feat(ml): add more search models (#11468)

* update export code

* add uuid glob, sort model names

* add new models to ml, sort names

* add new models to server, sort by dims and name

* typo in name

* update export dependencies

* onnx save function

* format

machine-learning/ann/ann.py

@@ -65,7 +65,7 @@ class Ann(metaclass=_Singleton):
         self.input_shapes: dict[int, tuple[tuple[int], ...]] = {}
         self.ann: int | None = None
         self.new()
-        
+
         if self.tuning_file is not None:
             # make sure tuning file exists (without clearing contents)
             # once filled, the tuning file reduces the cost/time of the first
@@ -105,7 +105,7 @@ class Ann(metaclass=_Singleton):
             raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx")
         if not exists(model_path):
             raise ValueError("model_path must point to an existing file!")
-        
+
         save_cached_network = False
         if cached_network_path is not None and not exists(cached_network_path):
             save_cached_network = True

machine-learning/app/models/constants.py

@@ -2,53 +2,64 @@ from app.config import clean_name
 from app.schemas import ModelSource
 
 _OPENCLIP_MODELS = {
-    "RN50__openai",
-    "RN50__yfcc15m",
-    "RN50__cc12m",
     "RN101__openai",
     "RN101__yfcc15m",
-    "RN50x4__openai",
+    "RN50__cc12m",
+    "RN50__openai",
+    "RN50__yfcc15m",
     "RN50x16__openai",
+    "RN50x4__openai",
     "RN50x64__openai",
-    "ViT-B-32__openai",
+    "ViT-B-16-SigLIP-256__webli",
+    "ViT-B-16-SigLIP-384__webli",
+    "ViT-B-16-SigLIP-512__webli",
+    "ViT-B-16-SigLIP-i18n-256__webli",
+    "ViT-B-16-SigLIP__webli",
+    "ViT-B-16-plus-240__laion400m_e31",
+    "ViT-B-16-plus-240__laion400m_e32",
+    "ViT-B-16__laion400m_e31",
+    "ViT-B-16__laion400m_e32",
+    "ViT-B-16__openai",
+    "ViT-B-32__laion2b-s34b-b79k",
     "ViT-B-32__laion2b_e16",
     "ViT-B-32__laion400m_e31",
     "ViT-B-32__laion400m_e32",
-    "ViT-B-32__laion2b-s34b-b79k",
+    "ViT-B-32__openai",
-    "ViT-B-16__openai",
+    "ViT-H-14-378-quickgelu__dfn5b",
-    "ViT-B-16__laion400m_e31",
+    "ViT-H-14-quickgelu__dfn5b",
-    "ViT-B-16__laion400m_e32",
+    "ViT-H-14__laion2b-s32b-b79k",
-    "ViT-B-16-plus-240__laion400m_e31",
+    "ViT-L-14-336__openai",
-    "ViT-B-16-plus-240__laion400m_e32",
+    "ViT-L-14-quickgelu__dfn2b",
-    "ViT-L-14__openai",
+    "ViT-L-14__laion2b-s32b-b82k",
     "ViT-L-14__laion400m_e31",
     "ViT-L-14__laion400m_e32",
-    "ViT-L-14__laion2b-s32b-b82k",
+    "ViT-L-14__openai",
-    "ViT-L-14-336__openai",
+    "ViT-L-16-SigLIP-256__webli",
-    "ViT-H-14__laion2b-s32b-b79k",
+    "ViT-L-16-SigLIP-384__webli",
+    "ViT-SO400M-14-SigLIP-384__webli",
     "ViT-g-14__laion2b-s12b-b42k",
-    "ViT-L-14-quickgelu__dfn2b",
+    "XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k",
-    "ViT-H-14-quickgelu__dfn5b",
-    "ViT-H-14-378-quickgelu__dfn5b",
     "XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k",
+    "nllb-clip-base-siglip__mrl",
     "nllb-clip-base-siglip__v1",
+    "nllb-clip-large-siglip__mrl",
     "nllb-clip-large-siglip__v1",
 }
 
 
 _MCLIP_MODELS = {
     "LABSE-Vit-L-14",
-    "XLM-Roberta-Large-Vit-B-32",
     "XLM-Roberta-Large-Vit-B-16Plus",
+    "XLM-Roberta-Large-Vit-B-32",
     "XLM-Roberta-Large-Vit-L-14",
 }
 
 
 _INSIGHTFACE_MODELS = {
     "antelopev2",
-    "buffalo_l",
-    "buffalo_m",
     "buffalo_s",
+    "buffalo_m",
+    "buffalo_l",
 }
 
 

machine-learning/export/env.yaml

@@ -2,7 +2,7 @@ name: base
 channels:
   - conda-forge
   - nvidia
-  - pytorch-nightly
+  - pytorch
 platforms:
   - linux-64
 dependencies:
@@ -13,7 +13,7 @@ dependencies:
   - orjson==3.*
   - pip
   - python==3.11.*
-  - pytorch
+  - pytorch>=2.3
   - rich==13.*
   - safetensors==0.*
   - setuptools==68.*
@@ -21,5 +21,5 @@ dependencies:
   - transformers==4.*
   - pip:
     - multilingual-clip
-    - onnx-simplifier
+    - onnxsim
 category: main

machine-learning/export/models/mclip.py

@@ -1,3 +1,4 @@
+import os
 import tempfile
 import warnings
 from pathlib import Path
@@ -8,7 +9,6 @@ from transformers import AutoTokenizer
 
 from .openclip import OpenCLIPModelConfig
 from .openclip import to_onnx as openclip_to_onnx
-from .optimize import optimize
 from .util import get_model_path
 
 _MCLIP_TO_OPENCLIP = {
@@ -23,18 +23,20 @@ def to_onnx(
     model_name: str,
     output_dir_visual: Path | str,
     output_dir_textual: Path | str,
-) -> None:
+) -> tuple[Path, Path]:
     textual_path = get_model_path(output_dir_textual)
     with tempfile.TemporaryDirectory() as tmpdir:
-        model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir)
+        model = MultilingualCLIP.from_pretrained(model_name, cache_dir=os.environ.get("CACHE_DIR", tmpdir))
         AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
 
+        model.eval()
         for param in model.parameters():
             param.requires_grad_(False)
 
         export_text_encoder(model, textual_path)
-        openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual)
+        visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual)
-        optimize(textual_path)
+        assert visual_path is not None, "Visual model export failed"
+    return visual_path, textual_path
 
 
 def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None:
@@ -58,10 +60,10 @@ def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> Non
             args,
             output_path.as_posix(),
             input_names=["input_ids", "attention_mask"],
-            output_names=["text_embedding"],
+            output_names=["embedding"],
             opset_version=17,
-            dynamic_axes={
+            # dynamic_axes={
-                "input_ids": {0: "batch_size", 1: "sequence_length"},
+            #     "input_ids": {0: "batch_size", 1: "sequence_length"},
-                "attention_mask": {0: "batch_size", 1: "sequence_length"},
+            #     "attention_mask": {0: "batch_size", 1: "sequence_length"},
-            },
+            # },
         )

machine-learning/export/models/openclip.py

@@ -1,3 +1,4 @@
+import os
 import tempfile
 import warnings
 from dataclasses import dataclass, field
@@ -7,7 +8,6 @@ import open_clip
 import torch
 from transformers import AutoTokenizer
 
-from .optimize import optimize
 from .util import get_model_path, save_config
 
 
@@ -23,25 +23,28 @@ class OpenCLIPModelConfig:
         if open_clip_cfg is None:
             raise ValueError(f"Unknown model {self.name}")
         self.image_size = open_clip_cfg["vision_cfg"]["image_size"]
-        self.sequence_length = open_clip_cfg["text_cfg"]["context_length"]
+        self.sequence_length = open_clip_cfg["text_cfg"].get("context_length", 77)
 
 
 def to_onnx(
     model_cfg: OpenCLIPModelConfig,
     output_dir_visual: Path | str | None = None,
     output_dir_textual: Path | str | None = None,
-) -> None:
+) -> tuple[Path | None, Path | None]:
+    visual_path = None
+    textual_path = None
     with tempfile.TemporaryDirectory() as tmpdir:
         model = open_clip.create_model(
             model_cfg.name,
             pretrained=model_cfg.pretrained,
             jit=False,
-            cache_dir=tmpdir,
+            cache_dir=os.environ.get("CACHE_DIR", tmpdir),
             require_pretrained=True,
         )
 
         text_vision_cfg = open_clip.get_model_config(model_cfg.name)
 
+        model.eval()
         for param in model.parameters():
             param.requires_grad_(False)
 
@@ -53,8 +56,6 @@ def to_onnx(
             save_config(text_vision_cfg, output_dir_visual.parent / "config.json")
             export_image_encoder(model, model_cfg, visual_path)
 
-            optimize(visual_path)
-
         if output_dir_textual is not None:
             output_dir_textual = Path(output_dir_textual)
             textual_path = get_model_path(output_dir_textual)
@@ -62,7 +63,7 @@ def to_onnx(
             tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32")
             AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual)
             export_text_encoder(model, model_cfg, textual_path)
-            optimize(textual_path)
+    return visual_path, textual_path
 
 
 def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
@@ -83,9 +84,9 @@ def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig,
             args,
             output_path.as_posix(),
             input_names=["image"],
-            output_names=["image_embedding"],
+            output_names=["embedding"],
             opset_version=17,
-            dynamic_axes={"image": {0: "batch_size"}},
+            # dynamic_axes={"image": {0: "batch_size"}},
         )
 
 
@@ -107,7 +108,7 @@ def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, o
             args,
             output_path.as_posix(),
             input_names=["text"],
-            output_names=["text_embedding"],
+            output_names=["embedding"],
             opset_version=17,
-            dynamic_axes={"text": {0: "batch_size"}},
+            # dynamic_axes={"text": {0: "batch_size"}},
         )

machine-learning/export/models/optimize.py

@@ -5,13 +5,26 @@ import onnxruntime as ort
 import onnxsim
 
 
+def save_onnx(model: onnx.ModelProto, output_path: Path | str) -> None:
+    try:
+        onnx.save(model, output_path)
+    except ValueError as e:
+        if "The proto size is larger than the 2 GB limit." in str(e):
+            onnx.save(model, output_path, save_as_external_data=True, size_threshold=1_000_000)
+        else:
+            raise e
+
+
 def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None:
     model_path = Path(model_path)
     output_path = Path(output_path)
     model = onnx.load(model_path.as_posix())
-    model, check = onnxsim.simplify(model, skip_shape_inference=True)
+    model, check = onnxsim.simplify(model)
     assert check, "Simplified ONNX model could not be validated"
-    onnx.save(model, output_path.as_posix())
+    for file in model_path.parent.iterdir():
+        if file.name.startswith("Constant") or "onnx" in file.name or file.suffix == ".weight":
+            file.unlink()
+    save_onnx(model, output_path)
 
 
 def optimize_ort(
@@ -33,6 +46,4 @@ def optimize(model_path: Path | str) -> None:
     model_path = Path(model_path)
 
     optimize_ort(model_path, model_path)
-    # onnxsim serializes large models as a blob, which uses much more memory when loading the model at runtime
+    optimize_onnxsim(model_path, model_path)
-    if not any(file.name.startswith("Constant") for file in model_path.parent.iterdir()):
-        optimize_onnxsim(model_path, model_path)

machine-learning/export/run.py

@@ -3,74 +3,111 @@ import os
 from pathlib import Path
 from tempfile import TemporaryDirectory
 
-from huggingface_hub import create_repo, login, upload_folder
+import torch
+from huggingface_hub import create_repo, upload_folder
 from models import mclip, openclip
+from models.optimize import optimize
 from rich.progress import Progress
 
 models = [
-    "RN50::openai",
+    "M-CLIP/LABSE-Vit-L-14",
-    "RN50::yfcc15m",
+    "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus",
-    "RN50::cc12m",
+    "M-CLIP/XLM-Roberta-Large-Vit-B-32",
+    "M-CLIP/XLM-Roberta-Large-Vit-L-14",
     "RN101::openai",
     "RN101::yfcc15m",
-    "RN50x4::openai",
+    "RN50::cc12m",
+    "RN50::openai",
+    "RN50::yfcc15m",
     "RN50x16::openai",
+    "RN50x4::openai",
     "RN50x64::openai",
-    "ViT-B-32::openai",
+    "ViT-B-16-SigLIP-256::webli",
+    "ViT-B-16-SigLIP-384::webli",
+    "ViT-B-16-SigLIP-512::webli",
+    "ViT-B-16-SigLIP-i18n-256::webli",
+    "ViT-B-16-SigLIP::webli",
+    "ViT-B-16-plus-240::laion400m_e31",
+    "ViT-B-16-plus-240::laion400m_e32",
+    "ViT-B-16::laion400m_e31",
+    "ViT-B-16::laion400m_e32",
+    "ViT-B-16::openai",
+    "ViT-B-32::laion2b-s34b-b79k",
     "ViT-B-32::laion2b_e16",
     "ViT-B-32::laion400m_e31",
     "ViT-B-32::laion400m_e32",
-    "ViT-B-32::laion2b-s34b-b79k",
+    "ViT-B-32::openai",
-    "ViT-B-16::openai",
+    "ViT-H-14-378-quickgelu::dfn5b",
-    "ViT-B-16::laion400m_e31",
+    "ViT-H-14-quickgelu::dfn5b",
-    "ViT-B-16::laion400m_e32",
+    "ViT-H-14::laion2b-s32b-b79k",
-    "ViT-B-16-plus-240::laion400m_e31",
+    "ViT-L-14-336::openai",
-    "ViT-B-16-plus-240::laion400m_e32",
+    "ViT-L-14-quickgelu::dfn2b",
-    "ViT-L-14::openai",
+    "ViT-L-14::laion2b-s32b-b82k",
     "ViT-L-14::laion400m_e31",
     "ViT-L-14::laion400m_e32",
-    "ViT-L-14::laion2b-s32b-b82k",
+    "ViT-L-14::openai",
-    "ViT-L-14-336::openai",
+    "ViT-L-16-SigLIP-256::webli",
-    "ViT-H-14::laion2b-s32b-b79k",
+    "ViT-L-16-SigLIP-384::webli",
+    "ViT-SO400M-14-SigLIP-384::webli",
     "ViT-g-14::laion2b-s12b-b42k",
-    "M-CLIP/LABSE-Vit-L-14",
+    "nllb-clip-base-siglip::mrl",
-    "M-CLIP/XLM-Roberta-Large-Vit-B-32",
+    "nllb-clip-base-siglip::v1",
-    "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus",
+    "nllb-clip-large-siglip::mrl",
-    "M-CLIP/XLM-Roberta-Large-Vit-L-14",
+    "nllb-clip-large-siglip::v1",
+    "xlm-roberta-base-ViT-B-32::laion5b_s13b_b90k",
+    "xlm-roberta-large-ViT-H-14::frozen_laion5b_s13b_b90k",
 ]
 
-login(token=os.environ["HF_AUTH_TOKEN"])
+# glob to delete old UUID blobs when reuploading models
+uuid_char = "[a-fA-F0-9]"
+uuid_glob = uuid_char * 8 + "-" + uuid_char * 4 + "-" + uuid_char * 4 + "-" + uuid_char * 4 + "-" + uuid_char * 12
 
-with Progress() as progress:
+# remote repo files to be deleted before uploading
-    task1 = progress.add_task("[green]Exporting models...", total=len(models))
+# deletion is in the same commit as the upload, so it's atomic
-    task2 = progress.add_task("[yellow]Uploading models...", total=len(models))
+delete_patterns = ["**/*onnx*", "**/Constant*", "**/*.weight", "**/*.bias", f"**/{uuid_glob}"]
 
+with Progress() as progress:
+    task = progress.add_task("[green]Exporting models...", total=len(models))
+    token = os.environ.get("HF_AUTH_TOKEN")
+    torch.backends.mha.set_fastpath_enabled(False)
     with TemporaryDirectory() as tmp:
         tmpdir = Path(tmp)
         for model in models:
             model_name = model.split("/")[-1].replace("::", "__")
+            hf_model_name = model_name.replace("xlm-roberta-large", "XLM-Roberta-Large")
+            hf_model_name = model_name.replace("xlm-roberta-base", "XLM-Roberta-Base")
             config_path = tmpdir / model_name / "config.json"
 
-            def upload() -> None:
-                progress.update(task2, description=f"[yellow]Uploading {model_name}")
-                repo_id = f"immich-app/{model_name}"
-
-                create_repo(repo_id, exist_ok=True)
-                upload_folder(repo_id=repo_id, folder_path=tmpdir / model_name)
-                progress.update(task2, advance=1)
-
             def export() -> None:
-                progress.update(task1, description=f"[green]Exporting {model_name}")
+                progress.update(task, description=f"[green]Exporting {hf_model_name}")
-                visual_dir = tmpdir / model_name / "visual"
+                visual_dir = tmpdir / hf_model_name / "visual"
-                textual_dir = tmpdir / model_name / "textual"
+                textual_dir = tmpdir / hf_model_name / "textual"
                 if model.startswith("M-CLIP"):
-                    mclip.to_onnx(model, visual_dir, textual_dir)
+                    visual_path, textual_path = mclip.to_onnx(model, visual_dir, textual_dir)
                 else:
                     name, _, pretrained = model_name.partition("__")
-                    openclip.to_onnx(openclip.OpenCLIPModelConfig(name, pretrained), visual_dir, textual_dir)
+                    config = openclip.OpenCLIPModelConfig(name, pretrained)
+                    visual_path, textual_path = openclip.to_onnx(config, visual_dir, textual_dir)
+                progress.update(task, description=f"[green]Optimizing {hf_model_name} (visual)")
+                optimize(visual_path)
+                progress.update(task, description=f"[green]Optimizing {hf_model_name} (textual)")
+                optimize(textual_path)
 
-                progress.update(task1, advance=1)
                 gc.collect()
 
+            def upload() -> None:
+                progress.update(task, description=f"[yellow]Uploading {hf_model_name}")
+                repo_id = f"immich-app/{hf_model_name}"
+
+                create_repo(repo_id, exist_ok=True)
+                upload_folder(
+                    repo_id=repo_id,
+                    folder_path=tmpdir / hf_model_name,
+                    delete_patterns=delete_patterns,
+                    token=token,
+                )
+
             export()
-            upload()
+            if token is not None:
+                upload()
+            progress.update(task, advance=1)

server/src/constants.ts

@@ -93,39 +93,50 @@ export const supportedPresetTokens = [
 
 type ModelInfo = { dimSize: number };
 export const CLIP_MODEL_INFO: Record<string, ModelInfo> = {
-  RN50__openai: { dimSize: 1024 },
-  RN50__yfcc15m: { dimSize: 1024 },
-  RN50__cc12m: { dimSize: 1024 },
   RN101__openai: { dimSize: 512 },
   RN101__yfcc15m: { dimSize: 512 },
-  RN50x4__openai: { dimSize: 640 },
+  'ViT-B-16__laion400m_e31': { dimSize: 512 },
-  RN50x16__openai: { dimSize: 768 },
+  'ViT-B-16__laion400m_e32': { dimSize: 512 },
-  RN50x64__openai: { dimSize: 1024 },
+  'ViT-B-16__openai': { dimSize: 512 },
-  'ViT-B-32__openai': { dimSize: 512 },
+  'ViT-B-32__laion2b-s34b-b79k': { dimSize: 512 },
   'ViT-B-32__laion2b_e16': { dimSize: 512 },
   'ViT-B-32__laion400m_e31': { dimSize: 512 },
   'ViT-B-32__laion400m_e32': { dimSize: 512 },
-  'ViT-B-32__laion2b-s34b-b79k': { dimSize: 512 },
+  'ViT-B-32__openai': { dimSize: 512 },
-  'ViT-B-16__openai': { dimSize: 512 },
+  'XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k': { dimSize: 512 },
-  'ViT-B-16__laion400m_e31': { dimSize: 512 },
+  'XLM-Roberta-Large-Vit-B-32': { dimSize: 512 },
-  'ViT-B-16__laion400m_e32': { dimSize: 512 },
+  RN50x4__openai: { dimSize: 640 },
   'ViT-B-16-plus-240__laion400m_e31': { dimSize: 640 },
   'ViT-B-16-plus-240__laion400m_e32': { dimSize: 640 },
-  'ViT-L-14__openai': { dimSize: 768 },
+  'XLM-Roberta-Large-Vit-B-16Plus': { dimSize: 640 },
-  'ViT-L-14__laion400m_e31': { dimSize: 768 },
+  'LABSE-Vit-L-14': { dimSize: 768 },
-  'ViT-L-14__laion400m_e32': { dimSize: 768 },
+  RN50x16__openai: { dimSize: 768 },
-  'ViT-L-14__laion2b-s32b-b82k': { dimSize: 768 },
+  'ViT-B-16-SigLIP-256__webli': { dimSize: 768 },
+  'ViT-B-16-SigLIP-384__webli': { dimSize: 768 },
+  'ViT-B-16-SigLIP-512__webli': { dimSize: 768 },
+  'ViT-B-16-SigLIP-i18n-256__webli': { dimSize: 768 },
+  'ViT-B-16-SigLIP__webli': { dimSize: 768 },
   'ViT-L-14-336__openai': { dimSize: 768 },
   'ViT-L-14-quickgelu__dfn2b': { dimSize: 768 },
-  'ViT-H-14__laion2b-s32b-b79k': { dimSize: 1024 },
+  'ViT-L-14__laion2b-s32b-b82k': { dimSize: 768 },
-  'ViT-H-14-quickgelu__dfn5b': { dimSize: 1024 },
+  'ViT-L-14__laion400m_e31': { dimSize: 768 },
+  'ViT-L-14__laion400m_e32': { dimSize: 768 },
+  'ViT-L-14__openai': { dimSize: 768 },
+  'XLM-Roberta-Large-Vit-L-14': { dimSize: 768 },
+  'nllb-clip-base-siglip__mrl': { dimSize: 768 },
+  'nllb-clip-base-siglip__v1': { dimSize: 768 },
+  RN50__cc12m: { dimSize: 1024 },
+  RN50__openai: { dimSize: 1024 },
+  RN50__yfcc15m: { dimSize: 1024 },
+  RN50x64__openai: { dimSize: 1024 },
   'ViT-H-14-378-quickgelu__dfn5b': { dimSize: 1024 },
+  'ViT-H-14-quickgelu__dfn5b': { dimSize: 1024 },
+  'ViT-H-14__laion2b-s32b-b79k': { dimSize: 1024 },
+  'ViT-L-16-SigLIP-256__webli': { dimSize: 1024 },
+  'ViT-L-16-SigLIP-384__webli': { dimSize: 1024 },
   'ViT-g-14__laion2b-s12b-b42k': { dimSize: 1024 },
-  'LABSE-Vit-L-14': { dimSize: 768 },
-  'XLM-Roberta-Large-Vit-B-32': { dimSize: 512 },
-  'XLM-Roberta-Large-Vit-B-16Plus': { dimSize: 640 },
-  'XLM-Roberta-Large-Vit-L-14': { dimSize: 768 },
   'XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k': { dimSize: 1024 },
-  'nllb-clip-base-siglip__v1': { dimSize: 768 },
+  'ViT-SO400M-14-SigLIP-384__webli': { dimSize: 1152 },
+  'nllb-clip-large-siglip__mrl': { dimSize: 1152 },
   'nllb-clip-large-siglip__v1': { dimSize: 1152 },
 };