GQ-CNN¶
GQ-CNN and FC-GQ-CNN classes are never accessed directly, but through a lightweight factory function that returns the corresponding class depending on the specified backend.
$ from gqcnn import get_gqcnn_model
$
$ backend = 'tf'
$ my_gqcnn = get_gqcnn_model(backend)(<class initializer args>)
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gqcnn.
get_gqcnn_model
(backend='tf', verbose=True)¶ Get the GQ-CNN model for the provided backend.
Note
Currently only TensorFlow is supported.
- Parameters
backend (str) – The backend to use, currently only “tf” is supported.
verbose (bool) – Whether or not to log initialization output to stdout.
- Returns
GQ-CNN model with TensorFlow backend.
- Return type
-
gqcnn.
get_fc_gqcnn_model
(backend='tf', verbose=True)¶ Get the FC-GQ-CNN model for the provided backend.
Note
Currently only TensorFlow is supported.
- Parameters
backend (str) – The backend to use, currently only “tf” is supported.
verbose (bool) – Whether or not to log initialization output to stdout.
- Returns
FC-GQ-CNN model with TensorFlow backend.
- Return type
GQCNNTF¶
Tensorflow implementation of GQ-CNN model.
-
class
gqcnn.model.tf.
GQCNNTF
(gqcnn_config, verbose=True, log_file=None)¶ Bases:
object
GQ-CNN network implemented in Tensorflow.
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__init__
(gqcnn_config, verbose=True, log_file=None)¶ - Parameters
gqcnn_config (dict) – Python dictionary of model configuration parameters.
verbose (bool) – Whether or not to log model output to stdout.
log_file (str) – If provided, model output will also be logged to this file.
-
static
load
(model_dir, verbose=True, log_file=None)¶ Instantiate a trained GQ-CNN for fine-tuning or inference.
- Parameters
model_dir (str) – Path to trained GQ-CNN model.
verbose (bool) – Whether or not to log model output to stdout.
log_file (str) – If provided, model output will also be logged to this file.
- Returns
Initialized GQ-CNN.
- Return type
-
open_session
()¶ Open Tensorflow session.
-
close_session
()¶ Close Tensorflow session.
-
__del__
()¶ Destructor that basically just makes sure the Tensorflow session has been closed.
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property
filters
¶ Evaluate the filters of the first convolution layer.
- Returns
Filters (weights) from first convolution layer of the network.
- Return type
numpy.ndarray
-
predict
(image_arr, pose_arr, verbose=False)¶ Predict the probability of grasp success given a depth image and gripper pose.
- Parameters
image_arr (
numpy ndarray
) – 4D tensor of depth images.pose_arr (
numpy ndarray
) – Tensor of gripper poses.verbose (bool) – Whether or not to log progress to stdout, useful to turn off during training.
-
featurize
(image_arr, pose_arr=None, feature_layer='conv1_1', verbose=False)¶ Featurize a set of inputs.
- Parameters
image_arr (
numpy ndarray
) – 4D tensor of depth images.pose_arr (
numpy ndarray
) – Optional tensor of gripper poses.feature_layer (str) – The network layer to featurize.
verbose (bool) – Whether or not to log progress to stdout.
-
FCGQCNNTF¶
Tensorflow implementation of FC-GQ-CNN model.
-
class
gqcnn.model.tf.
FCGQCNNTF
(gqcnn_config, fc_config, verbose=True, log_file=None)¶ Bases:
gqcnn.model.tf.network_tf.GQCNNTF
FC-GQ-CNN network implemented in Tensorflow.
Note
FC-GQ-CNNs are never directly trained, but instead a pre-trained GQ-CNN is converted to an FC-GQ-CNN at inference time.
-
static
load
(model_dir, fc_config, log_file=None)¶ Load an FC-GQ-CNN from a pre-trained GQ-CNN.
- Parameters
model_dir (str) – Path to pre-trained GQ-CNN model.
fc_config (dict) – Python dictionary of FC-GQ-CNN model configuration parameters.
log_file (str) – If provided, model output will also be logged to this file.
- Returns
Initialized FC-GQ-CNN.
- Return type
-
static