Hvd

README.md

@@ -23,19 +23,13 @@ This repo is the official implementation of ["Swin Transformer: Hierarchical Vis
 
 > **Mixture-of-Experts**: See [get_started](get_started.md#mixture-of-experts-support) for more instructions.
 
-> **Feature-Distillation**: Will appear in [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation).
-
-## Activity notification
-
-* 09/18/2022: Organizing ECCV Workshop [*Computer Vision in the Wild (CVinW)*](https://computer-vision-in-the-wild.github.io/eccv-2022/), where two challenges are hosted to evaluate the zero-shot, few-shot and full-shot performance of pre-trained vision models in downstream tasks:
-  - [``*Image Classification in the Wild (ICinW)*''](https://eval.ai/web/challenges/challenge-page/1832/overview) Challenge evaluates on 20 image classification tasks.
-  - [``*Object Detection in the Wild (ODinW)*''](https://eval.ai/web/challenges/challenge-page/1839/overview) Challenge evaluates on 35 object detection tasks.
+> **Feature-Distillation**: See [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation).
 
+## Updates
 
-$\qquad$ [ <img src="https://computer-vision-in-the-wild.github.io/eccv-2022/static/eccv2022/img/ECCV-logo3.png" width=10%/> [Workshop]](https://computer-vision-in-the-wild.github.io/eccv-2022/)    $\qquad$    [<img src="https://evalai.s3.amazonaws.com/media/logos/4e939412-a9c0-46bd-9797-5ba0bd0a9095.jpg" width=10%/> [IC Challenge] ](https://eval.ai/web/challenges/challenge-page/1832/overview)
-$\qquad$    [<img src="https://evalai.s3.amazonaws.com/media/logos/3a31ae6e-a990-48fb-b2c3-1e7da9d17a20.jpg" width=10%/> [OD Challenge] ](https://eval.ai/web/challenges/challenge-page/1839/overview)
+***11/30/2022***
 
-## Updates
+1. Models and codes of **Feature Distillation** are released. Please refer to [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation) for details, and the checkpoints (FD-EsViT-Swin-B, FD-DeiT-ViT-B, FD-DINO-ViT-B, FD-CLIP-ViT-B, FD-CLIP-ViT-L).
 
 ***09/24/2022***
 

config.py

@@ -245,7 +245,7 @@
 # Tag of experiment, overwritten by command line argument
 _C.TAG = 'default'
 # Frequency to save checkpoint
-_C.SAVE_FREQ = 1
+_C.SAVE_FREQ = 10
 # Frequency to logging info
 _C.PRINT_FREQ = 10
 # Fixed random seed

configs/swinv2/swinv2_base_patch4_window8_128_hvd.yaml

@@ -0,0 +1,11 @@
+DATA:
+  IMG_SIZE: 128
+MODEL:
+  TYPE: swinv2
+  NAME: swinv2_base_patch4_window8_128_hvd
+  DROP_PATH_RATE: 0.5
+  SWINV2:
+    EMBED_DIM: 128
+    DEPTHS: [ 2, 2, 18, 2 ]
+    NUM_HEADS: [ 4, 8, 16, 32 ]
+    WINDOW_SIZE: 8

data/__init__.py

@@ -1,4 +1,5 @@
 from .build import build_loader as _build_loader
+from .build import build_loader_hvd as _build_loader_hvd
 from .data_simmim_pt import build_loader_simmim
 from .data_simmim_ft import build_loader_finetune
 
@@ -10,3 +11,7 @@ def build_loader(config, simmim=False, is_pretrain=False):
         return build_loader_simmim(config)
     else:
         return build_loader_finetune(config)
+
+def build_loader_hvd(config):
+    return _build_loader_hvd(config)
+

data/build.py

@@ -9,6 +9,7 @@
 import torch
 import numpy as np
 import torch.distributed as dist
+import horovod.torch as hvd
 from torchvision import datasets, transforms
 from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.data import Mixup
@@ -95,6 +96,60 @@ def build_loader(config):
     return dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn
 
 
+def build_loader_hvd(config):
+    config.defrost()
+    dataset_train, config.MODEL.NUM_CLASSES = build_dataset(is_train=True, config=config)
+    config.freeze()
+    print(f"local rank {config.LOCAL_RANK} / global rank {hvd.rank()} successfully build train dataset")
+    dataset_val, _ = build_dataset(is_train=False, config=config)
+    print(f"local rank {config.LOCAL_RANK} / global rank {hvd.rank()} successfully build val dataset")
+
+    num_tasks = hvd.size()
+    global_rank = hvd.rank()
+    if config.DATA.ZIP_MODE and config.DATA.CACHE_MODE == 'part':
+        indices = np.arange(hvd.rank(), len(dataset_train), hvd.size())
+        sampler_train = SubsetRandomSampler(indices)
+    else:
+        sampler_train = torch.utils.data.DistributedSampler(
+            dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
+        )
+
+    if config.TEST.SEQUENTIAL:
+        sampler_val = torch.utils.data.SequentialSampler(dataset_val)
+    else:
+        sampler_val = torch.utils.data.distributed.DistributedSampler(
+            dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=config.TEST.SHUFFLE
+        )
+
+    data_loader_train = torch.utils.data.DataLoader(
+        dataset_train, sampler=sampler_train,
+        batch_size=config.DATA.BATCH_SIZE,
+        num_workers=config.DATA.NUM_WORKERS,
+        pin_memory=config.DATA.PIN_MEMORY,
+        drop_last=True,
+    )
+
+    data_loader_val = torch.utils.data.DataLoader(
+        dataset_val, sampler=sampler_val,
+        batch_size=config.DATA.BATCH_SIZE,
+        shuffle=False,
+        num_workers=config.DATA.NUM_WORKERS,
+        pin_memory=config.DATA.PIN_MEMORY,
+        drop_last=False
+    )
+
+    # setup mixup / cutmix
+    mixup_fn = None
+    mixup_active = config.AUG.MIXUP > 0 or config.AUG.CUTMIX > 0. or config.AUG.CUTMIX_MINMAX is not None
+    if mixup_active:
+        mixup_fn = Mixup(
+            mixup_alpha=config.AUG.MIXUP, cutmix_alpha=config.AUG.CUTMIX, cutmix_minmax=config.AUG.CUTMIX_MINMAX,
+            prob=config.AUG.MIXUP_PROB, switch_prob=config.AUG.MIXUP_SWITCH_PROB, mode=config.AUG.MIXUP_MODE,
+            label_smoothing=config.MODEL.LABEL_SMOOTHING, num_classes=config.MODEL.NUM_CLASSES)
+
+    return dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn
+
+
 def build_dataset(is_train, config):
     transform = build_transform(is_train, config)
     if config.DATA.DATASET == 'imagenet':