Add LoRA Implementation (computational-cell-analytics#611)

Add LoRA based PEFT finetuning

finetuning/livecell/lora/train_livecell.py

@@ -0,0 +1,184 @@
+import os
+import argparse
+
+import torch
+
+from torch_em.model import UNETR
+from torch_em.loss import DiceBasedDistanceLoss
+from torch_em.data.datasets import get_livecell_loader
+from torch_em.transform.label import PerObjectDistanceTransform
+
+import micro_sam.training as sam_training
+from micro_sam.util import export_custom_sam_model
+
+
+def get_dataloaders(patch_shape, data_path, cell_type=None):
+    """This returns the livecell data loaders implemented in torch_em:
+    https://github.com/constantinpape/torch-em/blob/main/torch_em/data/datasets/livecell.py
+    It will automatically download the livecell data.
+
+    Note: to replace this with another data loader you need to return a torch data loader
+    that retuns `x, y` tensors, where `x` is the image data and `y` are the labels.
+    The labels have to be in a label mask instance segmentation format.
+    I.e. a tensor of the same spatial shape as `x`, with each object mask having its own ID.
+    Important: the ID 0 is reseved for background, and the IDs must be consecutive
+    """
+    label_transform = PerObjectDistanceTransform(
+        distances=True, boundary_distances=True, directed_distances=False, foreground=True, instances=True, min_size=25
+    )
+    raw_transform = sam_training.identity  # the current workflow avoids rescaling the inputs to [-1, 1]
+    train_loader = get_livecell_loader(
+        path=data_path, patch_shape=patch_shape, split="train", batch_size=2, num_workers=16,
+        cell_types=cell_type, download=True, shuffle=True, label_transform=label_transform,
+        raw_transform=raw_transform, label_dtype=torch.float32,
+    )
+    val_loader = get_livecell_loader(
+        path=data_path, patch_shape=patch_shape, split="val", batch_size=4, num_workers=16,
+        cell_types=cell_type, download=True, shuffle=True, label_transform=label_transform,
+        raw_transform=raw_transform, label_dtype=torch.float32,
+    )
+
+    return train_loader, val_loader
+
+
+def count_parameters(model):
+    params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    params = params / 1e6
+    return f"The number of trainable parameters for the provided model is {round(params, 2)}M"
+
+
+def finetune_livecell(args):
+    """Code for finetuning SAM (using LoRA) on LIVECell
+
+    Initial observations: There's no real memory advantage actually unless it's "truly" scaled up
+    # vit_b
+    # SAM: 93M (takes ~50GB)
+    # SAM-LoRA: 4.2M (takes ~49GB)
+
+    # vit_l
+    # SAM: 312M (takes ~63GB)
+    # SAM-LoRA: 4.4M (takes ~61GB)
+
+    # vit_h
+    # SAM: 641M (takes ~73GB)
+    # SAM-LoRA: 4.7M (takes ~67GB)
+
+    # Q: Would quantization lead to better results? (eg. QLoRA / DoRA)
+    """
+    # override this (below) if you have some more complex set-up and need to specify the exact gpu
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # training settings:
+    model_type = args.model_type
+    checkpoint_path = None  # override this to start training from a custom checkpoint
+    patch_shape = (520, 704)  # the patch shape for training
+    n_objects_per_batch = 5  # this is the number of objects per batch that will be sampled
+    freeze_parts = args.freeze  # override this to freeze different parts of the model
+    rank = 4  # the rank
+
+    # get the trainable segment anything model
+    model = sam_training.get_trainable_sam_model(
+        model_type=model_type,
+        device=device,
+        checkpoint_path=checkpoint_path,
+        freeze=freeze_parts,
+        use_lora=True,
+        rank=rank,
+    )
+    model.to(device)
+
+    # let's get the UNETR model for automatic instance segmentation pipeline
+    unetr = UNETR(
+        backbone="sam",
+        encoder=model.sam.image_encoder,
+        out_channels=3,
+        use_sam_stats=True,
+        final_activation="Sigmoid",
+        use_skip_connection=False,
+        resize_input=True,
+    )
+    unetr.to(device)
+
+    # let's check the total number of trainable parameters
+    print(count_parameters(model))
+
+    # let's get the parameters for SAM and the decoder from UNETR
+    joint_model_params = model.parameters()
+
+    joint_model_params = [params for params in joint_model_params]  # sam parameters
+    for name, params in unetr.named_parameters():  # unetr's decoder parameters
+        if not name.startswith("encoder"):
+            joint_model_params.append(params)
+
+    optimizer = torch.optim.Adam(joint_model_params, lr=1e-5)
+    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.9, patience=10)
+    train_loader, val_loader = get_dataloaders(patch_shape=patch_shape, data_path=args.input_path)
+
+    # this class creates all the training data for a batch (inputs, prompts and labels)
+    convert_inputs = sam_training.ConvertToSamInputs(transform=model.transform, box_distortion_factor=0.025)
+
+    trainer = sam_training.JointSamTrainer(
+        name="livecell_lora",
+        save_root=args.save_root,
+        train_loader=train_loader,
+        val_loader=val_loader,
+        model=model,
+        optimizer=optimizer,
+        device=device,
+        lr_scheduler=scheduler,
+        logger=sam_training.JointSamLogger,
+        log_image_interval=100,
+        mixed_precision=True,
+        convert_inputs=convert_inputs,
+        n_objects_per_batch=n_objects_per_batch,
+        n_sub_iteration=8,
+        compile_model=False,
+        mask_prob=0.5,  # (optional) overwrite to provide the probability of using mask inputs while training
+        unetr=unetr,
+        instance_loss=DiceBasedDistanceLoss(mask_distances_in_bg=True),
+        instance_metric=DiceBasedDistanceLoss(mask_distances_in_bg=True)
+    )
+    trainer.fit(args.iterations)
+    if args.export_path is not None:
+        checkpoint_path = os.path.join(
+            "" if args.save_root is None else args.save_root, "checkpoints", args.name, "best.pt"
+        )
+        export_custom_sam_model(
+            checkpoint_path=checkpoint_path,
+            model_type=model_type,
+            save_path=args.export_path,
+        )
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Finetune Segment Anything for the LiveCELL dataset.")
+    parser.add_argument(
+        "--input_path", "-i", default="/scratch/projects/nim00007/sam/data/livecell/",
+        help="The filepath to the LiveCELL data. If the data does not exist yet it will be downloaded."
+    )
+    parser.add_argument(
+        "--model_type", "-m", default="vit_b",
+        help="The model type to use for fine-tuning. Either vit_h, vit_b or vit_l."
+    )
+    parser.add_argument(
+        "--save_root", "-s", default=None,
+        help="Where to save the checkpoint and logs. By default they will be saved where this script is run."
+    )
+    parser.add_argument(
+        "--iterations", type=int, default=int(1e4),
+        help="For how many iterations should the model be trained? By default 100k."
+    )
+    parser.add_argument(
+        "--export_path", "-e",
+        help="Where to export the finetuned model to. The exported model can be used in the annotation tools."
+    )
+    parser.add_argument(
+        "--freeze", type=str, nargs="+", default=None,
+        help="Which parts of the model to freeze for finetuning."
+    )
+    args = parser.parse_args()
+    finetune_livecell(args)
+
+
+if __name__ == "__main__":
+    main()

micro_sam/training/peft_sam.py

@@ -0,0 +1,105 @@
+import math
+from typing import List, Union
+
+import torch.nn as nn
+
+from segment_anything.modeling import Sam
+
+
+class LoRASurgery(nn.Module):
+    """Operates on the attention layers for performing low-rank adaptation.
+
+    (Inspired from: https://github.com/JamesQFreeman/Sam_LoRA/)
+
+    In SAM, it is implemented as:
+    ```python
+    self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+    B, N, C = x.shape
+    qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+    q, k, v = qkv.unbind(0)
+    ```
+    """
+    def __init__(
+        self,
+        rank: int,
+        block: nn.Module,
+    ):
+        super().__init__()
+        self.qkv = block.attn.qkv
+        self.dim = self.qkv.in_features
+
+        self.w_a_linear_q = nn.Linear(self.dim, rank, bias=False)
+        self.w_b_linear_q = nn.Linear(rank, self.dim, bias=False)
+        self.w_a_linear_v = nn.Linear(self.dim, rank, bias=False)
+        self.w_b_linear_v = nn.Linear(rank, self.dim, bias=False)
+
+        self.reset_parameters()
+
+        block.attn.qkv = self
+
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.w_a_linear_q.weight, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.w_a_linear_v.weight, a=math.sqrt(5))
+        nn.init.zeros_(self.w_b_linear_q.weight)
+        nn.init.zeros_(self.w_b_linear_v.weight)
+
+    def forward(self, x):
+        qkv = self.qkv(x)  # B, N, N, 3 * org_C
+        new_q = self.w_b_linear_q(self.w_a_linear_q(x))
+        new_v = self.w_b_linear_v(self.w_a_linear_v(x))
+        qkv[:, :, :, :self.dim] += new_q
+        qkv[:, :, :, -self.dim:] += new_v
+        return qkv
+
+
+class PEFT_Sam(nn.Module):
+    """Inspired from: https://github.com/JamesQFreeman/Sam_LoRA/
+
+    Wraps the Segment Anything model's image encoder to different parameter efficient finetuning methods.
+
+    Args:
+        model: The Segment Anything model.
+        rank: The rank for low-rank adaptation.
+        peft_module: Wrapper to operate on the image encoder blocks for the PEFT method.
+        attention_layers_to_update: Which specific layers we apply PEFT methods to.
+    """
+
+    def __init__(
+        self,
+        model: Sam,
+        rank: int,
+        peft_module: nn.Module = LoRASurgery,
+        attention_layers_to_update: Union[List[int]] = None
+    ):
+        super(PEFT_Sam, self).__init__()
+
+        assert rank > 0
+
+        if attention_layers_to_update:
+            self.peft_layers = attention_layers_to_update
+        else:   # Applies PEFT to the image encoder by default
+            self.peft_layers = list(
+                range(len(model.image_encoder.blocks))
+            )
+
+        self.peft_module = peft_module
+        self.peft_blocks = []
+
+        # let's freeze all the pretrained image encoder layers first
+        for param in model.image_encoder.parameters():
+            param.requires_grad = False
+
+        for t_layer_i, blk in enumerate(model.image_encoder.blocks):
+            # If we only want specific layers with PEFT instead of all
+            if t_layer_i not in self.peft_layers:
+                continue
+
+            peft_block = self.peft_module(rank=rank, block=blk)
+            self.peft_blocks.append(peft_block)
+
+        self.peft_blocks = nn.ModuleList(self.peft_blocks)
+
+        self.sam = model
+
+    def forward(self, batched_input, multimask_output):
+        return self.sam(batched_input, multimask_output)

micro_sam/training/util.py

@@ -12,6 +12,7 @@
     get_centers_and_bounding_boxes, get_sam_model, get_device,
     segmentation_to_one_hot, _DEFAULT_MODEL,
 )
+from .peft_sam import PEFT_Sam
 from .trainable_sam import TrainableSAM
 
 from torch_em.transform.label import PerObjectDistanceTransform
@@ -42,6 +43,8 @@ def get_trainable_sam_model(
     checkpoint_path: Optional[Union[str, os.PathLike]] = None,
     freeze: Optional[List[str]] = None,
     return_state: bool = False,
+    use_lora: bool = False,
+    rank: Optional[int] = None,
 ) -> TrainableSAM:
     """Get the trainable sam model.
 
@@ -54,6 +57,8 @@ def get_trainable_sam_model(
         freeze: Specify parts of the model that should be frozen, namely: image_encoder, prompt_encoder and mask_decoder
             By default nothing is frozen and the full model is updated.
         return_state: Whether to return the full checkpoint state.
+        use_lora: Whether to use the low rank adaptation method for finetuning.
+        rank: The rank of the decomposition matrices for updating weights in each attention layer.
 
     Returns:
         The trainable segment anything model.
@@ -80,8 +85,14 @@ def get_trainable_sam_model(
                 if name.startswith(f"{freeze}"):
                     param.requires_grad = False
 
+    if use_lora:  # overwrites the SAM model by freezing the backbone and allow low rank adaption to attention layers
+        if rank is None:
+            rank = 4  # HACK: in case the user does not pass the rank, we provide a random rank to them
+        sam = PEFT_Sam(sam, rank=rank).sam
+
     # convert to trainable sam
     trainable_sam = TrainableSAM(sam)
+
     if return_state:
         return trainable_sam, state
     return trainable_sam

micro_sam/util.py

@@ -270,6 +270,8 @@ def get_sam_model(
     checkpoint_path: Optional[Union[str, os.PathLike]] = None,
     return_sam: bool = False,
     return_state: bool = False,
+    use_lora: bool = False,
+    rank: Optional[int] = None,
 ) -> SamPredictor:
     r"""Get the SegmentAnything Predictor.
 
@@ -302,6 +304,8 @@ def get_sam_model(
             then `model_type` must be given as "vit_b".
         return_sam: Return the sam model object as well as the predictor.
         return_state: Return the unpickled checkpoint state.
+        use_lora: Whether to use the low rank adaptation method for finetuning.
+        rank: The rank of the decomposition matrices for updating weights in each attention layer.
 
     Returns:
         The segment anything predictor.
@@ -347,6 +351,13 @@ def get_sam_model(
 
     state, model_state = _load_checkpoint(checkpoint_path)
     sam = sam_model_registry[abbreviated_model_type]()
+
+    if use_lora:  # overwrites the SAM model by freezing the backbone and allow low rank adaption to attention layers
+        from micro_sam.training.peft_sam import PEFT_Sam
+        if rank is None:
+            rank = 4  # HACK: in case the user does not pass the rank, we provide a random rank to them
+        sam = PEFT_Sam(sam, rank=rank).sam
+
     sam.load_state_dict(model_state)
     sam.to(device=device)