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main.py
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main.py
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import numpy as np
import random
import tensorflow as tf
import logging
import imageio
from data_generator import DataGenerator
from mil import MIL
from evaluation.eval_reach import evaluate_vision_reach
from evaluation.eval_push import evaluate_push
from tensorflow.python.platform import flags
FLAGS = flags.FLAGS
LOGGER = logging.getLogger(__name__)
## Dataset/method options
flags.DEFINE_string('experiment', 'sim_reach', 'sim_vision_reach or sim_push')
flags.DEFINE_string('demo_file', None, 'path to the directory where demo files that containing robot states and actions are stored')
flags.DEFINE_string('demo_gif_dir', None, 'path to the videos of demonstrations')
flags.DEFINE_string('gif_prefix', 'object', 'prefix of the video directory for each task, e.g. object_0 for task 0')
flags.DEFINE_integer('im_width', 100, 'width of the images in the demo videos, 125 for sim_push, and 80 for sim_vision_reach')
flags.DEFINE_integer('im_height', 90, 'height of the images in the demo videos, 125 for sim_push, and 64 for sim_vision_reach')
flags.DEFINE_integer('num_channels', 3, 'number of channels of the images in the demo videos')
flags.DEFINE_integer('T', 50, 'time horizon of the demo videos, 50 for reach, 100 for push')
flags.DEFINE_bool('hsv', False, 'convert the image to HSV format')
flags.DEFINE_bool('use_noisy_demos', False, 'use noisy demonstrations or not (for domain shift)')
flags.DEFINE_string('noisy_demo_gif_dir', None, 'path to the videos of noisy demonstrations')
flags.DEFINE_string('noisy_demo_file', None, 'path to the directory where noisy demo files that containing robot states and actions are stored')
flags.DEFINE_bool('no_action', False, 'do not include actions in the demonstrations for inner update')
flags.DEFINE_bool('no_state', False, 'do not include states in the demonstrations during training')
flags.DEFINE_bool('no_final_eept', False, 'do not include final ee pos in the demonstrations for inner update')
flags.DEFINE_bool('zero_state', False, 'zero-out states (meta-learn state) in the demonstrations for inner update (used in the paper with video-only demos)')
flags.DEFINE_bool('two_arms', False, 'use two-arm structure when state is zeroed-out')
flags.DEFINE_integer('training_set_size', -1, 'size of the training set, 1500 for sim_reach, 693 for sim push, and \
-1 for all data except those in validation set')
flags.DEFINE_integer('val_set_size', 150, 'size of the training set, 150 for sim_reach and 76 for sim push')
## Training options
flags.DEFINE_integer('metatrain_iterations', 50000, 'number of metatraining iterations.') # 30k for pushing, 50k for reaching and placing
flags.DEFINE_integer('meta_batch_size', 12, 'number of tasks sampled per meta-update') # 5 for reaching, 15 for pushing, 12 for placing
flags.DEFINE_float('meta_lr', 0.001, 'the base learning rate of the generator')
flags.DEFINE_integer('update_batch_size', 1, 'number of examples used for inner gradient update (K for K-shot learning).')
flags.DEFINE_float('train_update_lr', 1e-3, 'step size alpha for inner gradient update.') # 0.001 for reaching, 0.01 for pushing and placing
flags.DEFINE_integer('num_updates', 1, 'number of inner gradient updates during training.') # 5 for placing
flags.DEFINE_bool('clip', False, 'use gradient clipping for fast gradient')
flags.DEFINE_float('clip_max', 10.0, 'maximum clipping value for fast gradient')
flags.DEFINE_float('clip_min', -10.0, 'minimum clipping value for fast gradient')
flags.DEFINE_bool('fc_bt', True, 'use bias transformation for the first fc layer')
flags.DEFINE_bool('all_fc_bt', False, 'use bias transformation for all fc layers')
flags.DEFINE_bool('conv_bt', True, 'use bias transformation for the first conv layer, N/A for using pretraining')
flags.DEFINE_integer('bt_dim', 10, 'the dimension of bias transformation for FC layers')
flags.DEFINE_string('pretrain_weight_path', 'N/A', 'path to pretrained weights')
flags.DEFINE_bool('train_pretrain_conv1', False, 'whether to finetune the pretrained weights')
flags.DEFINE_bool('two_head', False, 'use two-head architecture')
flags.DEFINE_bool('learn_final_eept', False, 'learn an auxiliary loss for predicting final end-effector pose')
flags.DEFINE_bool('learn_final_eept_whole_traj', False, 'learn an auxiliary loss for predicting final end-effector pose \
by passing the whole trajectory of eepts (used for video-only models)')
flags.DEFINE_bool('stopgrad_final_eept', True, 'stop the gradient when concatenate the predicted final eept with the feature points')
flags.DEFINE_integer('final_eept_min', 6, 'first index of the final eept in the action array')
flags.DEFINE_integer('final_eept_max', 8, 'last index of the final eept in the action array')
flags.DEFINE_float('final_eept_loss_eps', 0.1, 'the coefficient of the auxiliary loss')
flags.DEFINE_float('act_loss_eps', 1.0, 'the coefficient of the action loss')
flags.DEFINE_float('loss_multiplier', 100.0, 'the constant multiplied with the loss value, 100 for reach and 50 for push')
flags.DEFINE_bool('use_l1_l2_loss', False, 'use a loss with combination of l1 and l2')
flags.DEFINE_float('l2_eps', 0.01, 'coeffcient of l2 loss')
flags.DEFINE_bool('shuffle_val', False, 'whether to choose the validation set via shuffling or not')
## Model options
flags.DEFINE_integer('random_seed', 0, 'random seed for training')
flags.DEFINE_bool('fp', True, 'use spatial soft-argmax or not')
flags.DEFINE_string('norm', 'layer_norm', 'batch_norm, layer_norm, or None')
flags.DEFINE_bool('dropout', False, 'use dropout for fc layers or not')
flags.DEFINE_float('keep_prob', 0.5, 'keep probability for dropout')
flags.DEFINE_integer('num_filters', 64, 'number of filters for conv nets -- 64 for placing, 16 for pushing, 40 for reaching.')
flags.DEFINE_integer('filter_size', 3, 'filter size for conv nets -- 3 for placing, 5 for pushing, 3 for reaching.')
flags.DEFINE_integer('num_conv_layers', 5, 'number of conv layers -- 5 for placing, 4 for pushing, 3 for reaching.')
flags.DEFINE_integer('num_strides', 3, 'number of conv layers with strided filters -- 3 for placing, 4 for pushing, 3 for reaching.')
flags.DEFINE_bool('conv', True, 'whether or not to use a convolutional network, only applicable in some cases')
flags.DEFINE_integer('num_fc_layers', 3, 'number of fully-connected layers')
flags.DEFINE_integer('layer_size', 100, 'hidden dimension of fully-connected layers')
flags.DEFINE_bool('temporal_conv_2_head', False, 'whether or not to use temporal convolutions for the two-head architecture in video-only setting.')
flags.DEFINE_bool('temporal_conv_2_head_ee', False, 'whether or not to use temporal convolutions for the two-head architecture in video-only setting \
for predicting the ee pose.')
flags.DEFINE_integer('temporal_filter_size', 5, 'filter size for temporal convolution')
flags.DEFINE_integer('temporal_num_filters', 64, 'number of filters for temporal convolution')
flags.DEFINE_integer('temporal_num_filters_ee', 64, 'number of filters for temporal convolution for ee pose prediction')
flags.DEFINE_integer('temporal_num_layers', 3, 'number of layers for temporal convolution for ee pose prediction')
flags.DEFINE_integer('temporal_num_layers_ee', 3, 'number of layers for temporal convolution for ee pose prediction')
flags.DEFINE_string('init', 'random', 'initializer for conv weights. Choose among random, xavier, and he')
flags.DEFINE_bool('max_pool', False, 'Whether or not to use max pooling rather than strided convolutions')
flags.DEFINE_bool('stop_grad', False, 'if True, do not use second derivatives in meta-optimization (for speed)')
## Logging, saving, and testing options
flags.DEFINE_bool('log', True, 'if false, do not log summaries, for debugging code.')
flags.DEFINE_string('log_dir', '/tmp/data', 'directory for summaries and checkpoints.')
flags.DEFINE_bool('resume', False, 'resume training if there is a model available')
flags.DEFINE_bool('train', True, 'True to train, False to test.')
flags.DEFINE_integer('restore_iter', 0, 'iteration to load model (-1 for latest model)')
flags.DEFINE_integer('train_update_batch_size', -1, 'number of examples used for gradient update during training \
(use if you want to test with a different number).')
flags.DEFINE_integer('test_update_batch_size', 1, 'number of demos used during test time')
flags.DEFINE_float('gpu_memory_fraction', 1.0, 'fraction of memory used in gpu')
flags.DEFINE_bool('record_gifs', True, 'record gifs during evaluation')
def train(graph, model, saver, sess, data_generator, log_dir, restore_itr=0):
"""
Train the model.
"""
PRINT_INTERVAL = 100
TEST_PRINT_INTERVAL = PRINT_INTERVAL*5
SUMMARY_INTERVAL = 100
SAVE_INTERVAL = 1000
TOTAL_ITERS = FLAGS.metatrain_iterations
prelosses, postlosses = [], []
save_dir = log_dir + '/model'
train_writer = tf.summary.FileWriter(log_dir, graph)
# actual training.
if restore_itr == 0:
training_range = range(TOTAL_ITERS)
else:
training_range = range(restore_itr+1, TOTAL_ITERS)
for itr in training_range:
state, tgt_mu = data_generator.generate_data_batch(itr)
statea = state[:, :FLAGS.update_batch_size*FLAGS.T, :]
stateb = state[:, FLAGS.update_batch_size*FLAGS.T:, :]
actiona = tgt_mu[:, :FLAGS.update_batch_size*FLAGS.T, :]
actionb = tgt_mu[:, FLAGS.update_batch_size*FLAGS.T:, :]
feed_dict = {model.statea: statea,
model.stateb: stateb,
model.actiona: actiona,
model.actionb: actionb}
input_tensors = [model.train_op]
if itr % SUMMARY_INTERVAL == 0 or itr % PRINT_INTERVAL == 0:
input_tensors.extend([model.train_summ_op, model.total_loss1, model.total_losses2[model.num_updates-1]])
with graph.as_default():
results = sess.run(input_tensors, feed_dict=feed_dict)
if itr != 0 and itr % SUMMARY_INTERVAL == 0:
prelosses.append(results[-2])
train_writer.add_summary(results[-3], itr)
postlosses.append(results[-1])
if itr != 0 and itr % PRINT_INTERVAL == 0:
print 'Iteration %d: average preloss is %.2f, average postloss is %.2f' % (itr, np.mean(prelosses), np.mean(postlosses))
prelosses, postlosses = [], []
if itr != 0 and itr % TEST_PRINT_INTERVAL == 0:
if FLAGS.val_set_size > 0:
input_tensors = [model.val_summ_op, model.val_total_loss1, model.val_total_losses2[model.num_updates-1]]
val_state, val_act = data_generator.generate_data_batch(itr, train=False)
statea = val_state[:, :FLAGS.update_batch_size*FLAGS.T, :]
stateb = val_state[:, FLAGS.update_batch_size*FLAGS.T:, :]
actiona = val_act[:, :FLAGS.update_batch_size*FLAGS.T, :]
actionb = val_act[:, FLAGS.update_batch_size*FLAGS.T:, :]
feed_dict = {model.statea: statea,
model.stateb: stateb,
model.actiona: actiona,
model.actionb: actionb}
with graph.as_default():
results = sess.run(input_tensors, feed_dict=feed_dict)
train_writer.add_summary(results[0], itr)
print 'Test results: average preloss is %.2f, average postloss is %.2f' % (np.mean(results[1]), np.mean(results[2]))
if itr != 0 and (itr % SAVE_INTERVAL == 0 or itr == training_range[-1]):
print 'Saving model to: %s' % (save_dir + '_%d' % itr)
with graph.as_default():
saver.save(sess, save_dir + '_%d' % itr)
def generate_test_demos(data_generator):
if not FLAGS.use_noisy_demos:
n_folders = len(data_generator.demos.keys())
demos = data_generator.demos
else:
n_folders = len(data_generator.noisy_demos.keys())
demos = data_generator.noisy_demos
policy_demo_idx = [np.random.choice(n_demo, replace=False, size=FLAGS.test_update_batch_size) \
for n_demo in [demos[i]['demoX'].shape[0] for i in xrange(n_folders)]]
selected_demoO, selected_demoX, selected_demoU = [], [], []
for i in xrange(n_folders):
selected_cond = np.array(demos[i]['demoConditions'])[np.arange(len(demos[i]['demoConditions'])) == policy_demo_idx[i]]
Xs, Us, Os = [], [], []
for idx in selected_cond:
if FLAGS.use_noisy_demos:
demo_gif_dir = data_generator.noisy_demo_gif_dir
else:
demo_gif_dir = data_generator.demo_gif_dir
O = np.array(imageio.mimread(demo_gif_dir + data_generator.gif_prefix + '_%d/cond%d.samp0.gif' % (i, idx)))[:, :, :, :3]
O = np.transpose(O, [0, 3, 2, 1]) # transpose to mujoco setting for images
O = O.reshape(FLAGS.T, -1) / 255.0 # normalize
Os.append(O)
Xs.append(demos[i]['demoX'][np.arange(demos[i]['demoX'].shape[0]) == policy_demo_idx[i]].squeeze())
Us.append(demos[i]['demoU'][np.arange(demos[i]['demoU'].shape[0]) == policy_demo_idx[i]].squeeze())
selected_demoO.append(np.array(Os))
selected_demoX.append(np.array(Xs))
selected_demoU.append(np.array(Us))
print "Finished collecting demos for testing"
selected_demo = dict(selected_demoX=selected_demoX, selected_demoU=selected_demoU, selected_demoO=selected_demoO)
data_generator.selected_demo = selected_demo
def main():
tf.set_random_seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
# Build up environment to prevent segfault
if not FLAGS.train:
if 'reach' in FLAGS.experiment:
env = gym.make('ReacherMILTest-v1')
ob = env.reset()
# import pdb; pdb.set_trace()
graph = tf.Graph()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
tf_config = tf.ConfigProto(gpu_options=gpu_options)
sess = tf.Session(graph=graph, config=tf_config)
network_config = {
'num_filters': [FLAGS.num_filters]*FLAGS.num_conv_layers,
'strides': [[1, 2, 2, 1]]*FLAGS.num_strides + [[1, 1, 1, 1]]*(FLAGS.num_conv_layers-FLAGS.num_strides),
'filter_size': FLAGS.filter_size,
'image_width': FLAGS.im_width,
'image_height': FLAGS.im_height,
'image_channels': FLAGS.num_channels,
'n_layers': FLAGS.num_fc_layers,
'layer_size': FLAGS.layer_size,
'initialization': FLAGS.init,
}
data_generator = DataGenerator()
state_idx = data_generator.state_idx
img_idx = range(len(state_idx), len(state_idx)+FLAGS.im_height*FLAGS.im_width*FLAGS.num_channels)
# need to compute x_idx and img_idx from data_generator
model = MIL(data_generator._dU, state_idx=state_idx, img_idx=img_idx, network_config=network_config)
# TODO: figure out how to save summaries and checkpoints
exp_string = FLAGS.experiment+ '.' + FLAGS.init + '_init.' + str(FLAGS.num_conv_layers) + '_conv' + '.' + str(FLAGS.num_strides) + '_strides' + '.' + str(FLAGS.num_filters) + '_filters' + \
'.' + str(FLAGS.num_fc_layers) + '_fc' + '.' + str(FLAGS.layer_size) + '_dim' + '.bt_dim_' + str(FLAGS.bt_dim) + '.mbs_'+str(FLAGS.meta_batch_size) + \
'.ubs_' + str(FLAGS.update_batch_size) + '.numstep_' + str(FLAGS.num_updates) + '.updatelr_' + str(FLAGS.train_update_lr)
if FLAGS.clip:
exp_string += '.clip_' + str(int(FLAGS.clip_max))
if FLAGS.conv_bt:
exp_string += '.conv_bt'
if FLAGS.all_fc_bt:
exp_string += '.all_fc_bt'
if FLAGS.fp:
exp_string += '.fp'
if FLAGS.learn_final_eept:
exp_string += '.learn_ee_pos'
if FLAGS.no_action:
exp_string += '.no_action'
if FLAGS.zero_state:
exp_string += '.zero_state'
if FLAGS.two_head:
exp_string += '.two_heads'
if FLAGS.two_arms:
exp_string += '.two_arms'
if FLAGS.temporal_conv_2_head:
exp_string += '.1d_conv_act_' + str(FLAGS.temporal_num_layers) + '_' + str(FLAGS.temporal_num_filters)
if FLAGS.temporal_conv_2_head_ee:
exp_string += '_ee_' + str(FLAGS.temporal_num_layers_ee) + '_' + str(FLAGS.temporal_num_filters_ee)
exp_string += '_' + str(FLAGS.temporal_filter_size) + 'x1_filters'
if FLAGS.training_set_size != -1:
exp_string += '.' + str(FLAGS.training_set_size) + '_trials'
log_dir = FLAGS.log_dir + '/' + exp_string
# put here for now
if FLAGS.train:
data_generator.generate_batches(noisy=FLAGS.use_noisy_demos)
with graph.as_default():
train_image_tensors = data_generator.make_batch_tensor(network_config, restore_iter=FLAGS.restore_iter)
inputa = train_image_tensors[:, :FLAGS.update_batch_size*FLAGS.T, :]
inputb = train_image_tensors[:, FLAGS.update_batch_size*FLAGS.T:, :]
train_input_tensors = {'inputa': inputa, 'inputb': inputb}
val_image_tensors = data_generator.make_batch_tensor(network_config, restore_iter=FLAGS.restore_iter, train=False)
inputa = val_image_tensors[:, :FLAGS.update_batch_size*FLAGS.T, :]
inputb = val_image_tensors[:, FLAGS.update_batch_size*FLAGS.T:, :]
val_input_tensors = {'inputa': inputa, 'inputb': inputb}
model.init_network(graph, input_tensors=train_input_tensors, restore_iter=FLAGS.restore_iter)
model.init_network(graph, input_tensors=val_input_tensors, restore_iter=FLAGS.restore_iter, prefix='Validation_')
else:
model.init_network(graph, prefix='Testing')
with graph.as_default():
# Set up saver.
saver = tf.train.Saver(max_to_keep=10)
# Initialize variables.
init_op = tf.global_variables_initializer()
sess.run(init_op, feed_dict=None)
# Start queue runners (used for loading videos on the fly)
tf.train.start_queue_runners(sess=sess)
if FLAGS.resume:
model_file = tf.train.latest_checkpoint(log_dir)
if FLAGS.restore_iter > 0:
model_file = model_file[:model_file.index('model')] + 'model_' + str(FLAGS.restore_iter)
if model_file:
ind1 = model_file.index('model')
resume_itr = int(model_file[ind1+6:])
print("Restoring model weights from " + model_file)
with graph.as_default():
saver.restore(sess, model_file)
if FLAGS.train:
train(graph, model, saver, sess, data_generator, log_dir, restore_itr=FLAGS.restore_iter)
else:
if 'reach' in FLAGS.experiment:
generate_test_demos(data_generator)
evaluate_vision_reach(env, graph, model, data_generator, sess, exp_string, FLAGS.record_gifs, log_dir)
elif 'push' in FLAGS.experiment:
evaluate_push(sess, graph, model, data_generator, exp_string, log_dir, FLAGS.demo_file + '/', save_video=FLAGS.record_gifs)
else:
raise NotImplementedError
if __name__ == "__main__":
main()