DeepLabCut · AlexEMG · Nov 6, 2023 · Aug 9, 2023 · Aug 9, 2023 · Aug 9, 2023
diff --git a/deeplabcut/generate_training_dataset/frame_extraction.py b/deeplabcut/generate_training_dataset/frame_extraction.py
@@ -262,7 +262,6 @@ def extract_frames(
     from deeplabcut.utils import frameselectiontools
     from deeplabcut.utils import auxiliaryfunctions
 
-
     config_file = Path(config).resolve()
     cfg = auxiliaryfunctions.read_config(config_file)
     print("Config file read successfully.")

diff --git a/deeplabcut/generate_training_dataset/trainingsetmanipulation.py b/deeplabcut/generate_training_dataset/trainingsetmanipulation.py
@@ -31,6 +31,8 @@
     auxfun_multianimal,
 )
 from deeplabcut.utils.auxfun_videos import VideoReader
+from deeplabcut.pose_estimation_tensorflow.config import load_config
+from deeplabcut.modelzoo.utils import parse_available_supermodels
 
 
 def comparevideolistsanddatafolders(config):
@@ -482,7 +484,9 @@ def merge_annotateddatasets(cfg, trainingsetfolder_full):
             data = pd.read_hdf(file_path)
             conversioncode.guarantee_multiindex_rows(data)
             if data.columns.levels[0][0] != cfg["scorer"]:
-                print(f"{file_path} labeled by a different scorer. This data will not be utilized in training dataset creation. If you need to merge datasets across scorers, see https://github.com/DeepLabCut/DeepLabCut/wiki/Using-labeled-data-in-DeepLabCut-that-was-annotated-elsewhere-(or-merge-across-labelers)")
+                print(
+                    f"{file_path} labeled by a different scorer. This data will not be utilized in training dataset creation. If you need to merge datasets across scorers, see https://github.com/DeepLabCut/DeepLabCut/wiki/Using-labeled-data-in-DeepLabCut-that-was-annotated-elsewhere-(or-merge-across-labelers)"
+                )
                 continue
             AnnotationData.append(data)
         except FileNotFoundError:
@@ -728,6 +732,7 @@ def create_training_dataset(
     net_type=None,
     augmenter_type=None,
     posecfg_template=None,
+    superanimal_name="",
 ):
     """Creates a training dataset.
 
@@ -791,6 +796,10 @@ def create_training_dataset(
         parameters a previous training iteration. None uses the default
         ``pose_cfg.yaml``.
 
+    superanimal_name: string, optional, default=""
+        Specify the superanimal name is transfer learning with superanimal is desired. This makes sure the pose config template uses superanimal configs as template
+
+
     Returns
     -------
     list(tuple) or None
@@ -832,23 +841,40 @@ def create_training_dataset(
 
     # Loading metadata from config file:
     cfg = auxiliaryfunctions.read_config(config)
+    dlc_root_path = auxiliaryfunctions.get_deeplabcut_path()
+
+    if superanimal_name != "":
+        supermodels = parse_available_supermodels()
+        posecfg_template = os.path.join(
+            dlc_root_path,
+            "pose_estimation_tensorflow",
+            "superanimal_configs",
+            supermodels[superanimal_name],
+        )
+
     if posecfg_template:
-        if not posecfg_template.endswith("pose_cfg.yaml"):
+        if (
+            not posecfg_template.endswith("pose_cfg.yaml")
+            and not posecfg_template.endswith("superquadruped.yaml")
+            and not posecfg_template.endswith("supertopview.yaml")
+        ):
             raise ValueError(
                 "posecfg_template argument must contain path to a pose_cfg.yaml file"
             )
         else:
             print("Reloading pose_cfg parameters from " + posecfg_template + "\n")
             from deeplabcut.utils.auxiliaryfunctions import read_plainconfig
 
-            prior_cfg = read_plainconfig(posecfg_template)
+        prior_cfg = read_plainconfig(posecfg_template)
     if cfg.get("multianimalproject", False):
         from deeplabcut.generate_training_dataset.multiple_individuals_trainingsetmanipulation import (
             create_multianimaltraining_dataset,
         )
 
         create_multianimaltraining_dataset(
-            config, num_shuffles, Shuffles,
+            config,
+            num_shuffles,
+            Shuffles,
             net_type=net_type,
             trainIndices=trainIndices,
             testIndices=testIndices,
@@ -880,6 +906,7 @@ def create_training_dataset(
                 "resnet" in net_type
                 or "mobilenet" in net_type
                 or "efficientnet" in net_type
+                or "dlcrnet" in net_type
             ):
                 pass
             else:

diff --git a/deeplabcut/gui/tabs/extract_frames.py b/deeplabcut/gui/tabs/extract_frames.py
@@ -91,7 +91,11 @@ def _set_page(self):
         self._generate_layout_attributes(self.layout_attributes)
         self.main_layout.addLayout(self.layout_attributes)
 
-        self.main_layout.addWidget(_create_label_widget("Optional: frame extraction from a video subset", "font:bold"))
+        self.main_layout.addWidget(
+            _create_label_widget(
+                "Optional: frame extraction from a video subset", "font:bold"
+            )
+        )
         self.video_selection_widget = VideoSelectionWidget(self.root, self)
         self.main_layout.addWidget(self.video_selection_widget)
 

diff --git a/deeplabcut/gui/tracklet_toolbox.py b/deeplabcut/gui/tracklet_toolbox.py
@@ -358,7 +358,7 @@ def _prepare_canvas(self, manager, fig):
 
         img = self.video.read_frame()
         self.im = self.ax1.imshow(img)
-        self.scat = self.ax1.scatter([], [], s=self.dotsize**2, picker=True)
+        self.scat = self.ax1.scatter([], [], s=self.dotsize ** 2, picker=True)
         self.scat.set_offsets(manager.xy[:, 0])
         self.scat.set_color(self.colors)
         self.trails = sum(
@@ -807,7 +807,7 @@ def on_change(self, val):
 
     def update_dotsize(self, val):
         self.dotsize = val
-        self.scat.set_sizes([self.dotsize**2])
+        self.scat.set_sizes([self.dotsize ** 2])
 
     @staticmethod
     def calc_distance(x1, y1, x2, y2):

diff --git a/deeplabcut/gui/window.py b/deeplabcut/gui/window.py
@@ -407,13 +407,17 @@ def _update_project_state(self, config, loaded):
     def _ask_for_help(self):
         dlg = QMessageBox(self)
         dlg.setWindowTitle("Ask for help")
-        dlg.setText('''Ask our community for help on <a href='https://forum.image.sc/tag/deeplabcut'>the forum</a>!''')
+        dlg.setText(
+            """Ask our community for help on <a href='https://forum.image.sc/tag/deeplabcut'>the forum</a>!"""
+        )
         _ = dlg.exec()
 
     def _learn_dlc(self):
         dlg = QMessageBox(self)
         dlg.setWindowTitle("Learn DLC")
-        dlg.setText('''Learn DLC with <a href='https://deeplabcut.github.io/DeepLabCut/docs/UseOverviewGuide.html'>our docs and how-to guides</a>!''')
+        dlg.setText(
+            """Learn DLC with <a href='https://deeplabcut.github.io/DeepLabCut/docs/UseOverviewGuide.html'>our docs and how-to guides</a>!"""
+        )
         _ = dlg.exec()
 
     def _create_project(self):

diff --git a/deeplabcut/modelzoo/api/spatiotemporal_adapt.py b/deeplabcut/modelzoo/api/spatiotemporal_adapt.py
@@ -119,7 +119,10 @@ def before_adapt_inference(self, make_video=False, **kwargs):
                 customized_test_config=self.customized_pose_config,
             )
         if kwargs.pop("plot_trajectories", True):
-            _plot_trajectories(datafiles[0])
+            if len(datafiles) == 0:
+                print("No data files found for plotting trajectory")
+            else:
+                _plot_trajectories(datafiles[0])
 
         if make_video:
             deeplabcut.create_labeled_video(

diff --git a/deeplabcut/modelzoo/api/superanimal_inference.py b/deeplabcut/modelzoo/api/superanimal_inference.py
@@ -341,7 +341,7 @@ def video_inference(
             print("Loading ", video)
             vid = VideoWriter(video)
             if len(scale_list) == 0:
-                # spatial pyramid can still be useful for reducing jittering and quantization error                
+                # spatial pyramid can still be useful for reducing jittering and quantization error
                 scale_list = [vid.height - 50, vid.height, vid.height + 50]
             if robust_nframes:
                 nframes = vid.get_n_frames(robust=True)

diff --git a/deeplabcut/pose_estimation_tensorflow/core/train_multianimal.py b/deeplabcut/pose_estimation_tensorflow/core/train_multianimal.py
@@ -46,7 +46,7 @@ def train(
     traintime_resize=False,
     video_path="",
     superanimal=None,
-    trim_ends = None  # trim the both ends of the video for video adaptation
+    remove_head=False,
 ):
     # in case there was already a graph
     tf.compat.v1.reset_default_graph()
@@ -93,6 +93,7 @@ def train(
         cfg["pairwise_predict"] = True
 
     dataset = PoseDatasetFactory.create(cfg)
+
     batch_spec = get_batch_spec(cfg)
     batch, enqueue_op, placeholders = setup_preloading(batch_spec)
 
@@ -107,6 +108,7 @@ def train(
     if init_weights != "":
         cfg["init_weights"] = init_weights
         cfg["resume_weights_only"] = True
+        print("replacing default init weights with: ", init_weights)
 
     stem = Path(cfg["init_weights"]).stem
     if "snapshot" in stem and keepdeconvweights:
@@ -117,6 +119,14 @@ def train(
         else:
             start_iter = int(stem.split("-")[1])
 
+        if remove_head:
+            # removing the decoding layer from the checkpoint
+            temp = []
+            for variable in variables_to_restore:
+                if "pose" not in variable.name:
+                    temp.append(variable)
+            variables_to_restore = temp
+
     else:
         print("Loading ImageNet-pretrained", net_type)
         # loading backbone from ResNet, MobileNet etc.

diff --git a/deeplabcut/pose_estimation_tensorflow/datasets/pose_deterministic.py b/deeplabcut/pose_estimation_tensorflow/datasets/pose_deterministic.py
@@ -228,7 +228,7 @@ def make_batch(self, data_item, scale, mirror):
 
     def compute_target_part_scoremap(self, joint_id, coords, data_item, size, scale):
         dist_thresh = self.cfg["pos_dist_thresh"] * scale
-        dist_thresh_sq = dist_thresh**2
+        dist_thresh_sq = dist_thresh ** 2
         num_joints = self.cfg["num_joints"]
         scmap = np.zeros(np.concatenate([size, np.array([num_joints])]))
         locref_size = np.concatenate([size, np.array([num_joints * 2])])
@@ -260,7 +260,7 @@ def compute_target_part_scoremap(self, joint_id, coords, data_item, size, scale)
                         pt_x = i * self.stride + self.half_stride
                         dx = j_x - pt_x
                         dy = j_y - pt_y
-                        dist = dx**2 + dy**2
+                        dist = dx ** 2 + dy ** 2
                         # print(la.norm(diff))
                         if dist <= dist_thresh_sq:
                             scmap[j, i, j_id] = 1

diff --git a/deeplabcut/pose_estimation_tensorflow/datasets/pose_imgaug.py b/deeplabcut/pose_estimation_tensorflow/datasets/pose_imgaug.py
@@ -487,7 +487,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
         width = size[1]
         height = size[0]
         dist_thresh = float((width + height) / 6)
-        dist_thresh_sq = dist_thresh**2
+        dist_thresh_sq = dist_thresh ** 2
 
         std = dist_thresh / 4
         # Grid of coordinates
@@ -503,7 +503,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
                 map_j = grid.copy()
                 # Distance between the joint point and each coordinate
                 dist = np.linalg.norm(grid - (j_y, j_x), axis=2) ** 2
-                scmap_j = np.exp(-dist / (2 * (std**2)))
+                scmap_j = np.exp(-dist / (2 * (std ** 2)))
                 scmap[..., j_id] = scmap_j
                 locref_mask[dist <= dist_thresh_sq, j_id * 2 + 0] = 1
                 locref_mask[dist <= dist_thresh_sq, j_id * 2 + 1] = 1
@@ -528,7 +528,7 @@ def compute_target_part_scoremap_numpy(
         self, joint_id, coords, data_item, size, scale
     ):
         dist_thresh = float(self.cfg["pos_dist_thresh"] * scale)
-        dist_thresh_sq = dist_thresh**2
+        dist_thresh_sq = dist_thresh ** 2
         num_joints = self.cfg["num_joints"]
 
         scmap = np.zeros(np.concatenate([size, np.array([num_joints])]))
@@ -555,7 +555,7 @@ def compute_target_part_scoremap_numpy(
                 y = grid.copy()[:, :, 0]
                 dx = j_x - x * self.stride - self.half_stride
                 dy = j_y - y * self.stride - self.half_stride
-                dist = dx**2 + dy**2
+                dist = dx ** 2 + dy ** 2
                 mask1 = dist <= dist_thresh_sq
                 mask2 = (x >= min_x) & (x <= max_x)
                 mask3 = (y >= min_y) & (y <= max_y)

diff --git a/deeplabcut/pose_estimation_tensorflow/datasets/pose_multianimal_imgaug.py b/deeplabcut/pose_estimation_tensorflow/datasets/pose_multianimal_imgaug.py
@@ -131,12 +131,14 @@ def load_dataset(self):
         self.has_gt = has_gt
         return data
 
-    def _load_pseudo_data_from_h5(self, cfg, threshold=0.5, mask_kpts_below_thresh=False):
+    def _load_pseudo_data_from_h5(
+        self, cfg, threshold=0.5, mask_kpts_below_thresh=False
+    ):
         gt_file = cfg["pseudo_label"]
         assert os.path.exists(gt_file)
         path_ = Path(gt_file)
         print("Using gt file:", path_.name)
-        num_kpts = len(cfg['all_joints_names'])
+        num_kpts = len(cfg["all_joints_names"])
         df = pd.read_hdf(gt_file)
         video_name = path_.name.split("DLC")[0]
         video_root = str(path_.parents[0] / video_name)
@@ -353,11 +355,13 @@ def get_batch_from_video(self):
         batch_joints = []
         joint_ids = []
         data_items = []
-        trim_ends = self.cfg.get('trim_ends', None)
+        trim_ends = self.cfg.get("trim_ends", None)
         if trim_ends is None:
             trim_ends = 0
         # because of the existence of threshold, sampling population is adjusted to len(self.data)
-        img_idx = np.random.choice(len(self.data) - trim_ends *2, size=self.batch_size, replace=True)
+        img_idx = np.random.choice(
+            len(self.data) - trim_ends * 2, size=self.batch_size, replace=True
+        )
         for i in range(self.batch_size):
             index = img_idx[i]
             offset = trim_ends
@@ -614,7 +618,7 @@ def compute_target_part_scoremap_numpy(
         locref_size = *size, num_joints * 2
         locref_map = np.zeros(locref_size)
         locref_scale = 1.0 / self.cfg["locref_stdev"]
-        dist_thresh_sq = dist_thresh**2
+        dist_thresh_sq = dist_thresh ** 2
 
         partaffinityfield_shape = *size, self.cfg["num_limbs"] * 2
         partaffinityfield_map = np.zeros(partaffinityfield_shape)
@@ -640,7 +644,7 @@ def compute_target_part_scoremap_numpy(
         dx_ = dx * locref_scale
         dy = coords[:, 1] - yy * stride - half_stride
         dy_ = dy * locref_scale
-        dist = dx**2 + dy**2
+        dist = dx ** 2 + dy ** 2
         mask1 = dist <= dist_thresh_sq
         mask2 = (xx >= mins[:, 0]) & (xx <= maxs[:, 0])
         mask3 = (yy >= mins[:, 1]) & (yy <= maxs[:, 1])
@@ -747,7 +751,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
         locref_map = np.zeros(locref_size)
 
         locref_scale = 1.0 / self.cfg["locref_stdev"]
-        dist_thresh_sq = dist_thresh**2
+        dist_thresh_sq = dist_thresh ** 2
 
         partaffinityfield_shape = np.concatenate(
             [size, np.array([self.cfg["num_limbs"] * 2])]
@@ -779,7 +783,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
             map_j = grid.copy()
             # Distance between the joint point and each coordinate
             dist = np.linalg.norm(grid - (j_y, j_x), axis=2) ** 2
-            scmap_j = np.exp(-dist / (2 * (std**2)))
+            scmap_j = np.exp(-dist / (2 * (std ** 2)))
             scmap[..., j_id] = scmap_j
             locref_mask[dist <= dist_thresh_sq, j_id * 2 + 0] = 1
             locref_mask[dist <= dist_thresh_sq, j_id * 2 + 1] = 1

diff --git a/deeplabcut/pose_estimation_tensorflow/datasets/pose_tensorpack.py b/deeplabcut/pose_estimation_tensorflow/datasets/pose_tensorpack.py
@@ -350,7 +350,7 @@ def compute_target_part_scoremap(self, components):
         locref_map = np.zeros(locref_size)
 
         locref_scale = 1.0 / self.cfg["locref_stdev"]
-        dist_thresh_sq = dist_thresh**2
+        dist_thresh_sq = dist_thresh ** 2
 
         width = size[1]
         height = size[0]
@@ -375,7 +375,7 @@ def compute_target_part_scoremap(self, components):
                         pt_x = i * stride + half_stride
                         dx = j_x - pt_x
                         dy = j_y - pt_y
-                        dist = dx**2 + dy**2
+                        dist = dx ** 2 + dy ** 2
                         # print(la.norm(diff))
                         if dist <= dist_thresh_sq:
                             scmap[j, i, j_id] = 1

diff --git a/deeplabcut/pose_estimation_tensorflow/lib/inferenceutils.py b/deeplabcut/pose_estimation_tensorflow/lib/inferenceutils.py
@@ -359,7 +359,7 @@ def calc_link_probability(self, link):
         ind = _conv_square_to_condensed_indices(i, j, self.n_multibodyparts)
         mu = self._kde.mean[ind]
         sigma = self._kde.covariance[ind, ind]
-        z = (link.length**2 - mu) / sigma
+        z = (link.length ** 2 - mu) / sigma
         return 2 * (1 - 0.5 * (1 + erf(abs(z) / sqrt(2))))
 
     @staticmethod
@@ -809,13 +809,13 @@ def wrapped(i):
                         if unique is not None:
                             self.unique[i] = unique
                         pbar.update()
-    
+
     def from_pickle(self, pickle_path):
         with open(pickle_path, "rb") as file:
             data = pickle.load(file)
         self.unique = data.pop("single", {})
         self.assemblies = data
-        
+
     @staticmethod
     def parse_metadata(data):
         params = dict()

diff --git a/deeplabcut/pose_estimation_tensorflow/nnets/utils.py b/deeplabcut/pose_estimation_tensorflow/nnets/utils.py
@@ -86,8 +86,8 @@ def get_batch_spec(cfg):
 def make_2d_gaussian_kernel(sigma, size):
     sigma = tf.convert_to_tensor(sigma, dtype=tf.float32)
     k = tf.range(-size // 2 + 1, size // 2 + 1)
-    k = tf.cast(k**2, sigma.dtype)
-    k = tf.nn.softmax(-k / (2 * (sigma**2)))
+    k = tf.cast(k ** 2, sigma.dtype)
+    k = tf.nn.softmax(-k / (2 * (sigma ** 2)))
     return tf.einsum("i,j->ij", k, k)