def main():
cudnn.benchmark = False
test_video = Test_video(short_side=[224, 256])
model = slowfastnet.resnet50(class_num=Config.CLASS_NUM)
assert Config.LOAD_MODEL_PATH is not None
print("load model from:", Config.LOAD_MODEL_PATH)
pretrained_dict = torch.load(Config.LOAD_MODEL_PATH, map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda(params['gpu'][0])
validation(model, test_video)
def main():
cudnn.benchmark = False
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
logdir = os.path.join(params['log'], cur_time)
if not os.path.exists(logdir):
os.makedirs(logdir)
writer = SummaryWriter(log_dir=logdir)
print("Loading dataset")
train_dataloader = \
DataLoader(
VideoDataset(params['dataset'], mode='train', clip_len=params['clip_len'], frame_sample_rate=params['frame_sample_rate']),
batch_size=params['batch_size'], shuffle=True, num_workers=params['num_workers'])
val_dataloader = \
DataLoader(
VideoDataset(params['dataset'], mode='train', clip_len=params['clip_len'], frame_sample_rate=params['frame_sample_rate']),
batch_size=params['batch_size'], shuffle=False, num_workers=params['num_workers'])
print("load model")
model = slowfastnet.resnet50(class_num=params['num_classes'])
if params['pretrained'] is not None:
pretrained_dict = torch.load(params['pretrained'], map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda(params['gpu'][0])
model = nn.DataParallel(model, device_ids=params['gpu']) # multi-Gpu
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(model.parameters(),
lr=params['learning_rate'],
momentum=params['momentum'],
weight_decay=params['weight_decay'])
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=params['step'],
gamma=0.1)
model_save_dir = os.path.join(params['save_path'], cur_time)
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
for epoch in range(params['epoch_num']):
train(model, train_dataloader, epoch, criterion, optimizer, writer)
if (epoch + 1) % 5 == 0:
validation(model, val_dataloader, epoch, criterion, optimizer,
writer)
scheduler.step()
if epoch % 1 == 0:
checkpoint = os.path.join(
model_save_dir, "clip_len_" + str(params['clip_len']) +
"frame_sample_rate_" + str(params['frame_sample_rate']) +
"_checkpoint_" + str(epoch) + ".pth.tar")
torch.save(model.module.state_dict(), checkpoint)
writer.close()
def main():
# prepare images path
cls_name = os.listdir(params["test_video_path"])
videos_name = []
labels = np.empty([0], np.int64)
for i, cls in enumerate(cls_name):
sub_path = os.path.join(params["test_video_path"], cls)
sub_names = os.listdir(sub_path)
videos_name += [os.path.join(sub_path, name) for name in sub_names]
labels = np.concatenate(
(labels, np.ones([len(sub_names)], np.int64) * i), 0)
if len(videos_name) == 0:
raise Exception("no image found in {}".format(
params["test_video_path"]))
# Start inference
batch_size = 1
model = slowfastnet.resnet50(class_num=params['num_classes'])
model.train(False)
if params['pretrained'] is not None:
pretrained_dict = torch.load(params['pretrained'], map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda(params['gpu'][0])
with torch.no_grad():
def fix_images(img_list, frame_num):
img_list = img_list[frame_num:]
return img_list
top1 = AverageMeter()
for i in range(0, len(videos_name)):
capture = cv2.VideoCapture(videos_name[i])
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print('total frame:', frame_count)
frame_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
left = 0
top = 0
if frame_height < frame_width:
resize_height = CROP_SIZE
resize_width = int(
float(resize_height) / frame_height * frame_width)
left = (resize_width - CROP_SIZE) // 2
else:
resize_width = CROP_SIZE
resize_height = int(
float(resize_width) / frame_width * frame_height)
top = (resize_height - CROP_SIZE) // 2
frame_list = []
trace_torch_model = True
while True:
ret, frame = capture.read()
if ret:
# will resize frames if not already final size
# if (frame_height != resize_height) or (frame_width != resize_width):
# frame = cv2.resize(frame, (resize_width, resize_height))
frame = cv2.resize(frame, (CROP_SIZE, CROP_SIZE))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = frame.astype(np.float32)
# frame = frame[top:top+CROP_SIZE,left:left+CROP_SIZE]
frame = (frame / 128.0).astype(np.float32) - np.array(
[[[1.0, 1.0, 1.0]]], np.float32)
frame_list.append(frame)
if len(frame_list) == params['clip_len']:
tensor_frame = np.array(frame_list).transpose(
(3, 0, 1, 2))
inputs = torch.from_numpy(tensor_frame).cuda()
outputs = model(inputs.unsqueeze(0))
# if trace_torch_model:
# trace_model = torch.jit.trace(model,inputs.unsqueeze(0))
# pred_out = trace_model(inputs.unsqueeze(0))
# pred_out1 = trace_model(torch.ones_like(inputs).unsqueeze(0).cuda())
# trace_torch_model = False
# os.makedirs('./weights',exist_ok=True)
# trace_model.save('./weights/fighting_detect_model.pt')
prec1, = accuracy(outputs.data, labels[i])
if prec1.item() < 1:
print('current presicion ', prec1.item(),
'avg presicon ', top1.avg, 'video name ',
videos_name[i])
top1.update(prec1.item(), outputs.size(0))
frame_list = fix_images(frame_list, 1)
else:
break
capture.release()
print_string = 'Top-1 accuracy: {top1_acc:.2f}%'.format(
top1_acc=top1.avg)
print(print_string)
def main():
cudnn.benchmark = False
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
logdir = os.path.join(params.log, cur_time)
if not os.path.exists(logdir):
os.makedirs(logdir)
writer = SummaryWriter(log_dir=logdir)
print("Loading dataset")
train_dataloader = \
DataLoader(
VideoDataset(params.dataset, mode='train', clip_len=params.clip_len, frame_sample_rate=params.frame_sample_rate),
batch_size=params.batch_size, shuffle=True, num_workers=params.num_workers)
val_dataloader = \
DataLoader(
VideoDataset(params.dataset, mode='validation', clip_len=params.clip_len, frame_sample_rate=params.frame_sample_rate),
batch_size=params.batch_size, shuffle=False, num_workers=params.num_workers)
print("load model")
model = slowfastnet.resnet50(class_num=params.num_classes)
if params.pretrained is not None:
pretrained_dict = torch.load(params.pretrained, map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("Load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda(params.gpu[0])
model = nn.DataParallel(model, device_ids=params.gpu) # multi-Gpu
criterion = nn.CrossEntropyLoss().cuda(params.gpu[0])
optimizer = optim.SGD(model.parameters(),
lr=params.learning_rate,
momentum=params.momentum,
weight_decay=params.weight_decay)
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=params.step,
gamma=0.1)
model_save_dir = os.path.join(params.save_path, cur_time)
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
best_loss = 1e6
best_valid = dict(top1_acc=0., top5_acc=0., epoch=0)
no_loss_decrease_count = 0
for epoch in range(params.epoch_num):
train_top1_acc, train_top5_acc, train_loss = train(
model, train_dataloader, epoch, criterion, optimizer, writer)
if (epoch + 1) % params.val_freq == 0:
val_top1_acc, val_top5_acc, val_loss = validation(
model, val_dataloader, epoch, criterion, writer)
if val_top1_acc > best_valid['top1_acc']:
best_valid['top1_acc'] = val_top1_acc
best_valid['top5_acc'] = val_top5_acc
best_valid['epoch'] = epoch
if train_loss < best_loss:
best_loss = train_loss
no_loss_decrease_count = 0
else:
no_loss_decrease_count += 1
if no_loss_decrease_count >= params.patience:
print(
f'Early stop on Epoch {epoch} with patience {params.patience}')
write_exp_log(f'[{epoch}] Early stop')
break
scheduler.step()
if epoch % 1 == 0:
checkpoint = os.path.join(
model_save_dir, "clip_len_" + str(params.clip_len) +
"frame_sample_rate_" + str(params.frame_sample_rate) +
"_checkpoint_" + str(epoch) + ".pth.tar")
torch.save(model.module.state_dict(), checkpoint)
print(
f'Best Validated model was found on epoch {best_valid["epoch"]}: Top1 acc: {best_valid["top1_acc"]} Top5 acc: {best_valid["top5_acc"]}'
)
write_exp_log(
f'Best model found on epoch {best_valid["epoch"]}: Top1 acc: {best_valid["top1_acc"]} Top5 acc: {best_valid["top5_acc"]}'
)
writer.close()
lr=params['learning_rate'],
momentum=params['momentum'],
weight_decay=params['weight_decay'])
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=params['step'],
gamma=0.1)
model_save_dir = os.path.join(params['save_path'], cur_time)
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
for epoch in range(params['epoch_num']):
train(model, train_dataloader, epoch, criterion, optimizer, writer)
if epoch % 2 == 0:
validation(model, val_dataloader, epoch, criterion, optimizer,
writer)
scheduler.step()
if epoch % 1 == 0:
checkpoint = os.path.join(
model_save_dir, "clip_len_" + str(params['clip_len']) +
"frame_sample_rate_" + str(params['frame_sample_rate']) +
"_checkpoint_" + str(epoch) + ".pth.tar")
torch.save(model.module.state_dict(), checkpoint)
writer.close()
if __name__ == '__main__':
model = slowfastnet.resnet50(class_num=params['num_classes'])
print(model)
main()
def main():
'''
torch.backends.cudnn.benchmark = True
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
logdir = os.path.join(params['log'], cur_time)
if not os.path.exists(logdir):
os.makedirs(logdir)
'''
#writer = SummaryWriter(log_dir=logdir)
#videos = videos_reales()
with open('index.txt', 'w') as f:
f.close()
print("Loading dataset")
data = VideoDataset(params['dataset'],
mode='validation',
clip_len=64,
frame_sample_rate=1)
#print('hola')
val_dataloader = DataLoader(data,
batch_size=1,
shuffle=False,
num_workers=8)
print("load model")
model = slowfastnet.resnet50(class_num=params['num_classes'])
if params['pretrained'] is not None:
pretrained_dict = torch.load(params['pretrained'], map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda(params['gpu'][0])
model = nn.DataParallel(model, device_ids=params['gpu']) # multi-Gpu
model.eval()
#contadores agregados para guardar video
n = 0
m = 0
#esto se puede generalizar recorriendo una lista de las clases y haciendo un for despues de otro
dic_predict_1 = dict()
dic_predict_1['cellphone'] = 0
dic_predict_1['nothing'] = 0
dic_predict_2 = dict()
dic_predict_2['cellphone'] = 0
dic_predict_2['nothing'] = 0
predicciones = dict()
predicciones['cellphone'] = dic_predict_1
predicciones['nothing'] = dic_predict_2
end = time.time()
with torch.no_grad():
for step, (inputs, labels) in enumerate(val_dataloader):
#data_time.update(time.time() - end)
#largo batch, canales, numero de imagenes, (112, 112)
with open('index.txt', 'r') as f:
list_index = f.readlines()
f.close()
labels_2 = labels
inputs_cpu = np.array(inputs)
inputs = inputs.cuda()
#largo 16 por que hay 16 videos por batch
labels = labels.cuda()
#videos,clases 16,101, filas,columnas
outputs = model(inputs)
res, correct_k = accuracy(outputs.data, labels)
video_path = data.fnames[int(list_index[n].rstrip('\n'))]
capture = cv2.VideoCapture(video_path)
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
frame_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
out_np = outputs.cpu().detach().numpy()
index = np.argmax(out_np)
'''
if (int(correct_k.cpu()/100) != (index == np.array(labels_2)[0])):
print('ESTA MAL')
print(int(correct_k.cpu() / 100), index, np.array(labels_2)[0])
'''
#ESTO DEJA AL DICCIONARIO COMO predicciones[lo que realemente es][lo que penso que era]
#se puede mejorar leyendo el archivo class_labels.txt para hacerlo general
if index == 0:
texto = 'cellphone'
if np.array(labels_2) == 0:
predicciones['cellphone']['cellphone'] += 1
else:
predicciones['nothing']['cellphone'] += 1
else:
texto = 'nothing'
if np.array(labels_2) == 0:
predicciones['cellphone']['nothing'] += 1
else:
predicciones['nothing']['nothing'] += 1
#print(list_index, outputs)
#modificacion para guardar ciertos videos de la validacion y escribir lo que infiere la red
vid_writer = cv2.VideoWriter('videos_out/video_' + str(n) + '.avi',
cv2.VideoWriter_fourcc(*'MPEG'), 20,
(frame_width, frame_height), True)
while capture.isOpened():
ret, imag = capture.read()
if imag is None:
break
cv2.putText(imag, texto,
(frame_width - 200, frame_height - 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
vid_writer.write(imag.astype(np.uint8))
capture.release()
n += 1
print(predicciones)
def main():
cudnn.benchmark = False
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
logdir = os.path.join(params['log'], cur_time)
if not os.path.exists(logdir):
os.makedirs(logdir)
writer = SummaryWriter(log_dir=logdir)
if params['train_val_Shuffle']:
with open(params['tagg_info_txt'], 'r', encoding='utf-8') as f:
lines = f.readlines()
random.shuffle(lines)
train_nums = 0.9 * len(lines)
out_file_train = open(params['filename_imglist_train'],
'w',
encoding='utf-8')
out_file_val = open(params['filename_imglist_val'],
'w',
encoding='utf-8')
try:
for index, line in tqdm.tqdm(enumerate(lines),
total=len(lines)):
out_file = out_file_train if index < train_nums else out_file_val
line = line.strip()
if len(line.split('\t')) != 2: return
path, label_strings = line.split('\t')
vid = os.path.basename(path)
out_file.write(vid + '\t' + label_strings + '\n')
except Exception as e:
print(e)
else:
if os.path.exists(params['filename_imglist_train']) and os.path.exists(
params['filename_imglist_val']):
print("loading existing train and val splits!!")
else:
return 0
print("Loading dataset")
train_dataloader = \
DataLoader(
VideoDataset(params['dataset'], params['filename_imglist_train'], params['label_id_file'], mode='train', clip_len=params['clip_len'], frame_sample_rate=params['frame_sample_rate']),
batch_size=params['batch_size'], shuffle=True, num_workers=params['num_workers'])
val_dataloader = \
DataLoader(
VideoDataset(params['dataset'], params['filename_imglist_val'],params['label_id_file'], mode='validation', clip_len=params['clip_len'], frame_sample_rate=params['frame_sample_rate']),
batch_size=params['batch_size'], shuffle=False, num_workers=params['num_workers'])
print("load model")
model = slowfastnet.resnet50(class_num=params['num_classes'])
if params['pretrained'] is not None:
pretrained_dict = torch.load(params['pretrained'], map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda(params['gpu'][0])
model = nn.DataParallel(model, device_ids=params['gpu']) # multi-Gpu
criterion = loss.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=params['learning_rate'],
momentum=params['momentum'],
weight_decay=params['weight_decay'])
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=params['step'],
gamma=0.1)
model_save_dir = os.path.join(params['save_path'], cur_time)
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
gamma = params['gamma']
for epoch in range(params['epoch_num']):
if params['train_rule'] == 'DRW':
idx = epoch // 30
# idx = 1
betas = [0, 0.99]
cls_num_list = [100] * params['num_classes']
effective_num = 1.0 - np.power(betas[idx], cls_num_list)
per_cls_weights = (1.0 - betas[idx]) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
cls_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights)
else:
per_cls_weights = torch.ones(1)
print(f"per_cls_weights{per_cls_weights}")
train(train_dataloader, model, criterion, optimizer, epoch,
per_cls_weights, gamma, writer)
if epoch % 2 == 0:
gap = validate2(val_dataloader,
model,
criterion,
epoch,
tfwriter=writer)
is_best = gap > best_gap
best_gap = max(gap, best_gap)
writer.add_scalar('acc/val_gap_best', best_gap, epoch)
output_best = 'Best val gap: %.3f\n' % (best_gap)
print(output_best)
if is_best and gap > 0.75:
checkpoint = os.path.join(
model_save_dir, "clip_len_" + str(params['clip_len']) +
"frame_sample_rate_" + str(params['frame_sample_rate']) +
"_checkpoint_" + str(epoch) + "_gap" + str(gap) +
".pth.tar")
torch.save(model.module.state_dict(), checkpoint)
scheduler.step()
writer.close
