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r""" training (validation) code """
import torch.optim as optim
import torch.nn as nn
import torch
from model.DCAMA import DCAMA
from common.logger import Logger, AverageMeter
from common.evaluation import Evaluator
from common.config import parse_opts
from common import utils
from data.dataset import FSSDataset # FSDataset4SAM
# from transformers import SamProcessor
from PIL import Image
import numpy as np
import torch.nn.functional as F
from torchvision import transforms
import pickle
import pycocotools.coco as COCO
import cv2
def train(epoch, model, dataloader, optimizer, training, shot=1):
r""" Train """
# Force randomness during training / freeze randomness during testing
utils.fix_randseed(None) if training else utils.fix_randseed(0)
if hasattr(model, "module"):
model.module.train_mode() if training else model.module.eval()
else:
model.train_mode() if training else model.module.eval()
average_meter = AverageMeter(dataloader.dataset)
average_loss = torch.tensor(0.).float().cuda()
stats = [[], []]
criterion_score = nn.BCEWithLogitsLoss()
for idx, batch in enumerate(dataloader):
# batch = process_batch4SAM(batch)
shot = batch['support_imgs'].size(1)
# 1. forward pass
batch = utils.to_cuda(batch)
logit_mask, score_preds = model(batch['query_img'], batch['support_imgs'], batch['support_masks'], nshot=shot)
pred_mask = logit_mask.argmax(dim=1)
# 2. Compute loss & update model parameters
loss = model.compute_objective(logit_mask, batch['query_mask'])
# loss_obj = loss.detach()
area_inter, area_union = Evaluator.classify_prediction(pred_mask, batch)
iou = (area_inter[1] / area_union[1]).float()
if iou > 0.7 or iou < 0.1:
'''
if iou < 0.1:
img = batch['query_img'][0].permute(1, 2, 0).detach().cpu().numpy()
img = img - img.min()
img = img / img.max()
cv2.imwrite('query_image.png', (img * 255).astype(np.uint8))
img = batch['support_imgs'][0][0].permute(1, 2, 0).detach().cpu().numpy()
img = img - img.min()
img = img / img.max()
cv2.imwrite('support_image.png', (img * 255).astype(np.uint8))
cv2.imwrite('query_mask.png', (batch['query_mask'][0] * 255).detach().cpu().numpy().astype(np.uint8))
cv2.imwrite('pred_mask.png', (pred_mask[0] * 255).detach().cpu().numpy().astype(np.uint8))
cv2.imwrite('support_mask.png', (batch['support_masks'][0][0] * 255).detach().cpu().numpy().astype(np.uint8))
'''
if iou > 0.7:
iou = torch.tensor(1.).float().cuda()
else:
iou = torch.tensor(0.).float().cuda()
score_loss = criterion_score(score_preds, iou)
stats[0].append(score_preds.detach().cpu().numpy())
stats[1].append((area_inter[1] / area_union[1]).detach().cpu().numpy())
print(score_preds, (area_inter[1] / area_union[1]))
if training:
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 3. Evaluate prediction
# img = batch['support_imgs'][0][0].permute(1, 2, 0)
# img = img - img.min()
# img /= img.max()
# import cv2
# cv2.imwrite("debug.png", (img * 255).detach().cpu().numpy())
# cv2.imwrite("debug2.png", (batch['support_masks'][0][0] * 255).detach().cpu().numpy())
# import ipdb;ipdb.set_trace()
area_inter, area_union = Evaluator.classify_prediction(pred_mask, batch)
average_meter.update(area_inter, area_union, batch['class_id'], loss.detach().clone())
average_meter.write_process(idx, len(dataloader), epoch, write_batch_idx=50)
# Write evaluation results
average_meter.write_result('Training' if training else 'Validation', epoch)
avg_loss = utils.mean(average_meter.loss_buf)
miou, fb_iou = average_meter.compute_iou()
import matplotlib.pyplot as plt
plt.scatter(stats[0], stats[1], c="red", s=2, alpha=0.02)
plt.savefig("stats.png")
return avg_loss, miou, fb_iou
if __name__ == '__main__':
# Arguments parsing
args = parse_opts()
# Model initialization
model = DCAMA(args.backbone, args.feature_extractor_path, False)
device = torch.device("cuda", args.local_rank)
model.to(device)
params = model.state_dict()
state_dict = torch.load(args.load)
if 'state_dict' in state_dict.keys():
state_dict = state_dict['state_dict']
state_dict2 = {}
for k in state_dict.keys():
if "scorer" in k:
continue
state_dict2[k] = state_dict[k]
state_dict = state_dict2
for k1, k2 in zip(list(state_dict.keys()), params.keys()):
state_dict[k2] = state_dict.pop(k1)
model.load_state_dict(state_dict, strict=False)
## TODO:
for i in range(len(model.model.DCAMA_blocks)):
torch.nn.init.constant_(model.model.DCAMA_blocks[i].linears[1].weight, 0.)
torch.nn.init.constant_(model.model.DCAMA_blocks[i].linears[1].bias, 1.)
# Helper classes (for training) initialization
optimizer = optim.SGD([{"params": model.parameters(), "lr": args.lr,
"momentum": 0.9, "weight_decay": args.lr/10, "nesterov": True}])
Evaluator.initialize()
if args.local_rank == 0:
Logger.initialize(args, training=True)
Logger.info('# available GPUs: %d' % torch.cuda.device_count())
# Dataset initialization
FSSDataset.initialize(img_size=384, datapath=args.datapath, use_original_imgsize=False)
dataloader_trn = FSSDataset.build_dataloader(args.benchmark, args.bsz, args.nworker, args.fold, 'trn', shot=args.nshot)
if args.local_rank == 0:
dataloader_val = FSSDataset.build_dataloader(args.benchmark, args.bsz, args.nworker, args.fold, 'val', shot=args.nshot)
# Train
best_val_miou = float('-inf')
best_val_loss = float('inf')
for epoch in range(args.nepoch):
trn_loss, trn_miou, trn_fb_iou = train(epoch, model, dataloader_trn, optimizer, training=True, shot=args.nshot)
# evaluation
if args.local_rank == 0:
# with torch.no_grad():
# val_loss, val_miou, val_fb_iou = train(epoch, model, dataloader_val, optimizer, training=False)
# Save the best model
# if val_miou > best_val_miou:
# best_val_miou = val_miou
Logger.save_model_miou(model, epoch, 1.)
# Logger.tbd_writer.add_scalars('data/loss', {'trn_loss': trn_loss, 'val_loss': val_loss}, epoch)
# Logger.tbd_writer.add_scalars('data/miou', {'trn_miou': trn_miou, 'val_miou': val_miou}, epoch)
# Logger.tbd_writer.add_scalars('data/fb_iou', {'trn_fb_iou': trn_fb_iou, 'val_fb_iou': val_fb_iou}, epoch)
# Logger.tbd_writer.flush()
if args.local_rank == 0:
Logger.tbd_writer.close()
Logger.info('==================== Finished Training ====================')
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