Merge branch 'master' of https://github.com/unifyai/memory

install_dependencies.sh

@@ -0,0 +1,2 @@
+pip install -r requirements.txt || exit 1
+pip install -r optional.txt || exit 1

ivy_memory/geometric/esm.py

@@ -226,7 +226,8 @@ def _frame_to_omni_frame_projection(
         cam_rel_poses
             Relative pose of camera to agent *[batch_size, n, c, 6]*
         cam_rel_mats
-            Relative transformation matrix from camera to agent *[batch_size, n, c, 3, 4]*
+            Relative transformation matrix from camera to agent
+            *[batch_size, n, c, 3, 4]*
         uniform_sphere_pixel_coords
             Pixel coords *[batch_size, n, h, w, 3]*
         cam_coords_f1
@@ -260,7 +261,6 @@ def _frame_to_omni_frame_projection(
             *[batch_size, 1, h, w, 3+f]*
 
         """
-
         # cam 1 to cam 2 coords
 
         if ivy.is_bool_dtype(cam_coords_f1):
@@ -383,7 +383,6 @@ def _omni_frame_to_omni_frame_projection(
             *[batch_size, h, w, 3+f]*
 
         """
-
         # Frame 1 #
         # --------#
 
@@ -500,7 +499,6 @@ def _convert_images_to_omni_observations(
             batch_size, n, oh, ow, 3+f]*    *[batch_size, n, oh, ow, 3+f]*
 
         """
-
         # coords from all scene cameras wrt world
 
         images_list = list()
@@ -632,7 +630,8 @@ def _kalman_filter_on_measurement_sequence(
         uniform_sphere_pixel_coords
             Uniform sphere pixel co-ordinates *[batch_size, oh, ow, 3]*
         agent_rel_poses
-            Relative poses of agents to the previous step *[batch_size, num_timesteps, 6]*
+            Relative poses of agents to the previous step
+            *[batch_size, num_timesteps, 6]*
         agent_rel_pose_covs
             Agent relative pose covariances *[batch_size, num_timesteps, 6, 6]*
         agent_rel_mats
@@ -649,7 +648,6 @@ def _kalman_filter_on_measurement_sequence(
             list of *[batch_size, oh, ow, (3+f)]*, list of *[batch_size, oh, ow, (3+f)]*
 
         """
-
         fused_list = list()
         fused_variances_list = list()
 
@@ -933,7 +931,8 @@ def smooth(
             )
 
             # replace temporary zeros with their prior values
-            # This ensures that the smoothing operation only changes the values for regions of high variance
+            # This ensures that the smoothing operation only
+            # changes the values for regions of high variance
             if fix_low_var_pixels:
                 fused_val = ivy.where(low_var_mask, fused_val, smoothed_fused_val)
             else:
@@ -977,7 +976,6 @@ def _forward(
             New memory of type ESMMemory
 
         """
-
         # get shapes
         img_meas = (next(iter(obs.img_meas.values()))).img_mean
         if batch_size is None:

ivy_memory_demos/interactive/learning_to_copy_with_ntm.py

@@ -105,9 +105,6 @@ def main(
             lambda v, ttl_sq, trgt_sq, sq_ln: loss_fn(ntm, v, ttl_sq, trgt_sq, sq_ln),
             return_backend_compiled_fn=True,
         )
-        # loss_fn_maybe_compiled = lambda v, ttl_sq, trgt_sq, sq_ln: loss_fn(
-        #     ntm, v, ttl_sq, trgt_sq, sq_ln
-        # )
     else:
         loss_fn_maybe_compiled = lambda v, ttl_sq, trgt_sq, sq_ln: loss_fn(
             ntm, v, ttl_sq, trgt_sq, sq_ln

ivy_memory_demos/interactive/mapping_a_room_with_esm.py

@@ -160,32 +160,35 @@ def __init__(self, interactive, try_use_sim):
             self._default_camera.set_orientation(np.array([2.642, 0.596, -0.800]))
 
             # make vision sensor child of drone
-            vision_sensor = self._vision_sensors[0]
+            self._vision_sensors[0]
             vision_sensor_body = self._vision_sensor_bodies[0]
             vision_sensor_body.set_quaternion([0.5, 0.5, 0.5, 0.5])
             vision_sensor_body.set_position(self._drone.get_position())
             vision_sensor_body.set_position([0.0, 0.0, 0.15], vision_sensor_body)
             vision_sensor_body.set_parent(self._drone)
 
             # public drone
-            cam_rel_to_mat = ivy.array(
-                vision_sensor.get_matrix(self._drone)[0:3].tolist()
-            )
             self.drone = Drone(self._drone, DroneCam(self._vision_sensors[0]))
 
             # wait for user input
             self._user_prompt(
                 "\nInitialized scene with a drone in the centre.\n\n"
                 "You can click on the drone,"
-                "then select the box icon with four arrows in the top panel of the simulator, "
+                "then select the box icon with four arrows in the top panel "
+                "of the simulator, "
                 "and then drag the drone around dynamically.\n"
-                "Starting to drag and then holding ctrl allows you to also drag the camera up and down. \n\n"
-                "This demo enables you to capture 10 different images from the drone forward facing camera, "
-                "and render the first 10 point cloud representations of the ESM memory in an open3D visualizer.\n\n"
-                "Both visualizers can be translated and rotated by clicking either the left mouse button or the wheel, "
+                "Starting to drag and then holding ctrl allows you to also "
+                "drag the camera up and down. \n\n"
+                "This demo enables you to capture 10 different images "
+                "from the drone forward facing camera, "
+                "and render the first 10 point cloud representations of the "
+                "ESM memory in an open3D visualizer.\n\n"
+                "Both visualizers can be translated and rotated by clicking "
+                "either the left mouse button or the wheel, "
                 "and then dragging the mouse.\n"
                 "Scrolling the mouse wheel zooms the view in and out.\n\n"
-                "Both visualizers can be rotated and zoomed by clicking either the left mouse button or the wheel, "
+                "Both visualizers can be rotated and zoomed by clicking "
+                "either the left mouse button or the wheel, "
                 "and then dragging with the mouse.\n\n"
                 "Press enter in the terminal to start the demo.\n\n"
             )

ivy_memory_tests/test_ivy_memory_demos.py

@@ -20,7 +20,8 @@ def test_demo_ntm_copy(compile_flag, dev_str, f, fw):
     from ivy_memory_demos.interactive.learning_to_copy_with_ntm import main
 
     if fw in ["numpy", "tensorflow_graph"]:
-        # numpy does not support gradients, and demo compiles already, so no need to use tf_graph_call
+        # numpy does not support gradients, and demo compiles already,
+        # so no need to use tf_graph_call
         pytest.skip()
     if fw in ["torch", "jax"] and compile_flag:
         # PyTorch Dictionary inputs to traced functions must have consistent type

setup.py

@@ -30,7 +30,7 @@ def _replace_logos_html(txt):
     backends_chunk = chunks[2]
     bc = backends_chunk.split("\n\n")
     img_str = (
-        ".. image:: https://github.com/unifyai/unifyai.github.io/blob/master/img/externally_linked/logos/supported/frameworks.png?raw=true\n"
+        ".. image:: https://github.com/unifyai/unifyai.github.io/blob/master/img/externally_linked/logos/supported/frameworks.png?raw=true\n"  # noqa
         "   :width: 100%"
     )
     backends_chunk = "\n\n".join(bc[0:1] + [img_str] + bc[2:])
@@ -39,7 +39,7 @@ def _replace_logos_html(txt):
     libraries_chunk = chunks[3]
     lc = libraries_chunk.split("\n\n")
     img_str = (
-        ".. image:: https://github.com/unifyai/unifyai.github.io/blob/master/img/externally_linked/ivy_libraries.png?raw=true\n"
+        ".. image:: https://github.com/unifyai/unifyai.github.io/blob/master/img/externally_linked/ivy_libraries.png?raw=true\n"  # noqa
         "   :width: 100%"
     )
     libraries_chunk = "\n\n".join(lc[0:1] + [img_str] + lc[2:])
@@ -117,7 +117,9 @@ def _is_raw_block(line):
     version="1.1.9",
     author="Ivy Team",
     author_email="ivydl.team@gmail.com",
-    description="End-to-end memory modules for machine learning developers, written in Ivy.",
+    description=(
+        "End-to-end memory modules for machine learning developers, " "written in Ivy."
+    ),
     long_description=long_description,
     long_description_content_type="text/x-rst",
     url="https://lets-unify.ai/memory",