Allow setting per-layer learning rates"

src/super_gradients/recipes/cityscapes_ddrnet.yaml

@@ -55,7 +55,17 @@ architecture: ddrnet_23
 
 training_hyperparams:
   max_epochs: 500
-  initial_lr: 0.0075   # batch size 24
+  initial_lr: # batch size 24
+    default: 0.075
+    # backbone layers
+    _backbone: 0.0075
+    compression3: 0.0075
+    compression4: 0.0075
+    down3: 0.0075
+    down4: 0.0075
+    layer3_skip: 0.0075
+    layer4_skip: 0.0075
+    layer5_skip: 0.0075
   loss:
     DiceCEEdgeLoss:
       num_classes: 19

src/super_gradients/recipes/cityscapes_kd_base.yaml

@@ -48,7 +48,19 @@ resume: False
 training_hyperparams:
   sync_bn: True
   max_epochs: 500
-  initial_lr: 0.0075   # batch size 24
+
+  initial_lr: # batch size 24
+    default: 0.075
+    # backbone layers
+    _backbone: 0.0075
+    compression3: 0.0075
+    compression4: 0.0075
+    down3: 0.0075
+    down4: 0.0075
+    layer3_skip: 0.0075
+    layer4_skip: 0.0075
+    layer5_skip: 0.0075
+
   resume: ${resume}
   loss:
     _target_: super_gradients.training.losses.seg_kd_loss.SegKDLoss

src/super_gradients/recipes/cityscapes_pplite_seg50.yaml

@@ -67,6 +67,9 @@ checkpoint_params:
 
 training_hyperparams:
   sync_bn: True
+  initial_lr:
+    "encoder.backbone": 0.01
+    default: 0.1
   loss:
     DiceCEEdgeLoss:
       num_classes: 19

src/super_gradients/recipes/cityscapes_pplite_seg75.yaml

@@ -62,6 +62,10 @@ checkpoint_params:
 
 training_hyperparams:
   sync_bn: True
+  initial_lr:
+    "encoder.backbone": 0.01
+    default: 0.1
+
   loss:
     DiceCEEdgeLoss:
       num_classes: 19

src/super_gradients/recipes/cityscapes_stdc_seg50.yaml

@@ -60,6 +60,9 @@ checkpoint_params:
   strict_load: no_key_matching
 
 training_hyperparams:
+  initial_lr:
+    cp: 0.01
+    default: 0.1
   sync_bn: True
   loss:
     DiceCEEdgeLoss:

src/super_gradients/recipes/cityscapes_stdc_seg75.yaml

@@ -64,7 +64,10 @@ checkpoint_params:
   strict_load: no_key_matching
 
 training_hyperparams:
-  initial_lr: 0.005
+  initial_lr:
+    cp: 0.005
+    default: 0.05
+
   sync_bn: True
 
   loss:

src/super_gradients/recipes/training_hyperparams/coco2017_yolo_nas_train_params.yaml

@@ -8,7 +8,10 @@ warmup_initial_lr:  1e-6
 lr_warmup_steps: 1000
 lr_warmup_epochs: 0
 
-initial_lr:  2e-4
+initial_lr:
+  default: 2e-4
+
+
 lr_mode: CosineLRScheduler
 cosine_final_lr_ratio: 0.1
 

src/super_gradients/training/datasets/pose_estimation_datasets/yolo_nas_pose_collate_fn.py

@@ -3,7 +3,7 @@
 import numpy as np
 import torch
 from torch import Tensor
-from torch.utils.data import default_collate
+from torch.utils.data.dataloader import default_collate
 
 from super_gradients.common.registry.registry import register_collate_function
 from super_gradients.training.samples import PoseEstimationSample

src/super_gradients/training/models/segmentation_models/segformer.py

@@ -458,7 +458,7 @@ def _separate_lr_multiply_params(self):
         backbone_names = [n for n, p in self.backbone.named_parameters()]
         multiply_lr_params, no_multiply_params = {}, {}
         for name, param in self.named_parameters():
-            if name in backbone_names:
+            if any([backbone_name in name for backbone_name in backbone_names]):
                 no_multiply_params[name] = param
             else:
                 multiply_lr_params[name] = param

src/super_gradients/training/params.py

@@ -51,6 +51,7 @@
     "warmup_mode": "LinearEpochLRWarmup",
     "step_lr_update_freq": None,
     "lr_updates": [],
+    "initial_lr": None,
     "clip_grad_norm": None,
     "pre_prediction_callback": None,
     "ckpt_best_name": "ckpt_best.pth",
@@ -98,7 +99,7 @@
         # "lr_updates": {"type": "array", "minItems": 1},
         "lr_decay_factor": {"type": "number", "minimum": 0, "maximum": 1},
         "lr_warmup_epochs": {"type": "number", "minimum": 0, "maximum": 10},
-        "initial_lr": {"type": "number", "exclusiveMinimum": 0, "maximum": 10},
+        # "initial_lr": {"type": "number", "exclusiveMinimum": 0, "maximum": 10},
     },
     "if": {"properties": {"lr_mode": {"const": "StepLRScheduler"}}},
     "then": {"required": ["lr_updates", "lr_decay_factor"]},

src/super_gradients/training/sg_trainer/sg_trainer.py

@@ -76,7 +76,7 @@
     broadcast_from_master,
 )
 from super_gradients.training.utils.ema import ModelEMA
-from super_gradients.training.utils.optimizer_utils import build_optimizer
+from super_gradients.training.utils.optimizer_utils import build_optimizer, get_initial_lr_from_optimizer
 from super_gradients.training.utils.sg_trainer_utils import MonitoredValue, log_main_training_params
 from super_gradients.training.utils.utils import fuzzy_idx_in_list, unwrap_model
 from super_gradients.training.utils.weight_averaging_utils import ModelWeightAveraging
@@ -890,9 +890,16 @@ def train(
                     Final learning rate ratio (only relevant when `lr_mode`='CosineLRScheduler'). The cosine starts from initial_lr and reaches
                      initial_lr * cosine_final_lr_ratio in last epoch
 
-                - `inital_lr` : float
+                - `inital_lr` : Union[float, Dict[str, float]
 
-                    Initial learning rate.
+                    Initial learning rate as:
+                    float - learning rate value when passed as a scalar
+                    Dictionary where keys are group names and values are the learning rates.
+                    For example {"default": 0.01, "head": 0.1}
+
+                    - Keys in such mapping are prefixes of named parameters of the model.
+                    - The "default" key is mandatory, and it's lr value is set for any group not specified in the other keys
+                    - It is also possible to freeze some parts of the model by assigning 0 as a lr value.
 
                 - `loss` : Union[nn.module, str]
 
@@ -1289,6 +1296,20 @@ def forward(self, inputs, targets):
         else:
             raise RuntimeError("warmup_mode has to be either a name of a mode (str) or a subclass of PhaseCallback")
 
+        if isinstance(self.training_params.optimizer, str) or (
+            inspect.isclass(self.training_params.optimizer) and issubclass(self.training_params.optimizer, torch.optim.Optimizer)
+        ):
+            self.optimizer = build_optimizer(net=unwrap_model(self.net), lr=self.training_params.initial_lr, training_params=self.training_params)
+        elif isinstance(self.training_params.optimizer, torch.optim.Optimizer):
+            if self.training_params.initial_lr is not None:
+                raise RuntimeError("An instantiated optimizer cannot be passed along initial_lr != None")
+            self.optimizer = self.training_params.optimizer
+
+            # NEED TO EXTRACT INITAL_LR FROM THE OPTIMIZER PARAM GROUPS
+            self.training_params.initial_lr = get_initial_lr_from_optimizer(self.optimizer)
+        else:
+            raise UnsupportedOptimizerFormat()
+
         if warmup_callback_cls is not None:
             self.phase_callbacks.append(
                 warmup_callback_cls(
@@ -1322,15 +1343,6 @@ def forward(self, inputs, targets):
             self._reset_best_metric()
             load_opt_params = False
 
-        if isinstance(self.training_params.optimizer, str) or (
-            inspect.isclass(self.training_params.optimizer) and issubclass(self.training_params.optimizer, torch.optim.Optimizer)
-        ):
-            self.optimizer = build_optimizer(net=unwrap_model(self.net), lr=self.training_params.initial_lr, training_params=self.training_params)
-        elif isinstance(self.training_params.optimizer, torch.optim.Optimizer):
-            self.optimizer = self.training_params.optimizer
-        else:
-            raise UnsupportedOptimizerFormat()
-
         if self.lr_mode is not None:
             lr_scheduler_callback = create_lr_scheduler_callback(
                 lr_mode=self.lr_mode,
@@ -1972,7 +1984,11 @@ def _get_epoch_start_logging_values(self) -> dict:
         """Get all the values that should be logged at the start of each epoch.
         This is useful for values like Learning Rate that can change over an epoch."""
         lrs = [self.optimizer.param_groups[i]["lr"] for i in range(len(self.optimizer.param_groups))]
-        lr_titles = ["LR/Param_group_" + str(i) for i in range(len(self.optimizer.param_groups))] if len(self.optimizer.param_groups) > 1 else ["LR"]
+        lr_titles = (
+            ["LR/" + self.optimizer.param_groups[i].get("name", str(i)) for i in range(len(self.optimizer.param_groups))]
+            if len(self.optimizer.param_groups) > 1
+            else ["LR"]
+        )
         lr_dict = {lr_titles[i]: lrs[i] for i in range(len(lrs))}
         return lr_dict