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demo.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 sys
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, compute_box_iou
from utils.vis_utils import overlay_image
from data.customize import CustomizedDataset, detection_collate, WIDTH, HEIGHT
from data.augmentations import BaseTransform
def main():
################## Customize your configuratons here ###################
checkpoint_path = 'pretrained/ava_step.pth'
if os.path.isfile(checkpoint_path):
print ("Loading pretrain model from %s" % checkpoint_path)
map_location = 'cuda:0'
checkpoint = torch.load(checkpoint_path, map_location=map_location)
args = checkpoint['cfg']
else:
raise ValueError("Pretrain model not found!", checkpoint_path)
# TODO: Set data_root to the customized input dataset
args.data_root = '/datasets/demo/frames/'
args.save_root = os.path.join(os.path.dirname(args.data_root), 'results/')
if not os.path.isdir(args.save_root):
os.makedirs(args.save_root)
# TODO: modify this setting according to the actual frame rate and file name
source_fps = 30
im_format = 'frame%04d.jpg'
conf_thresh = 0.4
global_thresh = 0.8 # used for cross-class NMS
################ Define models #################
gpu_count = torch.cuda.device_count()
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()
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):
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))
# load pretrained model
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):
pretrained_dict = checkpoint['det_net%d' % i]
nets['det_net%d' % i].load_state_dict(pretrained_dict)
################ DataLoader setup #################
dataset = CustomizedDataset(args.data_root, args.T, args.NUM_CHUNKS[args.max_iter], source_fps, args.fps, BaseTransform(args.image_size, args.means, args.stds,args.scale_norm), anchor_mode=args.anchor_mode, im_format=im_format)
dataloader = torch.utils.data.DataLoader(dataset, args.batch_size, num_workers=args.num_workers,
shuffle=False, collate_fn=detection_collate, pin_memory=True)
################ Inference #################
for _, net in nets.items():
net.eval()
fout = open(os.path.join(args.save_root, 'results.txt'), 'w')
torch.cuda.synchronize()
t0 = time.time()
with torch.no_grad():
for _, (images, tubes, infos) in enumerate(dataloader):
_, _, 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)
history, _ = inference(args, conv_feat, context_feat, nets, args.max_iter, tubes)
# collect result of the last step
pred_prob = history[-1]['pred_prob'].cpu()
pred_prob = pred_prob[:,int(pred_prob.shape[1]/2)]
pred_tubes = history[-1]['pred_loc'].cpu()
pred_tubes = pred_tubes[:,int(pred_tubes.shape[1]/2)]
tubes_nums = history[-1]['tubes_nums']
# loop for each 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()
c_mask = scores.gt(conf_thresh) # greater than a 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]
# merge high overlapping boxes (a simple greedy method)
merged_result = {}
flag = [1 for _ in range(len(scores_list))]
for i in range(len(scores_list)):
if flag[i]:
s, cl_ind, j = scores_list[i]
box = all_boxes[cl_ind][j]
temp = ([box], [args.label_dict[cl_ind]], [s])
# find all high IoU boxes
for ii in range(i+1, len(scores_list)):
if flag[ii]:
s2, cl_ind2, j2 = scores_list[ii]
box2 = all_boxes[cl_ind2][j2]
if compute_box_iou(box, box2) > global_thresh:
flag[ii] = 0
temp[0].append(box2)
temp[1].append(args.label_dict[cl_ind2])
temp[2].append(s2)
merged_box = np.mean(np.concatenate(temp[0], axis=0).reshape(-1,4), axis=0)
key = ','.join(merged_box.astype(str).tolist())
merged_result[key] = [(l, s) for l,s in zip(temp[1], temp[2])]
# visualize results
if not os.path.isdir(os.path.join(args.save_root, info['video_name'])):
os.makedirs(os.path.join(args.save_root, info['video_name']))
print (info)
overlay_image(os.path.join(args.data_root, info['video_name'], im_format % info['fid']),
os.path.join(args.save_root, info['video_name'], im_format % info['fid']),
pred_boxes = merged_result,
id2class = args.id2class)
# write to files
for key in merged_result:
box = np.asarray(key.split(','), dtype=np.float32)
for l, s in merged_result[key]:
fout.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],
l, s))
torch.cuda.synchronize()
t1 = time.time()
print ("Batch time: ", t1-t0)
torch.cuda.synchronize()
t0 = time.time()
fout.close()
if __name__ == "__main__":
main()