import config
import data
import model
import utils
import os
import tensorflow as tf
if __name__ == "__main__":
# LOAD EMBEDDING
word_to_index, index_to_word, word_to_vec, emb_matrix = utils.read_glove_vecs(
os.path.join(config.EMBEDDING_DIR, config.EMBEDDING_PATH))
print("Pretrained Embedding Loaded")
#LOAD CONFIG
train_config = config.TrainConfig()
test_config = config.TestConfig()
# LOAD DATA
train_data = data.DATA(train_config)
train_data.read_file(config.TRAIN_PATH, word_to_index)
print("Train data Loaded")
test_data = data.DATA(test_config)
test_data.read_file(config.TEST_PATH, word_to_index)
print("Test data Loaded")
# BUILD MODEL
#initializer = tf.random_uniform_initializer(train_config.init_scale, train_config.init_scale)
with tf.name_scope("Train"):
with tf.variable_scope("Model", reuse=None):
train_model = model.MODEL(train_config,
len(word_to_index),
# config.data_mean = data_mean
# config.data_std = data_std
# config.dim_to_ignore = dim_to_ignore
# config.dim_to_use = dim_to_use
#set = utils.normalize_data(set, config.data_mean, config.data_std, list(range(0, 48)))
self.data = set
def __getitem__(self, idx):
if self.config.datatype == 'lie':
sample = self.data[idx]
elif self.config.datatype == 'xyz':
pass
sample = self.formatdata(sample, False)
return sample
def __len__(self):
return len(self.data)
if __name__ == '__main__':
import config
config = config.TrainConfig(dataset='Human',
datatype='smpl',
action='walking',
gpu=[0],
training=True,
visualize=False)
data = CMUDataset(config, train=True)
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import config
import tools
import datautil
from bilstm_seq2seq import BilstmSeq2Seq
import subprocess
import argparse
import tools
import threading
import platform
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_path", help="the path of data", default="./data/")
parser.add_argument("-s", "--save_path", help="the path of the saved model", default="./models/")
parser.add_argument("-e", "--epoch", help="the number of epoch", default=100, type=int)
parser.add_argument("-c", "--char_emb", help="the char embedding file", default="char.emb")
parser.add_argument("-w", "--word_emb", help="the word embedding file", default="term.emb.np")
parser.add_argument("-ed", "--emb_dim", help="the word embedding size", default=128)
parser.add_argument("-hd", "--hid_dim", help="the hidden size", default=128)
parser.add_argument("-g", "--gpu", help="the id of gpu, the default is 0", default=0, type=int)
parser.add_argument("-j", "--job", help="job name.", default="bilstm term-level crf", type=str)
args = parser.parse_args()
cf = config.TrainConfig(args)
if platform.system() == 'Darwin':
cf.use_gpu = False
class TrainThread(threading.Thread):
def __init__(self):
train_iter = datautil.queue_iter()
# create time: 7/20/2019
import numpy as np
import torch
import config
from loss import linearizedlie_loss
import utils
from choose_dataset import DatasetChooser
from torch import autograd
from STLN import ST_HMR, ST_LSTM
import choose_dataset
from torch.utils.data import DataLoader
if __name__ == '__main__':
config = config.TrainConfig('Human', 'lie', 'all')
# choose = DatasetChooser(config)
# prediction_dataset, _ = choose(prediction=True)
# prediction_loader = DataLoader(prediction_dataset, batch_size=config.batch_size, shuffle=True)
choose = DatasetChooser(config)
train_dataset, bone_length = choose(train=True)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=True)
test_dataset, _ = choose(train=False)
test_loader = DataLoader(test_dataset,
batch_size=config.batch_size,
shuffle=True)
prediction_dataset, bone_length = choose(prediction=True)
prediction_loader = DataLoader(prediction_dataset,
type=str,
default='all',
dest="action",
help="choose one action in the dataset:"
"h3.6m_actions = ['directions', 'discussion', 'eating', 'greeting', 'phoning', 'posing', 'purchases', 'sitting',"
"'sittingdown', 'smoking', 'takingphoto', 'waiting', 'walking', 'walkingdog', 'walkingtogether']"
"'all means all of the above")
parser.add_argument("--dataset",
type=str,
required=True,
dest="dataset",
help="choose dataset from 'Human' or 'Mouse'")
parser.add_argument("--datatype",
type=str,
default='smpl',
dest="datatype",
help="only lie is usable")
parser.add_argument("--visualize",
type=bool,
default=False,
dest="visualize",
help="visualize the prediction or not ")
args = parser.parse_args()
config = config.TrainConfig(args.dataset, args.datatype, args.action,
args.gpu, args.training, args.visualize)
checkpoint_dir, output_dir = utils.create_directory(config)
if config.train_model is True:
train(config, checkpoint_dir)
else:
prediction(config, checkpoint_dir, output_dir)
from model import LightningModel
def init_trainer():
""" Init a Lightning Trainer using from_argparse_args
Thus every CLI command (--gpus, distributed_backend, ...) become available.
"""
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
lr_logger = LearningRateMonitor()
early_stopping = EarlyStopping(monitor='val_loss',
mode='min',
min_delta=0.001,
patience=10,
verbose=True)
return Trainer.from_argparse_args(args,
callbacks=[lr_logger, early_stopping])
def run_training(config):
""" Instanciate a datamodule, a model and a trainer and run trainer.fit(model, data) """
data = CIFAR10DataModule(config.rootdir)
model = LightningModel(config)
trainer = init_trainer()
trainer.fit(model, data)
if __name__ == '__main__':
run_training(cfg.TrainConfig())
