version bump and formatting

examples/policy.py

@@ -38,7 +38,8 @@
 
 import numpy as np
 
-from autolab_core import YamlConfig, Logger, BinaryImage, CameraIntrinsics, ColorImage, DepthImage, RgbdImage
+from autolab_core import (YamlConfig, Logger, BinaryImage, CameraIntrinsics,
+                          ColorImage, DepthImage, RgbdImage)
 from visualization import Visualizer2D as vis
 
 from gqcnn.grasping import (RobustGraspingPolicy,

examples/policy_ros.py

@@ -39,7 +39,8 @@
 
 from cv_bridge import CvBridge, CvBridgeError
 
-from autolab_core import Point, Logger, BinaryImage, CameraIntrinsics, ColorImage, DepthImage
+from autolab_core import (Point, Logger, BinaryImage, CameraIntrinsics,
+                          ColorImage, DepthImage)
 from visualization import Visualizer2D as vis
 
 from gqcnn.grasping import Grasp2D, SuctionPoint2D, GraspAction

examples/policy_with_image_proc.py

@@ -37,7 +37,10 @@
 import pcl
 import skimage
 
-from autolab_core import PointCloud, RigidTransform, YamlConfig, Logger, BinaryImage, CameraIntrinsics, ColorImage, DepthImage, RgbdImage, SegmentationImage
+from autolab_core import (PointCloud, RigidTransform, YamlConfig,
+                          Logger, BinaryImage, CameraIntrinsics,
+                          ColorImage, DepthImage, RgbdImage,
+                          SegmentationImage)
 from visualization import Visualizer2D as vis
 
 from gqcnn import (RobustGraspingPolicy, CrossEntropyRobustGraspingPolicy,

gqcnn/analysis/analyzer.py

@@ -35,7 +35,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from autolab_core import BinaryClassificationResult, TensorDataset, Logger, DepthImage
+from autolab_core import (BinaryClassificationResult, TensorDataset,
+                          Logger, DepthImage)
 from autolab_core.constants import JSON_INDENT
 import autolab_core.utils as utils
 from visualization import Visualizer2D as vis2d

gqcnn/grasping/constraint_fn.py

@@ -117,6 +117,7 @@ def satisfies_constraints(self, grasp):
 
 
 class GraspConstraintFnFactory(object):
+
     @staticmethod
     def constraint_fn(fn_type, config):
         if fn_type == "none":

gqcnn/grasping/grasp_quality_function.py

@@ -816,6 +816,7 @@ def quality(self, state, actions, params=None):
 
 class DiscCurvatureSuctionQualityFunction(
         GaussianCurvatureSuctionQualityFunction):
+
     def __init__(self, config):
         """Create approach planarity suction metric."""
         self._radius = config["radius"]
@@ -848,6 +849,7 @@ def _points_in_window(self, point_cloud_image, action, segmask=None):
 
 class ComDiscCurvatureSuctionQualityFunction(
         DiscCurvatureSuctionQualityFunction):
+
     def __init__(self, config):
         """Create approach planarity suction metric."""
         self._curvature_pctile = config["curvature_pctile"]
@@ -911,6 +913,7 @@ def quality(self, state, actions, params=None):
 
 
 class GQCnnQualityFunction(GraspQualityFunction):
+
     def __init__(self, config):
         """Create a GQCNN suction quality function."""
         GraspQualityFunction.__init__(self)
@@ -1067,6 +1070,7 @@ def quality(self, state, actions, params):
 
 
 class NoMagicQualityFunction(GraspQualityFunction):
+
     def __init__(self, config):
         """Create a quality that uses `nomagic_net` as a quality function."""
         from nomagic_submission import ConvNetModel
@@ -1154,8 +1158,8 @@ def grasps_to_tensors(self, grasps, state):
                                                        255))[1].tostring()
             im_decoded = cv2.imdecode(np.frombuffer(im_encoded, np.uint8),
                                       0) / 255.0
-            image_tensor[i, :, :, 0] = ((im_decoded - self._data_mean) /
-                                        self._data_std)
+            image_tensor[i, :, :,
+                         0] = ((im_decoded - self._data_mean) / self._data_std)
 
             if gripper_mode == GripperMode.PARALLEL_JAW:
                 pose_tensor[i] = grasp.depth
@@ -1230,6 +1234,7 @@ def quality(self, state, actions, params):
 
 
 class FCGQCnnQualityFunction(GraspQualityFunction):
+
     def __init__(self, config):
         """Grasp quality function using the fully-convolutional gqcnn."""
         GraspQualityFunction.__init__(self)

gqcnn/grasping/image_grasp_sampler.py

@@ -39,7 +39,8 @@
 import scipy.spatial.distance as ssd
 import scipy.stats as ss
 
-from autolab_core import Point, RigidTransform, Logger, DepthImage, RgbdImage, GdImage
+from autolab_core import (Point, RigidTransform, Logger,
+                          DepthImage, RgbdImage, GdImage)
 from visualization import Visualizer2D as vis
 
 from .grasp import Grasp2D, SuctionPoint2D, MultiSuctionPoint2D
@@ -558,8 +559,8 @@ def _sample_antipodal_grasps(self,
             for i in range(self._depth_samples_per_grasp):
                 # Get depth in the neighborhood of the center pixel.
                 depth_win = depth_im.data[grasp_center[0] -
-                                          self._h:grasp_center[0] +
-                                          self._h, grasp_center[1] -
+                                          self._h:grasp_center[0] + self._h,
+                                          grasp_center[1] -
                                           self._w:grasp_center[1] + self._w]
                 center_depth = np.min(depth_win)
                 if center_depth == 0 or np.isnan(center_depth):

gqcnn/grasping/policy/fc_policy.py

@@ -105,8 +105,8 @@ def _mask_predictions(self, preds, raw_segmask):
         preds_masked = np.zeros_like(preds)
         raw_segmask_cropped = raw_segmask[self._gqcnn_recep_h //
                                           2:raw_segmask.shape[0] -
-                                          self._gqcnn_recep_h //
-                                          2, self._gqcnn_recep_w //
+                                          self._gqcnn_recep_h // 2,
+                                          self._gqcnn_recep_w //
                                           2:raw_segmask.shape[1] -
                                           self._gqcnn_recep_w // 2, 0]
         raw_segmask_downsampled = raw_segmask_cropped[::self._gqcnn_stride, ::
@@ -321,8 +321,8 @@ def _action(self, state, num_actions=1):
                                            self._vis_scale,
                                            output_dir=state_output_dir)
 
-        return actions[-1] if (self._filter_grasps or num_actions == 1
-                               ) else actions[-(num_actions + 1):]
+        return actions[-1] if (self._filter_grasps or num_actions
+                               == 1) else actions[-(num_actions + 1):]
 
     def action_set(self, state, num_actions):
         """Plan a set of actions.
@@ -411,8 +411,8 @@ def _get_actions(self, preds, ind, images, depths, camera_intr,
                             depth,
                             width=self._gripper_width,
                             camera_intr=camera_intr)
-            grasp_action = GraspAction(grasp,
-                                       preds[im_idx, h_idx, w_idx, ang_idx],
+            grasp_action = GraspAction(grasp, preds[im_idx, h_idx, w_idx,
+                                                    ang_idx],
                                        DepthImage(images[im_idx]))
             actions.append(grasp_action)
         return actions

gqcnn/grasping/policy/policy.py

@@ -42,7 +42,9 @@
 from sklearn.mixture import GaussianMixture
 
 import autolab_core.utils as utils
-from autolab_core import Point, Logger, BinaryImage, ColorImage, DepthImage, RgbdImage, SegmentationImage, CameraIntrinsics
+from autolab_core import (Point, Logger, BinaryImage, ColorImage,
+                          DepthImage, RgbdImage, SegmentationImage,
+                          CameraIntrinsics)
 from visualization import Visualizer2D as vis
 
 from ..constraint_fn import GraspConstraintFnFactory
@@ -1238,7 +1240,7 @@ def action_set(self, state):
                     q_values=norm_q_values,
                     scale=2.0,
                     title=title,
-                    save_fname="final_sampled_grasps.png".format(j),
+                    save_fname="final_sampled_grasps.png",
                     save_path=state_output_dir)
             display_grasps_and_q_values = zip(grasps, q_values)
             display_grasps_and_q_values = sorted(display_grasps_and_q_values,
@@ -1545,6 +1547,7 @@ def set_constraint_fn(self, constraint_fn):
 
 
 class PriorityCompositeGraspingPolicy(CompositeGraspingPolicy):
+
     def __init__(self, policies, priority_list):
         # Check validity.
         for name in priority_list:
@@ -1613,6 +1616,7 @@ def action_set(self, state, policy_subset=None, min_q_value=-1.0):
 
 
 class GreedyCompositeGraspingPolicy(CompositeGraspingPolicy):
+
     def __init__(self, policies):
         CompositeGraspingPolicy.__init__(self, policies)
 

gqcnn/model/tf/network_tf.py

@@ -878,21 +878,21 @@ def _predict(self, image_arr, pose_arr, verbose=False):
                 end_ind = cur_ind + dim
 
                 if self._input_depth_mode == InputDepthMode.POSE_STREAM:
-                    self._input_im_arr[:dim, ...] = (
-                        image_arr[cur_ind:end_ind, ...] -
-                        self._im_mean) / self._im_std
+                    self._input_im_arr[:dim,
+                                       ...] = (image_arr[cur_ind:end_ind, ...]
+                                               - self._im_mean) / self._im_std
                     self._input_pose_arr[:dim, :] = (
                         pose_arr[cur_ind:end_ind, :] -
                         self._pose_mean) / self._pose_std
                 elif self._input_depth_mode == InputDepthMode.SUB:
                     self._input_im_arr[:dim, ...] = image_arr[cur_ind:end_ind,
                                                               ...]
-                    self._input_pose_arr[:dim, :] = pose_arr[cur_ind:
-                                                             end_ind, :]
+                    self._input_pose_arr[:dim, :] = pose_arr[
+                        cur_ind:end_ind, :]
                 elif self._input_depth_mode == InputDepthMode.IM_ONLY:
-                    self._input_im_arr[:dim, ...] = (
-                        image_arr[cur_ind:end_ind, ...] -
-                        self._im_mean) / self._im_std
+                    self._input_im_arr[:dim,
+                                       ...] = (image_arr[cur_ind:end_ind, ...]
+                                               - self._im_mean) / self._im_std
 
                 gqcnn_output = self._sess.run(
                     self._output_tensor,
@@ -1417,9 +1417,9 @@ def _build_network(self, input_im_node, input_pose_node,
                 self._input_depth_mode == InputDepthMode.IM_ONLY:
             extraneous_stream_msg = ("When using input depth mode '{}', only"
                                      " im stream is allowed!")
-            assert not ("pose_stream" in self._architecture
-                        or "merge_stream" in self._architecture
-                        ), extraneous_stream_msg.format(self._input_depth_mode)
+            assert not ("pose_stream" in self._architecture or "merge_stream"
+                        in self._architecture), extraneous_stream_msg.format(
+                            self._input_depth_mode)
             with tf.name_scope("im_stream"):
                 return self._build_im_stream(input_im_node,
                                              input_pose_node,

gqcnn/search/resource_manager.py

@@ -48,6 +48,7 @@
 
 
 class ResourceManager(object):
+
     def __init__(self,
                  trial_cpu_load,
                  trial_gpu_load,

gqcnn/search/search.py

@@ -50,6 +50,7 @@
 
 
 class GQCNNSearch(object):
+
     def __init__(self,
                  analysis_config,
                  train_configs,

gqcnn/search/trial.py

@@ -50,6 +50,7 @@ class TrialStatus:
 
 
 class GQCNNTrialWithAnalysis(ABC):
+
     def __init__(self, analysis_cfg, train_cfg, dataset_dir, split_name,
                  output_dir, model_name, hyperparam_summary):
         self._analysis_cfg = analysis_cfg
@@ -198,11 +199,13 @@ def __str__(self):
 
 
 class GQCNNTrainingAndAnalysisTrial(GQCNNTrialWithAnalysis):
+
     def _run(self, trainer):
         trainer.train()
 
 
 class GQCNNFineTuningAndAnalysisTrial(GQCNNTrialWithAnalysis):
+
     def __init__(self, analysis_cfg, train_cfg, dataset_dir, base_model_dir,
                  split_name, output_dir, model_name, hyperparam_summary):
         GQCNNTrialWithAnalysis.__init__(self, analysis_cfg, train_cfg,

gqcnn/training/tf/trainer_tf.py

@@ -1517,8 +1517,9 @@ def _load_and_enqueue(self, seed):
                         (train_label_arr.shape[0], self._angular_bins * 2))
                     for i in range(angles.shape[0]):
                         bins[i] = angles[i] // self._bin_width
-                        train_pred_mask_arr[
-                            i, int((angles[i] // self._bin_width) * 2)] = 1
+                        train_pred_mask_arr[i,
+                                            int((angles[i] //
+                                                 self._bin_width) * 2)] = 1
                         train_pred_mask_arr[
                             i, int((angles[i] // self._bin_width) * 2 + 1)] = 1
 

gqcnn/utils/policy_exceptions.py

@@ -34,7 +34,10 @@ class NoValidGraspsException(Exception):
     """Exception for when antipodal point pairs can be found in the depth
     image but none are valid grasps that can be executed."""
 
-    def __init__(self, in_collision=True, not_confident=False, *args,
+    def __init__(self,
+                 in_collision=True,
+                 not_confident=False,
+                 *args,
                  **kwargs):
         self.in_collision = in_collision
         self.not_confident = not_confident

gqcnn/version.py

@@ -1 +1 @@
-__version__ = "1.1.0"
+__version__ = "1.3.0"

package.xml

@@ -24,7 +24,7 @@ MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
 -->
 <package>
   <name>gqcnn</name>
-  <version>1.1.0</version>
+  <version>1.3.0</version>
   <description>ROS package for deploying Grasp Quality Convolutional Neural Networks (GQ-CNNs).</description>
 
   <!-- One maintainer tag required, multiple allowed, one person per tag --> 

ros_nodes/grasp_planner_node.py

@@ -38,7 +38,8 @@
 import numpy as np
 import rospy
 
-from autolab_core import YamlConfig, CameraIntrinsics, ColorImage, DepthImage, BinaryImage, RgbdImage
+from autolab_core import (YamlConfig, CameraIntrinsics, ColorImage,
+                          DepthImage, BinaryImage, RgbdImage)
 from visualization import Visualizer2D as vis
 from gqcnn.grasping import (Grasp2D, SuctionPoint2D, RgbdImageState,
                             RobustGraspingPolicy,
@@ -55,6 +56,7 @@
 
 
 class GraspPlanner(object):
+
     def __init__(self, cfg, cv_bridge, grasping_policy, grasp_pose_publisher):
         """
         Parameters
@@ -118,8 +120,8 @@ def read_images(self, req):
             raw_camera_info.K[1], raw_camera_info.height,
             raw_camera_info.width)
 
-        # Create wrapped BerkeleyAutomation/autolab_core RGB and depth images by
-        # unpacking the ROS images using ROS `CvBridge`
+        # Create wrapped BerkeleyAutomation/autolab_core RGB and depth images
+        # by unpacking the ROS images using ROS `CvBridge`
         try:
             color_im = ColorImage(self.cv_bridge.imgmsg_to_cv2(
                 raw_color, "rgb8"),