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train.py
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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision
import numpy as np
from collections import OrderedDict
import time
from datetime import datetime
#from tensorboardX import SummaryWriter
import glob
from config import parse_config
from models import BaseNet, ROINet, TwoBranchNet, ContextNet
from external.maskrcnn_benchmark.roi_layers import nms
from utils.utils import inference, train_select, AverageMeter, get_gpu_memory
from utils.tube_utils import flatten_tubes, valid_tubes
from utils.solver import WarmupCosineLR, WarmupStepLR, get_params
from data.ava import AVADataset, detection_collate, WIDTH, HEIGHT
from data.augmentations import TubeAugmentation, BaseTransform
from utils.eval_utils import ava_evaluation
from external.ActivityNet.Evaluation.get_ava_performance import read_labelmap
args = parse_config()
try:
import apex
from apex import amp
from apex.fp16_utils import *
except ImportError:
print ('Warning: If you want to use fp16, please apex with cuda support (https://github.com/NVIDIA/apex) and update pytorch to 1.0')
args.fp16 = False
pass
args.image_size = (WIDTH, HEIGHT)
label_dict = {}
if args.num_classes == 60:
label_map = os.path.join(args.data_root, 'label/ava_action_list_v2.1_for_activitynet_2018.pbtxt')
categories, class_whitelist = read_labelmap(open(label_map, 'r'))
classes = [(val['id'], val['name']) for val in categories]
id2class = {c[0]: c[1] for c in classes} # gt class id (1~80) --> class name
for i, c in enumerate(sorted(list(class_whitelist))):
label_dict[i] = c
else:
for i in range(80):
label_dict[i] = i+1
args.label_dict = label_dict
args.id2class = id2class
## set random seeds
np.random.seed(args.man_seed)
torch.manual_seed(args.man_seed)
if args.cuda:
torch.cuda.manual_seed_all(args.man_seed)
gpu_count = torch.cuda.device_count()
torch.backends.cudnn.benchmark=True
best_mAP = 0
def main():
global best_mAP
args.exp_name = '{}-max{}-{}-{}'.format(args.name, args.max_iter, args.base_net, args.det_net)
args.save_root = os.path.join(args.save_root, args.exp_name+'/')
if not os.path.isdir(args.save_root):
os.makedirs(args.save_root)
log_name = args.save_root+"training-"+datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')+".log"
log_file = open(log_name, "w", 1)
log_file.write(args.exp_name+'\n')
################ DataLoader setup #################
print('Loading Dataset...')
augmentation = TubeAugmentation(args.image_size, args.means, args.stds, do_flip=args.do_flip, do_crop=args.do_crop, do_photometric=args.do_photometric, scale=args.scale_norm, do_erase=args.do_erase)
log_file.write("Data agumentation: "+ str(augmentation))
train_dataset = AVADataset(args.data_root, 'train', args.input_type, args.T, args.NUM_CHUNKS[args.max_iter], args.fps, augmentation, proposal_path=args.proposal_path_train, stride=1, anchor_mode=args.anchor_mode, num_classes=args.num_classes, foreground_only=True)
val_dataset = AVADataset(args.data_root, 'val', args.input_type, args.T, args.NUM_CHUNKS[args.max_iter], args.fps, BaseTransform(args.image_size, args.means, args.stds,args.scale_norm), proposal_path=args.proposal_path_val, stride=1, anchor_mode=args.anchor_mode, num_classes=args.num_classes, foreground_only=False)
if args.milestones[0] == -1:
args.milestones = [int(np.ceil(len(train_dataset) / args.batch_size) * args.max_epochs)]
train_dataloader = torch.utils.data.DataLoader(train_dataset, args.batch_size, num_workers=args.num_workers,
shuffle=True, collate_fn=detection_collate, pin_memory=True)
val_dataloader = torch.utils.data.DataLoader(val_dataset, args.batch_size, num_workers=args.num_workers,
shuffle=False, collate_fn=detection_collate, pin_memory=True)
log_file.write("Training size: " + str(len(train_dataset)) + "\n")
log_file.write("Validation size: " + str(len(val_dataset)) + "\n")
print('Training STEP on ', train_dataset.name)
################ define models #################
nets = OrderedDict()
# backbone network
nets['base_net'] = BaseNet(args)
# ROI pooling
nets['roi_net'] = ROINet(args.pool_mode, args.pool_size)
# detection network
for i in range(args.max_iter):
if args.det_net == "two_branch":
nets['det_net%d' % i] = TwoBranchNet(args)
else:
raise NotImplementedError
if not args.no_context:
# context branch
nets['context_net'] = ContextNet(args)
for key in nets:
nets[key] = nets[key].cuda()
################ Training setup #################
params = get_params(nets, args)
if args.optimizer == 'sgd':
optimizer = optim.SGD(params, lr=args.det_lr, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
optimizer = optim.Adam(params, lr=args.det_lr)
else:
raise NotImplementedError
if args.scheduler == "cosine":
scheduler = WarmupCosineLR(optimizer, args.milestones, args.min_ratio, args.cycle_decay, args.warmup_iters)
else:
scheduler = WarmupStepLR(optimizer, args.milestones, args.warmup_iters)
# Initialize AMP if needed
if args.fp16:
models, optimizer = amp.initialize([net for _,net in nets.items()], optimizer, opt_level="O1")
for i, key in enumerate(nets):
nets[key] = models[i]
# DataParallel is used
nets['base_net'] = torch.nn.DataParallel(nets['base_net'])
if not args.no_context:
nets['context_net'] = torch.nn.DataParallel(nets['context_net'])
for i in range(args.max_iter):
# distribute models to fit in GPU memory
nets['det_net%d' % i].to('cuda:%d' % ((i+1)%gpu_count))
nets['det_net%d' % i].set_device('cuda:%d' % ((i+1)%gpu_count))
############ Pretrain & Resume ###########
# load pretrained model if needed
if args.pretrain_path is not None:
if os.path.isfile(args.pretrain_path):
print ("Loading pretrain model from %s" % args.pretrain_path)
checkpoint = torch.load(args.pretrain_path, map_location='cuda:0')
nets['base_net'].load_state_dict(checkpoint['base_net'])
if not args.no_context and 'context_net' in checkpoint:
nets['context_net'].load_state_dict(checkpoint['context_net'])
for i in range(args.max_iter):
model_dict = nets['det_net%d' % i].state_dict()
pretrained_dict = checkpoint.get('det_net%d' % i, checkpoint["det_net0"]) # load from classfication pretrained model, so only det_net0 is loaded
pretrained_dict = {k:v for k,v in pretrained_dict.items() if k in model_dict and k.find('global_cls') <= -1} # last layer (classifier) is not loaded
model_dict.update(pretrained_dict)
nets['det_net%d' % i].load_state_dict(model_dict)
else:
raise ValueError("Pretrain model not found!", args.pretrain_path)
del checkpoint
torch.cuda.empty_cache()
# resume trained model if needed
if args.resume_path is not None:
if args.resume_path.lower() == "best":
model_path = args.save_root+'/checkpoint_best.pth'
if not os.path.isfile(model_path):
model_path = None
elif args.resume_path.lower() == "auto":
# automatically get the latest model
model_paths = glob.glob(os.path.join(args.save_root, 'checkpoint_*.pth'))
best_path = os.path.join(args.save_root, 'checkpoint_best.pth')
if best_path in model_paths:
model_paths.remove(best_path)
if len(model_paths):
iters = [int(val.split('_')[-1].split('.')[0]) for val in model_paths]
model_path = model_paths[np.argmax(iters)]
else:
model_path = None
else:
model_path = args.resume_path
if not os.path.isfile(model_path):
raise ValueError("Resume model not found!", args.resume_path)
if model_path is not None:
print ("Resuming trained model from %s" % model_path)
checkpoint = torch.load(model_path, map_location='cuda:0')
nets['base_net'].load_state_dict(checkpoint['base_net'])
if not args.no_context and 'context_net' in checkpoint:
nets['context_net'].load_state_dict(checkpoint['context_net'])
for i in range(args.max_iter):
nets['det_net%d' % i].load_state_dict(checkpoint['det_net%d' % i])
optimizer.load_state_dict(checkpoint['optimizer'])
if 'scheduler' in checkpoint:
scheduler.load_state_dict(checkpoint['scheduler'])
args.start_iteration = checkpoint['iteration']
if checkpoint['iteration'] % int(np.ceil(len(train_dataset)/args.batch_size)) == 0:
args.start_epochs = checkpoint['epochs']
else:
args.start_epochs = checkpoint['epochs'] - 1
best_mAP = checkpoint['val_mAP']
del checkpoint
torch.cuda.empty_cache()
######################################################
for arg in sorted(vars(args)):
print(arg, getattr(args, arg))
log_file.write(str(arg)+': '+str(getattr(args, arg))+'\n')
for i in range(args.max_iter):
log_file.write(str(nets['det_net%d' % i])+'\n\n')
# Start training
train(args, nets, optimizer, scheduler, train_dataloader, val_dataloader, log_file)
def train(args, nets, optimizer, scheduler, train_dataloader, val_dataloader, log_file):
global best_mAP
for _, net in nets.items():
net.train()
# loss counters
batch_time = AverageMeter(200)
losses = [AverageMeter(200) for _ in range(args.max_iter)]
losses_global_cls = AverageMeter(200)
losses_local_loc = AverageMeter(200)
losses_neighbor_loc = AverageMeter(200)
# writer = SummaryWriter(args.save_root+"summary"+datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
################ Training loop #################
torch.cuda.synchronize()
t0 = time.perf_counter()
epochs = args.start_epochs
iteration = args.start_iteration
epoch_size = int(np.ceil(len(train_dataloader.dataset) / args.batch_size))
while epochs < args.max_epochs:
for _, (images, targets, tubes, infos) in enumerate(train_dataloader):
images = images.cuda()
# adjust learning rate
scheduler.step()
lr = optimizer.param_groups[-1]['lr']
# get conv features
conv_feat = nets['base_net'](images)
context_feat = None
if not args.no_context:
context_feat = nets['context_net'](conv_feat)
############# Inference to get candidates for each iteration ########
# randomly sample a fixed number of tubes
if args.NUM_SAMPLE > 0 and args.NUM_SAMPLE < tubes[0].shape[0]:
sampled_idx = np.random.choice(tubes[0].shape[0], args.NUM_SAMPLE, replace=False)
for i in range(len(tubes)):
tubes[i] = tubes[i][sampled_idx]
for _, net in nets.items():
net.eval()
with torch.no_grad():
history, _ = inference(args, conv_feat, context_feat, nets, args.max_iter-1, tubes)
for _, net in nets.items():
net.train()
########### Forward pass for each iteration ############
optimizer.zero_grad()
loss_back = 0.
# loop for each step
for i in range(1, args.max_iter+1): # index from 1
# adaptively get the start chunk
chunks = args.NUM_CHUNKS[i]
max_chunks = args.NUM_CHUNKS[args.max_iter]
T_start = int((args.NUM_CHUNKS[args.max_iter] - chunks) / 2) * args.T
T_length = chunks * args.T
T_mid = int(chunks/2) * args.T # center chunk within T_length
chunk_idx = [j*args.T + int(args.T/2) for j in range(chunks)] # used to index the middel frame of each chunk
# select training samples
selected_tubes, target_tubes = train_select(i, history[i-2], targets, tubes, args)
######### Start training ########
# flatten list of tubes
flat_targets, _ = flatten_tubes(target_tubes, batch_idx=False)
flat_tubes, _ = flatten_tubes(selected_tubes, batch_idx=True) # add batch_idx for ROI pooling
flat_targets = torch.FloatTensor(flat_targets).to(conv_feat)
flat_tubes = torch.FloatTensor(flat_tubes).to(conv_feat)
# ROI Pooling
pooled_feat = nets['roi_net'](conv_feat[:, T_start:T_start+T_length].contiguous(), flat_tubes)
_,C,W,H = pooled_feat.size()
pooled_feat = pooled_feat.view(-1, T_length, C, W, H)
temp_context_feat = None
if not args.no_context:
temp_context_feat = torch.zeros((pooled_feat.size(0),context_feat.size(1),T_length,1,1)).to(context_feat)
for p in range(pooled_feat.size(0)):
temp_context_feat[p] = context_feat[int(flat_tubes[p,0,0].item()/T_length),:,T_start:T_start+T_length].contiguous().clone()
_,_,_,_, cur_loss_global_cls, cur_loss_local_loc, cur_loss_neighbor_loc = nets['det_net%d' % (i-1)](pooled_feat, context_feat=temp_context_feat, tubes=flat_tubes, targets=flat_targets)
cur_loss_global_cls = cur_loss_global_cls.mean()
cur_loss_local_loc = cur_loss_local_loc.mean()
cur_loss_neighbor_loc = cur_loss_neighbor_loc.mean()
cur_loss = cur_loss_global_cls + \
cur_loss_local_loc * args.lambda_reg + \
cur_loss_neighbor_loc * args.lambda_neighbor
loss_back += cur_loss.to(conv_feat.device)
losses[i-1].update(cur_loss.item())
if cur_loss_neighbor_loc.item() > 0:
losses_neighbor_loc.update(cur_loss_neighbor_loc.item())
########### Gradient updates ############
# record last step only
losses_global_cls.update(cur_loss_global_cls.item())
losses_local_loc.update(cur_loss_local_loc.item())
if args.fp16:
loss_back /= args.max_iter # prevent gradient overflow
with amp.scale_loss(loss_back, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss_back.backward()
optimizer.step()
############### Print logs and save models ############
iteration += 1
if iteration % args.print_step == 0 and iteration>0:
gpu_memory = get_gpu_memory()
torch.cuda.synchronize()
t1 = time.perf_counter()
batch_time.update(t1 - t0)
print_line = 'Epoch {}/{}({}) Iteration {:06d} lr {:.2e} '.format(
epochs+1, args.max_epochs, epoch_size, iteration, lr)
for i in range(args.max_iter):
print_line += 'loss-{} {:.3f} '.format(i+1, losses[i].avg)
print_line += 'loss_global_cls {:.3f} loss_local_loc {:.3f} loss_neighbor_loc {:.3f} Timer {:0.3f}({:0.3f}) GPU usage: {}'.format(
losses_global_cls.avg, losses_local_loc.avg, losses_neighbor_loc.avg, batch_time.val, batch_time.avg, gpu_memory)
torch.cuda.synchronize()
t0 = time.perf_counter()
log_file.write(print_line+'\n')
print(print_line)
if (iteration % args.save_step == 0) and iteration>0:
print('Saving state, iter:', iteration)
save_name = args.save_root+'checkpoint_'+str(iteration) + '.pth'
save_dict = {
'epochs': epochs+1,
'iteration': iteration,
'base_net': nets['base_net'].state_dict(),
'context_net': nets['context_net'].state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'val_mAP': best_mAP,
'cfg': args}
for i in range(args.max_iter):
save_dict['det_net%d' % i] = nets['det_net%d' % i].state_dict()
torch.save(save_dict, save_name)
# only keep the latest model
if os.path.isfile(args.save_root+'checkpoint_'+str(iteration-args.save_step) + '.pth'):
os.remove(args.save_root+'checkpoint_'+str(iteration-args.save_step) + '.pth')
print (args.save_root+'checkpoint_'+str(iteration-args.save_step) + '.pth removed!')
# For consistency when resuming from the middle of an epoch
if iteration % epoch_size == 0 and iteration > 0:
break
##### Validation at the end of each epoch #####
validate_epochs = [0,1,5,9,13,14]
if epochs in validate_epochs:
torch.cuda.synchronize()
tvs = time.perf_counter()
for _, net in nets.items():
net.eval() # switch net to evaluation mode
print('Validating at ', iteration)
all_metrics = validate(args, val_dataloader, nets, iteration, iou_thresh=args.iou_thresh)
prt_str = ''
for i in range(args.max_iter):
prt_str += 'Iter '+str(i+1)+': MEANAP =>'+str(all_metrics[i]['PascalBoxes_Precision/[email protected]'])+'\n'
print(prt_str)
log_file.write(prt_str)
log_file.write("Best MEANAP so far => {}\n".format(best_mAP))
for i in class_whitelist:
log_file.write("({}) {}: {}\n".format(i,id2class[i],
all_metrics[-1]["PascalBoxes_PerformanceByCategory/[email protected]/{}".format(id2class[i])]))
# writer.add_scalar('mAP', all_metrics[-1]['PascalBoxes_Precision/[email protected]'], iteration)
# for key, ap in all_metrics[-1].items():
# writer.add_scalar(key, ap, iteration)
if all_metrics[-1]['PascalBoxes_Precision/[email protected]'] > best_mAP:
best_mAP = all_metrics[-1]['PascalBoxes_Precision/[email protected]']
print('Saving current best model, iter:', iteration)
save_name = args.save_root+'checkpoint_best.pth'
save_dict = {
'epochs': epochs+1,
'iteration': iteration,
'base_net': nets['base_net'].state_dict(),
'context_net': nets['context_net'].state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'val_mAP': best_mAP,
'cfg': args}
for i in range(args.max_iter):
save_dict['det_net%d' % i] = nets['det_net%d' % i].state_dict()
torch.save(save_dict, save_name)
for _, net in nets.items():
net.train() # switch net to training mode
torch.cuda.synchronize()
t0 = time.perf_counter()
prt_str2 = '\nValidation TIME::: {:0.3f}\n\n'.format(t0-tvs)
print(prt_str2)
log_file.write(prt_str2)
epochs += 1
log_file.close()
# writer.close()
def validate(args, val_dataloader, nets, iteration=0, iou_thresh=0.5):
"""
Test the model on validation set
"""
# write results to files for evaluation
output_files = []
fouts = []
for i in range(args.max_iter):
output_file = args.save_root+'val_result-'+str(iteration)+'-iter'+str(i+1)+'.csv'
output_files.append(output_file)
f = open(output_file, 'w')
fouts.append(f)
gt_file = args.save_root+'val_gt.csv'
fout = open(gt_file, 'w')
with torch.no_grad(): # for evaluation
for num, (images, targets, tubes, infos) in enumerate(val_dataloader):
if (num+1) % 100 == 0:
print ("%d / %d" % (num+1, len(val_dataloader.dataset)/args.batch_size))
for b in range(len(infos)):
for n in range(len(infos[b]['boxes'])):
mid = int(len(infos[b]['boxes'][n])/2)
box = infos[b]['boxes'][n][mid]
labels = infos[b]['labels'][n][mid]
for label in labels:
fout.write('{0},{1:04},{2:.4},{3:.4},{4:.4},{5:.4},{6}\n'.format(
infos[b]['video_name'],
infos[b]['fid'],
box[0], box[1], box[2], box[3],
label))
_, _, channels, height, width = images.size()
images = images.cuda()
# get conv features
conv_feat = nets['base_net'](images)
context_feat = None
if not args.no_context:
context_feat = nets['context_net'](conv_feat)
############## Inference ##############
history, _ = inference(args, conv_feat, context_feat, nets, args.max_iter, tubes)
#################### Evaluation #################
# loop for each iteration
for i in range(len(history)):
pred_prob = history[i]['pred_prob'].cpu()
pred_prob = pred_prob[:,int(pred_prob.shape[1]/2)]
pred_tubes = history[i]['pred_loc'].cpu()
pred_tubes = pred_tubes[:,int(pred_tubes.shape[1]/2)]
tubes_nums = history[i]['tubes_nums']
# loop for each sample in a batch
tubes_count = 0
for b in range(len(tubes_nums)):
info = infos[b]
seq_start = tubes_count
tubes_count = tubes_count + tubes_nums[b]
cur_pred_prob = pred_prob[seq_start:seq_start+tubes_nums[b]]
cur_pred_tubes = pred_tubes[seq_start:seq_start+tubes_nums[b]]
# do NMS first
all_scores = []
all_boxes = []
all_idx = []
for cl_ind in range(args.num_classes):
scores = cur_pred_prob[:, cl_ind].squeeze().reshape(-1)
c_mask = scores.gt(args.conf_thresh) # greater than minmum threshold
scores = scores[c_mask]
idx = np.where(c_mask.numpy())[0]
if len(scores) == 0:
all_scores.append([])
all_boxes.append([])
continue
boxes = cur_pred_tubes.clone()
l_mask = c_mask.unsqueeze(1).expand_as(boxes)
boxes = boxes[l_mask].view(-1, 4)
boxes = valid_tubes(boxes.view(-1,1,4)).view(-1,4)
keep = nms(boxes, scores, args.nms_thresh)
boxes = boxes[keep].numpy()
scores = scores[keep].numpy()
idx = idx[keep]
boxes[:, ::2] /= width
boxes[:, 1::2] /= height
all_scores.append(scores)
all_boxes.append(boxes)
all_idx.append(idx)
# get the top scores
scores_list = [(s,cl_ind,j) for cl_ind,scores in enumerate(all_scores) for j,s in enumerate(scores)]
if args.evaluate_topk > 0:
scores_list.sort(key=lambda x: x[0])
scores_list = scores_list[::-1]
scores_list = scores_list[:args.topk]
for s,cl_ind,j in scores_list:
# write to files
box = all_boxes[cl_ind][j]
fouts[i].write('{0},{1:04},{2:.4},{3:.4},{4:.4},{5:.4},{6},{7:.4}\n'.format(
info['video_name'],
info['fid'],
box[0],box[1],box[2],box[3],
label_dict[cl_ind],
s))
fout.close()
all_metrics = []
for i in range(args.max_iter):
fouts[i].close()
metrics = ava_evaluation(os.path.join(args.data_root, 'label/'), output_files[i], gt_file)
all_metrics.append(metrics)
return all_metrics
if __name__ == '__main__':
main()