diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 00497a8d6e..c2b28d654c 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -395,10 +395,10 @@ jobs: uv sync --extra cpu - name: Lint with ruff run: | - uv run ruff check --output-format=github app export + uv run ruff check --output-format=github app - name: Check black formatting run: | - uv run black --check app export + uv run black --check app - name: Run mypy type checking run: | uv run mypy --strict app/ diff --git a/machine-learning/export/.python-version b/machine-learning/export/.python-version deleted file mode 100644 index e4fba21835..0000000000 --- a/machine-learning/export/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.12 diff --git a/machine-learning/export/README.md b/machine-learning/export/README.md deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/machine-learning/export/immich_model_exporter/__init__.py b/machine-learning/export/immich_model_exporter/__init__.py deleted file mode 100644 index d252ce103a..0000000000 --- a/machine-learning/export/immich_model_exporter/__init__.py +++ /dev/null @@ -1,165 +0,0 @@ -import json -import resource -from pathlib import Path - -import typer -from tenacity import retry, stop_after_attempt, wait_fixed -from typing_extensions import Annotated - -from .exporters.constants import DELETE_PATTERNS, SOURCE_TO_METADATA, ModelSource, ModelTask -from .exporters.onnx import export as onnx_export -from .exporters.rknn import export as rknn_export - -app = typer.Typer(pretty_exceptions_show_locals=False) - - -def generate_readme(model_name: str, model_source: ModelSource) -> str: - (name, link, type) = SOURCE_TO_METADATA[model_source] - match model_source: - case ModelSource.MCLIP: - tags = ["immich", "clip", "multilingual"] - case ModelSource.OPENCLIP: - tags = ["immich", "clip"] - lowered = model_name.lower() - if "xlm" in lowered or "nllb" in lowered: - tags.append("multilingual") - case ModelSource.INSIGHTFACE: - tags = ["immich", "facial-recognition"] - case _: - raise ValueError(f"Unsupported model source {model_source}") - - return f"""--- -tags: -{" - " + "\n - ".join(tags)} ---- -# Model Description - -This repo contains ONNX exports for the associated {type} model by {name}. See the [{name}]({link}) repo for more info. - -This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library. -""" - - -def clean_name(model_name: str) -> str: - hf_model_name = model_name.split("/")[-1] - hf_model_name = hf_model_name.replace("xlm-roberta-large", "XLM-Roberta-Large") - hf_model_name = hf_model_name.replace("xlm-roberta-base", "XLM-Roberta-Base") - return hf_model_name - - -@app.command() -def export(model_name: str, model_source: ModelSource, output_dir: Path = Path("models"), cache: bool = True) -> None: - hf_model_name = clean_name(model_name) - output_dir = output_dir / hf_model_name - match model_source: - case ModelSource.MCLIP | ModelSource.OPENCLIP: - output_dir.mkdir(parents=True, exist_ok=True) - onnx_export(model_name, model_source, output_dir, cache=cache) - case ModelSource.INSIGHTFACE: - from huggingface_hub import snapshot_download - - # TODO: start from insightface dump instead of downloading from HF - snapshot_download(f"immich-app/{hf_model_name}", local_dir=output_dir) - case _: - raise ValueError(f"Unsupported model source {model_source}") - - try: - rknn_export(output_dir, cache=cache) - except Exception as e: - print(f"Failed to export model {model_name} to rknn: {e}") - (output_dir / "rknpu").unlink(missing_ok=True) - - readme_path = output_dir / "README.md" - if not (cache or readme_path.exists()): - with open(readme_path, "w") as f: - f.write(generate_readme(model_name, model_source)) - - -@app.command() -def profile(model_dir: Path, model_task: ModelTask, output_path: Path) -> None: - from timeit import timeit - - import numpy as np - import onnxruntime as ort - - np.random.seed(0) - - sess_options = ort.SessionOptions() - sess_options.enable_cpu_mem_arena = False - providers = ["CPUExecutionProvider"] - provider_options = [{"arena_extend_strategy": "kSameAsRequested"}] - match model_task: - case ModelTask.SEARCH: - textual = ort.InferenceSession( - model_dir / "textual" / "model.onnx", - sess_options=sess_options, - providers=providers, - provider_options=provider_options, - ) - tokens = {node.name: np.random.rand(*node.shape).astype(np.int32) for node in textual.get_inputs()} - - visual = ort.InferenceSession( - model_dir / "visual" / "model.onnx", - sess_options=sess_options, - providers=providers, - provider_options=provider_options, - ) - image = {node.name: np.random.rand(*node.shape).astype(np.float32) for node in visual.get_inputs()} - - def predict() -> None: - textual.run(None, tokens) - visual.run(None, image) - - case ModelTask.FACIAL_RECOGNITION: - detection = ort.InferenceSession( - model_dir / "detection" / "model.onnx", - sess_options=sess_options, - providers=providers, - provider_options=provider_options, - ) - image = {node.name: np.random.rand(1, 3, 640, 640).astype(np.float32) for node in detection.get_inputs()} - - recognition = ort.InferenceSession( - model_dir / "recognition" / "model.onnx", - sess_options=sess_options, - providers=providers, - provider_options=provider_options, - ) - face = {node.name: np.random.rand(1, 3, 112, 112).astype(np.float32) for node in recognition.get_inputs()} - - def predict() -> None: - detection.run(None, image) - recognition.run(None, face) - - case _: - raise ValueError(f"Unsupported model task {model_task}") - predict() - ms = timeit(predict, number=100) - rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss - json.dump({"pretrained_model": model_dir.name, "peak_rss": rss, "exec_time_ms": ms}, output_path.open("w")) - print(f"Model {model_dir.name} took {ms:.2f}ms per iteration using {rss / 1024:.2f}MiB of memory") - - -@app.command() -def upload( - model_dir: Path, - hf_organization: str = "immich-app", - hf_auth_token: Annotated[str | None, typer.Option(envvar="HF_AUTH_TOKEN")] = None, -) -> None: - from huggingface_hub import create_repo, upload_folder - - repo_id = f"{hf_organization}/{model_dir.name}" - - @retry(stop=stop_after_attempt(5), wait=wait_fixed(5)) - def upload_model() -> None: - create_repo(repo_id, exist_ok=True, token=hf_auth_token) - upload_folder( - repo_id=repo_id, - folder_path=model_dir, - # remote repo files to be deleted before uploading - # deletion is in the same commit as the upload, so it's atomic - delete_patterns=DELETE_PATTERNS, - token=hf_auth_token, - ) - - upload_model() diff --git a/machine-learning/export/immich_model_exporter/__main__.py b/machine-learning/export/immich_model_exporter/__main__.py deleted file mode 100644 index 9526293a7b..0000000000 --- a/machine-learning/export/immich_model_exporter/__main__.py +++ /dev/null @@ -1,3 +0,0 @@ -from immich_model_exporter import app - -app() diff --git a/machine-learning/export/immich_model_exporter/exporters/constants.py b/machine-learning/export/immich_model_exporter/exporters/constants.py deleted file mode 100644 index 45e08a5aef..0000000000 --- a/machine-learning/export/immich_model_exporter/exporters/constants.py +++ /dev/null @@ -1,54 +0,0 @@ -from enum import StrEnum -from typing import NamedTuple - - -class ModelSource(StrEnum): - INSIGHTFACE = "insightface" - MCLIP = "mclip" - OPENCLIP = "openclip" - - -class ModelTask(StrEnum): - FACIAL_RECOGNITION = "facial-recognition" - SEARCH = "clip" - - -class SourceMetadata(NamedTuple): - name: str - link: str - type: str - - -SOURCE_TO_METADATA = { - ModelSource.MCLIP: SourceMetadata("M-CLIP", "https://huggingface.co/M-CLIP", "CLIP"), - ModelSource.OPENCLIP: SourceMetadata("OpenCLIP", "https://github.com/mlfoundations/open_clip", "CLIP"), - ModelSource.INSIGHTFACE: SourceMetadata( - "InsightFace", "https://github.com/deepinsight/insightface/tree/master", "facial recognition" - ), -} - - -SOURCE_TO_TASK = { - ModelSource.MCLIP: ModelTask.SEARCH, - ModelSource.OPENCLIP: ModelTask.SEARCH, - ModelSource.INSIGHTFACE: ModelTask.FACIAL_RECOGNITION, -} - -RKNN_SOCS = ["rk3566", "rk3568", "rk3576", "rk3588"] - - -# 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 -DELETE_PATTERNS = [ - "**/*onnx*", - "**/Constant*", - "**/*.weight", - "**/*.bias", - "**/*.proj", - "**/*in_proj_bias", - "**/*.npy", - "**/*.latent", - "**/*.pos_embed", - f"**/{_uuid_glob}", -] diff --git a/machine-learning/export/immich_model_exporter/exporters/onnx/__init__.py b/machine-learning/export/immich_model_exporter/exporters/onnx/__init__.py deleted file mode 100644 index d4beac4405..0000000000 --- a/machine-learning/export/immich_model_exporter/exporters/onnx/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from pathlib import Path - -from ..constants import ModelSource -from .models import mclip, openclip - - -def export( - model_name: str, model_source: ModelSource, output_dir: Path, opset_version: int = 19, cache: bool = True -) -> None: - visual_dir = output_dir / "visual" - textual_dir = output_dir / "textual" - match model_source: - case ModelSource.MCLIP: - mclip.to_onnx(model_name, opset_version, visual_dir, textual_dir, cache=cache) - case ModelSource.OPENCLIP: - name, _, pretrained = model_name.partition("__") - config = openclip.OpenCLIPModelConfig(name, pretrained) - openclip.to_onnx(config, opset_version, visual_dir, textual_dir, cache=cache) - case _: - raise ValueError(f"Unsupported model source {model_source}") diff --git a/machine-learning/export/immich_model_exporter/exporters/onnx/models/__init__.py b/machine-learning/export/immich_model_exporter/exporters/onnx/models/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/machine-learning/export/immich_model_exporter/exporters/onnx/models/mclip.py b/machine-learning/export/immich_model_exporter/exporters/onnx/models/mclip.py deleted file mode 100644 index 8c0be1057a..0000000000 --- a/machine-learning/export/immich_model_exporter/exporters/onnx/models/mclip.py +++ /dev/null @@ -1,77 +0,0 @@ -import warnings -from pathlib import Path -from typing import Any - -from .openclip import OpenCLIPModelConfig -from .openclip import to_onnx as openclip_to_onnx -from .util import get_model_path - -_MCLIP_TO_OPENCLIP = { - "M-CLIP/XLM-Roberta-Large-Vit-B-32": OpenCLIPModelConfig("ViT-B-32", "openai"), - "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus": OpenCLIPModelConfig("ViT-B-16-plus-240", "laion400m_e32"), - "M-CLIP/LABSE-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"), - "M-CLIP/XLM-Roberta-Large-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"), -} - - -def to_onnx( - model_name: str, - opset_version: int, - output_dir_visual: Path | str, - output_dir_textual: Path | str, - cache: bool = True, -) -> tuple[Path, Path]: - textual_path = get_model_path(output_dir_textual) - if not cache or not textual_path.exists(): - import torch - from multilingual_clip.pt_multilingual_clip import MultilingualCLIP - from transformers import AutoTokenizer - - torch.backends.mha.set_fastpath_enabled(False) - - model = MultilingualCLIP.from_pretrained(model_name) - 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, opset_version) - else: - print(f"Model {textual_path} already exists, skipping") - visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], opset_version, output_dir_visual, cache=cache) - assert visual_path is not None, "Visual model export failed" - return visual_path, textual_path - - -def _export_text_encoder(model: Any, output_path: Path | str, opset_version: int) -> None: - import torch - from multilingual_clip.pt_multilingual_clip import MultilingualCLIP - - output_path = Path(output_path) - - def forward(self: MultilingualCLIP, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: - embs = self.transformer(input_ids, attention_mask)[0] - embs = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None] - embs = self.LinearTransformation(embs) - return torch.nn.functional.normalize(embs, dim=-1) - - # unfortunately need to monkeypatch for tracing to work here - # otherwise it hits the 2GiB protobuf serialization limit - MultilingualCLIP.forward = forward - - args = (torch.ones(1, 77, dtype=torch.int32), torch.ones(1, 77, dtype=torch.int32)) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", UserWarning) - torch.onnx.export( - model, - args, - output_path.as_posix(), - input_names=["input_ids", "attention_mask"], - output_names=["embedding"], - opset_version=opset_version, - # dynamic_axes={ - # "input_ids": {0: "batch_size", 1: "sequence_length"}, - # "attention_mask": {0: "batch_size", 1: "sequence_length"}, - # }, - ) diff --git a/machine-learning/export/immich_model_exporter/exporters/onnx/models/openclip.py b/machine-learning/export/immich_model_exporter/exporters/onnx/models/openclip.py deleted file mode 100644 index 0d34b4a1fd..0000000000 --- a/machine-learning/export/immich_model_exporter/exporters/onnx/models/openclip.py +++ /dev/null @@ -1,151 +0,0 @@ -import warnings -from dataclasses import dataclass -from functools import cached_property -from pathlib import Path -from typing import Any - -from .util import get_model_path, save_config - - -@dataclass -class OpenCLIPModelConfig: - name: str - pretrained: str - - @cached_property - def model_config(self) -> dict[str, Any]: - import open_clip - - config: dict[str, Any] | None = open_clip.get_model_config(self.name) - if config is None: - raise ValueError(f"Unknown model {self.name}") - return config - - @property - def image_size(self) -> int: - image_size: int = self.model_config["vision_cfg"]["image_size"] - return image_size - - @property - def sequence_length(self) -> int: - context_length: int = self.model_config["text_cfg"].get("context_length", 77) - return context_length - - -def to_onnx( - model_cfg: OpenCLIPModelConfig, - opset_version: int, - output_dir_visual: Path | str | None = None, - output_dir_textual: Path | str | None = None, - cache: bool = True, -) -> tuple[Path | None, Path | None]: - visual_path = None - textual_path = None - if output_dir_visual is not None: - output_dir_visual = Path(output_dir_visual) - visual_path = get_model_path(output_dir_visual) - - if output_dir_textual is not None: - output_dir_textual = Path(output_dir_textual) - textual_path = get_model_path(output_dir_textual) - - if cache and ((textual_path is None or textual_path.exists()) and (visual_path is None or visual_path.exists())): - print(f"Models {textual_path} and {visual_path} already exist, skipping") - return visual_path, textual_path - - import open_clip - import torch - from transformers import AutoTokenizer - - torch.backends.mha.set_fastpath_enabled(False) - - model = open_clip.create_model( - model_cfg.name, - pretrained=model_cfg.pretrained, - jit=False, - 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) - - if visual_path is not None and output_dir_visual is not None: - if not cache or not visual_path.exists(): - save_config( - open_clip.get_model_preprocess_cfg(model), - output_dir_visual / "preprocess_cfg.json", - ) - save_config(text_vision_cfg, output_dir_visual.parent / "config.json") - _export_image_encoder(model, model_cfg, visual_path, opset_version) - else: - print(f"Model {visual_path} already exists, skipping") - - if textual_path is not None and output_dir_textual is not None: - if not cache or not textual_path.exists(): - 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, opset_version) - else: - print(f"Model {textual_path} already exists, skipping") - return visual_path, textual_path - - -def _export_image_encoder( - model: Any, model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int -) -> None: - import torch - - output_path = Path(output_path) - - def encode_image(image: torch.Tensor) -> torch.Tensor: - output = model.encode_image(image, normalize=True) - assert isinstance(output, torch.Tensor) - return output - - model.forward = encode_image - - args = (torch.randn(1, 3, model_cfg.image_size, model_cfg.image_size),) - - with warnings.catch_warnings(): - warnings.simplefilter("ignore", UserWarning) - torch.onnx.export( - model, - args, - output_path.as_posix(), - input_names=["image"], - output_names=["embedding"], - opset_version=opset_version, - # dynamic_axes={"image": {0: "batch_size"}}, - ) - - -def _export_text_encoder( - model: Any, model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int -) -> None: - import torch - - output_path = Path(output_path) - - def encode_text(text: torch.Tensor) -> torch.Tensor: - output = model.encode_text(text, normalize=True) - assert isinstance(output, torch.Tensor) - return output - - model.forward = encode_text - - args = (torch.ones(1, model_cfg.sequence_length, dtype=torch.int32),) - - with warnings.catch_warnings(): - warnings.simplefilter("ignore", UserWarning) - torch.onnx.export( - model, - args, - output_path.as_posix(), - input_names=["text"], - output_names=["embedding"], - opset_version=opset_version, - # dynamic_axes={"text": {0: "batch_size"}}, - ) diff --git a/machine-learning/export/immich_model_exporter/exporters/onnx/models/util.py b/machine-learning/export/immich_model_exporter/exporters/onnx/models/util.py deleted file mode 100644 index 67e582af46..0000000000 --- a/machine-learning/export/immich_model_exporter/exporters/onnx/models/util.py +++ /dev/null @@ -1,15 +0,0 @@ -import json -from pathlib import Path -from typing import Any - - -def get_model_path(output_dir: Path | str) -> Path: - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - return output_dir / "model.onnx" - - -def save_config(config: Any, output_path: Path | str) -> None: - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - json.dump(config, output_path.open("w")) diff --git a/machine-learning/export/immich_model_exporter/exporters/rknn.py b/machine-learning/export/immich_model_exporter/exporters/rknn.py deleted file mode 100644 index c45c4a6336..0000000000 --- a/machine-learning/export/immich_model_exporter/exporters/rknn.py +++ /dev/null @@ -1,96 +0,0 @@ -from pathlib import Path - -from .constants import RKNN_SOCS - - -def _export_platform( - model_dir: Path, - target_platform: str, - inputs: list[str] | None = None, - input_size_list: list[list[int]] | None = None, - fuse_matmul_softmax_matmul_to_sdpa: bool = True, - cache: bool = True, -) -> None: - from rknn.api import RKNN - - input_path = model_dir / "model.onnx" - output_path = model_dir / "rknpu" / target_platform / "model.rknn" - if cache and output_path.exists(): - print(f"Model {input_path} already exists at {output_path}, skipping") - return - - print(f"Exporting model {input_path} to {output_path}") - - rknn = RKNN(verbose=False) - - rknn.config( - target_platform=target_platform, - disable_rules=["fuse_matmul_softmax_matmul_to_sdpa"] if not fuse_matmul_softmax_matmul_to_sdpa else [], - enable_flash_attention=False, - model_pruning=True, - ) - ret = rknn.load_onnx(model=input_path.as_posix(), inputs=inputs, input_size_list=input_size_list) - - if ret != 0: - raise RuntimeError("Load failed!") - - ret = rknn.build(do_quantization=False) - - if ret != 0: - raise RuntimeError("Build failed!") - - output_path.parent.mkdir(parents=True, exist_ok=True) - ret = rknn.export_rknn(output_path.as_posix()) - if ret != 0: - raise RuntimeError("Export rknn model failed!") - - -def _export_platforms( - model_dir: Path, - inputs: list[str] | None = None, - input_size_list: list[list[int]] | None = None, - cache: bool = True, -) -> None: - fuse_matmul_softmax_matmul_to_sdpa = True - for soc in RKNN_SOCS: - try: - _export_platform( - model_dir, - soc, - inputs=inputs, - input_size_list=input_size_list, - fuse_matmul_softmax_matmul_to_sdpa=fuse_matmul_softmax_matmul_to_sdpa, - cache=cache, - ) - except Exception as e: - print(f"Failed to export model for {soc}: {e}") - if "inputs or 'outputs' must be set" in str(e): - print("Retrying without fuse_matmul_softmax_matmul_to_sdpa") - fuse_matmul_softmax_matmul_to_sdpa = False - _export_platform( - model_dir, - soc, - inputs=inputs, - input_size_list=input_size_list, - fuse_matmul_softmax_matmul_to_sdpa=fuse_matmul_softmax_matmul_to_sdpa, - cache=cache, - ) - - -def export(model_dir: Path, cache: bool = True) -> None: - textual = model_dir / "textual" - visual = model_dir / "visual" - detection = model_dir / "detection" - recognition = model_dir / "recognition" - - if textual.is_dir(): - _export_platforms(textual, cache=cache) - - if visual.is_dir(): - _export_platforms(visual, cache=cache) - - if detection.is_dir(): - _export_platforms(detection, inputs=["input.1"], input_size_list=[[1, 3, 640, 640]], cache=cache) - - if recognition.is_dir(): - _export_platforms(recognition, inputs=["input.1"], input_size_list=[[1, 3, 112, 112]], cache=cache) diff --git a/machine-learning/export/immich_model_exporter/get_dims.py b/machine-learning/export/immich_model_exporter/get_dims.py deleted file mode 100644 index 32f307ef7c..0000000000 --- a/machine-learning/export/immich_model_exporter/get_dims.py +++ /dev/null @@ -1,22 +0,0 @@ -import json -from pathlib import Path - -models_dir = Path("models") -model_to_embed_dim = {} -for model_dir in models_dir.iterdir(): - if not model_dir.is_dir(): - continue - - config_path = model_dir / "config.json" - if not config_path.exists(): - print(f"Skipping {model_dir.name} as it does not have a config.json") - continue - with open(config_path) as f: - config = json.load(f) - embed_dim = config.get("embed_dim") - if embed_dim is None: - print(f"Skipping {model_dir.name} as it does not have an embed_dim") - continue - print(f"{model_dir.name}: {embed_dim}") - model_to_embed_dim[model_dir.name] = {"dimSize": embed_dim} -print(json.dumps(model_to_embed_dim)) diff --git a/machine-learning/export/immich_model_exporter/parse_eval_data.py b/machine-learning/export/immich_model_exporter/parse_eval_data.py deleted file mode 100644 index 8f66366eb3..0000000000 --- a/machine-learning/export/immich_model_exporter/parse_eval_data.py +++ /dev/null @@ -1,121 +0,0 @@ -import polars as pl - - -def collapsed_table(language: str, df: pl.DataFrame) -> str: - with pl.Config( - tbl_formatting="ASCII_MARKDOWN", - tbl_hide_column_data_types=True, - tbl_hide_dataframe_shape=True, - fmt_str_lengths=100, - tbl_rows=1000, - tbl_width_chars=1000, - ): - return f"
\n{language}\n{str(df)}\n
" - - -languages = { - "en": "English", - "ar": "Arabic", - "bn": "Bengali", - "zh": "Chinese (Simplified)", - "hr": "Croatian", - "quz": "Cusco Quechua", - "cs": "Czech", - "da": "Danish", - "nl": "Dutch", - "fil": "Filipino", - "fi": "Finnish", - "fr": "French", - "de": "German", - "el": "Greek", - "he": "Hebrew", - "hi": "Hindi", - "hu": "Hungarian", - "id": "Indonesian", - "it": "Italian", - "ja": "Japanese", - "ko": "Korean", - "mi": "Maori", - "no": "Norwegian", - "fa": "Persian", - "pl": "Polish", - "pt": "Portuguese", - "ro": "Romanian", - "ru": "Russian", - "es": "Spanish", - "sw": "Swahili", - "sv": "Swedish", - "te": "Telugu", - "th": "Thai", - "tr": "Turkish", - "uk": "Ukrainian", - "vi": "Vietnamese", -} - -profile_df = pl.scan_ndjson("profiling/*.json").select("pretrained_model", "peak_rss", "exec_time_ms") -eval_df = pl.scan_ndjson("results/*.json").select("model", "pretrained", "language", "metrics") - -eval_df = eval_df.with_columns( - model=pl.col("model") - .str.replace("xlm-roberta-base", "XLM-Roberta-Base") - .str.replace("xlm-roberta-large", "XLM-Roberta-Large") -) -eval_df = eval_df.with_columns(pretrained_model=pl.concat_str(pl.col("model"), pl.col("pretrained"), separator="__")) -eval_df = eval_df.drop("model", "pretrained") -eval_df = eval_df.join(profile_df, on="pretrained_model") - -eval_df = eval_df.with_columns( - recall=( - pl.col("metrics").struct.field("image_retrieval_recall@1") - + pl.col("metrics").struct.field("image_retrieval_recall@5") - + pl.col("metrics").struct.field("image_retrieval_recall@10") - ) - * (100 / 3) -) - -pareto_front = eval_df.join_where( - eval_df.select("language", "peak_rss", "exec_time_ms", "recall").rename( - { - "language": "language_other", - "peak_rss": "peak_rss_other", - "exec_time_ms": "exec_time_ms_other", - "recall": "recall_other", - } - ), - (pl.col("language") == pl.col("language_other")) - & (pl.col("peak_rss_other") <= pl.col("peak_rss")) - & (pl.col("exec_time_ms_other") <= pl.col("exec_time_ms")) - & (pl.col("recall_other") >= pl.col("recall")) - & ( - (pl.col("peak_rss_other") < pl.col("peak_rss")) - | (pl.col("exec_time_ms_other") < pl.col("exec_time_ms")) - | (pl.col("recall_other") > pl.col("recall")) - ), -) -eval_df = eval_df.join(pareto_front, on=["pretrained_model", "language"], how="left") -eval_df = eval_df.with_columns(is_pareto=pl.col("recall_other").is_null()) -eval_df = ( - eval_df.drop("peak_rss_other", "exec_time_ms_other", "recall_other", "language_other") - .unique(subset=["pretrained_model", "language"]) - .collect() -) -eval_df.write_parquet("model_info.parquet") - -eval_df = eval_df.drop("metrics") -eval_df = eval_df.filter(pl.col("recall") >= 20) -eval_df = eval_df.sort("recall", descending=True) -eval_df = eval_df.select( - pl.col("pretrained_model").alias("Model"), - (pl.col("peak_rss") / 1024).round().cast(pl.UInt32).alias("Memory (MiB)"), - pl.col("exec_time_ms").round(2).alias("Execution Time (ms)"), - pl.col("language").alias("Language"), - pl.col("recall").round(2).alias("Recall (%)"), - pl.when(pl.col("is_pareto")).then(pl.lit("✅")).otherwise(pl.lit("❌")).alias("Pareto Optimal"), -) - - -for language in languages: - lang_df = eval_df.filter(pl.col("Language") == language).drop("Language") - if lang_df.shape[0] == 0: - continue - print(collapsed_table(languages[language], lang_df)) diff --git a/machine-learning/export/immich_model_exporter/run.py b/machine-learning/export/immich_model_exporter/run.py deleted file mode 100644 index 5384a8ba2a..0000000000 --- a/machine-learning/export/immich_model_exporter/run.py +++ /dev/null @@ -1,171 +0,0 @@ -import subprocess -from pathlib import Path - -from exporters.constants import ModelSource - -from immich_model_exporter import clean_name -from immich_model_exporter.exporters.constants import SOURCE_TO_TASK - -mclip = [ - "M-CLIP/LABSE-Vit-L-14", - "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus", - "M-CLIP/XLM-Roberta-Large-Vit-B-32", - "M-CLIP/XLM-Roberta-Large-Vit-L-14", -] - -openclip = [ - "RN101__openai", - "RN101__yfcc15m", - "RN50__cc12m", - "RN50__openai", - "RN50__yfcc15m", - "RN50x16__openai", - "RN50x4__openai", - "RN50x64__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-SigLIP2__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-SigLIP2-256__webli", - "ViT-B-32__laion2b-s34b-b79k", - "ViT-B-32__laion2b_e16", - "ViT-B-32__laion400m_e31", - "ViT-B-32__laion400m_e32", - "ViT-B-32__openai", - "ViT-H-14-378-quickgelu__dfn5b", - "ViT-H-14-quickgelu__dfn5b", - "ViT-H-14__laion2b-s32b-b79k", - "ViT-L-14-336__openai", - "ViT-L-14-quickgelu__dfn2b", - "ViT-L-14__laion2b-s32b-b82k", - "ViT-L-14__laion400m_e31", - "ViT-L-14__laion400m_e32", - "ViT-L-14__openai", - "ViT-L-16-SigLIP-256__webli", - "ViT-L-16-SigLIP-384__webli", - "ViT-L-16-SigLIP2-256__webli", - "ViT-L-16-SigLIP2-384__webli", - "ViT-L-16-SigLIP2-512__webli", - "ViT-SO400M-14-SigLIP-384__webli", - "ViT-SO400M-14-SigLIP2-378__webli", - "ViT-SO400M-14-SigLIP2__webli", - "ViT-SO400M-16-SigLIP2-256__webli", - "ViT-SO400M-16-SigLIP2-384__webli", - "ViT-SO400M-16-SigLIP2-512__webli", - "ViT-gopt-16-SigLIP2-256__webli", - "ViT-gopt-16-SigLIP2-384__webli", - "nllb-clip-base-siglip__mrl", - "nllb-clip-base-siglip__v1", - "nllb-clip-large-siglip__mrl", - "nllb-clip-large-siglip__v1", - "xlm-roberta-base-ViT-B-32__laion5b_s13b_b90k", - "xlm-roberta-large-ViT-H-14__frozen_laion5b_s13b_b90k", -] - -insightface = [ - "antelopev2", - "buffalo_l", - "buffalo_m", - "buffalo_s", -] - - -def export_models(models: list[str], source: ModelSource) -> None: - profiling_dir = Path("profiling") - profiling_dir.mkdir(exist_ok=True) - for model in models: - try: - model_dir = f"models/{clean_name(model)}" - task = SOURCE_TO_TASK[source] - - print(f"Processing model {model}") - subprocess.check_call(["python", "-m", "immich_model_exporter", "export", model, source]) - subprocess.check_call( - [ - "python", - "-m", - "immich_model_exporter", - "profile", - model_dir, - task, - "--output_path", - profiling_dir / f"{model}.json", - ] - ) - subprocess.check_call(["python", "-m", "immich_model_exporter", "upload", model_dir]) - except Exception as e: - print(f"Failed to export model {model}: {e}") - - -if __name__ == "__main__": - export_models(mclip, ModelSource.MCLIP) - export_models(openclip, ModelSource.OPENCLIP) - export_models(insightface, ModelSource.INSIGHTFACE) - - Path("results").mkdir(exist_ok=True) - subprocess.check_call( - [ - "python", - "clip_benchmark", - "eval", - "--pretrained_model", - *[name.replace("__", ",") for name in openclip], - "--task", - "zeroshot_retrieval", - "--dataset", - "crossmodal3600", - "--batch_size", - "64", - "--language", - "ar", - "bn", - "cs", - "da", - "de", - "el", - "en", - "es", - "fa", - "fi", - "fil", - "fr", - "he", - "hi", - "hr", - "hu", - "id", - "it", - "ja", - "ko", - "mi", - "nl", - "no", - "pl", - "pt", - "quz", - "ro", - "ru", - "sv", - "sw", - "te", - "th", - "tr", - "uk", - "vi", - "zh", - "--recall_k", - "1", - "5", - "10", - "--no_amp", - "--output", - "results/{dataset}_{language}_{model}_{pretrained}.json", - ] - ) diff --git a/machine-learning/export/pyproject.toml b/machine-learning/export/pyproject.toml deleted file mode 100644 index fa74427b1d..0000000000 --- a/machine-learning/export/pyproject.toml +++ /dev/null @@ -1,60 +0,0 @@ -[project] -name = "immich_model_exporter" -version = "0.1.0" -description = "Add your description here" -readme = "README.md" -requires-python = ">=3.10, <4.0" -dependencies = [ - "huggingface-hub>=0.29.3", - "multilingual-clip>=1.0.10", - "onnx>=1.14.1", - "onnxruntime>=1.16.0", - "open-clip-torch>=2.31.0", - "typer>=0.15.2", - "rknn-toolkit2>=2.3.0", - "transformers>=4.49.0", - "tenacity>=9.0.0", - "clip-benchmark>=1.6.1", - "polars>=1.25.2", -] - -[dependency-groups] -dev = ["black>=23.3.0", "mypy>=1.3.0", "ruff>=0.0.272"] - -[tool.uv] -override-dependencies = [ - "onnx>=1.16.0,<2", - "onnxruntime>=1.18.2,<2", - "torch>=2.4", - "torchvision>=0.21", -] - -[tool.hatch.build.targets.sdist] -include = ["immich_model_exporter"] - -[tool.hatch.build.targets.wheel] -include = ["immich_model_exporter"] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.mypy] -python_version = "3.12" -follow_imports = "silent" -warn_redundant_casts = true -disallow_any_generics = true -check_untyped_defs = true -disallow_untyped_defs = true -ignore_missing_imports = true - -[tool.ruff] -line-length = 120 -target-version = "py312" - -[tool.ruff.lint] -select = ["E", "F", "I"] - -[tool.black] -line-length = 120 -target-version = ['py312'] diff --git a/machine-learning/export/uv.lock b/machine-learning/export/uv.lock deleted file mode 100644 index a46930303a..0000000000 --- a/machine-learning/export/uv.lock +++ /dev/null @@ -1,1924 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.10, <4.0" -resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'darwin'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", - 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