Set torchserve version to 0.7.1 for compatibility

configs/clip/clip_vit-base-p16_zeroshot-cls_cifar100.py

@@ -0,0 +1,68 @@
+_base_ = '../_base_/default_runtime.py'
+
+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
+    std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
+    to_rgb=False,
+)
+
+test_pipeline = [
+    dict(type='Resize', scale=(224, 224), interpolation='bicubic'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text'],
+        meta_keys=['image_id', 'scale_factor'],
+    ),
+]
+
+train_dataloader = None
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=8,
+    dataset=dict(
+        type='CIFAR100',
+        data_root='data/cifar100',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# schedule settings
+train_cfg = None
+val_cfg = None
+test_cfg = dict()
+
+# model settings
+model = dict(
+    type='CLIPZeroShot',
+    vision_backbone=dict(
+        type='VisionTransformer',
+        arch='base',
+        img_size=224,
+        patch_size=16,
+        drop_rate=0.,
+        layer_cfgs=dict(act_cfg=dict(type='QuickGELU')),
+        pre_norm=True,
+    ),
+    projection=dict(type='CLIPProjection', in_channels=768, out_channels=512),
+    text_backbone=dict(
+        type='CLIPTransformer',
+        width=512,
+        layers=12,
+        heads=8,
+        attn_mask=True,
+    ),
+    tokenizer=dict(
+        type='AutoTokenizer',
+        name_or_path='openai/clip-vit-base-patch16',
+        use_fast=False),
+    vocab_size=49408,
+    transformer_width=512,
+    proj_dim=512,
+    text_prototype='cifar100',
+    text_prompt='openai_cifar100',
+    context_length=77,
+)

configs/clip/clip_vit-base-p16_zeroshot-cls_in1k.py

@@ -0,0 +1,69 @@
+_base_ = '../_base_/default_runtime.py'
+
+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
+    std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
+    to_rgb=True,
+)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(224, 224), interpolation='bicubic'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text'],
+        meta_keys=['image_id', 'scale_factor'],
+    ),
+]
+
+train_dataloader = None
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=8,
+    dataset=dict(
+        type='ImageNet',
+        data_root='data/imagenet',
+        split='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# schedule settings
+train_cfg = None
+val_cfg = None
+test_cfg = dict()
+
+# model settings
+model = dict(
+    type='CLIPZeroShot',
+    vision_backbone=dict(
+        type='VisionTransformer',
+        arch='base',
+        img_size=224,
+        patch_size=16,
+        drop_rate=0.,
+        layer_cfgs=dict(act_cfg=dict(type='QuickGELU')),
+        pre_norm=True,
+    ),
+    projection=dict(type='CLIPProjection', in_channels=768, out_channels=512),
+    text_backbone=dict(
+        type='CLIPTransformer',
+        width=512,
+        layers=12,
+        heads=8,
+        attn_mask=True,
+    ),
+    tokenizer=dict(
+        type='AutoTokenizer',
+        name_or_path='openai/clip-vit-base-patch16',
+        use_fast=False),
+    vocab_size=49408,
+    transformer_width=512,
+    proj_dim=512,
+    text_prototype='imagenet',
+    text_prompt='openai_imagenet_sub',  # openai_imagenet, openai_imagenet_sub
+    context_length=77,
+)

configs/clip/clip_vit-large-p14_zeroshot-cls_cifar100.py

@@ -0,0 +1,68 @@
+_base_ = '../_base_/default_runtime.py'
+
+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
+    std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
+    to_rgb=False,
+)
+
+test_pipeline = [
+    dict(type='Resize', scale=(224, 224), interpolation='bicubic'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text'],
+        meta_keys=['image_id', 'scale_factor'],
+    ),
+]
+
+train_dataloader = None
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=8,
+    dataset=dict(
+        type='CIFAR100',
+        data_root='data/cifar100',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# schedule settings
+train_cfg = None
+val_cfg = None
+test_cfg = dict()
+
+# model settings
+model = dict(
+    type='CLIPZeroShot',
+    vision_backbone=dict(
+        type='VisionTransformer',
+        arch='large',
+        img_size=224,
+        patch_size=14,
+        drop_rate=0.,
+        layer_cfgs=dict(act_cfg=dict(type='QuickGELU')),
+        pre_norm=True,
+    ),
+    projection=dict(type='CLIPProjection', in_channels=1024, out_channels=768),
+    text_backbone=dict(
+        type='CLIPTransformer',
+        width=768,
+        layers=12,
+        heads=12,
+        attn_mask=True,
+    ),
+    tokenizer=dict(
+        type='AutoTokenizer',
+        name_or_path='openai/clip-vit-large-patch14',
+        use_fast=False),
+    vocab_size=49408,
+    transformer_width=768,
+    proj_dim=768,
+    text_prototype='cifar100',
+    text_prompt='openai_cifar100',
+    context_length=77,
+)

configs/clip/clip_vit-large-p14_zeroshot-cls_in1k.py

@@ -0,0 +1,69 @@
+_base_ = '../_base_/default_runtime.py'
+
+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
+    std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
+    to_rgb=True,
+)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(224, 224), interpolation='bicubic'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text'],
+        meta_keys=['image_id', 'scale_factor'],
+    ),
+]
+
+train_dataloader = None
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=8,
+    dataset=dict(
+        type='ImageNet',
+        data_root='data/imagenet',
+        split='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# schedule settings
+train_cfg = None
+val_cfg = None
+test_cfg = dict()
+
+# model settings
+model = dict(
+    type='CLIPZeroShot',
+    vision_backbone=dict(
+        type='VisionTransformer',
+        arch='large',
+        img_size=224,
+        patch_size=14,
+        drop_rate=0.,
+        layer_cfgs=dict(act_cfg=dict(type='QuickGELU')),
+        pre_norm=True,
+    ),
+    projection=dict(type='CLIPProjection', in_channels=1024, out_channels=768),
+    text_backbone=dict(
+        type='CLIPTransformer',
+        width=768,
+        layers=12,
+        heads=12,
+        attn_mask=True,
+    ),
+    tokenizer=dict(
+        type='AutoTokenizer',
+        name_or_path='openai/clip-vit-large-patch14',
+        use_fast=False),
+    vocab_size=49408,
+    transformer_width=768,
+    proj_dim=768,
+    text_prototype='imagenet',
+    text_prompt='openai_imagenet_sub',  # openai_imagenet, openai_imagenet_sub
+    context_length=77,
+)

docker/serve/Dockerfile

@@ -1,6 +1,7 @@
 ARG PYTORCH="1.12.1"
 ARG CUDA="11.3"
 ARG CUDNN="8"
+ARG TORCHSERVE="0.7.1"
 FROM pytorch/torchserve:latest-gpu
 
 ARG MMPRE="1.0.2"
@@ -11,8 +12,8 @@ ENV HOME="/home/model-server"
 ENV PATH="/opt/conda/bin:$HOME/.local/bin:$PATH"
 RUN export FORCE_CUDA=1
 
-# TORCHSEVER
-RUN pip install torchserve torch-model-archiver
+# TORCHSERVE
+RUN pip install torchserve==${TORCHSERVE} torch-model-archiver==${TORCHSERVE}
 RUN pip install nvgpu
 
 # OPEN-MMLAB

mmpretrain/apis/image_retrieval.py

@@ -108,6 +108,7 @@ def build_dataloader(dataset):
             # A config of dataset
             from mmpretrain.registry import DATASETS
             test_pipeline = [dict(type='LoadImageFromFile'), self.pipeline]
+            prototype.setdefault('pipeline', test_pipeline)
             dataset = DATASETS.build(prototype)
             dataloader = build_dataloader(dataset)
         elif isinstance(prototype, DataLoader):

mmpretrain/configs/_base_/datasets/cifar10_bs16.py

@@ -0,0 +1,52 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.dataset import DefaultSampler
+
+from mmpretrain.datasets import CIFAR10, PackInputs, RandomCrop, RandomFlip
+from mmpretrain.evaluation import Accuracy
+
+# dataset settings
+dataset_type = CIFAR10
+data_preprocessor = dict(
+    num_classes=10,
+    # RGB format normalization parameters
+    mean=[125.307, 122.961, 113.8575],
+    std=[51.5865, 50.847, 51.255],
+    # loaded images are already RGB format
+    to_rgb=False)
+
+train_pipeline = [
+    dict(type=RandomCrop, crop_size=32, padding=4),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=PackInputs),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/cifar10',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/cifar10/',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=False),
+)
+val_evaluator = dict(type=Accuracy, topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator