add supervised training pipeline

configs/supervised_wilds_vith14_ep300.yaml

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+meta:
+  model_name: vit_huge
+  embed_dim: 1280
+  load_checkpoint: true
+  read_checkpoint: jepa-latest.pth.tar
+  use_bfloat16: true
+  num_classes: 182
+
+data:
+  batch_size: 128
+  root_path: ./wilds_data
+  num_workers: 10
+  pin_mem: true
+  crop_size: 224
+
+optimization:
+  optimizer: adamw # 'adamw' or 'sgd'
+  freeze_weights: true # true for linear probing, false for full fine-tuning
+  epochs: 50
+  lr: 0.001
+  weight_decay: 0.05
+  start_lr: 0.0001
+  final_lr: 1.0e-06
+  warmup: 5
+  ipe_scale: 1.0
+
+logging:
+  folder: experiment_logs/supervised-vith14.224-bs.128-ep.300/
+  write_tag: linear_probe

main_distributed_supervised.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+import logging
+import os
+import pprint
+import sys
+import yaml
+
+import submitit
+
+from src.train_supervised import main as app_main
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logger = logging.getLogger()
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--folder", type=str, help="location to save submitit logs")
+parser.add_argument(
+    "--batch-launch",
+    action="store_true",
+    help="whether fname points to a file to batch-lauch several config files",
+)
+parser.add_argument(
+    "--fname",
+    type=str,
+    help="yaml file containing config file names to launch",
+    default="configs.yaml",
+)
+parser.add_argument("--partition", type=str, help="cluster partition to submit jobs on")
+parser.add_argument(
+    "--nodes", type=int, default=1, help="num. nodes to request for job"
+)
+parser.add_argument(
+    "--tasks-per-node", type=int, default=1, help="num. procs to per node"
+)
+parser.add_argument("--time", type=int, default=4300, help="time in minutes to run job")
+
+
+class Trainer:
+    def __init__(self, fname="configs.yaml", load_model=None):
+        self.fname = fname
+        self.load_model = load_model
+
+    def __call__(self):
+        fname = self.fname
+        load_model = self.load_model
+        logger.info(f"called-params {fname}")
+
+        # -- load script params
+        params = None
+        with open(fname, "r") as y_file:
+            params = yaml.load(y_file, Loader=yaml.FullLoader)
+            logger.info("loaded params...")
+            pp = pprint.PrettyPrinter(indent=4)
+            pp.pprint(params)
+
+        resume_preempt = False if load_model is None else load_model
+        app_main(args=params, resume_preempt=resume_preempt)
+
+    def checkpoint(self):
+        fb_trainer = Trainer(self.fname, True)
+        return submitit.helpers.DelayedSubmission(
+            fb_trainer,
+        )
+
+
+def launch():
+    executor = submitit.SlurmExecutor(
+        folder=os.path.join(args.folder, "job_%j"), max_num_timeout=20
+    )
+    executor.update_parameters(
+        partition=args.partition,
+        mem_per_gpu="55G",
+        time=args.time,
+        nodes=args.nodes,
+        ntasks_per_node=args.tasks_per_node,
+        cpus_per_task=10,
+        gpus_per_node=args.tasks_per_node,
+    )
+
+    config_fnames = [args.fname]
+
+    jobs, trainers = [], []
+    with executor.batch():
+        for cf in config_fnames:
+            fb_trainer = Trainer(cf)
+            job = executor.submit(
+                fb_trainer,
+            )
+            trainers.append(fb_trainer)
+            jobs.append(job)
+
+    for job in jobs:
+        print(job.job_id)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    launch()

src/train_supervised.py

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+import os
+import sys
+import yaml
+import logging
+import torch
+import torch.nn as nn
+from torch.nn.parallel import DistributedDataParallel
+import torch.nn.functional as F
+import numpy as np
+
+from src.datasets.wilds import make_iwildcam
+from src.helper import load_checkpoint, init_model, init_opt
+from src.transforms import make_transforms
+from src.models.head import ViTClassifier  # Import our new class
+from src.utils.distributed import init_distributed
+from src.utils.logging import CSVLogger, AverageMeter
+
+# --
+log_timings = True
+log_freq = 10
+checkpoint_freq = 50
+# --
+
+_GLOBAL_SEED = 0
+np.random.seed(_GLOBAL_SEED)
+torch.manual_seed(_GLOBAL_SEED)
+torch.backends.cudnn.benchmark = True
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logger = logging.getLogger()
+
+
+def main(args):
+    # -- Init Distributed
+    world_size, rank = init_distributed()
+    device = torch.device(f"cuda:{torch.cuda.current_device()}")
+
+    # -- Extract Config Params
+    m_args = args["meta"]
+    o_args = args["optimization"]
+    d_args = args["data"]
+
+    # -- 1. Initialize Encoder
+    encoder, _ = init_model(
+        device=device,
+        patch_size=args.get("mask", {}).get("patch_size", 14),
+        crop_size=d_args["crop_size"],
+        model_name=m_args["model_name"],
+    )
+
+    # -- 2. Wrap in Classification Head
+    embed_dim = m_args.get("embed_dim")
+    model = ViTClassifier(encoder, m_args["num_classes"], embed_dim).to(device)
+
+    # -- 3. Handle Freezing (Linear Probing vs Fine-Tuning)
+    if o_args["freeze_weights"]:
+        logger.info("Freezing encoder weights (Linear Probing mode)")
+        for name, param in model.encoder.named_parameters():
+            param.requires_grad = False
+    else:
+        logger.info("Training full model (Fine-tuning mode)")
+
+    # -- 4. Load Pre-trained Weights
+    if m_args["load_checkpoint"]:
+        load_path = os.path.join(args["logging"]["folder"], m_args["read_checkpoint"])
+        checkpoint = torch.load(load_path, map_location="cpu")
+        msg = model.encoder.load_state_dict(checkpoint["encoder"], strict=False)
+        logger.info(f"Loaded encoder from {load_path} with msg: {msg}")
+
+    # -- 5. Data Setup
+    transform = make_transforms(crop_size=d_args["crop_size"])
+    _, loader, sampler = make_iwildcam(
+        transform=transform,
+        split="train",
+        batch_size=d_args["batch_size"],
+        root_path=d_args["root_path"],
+        rank=rank,
+        world_size=world_size,
+        collator=None,  # No mask collator needed
+    )
+
+    # -- 6. Optimizer Selection
+    params = [p for p in model.parameters() if p.requires_grad]
+    if o_args["optimizer"].lower() == "adamw":
+        optimizer = torch.optim.AdamW(
+            params, lr=o_args["lr"], weight_decay=o_args["weight_decay"]
+        )
+    else:
+        optimizer = torch.optim.SGD(
+            params, lr=o_args["lr"], momentum=0.9, weight_decay=o_args["weight_decay"]
+        )
+
+    criterion = nn.CrossEntropyLoss().to(device)
+    model = DistributedDataParallel(model, device_ids=[torch.cuda.current_device()])
+
+    # -- 7. Training Loop
+    for epoch in range(o_args["epochs"]):
+        sampler.set_epoch(epoch)
+        model.train()
+        loss_meter = AverageMeter()
+
+        for itr, (imgs, labels, _) in enumerate(loader):
+            imgs, labels = imgs.to(device), labels.to(device)
+
+            with torch.cuda.amp.autocast(
+                enabled=m_args["use_bfloat16"], dtype=torch.bfloat16
+            ):
+                outputs = model(imgs)
+                loss = criterion(outputs, labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            loss_meter.update(loss.item())
+
+            if itr % 10 == 0 and rank == 0:
+                logger.info(
+                    f"Epoch {epoch} [{itr}/{len(loader)}] Loss: {loss_meter.avg:.4f}"
+                )
+
+
+if __name__ == "__main__":
+    main()