MaxVit model

references/classification/presets.py

@@ -13,14 +13,16 @@ def __init__(
         interpolation=InterpolationMode.BILINEAR,
         hflip_prob=0.5,
         auto_augment_policy=None,
+        policy_magnitude=9,
         random_erase_prob=0.0,
+        center_crop=False,
     ):
-        trans = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
+        trans = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)] if center_crop else [transforms.CenterCrop(crop_size)]
         if hflip_prob > 0:
             trans.append(transforms.RandomHorizontalFlip(hflip_prob))
         if auto_augment_policy is not None:
             if auto_augment_policy == "ra":
-                trans.append(autoaugment.RandAugment(interpolation=interpolation))
+                trans.append(autoaugment.RandAugment(interpolation=interpolation, magnitude=policy_magnitude))
             elif auto_augment_policy == "ta_wide":
                 trans.append(autoaugment.TrivialAugmentWide(interpolation=interpolation))
             elif auto_augment_policy == "augmix":

references/classification/run_with_submitit.py

@@ -0,0 +1,122 @@
+import argparse
+import os
+import uuid
+from pathlib import Path
+
+import train
+import submitit
+
+
+def parse_args():
+    train_parser = train.get_args_parser(add_help=False)
+    parser = argparse.ArgumentParser("Submitit for train", parents=[train_parser], add_help=True)
+    parser.add_argument("--ngpus", default=8, type=int, help="Number of gpus to request on each node")
+    parser.add_argument("--nodes", default=1, type=int, help="Number of nodes to request")
+    parser.add_argument("--timeout", default=60*24*30, type=int, help="Duration of the job")
+    parser.add_argument("--job_dir", default="", type=str, help="Job dir. Leave empty for automatic.")
+    parser.add_argument("--partition", default="train", type=str, help="the partition (default train).")
+    return parser.parse_args()
+
+
+def get_shared_folder() -> Path:
+    user = os.getenv("USER")
+    path = "/data/checkpoints"
+    if Path(path).is_dir():
+        p = Path(f"{path}/{user}/experiments")
+        p.mkdir(exist_ok=True)
+        return p
+    raise RuntimeError("No shared folder available")
+
+
+def get_init_file_folder() -> Path:
+    user = os.getenv("USER")
+    path = "/shared"
+    if Path(path).is_dir():
+        p = Path(f"{path}/{user}")
+        p.mkdir(exist_ok=True)
+        return p
+    raise RuntimeError("No shared folder available")
+
+
+def get_init_file():
+    # Init file must not exist, but it's parent dir must exist.
+    os.makedirs(str(get_init_file_folder()), exist_ok=True)
+    init_file = get_init_file_folder() / f"{uuid.uuid4().hex}_init"
+    if init_file.exists():
+        os.remove(str(init_file))
+    return init_file
+
+
+class Trainer(object):
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        import train
+
+        self._setup_gpu_args()
+        train.main(self.args)
+
+    def checkpoint(self):
+        import os
+        import submitit
+        from pathlib import Path
+
+        self.args.dist_url = get_init_file().as_uri()
+        checkpoint_file = os.path.join(self.args.output_dir, "checkpoint.pth")
+        if os.path.exists(checkpoint_file):
+            self.args.resume = checkpoint_file
+        print("Requeuing ", self.args)
+        empty_trainer = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty_trainer)
+
+    def _setup_gpu_args(self):
+        import submitit
+        from pathlib import Path
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = Path(str(self.args.output_dir).replace("%j", str(job_env.job_id)))
+        self.args.gpu = job_env.local_rank
+        self.args.rank = job_env.global_rank
+        self.args.world_size = job_env.num_tasks
+        print(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+
+
+def main():
+    args = parse_args()
+    if args.job_dir == "":
+        args.job_dir = get_shared_folder() / "%j"
+
+    # Note that the folder will depend on the job_id, to easily track experiments
+    executor = submitit.AutoExecutor(folder=args.job_dir, slurm_max_num_timeout=300)
+
+    # cluster setup is defined by environment variables
+    num_gpus_per_node = args.ngpus
+    nodes = args.nodes
+    timeout_min = args.timeout
+
+    executor.update_parameters(
+        #mem_gb=96 * num_gpus_per_node,  # 768GB per machine
+        gpus_per_node=num_gpus_per_node,
+        tasks_per_node=num_gpus_per_node,  # one task per GPU
+        cpus_per_task=12,  # 96 cpus per machine
+        nodes=nodes,
+        timeout_min=timeout_min,  # max is 60 * 72
+        slurm_partition=args.partition,
+        slurm_signal_delay_s=120,
+    )
+
+
+    executor.update_parameters(name="torchvision")
+
+    args.dist_url = get_init_file().as_uri()
+    args.output_dir = args.job_dir
+
+    trainer = Trainer(args)
+    job = executor.submit(trainer)
+
+    print("Submitted job_id:", job.job_id)
+
+
+if __name__ == "__main__":
+    main()

references/classification/train.py

@@ -1,5 +1,6 @@
 import datetime
 import os
+import random
 import time
 import warnings
 
@@ -15,7 +16,7 @@
 from torchvision.transforms.functional import InterpolationMode
 
 
-def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema=None, scaler=None):
+def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema=None, scaler=None, scheduler=None):
     model.train()
     metric_logger = utils.MetricLogger(delimiter="  ")
     metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value}"))
@@ -43,6 +44,9 @@ def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, arg
             if args.clip_grad_norm is not None:
                 nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)
             optimizer.step()
+
+        if scheduler is not None and args.lr_step_every_batch:
+            scheduler.step()
 
         if model_ema and i % args.model_ema_steps == 0:
             model_ema.update_parameters(model)
@@ -113,7 +117,7 @@ def _get_cache_path(filepath):
 def load_data(traindir, valdir, args):
     # Data loading code
     print("Loading data")
-    val_resize_size, val_crop_size, train_crop_size = args.val_resize_size, args.val_crop_size, args.train_crop_size
+    val_resize_size, val_crop_size, train_crop_size, center_crop, policy_magnitude = args.val_resize_size, args.val_crop_size, args.train_crop_size, args.train_center_crop, args.policy_magnitude
     interpolation = InterpolationMode(args.interpolation)
 
     print("Loading training data")
@@ -129,10 +133,12 @@ def load_data(traindir, valdir, args):
         dataset = torchvision.datasets.ImageFolder(
             traindir,
             presets.ClassificationPresetTrain(
+                center_crop=center_crop,
                 crop_size=train_crop_size,
                 interpolation=interpolation,
                 auto_augment_policy=auto_augment_policy,
                 random_erase_prob=random_erase_prob,
+                policy_magnitude=policy_magnitude,
             ),
         )
         if args.cache_dataset:
@@ -182,7 +188,12 @@ def load_data(traindir, valdir, args):
 def main(args):
     if args.output_dir:
         utils.mkdir(args.output_dir)
-
+
+    if args.seed is None:
+        # randomly choose a seed
+        args.seed = random.randint(0, 2 ** 32)
+    utils.set_seed(args.seed)
+
     utils.init_distributed_mode(args)
     print(args)
 
@@ -261,13 +272,21 @@ def main(args):
         raise RuntimeError(f"Invalid optimizer {args.opt}. Only SGD, RMSprop and AdamW are supported.")
 
     scaler = torch.cuda.amp.GradScaler() if args.amp else None
+
+    batches_per_epoch = len(data_loader)
+    warmup_iters = args.lr_warmup_epochs
+    total_iters = args.epochs
+
+    if args.lr_step_every_batch:
+        warmup_iters *= batches_per_epoch
+        total_iters *= batches_per_epoch
 
     args.lr_scheduler = args.lr_scheduler.lower()
     if args.lr_scheduler == "steplr":
         main_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
     elif args.lr_scheduler == "cosineannealinglr":
         main_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer, T_max=args.epochs - args.lr_warmup_epochs, eta_min=args.lr_min
+            optimizer, T_max=total_iters - warmup_iters, eta_min=args.lr_min
         )
     elif args.lr_scheduler == "exponentiallr":
         main_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=args.lr_gamma)
@@ -280,18 +299,18 @@ def main(args):
     if args.lr_warmup_epochs > 0:
         if args.lr_warmup_method == "linear":
             warmup_lr_scheduler = torch.optim.lr_scheduler.LinearLR(
-                optimizer, start_factor=args.lr_warmup_decay, total_iters=args.lr_warmup_epochs
+                optimizer, start_factor=args.lr_warmup_decay, total_iters=warmup_iters
             )
         elif args.lr_warmup_method == "constant":
             warmup_lr_scheduler = torch.optim.lr_scheduler.ConstantLR(
-                optimizer, factor=args.lr_warmup_decay, total_iters=args.lr_warmup_epochs
+                optimizer, factor=args.lr_warmup_decay, total_iters=warmup_iters
             )
         else:
             raise RuntimeError(
                 f"Invalid warmup lr method '{args.lr_warmup_method}'. Only linear and constant are supported."
             )
         lr_scheduler = torch.optim.lr_scheduler.SequentialLR(
-            optimizer, schedulers=[warmup_lr_scheduler, main_lr_scheduler], milestones=[args.lr_warmup_epochs]
+            optimizer, schedulers=[warmup_lr_scheduler, main_lr_scheduler], milestones=[warmup_iters]
         )
     else:
         lr_scheduler = main_lr_scheduler
@@ -341,8 +360,9 @@ def main(args):
     for epoch in range(args.start_epoch, args.epochs):
         if args.distributed:
             train_sampler.set_epoch(epoch)
-        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema, scaler)
-        lr_scheduler.step()
+        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema, scaler, lr_scheduler)
+        if not args.lr_step_every_batch:
+            lr_scheduler.step()
         evaluate(model, criterion, data_loader_test, device=device)
         if model_ema:
             evaluate(model_ema, criterion, data_loader_test, device=device, log_suffix="EMA")
@@ -371,7 +391,7 @@ def get_args_parser(add_help=True):
 
     parser = argparse.ArgumentParser(description="PyTorch Classification Training", add_help=add_help)
 
-    parser.add_argument("--data-path", default="/datasets01/imagenet_full_size/061417/", type=str, help="dataset path")
+    parser.add_argument("--data-path", default="/datasets01_ontap/imagenet_full_size/061417/", type=str, help="dataset path")
     parser.add_argument("--model", default="resnet18", type=str, help="model name")
     parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
     parser.add_argument(
@@ -425,6 +445,7 @@ def get_args_parser(add_help=True):
     parser.add_argument("--lr-step-size", default=30, type=int, help="decrease lr every step-size epochs")
     parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")
     parser.add_argument("--lr-min", default=0.0, type=float, help="minimum lr of lr schedule (default: 0.0)")
+    parser.add_argument("--lr-step-every-batch", action="store_true", help="decrease lr every step-size batches", default=False)
     parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
     parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
     parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
@@ -448,6 +469,7 @@ def get_args_parser(add_help=True):
         action="store_true",
     )
     parser.add_argument("--auto-augment", default=None, type=str, help="auto augment policy (default: None)")
+    parser.add_argument("--policy-magnitude", default=9, type=int, help="magnitude of auto augment policy")
     parser.add_argument("--random-erase", default=0.0, type=float, help="random erasing probability (default: 0.0)")
 
     # Mixed precision training parameters
@@ -486,13 +508,16 @@ def get_args_parser(add_help=True):
     parser.add_argument(
         "--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
     )
+    parser.add_argument(
+        "--train-center-crop", action="store_true", help="use center crop instead of random crop for training (default: False)"
+    )
     parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")
     parser.add_argument("--ra-sampler", action="store_true", help="whether to use Repeated Augmentation in training")
     parser.add_argument(
         "--ra-reps", default=3, type=int, help="number of repetitions for Repeated Augmentation (default: 3)"
     )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
-
+    parser.add_argument("--seed", default=None, type=int, help="the seed for randomness (default: None). A `None` value means a seed will be randomly generated")
     return parser
 
 

references/classification/utils.py

@@ -9,6 +9,8 @@
 
 import torch
 import torch.distributed as dist
+import numpy as np
+import random
 
 
 class SmoothedValue:
@@ -463,3 +465,13 @@ def _add_params(module, prefix=""):
         if len(params[key]) > 0:
             param_groups.append({"params": params[key], "weight_decay": params_weight_decay[key]})
     return param_groups
+
+def set_seed(seed: int):
+    """
+    Function for setting all the RNGs to the same seed
+    """
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)