def __init__(self, root_path, video_path, annotation_path, result_path,
model_path, modality):
print('***checkpoint***')
opt = parse_opts_offline([
'--root_path', root_path, '--video_path', video_path,
'--annotation_path', annotation_path, '--result_path', result_path,
'--resume_path', model_path, '--dataset', 'ems',
'--sample_duration', '32', '--model', 'resnext', '--model_depth',
'101', '--resnet_shortcut', 'B', '--batch_size', '1',
'--n_finetune_classes', '4', '--n_threads', '1', '--checkpoint',
'1', '--modality', modality, '--n_val_samples', '1',
'--test_subset', 'test'
])
if opt.root_path != '':
opt.video_path = os.path.join(opt.root_path, opt.video_path)
opt.annotation_path = os.path.join(opt.root_path,
opt.annotation_path)
# opt.result_path = os.path.join(opt.root_path, opt.result_path)
# if opt.resume_path:
# opt.resume_path = os.path.join(opt.root_path, opt.resume_path)
if opt.pretrain_path:
opt.pretrain_path = os.path.join(opt.root_path,
opt.pretrain_path)
opt.scales = [opt.initial_scale]
for i in range(1, opt.n_scales):
opt.scales.append(opt.scales[-1] * opt.scale_step)
opt.arch = '{}-{}'.format(opt.model, opt.model_depth)
opt.mean = get_mean(opt.norm_value)
opt.std = get_std(opt.norm_value)
print(opt)
#%%
warnings.filterwarnings('ignore')
torch.manual_seed(opt.manual_seed)
model, parameters = generate_model(opt)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
self.opt = opt
self.model = model
self.parameters = parameters
def main():
gpu_num = "1"
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_num
import gc
gc.disable()
fix_gpu_memory()
global opt
opt = parse_opts_offline()
if opt.cmd == "train":
train_LSTM(opt.training_path, opt.validation_path, opt.model_path,
opt.weight_path)
if opt.cmd == "test":
test_LSTM(opt.testing_path, opt.oldmodel_path, opt.testing_res,
opt.testing_res_CDF)
if opt.cmd == "transfer":
train(opt.training_path, opt.validation_path, opt.oldmodel_weight_path,
opt.model_path, opt.weight_path)
def calculate_accuracy(outputs, targets, topk=(1, )):
maxk = max(topk)
batch_size = targets.size(0)
_, pred = outputs.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(targets.view(1, -1).expand_as(pred))
ret = []
for k in topk:
correct_k = correct[:k].float().sum().item()
ret.append(correct_k / batch_size)
return ret
opt = parse_opts_offline()
if opt.root_path != '':
opt.video_path = os.path.join(opt.root_path, opt.video_path)
opt.annotation_path = os.path.join(opt.root_path, opt.annotation_path)
opt.result_path = os.path.join(opt.root_path, opt.result_path)
if opt.resume_path:
opt.resume_path = os.path.join(opt.root_path, opt.resume_path)
if opt.pretrain_path:
opt.pretrain_path = os.path.join(opt.root_path, opt.pretrain_path)
opt.scales = [opt.initial_scale]
for i in range(1, opt.n_scales):
opt.scales.append(opt.scales[-1] * opt.scale_step)
opt.arch = '{}-{}'.format(opt.model, opt.model_depth)
opt.mean = get_mean(opt.norm_value)
opt.std = get_std(opt.norm_value)