[misc] Small ddp script adjustments

references/detection/README.md

@@ -24,9 +24,10 @@ or PyTorch:
 ```shell
 python references/detection/train_pytorch.py db_resnet50 --train_path path/to/your/train_set --val_path path/to/your/val_set --epochs 5
 ```
+
 ### Multi-GPU support (PyTorch only)
 
-Multi-GPU support on Detection task with PyTorch has been added. 
+Multi-GPU support on Detection task with PyTorch has been added.
 Arguments are the same than the ones from single GPU, except:
 
 - `--devices`: **by default, if you do not pass `--devices`, it will use all GPUs on your computer**.
@@ -41,7 +42,6 @@ device_names = [torch.cuda.get_device_name(d) for d in devices]
 - `--backend`: you can specify another `backend` for `DistribuedDataParallel` if the default one is not available on
 your operating system. Fastest one is `nccl` according to [PyTorch Documentation](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html).
 
-
 ```shell
 python references/detection/train_pytorch_ddp.py db_resnet50 --train_path path/to/your/train_set --val_path path/to/your/val_set --epochs 5 --devices 0 1 --backend nccl
 ```

references/detection/train_pytorch_DDP.py → references/detection/train_pytorch_ddp.py

@@ -11,6 +11,7 @@
 import hashlib
 import multiprocessing
 import time
+
 import numpy as np
 import torch
 
@@ -330,9 +331,7 @@ def main(rank: int, world_size: int, args):
         pin_memory=torch.cuda.is_available(),
         collate_fn=train_set.collate_fn,
     )
-    print(
-        f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in {len(train_loader)} batches)"
-    )
+    print(f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in {len(train_loader)} batches)")
 
     with open(os.path.join(args.train_path, "labels.json"), "rb") as f:
         train_hash = hashlib.sha256(f.read()).hexdigest()
@@ -446,7 +445,7 @@ def parse_args():
     import argparse
 
     parser = argparse.ArgumentParser(
-        description="DocTR training script for text detection (PyTorch)",
+        description="DocTR DDP training script for text detection (PyTorch)",
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
 
@@ -505,7 +504,7 @@ def parse_args():
 if __name__ == "__main__":
     args = parse_args()
     if not torch.cuda.is_available():
-        raise AssertionError("PyTorch cannot access your GPUs. please look into it bro !!!")
+        raise AssertionError("PyTorch cannot access your GPUs. Please investigate!")
 
     if not isinstance(args.devices, list):
         args.devices = list(range(torch.cuda.device_count()))

references/recognition/train_pytorch_ddp.py

@@ -357,10 +357,13 @@ def parse_args():
     import argparse
 
     parser = argparse.ArgumentParser(
-        description="DocTR training script for text recognition (PyTorch)",
+        description="DocTR DDP training script for text recognition (PyTorch)",
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
 
+    # DDP related args
+    parser.add_argument("--backend", default="nccl", type=str, help="Backend to use for Torch DDP")
+
     parser.add_argument("arch", type=str, help="text-recognition model to train")
     parser.add_argument("--train_path", type=str, default=None, help="path to train data folder(s)")
     parser.add_argument("--val_path", type=str, default=None, help="path to val data folder")
@@ -384,7 +387,6 @@ def parse_args():
     parser.add_argument("--name", type=str, default=None, help="Name of your training experiment")
     parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train the model on")
     parser.add_argument("-b", "--batch_size", type=int, default=64, help="batch size for training")
-    parser.add_argument("--backend", default="nccl", type=str, help="Backend to use for Torch DDP")
     parser.add_argument("--devices", default=None, nargs="+", type=int, help="GPU devices to use for training")
     parser.add_argument("--input_size", type=int, default=32, help="input size H for the model, W = 4*H")
     parser.add_argument("--lr", type=float, default=0.001, help="learning rate for the optimizer (Adam)")