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test.py
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test.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
from utils.tube_utils import valid_tubes, compute_box_iou
from data.ava import AVADataset, detection_collate, WIDTH, HEIGHT
from data.augmentations import BaseTransform
from utils.eval_utils import ava_evaluation
from external.ActivityNet.Evaluation.get_ava_performance import read_labelmap
def main():
################## Load pretrained model and configurations ###################
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)
if not os.path.isdir(args.save_root):
os.makedirs(args.save_root)
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
################ 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 weights
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 = AVADataset(args.data_root, 'test', 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)
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()
# write results to files for evaluation
output_files = []
fouts = []
for i in range(args.max_iter):
output_file = args.save_root+'testing_result-iter'+str(i+1)+'.csv'
output_files.append(output_file)
f = open(output_file, 'w')
fouts.append(f)
gt_file = args.save_root+'testing_gt.csv'
fout = open(gt_file, 'w')
torch.cuda.synchronize()
t0 = time.time()
with torch.no_grad(): # for evaluation
for num, (images, targets, tubes, infos) in enumerate(dataloader):
if (num+1) % 100 == 0:
print ("%d / %d" % (num+1, len(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)
# Logging
log_name = args.save_root+"testing_results.log"
log_file = open(log_name, "w", 1)
prt_str = ''
for i in range(args.max_iter):
prt_str += 'Iter '+str(i+1)+': MEANAP =>'+str(all_metrics[i]['PascalBoxes_Precision/mAP@0.5IOU'])+'\n'
log_file.write(prt_str)
for i in class_whitelist:
log_file.write("({}) {}: {}\n".format(i,id2class[i],
all_metrics[-1]["PascalBoxes_PerformanceByCategory/AP@0.5IOU/{}".format(id2class[i])]))
log_file.close()
if __name__ == "__main__":
main()