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run_i3d_net.py
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run_i3d_net.py
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#!/usr/bin/env python
import os
import sys
import argparse
import numpy as np
from models.I3D_Net import I3D_Net
from data_providers.utils import get_data_provider_by_path
# resource.setrlimit(resource.RLIMIT_STACK, (2**29,-1))
sys.setrecursionlimit(2000)
train_params = {
'num_classes': 2,
'batch_size': 10,
'n_epochs': 100,
'crop_size': (224, 224),
'sequence_length': 10,
'initial_learning_rate': 0.001,
'reduce_lr_epoch_1': 30, # epochs * 0.5
'reduce_lr_epoch_2': 55, # epochs * 0.75
'validation_set': True,
'validation_split': None, # None or float
'queue_size': 500,
'normalization': 'std', # None, divide_256, divide_255, std
}
# Inception module (Inc.) parameters --> dict, each key denotes the parameters of the four branches
# e.g. 'Inc_1': {'Branch_0': [out_channels, ksize], 'Branch_1': [out_channels_1, ksize_1, out_channels_2, ksize_2],
# 'Branch_2': [out_channels_1, ksize_1, out_channels_2, ksize_2], 'Branch_3': [pool_ksize,
# pool_strides, out_channles, ksize]}
# the Inception module (Inc.) architecture can get in the paper: https://arxiv.org/pdf/1705.07750.pdf
Inc_params = {'Inc_1': {'Branch_0': [64, 1], 'Branch_1': [96, 1, 128, 3], 'Branch_2': [16, 1, 32, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 32, 1]},
'Inc_2': {'Branch_0': [128, 1], 'Branch_1': [128, 1, 192, 3], 'Branch_2': [32, 1, 96, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 64, 1]},
'Inc_3': {'Branch_0': [192, 1], 'Branch_1': [96, 1, 208, 3], 'Branch_2': [16, 1, 48, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 64, 1]},
'Inc_4': {'Branch_0': [160, 1], 'Branch_1': [112, 1, 224, 3], 'Branch_2': [24, 1, 64, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 64, 1]},
'Inc_5': {'Branch_0': [128, 1], 'Branch_1': [128, 1, 256, 3], 'Branch_2': [24, 1, 64, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 64, 1]},
'Inc_6': {'Branch_0': [112, 1], 'Branch_1': [144, 1, 288, 3], 'Branch_2': [32, 1, 64, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 64, 1]},
'Inc_7': {'Branch_0': [256, 1], 'Branch_1': [160, 1, 320, 3], 'Branch_2': [32, 1, 128, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 128, 1]},
'Inc_8': {'Branch_0': [256, 1], 'Branch_1': [160, 1, 320, 3], 'Branch_2': [32, 1, 128, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 128, 1]},
'Inc_9': {'Branch_0': [384, 1], 'Branch_1': [192, 1, 384, 3], 'Branch_2': [48, 1, 128, 3],
'Branch_3': [[1, 3, 3, 3, 1], [1, 1, 1, 1, 1], 128, 1]},
}
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--train', action='store_true',
help='Train the model')
parser.add_argument(
'--test', action='store_true',
help='Test model for required dataset if pretrained model exists.'
'If provided together with `--train` flag testing will be'
'performed right after training.')
parser.add_argument(
'--demo', action='store_true',
help='Test model for required video if pretrained model exists.')
parser.add_argument(
'--dataset', '-ds', type=str,
help='Path to the dataset')
parser.add_argument(
'--video_path1', '-ds1', type=str,
help='Path to the video1')
parser.add_argument(
'--video_path2', '-ds2', type=str,
help='Path to the video2')
parser.add_argument(
'--keep_prob', '-kp', type=float, default=1.0, metavar='',
help="Keep probability for dropout.")
parser.add_argument(
'--gpu_id', '-gid', type=str, default='0',
help='Specify the gpu ID to run the program')
parser.add_argument(
'--weight_decay', '-wd', type=float, default=1e-4, metavar='',
help='Weight decay for optimizer (default: %(default)s)')
parser.add_argument(
'--nesterov_momentum', '-nm', type=float, default=0.9, metavar='',
help='Nesterov momentum (default: %(default)s)')
parser.add_argument(
'--eval_type', '-type', type=str, default='joint',
help='use to specify the input type of the model, should be ‘rgb’, ‘flow’ , ’motempl‘, or ‘joint’')
parser.add_argument(
'--logs', dest='should_save_logs', action='store_true',
help='Write tensorflow logs')
parser.add_argument(
'--no-logs', dest='should_save_logs', action='store_false',
help='Do not write tensorflow logs')
parser.set_defaults(should_save_logs=True)
parser.add_argument(
'--saves', dest='should_save_model', action='store_true',
help='Save model during training')
parser.add_argument(
'--no-saves', dest='should_save_model', action='store_false',
help='Do not save model during training')
parser.set_defaults(should_save_model=True)
parser.add_argument(
'--renew-logs', dest='renew_logs', action='store_true',
help='Erase previous logs for model if exists.')
parser.add_argument(
'--not-renew-logs', dest='renew_logs', action='store_false',
help='Do not erase previous logs for model if exists.')
parser.set_defaults(renew_logs=False)
args = parser.parse_args()
print('args.video_path1', args.video_path1)
print('args.video_path2', args.video_path2)
model_params = vars(args)
# if not args.train and not args.test or not args.dataset or not args.evaluate:
# print("You should train or test your network. Please check params.")
# parser.print_help()
# exit()
# ==========================================================================
# LIMITE THE USAGE OF THE GPU
# =========================================================================
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
# ==========================================================================
# LIMITE THE USAGE OF THE GPU
# =========================================================================
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
# ==========================================================================
# LOG FILE SETTING
# ==========================================================================
# write all the log to the file without buffer
# f = open('log.txt', 'wb', 0)
# sys.stdout = f
# sys.stderr = f
# ==========================================================================
# PARAMETERS PRINTING
# ==========================================================================
# some default params dataset/architecture related
print("Params:")
for k, v in model_params.items():
print("\t%s: %s" % (k, v))
print("Train params:")
for k, v in train_params.items():
print("\t%s: %s" % (k, v))
# ==========================================================================
# DATA PREPARATION
# ==========================================================================
train_params['test'] = args.test
train_params['train'] = args.train
if not args.train:
train_params['validation_set'] = False
data_provider = get_data_provider_by_path(args.dataset, train_params)
# ==========================================================================
# TRAINING & TESTING & EVALUATING
# ==========================================================================
print("Initialize the model..")
model_params['sequence_length'] = train_params['sequence_length']
model_params['crop_size'] = train_params['crop_size']
model_params['Inc_params'] = Inc_params
# print('model_params:', model_params['crop_size'])
# print('model_params[crop_size][0]:', model_params['crop_size'][0])
# print('model_params[crop_size][1]:', model_params['crop_size'][1])
model = I3D_Net(data_provider=data_provider, **model_params)
# data_provider.data_shape
if args.train:
print("Data provider train videos: ", data_provider.train.num_examples)
model.train_all_epochs(train_params)
# Test the trained model on test sets
if args.test:
if not args.train:
model.load_model()
print("Data provider test videos: ", data_provider.test.num_examples)
print("Testing...")
losses = []
accuracies = []
for i in range(10):
loss, accuracy = model.test(data_provider.test, batch_size=10)
losses.append(loss)
accuracies.append(accuracy)
loss = np.mean(losses)
accuracy = np.mean(accuracies)
print("mean cross_entropy: %f, mean accuracy: %f" % (loss, accuracy))
# evaluate the model on camera
if args.demo:
if not args.train and not args.test:
model.load_model()
print("Evaluate the trained model on video.")
if args.eval_type == 'rgb':
print('Evaluate the trained model on rgb input data!!!')
model.evaluate_rgb_model(args.video_path1, args.video_path2, model_params)
elif args.eval_type == 'flow':
print('Evaluate the trained model on flow input data!!!')
model.evaluate_flow_model(args.video_path1, args.video_path2, model_params)
elif args.eval_type == 'motempl':
print('Evaluate the trained model on motempl input data!!!')
model.evaluate_motempl_model(args.video_path1, args.video_path2, model_params)