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import os
import time
from tqdm import tqdm
import cv2
import numpy as np
import torch
import torch.cuda.amp as amp
import torch.distributed as dist
import torch.nn.functional as F
import wandb
from PIL import Image
from loguru import logger
from utils.misc import (AverageMeter, ProgressMeter, concat_all_gather, trainMetricGPU)
def train(train_loader, model, optimizer, scheduler, scaler, epoch, args):
batch_time = AverageMeter('Batch', ':2.2f')
data_time = AverageMeter('Data', ':2.2f')
lr = AverageMeter('Lr', ':1.6f')
loss_meter = AverageMeter('Loss', ':2.4f')
iou_meter = AverageMeter('IoU', ':2.2f')
pr_meter = AverageMeter('Prec@50', ':2.2f')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, lr, loss_meter, iou_meter, pr_meter],
prefix="Training: Epoch=[{}/{}] ".format(epoch, args.epochs))
model.train()
time.sleep(2)
end = time.time()
# size_list = [320, 352, 384, 416, 448, 480, 512]
# idx = np.random.choice(len(size_list))
# new_size = size_list[idx]
for i, (image, text, target, l_mask) in enumerate(train_loader):
data_time.update(time.time() - end)
# data
# image = torch.stack(image).cuda(non_blocking=True)
# text = torch.stack(text).cuda(non_blocking=True)
# target = torch.stack(target).cuda(non_blocking=True)
# l_mask = torch.stack(l_mask).cuda(non_blocking=True)
image = image.cuda(non_blocking=True)
text = text.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
l_mask = l_mask.cuda(non_blocking=True)
# # multi-scale training
# image = F.interpolate(image, size=(new_size, new_size), mode='bilinear', align_corners=True)
text = text.squeeze(1)
l_mask = l_mask.squeeze(1)
# forward
with amp.autocast():
pred, target, loss = model(image, text, l_mask, target)
# backward
#optimizer.zero_grad()
#scaler.scale(loss).backward()
#loss.backward()
# if args.max_norm:
# torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
# scaler.step(optimizer)
# scaler.update()
model.backward(loss)
model.step()
#optimizer.step()
scheduler.step()
# metric
iou, pr5 = trainMetricGPU(pred, target, 0.35)
dist.all_reduce(loss.detach())
dist.all_reduce(iou)
dist.all_reduce(pr5)
loss = loss / dist.get_world_size()
iou = iou / dist.get_world_size()
pr5 = pr5 / dist.get_world_size()
loss_meter.update(loss.item(), image.size(0))
iou_meter.update(iou.item(), image.size(0))
pr_meter.update(pr5.item(), image.size(0))
lr.update(optimizer.param_groups[0]["lr"])
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
progress.display(i + 1)
if dist.get_rank() in [-1, 0]:
wandb.log(
{
"time/batch": batch_time.val,
"time/data": data_time.val,
"training/lr": lr.val,
"training/loss": loss_meter.val,
"training/iou": iou_meter.val,
"training/prec@50": pr_meter.val,
},
step=epoch * len(train_loader) + (i + 1))
@torch.no_grad()
def validate(val_loader, model, epoch, args):
iou_list = []
I = []
U = []
model.eval()
time.sleep(2)
for imgs, text, masks, l_mask in val_loader:
# data
# imgs = torch.stack(imgs).cuda(non_blocking=True)
# text = torch.stack(text).cuda(non_blocking=True)
# l_mask = torch.stack(l_mask).cuda(non_blocking=True)
imgs = imgs.cuda(non_blocking=True)
text = text.cuda(non_blocking=True)
l_mask = l_mask.cuda(non_blocking=True)
text = text.squeeze(1)
l_mask = l_mask.squeeze(1)
# inference
with amp.autocast(): # does inference need fp16?
preds, maps = model(imgs, text, l_mask)
preds = torch.sigmoid(preds)
# process one batch
for pred, mask in zip(preds, masks):
# iou
pred = pred.cpu().numpy()
mask = mask.cpu().numpy()
pred = np.array(pred > 0.5)
inter = np.logical_and(pred, mask)
union = np.logical_or(pred, mask)
iou = np.sum(inter) / (np.sum(union) + 1e-6)
iou_list.append(iou)
I.append(np.sum(inter))
U.append(np.sum(union))
iou_list = np.stack(iou_list)
iou_list = torch.from_numpy(iou_list).to(imgs.device)
iou_list = concat_all_gather(iou_list)
I = np.stack(I)
I = torch.from_numpy(I).to(imgs.device)
I = concat_all_gather(I).sum()
U = np.stack(U)
U = torch.from_numpy(U).to(imgs.device)
U = concat_all_gather(U).sum()
oIoU = I/U
prec_list = []
for thres in torch.arange(0.5, 1.0, 0.1):
tmp = (iou_list > thres).float().mean()
prec_list.append(tmp)
iou = iou_list.mean()
prec = {}
temp = ' '
for i, thres in enumerate(range(5, 10)):
key = 'Pr@{}'.format(thres * 10)
value = prec_list[i].item()
prec[key] = value
temp += "{}: {:.2f} ".format(key, 100. * value)
head = 'Evaluation: Epoch=[{}/{}] mIoU={:.2f} oIoU={:.2f}'.format(
epoch, args.epochs, 100. * iou.item(), 100.*(oIoU))
logger.info(head + temp)
return oIoU, prec
@torch.no_grad()
def inference(test_loader, model, args):
iou_list = []
I = 0.
U = 0.
tbar = tqdm(test_loader, desc='Inference:', ncols=100)
model.eval()
time.sleep(2)
for ori_img, img, texts, mask, l_masks, seg_id, sents in tbar:
img = img.cuda(non_blocking=True)
mask = mask.cpu().numpy()
for text, l_mask, sent in zip(texts, l_masks, sents):
text = text.cuda(non_blocking=True)
l_mask = l_mask.cuda(non_blocking=True)
text = text.squeeze(1)
l_mask = l_mask.squeeze(1)
with amp.autocast():
pred, maps = model(img, text, l_mask)
pred = torch.sigmoid(pred)
if pred.shape[-2:] != ori_img.shape[:-1]:
#print(f"before** {pred.shape}, {ori_img.shape}, {mask.shape}")
pred = F.interpolate(pred, size=ori_img.shape[1:-1], mode='bicubic', align_corners=True)
#print(f"after** {pred.shape}, {ori_img.shape}, {mask.shape}")
# # process one sentence
pred = pred.cpu().numpy()
pred_ = np.array(pred > 0.5)
inter = np.logical_and(pred_, mask)
union = np.logical_or(pred_, mask)
I += np.sum(inter)
U += np.sum(union)
iou = np.sum(inter) / (np.sum(union) + 1e-6)
iou_list.append(iou)
logger.info('=> Metric Calculation <=')
iou_list = np.stack(iou_list)
iou_list = torch.from_numpy(iou_list).to(img.device)
prec_list = []
for thres in torch.arange(0.5, 1.0, 0.1):
tmp = (iou_list > thres).float().mean()
prec_list.append(tmp)
iou = iou_list.mean()
prec = {}
for i, thres in enumerate(range(5, 10)):
key = 'Pr@{}'.format(thres*10)
value = prec_list[i].item()
prec[key] = value
logger.info('oIoU={:.2f}'.format(100.*(I/U)))
logger.info('mIoU={:.2f}'.format(100.*iou.item()))
for k, v in prec.items():
logger.info('{}: {:.2f}.'.format(k, 100.*v))
return iou.item(), prec
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