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train_on_GAICD.py
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train_on_GAICD.py
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import os
import sys
import numpy as np
from tensorboardX import SummaryWriter
import torch
import time
import datetime
import csv
from tqdm import tqdm
import shutil
import pickle
from scipy.stats import spearmanr
import random
from torch.autograd import Variable
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
import math
from cropping_model import HumanCentricCroppingModel
from cropping_model import score_regression_loss, score_rank_loss, \
score_weighted_regression_loss, listwise_view_ranking_loss
from cropping_dataset import GAICDataset
from config_GAICD import cfg
from test import evaluate_on_GAICD
device = torch.device('cuda:{}'.format(cfg.gpu_id))
torch.cuda.set_device(cfg.gpu_id)
IMAGE_NET_MEAN = [0.485, 0.456, 0.406]
IMAGE_NET_STD = [0.229, 0.224, 0.225]
MOS_MEAN = 2.95
MOS_STD = 0.8
SEED = 0
torch.manual_seed(SEED)
np.random.seed(SEED)
random.seed(SEED)
def create_dataloader():
assert cfg.training_set == 'GAICD', cfg.training_set
dataset = GAICDataset(only_human_images=cfg.only_human,
keep_aspect_ratio=cfg.keep_aspect_ratio,
split='train')
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1,
shuffle=True, num_workers=cfg.num_workers,
drop_last=False, worker_init_fn=random.seed(SEED),
pin_memory=True)
print('training set has {} samples, {} batches'.format(len(dataset), len(dataloader)))
return dataloader
class Trainer:
def __init__(self, model):
self.model = model
self.epoch = 0
self.iters = 0
self.max_epoch = cfg.max_epoch
self.writer = SummaryWriter(log_dir=cfg.log_dir)
self.optimizer, self.lr_scheduler = self.get_optimizer()
self.train_loader = create_dataloader()
self.eval_results = []
self.best_results = {'human_srcc': 0, 'human_acc5': 0., 'human_acc10': 0.,
'srcc': 0, 'acc5': 0., 'acc10': 0.}
self.score_loss_type = cfg.loss_type if isinstance(cfg.loss_type, list) else [cfg.loss_type]
self.l1_loss = torch.nn.L1Loss()
def get_optimizer(self):
optim = torch.optim.Adam(
self.model.parameters(),
lr=cfg.lr,
weight_decay=cfg.weight_decay
)
if cfg.lr_scheduler == 'cosine':
warm_up_epochs = 5
warm_up_with_cosine_lr = lambda epoch: min(1.,(epoch+1) / warm_up_epochs) if epoch <= warm_up_epochs else 0.5 * (
math.cos((epoch - warm_up_epochs) / (self.max_epoch - warm_up_epochs) * math.pi) + 1)
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=warm_up_with_cosine_lr)
else:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optim, milestones=cfg.lr_decay_epoch, gamma=cfg.lr_decay
)
return optim, lr_scheduler
def run(self):
print(("======== Begin Training ========="))
for epoch in range(self.max_epoch):
self.epoch = epoch
self.train()
if epoch % cfg.eval_freq == 0 or (epoch == self.max_epoch-1):
self.eval()
self.record_eval_results()
self.lr_scheduler.step()
def visualize_partition_mask(self, im, pre_part, gt_part):
im = im.detach().cpu()
pre_part = torch.softmax(pre_part,dim=0).detach().cpu()
# pre_part = pre_part.detach().cpu()
gt_part = gt_part.detach().cpu()
im = im * torch.tensor(IMAGE_NET_STD).view(3,1,1) + torch.tensor(IMAGE_NET_MEAN).view(3,1,1)
trans_fn = transforms.ToPILImage()
im = trans_fn(im).convert('RGB')
width, height = im.size
joint_pre = None
joint_gt = None
for i in range(3):
h,w = pre_part.shape[1:]
col_band= torch.ones(h,1).float()
row_pre = torch.cat([pre_part[i*3], col_band, pre_part[i*3+1], col_band, pre_part[i*3+2]], dim=-1)
row_gt = torch.cat([gt_part[i*3], col_band, gt_part[i*3+1], col_band, gt_part[i*3+2]], dim=-1)
if joint_gt is None:
joint_gt = row_gt
joint_pre = row_pre
else:
row_band = torch.ones(1, row_pre.shape[-1]).float()
joint_pre = torch.cat([joint_pre, row_band, row_pre], dim=0)
joint_gt = torch.cat([joint_gt, row_band, row_gt], dim=0)
pre_part = trans_fn(joint_pre).convert('RGB').resize((width,height))
gt_part = trans_fn(joint_gt).convert('RGB').resize((width, height))
ver_band = (np.ones((height,5,3)) * 255).astype(np.uint8)
cat_im = np.concatenate([np.asarray(im), ver_band, np.asarray(gt_part), ver_band, np.asarray(pre_part)], axis=1)
cat_im = Image.fromarray(cat_im)
fig_dir = os.path.join(cfg.exp_path, 'visualization')
os.makedirs(fig_dir,exist_ok=True)
fig_file = os.path.join(fig_dir, str(self.iters) + '_part.jpg')
cat_im.save(fig_file)
def visualize_heat_map(self, im, pre_heat, gt_heat):
im = im.detach().cpu()
pre_heat = pre_heat.detach().cpu()
gt_heat = gt_heat.detach().cpu()
im = im * torch.tensor(IMAGE_NET_STD).view(3,1,1) + torch.tensor(IMAGE_NET_MEAN).view(3,1,1)
trans_fn = transforms.ToPILImage()
im = trans_fn(im).convert('RGB')
width, height = im.size
pre_heat = trans_fn(pre_heat).convert('RGB').resize((width,height))
gt_heat = trans_fn(gt_heat).convert('RGB').resize((width, height))
ver_band = (np.ones((height,5,3)) * 255).astype(np.uint8)
cat_im = np.concatenate([np.asarray(im), ver_band, np.asarray(gt_heat), ver_band, np.asarray(pre_heat)], axis=1)
cat_im = Image.fromarray(cat_im)
fig_dir = os.path.join(cfg.exp_path, 'visualization')
os.makedirs(fig_dir,exist_ok=True)
fig_file = os.path.join(fig_dir, str(self.iters) + '_content.jpg')
cat_im.save(fig_file)
def train(self):
self.model.train()
start = time.time()
batch_loss = 0
batch_score_loss = 0.
batch_content_loss = 0.
total_batch = len(self.train_loader)
human_cnt = 0.
for batch_idx, batch_data in enumerate(self.train_loader):
self.iters += 1
im = batch_data[0].to(device)
rois = batch_data[1].to(device)
human_box = batch_data[2].to(device)
heat_map = batch_data[3].to(device)
crop_mask = batch_data[4].to(device)
part_mask = batch_data[5].to(device)
score = batch_data[6].to(device)
# width = batch_data[7].to(device)
# height = batch_data[8].to(device)
contain_human = (torch.count_nonzero(part_mask[0, 4]) > 0)
random_ID = list(range(0, rois.shape[1]))
random.shuffle(random_ID)
chosen_ID = random_ID[:64]
rois = rois[:,chosen_ID]
crop_mask = crop_mask[:,chosen_ID]
score = score[:,chosen_ID]
pre_patition, pred_heat_map, pred_score = self.model(im, rois, human_box, crop_mask, part_mask)
score_loss = None
for loss_type in self.score_loss_type:
if loss_type == 'L1Loss':
cur_loss = score_regression_loss(pred_score, score)
elif loss_type == 'WeightedL1Loss':
cur_loss = score_weighted_regression_loss(pred_score, score, MOS_MEAN)
elif loss_type == 'RankLoss':
cur_loss = score_rank_loss(pred_score, score)
elif loss_type == 'LVRLoss':
cur_loss = listwise_view_ranking_loss(pred_score, score)
else:
raise Exception('Undefined score loss type', loss_type)
if score_loss:
score_loss += cur_loss
else:
score_loss = cur_loss
batch_score_loss += score_loss.item()
loss = score_loss
if cfg.use_content_preserve:
content_loss = self.l1_loss(pred_heat_map.reshape(-1), heat_map.reshape(-1))
loss += (content_loss * cfg.content_loss_weight)
batch_content_loss += content_loss.item()
if contain_human:
human_cnt += 1
if human_cnt % cfg.visualize_freq == 0:
if cfg.use_partition_aware and cfg.visualize_partition_feature:
self.visualize_partition_mask(im[0], pre_patition[0], part_mask[0])
if cfg.use_content_preserve and cfg.visualize_heat_map:
self.visualize_heat_map(im[0], pred_heat_map[0], heat_map[0])
batch_loss += loss.item()
loss = loss / cfg.batch_size
loss.backward()
if (batch_idx+1) % cfg.batch_size == 0 or batch_idx >= total_batch-1:
self.optimizer.step()
self.optimizer.zero_grad()
if batch_idx > 0 and batch_idx % cfg.display_freq == 0:
avg_loss = batch_loss / (1 + batch_idx)
cur_lr = self.optimizer.param_groups[0]['lr']
avg_score_loss = batch_score_loss / (1 + batch_idx)
self.writer.add_scalar('train/score_loss', avg_score_loss, self.iters)
self.writer.add_scalar('train/total_loss', avg_loss, self.iters)
self.writer.add_scalar('train/lr', cur_lr, self.iters)
if cfg.use_content_preserve:
avg_content_loss = batch_content_loss / (1 + batch_idx)
self.writer.add_scalar('train/content_loss', avg_content_loss, self.iters)
else:
avg_content_loss = 0.
time_per_batch = (time.time() - start) / (batch_idx + 1.)
last_batches = (self.max_epoch - self.epoch - 1) * total_batch + (total_batch - batch_idx - 1)
last_time = int(last_batches * time_per_batch)
time_str = str(datetime.timedelta(seconds=last_time))
print('=== epoch:{}/{}, step:{}/{} | Loss:{:.4f} | Score_Loss:{:.4f} | Content_Loss:{:.4f} | lr:{:.6f} | estimated last time:{} ==='.format(
self.epoch, self.max_epoch, batch_idx, total_batch, avg_loss, avg_score_loss, avg_content_loss, cur_lr, time_str
))
def eval(self):
self.model.eval()
human_srcc, human_acc5, human_acc10 = evaluate_on_GAICD(self.model, only_human=True)
srcc, acc5, acc10 = evaluate_on_GAICD(self.model, only_human=False)
self.eval_results.append([self.epoch, human_srcc, human_acc5, human_acc10,
srcc, acc5, acc10])
epoch_result = {'human_srcc': human_srcc, 'human_acc5': human_acc5, 'human_acc10': human_acc10,
'srcc': srcc, 'acc5': acc5, 'acc10': acc10}
for m in self.best_results.keys():
update = False
if (m != 'disp') and (epoch_result[m] > self.best_results[m]):
update = True
elif (m == 'disp') and (epoch_result[m] < self.best_results[m]):
update = True
if update:
self.best_results[m] = epoch_result[m]
checkpoint_path = os.path.join(cfg.checkpoint_dir, 'best-{}.pth'.format(m))
torch.save(self.model.state_dict(), checkpoint_path)
print('Update best {} model, best {}={:.4f}'.format(m, m, self.best_results[m]))
if m in ['human_srcc', 'srcc']:
self.writer.add_scalar('test/{}'.format(m), epoch_result[m], self.epoch)
self.writer.add_scalar('test/best-{}'.format(m), self.best_results[m], self.epoch)
if self.epoch % cfg.save_freq == 0:
checkpoint_path = os.path.join(cfg.checkpoint_dir, 'epoch-{}.pth'.format(self.epoch))
torch.save(self.model.state_dict(), checkpoint_path)
def record_eval_results(self):
csv_path = os.path.join(cfg.exp_path, '..', '{}.csv'.format(cfg.exp_name))
header = ['epoch', 'human_srcc', 'human_acc5', 'human_acc10',
'srcc', 'acc5', 'acc10']
rows = [header]
for i in range(len(self.eval_results)):
new_results = []
for j in range(len(self.eval_results[i])):
if 'srcc' in header[j]:
new_results.append(round(self.eval_results[i][j], 3))
elif 'acc' in header[j]:
new_results.append(round(self.eval_results[i][j], 3))
else:
new_results.append(self.eval_results[i][j])
self.eval_results[i] = new_results
rows += self.eval_results
metrics = [[] for i in header]
for result in self.eval_results:
for i, r in enumerate(result):
metrics[i].append(r)
for name, m in zip(header, metrics):
if name == 'epoch':
continue
index = m.index(max(m))
if name == 'disp':
index = m.index(min(m))
title = 'best {}(epoch-{})'.format(name, index)
row = [l[index] for l in metrics]
row[0] = title
rows.append(row)
with open(csv_path, 'w') as f:
cw = csv.writer(f)
cw.writerows(rows)
print('Save result to ', csv_path)
if __name__ == '__main__':
cfg.create_path()
for file in os.listdir('./'):
if file.endswith('.py'):
shutil.copy(file, cfg.exp_path)
print('backup', file)
net = HumanCentricCroppingModel(loadweights=True, cfg=cfg)
net = net.to(device)
trainer = Trainer(net)
trainer.run()