clean code

Motion/Animation.py

@@ -90,13 +90,7 @@ def __getitem__(self, k):
 
             try:
                 # since joints are re-indexed, we need to take spacial care on parents indices
-                reind = reindex(self.parents, k[1])
-
-                order_inversed = {num: i for i, num in enumerate(k[1])}
-                order_inversed[-1] = -1
-                reindexed_parents = np.array([order_inversed[self.parents[i]] for i in k[1]])
-                if not  all(reind==reindexed_parents):  # todo: delete prev 3 lines
-                    print('ABORT ABORT ABORT')
+                reindexed_parents = reindex(self.parents, k[1])
             except: # we will reach the 'except' if k[1] is Nones, i.e., if the sub-selection is over frames rather then over joints
                 reindexed_parents = self.parents
 
@@ -687,12 +681,7 @@ def animation_from_offsets(offsets, parents, shape=None):
     offsets = offsets[sorted_order]
 
     # reorder parents
-    p = reindex(parents, sorted_order)
-
-    sorted_order_inversed = {num: i for i, num in enumerate(sorted_order)}
-    sorted_order_inversed[-1] = -1
-    parents = np.array([sorted_order_inversed[parents[i]] for i in sorted_order])
-    assert all(p==parents) # todo: delete prev 3 lines if assert works well
+    parents = reindex(parents, sorted_order)
 
     if shape is None:
         shape=(1, offsets.shape[0])

Motion/AnimationStructure.py

@@ -130,7 +130,7 @@ def children_list(parents):
     """
 
     def joint_children(i):
-        return [j for j, p in enumerate(parents) if not isinstance(p, tuple) and p == i]  # todo: 'isinstance' is a hack. change later
+        return [j for j, p in enumerate(parents) if not isinstance(p, tuple) and p == i]
 
     return list(map(lambda j: np.array(joint_children(j)), joints(parents)))
 
@@ -477,4 +477,4 @@ def get_sorted_order(parents):
     sorted_order, _ = get_sorted_order_internal(sorted_order, 0, root_idx, children)
     return sorted_order
 
-
+

blender/import_multiple_bvh.py

@@ -17,8 +17,8 @@ def location(horiz_loc, vert_loc, entity):
 
 scale_factor = 3
 
-base_path = '/Users/sigalraab/momo/train_outputs/stylegan2_motion_skeleton'               # example: '/home/username/train_outputs'
-cur_path = 'Jasper_all_5K_norm_mixing_0p9_pool_edgerot_globpos_footvelo_tmpMoDi_rmnoise_shaeb57_deep_2.3508de29d4184dcb8eb60451a7dee025/models/079999_files/22_06_29_08_55_sample_10'  # example: 'experiment_name.362db4a171934333bea96e9c10712d95/models/079999_files/22_06_27_16_27_interp')'
+base_path = '<type the path to your models>'               # example: '/home/username/train_outputs'
+cur_path = '<type the rest of the path>'  # example: 'experiment_name.362db4a171934333bea96e9c10712d95/models/079999_files/22_06_16_15_10_sample_10')'
 path = osp.join(base_path, cur_path)
 files = glob.glob(osp.join(path, 'generated_*.bvh'))
 files = [osp.basename(file) for file in files if 'fixed' not in file]  # use only file name without path
@@ -35,7 +35,7 @@ def location(horiz_loc, vert_loc, entity):
     print('***********************')
     print('args.csv was not found.')
     print('***********************')
-    exit()
+    args = {'type': 'sample'}
 
 if 'entity' in args:
     entity = args['entity']

evaluate.py

@@ -76,7 +76,7 @@ def generate(args, g_ema, device, mean_joints, std_joints, entity):
     anim_dicts, frame_mults, is_sub_motion = edge_rot_dict_from_edge_motion_data(motion_data, type='sample', edge_rot_dict_general = edge_rot_dict_general)
 
     for j, (anim_dict, frame_mult) in enumerate(zip(anim_dicts, frame_mults)):
-        anim, names = anim_from_edge_rot_dict(anim_dict, root_name='Hips')  # todo: fix the hard coding
+        anim, names = anim_from_edge_rot_dict(anim_dict, root_name='Hips')
         # compute global positions using anim
         positions = Animation.positions_global(anim)
 

models/skeleton.py

@@ -37,8 +37,6 @@ def __init__(self, pooling_list, output_joints_num):
 
         for i, affecting_joints in self.pooling_list.items():
             for j in affecting_joints:
-                # todo: consider learning a weight for each unpooled vertex
-                # assign a fixed weight for each unpooled vertex which is 1/count
                 self.weight[j, i] = 1
 
         # if an output joint is affected by more than one input joint, it takes the average of all contributors

train.py

@@ -32,7 +32,7 @@
     get_world_size,
 )
 try:
-    from clear import Task
+    from clearml import Task
 except ImportError:
     Task = None
 

utils/data.py

@@ -12,7 +12,7 @@
 from utils.foot import get_foot_contact
 
 
-foot_names = ['LeftFoot', 'RightFoot']  # todo: convert to generic code
+foot_names = ['LeftFoot', 'RightFoot']
 
 class openpose_joints(object):
     def __init__(self):
@@ -66,8 +66,7 @@ def entity2index(obj, ordered_entities):
     return res
 
 
-class Joint(): # todo: move to a more appropriate file
-    # todo: deduce automatically from input structure
+class Joint():
     n_channels = 3
 
     @staticmethod
@@ -84,7 +83,6 @@ def str():
                            {0:[0], 1:[3]},
                            {0:[0], 1:[2], 2:[4], 3:[5], 4:[7], 5:[9]},
                            {0:[1], 1:[3], 2:[4], 3:[6], 4:[7], 5:[8], 6:[10], 7:[11], 8:[13], 9:[14]}]
-    # todo: deduce automatically from skeletal_pooling_dist_1
     oj = openpose_joints()
     parents_list = [[-1],
                [-1,0],
@@ -94,7 +92,7 @@ def str():
     parents_list = list(map(np.array, parents_list))  # convert list items to np
 
 
-class Edge():  # todo: instead of edge, relate motion to end joint (unlike start joint in bvh). then the toppology structure will be same as for Joint
+class Edge():
     n_channels = 4
 
     @staticmethod
@@ -169,9 +167,7 @@ def str():
     assert all([list(parents.keys()) == edges for (parents, edges) in zip(parents_list_edges, edges_list)])  # same order as edges_list
     assert all([list(parents_list.values())[0]==-1 for parents_list in parents_list_edges])  # 1st item is root
 
-    # todo: this is a temporary solution until we have a topology class
     feet_list_edges = [[], [(0, 1)], [(3, 5), (3, 4)], [(8, 9), (6, 7)], [(13, 14), (10, 11)]] # should be ordered [LeftFoot,RightFoot]
-    # n_feet = len(feet_list_edges[-1])
 
     n_hierarchical_stages = len(parents_list_edges)
     assert len(edges_list) == n_hierarchical_stages and len(parents_list_edges) == n_hierarchical_stages and \
@@ -289,7 +285,7 @@ def calc_bone_lengths(motion_data, parents=None, names=None):
     if motion_data.ndim == 3: # no batch dim
         motion_data = motion_data[np.newaxis]
 
-    # hack to support 16 joints skeleton
+    # support 16 joints skeleton
     if motion_data.shape[1] == 16:
         motion_data = motion_data[:,:15]
 
@@ -636,7 +632,7 @@ def edge_rot_dict_from_edge_motion_data(motion_data, type='sample', edge_rot_dic
             parents = Edge.parents_list[hierarchy_level]
             nearest_edge_idx_w_root_edge = np.array(
                 (list(Edge.skeletal_pooling_dist_0[hierarchy_level].values()))).flatten()
-            assert parents[0] == -1 and Edge.parents_list[hierarchy_level+1][0] == -1  # hack to make next line count on root location at index 0
+            assert parents[0] == -1 and Edge.parents_list[hierarchy_level+1][0] == -1  # make next line count on root location at index 0
             nearest_edge_idx = nearest_edge_idx_w_root_edge[1:] - 1   # root is first, hence we look from index 1 and reduce one because root is first on uppler level too.
             if feet:
                 # remove foot contact label
@@ -754,7 +750,7 @@ def motion_from_raw(args, motion_data_raw):
         edge_rot_dict_general['mean'] = mean_joints.transpose(0, 2, 1, 3)
         edge_rot_dict_general['std'] = std_joints.transpose(0, 2, 1, 3)
 
-        if args.foot:  # todo: depart np/torch functions
+        if args.foot:
             motion_data_torch = torch.from_numpy(motion_data).transpose(1, 2)
             motion_data_torch = append_foot_contact(motion_data_torch, args.glob_pos, args.axis_up, edge_rot_dict_general)
             motion_data = motion_data_torch.transpose(1, 2).numpy()
@@ -791,4 +787,4 @@ def append_foot_contact(motion_data, glob_pos, axis_up, edge_rot_dict_general):
 
 
 if __name__ == "__main__":
-    pass
+    pass

utils/preprocess_edges.py

@@ -194,9 +194,9 @@ def compute_mean_bone_length(animation_names, char_dir):
         anim, _, _ = BVH.load(anim_path)
 
         global_location = positions_global(anim)[:, :]
-        parents_hack = copy.deepcopy(anim.parents) # this hack is good for Jasper topology only
-        parents_hack[3] = 0
-        anim_offsets = (global_location[:, 1:] - global_location[:, parents_hack[1:]])
+        parents = copy.deepcopy(anim.parents)
+        parents[3] = 0
+        anim_offsets = (global_location[:, 1:] - global_location[:, parents[1:]])
         anim_offsets = np.concatenate([global_location[:, :1], anim_offsets],
                                       axis=1)  # for root joint (index 0), put the offset from the global zero
         if one_char_offsets is None:

utils/traits.py

@@ -264,7 +264,6 @@ def fixed_dim_pad(self, kernel_size):
         return (self.smaller_n_joints - 1,) + super().fixed_dim_pad(kernel_size)
 
     def weight_internal(self, in_channel, out_channel, kernel_size):
-        # todo: change weight initialization
         return torch.randn(out_channel, in_channel, self.smaller_n_joints, self.larger_n_joints, kernel_size)
 
     def mask_affectors(self, mask, out_channel, joint_idx, affectors_this_joint):

utils/visualization.py

@@ -145,7 +145,6 @@ def motion2fig(data, H=512, W=512, n_sampled_motions=5, n_sampled_frames=5, enti
 
         assert not isinstance(data, list) and not isinstance(data[0], dict)
 
-        # todo: not debugged for normalization!!!
         data = data * edge_rot_dict_general['std'].transpose(0, 2, 1, 3) + edge_rot_dict_general['mean'].transpose(0, 2, 1, 3)
 
         sampled_data = data[sampled_motions][:, :, :, sampled_frames]
@@ -164,7 +163,7 @@ def motion2fig(data, H=512, W=512, n_sampled_motions=5, n_sampled_frames=5, enti
             anim, _ = anim_from_edge_rot_dict(one_edge_rot_dict)
             joints[idx] = Animation.positions_global(anim)
 
-        anim_joints_1_to_open_pose = [7, 6, 15, 16, 17, 10, 11, 12, 0, 23, 24, 25, 19, 20, 21] # todo: change to non hard coded
+        anim_joints_1_to_open_pose = [7, 6, 15, 16, 17, 10, 11, 12, 0, 23, 24, 25, 19, 20, 21]
         data = joints[:, :, anim_joints_1_to_open_pose, :]
         data = data.transpose(0, 2, 3, 1)  # samples x frames x joints x features ==> samples x joints x features x frames
 
@@ -218,7 +217,7 @@ def motion2bvh_rot(motion_data, bvh_file_path, type, edge_rot_dict_general=None)
 
     # from this point input is a list of edge_rot_dicts
     for i, (edge_rot_dict, frame_mult) in enumerate(zip(edge_rot_dicts, frame_mults)):
-        anim, names = anim_from_edge_rot_dict(edge_rot_dict, root_name='Hips') # todo: fix the hard coding
+        anim, names = anim_from_edge_rot_dict(edge_rot_dict, root_name='Hips')
         if is_sub_motion:
             suffix = '_{}x{}'.format(edge_rot_dict['rot_edge_no_root'].shape[1], int(edge_rot_dict['rot_edge_no_root'].shape[0]/frame_mult))
         elif type == 'edit':