Merge branch 'main' into feature/torchvision-distillation-support

src/sparseml/pytorch/torchvision/train.py

@@ -23,7 +23,7 @@
 import warnings
 from functools import update_wrapper
 from types import SimpleNamespace
-from typing import Optional
+from typing import Callable, Optional
 
 import torch
 import torch.utils.data
@@ -62,23 +62,32 @@ def train_one_epoch(
     device: torch.device,
     epoch: int,
     args,
+    log_metrics_fn: Callable[[str, utils.MetricLogger, int, int], None],
     manager=None,
     model_ema=None,
     scaler=None,
 ) -> utils.MetricLogger:
+    accum_steps = args.gradient_accum_steps
+
     model.train()
     metric_logger = utils.MetricLogger(_LOGGER, delimiter="  ")
     metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value}"))
-    metric_logger.add_meter("img/s", utils.SmoothedValue(window_size=10, fmt="{value}"))
+    metric_logger.add_meter(
+        "imgs_per_sec", utils.SmoothedValue(window_size=10, fmt="{value}")
+    )
+    metric_logger.add_meter("loss", utils.SmoothedValue(window_size=accum_steps))
+    metric_logger.add_meter("acc1", utils.SmoothedValue(window_size=accum_steps))
+    metric_logger.add_meter("acc5", utils.SmoothedValue(window_size=accum_steps))
 
     steps_accumulated = 0
+    num_optim_steps = 0
 
     # initial zero grad for gradient accumulation
     optimizer.zero_grad()
 
     header = f"Epoch: [{epoch}]"
-    for i, (image, target) in enumerate(
-        metric_logger.log_every(data_loader, args.print_freq, header)
+    for (image, target) in metric_logger.log_every(
+        data_loader, args.logging_steps * accum_steps, header
     ):
         start_time = time.time()
         image, target = image.to(device), target.to(device)
@@ -89,7 +98,7 @@ def train_one_epoch(
                 output = output[0]
             loss = criterion(output, target)
 
-        if steps_accumulated % args.gradient_accum_steps == 0:
+        if steps_accumulated % accum_steps == 0:
             if manager is not None:
                 loss = manager.loss_update(
                     loss=loss,
@@ -119,9 +128,10 @@ def train_one_epoch(
 
             # zero grad here to start accumulating next set of gradients
             optimizer.zero_grad()
+            num_optim_steps += 1
         steps_accumulated += 1
 
-        if model_ema and i % args.model_ema_steps == 0:
+        if model_ema and num_optim_steps % args.model_ema_steps == 0:
             model_ema.update_parameters(model)
             if epoch < args.lr_warmup_epochs:
                 # Reset ema buffer to keep copying weights during warmup period
@@ -132,7 +142,12 @@ def train_one_epoch(
         metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
         metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
         metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
-        metric_logger.meters["img/s"].update(batch_size / (time.time() - start_time))
+        metric_logger.meters["imgs_per_sec"].update(
+            batch_size / (time.time() - start_time)
+        )
+
+        if num_optim_steps % args.logging_steps == 0:
+            log_metrics_fn("Train", metric_logger, epoch, num_optim_steps)
     return metric_logger
 
 
@@ -504,10 +519,17 @@ def collate_fn(batch):
                 criterion,
                 data_loader_test,
                 device,
+                print_freq=args.logging_steps,
                 log_suffix="EMA",
             )
         else:
-            evaluate(model, criterion, data_loader_test, device)
+            evaluate(
+                model,
+                criterion,
+                data_loader_test,
+                device,
+                print_freq=args.logging_steps,
+            )
         return
 
     if utils.is_main_process():
@@ -523,10 +545,13 @@ def collate_fn(batch):
     else:
         logger = LoggerManager(log_python=False)
 
-    def log_metrics(tag: str, metrics: utils.MetricLogger, epoch: int):
+    steps_per_epoch = len(data_loader) / args.gradient_accum_steps
+
+    def log_metrics(tag: str, metrics: utils.MetricLogger, epoch: int, epoch_step: int):
+        step = int(epoch * steps_per_epoch + epoch_step)
         for metric_name, smoothed_value in metrics.meters.items():
             logger.log_scalar(
-                f"{tag}/{metric_name}", smoothed_value.global_avg, step=epoch
+                f"{tag}/{metric_name}", smoothed_value.global_avg, step=step
             )
 
     if manager is not None:
@@ -537,7 +562,7 @@ def log_metrics(tag: str, metrics: utils.MetricLogger, epoch: int):
             distillation_teacher=args.distill_teacher,
         )
         optimizer = manager.modify(
-            model, optimizer, len(data_loader), epoch=args.start_epoch
+            model, optimizer, steps_per_epoch=steps_per_epoch, epoch=args.start_epoch
         )
 
     lr_scheduler = _get_lr_scheduler(
@@ -570,17 +595,18 @@ def log_metrics(tag: str, metrics: utils.MetricLogger, epoch: int):
             device,
             epoch,
             args,
+            log_metrics,
             manager=manager,
             model_ema=model_ema,
             scaler=scaler,
         )
-        log_metrics("Train", train_metrics, epoch)
+        log_metrics("Train", train_metrics, epoch, steps_per_epoch)
 
         if lr_scheduler:
             lr_scheduler.step()
 
         eval_metrics = evaluate(model, criterion, data_loader_test, device)
-        log_metrics("Test", eval_metrics, epoch)
+        log_metrics("Test", eval_metrics, epoch, steps_per_epoch)
 
         top1_acc = eval_metrics.acc1.global_avg
         if model_ema:
@@ -591,7 +617,7 @@ def log_metrics(tag: str, metrics: utils.MetricLogger, epoch: int):
                 device,
                 log_suffix="EMA",
             )
-            log_metrics("Test/EMA", ema_eval_metrics, epoch)
+            log_metrics("Test/EMA", ema_eval_metrics, epoch, steps_per_epoch)
 
         is_new_best = epoch >= args.save_best_after and top1_acc > best_top1_acc
         if is_new_best:
@@ -916,7 +942,13 @@ def new_func(*args, **kwargs):
     type=float,
     help="minimum lr of lr schedule",
 )
-@click.option("--print-freq", default=10, type=int, help="print frequency")
+@click.option("--print-freq", default=None, type=int, help="DEPRECATED. Does nothing.")
+@click.option(
+    "--logging-steps",
+    default=10,
+    type=int,
+    help="Frequency in number of batch updates for logging/printing",
+)
 @click.option("--output-dir", default=".", type=str, help="path to save outputs")
 @click.option("--resume", default=None, type=str, help="path of checkpoint")
 @click.option(