W&B: Proposal for supporting W&B sweeps

utils/wandb_logging/sweep.py

@@ -0,0 +1,33 @@
+import sys
+from pathlib import Path
+import wandb
+
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[2].as_posix())  # add utils/ to path
+
+from train import train, parse_opt
+import test
+from utils.general import increment_path
+from utils.torch_utils import select_device
+
+
+def sweep():
+    wandb.init()
+    # Get hyp dict from sweep agent
+    hyp_dict = vars(wandb.config).get("_items")
+
+    # Workaround: get necessary opt args
+    opt = parse_opt(known=True)
+    opt.batch_size = hyp_dict.get("batch_size")
+    opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
+    opt.epochs = hyp_dict.get("epochs")
+    opt.nosave = True
+    opt.data = hyp_dict.get("data")
+    device = select_device(opt.device, batch_size=opt.batch_size)
+
+    # train
+    train(hyp_dict, opt, device)
+
+
+if __name__ == "__main__":
+    sweep()

utils/wandb_logging/sweep.yaml

@@ -0,0 +1,143 @@
+# Hyperparameters for training
+# To set range- 
+# Provide min and max values as:
+#      parameter:
+#         
+#         min: scalar
+#         max: scalar
+# OR
+#
+# Set a specific list of search space-
+#     parameter: 
+#         values: [scalar1, scalar2, scalar3...]
+#         
+# You can use grid, bayesian and hyperopt search strategy 
+# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
+
+program: utils/wandb_logging/sweep.py
+method: random
+metric:
+  name: metrics/mAP_0.5
+  goal: maximize
+
+parameters:
+  # hyperparameters: set either min, max range or values list
+  data:
+    value: "data/coco128.yaml"
+  batch_size:
+    values: [ 64 ]
+  epochs:
+    values: [ 10 ]
+
+  lr0:
+    distribution: uniform
+    min: 1e-5
+    max: 1e-1
+  lrf:
+    distribution: uniform
+    min: 0.01
+    max: 1.0
+  momentum:
+    distribution: uniform
+    min: 0.6
+    max: 0.98
+  weight_decay:
+    distribution: uniform
+    min: 0.0
+    max: 0.001
+  warmup_epochs:
+    distribution: uniform
+    min: 0.0
+    max: 5.0
+  warmup_momentum:
+    distribution: uniform
+    min: 0.0
+    max: 0.95
+  warmup_bias_lr:
+    distribution: uniform
+    min: 0.0
+    max: 0.2
+  box:
+    distribution: uniform
+    min: 0.02
+    max: 0.2
+  cls:
+    distribution: uniform
+    min: 0.2
+    max: 4.0
+  cls_pw:
+    distribution: uniform
+    min: 0.5
+    max: 2.0
+  obj:
+    distribution: uniform
+    min: 0.2
+    max: 4.0
+  obj_pw:
+    distribution: uniform
+    min: 0.5
+    max: 2.0
+  iou_t:
+    distribution: uniform
+    min: 0.1
+    max: 0.7
+  anchor_t:
+    distribution: uniform
+    min: 2.0
+    max: 8.0
+  fl_gamma:
+    distribution: uniform
+    min: 0.0
+    max: 0.1
+  hsv_h:
+    distribution: uniform
+    min: 0.0
+    max: 0.1
+  hsv_s:
+    distribution: uniform
+    min: 0.0
+    max: 0.9
+  hsv_v:
+    distribution: uniform
+    min: 0.0
+    max: 0.9
+  degrees:
+    distribution: uniform
+    min: 0.0
+    max: 45.0
+  translate:
+    distribution: uniform
+    min: 0.0
+    max: 0.9
+  scale:
+    distribution: uniform
+    min: 0.0
+    max: 0.9
+  shear:
+    distribution: uniform
+    min: 0.0
+    max: 10.0
+  perspective:
+    distribution: uniform
+    min: 0.0
+    max: 0.001
+  flipud:
+    distribution: uniform
+    min: 0.0
+    max: 1.0
+  fliplr:
+    distribution: uniform
+    min: 0.0
+    max: 1.0
+  mosaic:
+    distribution: uniform
+    min: 0.0
+    max: 1.0
+  mixup:
+    distribution: uniform
+    min: 0.0
+    max: 1.0
+  copy_paste:
+    distribution: uniform
+    min: 0.0
+    max: 1.0

utils/wandb_logging/wandb_utils.py

@@ -153,7 +153,7 @@ def setup_training(self, opt, data_dict):
                 self.weights = Path(modeldir) / "last.pt"
                 config = self.wandb_run.config
                 opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
-                    self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \
+                    self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
                                                                                                        config.opt['hyp']
             data_dict = dict(self.wandb_run.config.data_dict)  # eliminates the need for config file to resume
         if 'val_artifact' not in self.__dict__:  # If --upload_dataset is set, use the existing artifact, don't download