def test_open(self):
# Try opening nonexistent dataset (should raise error)
open_successful = True
try:
TensorDataset.open(TEST_TENSOR_DATASET_NAME)
except FileNotFoundError:
open_successful = False
self.assertFalse(open_successful)
def __init__(self, config: Config, detector=None):
self.config = config
self.policy_name = config.policy_name
self.max_path_length = config.max_path_length
self.elevation_max = config.elevation_max
self.elevation_min = config.elevation_min
self.roll_max = config.roll_max
self.roll_min = config.roll_min
self.save_video = True
if self.save_video:
self.imgs = []
grasp_sampler_config = grasp_sampler.GraspSampler.Config()
grasp_sampler_config.data_collection_mode = self.config.data_collection_mode
grasp_sampler_config.data_collection_from_trained_model = self.config.data_collection_from_trained_model
grasp_sampler_config.camera_yaw_list = self.config.camera_yaw_list
grasp_sampler_config.fix_view = self.config.fix_view
grasp_sampler_config.save_data_name = self.config.save_data_name
self.grasp_sampler = grasp_sampler.GraspSampler(grasp_sampler_config)
if self.config.data_collection_mode:
npoints = 1024
self.npoints = npoints
import collections
data_config = collections.OrderedDict()
data_config['datapoints_per_file'] = 10
data_config['fields'] = collections.OrderedDict()
data_config['fields']["pc"] = dict()
data_config['fields']["pc"]['dtype'] = "float32"
data_config['fields']["pc"]['height'] = npoints
data_config['fields']["pc"]['width'] = 3
data_config['fields']["grasp_rt"] = dict()
data_config['fields']["grasp_rt"]['dtype'] = "float32"
data_config['fields']["grasp_rt"]['height'] = 4
data_config['fields']["grasp_rt"]['width'] = 4
data_config['fields']["label"] = dict()
data_config['fields']["label"]['dtype'] = "int32"
data_config['fields']["pc_pose"] = dict()
data_config['fields']["pc_pose"]['dtype'] = "float32"
data_config['fields']["pc_pose"]['height'] = 4
data_config['fields']["pc_pose"]['width'] = 4
from autolab_core import TensorDataset
self.tensordata = TensorDataset(self.config.save_data_name,
data_config)
def __init__(self, dataset_path, frame=None, loop=True):
self._dataset_path = dataset_path
self._frame = frame
self._color_frame = frame
self._ir_frame = frame
self._im_index = 0
self._running = False
from autolab_core import TensorDataset
self._dataset = TensorDataset.open(self._dataset_path)
self._num_images = self._dataset.num_datapoints
self._image_rescale_factor = 1.0
if "image_rescale_factor" in self._dataset.metadata.keys():
self._image_rescale_factor = (
1.0 / self._dataset.metadata["image_rescale_factor"])
datapoint = self._dataset.datapoint(
0, [TensorDatasetVirtualSensor.CAMERA_INTR_FIELD])
camera_intr_vec = datapoint[
TensorDatasetVirtualSensor.CAMERA_INTR_FIELD]
self._color_intr = CameraIntrinsics.from_vec(
camera_intr_vec,
frame=self._color_frame).resize(self._image_rescale_factor)
self._ir_intr = CameraIntrinsics.from_vec(
camera_intr_vec,
frame=self._ir_frame).resize(self._image_rescale_factor)
def get_dataset(config, args):
# print args
# tensor_config = config['dataset']['tensors']
# for field_name in ['vertices', 'normals', 'vertex_probs', 'min_required_forces']:
# tensor_config['fields'][field_name]['height'] = args.num_samples
# tensor_config['fields']['final_poses']['height']
# return TensorDataset(args.output, tensor_config)
return TensorDataset.open(args.output, access_mode='READ_WRITE')
def __init__(self, datasets):
self.datasets = {
dataset_name: TensorDataset.open(datasets[dataset_name])
for dataset_name in datasets.keys()
}
self.obj_ids, self.id_to_datapoint = {}, []
for dataset_name in datasets.keys():
with open(datasets[dataset_name] + "/obj_ids.json",
"r") as read_file:
obj_ids = json.load(read_file)
dataset = self.datasets[dataset_name]
for i in range(dataset.num_datapoints):
datapoint = dataset.datapoint(i)
self.id_to_datapoint.append((datapoint['obj_id'], i))
self.obj_ids[dataset_name] = obj_ids
self.id_to_datapoint = {
k: list(v)
for k, v in groupby(self.id_to_datapoint, key=lambda x: x[0])
}
def run_parallel_bin_picking_benchmark(input_dataset_path,
heap_ids,
timesteps,
output_dataset_path,
config_filename):
raise NotImplementedError('Cannot run in parallel. Need to split up the heap ids and timesteps')
# load config
config = YamlConfig(config_filename)
# init ray
ray_config = config['ray']
num_cpus = ray_config['num_cpus']
ray.init(num_cpus=num_cpus,
redirect_output=ray_config['redirect_output'])
# rollouts
num_rollouts = config['num_rollouts'] // num_cpus
dataset_ids = [rollout_bin_picking_policy_in_parallel.remote(dataset_path, config_filename, num_rollouts) for i in range(num_cpus)]
dataset_filenames = ray.get(dataset_ids)
if len(dataset_filenames) == 0:
return
# merge datasets
subproc_dataset = TensorDataset.open(dataset_filenames[0])
tensor_config = subproc_dataset.config
# open dataset
dataset = TensorDataset(dataset_path, tensor_config)
dataset.add_metadata('action_ids', subproc_dataset.metadata['action_ids'])
# add datapoints
obj_id = 0
heap_id = 0
obj_ids = {}
for dataset_filename in dataset_filenames:
logging.info('Aggregating data from %s' %(dataset_filename))
j = 0
subproc_dataset = TensorDataset.open(dataset_filename)
subproc_obj_ids = subproc_dataset.metadata['obj_ids']
for datapoint in subproc_dataset:
if j > 0 and datapoint['timesteps'] == 0:
heap_id += 1
# modify object ids
for i in range(datapoint['obj_ids'].shape[0]):
subproc_obj_id = datapoint['obj_ids'][i]
if subproc_obj_id != np.uint32(-1):
subproc_obj_key = subproc_obj_ids[str(subproc_obj_id)]
if subproc_obj_key not in obj_ids.keys():
obj_ids[subproc_obj_key] = obj_id
obj_id += 1
datapoint['obj_ids'][i] = obj_ids[subproc_obj_key]
# modify grasped obj id
subproc_grasped_obj_id = datapoint['grasped_obj_ids']
grasped_obj_key = subproc_obj_ids[str(subproc_grasped_obj_id)]
datapoint['grasped_obj_ids'] = obj_ids[grasped_obj_key]
# modify heap id
datapoint['heap_ids'] = heap_id
# add datapoint to dataset
dataset.add(datapoint)
j += 1
# write to disk
obj_ids = utils.reverse_dictionary(obj_ids)
dataset.add_metadata('obj_ids', obj_ids)
dataset.flush()
def _run_prediction_single_model(self, model_dir,
model_output_dir,
dataset_config):
""" Analyze the performance of a single model. """
# read in model config
model_config_filename = os.path.join(model_dir, 'config.json')
with open(model_config_filename) as data_file:
model_config = json.load(data_file)
# load model
self.logger.info('Loading model %s' %(model_dir))
log_file = None
for handler in self.logger.handlers:
if isinstance(handler, logging.FileHandler):
log_file = handler.baseFilename
gqcnn = get_gqcnn_model(verbose=self.verbose).load(model_dir, verbose=self.verbose, log_file=log_file)
gqcnn.open_session()
gripper_mode = gqcnn.gripper_mode
angular_bins = gqcnn.angular_bins
# read params from the config
if dataset_config is None:
dataset_dir = model_config['dataset_dir']
split_name = model_config['split_name']
image_field_name = model_config['image_field_name']
pose_field_name = model_config['pose_field_name']
metric_name = model_config['target_metric_name']
metric_thresh = model_config['metric_thresh']
else:
dataset_dir = dataset_config['dataset_dir']
split_name = dataset_config['split_name']
image_field_name = dataset_config['image_field_name']
pose_field_name = dataset_config['pose_field_name']
metric_name = dataset_config['target_metric_name']
metric_thresh = dataset_config['metric_thresh']
gripper_mode = dataset_config['gripper_mode']
self.logger.info('Loading dataset %s' %(dataset_dir))
dataset = TensorDataset.open(dataset_dir)
train_indices, val_indices, _ = dataset.split(split_name)
# visualize conv filters
conv1_filters = gqcnn.filters
num_filt = conv1_filters.shape[3]
d = utils.sqrt_ceil(num_filt)
vis2d.clf()
for k in range(num_filt):
filt = conv1_filters[:,:,0,k]
vis2d.subplot(d,d,k+1)
vis2d.imshow(DepthImage(filt))
figname = os.path.join(model_output_dir, 'conv1_filters.pdf')
vis2d.savefig(figname, dpi=self.dpi)
# aggregate training and validation true labels and predicted probabilities
all_predictions = []
if angular_bins > 0:
all_predictions_raw = []
all_labels = []
for i in range(dataset.num_tensors):
# log progress
if i % self.log_rate == 0:
self.logger.info('Predicting tensor %d of %d' %(i+1, dataset.num_tensors))
# read in data
image_arr = dataset.tensor(image_field_name, i).arr
pose_arr = read_pose_data(dataset.tensor(pose_field_name, i).arr,
gripper_mode)
metric_arr = dataset.tensor(metric_name, i).arr
label_arr = 1 * (metric_arr > metric_thresh)
label_arr = label_arr.astype(np.uint8)
if angular_bins > 0:
# form mask to extract predictions from ground-truth angular bins
raw_poses = dataset.tensor(pose_field_name, i).arr
angles = raw_poses[:, 3]
neg_ind = np.where(angles < 0)
angles = np.abs(angles) % GeneralConstants.PI
angles[neg_ind] *= -1
g_90 = np.where(angles > (GeneralConstants.PI / 2))
l_neg_90 = np.where(angles < (-1 * (GeneralConstants.PI / 2)))
angles[g_90] -= GeneralConstants.PI
angles[l_neg_90] += GeneralConstants.PI
angles *= -1 # hack to fix reverse angle convention
angles += (GeneralConstants.PI / 2)
pred_mask = np.zeros((raw_poses.shape[0], angular_bins*2), dtype=bool)
bin_width = GeneralConstants.PI / angular_bins
for i in range(angles.shape[0]):
pred_mask[i, int((angles[i] // bin_width)*2)] = True
pred_mask[i, int((angles[i] // bin_width)*2 + 1)] = True
# predict with GQ-CNN
predictions = gqcnn.predict(image_arr, pose_arr)
if angular_bins > 0:
raw_predictions = np.array(predictions)
predictions = predictions[pred_mask].reshape((-1, 2))
# aggregate
all_predictions.extend(predictions[:,1].tolist())
if angular_bins > 0:
all_predictions_raw.extend(raw_predictions.tolist())
all_labels.extend(label_arr.tolist())
# close session
gqcnn.close_session()
# create arrays
all_predictions = np.array(all_predictions)
all_labels = np.array(all_labels)
train_predictions = all_predictions[train_indices]
val_predictions = all_predictions[val_indices]
train_labels = all_labels[train_indices]
val_labels = all_labels[val_indices]
if angular_bins > 0:
all_predictions_raw = np.array(all_predictions_raw)
train_predictions_raw = all_predictions_raw[train_indices]
val_predictions_raw = all_predictions_raw[val_indices]
# aggregate results
train_result = BinaryClassificationResult(train_predictions, train_labels)
val_result = BinaryClassificationResult(val_predictions, val_labels)
train_result.save(os.path.join(model_output_dir, 'train_result.cres'))
val_result.save(os.path.join(model_output_dir, 'val_result.cres'))
# get stats, plot curves
self.logger.info('Model %s training error rate: %.3f' %(model_dir, train_result.error_rate))
self.logger.info('Model %s validation error rate: %.3f' %(model_dir, val_result.error_rate))
self.logger.info('Model %s training loss: %.3f' %(model_dir, train_result.cross_entropy_loss))
self.logger.info('Model %s validation loss: %.3f' %(model_dir, val_result.cross_entropy_loss))
# save images
vis2d.figure()
example_dir = os.path.join(model_output_dir, 'examples')
if not os.path.exists(example_dir):
os.mkdir(example_dir)
# train
self.logger.info('Saving training examples')
train_example_dir = os.path.join(example_dir, 'train')
if not os.path.exists(train_example_dir):
os.mkdir(train_example_dir)
# train TP
true_positive_indices = train_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
train_result.pred_probs[j],
train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(train_example_dir, 'true_positive_%03d.png' %(i)))
# train FP
false_positive_indices = train_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
train_result.pred_probs[j],
train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(train_example_dir, 'false_positive_%03d.png' %(i)))
# train TN
true_negative_indices = train_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
train_result.pred_probs[j],
train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(train_example_dir, 'true_negative_%03d.png' %(i)))
# train TP
false_negative_indices = train_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
train_result.pred_probs[j],
train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(train_example_dir, 'false_negative_%03d.png' %(i)))
# val
self.logger.info('Saving validation examples')
val_example_dir = os.path.join(example_dir, 'val')
if not os.path.exists(val_example_dir):
os.mkdir(val_example_dir)
# val TP
true_positive_indices = val_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
val_result.pred_probs[j],
val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(val_example_dir, 'true_positive_%03d.png' %(i)))
# val FP
false_positive_indices = val_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
val_result.pred_probs[j],
val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(val_example_dir, 'false_positive_%03d.png' %(i)))
# val TN
true_negative_indices = val_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
val_result.pred_probs[j],
val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(val_example_dir, 'true_negative_%03d.png' %(i)))
# val TP
false_negative_indices = val_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(k, field_names=[image_field_name,
pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode, angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name, gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %(k,
val_result.pred_probs[j],
val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(os.path.join(val_example_dir, 'false_negative_%03d.png' %(i)))
# save summary stats
train_summary_stats = {
'error_rate': train_result.error_rate,
'ap_score': train_result.ap_score,
'auc_score': train_result.auc_score,
'loss': train_result.cross_entropy_loss
}
train_stats_filename = os.path.join(model_output_dir, 'train_stats.json')
json.dump(train_summary_stats, open(train_stats_filename, 'w'),
indent=JSON_INDENT,
sort_keys=True)
val_summary_stats = {
'error_rate': val_result.error_rate,
'ap_score': val_result.ap_score,
'auc_score': val_result.auc_score,
'loss': val_result.cross_entropy_loss
}
val_stats_filename = os.path.join(model_output_dir, 'val_stats.json')
json.dump(val_summary_stats, open(val_stats_filename, 'w'),
indent=JSON_INDENT,
sort_keys=True)
return train_result, val_result
def _run_prediction_single_model(self, model_dir, model_output_dir,
dataset_config):
""" Analyze the performance of a single model. """
# read in model config
model_config_filename = os.path.join(model_dir, 'config.json')
with open(model_config_filename) as data_file:
model_config = json.load(data_file)
# load model
logging.info('Loading model %s' % (model_dir))
gqcnn = GQCNN.load(model_dir)
gqcnn.open_session()
gripper_mode = gqcnn.gripper_mode
# read params from the config
if dataset_config is None:
dataset_dir = model_config['dataset_dir']
split_name = model_config['split_name']
image_field_name = model_config['image_field_name']
pose_field_name = model_config['pose_field_name']
metric_name = model_config['target_metric_name']
metric_thresh = model_config['metric_thresh']
else:
dataset_dir = dataset_config['dataset_dir']
split_name = dataset_config['split_name']
image_field_name = dataset_config['image_field_name']
pose_field_name = dataset_config['pose_field_name']
metric_name = dataset_config['target_metric_name']
metric_thresh = dataset_config['metric_thresh']
gripper_mode = dataset_config['gripper_mode']
logging.info('Loading dataset %s' % (dataset_dir))
dataset = TensorDataset.open(dataset_dir)
train_indices, val_indices, _ = dataset.split(split_name)
# visualize conv filters
conv1_filters = gqcnn.filters
num_filt = conv1_filters.shape[3]
d = utils.sqrt_ceil(num_filt)
vis2d.clf()
for k in range(num_filt):
filt = conv1_filters[:, :, 0, k]
vis2d.subplot(d, d, k + 1)
vis2d.imshow(DepthImage(filt))
figname = os.path.join(model_output_dir, 'conv1_filters.pdf')
vis2d.savefig(figname, dpi=self.dpi)
# aggregate training and validation true labels and predicted probabilities
all_predictions = []
all_labels = []
for i in range(dataset.num_tensors):
# log progress
if i % self.log_rate == 0:
logging.info('Predicting tensor %d of %d' %
(i + 1, dataset.num_tensors))
# read in data
image_arr = dataset.tensor(image_field_name, i).arr
pose_arr = read_pose_data(
dataset.tensor(pose_field_name, i).arr, gripper_mode)
metric_arr = dataset.tensor(metric_name, i).arr
label_arr = 1 * (metric_arr > metric_thresh)
label_arr = label_arr.astype(np.uint8)
# predict with GQ-CNN
predictions = gqcnn.predict(image_arr, pose_arr)
# aggregate
all_predictions.extend(predictions[:, 1].tolist())
all_labels.extend(label_arr.tolist())
# close session
gqcnn.close_session()
# create arrays
all_predictions = np.array(all_predictions)
all_labels = np.array(all_labels)
train_predictions = all_predictions[train_indices]
val_predictions = all_predictions[val_indices]
train_labels = all_labels[train_indices]
val_labels = all_labels[val_indices]
# aggregate results
train_result = BinaryClassificationResult(train_predictions,
train_labels)
val_result = BinaryClassificationResult(val_predictions, val_labels)
train_result.save(os.path.join(model_output_dir, 'train_result.cres'))
val_result.save(os.path.join(model_output_dir, 'val_result.cres'))
# get stats, plot curves
logging.info('Model %s training error rate: %.3f' %
(model_dir, train_result.error_rate))
logging.info('Model %s validation error rate: %.3f' %
(model_dir, val_result.error_rate))
# save images
vis2d.figure()
example_dir = os.path.join(model_output_dir, 'examples')
if not os.path.exists(example_dir):
os.mkdir(example_dir)
# train
logging.info('Saving training examples')
train_example_dir = os.path.join(example_dir, 'train')
if not os.path.exists(train_example_dir):
os.mkdir(train_example_dir)
# train TP
true_positive_indices = train_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
'Datapoint %d: Pred: %.3f Label: %.3f' %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
'true_positive_%03d.png' % (i)))
# train FP
false_positive_indices = train_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
'Datapoint %d: Pred: %.3f Label: %.3f' %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
'false_positive_%03d.png' % (i)))
# train TN
true_negative_indices = train_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
'Datapoint %d: Pred: %.3f Label: %.3f' %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
'true_negative_%03d.png' % (i)))
# train TP
false_negative_indices = train_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
'Datapoint %d: Pred: %.3f Label: %.3f' %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
'false_negative_%03d.png' % (i)))
# val
logging.info('Saving validation examples')
val_example_dir = os.path.join(example_dir, 'val')
if not os.path.exists(val_example_dir):
os.mkdir(val_example_dir)
# val TP
true_positive_indices = val_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, 'true_positive_%03d.png' % (i)))
# val FP
false_positive_indices = val_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, 'false_positive_%03d.png' % (i)))
# val TN
true_negative_indices = val_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, 'true_negative_%03d.png' % (i)))
# val TP
false_negative_indices = val_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title('Datapoint %d: Pred: %.3f Label: %.3f' %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, 'false_negative_%03d.png' % (i)))
# save summary stats
train_summary_stats = {
'error_rate': train_result.error_rate,
'ap_score': train_result.ap_score,
'auc_score': train_result.auc_score
}
train_stats_filename = os.path.join(model_output_dir,
'train_stats.json')
json.dump(train_summary_stats,
open(train_stats_filename, 'w'),
indent=JSON_INDENT,
sort_keys=True)
val_summary_stats = {
'error_rate': val_result.error_rate,
'ap_score': val_result.ap_score,
'auc_score': val_result.auc_score
}
val_stats_filename = os.path.join(model_output_dir, 'val_stats.json')
json.dump(val_summary_stats,
open(val_stats_filename, 'w'),
indent=JSON_INDENT,
sort_keys=True)
return train_result, val_result
get_model_config(model_config, .3, .9, 6.6e-04, 6.5e-02, 2.2e-01,
True)),
TopplingModel(
get_model_config(model_config, .3, .9, 6.6e-04, 6.5e-02, 2.2e-01,
False)),
TopplingModel(
get_model_config(model_config, .3, .9, 6.6e-04, 6.5e-02, 2.2e-01,
True)),
TopplingModel(
get_model_config(model_config, .3, .9, 6.6e-04, 6.5e-02, 2.2e-01,
False))
]
use_sensitivities = [False, False, True, True]
model_names = [
'Baseline', 'Baseline+Rotations', 'Baseline+Robustness', 'Robust Model'
]
env = GraspingEnv(config, config['vis'])
env.reset()
datasets, obj_id_to_keys = [], []
for dataset_name in os.listdir(args.datasets):
dataset_name = dataset_name.split(' ')[0]
dataset_path = os.path.join(args.datasets, dataset_name)
datasets.append(TensorDataset.open(dataset_path))
with open(os.path.join(dataset_path, "obj_keys.json"),
"r") as read_file:
obj_id_to_keys.append(json.load(read_file))
visualize(env, datasets, obj_id_to_keys, models, model_names,
use_sensitivities)
def compute_dataset_statistics(dataset_path,
output_path,
config):
"""
Compute the statistics of fields of a TensorDataset
Parameters
----------
dataset_path : str
path to the dataset
output_dir : str
where to save the data
config : :obj:`YamlConfig`
parameters for the analysis
"""
# parse config
analysis_fields = config['analysis_fields']
num_percentiles = config['num_percentiles']
thresholds = config['thresholds']
log_rate = config['log_rate']
num_bins = config['num_bins']
font_size = config['font_size']
line_width = config['line_width']
dpi = config['dpi']
# create dataset for the aggregated results
dataset = TensorDataset.open(dataset_path)
num_datapoints = dataset.num_datapoints
# allocate buffers
analysis_data = {}
for field in analysis_fields:
analysis_data[field] = []
# loop through dataset
for i in range(num_datapoints):
if i % log_rate == 0:
logging.info('Reading datapoint %d of %d' %(i+1, num_datapoints))
# read datapoint
datapoint = dataset.datapoint(i, analysis_fields)
for key, value in datapoint.iteritems():
analysis_data[key].append(value)
# create output CSV
stats_headers = {
'name': 'str',
'mean': 'float',
'median': 'float',
'std': 'float'
}
for i in range(num_percentiles):
pctile = int((100.0 / num_percentiles) * i)
field = '%d_pctile' %(pctile)
stats_headers[field] = 'float'
for t in thresholds:
field = 'pct_above_%.3f' %(t)
stats_headers[field] = 'float'
# analyze statistics
for field, data in analysis_data.iteritems():
# init arrays
data = np.array(data)
# init filename
stats_filename = os.path.join(output_path, '%s_stats.json' %(field))
if os.path.exists(stats_filename):
logging.warning('Statistics file %s exists!' %(stats_filename))
# stats
mean = np.mean(data)
median = np.median(data)
std = np.std(data)
stats = {
'name': str(field),
'mean': float(mean),
'median': float(median),
'std': float(std),
}
for i in range(num_percentiles):
pctile = int((100.0 / num_percentiles) * i)
pctile_field = '%d_pctile' %(pctile)
stats[pctile_field] = float(np.percentile(data, pctile))
for t in thresholds:
t_field = 'pct_above_%.3f' %(t)
stats[t_field] = float(np.mean(1 * (data > t)))
json.dump(stats,
open(stats_filename, 'w'),
indent=2,
sort_keys=True)
# histogram
num_unique = np.unique(data).shape[0]
nb = min(num_bins, data.shape[0], num_unique)
bounds = (np.min(data), np.max(data))
vis2d.figure()
utils.histogram(data,
nb,
bounds,
normalized=False,
plot=True)
vis2d.xlabel(field, fontsize=font_size)
vis2d.ylabel('Count', fontsize=font_size)
data_filename = os.path.join(output_path, 'histogram_%s.pdf' %(field))
vis2d.show(data_filename, dpi=dpi)
def __init__(self, config: Config, detector=None):
self.config = config
self.policy_name = config.policy_name
self.max_path_length = config.max_path_length
self.elevation_max = config.elevation_max
self.elevation_min = config.elevation_min
self.roll_max = config.roll_max
self.roll_min = config.roll_min
self.save_video = False #True #False
if self.save_video:
self.imgs = []
#grasp_sampler_config = grasp_sampler.GraspSampler.Config()
#self.grasp_sampler = grasp_sampler.GraspSampler(grasp_sampler_config)
model_name = "gqcnn_example_pj_fish" #"GQCNN-4.0-PJ"
fully_conv = False
gqcnn_dir = "/home/htung/2020/gqcnn/"
# Set model if provided.
model_dir = os.path.join(gqcnn_dir, "models")
model_path = os.path.join(model_dir, model_name)
config_filename = None
# Get configs.
model_config = json.load(
open(os.path.join(model_path, "config.json"), "r"))
try:
gqcnn_config = model_config["gqcnn"]
gripper_mode = gqcnn_config["gripper_mode"]
except KeyError:
gqcnn_config = model_config["gqcnn_config"]
input_data_mode = gqcnn_config["input_data_mode"]
if input_data_mode == "tf_image":
gripper_mode = GripperMode.LEGACY_PARALLEL_JAW
elif input_data_mode == "tf_image_suction":
gripper_mode = GripperMode.LEGACY_SUCTION
elif input_data_mode == "suction":
gripper_mode = GripperMode.SUCTION
elif input_data_mode == "multi_suction":
gripper_mode = GripperMode.MULTI_SUCTION
elif input_data_mode == "parallel_jaw": # this is picked
gripper_mode = GripperMode.PARALLEL_JAW
else:
raise ValueError("Input data mode {} not supported!".format(
input_data_mode))
config_filename = os.path.join(gqcnn_dir, "cfg/examples/gqcnn_pj.yaml")
# Read config.
config = YamlConfig(config_filename)
inpaint_rescale_factor = config["inpaint_rescale_factor"]
policy_config = config["policy"]
# original_gripper_width = 0.05
original_gripper_width = policy_config["gripper_width"]
self.real_gripper_width = 0.112 #0.15 #0.112
self.rescale_factor = original_gripper_width / self.real_gripper_width
#self.config.camera_fov_y = self.config.camera_fov_y * self.rescale_factor
#policy_config["gripper_width"] = 0.112
# gripper distance to the grasp point
# min_depth_offset = config['policy']["sampling"]["min_depth_offset"] = 0.015
# max_depth_offset = config['policy']["sampling"]["max_depth_offset"] = 0.05
# Make relative paths absolute.
if "gqcnn_model" in policy_config["metric"]:
policy_config["metric"]["gqcnn_model"] = model_path
if not os.path.isabs(policy_config["metric"]["gqcnn_model"]):
policy_config["metric"]["gqcnn_model"] = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "..",
policy_config["metric"]["gqcnn_model"])
# Set input sizes for fully-convolutional policy.
if fully_conv:
policy_config["metric"]["fully_conv_gqcnn_config"][
"im_height"] = depth_im.shape[0]
policy_config["metric"]["fully_conv_gqcnn_config"][
"im_width"] = depth_im.shape[1]
# Init policy.
self.policy_config = policy_config
self.policy_config["vis"]["vmin"] = 0
self.policy_config["vis"]["vmax"] = 0.5
if self.config.data_collection_mode:
self.policy_config["num_iters"] = 0
self.policy_config["random_sample"] = True
self.gqcnn_image_size = 96
import collections
data_config = collections.OrderedDict()
data_config['datapoints_per_file'] = 50
data_config['fields'] = collections.OrderedDict()
data_config['fields']["grasp_metrics"] = dict()
data_config['fields']["grasp_metrics"]['dtype'] = "float32"
data_config['fields']["grasps"] = dict()
data_config['fields']["grasps"]['dtype'] = "float32"
data_config['fields']["grasps"]['height'] = 6
data_config['fields']["split"] = dict()
data_config['fields']["split"]['dtype'] = "uint8"
data_config['fields']["tf_depth_ims"] = dict()
data_config['fields']["tf_depth_ims"]['dtype'] = "float32"
data_config['fields']["tf_depth_ims"][
'height'] = self.gqcnn_image_size
data_config['fields']["tf_depth_ims"][
'width'] = self.gqcnn_image_size
data_config['fields']["tf_depth_ims"]['channels'] = 1
from autolab_core import TensorDataset
self.tensordata = TensorDataset(self.config.save_data_name,
data_config)
#import ipdb; ipdb.set_trace()
policy_type = "cem"
if "type" in policy_config:
policy_type = policy_config["type"]
if policy_type == "ranking":
self.policy = RobustGraspingPolicy(policy_config)
elif policy_type == "cem":
self.policy = CrossEntropyRobustGraspingPolicy(policy_config)
else:
raise ValueError("Invalid policy type: {}".format(policy_type))
input_dataset_names = cfg["input_datasets"]
output_dataset_name = cfg["output_dataset"]
display_rate = cfg["display_rate"]
# modify list of dataset names
all_input_dataset_names = []
for dataset_name in input_dataset_names:
tensor_dir = os.path.join(dataset_name, "tensors")
if os.path.exists(tensor_dir):
all_input_dataset_names.append(dataset_name)
else:
dataset_subdirs = utils.filenames(dataset_name, tag="dataset_")
all_input_dataset_names.extend(dataset_subdirs)
# open tensor dataset
dataset = TensorDataset.open(all_input_dataset_names[0])
tensor_config = copy.deepcopy(dataset.config)
for field_name in cfg["exclude_fields"]:
if field_name in tensor_config["fields"].keys():
del tensor_config["fields"][field_name]
field_names = tensor_config["fields"].keys()
alt_field_names = [
f if f != "rewards" else "grasp_metrics" for f in field_names
]
# init tensor dataset
output_dataset = TensorDataset(output_dataset_name, tensor_config)
# copy config
out_config_filename = os.path.join(output_dataset_name,
"merge_config.yaml")
def test_single_read_write(self):
# seed
np.random.seed(SEED)
random.seed(SEED)
# open dataset
create_successful = True
try:
dataset = TensorDataset(TEST_TENSOR_DATASET_NAME, TENSOR_CONFIG)
except:
create_successful = False
self.assertTrue(create_successful)
# check field names
write_datapoint = dataset.datapoint_template
for field_name in write_datapoint.keys():
self.assertTrue(field_name in dataset.field_names)
# add the datapoint
write_datapoint['float_value'] = np.random.rand()
write_datapoint['int_value'] = int(100 * np.random.rand())
write_datapoint['str_value'] = utils.gen_experiment_id()
write_datapoint['vector_value'] = np.random.rand(HEIGHT)
write_datapoint['matrix_value'] = np.random.rand(HEIGHT, WIDTH)
write_datapoint['image_value'] = np.random.rand(
HEIGHT, WIDTH, CHANNELS)
dataset.add(write_datapoint)
# check num datapoints
self.assertTrue(dataset.num_datapoints == 1)
# add metadata
metadata_num = np.random.rand()
dataset.add_metadata('test', metadata_num)
# check written arrays
dataset.flush()
for field_name in dataset.field_names:
filename = os.path.join(TEST_TENSOR_DATASET_NAME, 'tensors',
'%s_00000.npz' % (field_name))
value = np.load(filename)['arr_0']
if isinstance(value[0], str):
self.assertTrue(value[0] == write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(value[0], write_datapoint[field_name]))
# re-open the dataset
del dataset
dataset = TensorDataset.open(TEST_TENSOR_DATASET_NAME)
# read metadata
self.assertTrue(np.allclose(dataset.metadata['test'], metadata_num))
# read datapoint
read_datapoint = dataset.datapoint(0)
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(
read_datapoint[field_name] == write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# check iterator
for read_datapoint in dataset:
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# read individual fields
for field_name in dataset.field_names:
read_datapoint = dataset.datapoint(0, field_names=[field_name])
if isinstance(read_datapoint[field_name], str):
self.assertTrue(
read_datapoint[field_name] == write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# re-open the dataset in write-only
del dataset
dataset = TensorDataset.open(TEST_TENSOR_DATASET_NAME,
access_mode=READ_WRITE_ACCESS)
# delete datapoint
dataset.delete_last()
# check that the dataset is correct
self.assertTrue(dataset.num_datapoints == 0)
self.assertTrue(dataset.num_tensors == 0)
for field_name in dataset.field_names:
filename = os.path.join(TEST_TENSOR_DATASET_NAME, 'tensors',
'%s_00000.npz' % (field_name))
self.assertFalse(os.path.exists(filename))
# remove dataset
if os.path.exists(TEST_TENSOR_DATASET_NAME):
shutil.rmtree(TEST_TENSOR_DATASET_NAME)
def test_multi_tensor_read_write(self):
# seed
np.random.seed(SEED)
random.seed(SEED)
# open dataset
dataset = TensorDataset(TEST_TENSOR_DATASET_NAME, TENSOR_CONFIG)
write_datapoints = []
for i in range(DATAPOINTS_PER_FILE + 1):
write_datapoint = {}
write_datapoint['float_value'] = np.random.rand()
write_datapoint['int_value'] = int(100 * np.random.rand())
write_datapoint['str_value'] = utils.gen_experiment_id()
write_datapoint['vector_value'] = np.random.rand(HEIGHT)
write_datapoint['matrix_value'] = np.random.rand(HEIGHT, WIDTH)
write_datapoint['image_value'] = np.random.rand(
HEIGHT, WIDTH, CHANNELS)
dataset.add(write_datapoint)
write_datapoints.append(write_datapoint)
# check num datapoints
self.assertTrue(dataset.num_datapoints == DATAPOINTS_PER_FILE + 1)
self.assertTrue(dataset.num_tensors == 2)
# check read
dataset.flush()
del dataset
dataset = TensorDataset.open(TEST_TENSOR_DATASET_NAME,
access_mode=READ_WRITE_ACCESS)
for i, read_datapoint in enumerate(dataset):
write_datapoint = write_datapoints[i]
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
for i, read_datapoint in enumerate(dataset):
# check iterator item
write_datapoint = write_datapoints[i]
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# check random item
ind = np.random.choice(dataset.num_datapoints)
write_datapoint = write_datapoints[ind]
read_datapoint = dataset.datapoint(ind)
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# check deletion
dataset.delete_last()
self.assertTrue(dataset.num_datapoints == DATAPOINTS_PER_FILE)
self.assertTrue(dataset.num_tensors == 1)
for field_name in dataset.field_names:
filename = os.path.join(TEST_TENSOR_DATASET_NAME, 'tensors',
'%s_00001.npz' % (field_name))
dataset.add(write_datapoints[-1])
for write_datapoint in write_datapoints:
dataset.add(write_datapoint)
self.assertTrue(dataset.num_datapoints == 2 *
(DATAPOINTS_PER_FILE + 1))
self.assertTrue(dataset.num_tensors == 3)
# check valid
for i in range(dataset.num_datapoints):
read_datapoint = dataset.datapoint(i)
write_datapoint = write_datapoints[i % (len(write_datapoints))]
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# check read then write out of order
ind = np.random.choice(DATAPOINTS_PER_FILE)
write_datapoint = write_datapoints[ind]
read_datapoint = dataset.datapoint(ind)
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(
read_datapoint[field_name] == write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
write_datapoint = write_datapoints[0]
dataset.add(write_datapoint)
read_datapoint = dataset.datapoint(dataset.num_datapoints - 1)
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(
read_datapoint[field_name] == write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
dataset.delete_last()
# check data integrity
for i, read_datapoint in enumerate(dataset):
write_datapoint = write_datapoints[i % len(write_datapoints)]
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# delete last
dataset.delete_last(len(write_datapoints))
self.assertTrue(dataset.num_datapoints == DATAPOINTS_PER_FILE + 1)
self.assertTrue(dataset.num_tensors == 2)
for i, read_datapoint in enumerate(dataset):
write_datapoint = write_datapoints[i]
for field_name in dataset.field_names:
if isinstance(read_datapoint[field_name], str):
self.assertTrue(read_datapoint[field_name] ==
write_datapoint[field_name])
else:
self.assertTrue(
np.allclose(read_datapoint[field_name],
write_datapoint[field_name]))
# remove dataset
if os.path.exists(TEST_TENSOR_DATASET_NAME):
shutil.rmtree(TEST_TENSOR_DATASET_NAME)
def get_dataset(config, args):
tensor_config = config['experiments']['tensors']
return TensorDataset(args.output, tensor_config)
class Grasp6DGraspController(Policy):
class Config(Config):
policy_name = "grasp_6dgrasp_controller"
policy_model_path = ""
model_name = None
max_path_length = 150
# keypoint = center + alpha * haf_bounding_box + beta
# angle = elevation * gamma * elevation*90
# [alpha1, alpha2, alpha3, beta1, beta2, beta3, gamma]
# alpha1, alpha2, alph3 = [-1, 1], beta1, beta2, beta3 = [-0.4, 0.4]
params = []
elevation_max = 90
elevation_min = 0
roll_max = 90
roll_min = 0
# camera params
camera_img_height = 128
camera_img_width = 128
camera_radius = 0.3
camera_fov_y = 45
camera_pitch = [40, 41, 2]
camera_yaw = [0, 350, 36]
camera_yaw_list = None #[0, 60, 300]
camera_save_image = False
camera_recon_scene = True
camera_lookat_pos = [1.3, 0.75, 0.4]
data_collection_mode = False
data_collection_from_trained_model = False
save_data_name = ""
fix_view = False
def __init__(self, config: Config, detector=None):
self.config = config
self.policy_name = config.policy_name
self.max_path_length = config.max_path_length
self.elevation_max = config.elevation_max
self.elevation_min = config.elevation_min
self.roll_max = config.roll_max
self.roll_min = config.roll_min
self.save_video = True
if self.save_video:
self.imgs = []
grasp_sampler_config = grasp_sampler.GraspSampler.Config()
grasp_sampler_config.data_collection_mode = self.config.data_collection_mode
grasp_sampler_config.data_collection_from_trained_model = self.config.data_collection_from_trained_model
grasp_sampler_config.camera_yaw_list = self.config.camera_yaw_list
grasp_sampler_config.fix_view = self.config.fix_view
grasp_sampler_config.save_data_name = self.config.save_data_name
self.grasp_sampler = grasp_sampler.GraspSampler(grasp_sampler_config)
if self.config.data_collection_mode:
npoints = 1024
self.npoints = npoints
import collections
data_config = collections.OrderedDict()
data_config['datapoints_per_file'] = 10
data_config['fields'] = collections.OrderedDict()
data_config['fields']["pc"] = dict()
data_config['fields']["pc"]['dtype'] = "float32"
data_config['fields']["pc"]['height'] = npoints
data_config['fields']["pc"]['width'] = 3
data_config['fields']["grasp_rt"] = dict()
data_config['fields']["grasp_rt"]['dtype'] = "float32"
data_config['fields']["grasp_rt"]['height'] = 4
data_config['fields']["grasp_rt"]['width'] = 4
data_config['fields']["label"] = dict()
data_config['fields']["label"]['dtype'] = "int32"
data_config['fields']["pc_pose"] = dict()
data_config['fields']["pc_pose"]['dtype'] = "float32"
data_config['fields']["pc_pose"]['height'] = 4
data_config['fields']["pc_pose"]['width'] = 4
from autolab_core import TensorDataset
self.tensordata = TensorDataset(self.config.save_data_name,
data_config)
@staticmethod
def compute_geom_center_from_mujoco(env, object_name):
# first get the idx of the object_geoms
body_id = env.env.sim.model.body_name2id('object0')
geom_idxs = list()
for i, assoc_body in enumerate(env.env.sim.model.geom_bodyid):
if assoc_body == body_id:
geom_idxs.append(i)
# now get the xpos and xmat of these idxs
geom_xpos = env.env.sim.data.geom_xpos[geom_idxs]
geom_xmat = env.env.sim.data.geom_xmat[geom_idxs]
# now get the vertices of belonging to each geom
# first I get the idxs associated with the name of the mesh
object_mesh_idxs = list()
for m in env.env.sim.model.mesh_names:
if object_name in m:
object_mesh_idxs.append(env.env.sim.model.mesh_name2id(m))
# now get the vertex location address
addr_in_vert_array = list()
for idx in object_mesh_idxs:
addr_in_vert_array.append(env.env.sim.model.mesh_vertadr[idx])
# finally get the vertices for each geom
geom_mesh_vertices = list()
for i in range(len(addr_in_vert_array) - 1):
low_idx = addr_in_vert_array[i]
high_idx = addr_in_vert_array[i + 1]
verts = env.env.sim.model.mesh_vert[low_idx:high_idx]
geom_mesh_vertices.append(verts)
geom_mesh_vertices.append(
env.env.sim.model.mesh_vert[addr_in_vert_array[-1]:])
# now transform each of these vertices in world_coordinate frame
verts_in_world = list()
for i, vert in enumerate(geom_mesh_vertices):
trans = geom_xpos[i]
rot_mat = geom_xmat[i]
transform_mat = np.eye(4)
transform_mat[:3, :3] = rot_mat.reshape(3, 3)
transform_mat[:3, 3] = trans
h_vert = np.c_[vert, np.ones(len(vert))]
rot_vert = np.dot(transform_mat, h_vert.T).T[:, :3]
verts_in_world.append(rot_vert)
print(f'length in world {len(verts_in_world)}')
verts_in_world = np.concatenate(verts_in_world)
return verts_in_world
@staticmethod
def get_bbox_properties(env, obj_info):
obj_xml = obj_info.obj_xml_file
obj_xpos = env.env.sim.data.get_body_xpos('object0')
obj_xmat = env.env.sim.data.get_body_xmat('object0')
obj_xquat = env.env.sim.data.get_body_xquat('object0')
scale = obj_info.scale
coords, combined_mesh = pcp_utils.np_vis.compute_bounding_box_from_obj_xml(
obj_info.obj_xml_file,
obj_xpos,
obj_xmat,
scale=scale,
euler=obj_info.euler,
return_combined_mesh=True)
# now get the properties
bounds, center, extents = pcp_utils.np_vis.get_bbox_attribs(coords)
# check here if bounding box is fine for the object
# I will draw the box using my computed values
transform = np.eye(4)
transform[:3, 3] = center
bounding_box_outline = trimesh.primitives.Box(transform=transform,
extents=extents)
bounding_box_outline.visual.face_colors = [0, 0, 255, 100]
# just to make sure that the bounding box is tight here
assert np.allclose(bounding_box_outline.bounds, combined_mesh.bounds)
# # plot the box and mesh
# scene = trimesh.Scene()
# scene.add_geometry([combined_mesh, bounding_box_outline])
# scene.show()
### ... checking of bounding box for every step ends ... ###
bbox_xpos = center.copy()
return bounds, center, extents, obj_xquat, bbox_xpos
@staticmethod
def get_bbox_properties_norot(env, obj_info):
obj_xml = obj_info.obj_xml_file
obj_xpos = env.env.sim.data.get_body_xpos('object0')
obj_xmat = np.eye(3)
obj_xquat = np.zeros(4)
obj_xquat[0] = 1
#obj_xmat = env.env.sim.data.get_body_xmat('object0')
#obj_xquat = env.env.sim.data.get_body_xquat('object0')
scale = obj_info.scale
coords, combined_mesh = pcp_utils.np_vis.compute_bonuding_box_from_obj_xml(
obj_info.obj_xml_file,
obj_xpos,
obj_xmat,
scale,
return_combined_mesh=True)
# now get the properties
bounds, center, extents = pcp_utils.np_vis.get_bbox_attribs(coords)
# check here if bounding box is fine for the object
# I will draw the box using my computed values
transform = np.eye(4)
transform[:3, 3] = center
bounding_box_outline = trimesh.primitives.Box(transform=transform,
extents=extents)
bounding_box_outline.visual.face_colors = [0, 0, 255, 100]
# just to make sure that the bounding box is tight here
assert np.allclose(bounding_box_outline.bounds, combined_mesh.bounds)
# # plot the box and mesh
# scene = trimesh.Scene()
# scene.add_geometry([combined_mesh, bounding_box_outline])
# scene.show()
### ... checking of bounding box for every step ends ... ###
bbox_xpos = center.copy()
return bounds, center, extents, obj_xquat, bbox_xpos
@staticmethod
def compute_top_of_rim(center, extents, obj_quat=np.array([1, 0, 0, 0])):
# center : position of the object
# extents : size of the bounding box
# compute the position of the left side rim while looking at screen
d_rim_pos = np.zeros(4) #center.copy()
d_rim_pos[3] = 1
d_rim_pos[2] = extents[2] / 2.0 + 0.08
d_rim_pos[1] = -extents[1] / 2.0
ori_T_obj = transformations.quaternion_matrix(obj_quat)
rotated_d_rim_pos = np.dot(ori_T_obj, d_rim_pos)[:3]
rim_pos = center.copy() + rotated_d_rim_pos
return rim_pos
@staticmethod
def compute_pts_away_from_grasp_point(grasp_point, gripper_quat, dist=0):
# center : position of the object
# extents : size of the bounding box
# compute the position of the left side rim while looking at screen
gripper_xmat = transformations.quaternion_matrix(gripper_quat)
new_grasp_point = grasp_point + (-1) * gripper_xmat[:3, 0] * dist
return new_grasp_point
# @staticmethod
# def compute_rim_point(center, extents, obj_quat=np.array([1, 0, 0, 0])):
# # center : position of the object
# # extents : size of the bounding box
# d_rim_pos = np.zeros(4)
# d_rim_pos[3] = 1
# d_rim_pos[2] = extents[2] / 2.0 * self.alpha[2] + self.beta[2]
# d_rim_pos[1] = extents[1] / 2.0 * self.alpha[1] + self.beta[1]
# d_rim_pos[0] = extents[0] / 2.0 * self.alpha[0] + self.beta[0]
#
# import ipdb; ipdb.set_trace()
#
#
# #ori_T_obj = transformations.quaternion_matrix(obj_quat)
# #rotated_d_rim_pos = np.dot(ori_T_obj, d_rim_pos)[:3]
# object_rot = transformations.quaternion_matrix(obj_quat)
# rotated_d_rim_pos = np.dot(object_rot, d_rim_pos)[:3]
# rim_pos = center.copy() + rotated_d_rim_pos
#
# return rim_pos
@staticmethod
def compute_rotation(env):
# computes a quaternion to orient gripper and object.
obj_xmat = env.env.sim.data.get_body_xmat('object0')
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
# now I want the x-axis of gripper to be equal to z-axis of object
obj_zaxis = obj_xmat[:3, 2]
gripper_xmat[:3, 0] = -obj_zaxis
h_gripper = np.eye(4)
h_gripper[:3, :3] = gripper_xmat
# convert to quaternion
gripper_xquat = transformations.quaternion_from_matrix(h_gripper)
return gripper_xquat
@staticmethod
def vis_bbox(env, center, xquat):
env.env.move_indicator(center, xquat)
# env.env.sim.forward()
def run_forwards(self,
env,
num_rollouts,
path_length=None,
obj=None,
render=False,
accept_threshold=0,
cluster_name=""):
self.render = render
self.env = env
obj_info = obj
acc_reward = 0
acc_success = 0
max_num_failure = (1 - accept_threshold) * num_rollouts
num_failure = 0
for iter_id in range(num_rollouts):
obs = env.reset()
#ep_actions, ep_observations, ep_infos
self.reset_everything_on_table(env, mesh_obj_info=obj_info)
success, cur_reward = self.goToGoal(env,
obs,
mesh_obj_info=obj_info)
print("ITERATION NUMBER ", iter_id, 'success', success)
if self.config.data_collection_mode:
datapoint = dict()
datapoint["label"] = success
#grasp_pose = np.zeros((6), np.float32)
#grasp_pose[2] = self.current_grasp.depth
datapoint["pc"] = grasp_sampler._regularize_pc_point_count(
self.cache_pc, self.npoints)
datapoint["pc_pose"] = self.cache_pc_pose
datapoint["grasp_rt"] = self.cache_grasp_rt
self.tensordata.add(datapoint)
if self.save_video:
self.dump_video(
f"tmp/6dgrasp/{obj_info.name}_{iter_id}_{success}.mp4")
acc_reward += cur_reward
acc_success += success
if success < 0.1:
num_failure += 1
if num_failure > max_num_failure:
break
success_rate = acc_success / num_rollouts
avg_reward = acc_reward / num_rollouts
return {'avg_reward': avg_reward, 'success_rate': success_rate}
def reset_everything_on_table(self, env, mesh_obj_info, max_run=100):
_, center, _, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
center_old = center
for i in range(max_run):
obsDataNew, reward, done, info = env.step(np.zeros(8))
_, center, _, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
if np.linalg.norm(center_old - center) < 0.000001:
return
center_old = center
return
#raise ValueError
def unstable_grasp(self, finger_tip_states):
return np.var(finger_tip_states) > 0.00001
def gripper_goto_pos_orn(self,
env,
target_pos,
target_quat=np.array([1, 0, 1, 0]),
goal_current_thres=0.005,
speed=6,
open=True,
timeStep=0,
mesh_obj_info=None,
debug=False,
max_time_limit=10000):
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
gripper_quat = R.from_matrix(gripper_xmat).as_quat()[[3, 0, 1, 2]]
rel_pos = target_pos - gripper_position
cur_reward = []
episodeAcs = []
episodeObs = []
episodeInfo = []
finger_tip_states = []
grasp_harder = False
while np.linalg.norm(
rel_pos
) >= goal_current_thres and timeStep <= max_time_limit and timeStep <= self.max_path_length:
self.env_render()
action = np.zeros(8, )
finger_tip_state = env.get_finger_tip_state()
finger_tip_states.append(finger_tip_state)
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
gripper_quat = R.from_matrix(gripper_xmat).as_quat()[[3, 0, 1, 2]]
delta_quat = self._get_intermediate_delta_quats(
gripper_quat, target_quat, num_intermediates=4)
action[3:7] = delta_quat[1]
for i in range(len(rel_pos)):
action[i] = rel_pos[i] * speed
if open:
action[len(action) - 1] = 0.05 #open
else:
action[len(action) - 1] = -0.05
if not grasp_harder and self.unstable_grasp(finger_tip_states):
action[len(action) - 1] = -0.05
grasp_harder = True
elif grasp_harder:
action[len(action) - 1] = -0.05
obsDataNew, reward, done, info = env.step(action)
cur_reward.append(reward)
timeStep += 1
episodeAcs.append(action)
episodeInfo.append(info)
episodeObs.append(obsDataNew)
# compute the objectPos and object_rel_pos using bbox
if debug:
print("action", action, np.linalg.norm(rel_pos))
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
# move the gripper to the top of the rim
rel_pos = target_pos - gripper_position
# now before executing the action move the box, step calls forward
# which would actually move the box
if mesh_obj_info is None:
bounds, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
self.vis_bbox(env, bbox_xpos, obj_xquat)
# for step in range(10):
# self.env_render()
# timeStep += 1
# action = np.zeros(8,)
# action[3:7] = delta_quat[step]
# obsDataNew, reward, done, info = env.step(action)
# cur_reward.append(reward)
return cur_reward, timeStep
def close_gripper(self,
env,
iter=50,
timeStep=0,
gripper_pos=-0.03,
mesh_obj_info=None):
cur_reward = []
episodeAcs = []
episodeObs = []
episodeInfo = []
for i in range(iter):
self.env_render()
action = np.zeros(8, )
action[len(action) - 1] = gripper_pos
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
gripper_quat = R.from_matrix(gripper_xmat).as_quat()[[3, 0, 1, 2]]
action[
3:7] = gripper_quat #todo, replace with what the gipper is at
obsDataNew, reward, done, info = env.step(
action) # actually simulating for some timesteps
timeStep += 1
cur_reward.append(reward)
episodeAcs.append(action)
episodeInfo.append(info)
episodeObs.append(obsDataNew)
# keep on updating the object xpos
if mesh_obj_info is None:
bounds, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
self.vis_bbox(env, bbox_xpos, obj_xquat)
return cur_reward, timeStep
def _get_intermediate_delta_quats(self,
init_quat,
des_quat,
num_intermediates=10):
"""
init_quat: initial quaternion
des_quat: desired quaternion
TODO: ideally since the quaternions are like complex numbers in high
dimensions, the multiplication should give addition, but for some reason
the simple addtion is fine here, investigate why this is the case
"""
# class should be pyquaternion.Quaternion
from pyquaternion import Quaternion
quat_to_array = lambda q: np.asarray([q.w, q.x, q.y, q.z])
if isinstance(init_quat, np.ndarray):
init_quat = Quaternion(init_quat)
if isinstance(des_quat, np.ndarray):
des_quat = Quaternion(des_quat)
assert isinstance(init_quat, Quaternion)
assert isinstance(des_quat, Quaternion)
# generator for the intermediates
intermediate_quats = list()
for q in Quaternion.intermediates(init_quat,
des_quat,
num_intermediates,
include_endpoints=True):
qu = quat_to_array(q)
intermediate_quats.append(qu)
# go through the intermediates to generate the delta quats
delta_quats = list()
prev_quat = quat_to_array(init_quat).copy()
for q in intermediate_quats:
delta_quat = q - prev_quat
delta_quats.append(delta_quat)
prev_quat = q.copy()
# now delta quats when combined with initial quat should sum to 1
add_val = quat_to_array(init_quat) + np.sum(delta_quats, axis=0)
assert np.allclose(add_val, quat_to_array(des_quat))
return delta_quats
def compute_grasping_direction(self):
#gripper_xquat = transformations.quaternion_from_matrix(h_gripper)
# at 1, 0, gripper is pointing toward -y, gripper right is pointing -x,
ori_gripper_xmat = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]])
d_rim_pos = self.get_grasp_point_to_center()
d_xy = d_rim_pos[:2]
if d_xy[0] == 0 and d_xy[1] == 0:
d_xy[1] = 0.00000001
d_xy = d_xy / np.linalg.norm(d_xy)
cos_theta = d_xy[0]
sin_theta = d_xy[1]
elevation = self.get_grasp_point_elevation()
roll = self.get_grasp_point_roll()
#ori_gripper_quat = transformations.quaternion_from_matrix(ori_gripper_xmat)
# add rotation on the xy plane
xy_rot_xmat = np.array([[cos_theta, -sin_theta, 0],
[sin_theta, cos_theta, 0], [0, 0, 1]])
# add elevation: elevation higher means camera looking more downwards
roll_xmat = np.array(
[[1, 0, 0],
[0, np.cos(np.deg2rad(-roll)), -np.sin(np.deg2rad(-roll))],
[0, np.sin(np.deg2rad(-roll)),
np.cos(np.deg2rad(-roll))]])
ele_xmat = np.array([[
np.cos(np.deg2rad(-elevation)), 0,
np.sin(np.deg2rad(-elevation))
], [0, 1, 0],
[
-np.sin(np.deg2rad(-elevation)), 0,
np.cos(np.deg2rad(-elevation))
]])
final_rot = np.matmul(
np.matmul(xy_rot_xmat, np.matmul(ele_xmat, ori_gripper_xmat)),
roll_xmat)
# making the "thumb" of the gripper to point to y+
# if not: rotate gripper with 180 degree
if final_rot[1, 1] < 0:
rot = 180
xmat = np.array(
[[1, 0, 0],
[0, np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot))],
[0, np.sin(np.deg2rad(-rot)),
np.cos(np.deg2rad(-rot))]])
final_rot = np.matmul(final_rot, xmat)
final_rot_4x4 = np.eye(4)
final_rot_4x4[:3, :3] = final_rot
gripper_xquat = transformations.quaternion_from_matrix(final_rot_4x4)
return gripper_xquat
def get_grasp_point_roll(self):
return self.roll_min + self.eta * (self.roll_max - self.roll_min)
def get_grasp_point_elevation(self):
return self.elevation_min + self.gamma * (self.elevation_max -
self.elevation_min)
def get_grasp_point_to_center(self):
d_rim_pos = np.zeros(3)
d_rim_pos[2] = self.extents[2] / 2.0 * self.alpha[2] + self.beta[2]
d_rim_pos[1] = self.extents[1] / 2.0 * self.alpha[1] + self.beta[1]
d_rim_pos[0] = self.extents[0] / 2.0 * self.alpha[0] + self.beta[0]
return d_rim_pos
def get_grasp_point(self, center):
grasp_point = center + self.get_grasp_point_to_center()
return grasp_point
def split_grasps(self, grasp):
pos = grasp[:3, 3]
orn = grasp[:3, :3]
return pos, orn
def convert_grasp_orn_to_fetch(self, orn):
gripper_right = orn[:3, 0]
gripper_down = orn[:3, 1]
gripper_point = orn[:3, 2]
final_rot_4x4 = np.eye(4)
final_rot_4x4[:3, 0] = gripper_point
final_rot_4x4[:3, 1] = gripper_right
final_rot_4x4[:3, 2] = gripper_down
if final_rot_4x4[1, 1] < 0:
rot = 180
xmat = np.array(
[[1, 0, 0, 0],
[0, np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
[0, np.sin(np.deg2rad(-rot)),
np.cos(np.deg2rad(-rot)), 0], [0, 0, 0, 1]])
final_rot_4x4 = np.matmul(final_rot_4x4, xmat)
r = R.from_matrix(final_rot_4x4[:3, :3])
gripper_xquat = r.as_quat()
# change from xyzw to wxyz
wxyz = np.zeros(4, np.float32)
wxyz[1:] = gripper_xquat[:3]
wxyz[0] = gripper_xquat[3]
#sgripper_xquat = transformations.quaternion_from_matrix(final_rot_4x4)
return wxyz
def get_grasp_from_grasp_sampler(self, env, mesh_obj_info):
data = self.grasp_sampler.sample_grasp_from_camera(
env, mesh_obj_info, canonicalize_pcs=False)
#data = np.load("vae_generated_grasps/grasps_fn_159e56c18906830278d8f8c02c47cde0_15.npy", allow_pickle=True).item()
_, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
# pick one
top_k = min(10, len(data['generated_scores']))
if top_k == 0: # if no grasp is detect : just grasp from center
grasp_pos = center
gripper_target_quat = np.array([1, 0, 1, 0])
a_grasp = np.eye(4)
a_grasp[2, 3] = 0.02
else:
grasp_ids = np.argsort(data['generated_scores'])[-top_k:][::-1]
if self.config.data_collection_mode and self.config.data_collection_from_trained_model:
picked_id = np.random.randint(top_k)
grasp_id = grasp_ids[picked_id]
elif self.config.data_collection_mode:
grasp_id = np.random.randint(len(data['generated_scores']))
else:
picked_id = 0
grasp_id = grasp_ids[picked_id]
# if self.config.data_collection_mode and not self.config.data_collection_from_trained_model:
# grasp_id = np.random.randint(len(data['generated_scores']))
# else:
# grasp_ids = np.argsort(data['generated_scores'])[-top_k:][::-1]
# picked_id = 0#np.random.randint(top_k)
# grasp_id = grasp_ids[picked_id]
a_grasp = data['generated_grasps'][grasp_id]
#a_grasp_X = data['generated_grasps_X'][grasp_id]
grasp_pos, grasp_orn = self.split_grasps(a_grasp)
grasp_pos, grasp_orn = self.grasp_sampler.convert_grasp_to_mujoco(
grasp_pos, grasp_orn)
gripper_target_quat = self.convert_grasp_orn_to_fetch(
grasp_orn) #self.compute_grasping_direction()
if self.config.data_collection_mode:
self.cache_pc = data['obj_pcd_X'] # reduce number of points
self.cache_grasp_rt = a_grasp
self.cache_pc_pose = data["camR_T_camX"]
return grasp_pos, gripper_target_quat
def goToGoal(self, env, lastObs, mesh_obj_info=None):
goal = lastObs['desired_goal']
grasp_pos, gripper_target_quat = self.get_grasp_from_grasp_sampler(
env, mesh_obj_info)
grasp_point = grasp_pos #self.get_grasp_point(center)
_, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
self.extents = extents
# transform it from adam to
self.env_render()
self.vis_bbox(env, bbox_xpos, obj_xquat)
# while True:
# # env.render()
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
# move the gripper to the top of the rim
timeStep = 0 #count the total number of timesteps
cur_reward = []
episodeAcs = []
episodeObs = []
episodeInfo = []
target_pos = self.compute_pts_away_from_grasp_point(
grasp_point, gripper_target_quat,
dist=0) # the gripper is -0.105 away from the grasp point
## go to the top of the rim
#rim_top_pos = self.compute_top_of_rim(center, extents, gripper_target_quat)
#gripper_quat = np.array([1, 0, 1, 0])
new_gripper_quat = gripper_target_quat #transformations.quaternion_multiply(obj_xquat, gripper_quat)
rewards, timeStep = self.gripper_goto_pos_orn(
env,
target_pos,
new_gripper_quat,
goal_current_thres=0.002,
speed=6,
open=True,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info,
max_time_limit=self.max_path_length / 3)
cur_reward += rewards
# go toward rim pos
_, center, _, _, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
#grasp_point = self.get_grasp_point(center)
rim_pos = self.compute_pts_away_from_grasp_point(grasp_point,
gripper_target_quat,
dist=-0.105)
rewards, timeStep = self.gripper_goto_pos_orn(
env,
rim_pos,
new_gripper_quat,
goal_current_thres=0.002,
speed=6,
open=True,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info,
max_time_limit=self.max_path_length * 2 / 3)
cur_reward += rewards
# bounds, center_new, _, _, bbox_xpos = self.get_bbox_properties(env, mesh_obj_info)
# if np.linalg.norm(center - center_new) >= 0.02: # some objects are still dropping
# rim_pos = self.get_grasp_point(center_new)
# #rim_pos = self.compute_rim_point(center_new, extents)
# rewards, timeStep = self.gripper_goto_pos_orn(env, rim_pos, new_gripper_quat, goal_current_thres= 0.002, speed=6, open=True, timeStep=timeStep, mesh_obj_info=mesh_obj_info)
# cur_reward += rewards
# close gripper
rewards, timeStep = self.close_gripper(env,
iter=20,
gripper_pos=-0.01,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info)
cur_reward += rewards
# move to target location
goal_pos_for_gripper = goal - bbox_xpos + gripper_position
run_one = True
while np.linalg.norm(
goal - bbox_xpos) >= 0.01 and timeStep <= self.max_path_length:
#print(np.linalg.norm(goal - bbox_xpos), goal_pos_for_gripper, gripper_position)
if run_one: # first time, go a rroughly toward the goal
thres = 0.01
else:
thres = 0.002
rewards, timeStep = self.gripper_goto_pos_orn(
env,
goal_pos_for_gripper,
new_gripper_quat,
goal_current_thres=thres,
speed=6,
open=False,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info)
run_one = False
cur_reward += rewards
if cur_reward[-1] > 0:
break
# retriev again the poses
bounds, center, _, _, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
# recalculate
goal_pos_for_gripper = goal - bbox_xpos + gripper_position
if timeStep >= self.max_path_length:
break #env._max_episode_steps: 70
while True:
rewards, timeStep = self.close_gripper(env,
iter=1,
timeStep=timeStep,
gripper_pos=-0.005,
mesh_obj_info=mesh_obj_info)
cur_reward += rewards
if timeStep >= self.max_path_length:
break #env._max_episode_steps: 70
success = 0
curr_reward = np.sum(cur_reward)
if np.sum(curr_reward
) > -1 * self.max_path_length and cur_reward[-1] == 0:
success = 1
return success, curr_reward
def generate_segmask_dataset(output_dataset_path,
config,
save_tensors=True,
warm_start=False):
""" Generate a segmentation training dataset
Parameters
----------
dataset_path : str
path to store the dataset
config : dict
dictionary-like objects containing parameters of the simulator and visualization
save_tensors : bool
save tensor datasets (for recreating state)
warm_start : bool
restart dataset generation from a previous state
"""
# read subconfigs
dataset_config = config['dataset']
image_config = config['images']
vis_config = config['vis']
# debugging
debug = config['debug']
if debug:
np.random.seed(SEED)
# read general parameters
num_states = config['num_states']
num_images_per_state = config['num_images_per_state']
states_per_flush = config['states_per_flush']
states_per_garbage_collect = config['states_per_garbage_collect']
# set max obj per state
max_objs_per_state = config['state_space']['heap']['max_objs']
# read image parameters
im_height = config['state_space']['camera']['im_height']
im_width = config['state_space']['camera']['im_width']
segmask_channels = max_objs_per_state + 1
# create the dataset path and all subfolders if they don't exist
if not os.path.exists(output_dataset_path):
os.mkdir(output_dataset_path)
image_dir = os.path.join(output_dataset_path, 'images')
if not os.path.exists(image_dir):
os.mkdir(image_dir)
color_dir = os.path.join(image_dir, 'color_ims')
if image_config['color'] and not os.path.exists(color_dir):
os.mkdir(color_dir)
depth_dir = os.path.join(image_dir, 'depth_ims')
if image_config['depth'] and not os.path.exists(depth_dir):
os.mkdir(depth_dir)
amodal_dir = os.path.join(image_dir, 'amodal_masks')
if image_config['amodal'] and not os.path.exists(amodal_dir):
os.mkdir(amodal_dir)
modal_dir = os.path.join(image_dir, 'modal_masks')
if image_config['modal'] and not os.path.exists(modal_dir):
os.mkdir(modal_dir)
semantic_dir = os.path.join(image_dir, 'semantic_masks')
if image_config['semantic'] and not os.path.exists(semantic_dir):
os.mkdir(semantic_dir)
# setup logging
experiment_log_filename = os.path.join(output_dataset_path,
'dataset_generation.log')
if os.path.exists(experiment_log_filename) and not warm_start:
os.remove(experiment_log_filename)
Logger.add_log_file(logger, experiment_log_filename, global_log_file=True)
config.save(
os.path.join(output_dataset_path, 'dataset_generation_params.yaml'))
metadata = {}
num_prev_states = 0
# set dataset params
if save_tensors:
# read dataset subconfigs
state_dataset_config = dataset_config['states']
image_dataset_config = dataset_config['images']
state_tensor_config = state_dataset_config['tensors']
image_tensor_config = image_dataset_config['tensors']
obj_pose_dim = POSE_DIM * max_objs_per_state
obj_com_dim = POINT_DIM * max_objs_per_state
state_tensor_config['fields']['obj_poses']['height'] = obj_pose_dim
state_tensor_config['fields']['obj_coms']['height'] = obj_com_dim
state_tensor_config['fields']['obj_ids']['height'] = max_objs_per_state
image_tensor_config['fields']['camera_pose']['height'] = POSE_DIM
if image_config['color']:
image_tensor_config['fields']['color_im'] = {
'dtype': 'uint8',
'channels': 3,
'height': im_height,
'width': im_width
}
if image_config['depth']:
image_tensor_config['fields']['depth_im'] = {
'dtype': 'float32',
'channels': 1,
'height': im_height,
'width': im_width
}
if image_config['modal']:
image_tensor_config['fields']['modal_segmasks'] = {
'dtype': 'uint8',
'channels': segmask_channels,
'height': im_height,
'width': im_width
}
if image_config['amodal']:
image_tensor_config['fields']['amodal_segmasks'] = {
'dtype': 'uint8',
'channels': segmask_channels,
'height': im_height,
'width': im_width
}
if image_config['semantic']:
image_tensor_config['fields']['semantic_segmasks'] = {
'dtype': 'uint8',
'channels': 1,
'height': im_height,
'width': im_width
}
# create dataset filenames
state_dataset_path = os.path.join(output_dataset_path, 'state_tensors')
image_dataset_path = os.path.join(output_dataset_path, 'image_tensors')
if warm_start:
if not os.path.exists(state_dataset_path) or not os.path.exists(
image_dataset_path):
logger.error(
'Attempting to warm start without saved tensor dataset')
exit(1)
# open datasets
logger.info('Opening state dataset')
state_dataset = TensorDataset.open(state_dataset_path,
access_mode='READ_WRITE')
logger.info('Opening image dataset')
image_dataset = TensorDataset.open(image_dataset_path,
access_mode='READ_WRITE')
# read configs
state_tensor_config = state_dataset.config
image_tensor_config = image_dataset.config
# clean up datasets (there may be datapoints with indices corresponding to non-existent data)
num_state_datapoints = state_dataset.num_datapoints
num_image_datapoints = image_dataset.num_datapoints
num_prev_states = num_state_datapoints
# clean up images
image_ind = num_image_datapoints - 1
image_datapoint = image_dataset[image_ind]
while image_ind > 0 and image_datapoint[
'state_ind'] >= num_state_datapoints:
image_ind -= 1
image_datapoint = image_dataset[image_ind]
images_to_remove = num_image_datapoints - 1 - image_ind
logger.info('Deleting last %d image tensors' % (images_to_remove))
if images_to_remove > 0:
image_dataset.delete_last(images_to_remove)
num_image_datapoints = image_dataset.num_datapoints
else:
# create datasets from scratch
logger.info('Creating datasets')
state_dataset = TensorDataset(state_dataset_path,
state_tensor_config)
image_dataset = TensorDataset(image_dataset_path,
image_tensor_config)
# read templates
state_datapoint = state_dataset.datapoint_template
image_datapoint = image_dataset.datapoint_template
if warm_start:
if not os.path.exists(
os.path.join(output_dataset_path, 'metadata.json')):
logger.error(
'Attempting to warm start without previously created dataset')
exit(1)
# Read metadata and indices
metadata = json.load(
open(os.path.join(output_dataset_path, 'metadata.json'), 'r'))
test_inds = np.load(os.path.join(image_dir,
'test_indices.npy')).tolist()
train_inds = np.load(os.path.join(image_dir,
'train_indices.npy')).tolist()
# set obj ids and splits
reverse_obj_ids = metadata['obj_ids']
obj_id_map = utils.reverse_dictionary(reverse_obj_ids)
obj_splits = metadata['obj_splits']
obj_keys = obj_splits.keys()
mesh_filenames = metadata['meshes']
# Get list of images generated so far
generated_images = sorted(
os.listdir(color_dir)) if image_config['color'] else sorted(
os.listdir(depth_dir))
num_total_images = len(generated_images)
# Do our own calculation if no saved tensors
if num_prev_states == 0:
num_prev_states = num_total_images // num_images_per_state
# Find images to remove and remove them from all relevant places if they exist
num_images_to_remove = num_total_images - (num_prev_states *
num_images_per_state)
logger.info(
'Deleting last {} invalid images'.format(num_images_to_remove))
for k in range(num_images_to_remove):
im_name = generated_images[-(k + 1)]
im_basename = os.path.splitext(im_name)[0]
im_ind = int(im_basename.split('_')[1])
if os.path.exists(os.path.join(depth_dir, im_name)):
os.remove(os.path.join(depth_dir, im_name))
if os.path.exists(os.path.join(color_dir, im_name)):
os.remove(os.path.join(color_dir, im_name))
if os.path.exists(os.path.join(semantic_dir, im_name)):
os.remove(os.path.join(semantic_dir, im_name))
if os.path.exists(os.path.join(modal_dir, im_basename)):
shutil.rmtree(os.path.join(modal_dir, im_basename))
if os.path.exists(os.path.join(amodal_dir, im_basename)):
shutil.rmtree(os.path.join(amodal_dir, im_basename))
if im_ind in train_inds:
train_inds.remove(im_ind)
elif im_ind in test_inds:
test_inds.remove(im_ind)
else:
# Create initial env to generate metadata
env = BinHeapEnv(config)
obj_id_map = env.state_space.obj_id_map
obj_keys = env.state_space.obj_keys
obj_splits = env.state_space.obj_splits
mesh_filenames = env.state_space.mesh_filenames
save_obj_id_map = obj_id_map.copy()
save_obj_id_map[ENVIRONMENT_KEY] = np.iinfo(np.uint32).max
reverse_obj_ids = utils.reverse_dictionary(save_obj_id_map)
metadata['obj_ids'] = reverse_obj_ids
metadata['obj_splits'] = obj_splits
metadata['meshes'] = mesh_filenames
json.dump(metadata,
open(os.path.join(output_dataset_path, 'metadata.json'),
'w'),
indent=JSON_INDENT,
sort_keys=True)
train_inds = []
test_inds = []
# generate states and images
state_id = num_prev_states
while state_id < num_states:
# create env and set objects
create_start = time.time()
env = BinHeapEnv(config)
env.state_space.obj_id_map = obj_id_map
env.state_space.obj_keys = obj_keys
env.state_space.set_splits(obj_splits)
env.state_space.mesh_filenames = mesh_filenames
create_stop = time.time()
logger.info('Creating env took %.3f sec' %
(create_stop - create_start))
# sample states
states_remaining = num_states - state_id
for i in range(min(states_per_garbage_collect, states_remaining)):
# log current rollout
if state_id % config['log_rate'] == 0:
logger.info('State: %06d' % (state_id))
try:
# reset env
env.reset()
state = env.state
split = state.metadata['split']
# render state
if vis_config['state']:
env.view_3d_scene()
# Save state if desired
if save_tensors:
# set obj state variables
obj_pose_vec = np.zeros(obj_pose_dim)
obj_com_vec = np.zeros(obj_com_dim)
obj_id_vec = np.iinfo(
np.uint32).max * np.ones(max_objs_per_state)
j = 0
for obj_state in state.obj_states:
obj_pose_vec[j * POSE_DIM:(j + 1) *
POSE_DIM] = obj_state.pose.vec
obj_com_vec[j * POINT_DIM:(j + 1) *
POINT_DIM] = obj_state.center_of_mass
obj_id_vec[j] = int(obj_id_map[obj_state.key])
j += 1
# store datapoint env params
state_datapoint['state_id'] = state_id
state_datapoint['obj_poses'] = obj_pose_vec
state_datapoint['obj_coms'] = obj_com_vec
state_datapoint['obj_ids'] = obj_id_vec
state_datapoint['split'] = split
# store state datapoint
image_start_ind = image_dataset.num_datapoints
image_end_ind = image_start_ind + num_images_per_state
state_datapoint['image_start_ind'] = image_start_ind
state_datapoint['image_end_ind'] = image_end_ind
# clean up
del obj_pose_vec
del obj_com_vec
del obj_id_vec
# add state
state_dataset.add(state_datapoint)
# render images
for k in range(num_images_per_state):
# reset the camera
if num_images_per_state > 1:
env.reset_camera()
obs = env.render_camera_image(color=image_config['color'])
if image_config['color']:
color_obs, depth_obs = obs
else:
depth_obs = obs
# vis obs
if vis_config['obs']:
if image_config['depth']:
plt.figure()
plt.imshow(depth_obs)
plt.title('Depth Observation')
if image_config['color']:
plt.figure()
plt.imshow(color_obs)
plt.title('Color Observation')
plt.show()
if image_config['modal'] or image_config[
'amodal'] or image_config['semantic']:
# render segmasks
amodal_segmasks, modal_segmasks = env.render_segmentation_images(
)
# retrieve segmask data
modal_segmask_arr = np.iinfo(np.uint8).max * np.ones(
[im_height, im_width, segmask_channels],
dtype=np.uint8)
amodal_segmask_arr = np.iinfo(np.uint8).max * np.ones(
[im_height, im_width, segmask_channels],
dtype=np.uint8)
stacked_segmask_arr = np.zeros(
[im_height, im_width, 1], dtype=np.uint8)
modal_segmask_arr[:, :, :env.
num_objects] = modal_segmasks
amodal_segmask_arr[:, :, :env.
num_objects] = amodal_segmasks
if image_config['semantic']:
for j in range(env.num_objects):
this_obj_px = np.where(
modal_segmasks[:, :, j] > 0)
stacked_segmask_arr[this_obj_px[0],
this_obj_px[1], 0] = j + 1
# visualize
if vis_config['semantic']:
plt.figure()
plt.imshow(stacked_segmask_arr.squeeze())
plt.show()
if save_tensors:
# save image data as tensors
if image_config['color']:
image_datapoint['color_im'] = color_obs
if image_config['depth']:
image_datapoint['depth_im'] = depth_obs[:, :, None]
if image_config['modal']:
image_datapoint[
'modal_segmasks'] = modal_segmask_arr
if image_config['amodal']:
image_datapoint[
'amodal_segmasks'] = amodal_segmask_arr
if image_config['semantic']:
image_datapoint[
'semantic_segmasks'] = stacked_segmask_arr
image_datapoint['camera_pose'] = env.camera.pose.vec
image_datapoint[
'camera_intrs'] = env.camera.intrinsics.vec
image_datapoint['state_ind'] = state_id
image_datapoint['split'] = split
# add image
image_dataset.add(image_datapoint)
# Save depth image and semantic masks
if image_config['color']:
ColorImage(color_obs).save(
os.path.join(
color_dir, 'image_{:06d}.png'.format(
num_images_per_state * state_id + k)))
if image_config['depth']:
DepthImage(depth_obs).save(
os.path.join(
depth_dir, 'image_{:06d}.png'.format(
num_images_per_state * state_id + k)))
if image_config['modal']:
modal_id_dir = os.path.join(
modal_dir,
'image_{:06d}'.format(num_images_per_state *
state_id + k))
if not os.path.exists(modal_id_dir):
os.mkdir(modal_id_dir)
for i in range(env.num_objects):
BinaryImage(modal_segmask_arr[:, :, i]).save(
os.path.join(modal_id_dir,
'channel_{:03d}.png'.format(i)))
if image_config['amodal']:
amodal_id_dir = os.path.join(
amodal_dir,
'image_{:06d}'.format(num_images_per_state *
state_id + k))
if not os.path.exists(amodal_id_dir):
os.mkdir(amodal_id_dir)
for i in range(env.num_objects):
BinaryImage(amodal_segmask_arr[:, :, i]).save(
os.path.join(amodal_id_dir,
'channel_{:03d}.png'.format(i)))
if image_config['semantic']:
GrayscaleImage(stacked_segmask_arr.squeeze()).save(
os.path.join(
semantic_dir, 'image_{:06d}.png'.format(
num_images_per_state * state_id + k)))
# Save split
if split == TRAIN_ID:
train_inds.append(num_images_per_state * state_id + k)
else:
test_inds.append(num_images_per_state * state_id + k)
# auto-flush after every so many timesteps
if state_id % states_per_flush == 0:
np.save(os.path.join(image_dir, 'train_indices.npy'),
train_inds)
np.save(os.path.join(image_dir, 'test_indices.npy'),
test_inds)
if save_tensors:
state_dataset.flush()
image_dataset.flush()
# delete action objects
for obj_state in state.obj_states:
del obj_state
del state
gc.collect()
# update state id
state_id += 1
except Exception as e:
# log an error
logger.warning('Heap failed!')
logger.warning('%s' % (str(e)))
logger.warning(traceback.print_exc())
if debug:
raise
del env
gc.collect()
env = BinHeapEnv(config)
env.state_space.obj_id_map = obj_id_map
env.state_space.obj_keys = obj_keys
env.state_space.set_splits(obj_splits)
env.state_space.mesh_filenames = mesh_filenames
# garbage collect
del env
gc.collect()
# write all datasets to file, save indices
np.save(os.path.join(image_dir, 'train_indices.npy'), train_inds)
np.save(os.path.join(image_dir, 'test_indices.npy'), test_inds)
if save_tensors:
state_dataset.flush()
image_dataset.flush()
logger.info('Generated %d image datapoints' %
(state_id * num_images_per_state))
# parse args
parser = argparse.ArgumentParser(description='Subsamples a dataset')
parser.add_argument('dataset_path',
type=str,
default=None,
help='directory of the dataset to subsample')
parser.add_argument('output_path',
type=str,
default=None,
help='directory to store the subsampled dataset')
args = parser.parse_args()
dataset_path = args.dataset_path
output_path = args.output_path
dataset = TensorDataset.open(dataset_path)
out_dataset = TensorDataset(output_path, dataset.config)
ind = np.arange(dataset.num_datapoints)
np.random.shuffle(ind)
for i, j in enumerate(ind):
logging.info('Saving datapoint %d' % (i))
datapoint = dataset[j]
out_dataset.add(datapoint)
out_dataset.flush()
for split_name in dataset.split_names:
_, val_indices, _ = dataset.split(split_name)
new_val_indices = []
for i in range(ind.shape[0]):
# fix dataset config
dataset_config['fields']['raw_color_ims']['height'] = camera_intr.height
dataset_config['fields']['raw_color_ims']['width'] = camera_intr.width
dataset_config['fields']['raw_depth_ims']['height'] = camera_intr.height
dataset_config['fields']['raw_depth_ims']['width'] = camera_intr.width
dataset_config['fields']['color_ims']['height'] = camera_intr.height
dataset_config['fields']['color_ims']['width'] = camera_intr.width
dataset_config['fields']['depth_ims']['height'] = camera_intr.height
dataset_config['fields']['depth_ims']['width'] = camera_intr.width
dataset_config['fields']['segmasks']['height'] = camera_intr.height
dataset_config['fields']['segmasks']['width'] = camera_intr.width
# open dataset
sensor_dataset_filename = os.path.join(output_dir, sensor_name)
datasets[sensor_name] = TensorDataset(sensor_dataset_filename,
dataset_config)
# save raw
if save_raw:
sensor_dir = os.path.join(output_dir, sensor_name)
raw_dir = os.path.join(sensor_dir, 'raw')
if not os.path.exists(raw_dir):
os.mkdir(raw_dir)
camera_intr_filename = os.path.join(raw_dir, 'camera_intr.intr')
camera_intr.save(camera_intr_filename)
camera_pose_filename = os.path.join(raw_dir, 'T_camera_world.tf')
T_camera_world.save(camera_pose_filename)
# collect K images
for k in range(num_images):
class GraspDexnetController(Policy):
class Config(Config):
policy_name = "grasp_6dgrasp_controller"
policy_model_path = ""
model_name = None
max_path_length = 150
# keypoint = center + alpha * haf_bounding_box + beta
# angle = elevation * gamma * elevation*90
# [alpha1, alpha2, alpha3, beta1, beta2, beta3, gamma]
# alpha1, alpha2, alph3 = [-1, 1], beta1, beta2, beta3 = [-0.4, 0.4]
params = []
elevation_max = 90
elevation_min = 0
roll_max = 90
roll_min = 0
camera_img_height = 128 * 3
camera_img_width = 128 * 3
camera_radius = 0.3
camera_fov_y = 45
table_top = [1.3, 0.75, 0.4]
data_collection_mode = False
save_data_name = ""
def __init__(self, config: Config, detector=None):
self.config = config
self.policy_name = config.policy_name
self.max_path_length = config.max_path_length
self.elevation_max = config.elevation_max
self.elevation_min = config.elevation_min
self.roll_max = config.roll_max
self.roll_min = config.roll_min
self.save_video = False #True #False
if self.save_video:
self.imgs = []
#grasp_sampler_config = grasp_sampler.GraspSampler.Config()
#self.grasp_sampler = grasp_sampler.GraspSampler(grasp_sampler_config)
model_name = "gqcnn_example_pj_fish" #"GQCNN-4.0-PJ"
fully_conv = False
gqcnn_dir = "/home/htung/2020/gqcnn/"
# Set model if provided.
model_dir = os.path.join(gqcnn_dir, "models")
model_path = os.path.join(model_dir, model_name)
config_filename = None
# Get configs.
model_config = json.load(
open(os.path.join(model_path, "config.json"), "r"))
try:
gqcnn_config = model_config["gqcnn"]
gripper_mode = gqcnn_config["gripper_mode"]
except KeyError:
gqcnn_config = model_config["gqcnn_config"]
input_data_mode = gqcnn_config["input_data_mode"]
if input_data_mode == "tf_image":
gripper_mode = GripperMode.LEGACY_PARALLEL_JAW
elif input_data_mode == "tf_image_suction":
gripper_mode = GripperMode.LEGACY_SUCTION
elif input_data_mode == "suction":
gripper_mode = GripperMode.SUCTION
elif input_data_mode == "multi_suction":
gripper_mode = GripperMode.MULTI_SUCTION
elif input_data_mode == "parallel_jaw": # this is picked
gripper_mode = GripperMode.PARALLEL_JAW
else:
raise ValueError("Input data mode {} not supported!".format(
input_data_mode))
config_filename = os.path.join(gqcnn_dir, "cfg/examples/gqcnn_pj.yaml")
# Read config.
config = YamlConfig(config_filename)
inpaint_rescale_factor = config["inpaint_rescale_factor"]
policy_config = config["policy"]
# original_gripper_width = 0.05
original_gripper_width = policy_config["gripper_width"]
self.real_gripper_width = 0.112 #0.15 #0.112
self.rescale_factor = original_gripper_width / self.real_gripper_width
#self.config.camera_fov_y = self.config.camera_fov_y * self.rescale_factor
#policy_config["gripper_width"] = 0.112
# gripper distance to the grasp point
# min_depth_offset = config['policy']["sampling"]["min_depth_offset"] = 0.015
# max_depth_offset = config['policy']["sampling"]["max_depth_offset"] = 0.05
# Make relative paths absolute.
if "gqcnn_model" in policy_config["metric"]:
policy_config["metric"]["gqcnn_model"] = model_path
if not os.path.isabs(policy_config["metric"]["gqcnn_model"]):
policy_config["metric"]["gqcnn_model"] = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "..",
policy_config["metric"]["gqcnn_model"])
# Set input sizes for fully-convolutional policy.
if fully_conv:
policy_config["metric"]["fully_conv_gqcnn_config"][
"im_height"] = depth_im.shape[0]
policy_config["metric"]["fully_conv_gqcnn_config"][
"im_width"] = depth_im.shape[1]
# Init policy.
self.policy_config = policy_config
self.policy_config["vis"]["vmin"] = 0
self.policy_config["vis"]["vmax"] = 0.5
if self.config.data_collection_mode:
self.policy_config["num_iters"] = 0
self.policy_config["random_sample"] = True
self.gqcnn_image_size = 96
import collections
data_config = collections.OrderedDict()
data_config['datapoints_per_file'] = 50
data_config['fields'] = collections.OrderedDict()
data_config['fields']["grasp_metrics"] = dict()
data_config['fields']["grasp_metrics"]['dtype'] = "float32"
data_config['fields']["grasps"] = dict()
data_config['fields']["grasps"]['dtype'] = "float32"
data_config['fields']["grasps"]['height'] = 6
data_config['fields']["split"] = dict()
data_config['fields']["split"]['dtype'] = "uint8"
data_config['fields']["tf_depth_ims"] = dict()
data_config['fields']["tf_depth_ims"]['dtype'] = "float32"
data_config['fields']["tf_depth_ims"][
'height'] = self.gqcnn_image_size
data_config['fields']["tf_depth_ims"][
'width'] = self.gqcnn_image_size
data_config['fields']["tf_depth_ims"]['channels'] = 1
from autolab_core import TensorDataset
self.tensordata = TensorDataset(self.config.save_data_name,
data_config)
#import ipdb; ipdb.set_trace()
policy_type = "cem"
if "type" in policy_config:
policy_type = policy_config["type"]
if policy_type == "ranking":
self.policy = RobustGraspingPolicy(policy_config)
elif policy_type == "cem":
self.policy = CrossEntropyRobustGraspingPolicy(policy_config)
else:
raise ValueError("Invalid policy type: {}".format(policy_type))
@staticmethod
def compute_geom_center_from_mujoco(env, object_name):
# first get the idx of the object_geoms
body_id = env.env.sim.model.body_name2id('object0')
geom_idxs = list()
for i, assoc_body in enumerate(env.env.sim.model.geom_bodyid):
if assoc_body == body_id:
geom_idxs.append(i)
# now get the xpos and xmat of these idxs
geom_xpos = env.env.sim.data.geom_xpos[geom_idxs]
geom_xmat = env.env.sim.data.geom_xmat[geom_idxs]
# now get the vertices of belonging to each geom
# first I get the idxs associated with the name of the mesh
object_mesh_idxs = list()
for m in env.env.sim.model.mesh_names:
if object_name in m:
object_mesh_idxs.append(env.env.sim.model.mesh_name2id(m))
# now get the vertex location address
addr_in_vert_array = list()
for idx in object_mesh_idxs:
addr_in_vert_array.append(env.env.sim.model.mesh_vertadr[idx])
# finally get the vertices for each geom
geom_mesh_vertices = list()
for i in range(len(addr_in_vert_array) - 1):
low_idx = addr_in_vert_array[i]
high_idx = addr_in_vert_array[i + 1]
verts = env.env.sim.model.mesh_vert[low_idx:high_idx]
geom_mesh_vertices.append(verts)
geom_mesh_vertices.append(
env.env.sim.model.mesh_vert[addr_in_vert_array[-1]:])
# now transform each of these vertices in world_coordinate frame
verts_in_world = list()
for i, vert in enumerate(geom_mesh_vertices):
trans = geom_xpos[i]
rot_mat = geom_xmat[i]
transform_mat = np.eye(4)
transform_mat[:3, :3] = rot_mat.reshape(3, 3)
transform_mat[:3, 3] = trans
h_vert = np.c_[vert, np.ones(len(vert))]
rot_vert = np.dot(transform_mat, h_vert.T).T[:, :3]
verts_in_world.append(rot_vert)
print(f'length in world {len(verts_in_world)}')
verts_in_world = np.concatenate(verts_in_world)
return verts_in_world
@staticmethod
def get_bbox_properties(env, obj_info):
obj_xml = obj_info.obj_xml_file
obj_xpos = env.env.sim.data.get_body_xpos('object0')
obj_xmat = env.env.sim.data.get_body_xmat('object0')
obj_xquat = env.env.sim.data.get_body_xquat('object0')
scale = obj_info.scale
coords, combined_mesh = pcp_utils.np_vis.compute_bounding_box_from_obj_xml(
obj_info.obj_xml_file,
obj_xpos,
obj_xmat,
scale=scale,
euler=obj_info.euler,
return_combined_mesh=True)
# now get the properties
bounds, center, extents = pcp_utils.np_vis.get_bbox_attribs(coords)
# check here if bounding box is fine for the object
# I will draw the box using my computed values
transform = np.eye(4)
transform[:3, 3] = center
bounding_box_outline = trimesh.primitives.Box(transform=transform,
extents=extents)
bounding_box_outline.visual.face_colors = [0, 0, 255, 100]
# just to make sure that the bounding box is tight here
assert np.allclose(bounding_box_outline.bounds, combined_mesh.bounds)
# # plot the box and mesh
# scene = trimesh.Scene()
# scene.add_geometry([combined_mesh, bounding_box_outline])
# scene.show()
### ... checking of bounding box for every step ends ... ###
bbox_xpos = center.copy()
return bounds, center, extents, obj_xquat, bbox_xpos
@staticmethod
def get_bbox_properties_norot(env, obj_info):
obj_xml = obj_info.obj_xml_file
obj_xpos = env.env.sim.data.get_body_xpos('object0')
obj_xmat = np.eye(3)
obj_xquat = np.zeros(4)
obj_xquat[0] = 1
#obj_xmat = env.env.sim.data.get_body_xmat('object0')
#obj_xquat = env.env.sim.data.get_body_xquat('object0')
scale = obj_info.scale
coords, combined_mesh = pcp_utils.np_vis.compute_bonuding_box_from_obj_xml(
obj_info.obj_xml_file,
obj_xpos,
obj_xmat,
scale,
return_combined_mesh=True)
# now get the properties
bounds, center, extents = pcp_utils.np_vis.get_bbox_attribs(coords)
# check here if bounding box is fine for the object
# I will draw the box using my computed values
transform = np.eye(4)
transform[:3, 3] = center
bounding_box_outline = trimesh.primitives.Box(transform=transform,
extents=extents)
bounding_box_outline.visual.face_colors = [0, 0, 255, 100]
# just to make sure that the bounding box is tight here
assert np.allclose(bounding_box_outline.bounds, combined_mesh.bounds)
# # plot the box and mesh
# scene = trimesh.Scene()
# scene.add_geometry([combined_mesh, bounding_box_outline])
# scene.show()
### ... checking of bounding box for every step ends ... ###
bbox_xpos = center.copy()
return bounds, center, extents, obj_xquat, bbox_xpos
@staticmethod
def compute_top_of_rim(center, extents, obj_quat=np.array([1, 0, 0, 0])):
# center : position of the object
# extents : size of the bounding box
# compute the position of the left side rim while looking at screen
d_rim_pos = np.zeros(4) #center.copy()
d_rim_pos[3] = 1
d_rim_pos[2] = extents[2] / 2.0 + 0.08
d_rim_pos[1] = -extents[1] / 2.0
ori_T_obj = transformations.quaternion_matrix(obj_quat)
rotated_d_rim_pos = np.dot(ori_T_obj, d_rim_pos)[:3]
rim_pos = center.copy() + rotated_d_rim_pos
return rim_pos
@staticmethod
def compute_pts_away_from_grasp_point(grasp_point, gripper_quat, dist=0):
# center : position of the object
# extents : size of the bounding box
# compute the position of the left side rim while looking at screen
gripper_xmat = transformations.quaternion_matrix(gripper_quat)
new_grasp_point = grasp_point + (-1) * gripper_xmat[:3, 0] * dist
return new_grasp_point
# @staticmethod
# def compute_rim_point(center, extents, obj_quat=np.array([1, 0, 0, 0])):
# # center : position of the object
# # extents : size of the bounding box
# d_rim_pos = np.zeros(4)
# d_rim_pos[3] = 1
# d_rim_pos[2] = extents[2] / 2.0 * self.alpha[2] + self.beta[2]
# d_rim_pos[1] = extents[1] / 2.0 * self.alpha[1] + self.beta[1]
# d_rim_pos[0] = extents[0] / 2.0 * self.alpha[0] + self.beta[0]
#
# import ipdb; ipdb.set_trace()
#
#
# #ori_T_obj = transformations.quaternion_matrix(obj_quat)
# #rotated_d_rim_pos = np.dot(ori_T_obj, d_rim_pos)[:3]
# object_rot = transformations.quaternion_matrix(obj_quat)
# rotated_d_rim_pos = np.dot(object_rot, d_rim_pos)[:3]
# rim_pos = center.copy() + rotated_d_rim_pos
#
# return rim_pos
@staticmethod
def compute_rotation(env):
# computes a quaternion to orient gripper and object.
obj_xmat = env.env.sim.data.get_body_xmat('object0')
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
# now I want the x-axis of gripper to be equal to z-axis of object
obj_zaxis = obj_xmat[:3, 2]
gripper_xmat[:3, 0] = -obj_zaxis
h_gripper = np.eye(4)
h_gripper[:3, :3] = gripper_xmat
# convert to quaternion
gripper_xquat = transformations.quaternion_from_matrix(h_gripper)
return gripper_xquat
@staticmethod
def vis_bbox(env, center, xquat):
env.env.move_indicator(center, xquat)
# env.env.sim.forward()
def run_forwards(self,
env,
num_rollouts,
path_length=None,
obj=None,
render=False,
accept_threshold=0,
cluster_name=""):
"""
cluster_name: add as predix when saving the mp4
"""
self.render = render
self.env = env
obj_info = obj
acc_reward = 0
acc_success = 0
max_num_failure = (1 - accept_threshold) * num_rollouts
num_failure = 0
for iter_id in range(num_rollouts):
obs = env.reset()
self.iter_id = iter_id
#ep_actions, ep_observations, ep_infos
self.reset_everything_on_table(env, mesh_obj_info=obj_info)
success, cur_reward = self.goToGoal(env,
obs,
mesh_obj_info=obj_info)
print("ITERATION NUMBER ", iter_id, 'success', success)
if self.config.data_collection_mode:
datapoint = dict()
datapoint["grasp_metrics"] = success
grasp_pose = np.zeros((6), np.float32)
grasp_pose[2] = self.current_grasp.depth
datapoint["grasps"] = grasp_pose
datapoint["split"] = 0
datapoint["tf_depth_ims"] = self.current_patch
self.tensordata.add(datapoint)
if self.save_video:
self.dump_video(
f"tmp/dexnet2/{obj_info.name}_{iter_id}_{success}.mp4")
acc_reward += cur_reward
acc_success += success
if success < 0.1:
num_failure += 1
if num_failure > max_num_failure:
break
success_rate = acc_success / num_rollouts
avg_reward = acc_reward / num_rollouts
return {'avg_reward': avg_reward, 'success_rate': success_rate}
def reset_everything_on_table(self, env, mesh_obj_info, max_run=100):
_, center, _, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
center_old = center
for i in range(max_run):
obsDataNew, reward, done, info = env.step(np.zeros(8))
_, center, _, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
if np.linalg.norm(center_old - center) < 0.000001:
return
center_old = center
return
#raise ValueError
def unstable_grasp(self, finger_tip_states):
return np.var(finger_tip_states) > 0.00001
def gripper_goto_pos_orn(self,
env,
target_pos,
target_quat=np.array([1, 0, 1, 0]),
goal_current_thres=0.005,
speed=6,
open=True,
timeStep=0,
mesh_obj_info=None,
debug=False,
max_time_limit=10000):
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
gripper_quat = R.from_matrix(gripper_xmat).as_quat()[[3, 0, 1, 2]]
rel_pos = target_pos - gripper_position
cur_reward = []
episodeAcs = []
episodeObs = []
episodeInfo = []
finger_tip_states = []
grasp_harder = False
while np.linalg.norm(
rel_pos
) >= goal_current_thres and timeStep <= max_time_limit and timeStep <= self.max_path_length:
self.env_render()
action = np.zeros(8, )
finger_tip_state = env.get_finger_tip_state()
finger_tip_states.append(finger_tip_state)
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
gripper_quat = R.from_matrix(gripper_xmat).as_quat()[[3, 0, 1, 2]]
delta_quat = self._get_intermediate_delta_quats(
gripper_quat, target_quat, num_intermediates=4)
action[3:7] = delta_quat[1]
for i in range(len(rel_pos)):
action[i] = rel_pos[i] * speed
if open:
action[len(action) - 1] = 0.05 #open
else:
action[len(action) - 1] = -0.05
if not grasp_harder and self.unstable_grasp(finger_tip_states):
action[len(action) - 1] = -0.05
grasp_harder = True
elif grasp_harder:
action[len(action) - 1] = -0.05
obsDataNew, reward, done, info = env.step(action)
cur_reward.append(reward)
timeStep += 1
episodeAcs.append(action)
episodeInfo.append(info)
episodeObs.append(obsDataNew)
# compute the objectPos and object_rel_pos using bbox
if debug:
print("action", action, np.linalg.norm(rel_pos))
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
# move the gripper to the top of the rim
rel_pos = target_pos - gripper_position
# now before executing the action move the box, step calls forward
# which would actually move the box
if mesh_obj_info is None:
bounds, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
self.vis_bbox(env, bbox_xpos, obj_xquat)
# for step in range(10):
# self.env_render()
# timeStep += 1
# action = np.zeros(8,)
# action[3:7] = delta_quat[step]
# obsDataNew, reward, done, info = env.step(action)
# cur_reward.append(reward)
return cur_reward, timeStep
def close_gripper(self,
env,
iter=50,
timeStep=0,
gripper_pos=-0.03,
mesh_obj_info=None):
cur_reward = []
episodeAcs = []
episodeObs = []
episodeInfo = []
for i in range(iter):
self.env_render()
action = np.zeros(8, )
action[len(action) - 1] = gripper_pos
gripper_xmat = env.env.sim.data.get_site_xmat('robot0:grip')
gripper_quat = R.from_matrix(gripper_xmat).as_quat()[[3, 0, 1, 2]]
action[
3:7] = gripper_quat #todo, replace with what the gipper is at
obsDataNew, reward, done, info = env.step(
action) # actually simulating for some timesteps
timeStep += 1
cur_reward.append(reward)
episodeAcs.append(action)
episodeInfo.append(info)
episodeObs.append(obsDataNew)
# keep on updating the object xpos
if mesh_obj_info is None:
bounds, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
self.vis_bbox(env, bbox_xpos, obj_xquat)
return cur_reward, timeStep
def _get_intermediate_delta_quats(self,
init_quat,
des_quat,
num_intermediates=10):
"""
init_quat: initial quaternion
des_quat: desired quaternion
TODO: ideally since the quaternions are like complex numbers in high
dimensions, the multiplication should give addition, but for some reason
the simple addtion is fine here, investigate why this is the case
"""
# class should be pyquaternion.Quaternion
from pyquaternion import Quaternion
quat_to_array = lambda q: np.asarray([q.w, q.x, q.y, q.z])
if isinstance(init_quat, np.ndarray):
init_quat = Quaternion(init_quat)
if isinstance(des_quat, np.ndarray):
des_quat = Quaternion(des_quat)
assert isinstance(init_quat, Quaternion)
assert isinstance(des_quat, Quaternion)
# generator for the intermediates
intermediate_quats = list()
for q in Quaternion.intermediates(init_quat,
des_quat,
num_intermediates,
include_endpoints=True):
qu = quat_to_array(q)
intermediate_quats.append(qu)
# go through the intermediates to generate the delta quats
delta_quats = list()
prev_quat = quat_to_array(init_quat).copy()
for q in intermediate_quats:
delta_quat = q - prev_quat
delta_quats.append(delta_quat)
prev_quat = q.copy()
# now delta quats when combined with initial quat should sum to 1
add_val = quat_to_array(init_quat) + np.sum(delta_quats, axis=0)
assert np.allclose(add_val, quat_to_array(des_quat))
return delta_quats
def compute_grasping_direction(self):
#gripper_xquat = transformations.quaternion_from_matrix(h_gripper)
# at 1, 0, gripper is pointing toward -y, gripper right is pointing -x,
ori_gripper_xmat = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]])
d_rim_pos = self.get_grasp_point_to_center()
d_xy = d_rim_pos[:2]
if d_xy[0] == 0 and d_xy[1] == 0:
d_xy[1] = 0.00000001
d_xy = d_xy / np.linalg.norm(d_xy)
cos_theta = d_xy[0]
sin_theta = d_xy[1]
elevation = self.get_grasp_point_elevation()
roll = self.get_grasp_point_roll()
#ori_gripper_quat = transformations.quaternion_from_matrix(ori_gripper_xmat)
# add rotation on the xy plane
xy_rot_xmat = np.array([[cos_theta, -sin_theta, 0],
[sin_theta, cos_theta, 0], [0, 0, 1]])
# add elevation: elevation higher means camera looking more downwards
roll_xmat = np.array(
[[1, 0, 0],
[0, np.cos(np.deg2rad(-roll)), -np.sin(np.deg2rad(-roll))],
[0, np.sin(np.deg2rad(-roll)),
np.cos(np.deg2rad(-roll))]])
ele_xmat = np.array([[
np.cos(np.deg2rad(-elevation)), 0,
np.sin(np.deg2rad(-elevation))
], [0, 1, 0],
[
-np.sin(np.deg2rad(-elevation)), 0,
np.cos(np.deg2rad(-elevation))
]])
final_rot = np.matmul(
np.matmul(xy_rot_xmat, np.matmul(ele_xmat, ori_gripper_xmat)),
roll_xmat)
# making the "thumb" of the gripper to point to y+
# if not: rotate gripper with 180 degree
if final_rot[1, 1] < 0:
rot = 180
xmat = np.array(
[[1, 0, 0],
[0, np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot))],
[0, np.sin(np.deg2rad(-rot)),
np.cos(np.deg2rad(-rot))]])
final_rot = np.matmul(final_rot, xmat)
final_rot_4x4 = np.eye(4)
final_rot_4x4[:3, :3] = final_rot
gripper_xquat = transformations.quaternion_from_matrix(final_rot_4x4)
return gripper_xquat
def get_grasp_point_roll(self):
return self.roll_min + self.eta * (self.roll_max - self.roll_min)
def get_grasp_point_elevation(self):
return self.elevation_min + self.gamma * (self.elevation_max -
self.elevation_min)
def get_grasp_point_to_center(self):
d_rim_pos = np.zeros(3)
d_rim_pos[2] = self.extents[2] / 2.0 * self.alpha[2] + self.beta[2]
d_rim_pos[1] = self.extents[1] / 2.0 * self.alpha[1] + self.beta[1]
d_rim_pos[0] = self.extents[0] / 2.0 * self.alpha[0] + self.beta[0]
return d_rim_pos
def get_grasp_point(self, center):
grasp_point = center + self.get_grasp_point_to_center()
return grasp_point
def split_grasps(self, grasp):
pos = grasp[:3, 3]
orn = grasp[:3, :3]
return pos, orn
def convert_grasp_orn_to_fetch(self, orn):
gripper_right = orn[:3, 0]
gripper_down = orn[:3, 1]
gripper_point = orn[:3, 2]
final_rot_4x4 = np.eye(4)
final_rot_4x4[:3, 0] = gripper_point
final_rot_4x4[:3, 1] = gripper_right
final_rot_4x4[:3, 2] = gripper_down
if final_rot_4x4[1, 1] < 0:
rot = 180
xmat = np.array(
[[1, 0, 0, 0],
[0, np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
[0, np.sin(np.deg2rad(-rot)),
np.cos(np.deg2rad(-rot)), 0], [0, 0, 0, 1]])
final_rot_4x4 = np.matmul(final_rot_4x4, xmat)
r = R.from_matrix(final_rot_4x4[:3, :3])
gripper_xquat = r.as_quat()
# change from xyzw to wxyz
wxyz = np.zeros(4, np.float32)
wxyz[1:] = gripper_xquat[:3]
wxyz[0] = gripper_xquat[3]
#sgripper_xquat = transformations.quaternion_from_matrix(final_rot_4x4)
return wxyz
def get_grasp_from_grasp_sampler(self, env, mesh_obj_info):
data = self.grasp_sampler.sample_grasp_from_camera(env, mesh_obj_info)
#data = np.load("vae_generated_grasps/grasps_fn_159e56c18906830278d8f8c02c47cde0_15.npy", allow_pickle=True).item()
_, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
# pick one
top_k = min(5, len(data['generated_scores']))
if top_k == 0: # if no grasp is detect : just grasp from center
grasp_pos = center
gripper_target_quat = np.array([1, 0, 1, 0])
else:
grasp_ids = np.argsort(data['generated_scores'])[-top_k:][::-1]
picked_id = np.random.randint(top_k)
grasp_id = grasp_ids[picked_id]
a_grasp = data['generated_grasps'][grasp_id]
grasp_pos, grasp_orn = self.split_grasps(a_grasp)
grasp_pos, grasp_orn = self.grasp_sampler.convert_grasp_to_mujoco(
grasp_pos, grasp_orn)
gripper_target_quat = self.convert_grasp_orn_to_fetch(
grasp_orn) #self.compute_grasping_direction()
return grasp_pos, gripper_target_quat
def sample_grasp(self, env, mesh_obj, vis=True):
# Setup sensor.
#camera_intr = CameraIntrinsics.load(camera_intr_filename)
# Read images.
# get depth image from camera
inpaint_rescale_factor = 1.0
segmask_filename = None
_, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj)
camera_pos = copy.deepcopy(center)
camera_pos[2] += 0.5 * extents[2] + 0.2
env.set_camera(camera_pos,
np.array([0.7071068, 0, 0, -0.7071068]),
camera_name=f"ext_camera_0")
rgb, depth = env.render_from_camera(int(self.config.camera_img_height),
int(self.config.camera_img_width),
camera_name=f"ext_camera_0")
# enforce zoom out
from scipy.interpolate import interp2d
center_x = self.config.camera_img_height / 2 + 1
center_y = self.config.camera_img_width / 2 + 1
img_height = self.config.camera_img_height
img_width = self.config.camera_img_width
xdense = np.linspace(0, img_height - 1, img_height)
ydense = np.linspace(0, img_width - 1, img_width)
xintr = (xdense - center_x) * (1.0 / self.rescale_factor) + center_x
yintr = (ydense - center_y) * (1.0 / self.rescale_factor) + center_y
xintr[xintr < 0] = 0
xintr[xintr > (img_height - 1)] = img_height - 1
yintr[yintr < 0] = 0
yintr[yintr > (img_width - 1)] = img_width - 1
fr = interp2d(xdense, ydense, rgb[:, :, 0], kind="linear")
rgb_r_new = fr(xintr, yintr)
fg = interp2d(xdense, ydense, rgb[:, :, 1], kind="linear")
rgb_g_new = fg(xintr, yintr)
fb = interp2d(xdense, ydense, rgb[:, :, 2], kind="linear")
rgb_b_new = fb(xintr, yintr)
rgb_new = np.stack([rgb_r_new, rgb_g_new, rgb_b_new], axis=2)
fd = interp2d(xdense, ydense, depth, kind="linear")
depth_new = fd(xintr, yintr)
#from skimage.transform import resize
#rgb22, depth2 = env.render_from_camera(int(self.config.camera_img_height) , int(self.config.camera_img_width), camera_name=f"ext_camera_0")
#import ipdb; ipdb.set_trace()
# visualize the interpolation
#import imageio
#imageio.imwrite(f"tmp/rgb_{self.iter_id}.png", rgb)
#imageio.imwrite(f"tmp/rgb2_{self.iter_id}.png", rgb_new)
#imageio.imwrite(f"tmp/depth_{self.iter_id}.png", depth)
#imageio.imwrite(f"tmp/depth2_{self.iter_id}.png", depth_new)
#import ipdb; ipdb.set_trace()
rgb = rgb_new
depth = depth_new
depth = depth * self.rescale_factor
# rgb: 128 x 128 x 1
# depth: 128 x 128 x 1
scaled_camera_fov_y = self.config.camera_fov_y
aspect = 1
scaled_fovx = 2 * np.arctan(
np.tan(np.deg2rad(scaled_camera_fov_y) * 0.5) * aspect)
scaled_fovx = np.rad2deg(scaled_fovx)
scaled_fovy = scaled_camera_fov_y
cx = self.config.camera_img_width * 0.5
cy = self.config.camera_img_height * 0.5
scaled_fx = cx / np.tan(np.deg2rad(
scaled_fovx / 2.)) * (self.rescale_factor)
scaled_fy = cy / np.tan(np.deg2rad(
scaled_fovy / 2.)) * (self.rescale_factor)
camera_intr = CameraIntrinsics(frame='phoxi',
fx=scaled_fx,
fy=scaled_fy,
cx=self.config.camera_img_width * 0.5,
cy=self.config.camera_img_height * 0.5,
height=self.config.camera_img_height,
width=self.config.camera_img_width)
depth_im = DepthImage(depth, frame=camera_intr.frame)
color_im = ColorImage(np.zeros([depth_im.height, depth_im.width,
3]).astype(np.uint8),
frame=camera_intr.frame)
# Optionally read a segmask.
valid_px_mask = depth_im.invalid_pixel_mask().inverse()
segmask = valid_px_mask
# Inpaint.
depth_im = depth_im.inpaint(rescale_factor=inpaint_rescale_factor)
# Create state.
rgbd_im = RgbdImage.from_color_and_depth(color_im, depth_im)
state = RgbdImageState(rgbd_im, camera_intr, segmask=segmask)
# Query policy.
policy_start = time.time()
action = self.policy(state)
print("Planning took %.3f sec" % (time.time() - policy_start))
# numpy array with 2 values
grasp_center = action.grasp.center._data[:, 0] #(width, depth)
grasp_depth = action.grasp.depth * (1 / self.rescale_factor)
grasp_angle = action.grasp.angle #np.pi*0.3
if self.config.data_collection_mode:
self.current_grasp = action.grasp
depth_im = state.rgbd_im.depth
scale = 1.0
depth_im_scaled = depth_im.resize(scale)
translation = scale * np.array([
depth_im.center[0] - grasp_center[1],
depth_im.center[1] - grasp_center[0]
])
im_tf = depth_im_scaled
im_tf = depth_im_scaled.transform(translation, grasp_angle)
im_tf = im_tf.crop(self.gqcnn_image_size, self.gqcnn_image_size)
# get the patch
self.current_patch = im_tf.raw_data
XYZ_origin, gripper_quat = self.compute_grasp_pts_from_grasp_sample(
grasp_center, grasp_depth, grasp_angle, env)
return XYZ_origin[:, 0], gripper_quat
# Vis final grasp.
if vis:
from visualization import Visualizer2D as vis
vis.figure(size=(10, 10))
vis.imshow(rgbd_im.depth,
vmin=self.policy_config["vis"]["vmin"],
vmax=self.policy_config["vis"]["vmax"])
vis.grasp(action.grasp,
scale=2.5,
show_center=False,
show_axis=True)
vis.title("Planned grasp at depth {0:.3f}m with Q={1:.3f}".format(
action.grasp.depth, action.q_value))
vis.show(f"tmp/grasp2_{mesh_obj.name}_{self.iter_id}.png")
vis.figure(size=(10, 10))
vis.imshow(im_tf,
vmin=self.policy_config["vis"]["vmin"],
vmax=self.policy_config["vis"]["vmax"])
vis.show(f"tmp/cropd_{mesh_obj.name}_{self.iter_id}.png")
import ipdb
ipdb.set_trace()
return XYZ_origin[:, 0], gripper_quat
import ipdb
ipdb.set_trace()
def compute_grasp_pts_from_grasp_sample(self, grasp_center, grasp_depth,
grasp_angle, env):
xy_pixel = np.ones((3, 1))
xy_pixel[:2, 0] = grasp_center
# scale back the depth
depth = grasp_depth
#xy_pixel[2, 0] = depth
fovy = self.config.camera_fov_y
pix_T_cam = pcp_utils.cameras.get_intrinsics(
fovy, self.config.camera_img_width, self.config.camera_img_height)
pix_T_cam[0, 0] = pix_T_cam[0, 0] * self.rescale_factor
pix_T_cam[1, 1] = pix_T_cam[1, 1] * self.rescale_factor
cam_T_pix = np.linalg.inv(pix_T_cam)
XYZ_camX = np.matmul(cam_T_pix, xy_pixel) * depth
XYZ_camX[2, 0] = XYZ_camX[2, 0] #-0.09
#XYZ_camX[2,0] = depth
origin_T_camX = pcp_utils.cameras.gymenv_get_extrinsics(
env, f"ext_camera_0")
XYZ_camX_one = np.ones((4, 1))
XYZ_camX_one[:3, :] = XYZ_camX
XYZ_origin_one = np.matmul(origin_T_camX, XYZ_camX_one)
XYZ_origin = XYZ_origin_one[:3]
gripper_target_quat = np.array([1, 0, 1, 0])
gripper_rot = transformations.quaternion_matrix(
gripper_target_quat)[:3, :3]
cos_theta = np.cos(-grasp_angle)
sin_theta = np.sin(-grasp_angle)
xy_rot_xmat = np.array([[cos_theta, -sin_theta, 0],
[sin_theta, cos_theta, 0], [0, 0, 1]])
final_rot = np.matmul(xy_rot_xmat, gripper_rot)
if final_rot[1, 1] < 0:
rot = 180
xmat = np.array(
[[1, 0, 0],
[0, np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot))],
[0, np.sin(np.deg2rad(-rot)),
np.cos(np.deg2rad(-rot))]])
final_rot = np.matmul(final_rot, xmat)
final_rot_tmp = np.eye((4))
final_rot_tmp[:3, :3] = final_rot
gripper_quat = transformations.quaternion_from_matrix(final_rot_tmp)
# don't poke inside the table
XYZ_origin[2, 0] = np.maximum(XYZ_origin[2, 0],
self.config.table_top[2])
return XYZ_origin, gripper_quat
def goToGoal(self, env, lastObs, mesh_obj_info=None):
goal = lastObs['desired_goal']
#grasp_pos, gripper_target_quat = self.get_grasp_from_grasp_sampler(env, mesh_obj_info)
grasp_pos, gripper_target_quat = self.sample_grasp(env, mesh_obj_info)
#gripper_target_quat = np.array([1, 0, 1, 0])
grasp_point = grasp_pos #self.get_grasp_point(center)
_, center, extents, obj_xquat, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
self.extents = extents
# transform it from adam to
self.env_render()
self.vis_bbox(env, bbox_xpos, obj_xquat)
# while True:
# # env.render()
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
# move the gripper to the top of the rim
timeStep = 0 #count the total number of timesteps
cur_reward = []
episodeAcs = []
episodeObs = []
episodeInfo = []
target_pos = self.compute_pts_away_from_grasp_point(
grasp_point, gripper_target_quat,
dist=0.2) # the gripper is -0.105 away from the grasp point
## go to the top of the rim
#rim_top_pos = self.compute_top_of_rim(center, extents, gripper_target_quat)
# face down
new_gripper_quat = gripper_target_quat #transformations.quaternion_multiply(obj_xquat, gripper_quat)
rewards, timeStep = self.gripper_goto_pos_orn(
env,
target_pos,
new_gripper_quat,
goal_current_thres=0.002,
speed=6,
open=True,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info,
max_time_limit=self.max_path_length / 3)
cur_reward += rewards
# go toward rim pos
_, center, _, _, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
#grasp_point = self.get_grasp_point(center)
rim_pos = self.compute_pts_away_from_grasp_point(grasp_point,
gripper_target_quat,
dist=0.01)
rewards, timeStep = self.gripper_goto_pos_orn(
env,
rim_pos,
new_gripper_quat,
goal_current_thres=0.002,
speed=6,
open=True,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info,
max_time_limit=self.max_path_length * 2 / 3)
cur_reward += rewards
# bounds, center_new, _, _, bbox_xpos = self.get_bbox_properties(env, mesh_obj_info)
# if np.linalg.norm(center - center_new) >= 0.02: # some objects are still dropping
# rim_pos = self.get_grasp_point(center_new)
# #rim_pos = self.compute_rim_point(center_new, extents)
# rewards, timeStep = self.gripper_goto_pos_orn(env, rim_pos, new_gripper_quat, goal_current_thres= 0.002, speed=6, open=True, timeStep=timeStep, mesh_obj_info=mesh_obj_info)
# cur_reward += rewards
# close gripper
rewards, timeStep = self.close_gripper(env,
iter=20,
gripper_pos=-0.01,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info)
cur_reward += rewards
# move to target location
goal_pos_for_gripper = goal - bbox_xpos + gripper_position
run_one = True
while np.linalg.norm(
goal - bbox_xpos) >= 0.01 and timeStep <= self.max_path_length:
#print(np.linalg.norm(goal - bbox_xpos), goal_pos_for_gripper, gripper_position)
if run_one: # first time, go a rroughly toward the goal
thres = 0.01
else:
thres = 0.002
rewards, timeStep = self.gripper_goto_pos_orn(
env,
goal_pos_for_gripper,
new_gripper_quat,
goal_current_thres=thres,
speed=6,
open=False,
timeStep=timeStep,
mesh_obj_info=mesh_obj_info)
run_one = False
cur_reward += rewards
if cur_reward[-1] > 0:
break
# retriev again the poses
bounds, center, _, _, bbox_xpos = self.get_bbox_properties(
env, mesh_obj_info)
gripper_position = env.env.sim.data.get_site_xpos('robot0:grip')
# recalculate
goal_pos_for_gripper = goal - bbox_xpos + gripper_position
if timeStep >= self.max_path_length:
break #env._max_episode_steps: 70
while True:
rewards, timeStep = self.close_gripper(env,
iter=1,
timeStep=timeStep,
gripper_pos=-0.005,
mesh_obj_info=mesh_obj_info)
cur_reward += rewards
if timeStep >= self.max_path_length:
break #env._max_episode_steps: 70
success = 0
curr_reward = np.sum(cur_reward)
if np.sum(curr_reward
) > -1 * self.max_path_length and cur_reward[-1] == 0:
success = 1
return success, curr_reward
type=str,
default=None,
help="directory to store the subsampled dataset",
)
parser.add_argument(
"num_datapoints",
type=int,
default=None,
help="number of datapoints to subsample",
)
args = parser.parse_args()
dataset_path = args.dataset_path
output_path = args.output_path
num_datapoints = args.num_datapoints
dataset = TensorDataset.open(dataset_path)
out_dataset = TensorDataset(output_path, dataset.config)
num_datapoints = min(num_datapoints, dataset.num_datapoints)
ind = np.arange(dataset.num_datapoints)
np.random.shuffle(ind)
ind = ind[:num_datapoints]
ind = np.sort(ind)
for i in ind:
logging.info("Saving datapoint %d" % (i))
datapoint = dataset[i]
out_dataset.add(datapoint)
out_dataset.flush()
Permission to use, copy, modify, and distribute this software and its documentation for educational, research, and not-for-profit purposes, without fee and without a signed licensing agreement, is hereby granted, provided that the above copyright notice, this paragraph and the following two paragraphs appear in all copies, modifications, and distributions. Contact The Office of Technology Licensing, UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510) 643- 7201, [email protected], http://ipira.berkeley.edu/industry-info for commercial licensing opportunities. IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, PROVIDED HEREUNDER IS PROVIDED "AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. """ """ Print the size of a tensor dataset Author: Jeff Mahler """ import os import sys from autolab_core import TensorDataset if __name__ == '__main__': dataset = TensorDataset.open(sys.argv[1]) print 'Num datapoints', dataset.num_datapoints
def _run_prediction_single_model(self, model_dir, model_output_dir,
dataset_config):
"""Analyze the performance of a single model."""
# Read in model config.
model_config_filename = os.path.join(model_dir,
GQCNNFilenames.SAVED_CFG)
with open(model_config_filename) as data_file:
model_config = json.load(data_file)
# Load model.
self.logger.info("Loading model %s" % (model_dir))
log_file = None
for handler in self.logger.handlers:
if isinstance(handler, logging.FileHandler):
log_file = handler.baseFilename
gqcnn = get_gqcnn_model(verbose=self.verbose).load(
model_dir, verbose=self.verbose, log_file=log_file)
gqcnn.open_session()
gripper_mode = gqcnn.gripper_mode
angular_bins = gqcnn.angular_bins
# Read params from the config.
if dataset_config is None:
dataset_dir = model_config["dataset_dir"]
split_name = model_config["split_name"]
image_field_name = model_config["image_field_name"]
pose_field_name = model_config["pose_field_name"]
metric_name = model_config["target_metric_name"]
metric_thresh = model_config["metric_thresh"]
else:
dataset_dir = dataset_config["dataset_dir"]
split_name = dataset_config["split_name"]
image_field_name = dataset_config["image_field_name"]
pose_field_name = dataset_config["pose_field_name"]
metric_name = dataset_config["target_metric_name"]
metric_thresh = dataset_config["metric_thresh"]
gripper_mode = dataset_config["gripper_mode"]
self.logger.info("Loading dataset %s" % (dataset_dir))
dataset = TensorDataset.open(dataset_dir)
train_indices, val_indices, _ = dataset.split(split_name)
# Visualize conv filters.
conv1_filters = gqcnn.filters
num_filt = conv1_filters.shape[3]
d = utils.sqrt_ceil(num_filt)
vis2d.clf()
for k in range(num_filt):
filt = conv1_filters[:, :, 0, k]
vis2d.subplot(d, d, k + 1)
vis2d.imshow(DepthImage(filt))
figname = os.path.join(model_output_dir, "conv1_filters.pdf")
vis2d.savefig(figname, dpi=self.dpi)
# Aggregate training and validation true labels and predicted
# probabilities.
all_predictions = []
if angular_bins > 0:
all_predictions_raw = []
all_labels = []
for i in range(dataset.num_tensors):
# Log progress.
if i % self.log_rate == 0:
self.logger.info("Predicting tensor %d of %d" %
(i + 1, dataset.num_tensors))
# Read in data.
image_arr = dataset.tensor(image_field_name, i).arr
pose_arr = read_pose_data(
dataset.tensor(pose_field_name, i).arr, gripper_mode)
metric_arr = dataset.tensor(metric_name, i).arr
label_arr = 1 * (metric_arr > metric_thresh)
label_arr = label_arr.astype(np.uint8)
if angular_bins > 0:
# Form mask to extract predictions from ground-truth angular
# bins.
raw_poses = dataset.tensor(pose_field_name, i).arr
angles = raw_poses[:, 3]
neg_ind = np.where(angles < 0)
# TODO(vsatish): These should use the max angle instead.
angles = np.abs(angles) % GeneralConstants.PI
angles[neg_ind] *= -1
g_90 = np.where(angles > (GeneralConstants.PI / 2))
l_neg_90 = np.where(angles < (-1 * (GeneralConstants.PI / 2)))
angles[g_90] -= GeneralConstants.PI
angles[l_neg_90] += GeneralConstants.PI
# TODO(vsatish): Fix this along with the others.
angles *= -1 # Hack to fix reverse angle convention.
angles += (GeneralConstants.PI / 2)
pred_mask = np.zeros((raw_poses.shape[0], angular_bins * 2),
dtype=bool)
bin_width = GeneralConstants.PI / angular_bins
for i in range(angles.shape[0]):
pred_mask[i, int((angles[i] // bin_width) * 2)] = True
pred_mask[i, int((angles[i] // bin_width) * 2 + 1)] = True
# Predict with GQ-CNN.
predictions = gqcnn.predict(image_arr, pose_arr)
if angular_bins > 0:
raw_predictions = np.array(predictions)
predictions = predictions[pred_mask].reshape((-1, 2))
# Aggregate.
all_predictions.extend(predictions[:, 1].tolist())
if angular_bins > 0:
all_predictions_raw.extend(raw_predictions.tolist())
all_labels.extend(label_arr.tolist())
# Close session.
gqcnn.close_session()
# Create arrays.
all_predictions = np.array(all_predictions)
all_labels = np.array(all_labels)
train_predictions = all_predictions[train_indices]
val_predictions = all_predictions[val_indices]
train_labels = all_labels[train_indices]
val_labels = all_labels[val_indices]
if angular_bins > 0:
all_predictions_raw = np.array(all_predictions_raw)
train_predictions_raw = all_predictions_raw[train_indices]
val_predictions_raw = all_predictions_raw[val_indices]
# Aggregate results.
train_result = BinaryClassificationResult(train_predictions,
train_labels)
val_result = BinaryClassificationResult(val_predictions, val_labels)
train_result.save(os.path.join(model_output_dir, "train_result.cres"))
val_result.save(os.path.join(model_output_dir, "val_result.cres"))
# Get stats, plot curves.
self.logger.info("Model %s training error rate: %.3f" %
(model_dir, train_result.error_rate))
self.logger.info("Model %s validation error rate: %.3f" %
(model_dir, val_result.error_rate))
self.logger.info("Model %s training loss: %.3f" %
(model_dir, train_result.cross_entropy_loss))
self.logger.info("Model %s validation loss: %.3f" %
(model_dir, val_result.cross_entropy_loss))
# Save images.
vis2d.figure()
example_dir = os.path.join(model_output_dir, "examples")
if not os.path.exists(example_dir):
os.mkdir(example_dir)
# Train.
self.logger.info("Saving training examples")
train_example_dir = os.path.join(example_dir, "train")
if not os.path.exists(train_example_dir):
os.mkdir(train_example_dir)
# Train TP.
true_positive_indices = train_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"true_positive_%03d.png" % (i)))
# Train FP.
false_positive_indices = train_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"false_positive_%03d.png" % (i)))
# Train TN.
true_negative_indices = train_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"true_negative_%03d.png" % (i)))
# Train TP.
false_negative_indices = train_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"false_negative_%03d.png" % (i)))
# Val.
self.logger.info("Saving validation examples")
val_example_dir = os.path.join(example_dir, "val")
if not os.path.exists(val_example_dir):
os.mkdir(val_example_dir)
# Val TP.
true_positive_indices = val_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "true_positive_%03d.png" % (i)))
# Val FP.
false_positive_indices = val_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "false_positive_%03d.png" % (i)))
# Val TN.
true_negative_indices = val_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "true_negative_%03d.png" % (i)))
# Val TP.
false_negative_indices = val_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "false_negative_%03d.png" % (i)))
# Save summary stats.
train_summary_stats = {
"error_rate": train_result.error_rate,
"ap_score": train_result.ap_score,
"auc_score": train_result.auc_score,
"loss": train_result.cross_entropy_loss
}
train_stats_filename = os.path.join(model_output_dir,
"train_stats.json")
json.dump(train_summary_stats,
open(train_stats_filename, "w"),
indent=JSON_INDENT,
sort_keys=True)
val_summary_stats = {
"error_rate": val_result.error_rate,
"ap_score": val_result.ap_score,
"auc_score": val_result.auc_score,
"loss": val_result.cross_entropy_loss
}
val_stats_filename = os.path.join(model_output_dir, "val_stats.json")
json.dump(val_summary_stats,
open(val_stats_filename, "w"),
indent=JSON_INDENT,
sort_keys=True)
return train_result, val_result
default=None,
help="path to the dataset to use for training and validation",
)
parser.add_argument("split_name",
type=str,
default=None,
help="name to use for the split")
parser.add_argument(
"--train_pct",
type=float,
default=0.8,
help="percent of data to use for training",
)
parser.add_argument(
"--field_name",
type=str,
default=None,
help="name of the field to split on",
)
args = parser.parse_args()
dataset_dir = args.dataset_dir
split_name = args.split_name
train_pct = args.train_pct
field_name = args.field_name
# create split
dataset = TensorDataset.open(dataset_dir)
train_indices, val_indices = dataset.make_split(split_name,
train_pct=train_pct,
field_name=field_name)
