fix bugs highlited in pre-commit ci

agimus-project · Mar 25, 2024 · 4bf9b57 · 4bf9b57
1 parent 75451cc
commit 4bf9b57
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Showing 4 changed files with 5 additions and 45 deletions.
diff --git a/happypose/pose_estimators/cosypose/cosypose/datasets/detection_dataset.py b/happypose/pose_estimators/cosypose/cosypose/datasets/detection_dataset.py
@@ -28,38 +28,6 @@
 from happypose.toolbox.datasets.scene_dataset_wrappers import remove_invisible_objects
 
 
-def collate_fn(batch):
-    rgbs, targets = zip(*batch)
-    # Stack the rgbs and convert to a tensor
-    rgbs = torch.stack(rgbs, dim=0)
-
-    # Initialize the target dictionary
-    target = {
-        "boxes": [],
-        "labels": [],
-        "masks": [],
-        "image_id": [],
-        "area": [],
-        "iscrowd": [],
-    }
-
-    # Concatenate the target data for each image in the batch
-    for t in targets:
-        target["boxes"].append(t["boxes"])
-        target["labels"].append(t["labels"])
-        target["masks"].append(t["masks"])
-        target["image_id"].append(t["image_id"])
-        target["area"].append(t["area"])
-        target["iscrowd"].append(t["iscrowd"])
-
-    # Stack the target data and convert to tensors
-    for key in target.keys():
-        target[key] = torch.cat(target[key], dim=0)
-
-    # Return the batch data as a dictionary
-    return {"rgbs": rgbs, "targets": target}
-
-
 # TODO : Double check on types and add format documentation
 @dataclass
 class DetectionData:

diff --git a/happypose/pose_estimators/cosypose/cosypose/datasets/synthetic_dataset.py b/happypose/pose_estimators/cosypose/cosypose/datasets/synthetic_dataset.py
@@ -93,11 +93,6 @@ def __getitem__(self, idx):
                 )
             object_datas.append(obj_data)
 
-        state = {
-            "camera": cam,
-            "objects": objects,
-            "frame_info": self.frame_index.iloc[idx].to_dict(),
-        }
         image_infos = ObservationInfos(
             scene_id=self.frame_index.iloc[idx].to_dict()["scene_id"],
             view_id=self.frame_index.iloc[idx].to_dict()["view_id"],

diff --git a/happypose/pose_estimators/cosypose/cosypose/scripts/run_pose_training.py b/happypose/pose_estimators/cosypose/cosypose/scripts/run_pose_training.py
@@ -1,5 +1,6 @@
 import argparse
 import os
+import warnings
 
 import numpy as np
 from colorama import Fore, Style
@@ -9,12 +10,9 @@
 
 logger = get_logger(__name__)
 
-
-import warnings
-
+# TODO : Fix warnings
 warnings.filterwarnings("ignore")
 
-
 def make_cfg(args):
     cfg = argparse.ArgumentParser("").parse_args([])
     if args.config:

diff --git a/happypose/pose_estimators/cosypose/cosypose/training/train_detector.py b/happypose/pose_estimators/cosypose/cosypose/training/train_detector.py
@@ -17,8 +17,7 @@
 
 from happypose.pose_estimators.cosypose.cosypose.config import EXP_DIR
 from happypose.pose_estimators.cosypose.cosypose.datasets.detection_dataset import (
-    DetectionDataset,
-    collate_fn,
+    DetectionDataset
 )
 from happypose.pose_estimators.cosypose.cosypose.integrated.detector import Detector
 
@@ -51,11 +50,9 @@
 cudnn.benchmark = True
 logger = get_logger(__name__)
 
-
 def collate_fn(batch):
     return tuple(zip(*batch))
 
-
 def make_eval_configs(args, model_training, epoch):
     model = model_training.module
     model.config = args
@@ -335,6 +332,7 @@ def train_epoch():
                 if epoch < args.n_epochs_warmup:
                     lr_scheduler_warmup.step()
                 t = time.time()
+
             if epoch >= args.n_epochs_warmup:
                 lr_scheduler.step()
 
@@ -345,6 +343,7 @@ def validation():
                 loss = h(data=sample, meters=meters_val)
                 meters_val["loss_total"].add(loss.item())
 
+
         train_epoch()
         if epoch % args.val_epoch_interval == 0:
             validation()