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train_all_torque_task.py
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import os
import random
import argparse
import time
import pandas
import pickle
import sys
sys.path.append('../code')
sys.path.append('./code')
from nn_rmodels_torque import ConvRModel, RecurrentRModel, ConvRModel_new, RecurrentRModel_new
from nn_train_rutils_torque import *
from path_utils import PATH_TO_DATA_RL, PATH_TO_OLD
def load_df(model_type, arch_type):
""" Function to load dataframe based on model type and architecture type.
Outputs:
- all_conv_models: dataframe containing all the networks to train
- key_trained: key to set when train is finished
"""
if model_type == 'conv_new':
all_conv_models = pickle.load(open(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_newhyp_seed.p', 'rb'))
elif model_type == 'conv':
all_conv_models = pickle.load(open(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_seed.p', 'rb'))
elif model_type == 'rec':
all_conv_models = pickle.load(open(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_seed.p', 'rb'))
elif model_type == 'rec_new':
all_conv_models = pickle.load(open(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_newhyp_seed.p', 'rb'))
if (model_type == 'conv_new') or (model_type == 'conv'):
best_models_arch = all_conv_models[all_conv_models['arch_type'] == arch_type] #.nlargest(1, 'test_accuracy')
key_trained = 'is_trained'
else:
best_models_arch = all_conv_models[all_conv_models['rec_blocktype'] == arch_type]
key_trained = 'is_training'
return best_models_arch, key_trained
def save_df(best_models_arch, model_type, arch_type):
""" Function to save the updated dataframe.
"""
if model_type == 'conv_new':
best_models_arch.to_pickle(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_newhyp_seed.p')
elif model_type == 'conv':
best_models_arch.to_pickle(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_seed.p')
elif model_type == 'rec':
best_models_arch.to_pickle(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_seed.p')
elif model_type == 'rec_new':
best_models_arch.to_pickle(PATH_TO_OLD + '/torque/ALL_' + arch_type + '_newhyp_seed.p')
return
def sel_exp_id(model_type):
"""Return the experiment ID for loading the same initialization
Args:
model_type (str): Define the model type
Returns:
int: Experiment ID of the corresponding untrained models
"""
if model_type == 'conv_new':
old_exp_id = 115
elif model_type == 'conv':
old_exp_id = 15
elif model_type == 'rec':
old_exp_id = 45
elif model_type == 'rec_new':
old_exp_id = 5045
return old_exp_id
def main(args):
# Load dataset
exp_id = args.exp_id
train_data_path = os.path.join(PATH_TO_DATA_RL, 'dataset_train_rl_torque.hdf5')
val_data_path = os.path.join(PATH_TO_DATA_RL, 'dataset_val_rl_torque.hdf5')
train_data = Dataset(train_data_path, val_data_path, 'train', key='spindle_info', target_key='torque_coords', reach_key = 'target_coords')
test_data_path = os.path.join(PATH_TO_DATA_RL, 'dataset_test_rl_torque.hdf5')
test_data = Dataset(test_data_path, dataset_type='test', key='spindle_info', target_key='torque_coords', reach_key = 'target_coords')
n_outputs = train_data.train_targets.shape[1]
start_id = args.start_id
end_id = args.end_id
model_type = args.type
arch_type = args.arch_type
n_layersfc = args.n_layersfc
best_models_arch, key_trained = load_df(model_type, arch_type)
old_exp_id = sel_exp_id(model_type)
for i in range(start_id, end_id):
print('---------------------------------')
print('Training model: ', i)
print('---------------------------------')
if not best_models_arch.iloc[i][key_trained]:
latents = best_models_arch.iloc[i]
if model_type == 'conv':
# Create model
mymodel = ConvRModel(
experiment_id=exp_id, #i
arch_type=arch_type,
nlayers=latents['nlayers'],
n_skernels=latents['n_skernels'],
n_tkernels=latents['n_tkernels'],
s_kernelsize=latents['s_kernelsize'],
t_kernelsize=latents['t_kernelsize'],
s_stride=latents['s_stride'],
t_stride=latents['t_stride'],
n_outputs= n_outputs,
nlayers_fc=n_layersfc,
nunits= [args.proj_size for _ in range(n_layersfc)],
projector_rl=True) #,
elif model_type == 'conv_new':
# Create model
mymodel = ConvRModel_new(
experiment_id=exp_id, #i
arch_type=arch_type,
nlayers=latents['nlayers'],
n_skernels=latents['n_skernels'],
n_tkernels=latents['n_tkernels'],
s_kernelsize=latents['s_kernelsize'],
t_kernelsize=latents['t_kernelsize'],
s_stride=latents['s_stride'],
t_stride=latents['t_stride'],
n_outputs= n_outputs,
nlayers_fc=n_layersfc,
nunits= [args.proj_size for _ in range(n_layersfc)],
projector_rl=True) #,
elif model_type == 'rec':
mymodel = RecurrentRModel(
experiment_id=args.exp_id,
rec_blocktype=arch_type,
n_recunits=latents['n_recunits'],
npplayers=latents['npplayers'],
nppfilters=latents['nppfilters'],
s_kernelsize=latents['s_kernelsize'],
s_stride=latents['s_stride'],
n_outputs= n_outputs,
nlayers_fc=n_layersfc,
nunits= [args.proj_size for _ in range(n_layersfc)],
seed=latents['seed'])
elif model_type == 'rec_new':
mymodel = RecurrentRModel_new(
experiment_id=args.exp_id,
rec_blocktype=arch_type,
n_reclayers=latents['n_reclayers'],
n_recunits=latents['n_recunits'],
npplayers=latents['npplayers'],
nppfilters=latents['nppfilters'],
s_kernelsize=latents['s_kernelsize'],
s_stride=latents['s_stride'],
seed=latents['seed'],
n_outputs= n_outputs,
nlayers_fc=n_layersfc,
nunits= [args.proj_size for _ in range(n_layersfc)],
projector_rl=True)
print(mymodel.__dict__)
intime = time.time()
# Create trainer and train!
mytrainer = Trainer(mymodel, train_data, test_data)
if model_type == 'rec':
mytrainer.train(num_epochs=70, learning_rate=1e-3, batch_size=512,
early_stopping_epochs=1, verbose=True, save_rand=True, retrain_same_init=True, old_exp_dir = old_exp_id)
else:
mytrainer.train(num_epochs=50, batch_size = 512, verbose=True, save_rand=True, retrain_same_init=True, old_exp_dir = old_exp_id)
outt = time.time()
print(f'Successfully trained model {i+1} / {args.end_id - args.start_id} in {(outt-intime)/60} minutes.')
best_models_arch, key_trained = load_df(model_type, arch_type)
best_models_arch.at[i,key_trained] = True
save_df(best_models_arch, model_type, arch_type)
if __name__=='__main__':
parser = argparse.ArgumentParser(description='Training Convolutional Nets for PCR.')
# parser.add_argument('--old_models', type=str, help='Name of old conv models',default='ALL_spatial_temporal')
parser.add_argument('--type', type=str, help='Type of model',default='conv')
parser.add_argument('--arch_type', type=str, help='Architecture of specific model',default='spatial_temporal')
parser.add_argument('--exp_id', type=int, help='Experiment ID',default=42)
parser.add_argument('--proj_size', type=int, help='Size of the projector',default=128)
parser.add_argument('--n_layersfc', type=int, help='Number of fully connected layers',default=1)
parser.add_argument('--start_id', type=int, help='Id of net to start',default=0)
parser.add_argument('--end_id', type=int, help='Id of net to end',default=1)
main(parser.parse_args())