loss.item()
        epoch_loss /= len(train_loader)
        if epoch%(save_interval-1) == 0 and epoch > 0:
            filename = os.path.join(save_dir, 'epoch%d.pth' % epoch)
            torch.save(net.state_dict(), filename)
            print(filename, 'saved')
        scheduler.step()
    return net

def forward_pip(net, inputs, preprocess, input_size, net_stride, num_nb):
    net.eval()
    with torch.no_grad():
        outputs_cls, outputs_x, outputs_y, outputs_nb_x, outputs_nb_y = net(inputs)
        tmp_batch, tmp_channel, tmp_height, tmp_width = outputs_cls.size()
        assert tmp_batch == 1

        outputs_cls = outputs_cls.view(tmp_batch*tmp_channel, -1)
        max_ids = torch.argmax(outputs_cls, 1)
        max_cls = torch.max(outputs_cls, 1)[0]
        max_ids = max_ids.view(-1, 1)
        max_ids_nb = max_ids.repeat(1, num_nb).view(-1, 1)

        outputs_x = outputs_x.view(tmp_batch*tmp_channel, -1)
        outputs_x_select = torch.gather(outputs_x, 1, max_ids)
        outputs_x_select = outputs_x_select.squeeze(1)
        outputs_y = outputs_y.view(tmp_batch*tmp_channel, -1)
        outputs_y_select = torch.gather(outputs_y, 1, max_ids)
        outputs_y_select = outputs_y_select.squeeze(1)

        outputs_nb_x = outputs_nb_x.view(tmp_batch*num_nb*tmp_channel, -1)
        outputs_nb_x_select = torch.gather(outputs_nb_x, 1, max_ids_nb)
        outputs_nb_x_select = outputs_nb_x_select.squeeze(1).view(-1, num_nb)
        outputs_nb_y = outputs_nb_y.view(tmp_batch*num_nb*tmp_channel, -1)
        outputs_nb_y_select = torch.gather(outputs_nb_y, 1, max_ids_nb)
        outputs_nb_y_select = outputs_nb_y_select.squeeze(1).view(-1, num_nb)

        tmp_x = (max_ids%tmp_width).view(-1,1).float()+outputs_x_select.view(-1,1)
        tmp_y = (max_ids/tmp_width).view(-1,1).float()+outputs_y_select.view(-1,1)
        tmp_x /= 1.0 * input_size / net_stride
        tmp_y /= 1.0 * input_size / net_stride

        tmp_nb_x = (max_ids%tmp_width).view(-1,1).float()+outputs_nb_x_select
        tmp_nb_y = (max_ids/tmp_width).view(-1,1).float()+outputs_nb_y_select
        tmp_nb_x = tmp_nb_x.view(-1, num_nb)
        tmp_nb_y = tmp_nb_y.view(-1, num_nb)
        tmp_nb_x /= 1.0 * input_size / net_stride
        tmp_nb_y /= 1.0 * input_size / net_stride

    return tmp_x, tmp_y, tmp_nb_x, tmp_nb_y, outputs_cls, max_cls

def compute_nme(lms_pred, lms_gt, norm):
    lms_pred = lms_pred.reshape((-1, 2))
    lms_gt = lms_gt.reshape((-1, 2))
    nme = np.mean(np.linalg.norm(lms_pred - lms_gt, axis=1)) / norm 
    return nme

