diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index a6344aac8c09253b3b630fb776ae94478aa0275b..0000000000000000000000000000000000000000 --- a/.gitattributes +++ /dev/null @@ -1,35 +0,0 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index e5f53057fe1f25623d537f70bd20053843244099..ae712f7324944d6b7c232be26d294e72c7031d46 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,33 @@ --- title: MODUS -emoji: 💻 -colorFrom: purple +emoji: 🎨 +colorFrom: indigo colorTo: purple sdk: gradio -sdk_version: 6.19.0 -python_version: '3.13' +sdk_version: 4.44.1 app_file: app.py pinned: false +short_description: MODUS any-to-any multimodal demo (16 aligned modalities) --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# MODUS — any-to-any multimodal demo + +Three-tab Gradio demo for the MODUS 16-modality any-to-any model: + +1. **Any-to-Any** — one condition modality (image or caption) → any set of target + modalities, shown in a gallery (4M-style). +2. **Chained** — condition → intermediate (bridge) → target, showing both. +3. **Representation Analysis** — RGB→Depth/Normal conditioned on + {ViT only, VAE only, ViT+VAE}, side by side. + +## Setup (Space Settings → Variables and secrets) +- **Hardware:** ZeroGPU. +- **`HF_TOKEN`** (secret): a *read* token with access to the gated weights repo + `mqye/modus-16mod-stage3`. +- Optional **`MODUS_WEIGHTS_REPO`**: defaults to `mqye/modus-16mod-stage3`. + +The weights (bf16, ~30GB) + VAE + config + tokenizer are pulled once at startup +via `snapshot_download`; the model is built on CPU (~5min) and moved to the +ZeroGPU GPU per request. Inference config is baked in: modality config +`conf/modalities/instruction_16mod_stage2.yaml`, `use_instruction` off for the +generic modalities (seg/det/grounding keep their own instructions). diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..6d88a15e92374b4ae48bd100da67ccd732ad71c3 --- /dev/null +++ b/app.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""MODUS any-to-any demo — HuggingFace Space entrypoint (ZeroGPU). + +Thin ZeroGPU wrapper over the existing 3-tab demo backend (``demo_modus.py`` + +``demo_my/*``). The model is loaded ONCE at startup on CPU (no GPU, no time +limit); each inference call moves it to the ZeroGPU-provided GPU via the +``@spaces.GPU`` decorator (moving ~30GB bf16 over PCIe is seconds, whereas the +build+weight-load is ~5min and must NOT happen inside the GPU-time window). + +Space setup (Settings -> Variables and secrets): + HF_TOKEN read token for the gated weights repo (below) +Optional env (have sane defaults): + MODUS_WEIGHTS_REPO gated HF model repo with the bf16 weights + config + VAE +""" +import os +import sys + +try: + import torch + print(f"[app] torch={torch.__version__} python={sys.version.split()[0]}", flush=True) +except Exception as _e: + print(f"[app] torch import failed: {_e}", flush=True) + +# ── Env MUST be set before importing the demo backend (it reads these at import) ─ +os.environ.setdefault("MODUS_NO_MEAN_RESIZING", "1") # avoid gradio-BLAS deadlock +os.environ.setdefault("MODUS_FORCE_SDPA_ATTN", "1") # no flash-attn on the Space +os.environ.setdefault("MODUS_DEMO_MODALITY_CONFIG", + "conf/modalities/instruction_16mod_stage2.yaml") +os.environ.setdefault("MODUS_DEMO_MODEL_NAME", "bagel_from_json") +os.environ.setdefault("MODUS_DEMO_USE_EMA", "0") + +WEIGHTS_REPO = os.environ.get("MODUS_WEIGHTS_REPO", "mqye/modus-16mod-stage3") + +# ── Pull the gated weights once (model.safetensors + ae + config + tokenizer) ──── +from huggingface_hub import snapshot_download # noqa: E402 + +_weights_dir = snapshot_download( + repo_id=WEIGHTS_REPO, + repo_type="model", + token=os.environ.get("HF_TOKEN"), +) +# The snapshot dir holds BOTH the checkpoint (model.safetensors) and the base +# config/tokenizer/VAE, so it serves as CHECKPOINT_PATH and MODEL_PATH at once. +os.environ["MODUS_DEMO_CHECKPOINT"] = _weights_dir +os.environ["BAGEL_MODEL_PATH"] = _weights_dir +print(f"[app] weights ready at {_weights_dir}", flush=True) + +import spaces # noqa: E402 (ZeroGPU) + +# diffusers 0.20 (imported by the fourm VQVAE feature tokenizers) does +# `from huggingface_hub import cached_download`, which was removed in hub>=0.26 +# (the Space has 0.36). Shim it to hf_hub_download so `import diffusers` succeeds +# and the dino/clip/imagebind tokenizers can load. +import huggingface_hub as _hh # noqa: E402 +if not hasattr(_hh, "cached_download"): + _hh.cached_download = _hh.hf_hub_download + +# Importing demo_modus builds the UI + backend and reads the env set above. +import demo_modus # noqa: E402 + + +# ── ZeroGPU: wrap the two inference entry points so each call gets a GPU slice ─── +# tab1/tab2 call demo_modus.run_task; tab3 calls demo_modus.run_representation_task. +# The tab handlers resolve these by module-global name at call time, so replacing +# the module attribute makes them use the GPU-wrapped versions. +# duration = max GPU-seconds ZeroGPU reserves per call. The Chained tab runs TWO +# generations inside a single run_task call (~60s+), so 60 is too tight ("GPU task +# aborted"); 120 covers chained + cold-start model materialisation. (With PRO the +# reservation size is a non-issue quota-wise.) +_GPU_DURATION = int(os.environ.get("MODUS_GPU_DURATION", "120")) +demo_modus.run_task = spaces.GPU(duration=_GPU_DURATION)(demo_modus.run_task) +# Tab3 runs THREE generations (vit/vae/both) per click. Wrapping run_representation_task +# would take three separate GPU acquisitions in one handler → the 3rd hits "Expired +# ZeroGPU proxy token". Instead wrap the whole tab3 HANDLER so all three run inside a +# single GPU session (one token, one reservation big enough for 3 gens). +demo_modus.tab3_generate = spaces.GPU(duration=180)(demo_modus.tab3_generate) + +# Load the model ONCE at startup. The heavy CPU work (build arch + read 30GB +# weights, ~5min) runs here, OUTSIDE any @spaces.GPU window; the model's .cuda() +# moves are deferred by `spaces` and materialise on the first GPU call. +try: + demo_modus.HOLDER.ensure_loaded() + print("[app] model loaded (CPU) at startup", flush=True) +except Exception as e: # surface in UI, retry lazily on first request + demo_modus.HOLDER.load_error = str(e) + print(f"[app] startup model load failed: {e}", flush=True) + +# Work around a gradio 4.44.1 bug: get_api_info() (called when rendering the main +# "/" route) crashes with `TypeError: argument of type 'bool' is not iterable` when +# a component's JSON schema contains a bool (additionalProperties: true/false). +# Patch gradio_client's schema helpers to tolerate bool schemas. +try: + import gradio_client.utils as _gcu + + _orig_j2p = _gcu._json_schema_to_python_type + + def _safe_j2p(schema, defs=None): + if isinstance(schema, bool): + return "Any" + return _orig_j2p(schema, defs) + + _gcu._json_schema_to_python_type = _safe_j2p + + _orig_get_type = _gcu.get_type + + def _safe_get_type(schema): + if isinstance(schema, bool): + return "Any" + return _orig_get_type(schema) + + _gcu.get_type = _safe_get_type +except Exception as _e: + print(f"[app] gradio_client schema patch skipped: {_e}", flush=True) + +demo = demo_modus.build_ui() +demo.queue().launch(server_name="0.0.0.0", server_port=7860, show_api=False) diff --git a/conf/modalities/bagel_stage3_exact3cond.yaml b/conf/modalities/bagel_stage3_exact3cond.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f98f8ae4522a335acf3ab97ea26725806f8e59ff --- /dev/null +++ b/conf/modalities/bagel_stage3_exact3cond.yaml @@ -0,0 +1,191 @@ +# Stage 3 modality config for the standalone BAGEL exact-3-condition project. +# Intentionally duplicated from instruction_stage2.yaml so this project can +# evolve independently without affecting MODUS experiments. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + codebook_spatial_shape: [32, 32] + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + codebook_spatial_shape: [28, 28] + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + codebook_spatial_shape: [32, 32] + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 diff --git a/conf/modalities/hunyuan_image_3.yaml b/conf/modalities/hunyuan_image_3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..395f6ac5d5af6fd8f5e6fe5fa8aa6e8b6e4e95da --- /dev/null +++ b/conf/modalities/hunyuan_image_3.yaml @@ -0,0 +1,31 @@ +# Modality configuration for HunyuanImage-3.0 training. +# +# Only the RGB image modality is needed for text-to-image generation. +# Token keys (start_of_image / end_of_image) are populated by +# data_utils.add_special_tokens(), which adds <|vision_start|> and +# <|vision_end|> to the tokenizer when they are absent. +# These serve as sequence delimiters; actual image patches are placed at +# packed_vae_token_indexes and overwritten by the model's patch_embed. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vae: true + represent_vit: true diff --git a/conf/modalities/instruction.yaml b/conf/modalities/instruction.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e200d0ec94d4acfdbf081cecc3e72be89bb6bc5 --- /dev/null +++ b/conf/modalities/instruction.yaml @@ -0,0 +1,201 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + conditions: [rgb] + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 diff --git a/conf/modalities/instruction_10modality_stage2.yaml b/conf/modalities/instruction_10modality_stage2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0803edc6b4b728792d357156fbad8e9989ef44cc --- /dev/null +++ b/conf/modalities/instruction_10modality_stage2.yaml @@ -0,0 +1,142 @@ +# Stage 2 modality config — unconstrained conditions. +# +# Same modality definitions as instruction.yaml, but with ALL condition +# restrictions removed so every modality can be conditioned on every other. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + # conditions: unconstrained (any modality can condition caption) + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + # conditions: unconstrained + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + # conditions: unconstrained + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + diff --git a/conf/modalities/instruction_16mod_instr.yaml b/conf/modalities/instruction_16mod_instr.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b468f77ac60e49ae369e52b0ef39be1e995526c2 --- /dev/null +++ b/conf/modalities/instruction_16mod_instr.yaml @@ -0,0 +1,259 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: depth + id: 3 + kind: image + start_token_key: start_of_depth # REPLACE: per-modality start (token already exists in base vocab) + start_token: "<|depth_start|>" + end_token_key: end_of_image # shared end (image end is a structural terminator) + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_normal # REPLACE: per-modality start (token already exists in base vocab) + start_token: "<|normal_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + # NOTE: det's learnable pos_embed (grounding_pos_embed, size 4) is REMOVED here. + # The forward pos_embed loop is keyed by token-id range, and cocodet reuses + # det's x1/y1/x2/y2 tokens; with the pos_embed on, a multi-box cocodet sample + # (>4 coord tokens) would index the size-4 embedding out of bounds. det is not + # trained in modus (only present for coord-token-id alignment), so dropping its + # pos_embed is safe. reweight also removed (no reweight for det/cocodet). + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_seg # REPLACE: per-modality start (NEW token) + start_token: "<|seg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_canny # REPLACE: per-modality start (NEW token) + start_token: "<|canny_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + # ── NEW: cocodet (Pix2seq detection). stage1 = rgb→cocodet only. + # Coords reuse det's x1/y1/x2/y2 tokens (dedup, 0 new); class = COCO-id-aligned + # tokens (~91 new). dispersed_code_tokens → CE is gathered over the + # non-contiguous {coords ∪ class ∪ end} set, not a contiguous range. + # No reweight, no learnable pos_embed (RoPE + token-id carry order/role). + - name: cocodet + id: 13 + kind: codebook + start_token_key: start_of_cocodet + end_token_key: end_of_cocodet + start_token: "<|cocodet_start|>" + end_token: "<|cocodet_end|>" + represent_vae: true + dispersed_code_tokens: true + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|coco_cls_{i:02d}|>" + start: 0 + end: 90 + # Inference pipeline config. + # decode_method is 'cocodet' (NOT 'detection') — cocodet's output is + # coords+class per box ending on cocodet_end, a DIFFERENT format from the + # grounding/det coords-only decode (generate_detection_coordonly). Handled by + # the dedicated model.generate_cocodet + inferencer.gen_cocodet (which do NOT + # touch the grounding decode). + inference_decode_method: cocodet + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + # ── NEW image modalities (stage2): SAM segmentation / edge. Mirror seg/canny + # (direct-PNG image, VIT+VAE, MSE). REPLACE: per-modality start token, shared + # end_of_image. These start tokens are NEW (created via start_token field). + - name: samseg + id: 14 + kind: image + start_token_key: start_of_samseg + start_token: "<|samseg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: samedge + id: 15 + kind: image + start_token_key: start_of_samedge + start_token: "<|samedge_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true diff --git a/conf/modalities/instruction_16mod_stage1.yaml b/conf/modalities/instruction_16mod_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bae29aee53ced0d6ba4f6b1cb2b55f9890659818 --- /dev/null +++ b/conf/modalities/instruction_16mod_stage1.yaml @@ -0,0 +1,243 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + # NOTE: det's learnable pos_embed (grounding_pos_embed, size 4) is REMOVED here. + # The forward pos_embed loop is keyed by token-id range, and cocodet reuses + # det's x1/y1/x2/y2 tokens; with the pos_embed on, a multi-box cocodet sample + # (>4 coord tokens) would index the size-4 embedding out of bounds. det is not + # trained in modus (only present for coord-token-id alignment), so dropping its + # pos_embed is safe. reweight also removed (no reweight for det/cocodet). + conditions: [rgb] + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + # ── NEW: cocodet (Pix2seq detection). stage1 = rgb→cocodet only. + # Coords reuse det's x1/y1/x2/y2 tokens (dedup, 0 new); class = COCO-id-aligned + # tokens (~91 new). dispersed_code_tokens → CE is gathered over the + # non-contiguous {coords ∪ class ∪ end} set, not a contiguous range. + # No reweight, no learnable pos_embed (RoPE + token-id carry order/role). + - name: cocodet + id: 13 + kind: codebook + start_token_key: start_of_cocodet + end_token_key: end_of_cocodet + start_token: "<|cocodet_start|>" + end_token: "<|cocodet_end|>" + represent_vae: true + conditions: [rgb] + dispersed_code_tokens: true + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|coco_cls_{i:02d}|>" + start: 0 + end: 90 + # Inference pipeline config. + # decode_method is 'cocodet' (NOT 'detection') — cocodet's output is + # coords+class per box ending on cocodet_end, a DIFFERENT format from the + # grounding/det coords-only decode (generate_detection_coordonly). Handled by + # the dedicated model.generate_cocodet + inferencer.gen_cocodet (which do NOT + # touch the grounding decode). + inference_decode_method: cocodet + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false diff --git a/conf/modalities/instruction_16mod_stage1_rgbcond.yaml b/conf/modalities/instruction_16mod_stage1_rgbcond.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0c341b864361541e0f580d6010890ab33babf419 --- /dev/null +++ b/conf/modalities/instruction_16mod_stage1_rgbcond.yaml @@ -0,0 +1,266 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: depth + id: 3 + kind: image + start_token_key: start_of_depth # REPLACE: per-modality start (token already exists in base vocab) + start_token: "<|depth_start|>" + end_token_key: end_of_image # shared end (image end is a structural terminator) + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_normal # REPLACE: per-modality start (token already exists in base vocab) + start_token: "<|normal_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_det + represent_vae: true + # NOTE: det's learnable pos_embed (grounding_pos_embed, size 4) is REMOVED here. + # The forward pos_embed loop is keyed by token-id range, and cocodet reuses + # det's x1/y1/x2/y2 tokens; with the pos_embed on, a multi-box cocodet sample + # (>4 coord tokens) would index the size-4 embedding out of bounds. det is not + # trained in modus (only present for coord-token-id alignment), so dropping its + # pos_embed is safe. reweight also removed (no reweight for det/cocodet). + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_seg # REPLACE: per-modality start (NEW token) + start_token: "<|seg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_canny # REPLACE: per-modality start (NEW token) + start_token: "<|canny_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + # ── NEW: cocodet (Pix2seq detection). stage1 = rgb→cocodet only. + # Coords reuse det's x1/y1/x2/y2 tokens (dedup, 0 new); class = COCO-id-aligned + # tokens (~91 new). dispersed_code_tokens → CE is gathered over the + # non-contiguous {coords ∪ class ∪ end} set, not a contiguous range. + # No reweight, no learnable pos_embed (RoPE + token-id carry order/role). + - name: cocodet + id: 13 + kind: codebook + start_token_key: start_of_cocodet + conditions: ["rgb"] # stage1 rgbcond: condition on rgb only (rgb->X warmup) + end_token_key: end_of_cocodet + start_token: "<|cocodet_start|>" + end_token: "<|cocodet_end|>" + represent_vae: true + dispersed_code_tokens: true + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|coco_cls_{i:02d}|>" + start: 0 + end: 90 + # Inference pipeline config. + # decode_method is 'cocodet' (NOT 'detection') — cocodet's output is + # coords+class per box ending on cocodet_end, a DIFFERENT format from the + # grounding/det coords-only decode (generate_detection_coordonly). Handled by + # the dedicated model.generate_cocodet + inferencer.gen_cocodet (which do NOT + # touch the grounding decode). + inference_decode_method: cocodet + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + # ── NEW image modalities (stage2): SAM segmentation / edge. Mirror seg/canny + # (direct-PNG image, VIT+VAE, MSE). REPLACE: per-modality start token, shared + # end_of_image. These start tokens are NEW (created via start_token field). + - name: samseg + id: 14 + kind: image + start_token_key: start_of_samseg + start_token: "<|samseg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: samedge + id: 15 + kind: image + start_token_key: start_of_samedge + start_token: "<|samedge_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true diff --git a/conf/modalities/instruction_16mod_stage2.yaml b/conf/modalities/instruction_16mod_stage2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0ebcd90446388f17d435defd4566162a7489452 --- /dev/null +++ b/conf/modalities/instruction_16mod_stage2.yaml @@ -0,0 +1,272 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + inference_add_instruction: false # stage2/3 trained with use_target_instruction=false (no generic instr) + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + inference_add_instruction: false # any2caption (stage2) trained without a target instruction + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + inference_add_instruction: false # generic [start X] not trained in stage2/3 (use_target_instruction=false) + + - name: depth + id: 3 + kind: image + start_token_key: start_of_depth # REPLACE: per-modality start (token already exists in base vocab) + start_token: "<|depth_start|>" + end_token_key: end_of_image # shared end (image end is a structural terminator) + represent_vit: true + represent_vae: true + inference_add_instruction: false + + - name: normal + id: 4 + kind: image + start_token_key: start_of_normal # REPLACE: per-modality start (token already exists in base vocab) + start_token: "<|normal_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + inference_add_instruction: false + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + # NOTE: det's learnable pos_embed (grounding_pos_embed, size 4) is REMOVED here. + # The forward pos_embed loop is keyed by token-id range, and cocodet reuses + # det's x1/y1/x2/y2 tokens; with the pos_embed on, a multi-box cocodet sample + # (>4 coord tokens) would index the size-4 embedding out of bounds. det is not + # trained in modus (only present for coord-token-id alignment), so dropping its + # pos_embed is safe. reweight also removed (no reweight for det/cocodet). + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_seg # REPLACE: per-modality start (NEW token) + start_token: "<|seg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_canny # REPLACE: per-modality start (NEW token) + start_token: "<|canny_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + inference_add_instruction: false + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + inference_add_instruction: false # start_of_dino token triggers it; generic instr not in stage2/3 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + inference_add_instruction: false + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + inference_add_instruction: false + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + inference_add_instruction: false + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + inference_add_instruction: false + + # ── NEW: cocodet (Pix2seq detection). stage1 = rgb→cocodet only. + # Coords reuse det's x1/y1/x2/y2 tokens (dedup, 0 new); class = COCO-id-aligned + # tokens (~91 new). dispersed_code_tokens → CE is gathered over the + # non-contiguous {coords ∪ class ∪ end} set, not a contiguous range. + # No reweight, no learnable pos_embed (RoPE + token-id carry order/role). + - name: cocodet + id: 13 + kind: codebook + start_token_key: start_of_cocodet + end_token_key: end_of_cocodet + start_token: "<|cocodet_start|>" + end_token: "<|cocodet_end|>" + represent_vae: true + dispersed_code_tokens: true + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|coco_cls_{i:02d}|>" + start: 0 + end: 90 + # Inference pipeline config. + # decode_method is 'cocodet' (NOT 'detection') — cocodet's output is + # coords+class per box ending on cocodet_end, a DIFFERENT format from the + # grounding/det coords-only decode (generate_detection_coordonly). Handled by + # the dedicated model.generate_cocodet + inferencer.gen_cocodet (which do NOT + # touch the grounding decode). + inference_decode_method: cocodet + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + # ── NEW image modalities (stage2): SAM segmentation / edge. Mirror seg/canny + # (direct-PNG image, VIT+VAE, MSE). REPLACE: per-modality start token, shared + # end_of_image. These start tokens are NEW (created via start_token field). + - name: samseg + id: 14 + kind: image + start_token_key: start_of_samseg + start_token: "<|samseg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + inference_add_instruction: false + + - name: samedge + id: 15 + kind: image + start_token_key: start_of_samedge + start_token: "<|samedge_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + inference_add_instruction: false diff --git a/conf/modalities/instruction_2d_only_stage1.yaml b/conf/modalities/instruction_2d_only_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6a1ec1fce8f352ada534e5962746c3946cf5e202 --- /dev/null +++ b/conf/modalities/instruction_2d_only_stage1.yaml @@ -0,0 +1,65 @@ +# 2D-only stage1 modality registry. +# +# Keeps text/caption and image-like modalities only. Drops DET/DINO/CLIP/ +# ImageBind codebook modalities so this run can isolate whether 1D/codebook CE +# is the main source of Hunyuan T2I prior drift. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, depth, normal, seg, canny] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, depth, normal, seg, canny] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, rgb, normal, seg, canny] + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, rgb, depth, seg, canny] + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, rgb, depth, normal, canny] + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, rgb, depth, normal, seg] diff --git a/conf/modalities/instruction_9modality_stage1.yaml b/conf/modalities/instruction_9modality_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b6ffacd5a3b1f0959d24b3d038ac03ec1973f759 --- /dev/null +++ b/conf/modalities/instruction_9modality_stage1.yaml @@ -0,0 +1,114 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dino] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dino] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + conditions: [rgb] + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 diff --git a/conf/modalities/instruction_9modality_stage3.yaml b/conf/modalities/instruction_9modality_stage3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..576f01539c43c91cd7f2dc4b405922d5987057db --- /dev/null +++ b/conf/modalities/instruction_9modality_stage3.yaml @@ -0,0 +1,115 @@ +# Stage 3 modality config — 9 modalities (text + 8 task), unconstrained conditions. +# +# Mirrors instruction_9modality_stage1.yaml but drops every `conditions: [...]` +# restriction so any modality can be conditioned on any other (matches the old +# stage3 training run that was originally configured via CLI flags only). +# det reweight + det/dino learnable pos-embeds are preserved. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 diff --git a/conf/modalities/instruction_hunyuan_16mod_stage1.yaml b/conf/modalities/instruction_hunyuan_16mod_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5b929b00aafa949031f3dd97f1cb612e9ef5ecf --- /dev/null +++ b/conf/modalities/instruction_hunyuan_16mod_stage1.yaml @@ -0,0 +1,257 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + # det's learnable pos_embed (size 4) + reweight are REMOVED so cocodet — which + # reuses det's x1/y1/x2/y2 tokens (coco_cls is cocodet's own) — can carry >4 + # coord tokens per sample without indexing the size-4 pos_embed out of bounds. + # det is not a target here (only present for coord-token-id alignment); + # grounding reuses these tokens and is now RoPE-only for its bbox coords. + conditions: [rgb] + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + # ── cocodet (Pix2seq detection). Coords reuse det's x1/y1/x2/y2 tokens (dedup, + # 0 new); class = COCO-id-aligned tokens (~91 new). dispersed_code_tokens → CE + # is gathered over the non-contiguous {coords ∪ class ∪ end} set, not a + # contiguous range. No reweight, no learnable pos_embed (RoPE + token-id). + - name: cocodet + id: 13 + kind: codebook + start_token_key: start_of_cocodet + end_token_key: end_of_cocodet + start_token: "<|cocodet_start|>" + end_token: "<|cocodet_end|>" + represent_vae: true + conditions: [rgb] # rgb→cocodet, consistent with det/dino/clip in this config + dispersed_code_tokens: true + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|coco_cls_{i:02d}|>" + start: 0 + end: 90 + # Inference: dedicated model.generate_cocodet + inferencer.gen_cocodet + # (coords+class per box ending on cocodet_end; NOT the grounding/det decode). + inference_decode_method: cocodet + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + # ── SAM segmentation / edge. Mirror seg/canny (direct-PNG image, ViT+VAE, MSE). + # Per-modality start token, shared end_of_image. Start tokens are NEW. + - name: samseg + id: 14 + kind: image + start_token_key: start_of_samseg + start_token: "<|samseg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: samedge + id: 15 + kind: image + start_token_key: start_of_samedge + start_token: "<|samedge_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true diff --git a/conf/modalities/instruction_hunyuan_16mod_stage2.yaml b/conf/modalities/instruction_hunyuan_16mod_stage2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b9768ef74b56ddf5b6117b530542ef9bd17eed7e --- /dev/null +++ b/conf/modalities/instruction_hunyuan_16mod_stage2.yaml @@ -0,0 +1,250 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dino, dinolocal, clip, imagebind, imagebindlocal] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + # det's learnable pos_embed (size 4) + reweight are REMOVED so cocodet — which + # reuses det's x1/y1/x2/y2 tokens (coco_cls is cocodet's own) — can carry >4 + # coord tokens per sample without indexing the size-4 pos_embed out of bounds. + # det is not a target here (only present for coord-token-id alignment); + # grounding reuses these tokens and is now RoPE-only for its bbox coords. + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + # ── cocodet (Pix2seq detection). Coords reuse det's x1/y1/x2/y2 tokens (dedup, + # 0 new); class = COCO-id-aligned tokens (~91 new). dispersed_code_tokens → CE + # is gathered over the non-contiguous {coords ∪ class ∪ end} set, not a + # contiguous range. No reweight, no learnable pos_embed (RoPE + token-id). + - name: cocodet + id: 13 + kind: codebook + start_token_key: start_of_cocodet + end_token_key: end_of_cocodet + start_token: "<|cocodet_start|>" + end_token: "<|cocodet_end|>" + represent_vae: true + dispersed_code_tokens: true + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|coco_cls_{i:02d}|>" + start: 0 + end: 90 + # Inference: dedicated model.generate_cocodet + inferencer.gen_cocodet + # (coords+class per box ending on cocodet_end; NOT the grounding/det decode). + inference_decode_method: cocodet + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false + + # ── SAM segmentation / edge. Mirror seg/canny (direct-PNG image, ViT+VAE, MSE). + # Per-modality start token, shared end_of_image. Start tokens are NEW. + - name: samseg + id: 14 + kind: image + start_token_key: start_of_samseg + start_token: "<|samseg_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: samedge + id: 15 + kind: image + start_token_key: start_of_samedge + start_token: "<|samedge_start|>" + end_token_key: end_of_image + represent_vit: true + represent_vae: true diff --git a/conf/modalities/instruction_stage2.yaml b/conf/modalities/instruction_stage2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f78b8059c4c8a16a44c8de5ceb1bc89a7ef4aa6c --- /dev/null +++ b/conf/modalities/instruction_stage2.yaml @@ -0,0 +1,204 @@ +# Stage 2 modality config — unconstrained conditions. +# +# Same modality definitions as instruction.yaml, but with ALL condition +# restrictions removed so every modality can be conditioned on every other. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + # conditions: unconstrained (any modality can condition caption) + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + # conditions: unconstrained + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + # conditions: unconstrained + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + # conditions: unconstrained + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + diff --git a/conf/modalities/instruction_t2i_only_stage1.yaml b/conf/modalities/instruction_t2i_only_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e274080a2f07b0a7cf53b4ca10f6a8fc0ad60462 --- /dev/null +++ b/conf/modalities/instruction_t2i_only_stage1.yaml @@ -0,0 +1,27 @@ +# v65 = T2I-only stage1 modality registry. +# Keeps text + caption + rgb only. Drops every other modality. +# Pair with `hunyuan_image_3_v65_t2i_only.yaml`. + +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption] diff --git a/conf/modalities/legacy.yaml b/conf/modalities/legacy.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e20e6981855d35de026697690b8ed949b196486b --- /dev/null +++ b/conf/modalities/legacy.yaml @@ -0,0 +1,38 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: start_of_caption + end_token_key: end_of_caption + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: depth + id: 3 + kind: image + start_token_key: start_of_depth + end_token_key: end_of_depth + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_normal + end_token_key: end_of_normal + represent_vit: true + represent_vae: true + + diff --git a/conf/modalities/rebuttal_rgb2target.yaml b/conf/modalities/rebuttal_rgb2target.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df43d02399f5a3c9d8a86f2d666692bdbbde7d4a --- /dev/null +++ b/conf/modalities/rebuttal_rgb2target.yaml @@ -0,0 +1,116 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dino] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dino] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [rgb] + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [rgb] + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + conditions: [rgb] + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 diff --git a/conf/modalities/stage1_oversample_bbox2rgb_dinolocal2rgb.yaml b/conf/modalities/stage1_oversample_bbox2rgb_dinolocal2rgb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dc59cac6b5810a2cbb25d50b0edac0a4b3c0b38e --- /dev/null +++ b/conf/modalities/stage1_oversample_bbox2rgb_dinolocal2rgb.yaml @@ -0,0 +1,203 @@ +modalities: + - name: text + id: 0 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + + - name: caption + id: 1 + kind: text + start_token_key: bos_token_id + end_token_key: eos_token_id + represent_vae: true + conditions: [rgb, grounding, dinolocal] + condition_probs: [0.1, 0.45, 0.45] + + - name: rgb + id: 2 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + conditions: [caption, grounding, dinolocal] + condition_probs: [0.1, 0.45, 0.45] + + - name: depth + id: 3 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: normal + id: 4 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: det + id: 5 + kind: codebook + start_token_key: start_of_det + end_token_key: end_of_det + represent_vae: true + pos_embed_size: 4 + apply_pos_embed_in_forward: true + pos_embed_name: grounding # checkpoint compat: attr is "grounding_pos_embed" + conditions: [rgb] + loss: + reweight: true + reweight_min_w: 0.005 + start_token: "<|det_start|>" + end_token: "<|det_end|>" + extra_tokens: ["<|box_start|>", "<|box_end|>"] + code_token_groups: + - token_format: "<|x1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y1_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|x2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|y2_{i:03d}|>" + start: 0 + end: 999 + - token_format: "<|score_{i:02d}|>" + start: 0 + end: 99 + # Inference pipeline config + inference_decode_method: detection + inference_max_tokens: 1000 + inference_cfg_uncond: text + inference_add_instruction: false # det uses start_of_det token, not text instruction + + - name: seg + id: 6 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: canny + id: 7 + kind: image + start_token_key: start_of_image + end_token_key: end_of_image + represent_vit: true + represent_vae: true + + - name: dino + id: 8 + kind: codebook + start_token_key: start_of_dino + end_token_key: end_of_dino + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|dino_start|>" + end_token: "<|dino_end|>" + code_vocab_size: 8192 + code_token_format: "<|dino_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: dino + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: dinolocal + id: 9 + kind: codebook + start_token_key: start_of_dinolocal + end_token_key: end_of_dinolocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|dinolocal_start|>" + end_token: "<|dinolocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|dinolocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_DINOv2-B14_8k_224-448" + # Inference pipeline config + inference_decode_method: dinolocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: clip + id: 10 + kind: codebook + start_token_key: start_of_clip + end_token_key: end_of_clip + pos_embed_size: 784 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [28, 28] # 784 tokens, 28×28 grid → 2D RoPE + start_token: "<|clip_start|>" + end_token: "<|clip_end|>" + code_vocab_size: 8192 + code_token_format: "<|clip_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_CLIP-B16_8k_224-448" + # Inference pipeline config + inference_decode_method: clip + inference_max_tokens: 785 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebind + id: 11 + kind: codebook + start_token_key: start_of_imagebind + end_token_key: end_of_imagebind + pos_embed_size: 16 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + start_token: "<|imagebind_start|>" + end_token: "<|imagebind_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebind_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14-global_8k_16_224" + # Inference pipeline config + inference_decode_method: imagebind + inference_max_tokens: 17 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 + + - name: imagebindlocal + id: 12 + kind: codebook + start_token_key: start_of_imagebindlocal + end_token_key: end_of_imagebindlocal + pos_embed_size: 1024 + apply_pos_embed_in_forward: true + represent_vae: true + conditions: [rgb] + codebook_spatial_shape: [32, 32] # 1024 tokens, 32×32 grid → 2D RoPE + start_token: "<|imagebindlocal_start|>" + end_token: "<|imagebindlocal_end|>" + code_vocab_size: 8192 + code_token_format: "<|imagebindlocal_{i:04d}|>" + external_tokenizer_kind: vqvae + external_tokenizer_repo: "EPFL-VILAB/4M_tokenizers_ImageBind-H14_8k_224-448" + # Inference pipeline config + inference_decode_method: imagebindlocal + inference_max_tokens: 1025 + inference_cfg_uncond: img + inference_cfg_img_scale: 1.0 diff --git a/core/__init__.py b/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..02f6c24a5a0a3878899a1d777c40bbfcaec4f790 --- /dev/null +++ b/core/__init__.py @@ -0,0 +1,11 @@ +""" +Core utilities for MODUS. + +This package is intentionally small and dependency-light so it can be used by: +- training scripts +- inference/demo scripts +- dataset packing +- model code +""" + + diff --git a/core/modality.py b/core/modality.py new file mode 100644 index 0000000000000000000000000000000000000000..449ca97ed0d43115e101a139336acc84de92a3e1 --- /dev/null +++ b/core/modality.py @@ -0,0 +1,394 @@ +from __future__ import annotations + +from dataclasses import dataclass, field as dc_field, replace as dc_replace +from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple + + +TokenRange = Tuple[int, int] # (base_id, length) + + +@dataclass(frozen=True) +class LossConfig: + """Per-modality loss configuration (lives in the modality YAML).""" + reweight: bool = False + reweight_min_w: float = 0.005 + reweight_det_vocab_dim: int = 4000 # only used when reweight=True for coordinate codebooks + + +@dataclass(frozen=True) +class ModalitySpec: + """ + Config-driven definition of a single modality. + + Every tunable per-modality knob lives here so that the training script, + model forward, and dataset packing can all be driven from one YAML. + """ + + name: str + id: int + start_token_key: str + end_token_key: str + kind: str = "text" # text | image | codebook + + # Token range ``(base, length)`` for CE-loss vocab slicing. + # Codebook modalities get their own range from the tokenizer; text + # modalities are auto-assigned ``(0, text_vocab_end)`` in + # ``ModalityRegistry.from_config``. Image modalities (ViT/VAE) + # never produce CE tokens, so they keep ``None``. + code_token_range: Optional[TokenRange] = None + + # Explicit, possibly **non-contiguous** set of CE-loss token ids. Used for + # modalities whose alphabet is dispersed across the vocab and therefore + # cannot be expressed as a single ``(base, length)`` range (e.g. cocodet, + # which reuses det's low-vocab coordinate tokens + new high-vocab class/end + # tokens). When set, the model does gather-CE over exactly these ids instead + # of the contiguous-slice path. ``None`` for every existing modality. + code_token_ids: Optional[Tuple[int, ...]] = None + + # Learnable positional embedding for code tokens. + pos_embed_size: Optional[int] = None + apply_pos_embed_in_forward: bool = False + + # Image representation flags (used by dataset packing and inferencer). + represent_vit: bool = True + represent_vae: bool = False + + # Which other modalities may condition this one during training. + # ``None`` means "use all available"; an explicit list restricts. + conditions: Optional[Tuple[str, ...]] = None + # Optional sampling probabilities aligned with ``conditions`` order. + # If omitted, the dataset uses a uniform distribution. + condition_probs: Optional[Tuple[float, ...]] = None + + # Per-modality loss parameters (CE smoothing / reweighting). + loss: LossConfig = LossConfig() + + # Override the model attribute name for the pos-embed (for checkpoint compat). + # Defaults to ``name`` (e.g. modality "dino" → attr ``dino_pos_embed``). + # Set to e.g. "grounding" for det → ``grounding_pos_embed``. + pos_embed_name: Optional[str] = None + + # 2D spatial shape for codebook modalities (h, w). + # None → 1D sequential (uses text region of build_2d_rope, gets sequential positions). + # Set e.g. (32, 32) for dinolocal, (28, 28) for clip → gets 2D RoPE same as VAE latents. + codebook_spatial_shape: Optional[Tuple[int, int]] = None + + # External tokenizer for inference decoding/encoding (e.g. DINO VQVAE). + external_tokenizer_repo: Optional[str] = None + external_tokenizer_kind: str = "vqvae" + + # ── Inference-time configuration ───────────────────────────────────── + # These fields drive the unified inference pipeline so that the + # inferencer does not need per-modality if/elif chains. + + # Decode method: "auto" resolves from ``kind`` (image→image, text→text). + # Explicit values: "image", "text", "detection", "dino". + inference_decode_method: str = "auto" + + # Maximum AR tokens for text / codebook decoding (None → inferencer default). + inference_max_tokens: Optional[int] = None + + # Which CFG unconditional context to use when this modality is a *target*: + # "text" → cfg_text_context (drop text, keep image) e.g. detection + # "img" → cfg_img_context (drop image, keep text) e.g. dino + # "both" → dual CFG with both contexts e.g. image generation + # "none" → no CFG e.g. plain text + inference_cfg_uncond: str = "auto" + + # Whether to prepend "[start {name} x10]" instruction before generation. + inference_add_instruction: bool = True + + # Default CFG image scale for this modality when it is the *target*. + # ``None`` → use the global default from the base config. + inference_cfg_img_scale: Optional[float] = None + + +class ModalityRegistry: + """Runtime registry consumed by dataset packing, model forward, and inferencer.""" + + def __init__(self, specs: Iterable[ModalitySpec]): + specs = list(specs) + if len(specs) == 0: + raise ValueError("ModalityRegistry requires at least one ModalitySpec.") + + self._by_name: Dict[str, ModalitySpec] = {} + self._by_id: Dict[int, ModalitySpec] = {} + for s in specs: + if s.name in self._by_name: + raise ValueError(f"Duplicate modality name: {s.name}") + if s.id in self._by_id: + raise ValueError(f"Duplicate modality id: {s.id}") + self._by_name[s.name] = s + self._by_id[s.id] = s + + # ── lookups ────────────────────────────────────────────────────────── + + @property + def specs(self) -> List[ModalitySpec]: + return list(self._by_name.values()) + + def get(self, name: str) -> ModalitySpec: + return self._by_name[name] + + def get_by_id(self, modality_id: int) -> ModalitySpec: + return self._by_id[modality_id] + + def name_to_id(self) -> Dict[str, int]: + return {k: v.id for k, v in self._by_name.items()} + + def id_to_name(self) -> Dict[int, str]: + return {k: v.name for k, v in self._by_id.items()} + + def modality_name(self, modality_id: int) -> str: + spec = self._by_id.get(modality_id) + return spec.name if spec is not None else f"unknown_{modality_id}" + + def start_token_key(self, name: str) -> str: + return self._by_name[name].start_token_key + + def end_token_key(self, name: str) -> str: + return self._by_name[name].end_token_key + + def start_token_id(self, new_token_ids: Mapping[str, Any], name: str) -> int: + return int(new_token_ids[self.start_token_key(name)]) + + def end_token_id(self, new_token_ids: Mapping[str, Any], name: str) -> int: + return int(new_token_ids[self.end_token_key(name)]) + + def code_token_range(self, name: str) -> Optional[TokenRange]: + return self._by_name[name].code_token_range + + def has_codebook_modalities(self) -> bool: + """True if any registered modality is ``kind == 'codebook'``.""" + return any(s.kind == "codebook" for s in self._by_name.values()) + + def conditions_for(self, target_modality: str) -> Optional[List[str]]: + """ + Return the allowed conditioning modalities for *target_modality*. + + ``None`` means "no restriction — use all available". + """ + spec = self._by_name.get(target_modality) + if spec is None or spec.conditions is None: + return None + return list(spec.conditions) + + def condition_probs_for(self, target_modality: str) -> Optional[List[float]]: + """ + Return optional condition sampling probabilities for *target_modality*. + + The returned probabilities align with ``conditions_for(target_modality)``. + ``None`` means "use uniform condition sampling". + """ + spec = self._by_name.get(target_modality) + if spec is None or spec.condition_probs is None: + return None + return list(spec.condition_probs) + + def resolve_decode_method(self, name: str) -> str: + """Return the concrete decode method for *name* (resolves ``"auto"``).""" + spec = self._by_name[name] + if spec.inference_decode_method != "auto": + return spec.inference_decode_method + if spec.kind == "image": + return "image" + if spec.kind == "text": + return "text" + # codebook — guess from name + if "det" in spec.name: + return "detection" + if spec.name == "dinolocal": + return "dinolocal" + if "dino" in spec.name: + return "dino" + return "text" + + def resolve_cfg_uncond(self, name: str) -> str: + """Return the concrete CFG-uncond context type (resolves ``"auto"``).""" + spec = self._by_name[name] + if spec.inference_cfg_uncond != "auto": + return spec.inference_cfg_uncond + dm = self.resolve_decode_method(name) + if dm == "image": + return "both" + if dm == "detection": + return "text" + if dm in ("dino", "dinolocal"): + return "img" + return "none" + + def resolve_cfg_img_scale(self, name: str, default: float = 2.0) -> float: + """Return the per-modality CFG image scale, or *default* if not specified.""" + spec = self._by_name[name] + if spec.inference_cfg_img_scale is not None: + return spec.inference_cfg_img_scale + return default + + def needs_external_tokenizer(self, name: str) -> bool: + """Return True if the modality has an external tokenizer (e.g. DINO VQVAE).""" + spec = self._by_name.get(name) + return spec is not None and spec.external_tokenizer_repo is not None + + def modalities_with_forward_pos_embed(self) -> List[ModalitySpec]: + return [ + s + for s in self._by_name.values() + if s.pos_embed_size is not None and s.apply_pos_embed_in_forward + ] + + # ── construction from YAML dict ───────────────────────────────────── + + @staticmethod + def from_config( + cfg: Any, + *, + token_ranges: Optional[Mapping[str, TokenRange]] = None, + code_token_ids: Optional[Mapping[str, List[int]]] = None, + ) -> "ModalityRegistry": + """ + Build a registry from the parsed YAML config dict. + + Expected structure:: + + cfg["modalities"]: list[{name, id, start_token_key, end_token_key, kind, ...}] + + ``token_ranges`` maps modality name → ``(base, length)`` and is typically + computed after tokenizer token additions. + """ + if cfg is None: + raise ValueError("cfg is required") + + # Accept either ``{"modalities": [...]}`` or a bare list. + modalities = cfg.get("modalities") if isinstance(cfg, dict) else cfg + if modalities is None: + raise ValueError("No 'modalities' key found in cfg") + + specs: List[ModalitySpec] = [] + token_ranges = dict(token_ranges or {}) + code_token_ids = dict(code_token_ids or {}) + + for m in modalities: + name = str(m["name"]) + + # Resolve code_token_range: prefer runtime-computed, fall back to cfg. + _range = token_ranges.get(name) + if _range is None and m.get("code_token_range") is not None: + _range = tuple(m["code_token_range"]) + + # Resolve explicit (possibly dispersed) code_token_ids, if any. + _ids = code_token_ids.get(name) + if _ids is None and m.get("code_token_ids") is not None: + _ids = m["code_token_ids"] + _ids = tuple(int(i) for i in _ids) if _ids is not None else None + + # pos_embed_size may be None. + _pos = m.get("pos_embed_size") + pos_embed_size = int(_pos) if _pos is not None else None + + # Parse conditions (list of strings or None). + _conds = m.get("conditions") + conditions = tuple(_conds) if _conds is not None else None + _cond_probs = m.get("condition_probs") + condition_probs = tuple(float(x) for x in _cond_probs) if _cond_probs is not None else None + if condition_probs is not None: + if conditions is None: + raise ValueError( + f"Modality '{name}' sets 'condition_probs' without 'conditions'." + ) + if len(condition_probs) != len(conditions): + raise ValueError( + f"Modality '{name}' has {len(conditions)} conditions but " + f"{len(condition_probs)} condition_probs." + ) + if any(p < 0.0 for p in condition_probs): + raise ValueError(f"Modality '{name}' has negative values in condition_probs.") + probs_sum = float(sum(condition_probs)) + if abs(probs_sum - 1.0) > 1e-6: + raise ValueError( + f"Modality '{name}' condition_probs must sum to 1.0, got {probs_sum}." + ) + + # Parse per-modality loss config. + _loss_dict = m.get("loss") or {} + loss_cfg = LossConfig( + reweight=bool(_loss_dict.get("reweight", False)), + reweight_min_w=float(_loss_dict.get("reweight_min_w", 0.005)), + reweight_det_vocab_dim=int(_loss_dict.get("reweight_det_vocab_dim", 4000)), + ) + + _pe_name = m.get("pos_embed_name") + pe_name = str(_pe_name) if _pe_name is not None else None + + # 2D spatial shape for codebook modalities. + _shape = m.get("codebook_spatial_shape") + codebook_spatial_shape = tuple(int(x) for x in _shape) if _shape is not None else None + + # Inference-time fields (optional in YAML; sensible defaults). + _infer = m.get("inference") or {} + _infer_decode = str(_infer.get("decode_method", m.get("inference_decode_method", "auto"))) + _infer_max = _infer.get("max_tokens", m.get("inference_max_tokens")) + _infer_cfg = str(_infer.get("cfg_uncond", m.get("inference_cfg_uncond", "auto"))) + _infer_add_instr = bool(_infer.get("add_instruction", m.get("inference_add_instruction", True))) + _infer_cfg_img = _infer.get("cfg_img_scale", m.get("inference_cfg_img_scale")) + _infer_cfg_img = float(_infer_cfg_img) if _infer_cfg_img is not None else None + + spec = ModalitySpec( + name=name, + id=int(m["id"]), + start_token_key=str(m["start_token_key"]), + end_token_key=str(m["end_token_key"]), + kind=str(m.get("kind", "text")), + code_token_range=_range, + code_token_ids=_ids, + pos_embed_size=pos_embed_size, + apply_pos_embed_in_forward=bool(m.get("apply_pos_embed_in_forward", False)), + represent_vit=bool(m.get("represent_vit", True)), + represent_vae=bool(m.get("represent_vae", False)), + conditions=conditions, + condition_probs=condition_probs, + loss=loss_cfg, + pos_embed_name=pe_name, + codebook_spatial_shape=codebook_spatial_shape, + external_tokenizer_repo=m.get("external_tokenizer_repo"), + external_tokenizer_kind=str(m.get("external_tokenizer_kind", "vqvae")), + inference_decode_method=_infer_decode, + inference_max_tokens=int(_infer_max) if _infer_max is not None else None, + inference_cfg_uncond=_infer_cfg, + inference_add_instruction=_infer_add_instr, + inference_cfg_img_scale=_infer_cfg_img, + ) + specs.append(spec) + + # ── Auto-assign text token range to text-kind modalities ───────── + # Find text_vocab_end = min base across all codebook ranges. + text_vocab_end: Optional[int] = None + for s in specs: + if s.code_token_range is not None: + cb_base, _ = s.code_token_range + text_vocab_end = int(cb_base) if text_vocab_end is None else min(text_vocab_end, int(cb_base)) + + if text_vocab_end is not None: + text_range: TokenRange = (0, text_vocab_end) + specs = [ + dc_replace(s, code_token_range=text_range) + if s.kind == "text" and s.code_token_range is None else s + for s in specs + ] + + return ModalityRegistry(specs) + + +def infer_contiguous_token_range(token_ids: List[int]) -> TokenRange: + """Given a list of token IDs, infer ``(base, length)`` and validate contiguity.""" + if len(token_ids) == 0: + raise ValueError("token_ids cannot be empty") + token_ids_sorted = sorted(int(x) for x in token_ids) + base = token_ids_sorted[0] + length = len(token_ids_sorted) + expected = list(range(base, base + length)) + if token_ids_sorted != expected: + raise ValueError( + "Token IDs are not contiguous; cannot represent as a range efficiently. " + f"base={base}, length={length}, first10={token_ids_sorted[:10]}" + ) + return base, length diff --git a/core/model_registry.py b/core/model_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..7bb435638db964a740978cc8ab2237ee550b7cfa --- /dev/null +++ b/core/model_registry.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Callable, Dict, Optional, Tuple + + +ModelBuilder = Callable[..., Any] + + +class ModelRegistry: + """ + Minimal model registry to decouple scripts from concrete model classes. + + Goal: + - training/inference scripts choose `model.name` in config + - swapping architectures doesn't require editing scripts, only adding a builder registration + """ + + def __init__(self): + self._builders: Dict[str, ModelBuilder] = {} + + def register(self, name: str, builder: ModelBuilder) -> None: + if name in self._builders: + raise ValueError(f"Model '{name}' is already registered.") + self._builders[name] = builder + + def build(self, name: str, **kwargs) -> Any: + if name not in self._builders: + known = ", ".join(sorted(self._builders.keys())) + raise KeyError(f"Unknown model '{name}'. Known: {known}") + return self._builders[name](**kwargs) + + +GLOBAL_MODEL_REGISTRY = ModelRegistry() + + +def register_model(name: str) -> Callable[[ModelBuilder], ModelBuilder]: + def _decorator(fn: ModelBuilder) -> ModelBuilder: + GLOBAL_MODEL_REGISTRY.register(name, fn) + return fn + return _decorator + + +def build_model(name: str, **kwargs) -> Any: + return GLOBAL_MODEL_REGISTRY.build(name, **kwargs) + + diff --git a/core/tokenizer_utils.py b/core/tokenizer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..263923e4775adc96f3bd10f2a6eef329bdb1bcfd --- /dev/null +++ b/core/tokenizer_utils.py @@ -0,0 +1,257 @@ +""" +Config-driven tokenizer setup. + +All modality tokens (delimiters, codebook entries) are specified in the modality +YAML and added here. No hard-coded ``use_det`` / ``use_dino`` flags. +""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple + +from core.modality import TokenRange, infer_contiguous_token_range +from data.data_utils import add_special_tokens + + +@dataclass +class TokenizerArtifacts: + """Return value of :func:`build_tokenizer_and_special_tokens`.""" + tokenizer: Any + new_token_ids: Dict[str, Any] + token_ranges: Dict[str, TokenRange] + # Explicit dispersed CE-token id sets, for modalities that set + # ``dispersed_code_tokens: true`` (e.g. cocodet). modality name -> id list. + code_token_ids: Dict[str, List[int]] + + +def load_base_tokenizer(*, model_args: Any, training_args: Any): + """Load the base tokenizer (before any token additions). + + Dispatches to HunyuanImage3TokenizerFast when model_name == 'hunyuan_image_3', + otherwise falls back to the standard Qwen2Tokenizer. + """ + if getattr(model_args, "model_name", "bagel") == "hunyuan_image_3": + from modeling.hunyuan_image_3.src.tokenization_hunyuan_image_3 import ( + HunyuanImage3TokenizerFast, + ) + return HunyuanImage3TokenizerFast.from_pretrained( + model_args.model_path, + model_version="HunyuanImage-3.0-Instruct", + ) + + from modeling.qwen2.tokenization_qwen2 import Qwen2Tokenizer + + pretrained_path = ( + model_args.model_path + if getattr(training_args, "finetune_from_hf", False) + else model_args.llm_path + ) + return Qwen2Tokenizer.from_pretrained(pretrained_path) + + +# ── internal helpers ───────────────────────────────────────────────────────── + +def _add_tokens(tokenizer, tokens: List[str]) -> None: + """Add tokens in order, deduplicating while preserving first occurrence.""" + seen: set = set() + ordered: List[str] = [] + for t in tokens: + if t not in seen: + seen.add(t) + ordered.append(t) + tokenizer.add_tokens(ordered) + + +def _ensure_token_key( + tokenizer, + new_token_ids: Dict[str, Any], + *, + token_key: str, + token_str: Optional[str], +) -> None: + """ + Make sure ``new_token_ids[token_key]`` exists, creating the token if needed. + """ + if token_key in new_token_ids: + return + if hasattr(tokenizer, token_key): + new_token_ids[token_key] = int(getattr(tokenizer, token_key)) + return + if token_str is None: + raise KeyError( + f"Token key '{token_key}' not in tokenizer or new_token_ids and no token_str provided." + ) + try: + tokenizer.add_special_tokens({"additional_special_tokens": [token_str]}) + except Exception: + tokenizer.add_tokens([token_str]) + new_token_ids[token_key] = int(tokenizer.convert_tokens_to_ids(token_str)) + + +def _code_tokens_from_cfg(m: Dict[str, Any]) -> List[str]: + """Build the list of code-token strings from a modality config dict.""" + + # Preferred: explicit groups (e.g. DET coordinate tokens). + groups = m.get("code_token_groups") + if groups is not None: + out: List[str] = [] + for g in groups: + if not isinstance(g, dict): + g = dict(g) + token_format = str(g["token_format"]) + start = int(g.get("start", 0)) + end = int(g["end"]) + for i in range(start, end + 1): + out.append(token_format.format( + i=i, + prefix=m.get("code_token_prefix", m.get("name")), + )) + return out + + # Fallback: contiguous vocab with format / prefix. + vocab = m.get("code_vocab_size") + if vocab is None: + raise ValueError( + f"Codebook modality '{m.get('name')}' needs 'code_vocab_size' or 'code_token_groups'." + ) + vocab = int(vocab) + token_format = str(m.get("code_token_format", "<|{prefix}_{i:04d}|>")) + prefix = str(m.get("code_token_prefix", m.get("name"))) + return [token_format.format(prefix=prefix, i=i) for i in range(vocab)] + + +def _add_modality_tokens( + tokenizer, + *, + m: Dict[str, Any], + new_token_ids: Dict[str, Any], + token_ranges: Dict[str, TokenRange], + code_token_ids_out: Dict[str, List[int]], + deferred_tokens: List[Any], +) -> None: + """ + Add all tokens for one modality entry (text/image/codebook) from its YAML dict. + + For **codebook** modalities the addition order is *code tokens first, then + delimiter / extra tokens*. This matches the ordering used when the + original checkpoint was trained so that token IDs remain identical. + """ + kind = str(m.get("kind", "text")) + start_key = str(m["start_token_key"]) + end_key = str(m["end_token_key"]) + + if kind != "codebook": + # Image/text modalities: resolve already-existing start/end tokens now + # (shared start_of_image, or start_of_depth/normal added by + # add_special_tokens). Any genuinely NEW token (e.g. a per-modality + # start_of_seg/canny/samseg/samedge under REPLACE) is DEFERRED and added + # at the very tail in build_tokenizer_and_special_tokens — so introducing + # an image modality with its own start token does NOT shift the ids of + # codebook tokens added later in the loop, preserving cross-stage ckpt + # alignment. + vocab = tokenizer.get_vocab() + for token_key, token_str in ((start_key, m.get("start_token")), (end_key, m.get("end_token"))): + if token_key in new_token_ids: + continue + if token_str is not None and str(token_str) in vocab: + new_token_ids[token_key] = int(vocab[str(token_str)]) + elif token_str is not None: + deferred_tokens.append((token_key, str(token_str))) + else: + _ensure_token_key(tokenizer, new_token_ids, token_key=token_key, token_str=None) + return + + # ── codebook modality ──────────────────────────────────────────────── + code_tokens = _code_tokens_from_cfg(m) + + start_tok = m.get("start_token") + end_tok = m.get("end_token") + delim_tokens: List[str] = [] + if start_tok is not None: + delim_tokens.append(str(start_tok)) + if end_tok is not None: + delim_tokens.append(str(end_tok)) + delim_tokens.extend(str(t) for t in (m.get("extra_tokens") or [])) + + # One batch: code tokens first, delimiters after → matches checkpoint ordering. + _add_tokens(tokenizer, code_tokens + delim_tokens) + + # Populate delimiter keys. + if start_tok is not None and start_key not in new_token_ids: + new_token_ids[start_key] = int(tokenizer.convert_tokens_to_ids(str(start_tok))) + elif start_key not in new_token_ids: + _ensure_token_key(tokenizer, new_token_ids, token_key=start_key, token_str=None) + if end_tok is not None and end_key not in new_token_ids: + new_token_ids[end_key] = int(tokenizer.convert_tokens_to_ids(str(end_tok))) + elif end_key not in new_token_ids: + _ensure_token_key(tokenizer, new_token_ids, token_key=end_key, token_str=None) + + name = str(m["name"]) + token_ids = [int(tokenizer.convert_tokens_to_ids(t)) for t in code_tokens] + if bool(m.get("dispersed_code_tokens", False)): + # Dispersed alphabet (e.g. cocodet: reused det coords at low vocab ids + + # new class tokens at the tail) — cannot be a single contiguous range. + # Record the explicit CE-token id set = all code tokens + the end + # delimiter (a CE target / stop token). The start delimiter is + # input-only, so it is excluded. + ids = list(token_ids) + end_id = new_token_ids.get(end_key) + if end_id is not None: + ids.append(int(end_id)) + code_token_ids_out[name] = ids + else: + # Record contiguous code-token range. + token_ranges[name] = infer_contiguous_token_range(token_ids) + + +# ── public API ─────────────────────────────────────────────────────────────── + +def build_tokenizer_and_special_tokens( + tokenizer, + *, + modalities_cfg: Dict[str, Any], +) -> TokenizerArtifacts: + """ + Fully config-driven tokenizer setup. + + 1. Adds universal special tokens (``<|im_start|>``, ``<|vision_start|>``, …) + via the legacy ``add_special_tokens`` helper (for checkpoint compat). + 2. Iterates over ``modalities_cfg["modalities"]`` and adds delimiter / code + tokens for each entry. + + Returns a :class:`TokenizerArtifacts` with the final tokenizer, a dict of + all special-token IDs, and a dict of code-token ranges for codebook modalities. + """ + token_ranges: Dict[str, TokenRange] = {} + code_token_ids: Dict[str, List[int]] = {} + deferred_tokens: List[Any] = [] + + # Step 1: universal special tokens (checkpoint-compatible ordering). + tokenizer, new_token_ids, _num = add_special_tokens(tokenizer) + + # Step 2: per-modality tokens from YAML. + modalities = modalities_cfg.get("modalities", modalities_cfg) + for m in modalities: + if not isinstance(m, dict): + m = dict(m) + _add_modality_tokens( + tokenizer, + m=m, + new_token_ids=new_token_ids, + token_ranges=token_ranges, + code_token_ids_out=code_token_ids, + deferred_tokens=deferred_tokens, + ) + + # Step 3: append deferred NEW image-modality start/end tokens at the tail + # (after all codebook tokens) so they never shift existing ids across stages. + for token_key, token_str in deferred_tokens: + if token_key not in new_token_ids: + _ensure_token_key(tokenizer, new_token_ids, token_key=token_key, token_str=token_str) + + return TokenizerArtifacts( + tokenizer=tokenizer, + new_token_ids=new_token_ids, + token_ranges=token_ranges, + code_token_ids=code_token_ids, + ) diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..943b7d3a7a676d9a500bf048d4307f4f803221f5 --- /dev/null +++ b/data/__init__.py @@ -0,0 +1,2 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 \ No newline at end of file diff --git a/data/any2any_preprocess/_build_preview_and_montage.py b/data/any2any_preprocess/_build_preview_and_montage.py new file mode 100644 index 0000000000000000000000000000000000000000..d91129b3b0d90d2595667bee9aaaa8e1ebf39931 --- /dev/null +++ b/data/any2any_preprocess/_build_preview_and_montage.py @@ -0,0 +1,52 @@ +import io, os +import pyarrow.parquet as pq +from PIL import Image, ImageDraw + +BASE_SRC = ('datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding' + '_canny_dino_global_clip448_imagebind_samseg_samedge_cocodet') +Q95 = 'datasets/blip3o/parquet_16mod_normalq95_sample/sa_000000.parquet' +PREVIEW_DIR = 'datasets/blip3o/modus_preview' +MONTAGE = 'eval_outputs/modus_hero_montage.png' +N_ROWS = 500 + +# ---- 1. print sample uids per source to confirm the alignment key ---- +print('==== uid samples (alignment key check) ====', flush=True) +for f in ['sa_000000.parquet', 'webdataset_shard_000.parquet', 'webdataset_JDB_2.parquet']: + p = f'{BASE_SRC}/{f}' + t = pq.ParquetFile(p).read_row_group(0, columns=['uid']) + uids = t.column('uid').to_pylist()[:4] + print(f' {f}: {uids}', flush=True) + +# ---- 2. build small preview parquet (subset of q95-compressed sa_000000) ---- +os.makedirs(PREVIEW_DIR, exist_ok=True) +pf = pq.ParquetFile(Q95) +table = pf.read_row_group(0).slice(0, N_ROWS) +dst = f'{PREVIEW_DIR}/modus_preview_sa_500.parquet' +pq.write_table(table, dst, compression='snappy') +print(f'\npreview: {dst} rows={table.num_rows} size={os.path.getsize(dst)/1e6:.1f} MB', flush=True) + +# ---- 3. hero montage: rows=samples, cols=modalities ---- +MODS = ['rgb', 'depth', 'normal', 'canny', 'sam_seg', 'sam_edge'] +N_SHOW = 5 +cell, pad, hdr = 300, 6, 26 +cols = {m: table.column(m).to_pylist()[:N_SHOW] for m in MODS} + +def dec(b): + if b is None: return None + if isinstance(b, memoryview): b = b.tobytes() + return Image.open(io.BytesIO(b)).convert('RGB') + +W = len(MODS)*(cell+pad)+pad +H = hdr + N_SHOW*(cell+pad)+pad +grid = Image.new('RGB', (W, H), 'white') +d = ImageDraw.Draw(grid) +for c, m in enumerate(MODS): + d.text((pad+c*(cell+pad)+4, 6), m, fill='black') +for r in range(N_SHOW): + for c, m in enumerate(MODS): + im = dec(cols[m][r]) + if im is None: continue + grid.paste(im.resize((cell, cell)), (pad+c*(cell+pad), hdr+pad+r*(cell+pad))) +os.makedirs(os.path.dirname(MONTAGE), exist_ok=True) +grid.save(MONTAGE) +print(f'montage: {MONTAGE} ({W}x{H})', flush=True) diff --git a/data/any2any_preprocess/_cast_preview_images.py b/data/any2any_preprocess/_cast_preview_images.py new file mode 100644 index 0000000000000000000000000000000000000000..95317d89b1cf5dca3cdf5f7e8aca99e62941d8a2 --- /dev/null +++ b/data/any2any_preprocess/_cast_preview_images.py @@ -0,0 +1,34 @@ +"""Cast the preview parquet's raw-bytes image columns to the HF `Image` feature +so the dataset viewer renders thumbnails.""" +import json +import os +from datasets import load_dataset, Image, Value + +SRC = 'datasets/blip3o/modus_preview/modus_preview_sa_500.parquet' +OUT_DIR = 'datasets/blip3o/modus_preview_upload/data' +OUT = f'{OUT_DIR}/modus_preview_sa_500.parquet' +IMG_COLS = ['rgb', 'depth', 'normal', 'canny', 'sam_seg', 'sam_edge'] + +# Display-name order requested by the user (uid=key first; det_seg + grounding +# raw blobs moved to the very end). +ORDER = [ + 'uid', 'rgb', 'depth', 'normal', 'sam_seg', 'canny', 'sam_edge', + 'coco_det', 'caption', 'dino_global', 'dino', 'clip448', + 'imagebind_global', 'imagebind', 'det_seg', 'grounding', +] + +ds = load_dataset('parquet', data_files=SRC, split='train') +print('loaded:', ds.num_rows, 'rows; features before:', list(ds.features.keys())) +for c in IMG_COLS: + ds = ds.cast_column(c, Image()) +# Flatten deep-nested blobs to JSON strings so the HF viewer worker doesn't OOM. +for c in ['det_seg', 'grounding']: + ds = ds.map(lambda ex, col=c: {col: json.dumps(ex[col], default=str, ensure_ascii=False)}) + ds = ds.cast_column(c, Value('string')) +assert set(ORDER) == set(ds.column_names), set(ORDER) ^ set(ds.column_names) +ds = ds.select_columns(ORDER) +print('column order:', ds.column_names) + +os.makedirs(OUT_DIR, exist_ok=True) +ds.to_parquet(OUT) +print(f'wrote {OUT} size={os.path.getsize(OUT)/1e6:.1f} MB', flush=True) diff --git a/data/any2any_preprocess/_sb_full_rebuild.sh b/data/any2any_preprocess/_sb_full_rebuild.sh new file mode 100644 index 0000000000000000000000000000000000000000..cc251e9373542ad5984a4a10e0d365efdaf78949 --- /dev/null +++ b/data/any2any_preprocess/_sb_full_rebuild.sh @@ -0,0 +1,18 @@ +#!/bin/bash +#SBATCH --job-name=modus_full_rebuild +#SBATCH --partition=debug +#SBATCH --time=01:30:00 +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=128 +#SBATCH --mem=700GB +#SBATCH --account=a143 +#SBATCH --output=logs/full_rebuild_%j.out +#SBATCH --error=logs/full_rebuild_%j.err +#SBATCH --environment=/iopsstor/scratch/cscs/mye/workspace/BAGEL/environment/bagel.toml + +M=/iopsstor/scratch/cscs/mye/workspace/MODUS_RELEASE/MODUS +srun --environment=/iopsstor/scratch/cscs/mye/workspace/BAGEL/environment/bagel.toml \ + bash -c "cd $M && export PYTHONPATH='$M/data/any2any_preprocess:\$PYTHONPATH' && \ + python3 data/any2any_preprocess/run_full_rebuild.py \ + --num-tasks 1 --task-id 0 --concurrency 64" diff --git a/data/any2any_preprocess/_sb_recompress_normal_sample.sh b/data/any2any_preprocess/_sb_recompress_normal_sample.sh new file mode 100644 index 0000000000000000000000000000000000000000..54653aad7de6e2b4228c768039fea6fcfa6d1c9b --- /dev/null +++ b/data/any2any_preprocess/_sb_recompress_normal_sample.sh @@ -0,0 +1,24 @@ +#!/bin/bash +#SBATCH --job-name=norm_q95_smp +#SBATCH --partition=debug +#SBATCH --time=01:30:00 +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=64GB +#SBATCH --account=a143 +#SBATCH --output=logs/norm_q95_sample_%j.out +#SBATCH --error=logs/norm_q95_sample_%j.err +#SBATCH --environment=/iopsstor/scratch/cscs/mye/workspace/BAGEL/environment/bagel.toml + +M=/iopsstor/scratch/cscs/mye/workspace/MODUS_RELEASE/MODUS +SRC="$M/datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_samseg_samedge_cocodet" +OUT="$M/datasets/blip3o/parquet_16mod_normalq95_sample" + +srun --environment=/iopsstor/scratch/cscs/mye/workspace/BAGEL/environment/bagel.toml \ + bash -c "cd $M && python3 data/any2any_preprocess/recompress_normal_jpeg.py \ + --out_dir '$OUT' \ + '$SRC/sa_000000.parquet' \ + '$SRC/sa_000001.parquet' \ + '$SRC/sa_000002.parquet' \ + '$SRC/sa_000003.parquet'" diff --git a/data/any2any_preprocess/_sb_upload.sh b/data/any2any_preprocess/_sb_upload.sh new file mode 100644 index 0000000000000000000000000000000000000000..eaa5398f771d63215865022167a19b6e6feadf3e --- /dev/null +++ b/data/any2any_preprocess/_sb_upload.sh @@ -0,0 +1,21 @@ +#!/bin/bash +#SBATCH --job-name=modus_upload +#SBATCH --partition=debug +#SBATCH --time=01:30:00 +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=32 +#SBATCH --mem=200GB +#SBATCH --account=a143 +#SBATCH --output=logs/upload_%j.out +#SBATCH --error=logs/upload_%j.err +#SBATCH --environment=/iopsstor/scratch/cscs/mye/workspace/BAGEL/environment/bagel.toml + +# Resumable upload of the whole modus_full folder (all configs) + README. +M=/iopsstor/scratch/cscs/mye/workspace/MODUS_RELEASE/MODUS +srun --environment=/iopsstor/scratch/cscs/mye/workspace/BAGEL/environment/bagel.toml \ + bash -c "cd $M && export HF_TOKEN_FILE=/users/mye/.hf_token && \ + python3 -c \"from huggingface_hub import HfApi; t=open('/users/mye/.hf_token').read().strip(); \ + HfApi(token=t).upload_file(path_or_fileobj='datasets/blip3o/modus_preview_upload/README.md', path_in_repo='README.md', repo_id='epfl-vilab-modus/MODUS-16Modality', repo_type='dataset', commit_message='full configs card'); print('README ok')\" ; \ + python3 data/any2any_preprocess/upload_full.py \ + epfl-vilab-modus/MODUS-16Modality datasets/blip3o/modus_full '*/*.parquet'" diff --git a/data/any2any_preprocess/_verify_normal_q95.py b/data/any2any_preprocess/_verify_normal_q95.py new file mode 100644 index 0000000000000000000000000000000000000000..19b80666f014de5eb4fb651baee6f54a58da503b --- /dev/null +++ b/data/any2any_preprocess/_verify_normal_q95.py @@ -0,0 +1,34 @@ +import io, os +import pyarrow.parquet as pq +from PIL import Image + +SRC = ('datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding' + '_canny_dino_global_clip448_imagebind_samseg_samedge_cocodet/sa_000000.parquet') +NEW = 'datasets/blip3o/parquet_16mod_normalq95_sample/sa_000000.parquet' +OUT = 'eval_outputs/normal_q95_check' +os.makedirs(OUT, exist_ok=True) + +def load(path, col, n): + t = pq.ParquetFile(path).read_row_group(0, columns=[col]) + vals = t.column(col).to_pylist()[:n] + out = [] + for b in vals: + if isinstance(b, memoryview): b = b.tobytes() + out.append(Image.open(io.BytesIO(b)).convert('RGB')) + return out + +N = 3 +rgb = load(SRC, 'rgb', N) +nsrc = load(SRC, 'normal', N) +nnew = load(NEW, 'normal', N) + +cell = 384 +grid = Image.new('RGB', (cell*3, cell*N), 'white') +for r in range(N): + for c, im in enumerate([rgb[r], nsrc[r], nnew[r]]): + grid.paste(im.resize((cell, cell)), (c*cell, r*cell)) +path = f'{OUT}/rgb_normalPNG_normalJPEGq95.png' +grid.save(path) +print(f'saved {path} (cols: rgb | normal-PNG(src) | normal-JPEGq95(new))', flush=True) +for r in range(N): + print(f' row{r}: normal src size={nsrc[r].size} new size={nnew[r].size}', flush=True) diff --git a/data/any2any_preprocess/build_full_release.py b/data/any2any_preprocess/build_full_release.py new file mode 100644 index 0000000000000000000000000000000000000000..24b23ff52b2ff7e1e934e49138669ebd877ada8a --- /dev/null +++ b/data/any2any_preprocess/build_full_release.py @@ -0,0 +1,108 @@ +"""Build the MODUS full-release parquet for one source config. + +Per config: + sa1b / journeydb : annotations-only -> DROP ['rgb','caption'] ; recompress normal->JPEG q95 + cc12m : self-contained -> keep all columns ; recompress normal->JPEG q95 + +Streams by row group (bounded memory). Resumable: skips outputs that already +exist and are complete; writes to a .tmp then atomically renames. + +Usage: + python build_full_release.py --config sa1b --out_dir --workers 8 file1.parquet [...] +""" +import argparse +import io +import os +from concurrent.futures import ProcessPoolExecutor, as_completed + +import pyarrow as pa +import pyarrow.parquet as pq +from PIL import Image + +DROP = {'sa1b': ['rgb', 'caption'], 'journeydb': ['rgb', 'caption'], 'cc12m': []} +JPEG_QUALITY = 95 + + +def _recompress_normal(b): + if b is None: + return None + if isinstance(b, memoryview): + b = b.tobytes() + with Image.open(io.BytesIO(b)) as im: + img = im.convert('RGB') + out = io.BytesIO() + img.save(out, format='JPEG', quality=JPEG_QUALITY) + img.close() + return out.getvalue() + + +def process_one(src, dst, drop_cols): + if os.path.exists(dst) and os.path.getsize(dst) > 0: + return src, 'skip', os.path.getsize(dst) + tmp = dst + '.tmp' + pf = pq.ParquetFile(src) + names = pf.schema_arrow.names + keep = [n for n in names if n not in drop_cols] + out_schema = pa.schema([pf.schema_arrow.field(n) for n in keep]) + has_normal = 'normal' in keep + norm_field = out_schema.field('normal') if has_normal else None + ni = keep.index('normal') if has_normal else -1 + + # Stream in small batches so peak memory is bounded regardless of row-group size. + writer = pq.ParquetWriter(tmp, out_schema, compression='snappy') + try: + for batch in pf.iter_batches(batch_size=128, columns=keep): + t = pa.Table.from_batches([batch], schema=out_schema) + if has_normal: + col = [_recompress_normal(b) for b in t.column('normal').to_pylist()] + t = t.set_column(ni, norm_field, pa.array(col, type=norm_field.type)) + writer.write_table(t) + del t + finally: + writer.close() + os.replace(tmp, dst) + return src, 'done', os.path.getsize(dst) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--config', required=True, choices=list(DROP)) + ap.add_argument('--out_dir', required=True) + ap.add_argument('--workers', type=int, default=8) + ap.add_argument('files', nargs='+') + args = ap.parse_args() + os.makedirs(args.out_dir, exist_ok=True) + drop_cols = DROP[args.config] + + tasks = [(f, os.path.join(args.out_dir, os.path.basename(f)), drop_cols) + for f in args.files] + done = skip = 0 + + if args.workers == 1: + # Sequential, in-process (used when spawned as a per-chunk subprocess so + # the OS reclaims all memory on exit — no leak accumulation). + for src, dst, dc in tasks: + _, status, _ = process_one(src, dst, dc) + done += status == 'done' + skip += status == 'skip' + print(f'{status} {os.path.basename(src)}', flush=True) + print(f'CHUNK {args.config}: {done} built, {skip} skipped', flush=True) + return + + tot_out = 0 + with ProcessPoolExecutor(max_workers=args.workers) as ex: + futs = [ex.submit(process_one, *t) for t in tasks] + for i, fu in enumerate(as_completed(futs), 1): + src, status, sz = fu.result() + tot_out += sz + done += status == 'done' + skip += status == 'skip' + if i % 10 == 0 or status == 'done': + print(f'[{i}/{len(tasks)}] {status} {os.path.basename(src)} ' + f'done={done} skip={skip} out={tot_out/1e9:.1f}GB', flush=True) + print(f'CONFIG {args.config}: {done} built, {skip} skipped, ' + f'total_out={tot_out/1e9:.1f}GB', flush=True) + + +if __name__ == '__main__': + main() diff --git a/data/any2any_preprocess/check_vqa.py b/data/any2any_preprocess/check_vqa.py new file mode 100644 index 0000000000000000000000000000000000000000..72e2af559c3dbbba588f1b289d9dd847caa2012d --- /dev/null +++ b/data/any2any_preprocess/check_vqa.py @@ -0,0 +1,188 @@ +import os +import json +import argparse +from typing import Dict, Any, Tuple, List + + +def is_valid_name(name: Any) -> bool: + return isinstance(name, str) and len(name.strip()) > 0 + + +def process_labels_jsonl(images_dir: str, labels_path: str, max_lines: int = -1) -> Tuple[Dict[str, Any], List[str]]: + stats: Dict[str, Any] = dict( + num_records=0, + num_invalid_json=0, + num_image_refs=0, + num_images_missing=0, + num_unique_images_missing=0, + num_video_refs=0, + num_videos_missing=0, + ) + missing_images_unique = set() + + with open(labels_path, "r") as f: + for line_idx, line in enumerate(f): + if max_lines > 0 and line_idx >= max_lines: + break + try: + record = json.loads(line) + except Exception: + stats["num_invalid_json"] += 1 + continue + + stats["num_records"] += 1 + + # Check images (string or list) + if "image" in record: + if isinstance(record["image"], list): + image_names = record["image"] + else: + image_names = [record["image"]] + + for name in image_names: + stats["num_image_refs"] += 1 + if not is_valid_name(name): + stats["num_images_missing"] += 1 + missing_images_unique.add(str(name)) + continue + image_path = os.path.join(images_dir, name) + if not os.path.isfile(image_path): + stats["num_images_missing"] += 1 + missing_images_unique.add(name) + + # Check video if present (optional) + if "video" in record: + stats["num_video_refs"] += 1 + video_name = record["video"] + if not is_valid_name(video_name): + stats["num_videos_missing"] += 1 + else: + video_path = os.path.join(images_dir, video_name) + if not os.path.isfile(video_path): + stats["num_videos_missing"] += 1 + + stats["num_unique_images_missing"] = len(missing_images_unique) + # Return up to some examples of missing files for debugging + sample_missing = list(missing_images_unique)[:50] + return stats, sample_missing + + +def scan_base_dir(base_dir: str, max_lines: int = -1) -> Dict[str, Any]: + summary: Dict[str, Any] = dict(per_dataset={}, totals={}) + totals = dict( + datasets_scanned=0, + num_records=0, + num_invalid_json=0, + num_image_refs=0, + num_images_missing=0, + num_unique_images_missing=0, # accumulated across datasets (sum, not set-union) + num_video_refs=0, + num_videos_missing=0, + ) + + if not os.path.isdir(base_dir): + raise FileNotFoundError(f"Base dir does not exist: {base_dir}") + + # Pre-collect datasets that have labels.jsonl for better progress logging + ds_entries = [] + for entry in sorted(os.listdir(base_dir)): + ds_dir = os.path.join(base_dir, entry) + if not os.path.isdir(ds_dir): + continue + labels_path = os.path.join(ds_dir, "label", "labels.jsonl") + if os.path.isfile(labels_path): + ds_entries.append(entry) + + total_datasets = len(ds_entries) + for idx, entry in enumerate(ds_entries, 1): + ds_dir = os.path.join(base_dir, entry) + images_dir = os.path.join(ds_dir, "images") + labels_path = os.path.join(ds_dir, "label", "labels.jsonl") + + print(f"[{idx}/{total_datasets}] Scanning {entry} ...", flush=True) + + totals["datasets_scanned"] += 1 + ds_stats, sample_missing = process_labels_jsonl(images_dir, labels_path, max_lines=max_lines) + + summary["per_dataset"][entry] = dict(stats=ds_stats, sample_missing_images=sample_missing) + + # Accumulate totals (note: unique missing is summed per dataset) + for k in [ + "num_records", + "num_invalid_json", + "num_image_refs", + "num_images_missing", + "num_unique_images_missing", + "num_video_refs", + "num_videos_missing", + ]: + totals[k] += ds_stats[k] + + # Immediate per-dataset summary line + pct = 100.0 * ds_stats["num_images_missing"] / max(1, ds_stats["num_image_refs"]) + print( + f" -> records={ds_stats['num_records']}, image_refs={ds_stats['num_image_refs']}, " + f"missing_images={ds_stats['num_images_missing']} ({pct:.3f}%), videos_missing={ds_stats['num_videos_missing']}", + flush=True, + ) + + summary["totals"] = totals + return summary + + +def main(): + parser = argparse.ArgumentParser(description="Check VQA JSONL image/video references against images directories.") + parser.add_argument( + "--base_dir", + type=str, + default="./datasets/llava_onevision_vqa", + help="Root directory containing per-dataset subfolders with images/ and label/labels.jsonl", + ) + parser.add_argument( + "--max_lines", + type=int, + default=-1, + help="If >0, limit number of lines per JSONL file for a faster sample-based check.", + ) + parser.add_argument( + "--out_json", + type=str, + default="", + help="Optional path to write a JSON summary report.", + ) + + args = parser.parse_args() + + summary = scan_base_dir(args.base_dir, max_lines=args.max_lines) + + totals = summary["totals"] + print("==== VQA Consistency Check ====") + print( + f"Datasets: {totals['datasets_scanned']} | Records: {totals['num_records']} | " + f"Image refs: {totals['num_image_refs']} | Missing images: {totals['num_images_missing']} | " + f"Video refs: {totals['num_video_refs']} | Missing videos: {totals['num_videos_missing']}" + ) + if totals["num_image_refs"] > 0: + miss_pct = 100.0 * totals["num_images_missing"] / max(1, totals["num_image_refs"]) + print(f"Image missing rate: {miss_pct:.4f}%") + + # Print a few per-dataset highlights + for ds_name, data in list(summary["per_dataset"].items())[:20]: + s = data["stats"] + pct = 100.0 * s["num_images_missing"] / max(1, s["num_image_refs"]) + print( + f"- {ds_name}: records={s['num_records']}, image_refs={s['num_image_refs']}, " + f"missing_images={s['num_images_missing']} ({pct:.3f}%)" + ) + + if args.out_json: + with open(args.out_json, "w") as f: + json.dump(summary, f) + print(f"Summary written to {args.out_json}") + + +if __name__ == "__main__": + main() + + +# python data/any2any_preprocess/check_vqa.py --base_dir ./datasets/llava_onevision_vqa \ No newline at end of file diff --git a/data/any2any_preprocess/generate_parquest_grounding_canny_dino_global.py b/data/any2any_preprocess/generate_parquest_grounding_canny_dino_global.py new file mode 100644 index 0000000000000000000000000000000000000000..74d24faae7a8aea561a87f88107c9abfb86ef864 --- /dev/null +++ b/data/any2any_preprocess/generate_parquest_grounding_canny_dino_global.py @@ -0,0 +1,205 @@ +import os +import io +import glob +import tarfile +import logging +from typing import Dict, List, Optional + +import numpy as np +import pandas as pd + + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + + +class GlobalDinoReplacer: + def __init__(self, base_dir: str, + input_parquet_dir: Optional[str] = None, + global_dino_dir: Optional[str] = None, + output_parquet_dir: Optional[str] = None) -> None: + self.base_dir = base_dir + + # Defaults aligned with generate_parquet_grounding_canny_dino.py + self.input_parquet_dir = input_parquet_dir or os.path.join( + base_dir, 'parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino' + ) + self.global_dino_dir = global_dino_dir or os.path.join(base_dir, 'dinov2_global') + self.output_parquet_dir = output_parquet_dir or os.path.join( + base_dir, 'parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global' + ) + + os.makedirs(self.output_parquet_dir, exist_ok=True) + + def _list_input_parquets(self) -> List[str]: + files = glob.glob(os.path.join(self.input_parquet_dir, '*.parquet')) + return sorted(files) + + def _load_global_dino_from_tar(self, tar_path: str) -> Dict[str, np.ndarray]: + """Load all uid -> flattened tokens from a dinov2_global tar.""" + results: Dict[str, np.ndarray] = {} + + if not os.path.exists(tar_path): + logger.warning(f"Global DINO tar not found: {tar_path}") + return results + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if not member.isfile(): + continue + name = member.name + if not name.endswith('_tokens_global.npy'): + continue + + file_obj = tar.extractfile(member) + if file_obj is None: + continue + + content = file_obj.read() + npy = np.load(io.BytesIO(content)) + + # Derive uid by stripping extension and the trailing `_tokens` + uid = os.path.splitext(name)[0] + if uid.endswith('_tokens_global'): + uid = uid[:-len('_tokens_global')] + + results[uid] = npy.flatten() + except Exception as e: + logger.error(f"Failed to read {tar_path}: {e}") + + return results + + def _replace_dino_column(self, df: pd.DataFrame, uid_to_tokens: Dict[str, np.ndarray], + strict: bool = False) -> pd.DataFrame: + if 'uid' not in df.columns: + raise ValueError("Input parquet missing required 'uid' column") + + num_rows = len(df) + replaced = 0 + missing = 0 + + # Prepare a new column without mutating during iteration to avoid pandas warnings + new_dino_column: List[object] = [] + for _, row in df.iterrows(): + uid = row['uid'] + tokens = uid_to_tokens.get(uid) + if tokens is None: + missing += 1 + if strict: + new_dino_column.append(None) + else: + # Keep original if present + new_dino_column.append(row.get('dino_global', None)) + else: + new_dino_column.append(tokens) + replaced += 1 + + logger.info(f"Replaced DINO tokens for {replaced}/{num_rows} rows; missing {missing}") + + if strict and missing > 0: + logger.error("Strict mode: some UIDs are missing global DINO tokens") + + df = df.copy() + df['dino_global'] = new_dino_column + return df + + def process_one_parquet(self, parquet_path: str, overwrite: bool = False, strict: bool = False) -> Optional[str]: + base_name = os.path.splitext(os.path.basename(parquet_path))[0] + out_path = os.path.join(self.output_parquet_dir, f"{base_name}.parquet") + + if os.path.exists(out_path) and not overwrite: + logger.info(f"Output exists, skipping: {out_path}") + return out_path + + # Load the input parquet + df = pd.read_parquet(parquet_path, engine='pyarrow') + + # Load corresponding global dino tar + global_dino_tar = os.path.join(self.global_dino_dir, f"{base_name}.tar") + logger.info(f"Loading global DINO tokens from: {global_dino_tar}") + uid_to_tokens = self._load_global_dino_from_tar(global_dino_tar) + + + if not uid_to_tokens: + logger.warning(f"No tokens loaded from {global_dino_tar}; leaving original 'dino' values") + + # Replace column + df = self._replace_dino_column(df, uid_to_tokens, strict=strict) + + # Persist + df.to_parquet( + out_path, + index=False, + engine='pyarrow', + compression='snappy', + row_group_size=1000, + ) + logger.info(f"Wrote updated parquet with global DINO to: {out_path}") + return out_path + + def process_all(self, overwrite: bool = False, strict: bool = False, + start_from: int = 0) -> List[str]: + inputs = self._list_input_parquets() + if start_from > 0: + inputs = inputs[start_from:] + + if not inputs: + logger.error("No input parquet files found") + return [] + + logger.info(f"Found {len(inputs)} parquet files to update") + outputs: List[str] = [] + for parquet_path in inputs: + try: + result = self.process_one_parquet(parquet_path, overwrite=overwrite, strict=strict) + if result: + outputs.append(result) + except Exception as e: + logger.error(f"Error processing {parquet_path}: {e}") + + logger.info(f"Successfully wrote {len(outputs)} updated parquet files") + return outputs + + +def main() -> None: + import argparse + + parser = argparse.ArgumentParser(description='Replace DINO tokens in existing parquet files with dinov2_global tokens') + parser.add_argument('--base_dir', type=str, default='./datasets/blip3o', + help='Base directory (default: ./datasets/blip3o)') + parser.add_argument('--input_parquet_dir', type=str, default=None, + help='Input parquet directory; defaults to parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino') + parser.add_argument('--global_dino_dir', type=str, default=None, + help='Directory containing dinov2_global tars; defaults to base_dir/dinov2_global') + parser.add_argument('--output_parquet_dir', type=str, default=None, + help='Output parquet directory; defaults to parquet_*_dino_global') + parser.add_argument('--overwrite', action='store_true', help='Overwrite existing output parquet if present') + parser.add_argument('--strict', action='store_true', + help='If set, missing UIDs in global DINO will set dino=None and log error') + parser.add_argument('--start_from', type=int, default=0, + help='Index to start processing from (for resuming)') + + args = parser.parse_args() + + replacer = GlobalDinoReplacer( + base_dir=args.base_dir, + input_parquet_dir=args.input_parquet_dir, + global_dino_dir=args.global_dino_dir, + output_parquet_dir=args.output_parquet_dir, + ) + + outputs = replacer.process_all(overwrite=args.overwrite, strict=args.strict, start_from=args.start_from) + if outputs: + logger.info("Outputs written:") + for p in outputs: + logger.info(f" - {p}") + else: + logger.error("No outputs were created") + + +if __name__ == '__main__': + main() + + diff --git a/data/any2any_preprocess/generate_parquet.py b/data/any2any_preprocess/generate_parquet.py new file mode 100644 index 0000000000000000000000000000000000000000..6f0ca68fd42e61f4f49ac2a85cfd1453bde9918f --- /dev/null +++ b/data/any2any_preprocess/generate_parquet.py @@ -0,0 +1,229 @@ +import os +import tarfile +import pandas as pd +import numpy as np +from PIL import Image +import io +import glob +import json +from tqdm import tqdm +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +class TarProcessor: + def __init__(self, base_dir): + self.base_dir = base_dir + self.rgb_caption_dir = os.path.join(base_dir, 'datasets') + self.depth_dir = os.path.join(base_dir, 'datasets_depth') + self.normal_dir = os.path.join(base_dir, 'datasets_normal') + self.det_seg_dir = os.path.join(base_dir, 'datasets_seg_swinb') + self.parquet_dir = os.path.join(base_dir, 'parquet_rgb_caption_depth_normal_det_seg') + + # Create parquet directory if it doesn't exist + os.makedirs(self.parquet_dir, exist_ok=True) + + def get_tar_files(self): + """Get all tar files from rgb_caption directory""" + tar_files = glob.glob(os.path.join(self.rgb_caption_dir, '*.tar')) + return sorted(tar_files) + + def extract_from_tar(self, tar_path, file_type): + """ + Extract images and captions from tar file + + Args: + tar_path: Path to tar file + file_type: 'rgb_caption', 'depth', 'normal', 'det_seg' + """ + data = {} + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if member.isfile(): + filename = member.name + uid = os.path.splitext(filename)[0] # Remove extension + + # Extract file content + f = tar.extractfile(member) + if f is None: + continue + + content = f.read() + + if file_type == 'rgb_caption': + # Handle RGB + caption files + if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): + data[uid] = {'image': content, 'caption': None} + elif filename.endswith('.txt'): + # Find corresponding image + base_name = os.path.splitext(filename)[0] + if base_name in data: + data[base_name]['caption'] = content.decode('utf-8').strip() + else: + data[base_name] = {'image': None, 'caption': content.decode('utf-8').strip()} + + elif file_type == 'depth': + # Handle depth maps (PNG format) + if filename.endswith('.png'): + data[uid] = content + + elif file_type == 'normal': + # Handle normal maps (RGB format) + if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): + data[uid] = content + elif file_type == 'det_seg': + # Handle detection maps (JSON format) + if filename.endswith('.json'): + # Decode bytes to string and parse JSON + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + data[uid] = json_data + except Exception as e: + logger.error(f"Error processing {tar_path}: {e}") + return {} + + return data + + def process_tar_file(self, rgb_caption_tar): + """Process a single tar file and create corresponding parquet""" + + # Extract base name (e.g., 'sa_000000' from 'sa_000000.tar') + base_name = os.path.splitext(os.path.basename(rgb_caption_tar))[0] + + # Construct paths for corresponding depth and normal tar files + depth_tar = os.path.join(self.depth_dir, f"{base_name}.tar") + normal_tar = os.path.join(self.normal_dir, f"{base_name}.tar") + det_seg_tar = os.path.join(self.det_seg_dir, f"{base_name}.tar") + + logger.info(f"Processing {base_name}") + + # Check if corresponding files exist + if not os.path.exists(depth_tar): + logger.warning(f"Depth tar file not found: {depth_tar}") + return None + if not os.path.exists(normal_tar): + logger.warning(f"Normal tar file not found: {normal_tar}") + return None + if not os.path.exists(det_seg_tar): + logger.warning(f"Det seg tar file not found: {det_seg_tar}") + return None + + # Extract data from all three tar files + logger.info(f"Extracting from rgb_caption: {rgb_caption_tar}") + rgb_caption_data = self.extract_from_tar(rgb_caption_tar, 'rgb_caption') + + logger.info(f"Extracting from depth: {depth_tar}") + depth_data = self.extract_from_tar(depth_tar, 'depth') + + logger.info(f"Extracting from normal: {normal_tar}") + normal_data = self.extract_from_tar(normal_tar, 'normal') + + logger.info(f"Extracting from det_seg: {det_seg_tar}") + det_seg_data = self.extract_from_tar(det_seg_tar, 'det_seg') + + # Find common UIDs + logger.info(f"rgb_caption_data.keys() - det_seg_data.keys(): {len(set(rgb_caption_data.keys())) - len(set(det_seg_data.keys()))}") + common_uids = set(rgb_caption_data.keys()) & set(depth_data.keys()) & set(normal_data.keys()) & set(det_seg_data.keys()) + + if not common_uids: + logger.warning(f"No common UIDs found for {base_name}") + return None + + logger.info(f"Found {len(common_uids)} common UIDs") + + # Sort UIDs for consistent ordering + sorted_uids = sorted(common_uids) + logger.info(f"Sorted {len(sorted_uids)} UIDs") + + # Create DataFrame + rows = [] + for uid in tqdm(sorted_uids, desc=f"Processing {base_name}"): + rgb_info = rgb_caption_data[uid] + + # Skip if missing image or caption + if rgb_info['image'] is None or rgb_info['caption'] is None: + continue + + row = { + 'uid': uid, + 'rgb': rgb_info['image'], + 'caption': rgb_info['caption'], + 'depth': depth_data[uid], + 'normal': normal_data[uid], + 'det_seg': det_seg_data[uid] + } + rows.append(row) + + if not rows: + logger.warning(f"No valid rows for {base_name}") + return None + + # Create DataFrame + df = pd.DataFrame(rows) + + # Debug information + logger.info(f"DataFrame shape: {df.shape}") + if not df.empty: + logger.info(f"Sample UIDs: {df['uid'].head().tolist()}") + logger.info(f"Data types: {df.dtypes.to_dict()}") + + # Save to parquet with better settings for dataset compatibility + output_path = os.path.join(self.parquet_dir, f"{base_name}.parquet") + + # Use better parquet settings for dataset compatibility + df.to_parquet( + output_path, + index=False, + engine='pyarrow', + compression='snappy', + row_group_size=1000 # Smaller row group size for more groups + ) + logger.info(f"Saved {len(df)} rows to {output_path}") + + return output_path + + def process_all_tars(self): + """Process all tar files and create corresponding parquet files""" + tar_files = self.get_tar_files() + + if not tar_files: + logger.error("No tar files found in rgb_caption directory") + return + + logger.info(f"Found {len(tar_files)} tar files to process") + + processed_files = [] + for tar_file in tar_files: + try: + output_path = self.process_tar_file(tar_file) + if output_path: + processed_files.append(output_path) + except Exception as e: + logger.error(f"Error processing {tar_file}: {e}") + + logger.info(f"Successfully processed {len(processed_files)} files") + return processed_files + +def main(): + # Set the base directory + base_dir = './datasets/blip3o' + + # Create processor + processor = TarProcessor(base_dir) + + # Process all tar files + processed_files = processor.process_all_tars() + + if processed_files: + logger.info(f"Successfully created {len(processed_files)} parquet files:") + for file in processed_files: + logger.info(f" - {file}") + else: + logger.error("No parquet files were created") + +if __name__ == "__main__": + main() diff --git a/data/any2any_preprocess/generate_parquet_clip448_imagebind.py b/data/any2any_preprocess/generate_parquet_clip448_imagebind.py new file mode 100644 index 0000000000000000000000000000000000000000..82f266e0841ca55bdb3d780bd4bed89b0f323ac4 --- /dev/null +++ b/data/any2any_preprocess/generate_parquet_clip448_imagebind.py @@ -0,0 +1,318 @@ +import os +import io +import glob +import tarfile +import logging +from typing import Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd +from tqdm import tqdm + + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + + +class MultiModalEnricher: + """Read existing parquet files and add clip448 and imagebind columns from tar archives.""" + + def __init__(self, base_dir: str, + input_parquet_dir: Optional[str] = None, + clip448_dir: Optional[str] = None, + imagebind_dir: Optional[str] = None, + output_parquet_dir: Optional[str] = None) -> None: + self.base_dir = base_dir + + # Defaults + self.input_parquet_dir = input_parquet_dir or os.path.join( + base_dir, 'parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global' + ) + self.clip448_dir = clip448_dir or os.path.join(base_dir, 'datasets_clip448') + self.imagebind_dir = imagebind_dir or os.path.join(base_dir, 'datasets_imagebind') + self.output_parquet_dir = output_parquet_dir or os.path.join( + base_dir, 'parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind' + ) + + os.makedirs(self.output_parquet_dir, exist_ok=True) + + # ------------------------------------------------------------------ + # Listing helpers + # ------------------------------------------------------------------ + + def _list_input_parquets(self) -> List[str]: + files = glob.glob(os.path.join(self.input_parquet_dir, '*.parquet')) + return sorted(files) + + # ------------------------------------------------------------------ + # Tar loaders + # ------------------------------------------------------------------ + + def _load_clip448_from_tar(self, tar_path: str) -> Dict[str, np.ndarray]: + """Load uid -> flattened local tokens from a clip448 tar. + + Files inside: {uid}_tokens.npy (shape (28,28) int64) + """ + results: Dict[str, np.ndarray] = {} + + if not os.path.exists(tar_path): + logger.warning(f"CLIP448 tar not found: {tar_path}") + return results + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if not member.isfile(): + continue + name = member.name + if not name.endswith('_tokens.npy'): + continue + + file_obj = tar.extractfile(member) + if file_obj is None: + continue + + content = file_obj.read() + npy = np.load(io.BytesIO(content)) + + # Derive uid: strip .npy then _tokens + uid = os.path.splitext(name)[0] # remove .npy + if uid.endswith('_tokens'): + uid = uid[:-len('_tokens')] + + results[uid] = npy.flatten() + except Exception as e: + logger.error(f"Failed to read CLIP448 tar {tar_path}: {e}") + + return results + + def _load_imagebind_from_tar(self, tar_path: str) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray]]: + """Load uid -> flattened tokens from an imagebind tar. + + Files inside: + {uid}_tokens.npy (local, shape (32,32) int64) + {uid}_tokens_global.npy (global, shape (16,1,1) int64) + + Returns (local_map, global_map). + """ + local_results: Dict[str, np.ndarray] = {} + global_results: Dict[str, np.ndarray] = {} + + if not os.path.exists(tar_path): + logger.warning(f"ImageBind tar not found: {tar_path}") + return local_results, global_results + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if not member.isfile(): + continue + name = member.name + if not name.endswith('.npy'): + continue + + file_obj = tar.extractfile(member) + if file_obj is None: + continue + + content = file_obj.read() + npy = np.load(io.BytesIO(content)) + + base = os.path.splitext(name)[0] # remove .npy + + if base.endswith('_tokens_global'): + uid = base[:-len('_tokens_global')] + global_results[uid] = npy.flatten() + elif base.endswith('_tokens'): + uid = base[:-len('_tokens')] + local_results[uid] = npy.flatten() + except Exception as e: + logger.error(f"Failed to read ImageBind tar {tar_path}: {e}") + + return local_results, global_results + + # ------------------------------------------------------------------ + # Column addition + # ------------------------------------------------------------------ + + def _add_columns(self, df: pd.DataFrame, + clip448_map: Dict[str, np.ndarray], + ib_local_map: Dict[str, np.ndarray], + ib_global_map: Dict[str, np.ndarray], + strict: bool = False) -> pd.DataFrame: + """Add clip448, imagebind, and imagebind_global columns to *df*.""" + + if 'uid' not in df.columns: + raise ValueError("Input parquet missing required 'uid' column") + + num_rows = len(df) + stats = {'clip448': 0, 'imagebind': 0, 'imagebind_global': 0, + 'clip448_miss': 0, 'imagebind_miss': 0, 'imagebind_global_miss': 0} + + clip448_col: List[object] = [] + ib_local_col: List[object] = [] + ib_global_col: List[object] = [] + + for _, row in df.iterrows(): + uid = row['uid'] + + # clip448 + tokens = clip448_map.get(uid) + if tokens is None: + stats['clip448_miss'] += 1 + clip448_col.append(row.get('clip448', None) if not strict else None) + else: + clip448_col.append(tokens) + stats['clip448'] += 1 + + # imagebind local + tokens = ib_local_map.get(uid) + if tokens is None: + stats['imagebind_miss'] += 1 + ib_local_col.append(row.get('imagebind', None) if not strict else None) + else: + ib_local_col.append(tokens) + stats['imagebind'] += 1 + + # imagebind global + tokens = ib_global_map.get(uid) + if tokens is None: + stats['imagebind_global_miss'] += 1 + ib_global_col.append(row.get('imagebind_global', None) if not strict else None) + else: + ib_global_col.append(tokens) + stats['imagebind_global'] += 1 + + logger.info( + f"Rows: {num_rows} | " + f"clip448: {stats['clip448']} ok / {stats['clip448_miss']} miss | " + f"imagebind: {stats['imagebind']} ok / {stats['imagebind_miss']} miss | " + f"imagebind_global: {stats['imagebind_global']} ok / {stats['imagebind_global_miss']} miss" + ) + + if strict and (stats['clip448_miss'] or stats['imagebind_miss'] or stats['imagebind_global_miss']): + logger.error("Strict mode: some UIDs are missing tokens from one or more modalities") + + df = df.copy() + df['clip448'] = clip448_col + df['imagebind'] = ib_local_col + df['imagebind_global'] = ib_global_col + return df + + # ------------------------------------------------------------------ + # Processing + # ------------------------------------------------------------------ + + def process_one_parquet(self, parquet_path: str, overwrite: bool = False, + strict: bool = False) -> Optional[str]: + base_name = os.path.splitext(os.path.basename(parquet_path))[0] + out_path = os.path.join(self.output_parquet_dir, f"{base_name}.parquet") + + if os.path.exists(out_path) and not overwrite: + logger.info(f"Output exists, skipping: {out_path}") + return out_path + + # Load existing parquet + df = pd.read_parquet(parquet_path, engine='pyarrow') + + # Load clip448 tar + clip448_tar = os.path.join(self.clip448_dir, f"{base_name}.tar") + logger.info(f"Loading CLIP448 tokens from: {clip448_tar}") + clip448_map = self._load_clip448_from_tar(clip448_tar) + + # Load imagebind tar + imagebind_tar = os.path.join(self.imagebind_dir, f"{base_name}.tar") + logger.info(f"Loading ImageBind tokens from: {imagebind_tar}") + ib_local_map, ib_global_map = self._load_imagebind_from_tar(imagebind_tar) + + if not clip448_map: + logger.warning(f"No CLIP448 tokens loaded from {clip448_tar}") + if not ib_local_map: + logger.warning(f"No ImageBind local tokens loaded from {imagebind_tar}") + if not ib_global_map: + logger.warning(f"No ImageBind global tokens loaded from {imagebind_tar}") + + # Add new columns + df = self._add_columns(df, clip448_map, ib_local_map, ib_global_map, strict=strict) + + # Persist + df.to_parquet( + out_path, + index=False, + engine='pyarrow', + compression='snappy', + row_group_size=1000, + ) + logger.info(f"Wrote enriched parquet to: {out_path}") + return out_path + + def process_all(self, overwrite: bool = False, strict: bool = False, + start_from: int = 0) -> List[str]: + inputs = self._list_input_parquets() + if start_from > 0: + inputs = inputs[start_from:] + + if not inputs: + logger.error("No input parquet files found") + return [] + + logger.info(f"Found {len(inputs)} parquet files to enrich") + outputs: List[str] = [] + for parquet_path in tqdm(inputs, desc="Enriching parquets", unit="file"): + try: + result = self.process_one_parquet(parquet_path, overwrite=overwrite, strict=strict) + if result: + outputs.append(result) + except Exception as e: + logger.error(f"Error processing {parquet_path}: {e}") + + logger.info(f"Successfully wrote {len(outputs)} enriched parquet files") + return outputs + + +def main() -> None: + import argparse + + parser = argparse.ArgumentParser( + description='Add CLIP448 and ImageBind tokens to existing parquet files' + ) + parser.add_argument('--base_dir', type=str, default='./datasets/blip3o', + help='Base directory (default: ./datasets/blip3o)') + parser.add_argument('--input_parquet_dir', type=str, default=None, + help='Input parquet directory; defaults to parquet_*_dino_global') + parser.add_argument('--clip448_dir', type=str, default=None, + help='Directory containing clip448 tars; defaults to base_dir/datasets_clip448') + parser.add_argument('--imagebind_dir', type=str, default=None, + help='Directory containing imagebind tars; defaults to base_dir/datasets_imagebind') + parser.add_argument('--output_parquet_dir', type=str, default=None, + help='Output parquet directory; defaults to parquet_*_clip448_imagebind') + parser.add_argument('--overwrite', action='store_true', + help='Overwrite existing output parquet if present') + parser.add_argument('--strict', action='store_true', + help='If set, missing UIDs will set column=None and log error') + parser.add_argument('--start_from', type=int, default=0, + help='Index to start processing from (for resuming)') + + args = parser.parse_args() + + enricher = MultiModalEnricher( + base_dir=args.base_dir, + input_parquet_dir=args.input_parquet_dir, + clip448_dir=args.clip448_dir, + imagebind_dir=args.imagebind_dir, + output_parquet_dir=args.output_parquet_dir, + ) + + outputs = enricher.process_all(overwrite=args.overwrite, strict=args.strict, start_from=args.start_from) + if outputs: + logger.info("Outputs written:") + for p in outputs: + logger.info(f" - {p}") + else: + logger.error("No outputs were created") + + +if __name__ == '__main__': + main() + diff --git a/data/any2any_preprocess/generate_parquet_grounding.py b/data/any2any_preprocess/generate_parquet_grounding.py new file mode 100644 index 0000000000000000000000000000000000000000..a90bd23378371b64caa56aac900eb568c7020f15 --- /dev/null +++ b/data/any2any_preprocess/generate_parquet_grounding.py @@ -0,0 +1,583 @@ +import os +import tarfile +import pandas as pd +import numpy as np +from PIL import Image +import io +import glob +import json +from tqdm import tqdm +import logging +import pycocotools.mask as mask_util +import matplotlib.pyplot as plt + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +class GroundingProcessor: + def __init__(self, base_dir): + self.base_dir = base_dir + self.rgb_caption_dir = os.path.join(base_dir, 'datasets') + self.depth_dir = os.path.join(base_dir, 'datasets_depth') + self.normal_dir = os.path.join(base_dir, 'datasets_normal') + self.det_seg_dir = os.path.join(base_dir, 'datasets_seg_swinb') + self.grounding_dir = os.path.join(base_dir, 'datasets_grounding') # Pre-processed grounding tars + self.parquet_dir = os.path.join(base_dir, 'parquet_rgb_caption_depth_normal_det_seg_grounding') + + # Create parquet directory if it doesn't exist + os.makedirs(self.parquet_dir, exist_ok=True) + + def get_tar_files(self): + """Get all tar files from rgb_caption directory""" + tar_files = glob.glob(os.path.join(self.rgb_caption_dir, '*.tar')) + return sorted(tar_files) + + + def extract_from_tar(self, tar_path, file_type): + """ + Extract data from tar file + + Args: + tar_path: Path to tar file + file_type: 'rgb_caption', 'depth', 'normal', 'det_seg', 'grounding' + """ + data = {} + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if member.isfile(): + filename = member.name + uid = os.path.splitext(filename)[0] # Remove extension + + # Extract file content + f = tar.extractfile(member) + if f is None: + continue + + content = f.read() + + if file_type == 'rgb_caption': + # Handle RGB + caption files + if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): + data[uid] = {'image': content, 'caption': None} + elif filename.endswith('.txt'): + # Find corresponding image + base_name = os.path.splitext(filename)[0] + if base_name in data: + data[base_name]['caption'] = content.decode('utf-8').strip() + else: + data[base_name] = {'image': None, 'caption': content.decode('utf-8').strip()} + + elif file_type == 'depth': + # Handle depth maps (PNG format) + if filename.endswith('.png'): + data[uid] = content + + elif file_type == 'normal': + # Handle normal maps (RGB format) + if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): + data[uid] = content + + elif file_type == 'det_seg': + # Handle detection maps (JSON format) + if filename.endswith('.json'): + # Decode bytes to string and parse JSON + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + data[uid] = json_data + + elif file_type == 'grounding': + # Handle grounding JSON files + if filename.endswith('.json'): + # Decode bytes to string and parse JSON + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + data[uid] = json_data + except Exception as e: + logger.error(f"Error processing {tar_path}: {e}") + return {} + + return data + + + def extract_grounding_data(self, grounding_data): + """Extract grounding information from JSON data""" + if not grounding_data: + return None + + # Get the first (and usually only) key which is the image name + image_key = list(grounding_data.keys())[0] + image_data = grounding_data[image_key] + + # Extract objects and their bounding boxes + objects = image_data.get('objects', []) + floating_objects = image_data.get('floating_objects', []) + + # Extract grounding relationships + relationships = image_data.get('relationships', {}) + grounding_phrases = relationships.get('grounding', []) + + # Extract captions with grounding details + short_captions = image_data.get('short_captions', []) + dense_caption = image_data.get('dense_caption', {}) + + return { + 'objects': objects, + 'floating_objects': floating_objects, + 'grounding_phrases': grounding_phrases, + 'short_captions': short_captions, + 'dense_caption': dense_caption + } + + def normalize_bbox(self, bbox, image_size): + """Normalize bbox to 0-999 based on image dimensions""" + if not bbox or len(bbox) != 4: + return [] + + # RLE format: size = [width, height] + img_height, img_width = image_size + + x1, y1, x2, y2 = bbox + + # Normalize to 0-999 and clamp values + norm_x1 = max(0, min(999, int((x1 / img_width) * 999))) + norm_y1 = max(0, min(999, int((y1 / img_height) * 999))) + norm_x2 = max(0, min(999, int((x2 / img_width) * 999))) + norm_y2 = max(0, min(999, int((y2 / img_height) * 999))) + + return [norm_x1, norm_y1, norm_x2, norm_y2] + + + def resize_and_crop_bbox(self, bbox, orig_size, target_size=512): + """ + Resize and center-crop a bounding box using CLIP/BLIP-style transform. + + Args: + bbox: list or tuple [x1, y1, x2, y2] in pixel coords. + orig_size: [W, H] original image size. + target_size: int, final square crop size (default=512). + + Returns: + [x1', y1', x2', y2'] after resize+crop. + Also returns (scale, left, top) for debugging. + """ + if not bbox or len(bbox) != 4: + return [] + + H, W = orig_size + x1, y1, x2, y2 = bbox + + # --- 1. Resize (keep aspect ratio, short side -> target_size) --- + scale = target_size / min(W, H) + new_W, new_H = W * scale, H * scale + + # --- 2. Center crop to target_size×target_size --- + left = max(0, (new_W - target_size) / 2) + top = max(0, (new_H - target_size) / 2) + + # --- 3. Apply transform to bbox --- + x1_new = x1 * scale - left + y1_new = y1 * scale - top + x2_new = x2 * scale - left + y2_new = y2 * scale - top + + # Clip to [0, target_size] + x1_new, y1_new, x2_new, y2_new = np.clip( + [x1_new, y1_new, x2_new, y2_new], + 0, target_size + ) + + return [x1_new, y1_new, x2_new, y2_new] + + + def visualize_rle_mask(self, rle_data, save_path=None): + """Visualize RLE mask from segmentation data""" + try: + # Decode RLE to binary mask + mask = mask_util.decode(rle_data) + + # Create visualization + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) + + # Show mask + ax1.imshow(mask, cmap='gray') + ax1.set_title('RLE Mask') + ax1.axis('off') + + # Show mask with bbox overlay + ax2.imshow(mask, cmap='gray', alpha=0.7) + + # Add bbox if available + if 'bbox' in rle_data: + bbox = rle_data['bbox'] + x1, y1, x2, y2 = bbox + width = x2 - x1 + height = y2 - y1 + rect = plt.Rectangle((x1, y1), width, height, + fill=False, edgecolor='red', linewidth=2) + ax2.add_patch(rect) + ax2.set_title('Mask + Bbox') + else: + ax2.set_title('Mask Only') + ax2.axis('off') + + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=150, bbox_inches='tight') + logger.info(f"Mask visualization saved to {save_path}") + else: + plt.show() + + plt.close() + + except Exception as e: + logger.error(f"Error visualizing RLE mask: {e}") + + def visualize_object_mask(self, grounding_data, uid, object_id=0, save_path=None): + """Visualize a specific object's mask from grounding data""" + try: + if not grounding_data or 'objects' not in grounding_data: + logger.warning(f"No objects found for {uid}") + return + + objects = grounding_data['objects'] + if object_id >= len(objects): + logger.warning(f"Object ID {object_id} not found for {uid}") + return + + obj = objects[object_id] + if 'segmentation' not in obj: + logger.warning(f"No segmentation found for object {object_id} in {uid}") + return + + segmentation = obj['segmentation'] + bbox = obj.get('bbox', []) + + # Create RLE data for visualization + rle_data = { + 'size': segmentation['size'], + 'counts': segmentation['counts'], + 'bbox': bbox + } + + # Visualize + if save_path: + save_path_with_id = save_path.replace('.png', f'_obj_{object_id}.png') + else: + save_path_with_id = None + + self.visualize_rle_mask(rle_data, save_path_with_id) + + except Exception as e: + logger.error(f"Error visualizing object mask for {uid}: {e}") + + def create_grounding_samples(self, grounding_data, uid): + """Create training samples from grounding data""" + if not grounding_data: + return [] + + samples = [] + + # Get image size from first object's segmentation + image_size = None + if grounding_data['objects'] and 'segmentation' in grounding_data['objects'][0]: + image_size = grounding_data['objects'][0]['segmentation']['size'] + assert image_size is not None + else: + logger.warning(f"No segmentation data found for {uid}, skipping bbox processing") + return [] + + # Process grounding phrases + for phrase_info in grounding_data['grounding_phrases']: + phrase = phrase_info['phrase'] + object_ids = phrase_info['object_ids'] + + # Find corresponding objects and their bboxes + for obj_id in object_ids: + for obj in grounding_data['objects']: + if obj.get('id') == obj_id: + bbox = obj.get('bbox', []) + + # Process bbox if valid + if bbox and len(bbox) == 4: + bbox = self.resize_and_crop_bbox(bbox, image_size, 512) + bbox = self.normalize_bbox(bbox, [512, 512]) + else: + bbox = [] + + sample = { + 'uid': uid, + 'phrase': phrase, + 'object_id': obj_id, + 'bbox': bbox, + 'labels': obj.get('labels', []), + 'attributes': obj.get('attributes', []), + 'sample_type': 'grounding_phrase', + 'segmentation': obj.get('segmentation', []), + } + samples.append(sample) + break + + # Process short captions with details + for caption_info in grounding_data['short_captions']: + caption = caption_info['caption'] + for detail in caption_info.get('details', []): + phrase = detail['phrase'] + object_id = detail.get('id') + bbox = detail.get('bbox', []) + + # Process bbox if valid + if bbox and len(bbox) == 4: + bbox = self.resize_and_crop_bbox(bbox, image_size, 512) + bbox = self.normalize_bbox(bbox, [512, 512]) + else: + bbox = [] + + tokens_positive = detail.get('tokens_positive', []) + + sample = { + 'uid': uid, + 'phrase': phrase, + 'caption_id': object_id, + 'bbox': bbox, + 'tokens_positive': tokens_positive, + 'full_caption': caption, + 'sample_type': 'short_caption' + } + samples.append(sample) + + # # Process dense caption + # if grounding_data['dense_caption']: + # dense_caption = grounding_data['dense_caption'] + # caption = dense_caption.get('caption', '') + # for detail in dense_caption.get('details', []): + # phrase = detail['phrase'] + # object_ids = detail.get('ids', []) + # bboxes = detail.get('bbox', []) + # tokens_positive = detail.get('tokens_positive', []) + + # sample = { + # 'uid': uid, + # 'phrase': phrase, + # 'object_ids': object_ids, + # 'bboxes': bboxes, + # 'tokens_positive': tokens_positive, + # 'full_caption': caption, + # 'sample_type': 'dense_caption' + # } + # samples.append(sample) + + return samples + + def process_tar_file(self, rgb_caption_tar): + """Process a single tar file and create corresponding parquet""" + + # Extract base name (e.g., 'sa_000000' from 'sa_000000.tar') + base_name = os.path.splitext(os.path.basename(rgb_caption_tar))[0] + + # Construct paths for corresponding depth, normal, det_seg, grounding tar files + depth_tar = os.path.join(self.depth_dir, f"{base_name}.tar") + normal_tar = os.path.join(self.normal_dir, f"{base_name}.tar") + det_seg_tar = os.path.join(self.det_seg_dir, f"{base_name}.tar") + grounding_tar = os.path.join(self.grounding_dir, f"{base_name}.tar") + + logger.info(f"Processing {base_name}") + + # Check if corresponding files exist + if not os.path.exists(depth_tar): + logger.warning(f"Depth tar file not found: {depth_tar}") + return None + if not os.path.exists(normal_tar): + logger.warning(f"Normal tar file not found: {normal_tar}") + return None + if not os.path.exists(det_seg_tar): + logger.warning(f"Det seg tar file not found: {det_seg_tar}") + return None + # Initialize grounding_data + grounding_data = {} + + if not os.path.exists(grounding_tar): + logger.warning(f"Grounding tar file not found: {grounding_tar} - will process without grounding data") + else: + logger.info(f"Extracting from grounding: {grounding_tar}") + grounding_data = self.extract_from_tar(grounding_tar, 'grounding') + + # Extract data from all tar files + logger.info(f"Extracting from rgb_caption: {rgb_caption_tar}") + rgb_caption_data = self.extract_from_tar(rgb_caption_tar, 'rgb_caption') + + logger.info(f"Extracting from depth: {depth_tar}") + depth_data = self.extract_from_tar(depth_tar, 'depth') + + logger.info(f"Extracting from normal: {normal_tar}") + normal_data = self.extract_from_tar(normal_tar, 'normal') + + logger.info(f"Extracting from det_seg: {det_seg_tar}") + det_seg_data = self.extract_from_tar(det_seg_tar, 'det_seg') + + # Find common UIDs (grounding is optional) + common_uids = set(rgb_caption_data.keys()) & set(depth_data.keys()) & set(normal_data.keys()) & set(det_seg_data.keys()) + + if not common_uids: + logger.warning(f"No common UIDs found for {base_name}") + return None + + logger.info(f"Found {len(common_uids)} common UIDs") + + # Sort UIDs for consistent ordering + sorted_uids = sorted(common_uids) + + # Create samples for each UID + all_samples = [] + + for uid in sorted_uids: + rgb_info = rgb_caption_data[uid] + + # Skip if missing image or caption + if rgb_info['image'] is None or rgb_info['caption'] is None: + continue + + # Create base sample with all modalities + sample = { + 'uid': uid, + 'rgb': rgb_caption_data[uid]['image'], + 'caption': rgb_caption_data[uid]['caption'], + 'depth': depth_data[uid], + 'normal': normal_data[uid], + 'det_seg': det_seg_data[uid] + } + + # Add grounding data if available + if uid in grounding_data: + extracted_grounding = self.extract_grounding_data(grounding_data[uid]) + if extracted_grounding: + raw_image = Image.open(io.BytesIO(rgb_caption_data[uid]['image'])) + raw_image_size = raw_image.size + assert raw_image_size == (512, 512), f"Raw image size is {raw_image_size} but expected (512, 512) for {uid}" + grounding_samples = self.create_grounding_samples(extracted_grounding, uid) + if grounding_samples: + sample['grounding'] = grounding_samples + + # visualize_segmentation = sample['grounding'][1]['segmentation'] + # self.visualize_rle_mask(visualize_segmentation,save_path=f"visualize_{uid}_1.png") + all_samples.append(sample) + + if not all_samples: + logger.warning(f"No samples found for {base_name}") + return None + + # Create DataFrame + df = pd.DataFrame(all_samples) + + # Debug information + logger.info(f"DataFrame shape: {df.shape}") + if not df.empty: + logger.info(f"Sample UIDs: {df['uid'].head().tolist()}") + # Count grounding samples + grounding_counts = [] + for _, row in df.iterrows(): + if 'grounding' in row and row['grounding'] is not None and isinstance(row['grounding'], list): + grounding_counts.append(len(row['grounding'])) + else: + grounding_counts.append(0) + if grounding_counts: + logger.info(f"Grounding samples per row: {np.mean(grounding_counts):.2f} avg, {max(grounding_counts)} max") + else: + logger.info("No grounding samples found") + + # Save to parquet + output_path = os.path.join(self.parquet_dir, f"{base_name}.parquet") + + df.to_parquet( + output_path, + index=False, + engine='pyarrow', + compression='snappy', + row_group_size=1000 + ) + logger.info(f"Saved {len(df)} grounding samples to {output_path}") + + return output_path + + def process_all_tars(self): + """Process all tar files and create corresponding parquet files""" + tar_files = self.get_tar_files() + + if not tar_files: + logger.error("No tar files found in rgb_caption directory") + return + + logger.info(f"Found {len(tar_files)} tar files to process") + + processed_files = [] + for tar_file in tar_files: + try: + output_path = self.process_tar_file(tar_file) + if output_path: + processed_files.append(output_path) + except Exception as e: + logger.error(f"Error processing {tar_file}: {e}") + + logger.info(f"Successfully processed {len(processed_files)} files") + return processed_files + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='Generate parquet files with grounding data') + parser.add_argument('--base_dir', type=str, default='./datasets/blip3o', + help='Base directory (default: ./datasets/blip3o)') + parser.add_argument('--visualize_mask', action='store_true', + help='Visualize RLE masks for debugging') + parser.add_argument('--visualize_tar', type=str, default=None, + help='Specific tar file to visualize (e.g., sa_000001.tar)') + parser.add_argument('--visualize_uid', type=str, default=None, + help='Specific UID to visualize (e.g., sa_1)') + parser.add_argument('--visualize_object_id', type=int, default=0, + help='Object ID to visualize (default: 0)') + + args = parser.parse_args() + + # Set the base directory + base_dir = args.base_dir + + # Create processor + processor = GroundingProcessor(base_dir) + + # Visualize mask if requested + if args.visualize_mask and args.visualize_tar: + logger.info(f"Visualizing mask for {args.visualize_tar}, object {args.visualize_object_id}") + + # Find the grounding tar file for this UID + grounding_tar = os.path.join(processor.grounding_dir, f"{args.visualize_tar}.tar") + if os.path.exists(grounding_tar): + grounding_data = processor.extract_from_tar(grounding_tar, 'grounding') + if args.visualize_uid in grounding_data: + extracted_grounding = processor.extract_grounding_data(grounding_data[args.visualize_uid]) + if extracted_grounding: + save_path = f"mask_visualization_{args.visualize_uid}.png" + processor.visualize_object_mask(extracted_grounding, args.visualize_uid, + args.visualize_object_id, save_path) + else: + logger.error(f"No grounding data found for {args.visualize_uid}") + else: + logger.error(f"UID {args.visualize_uid} not found in grounding tar") + else: + logger.error(f"Grounding tar not found: {grounding_tar}") + return + + # Process all tar files + processed_files = processor.process_all_tars() + + if processed_files: + logger.info(f"Successfully created {len(processed_files)} grounding parquet files:") + for file in processed_files: + logger.info(f" - {file}") + else: + logger.error("No grounding parquet files were created") + +if __name__ == "__main__": + main() diff --git a/data/any2any_preprocess/generate_parquet_grounding_canny_dino.py b/data/any2any_preprocess/generate_parquet_grounding_canny_dino.py new file mode 100644 index 0000000000000000000000000000000000000000..d1be950b2ac8edad7ffd4f1887d25d58b48cc5b5 --- /dev/null +++ b/data/any2any_preprocess/generate_parquet_grounding_canny_dino.py @@ -0,0 +1,627 @@ +import os +import tarfile +import pandas as pd +import numpy as np +from PIL import Image +import io +import glob +import json +from tqdm import tqdm +import logging +import pycocotools.mask as mask_util +import matplotlib.pyplot as plt + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +class GroundingProcessor: + def __init__(self, base_dir): + self.base_dir = base_dir + self.rgb_caption_dir = os.path.join(base_dir, 'datasets') + self.depth_dir = os.path.join(base_dir, 'datasets_depth') + self.normal_dir = os.path.join(base_dir, 'datasets_normal') + self.det_seg_dir = os.path.join(base_dir, 'datasets_seg_swinb') + self.grounding_dir = os.path.join(base_dir, 'datasets_grounding') # Pre-processed grounding tars + self.canny_dir = os.path.join(base_dir, 'datasets_cannyedge') + self.dino_dir = os.path.join(base_dir, 'dinov2') + self.parquet_dir = os.path.join(base_dir, 'parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino') + + # Create parquet directory if it doesn't exist + os.makedirs(self.parquet_dir, exist_ok=True) + + def get_tar_files(self): + """Get all tar files from rgb_caption directory""" + tar_files = glob.glob(os.path.join(self.rgb_caption_dir, '*.tar')) + return sorted(tar_files) + + + def extract_from_tar(self, tar_path, file_type): + """ + Extract data from tar file + + Args: + tar_path: Path to tar file + file_type: 'rgb_caption', 'depth', 'normal', 'det_seg', 'grounding', 'canny', 'dino' + """ + data = {} + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if member.isfile(): + filename = member.name + uid = os.path.splitext(filename)[0] # Remove extension + + # Extract file content + f = tar.extractfile(member) + if f is None: + continue + + content = f.read() + + if file_type == 'rgb_caption': + # Handle RGB + caption files + if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): + data[uid] = {'image': content, 'caption': None} + elif filename.endswith('.txt'): + # Find corresponding image + base_name = os.path.splitext(filename)[0] + if base_name in data: + data[base_name]['caption'] = content.decode('utf-8').strip() + else: + data[base_name] = {'image': None, 'caption': content.decode('utf-8').strip()} + + elif file_type == 'depth': + # Handle depth maps (PNG format) + if filename.endswith('.png'): + data[uid] = content + + elif file_type == 'normal': + # Handle normal maps (RGB format) + if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): + data[uid] = content + + elif file_type == 'det_seg': + # Handle detection maps (JSON format) + if filename.endswith('.json'): + # Decode bytes to string and parse JSON + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + data[uid] = json_data + + elif file_type == 'grounding': + # Handle grounding JSON files + if filename.endswith('.json'): + # Decode bytes to string and parse JSON + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + data[uid] = json_data + + elif file_type == 'canny': + # Handle Canny maps (PNG format, similar to depth) + if filename.endswith('.png'): + data[uid] = content + + elif file_type == 'dino': + # Handle DINOv2 features (NPY format) + if filename.endswith('_tokens.npy'): + # Load NPY data from bytes + npy_data = np.load(io.BytesIO(content)) + uid = uid[:-len('_tokens')] + # Flatten the array for parquet storage + data[uid] = npy_data.flatten() + except Exception as e: + logger.error(f"Error processing {tar_path}: {e}") + return {} + + return data + + def extract_grounding_data(self, grounding_data): + """Extract grounding information from JSON data""" + if not grounding_data: + return None + + # Get the first (and usually only) key which is the image name + image_key = list(grounding_data.keys())[0] + image_data = grounding_data[image_key] + + # Extract objects and their bounding boxes + objects = image_data.get('objects', []) + floating_objects = image_data.get('floating_objects', []) + + # Extract grounding relationships + relationships = image_data.get('relationships', {}) + grounding_phrases = relationships.get('grounding', []) + + # Extract captions with grounding details + short_captions = image_data.get('short_captions', []) + dense_caption = image_data.get('dense_caption', {}) + + return { + 'objects': objects, + 'floating_objects': floating_objects, + 'grounding_phrases': grounding_phrases, + 'short_captions': short_captions, + 'dense_caption': dense_caption + } + + def normalize_bbox(self, bbox, image_size): + """Normalize bbox to 0-999 based on image dimensions""" + if not bbox or len(bbox) != 4: + return [] + + # RLE format: size = [width, height] + img_height, img_width = image_size + + x1, y1, x2, y2 = bbox + + # Normalize to 0-999 and clamp values + norm_x1 = max(0, min(999, int((x1 / img_width) * 999))) + norm_y1 = max(0, min(999, int((y1 / img_height) * 999))) + norm_x2 = max(0, min(999, int((x2 / img_width) * 999))) + norm_y2 = max(0, min(999, int((y2 / img_height) * 999))) + + return [norm_x1, norm_y1, norm_x2, norm_y2] + + + def resize_and_crop_bbox(self, bbox, orig_size, target_size=512): + """ + Resize and center-crop a bounding box using CLIP/BLIP-style transform. + + Args: + bbox: list or tuple [x1, y1, x2, y2] in pixel coords. + orig_size: [W, H] original image size. + target_size: int, final square crop size (default=512). + + Returns: + [x1', y1', x2', y2'] after resize+crop. + Also returns (scale, left, top) for debugging. + """ + if not bbox or len(bbox) != 4: + return [] + + H, W = orig_size + x1, y1, x2, y2 = bbox + + # --- 1. Resize (keep aspect ratio, short side -> target_size) --- + scale = target_size / min(W, H) + new_W, new_H = W * scale, H * scale + + # --- 2. Center crop to target_size×target_size --- + left = max(0, (new_W - target_size) / 2) + top = max(0, (new_H - target_size) / 2) + + # --- 3. Apply transform to bbox --- + x1_new = x1 * scale - left + y1_new = y1 * scale - top + x2_new = x2 * scale - left + y2_new = y2 * scale - top + + # Clip to [0, target_size] + x1_new, y1_new, x2_new, y2_new = np.clip( + [x1_new, y1_new, x2_new, y2_new], + 0, target_size + ) + + return [x1_new, y1_new, x2_new, y2_new] + + + def visualize_rle_mask(self, rle_data, save_path=None): + """Visualize RLE mask from segmentation data""" + try: + # Decode RLE to binary mask + mask = mask_util.decode(rle_data) + + # Create visualization + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) + + # Show mask + ax1.imshow(mask, cmap='gray') + ax1.set_title('RLE Mask') + ax1.axis('off') + + # Show mask with bbox overlay + ax2.imshow(mask, cmap='gray', alpha=0.7) + + # Add bbox if available + if 'bbox' in rle_data: + bbox = rle_data['bbox'] + x1, y1, x2, y2 = bbox + width = x2 - x1 + height = y2 - y1 + rect = plt.Rectangle((x1, y1), width, height, + fill=False, edgecolor='red', linewidth=2) + ax2.add_patch(rect) + ax2.set_title('Mask + Bbox') + else: + ax2.set_title('Mask Only') + ax2.axis('off') + + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=150, bbox_inches='tight') + logger.info(f"Mask visualization saved to {save_path}") + else: + plt.show() + + plt.close() + + except Exception as e: + logger.error(f"Error visualizing RLE mask: {e}") + + def visualize_object_mask(self, grounding_data, uid, object_id=0, save_path=None): + """Visualize a specific object's mask from grounding data""" + try: + if not grounding_data or 'objects' not in grounding_data: + logger.warning(f"No objects found for {uid}") + return + + objects = grounding_data['objects'] + if object_id >= len(objects): + logger.warning(f"Object ID {object_id} not found for {uid}") + return + + obj = objects[object_id] + if 'segmentation' not in obj: + logger.warning(f"No segmentation found for object {object_id} in {uid}") + return + + segmentation = obj['segmentation'] + bbox = obj.get('bbox', []) + + # Create RLE data for visualization + rle_data = { + 'size': segmentation['size'], + 'counts': segmentation['counts'], + 'bbox': bbox + } + + # Visualize + if save_path: + save_path_with_id = save_path.replace('.png', f'_obj_{object_id}.png') + else: + save_path_with_id = None + + self.visualize_rle_mask(rle_data, save_path_with_id) + + except Exception as e: + logger.error(f"Error visualizing object mask for {uid}: {e}") + + def create_grounding_samples(self, grounding_data, uid): + """Create training samples from grounding data""" + if not grounding_data: + return [] + + samples = [] + + # Get image size from first object's segmentation + image_size = None + if grounding_data['objects'] and 'segmentation' in grounding_data['objects'][0]: + image_size = grounding_data['objects'][0]['segmentation']['size'] + assert image_size is not None + else: + logger.warning(f"No segmentation data found for {uid}, skipping bbox processing") + return [] + + # Process grounding phrases + for phrase_info in grounding_data['grounding_phrases']: + phrase = phrase_info['phrase'] + object_ids = phrase_info['object_ids'] + + # Find corresponding objects and their bboxes + for obj_id in object_ids: + for obj in grounding_data['objects']: + if obj.get('id') == obj_id: + bbox = obj.get('bbox', []) + + # Process bbox if valid + if bbox and len(bbox) == 4: + bbox = self.resize_and_crop_bbox(bbox, image_size, 512) + bbox = self.normalize_bbox(bbox, [512, 512]) + else: + bbox = [] + + sample = { + 'uid': uid, + 'phrase': phrase, + 'object_id': obj_id, + 'bbox': bbox, + 'labels': obj.get('labels', []), + 'attributes': obj.get('attributes', []), + 'sample_type': 'grounding_phrase', + 'segmentation': obj.get('segmentation', []), + } + samples.append(sample) + break + + # Process short captions with details + for caption_info in grounding_data['short_captions']: + caption = caption_info['caption'] + for detail in caption_info.get('details', []): + phrase = detail['phrase'] + object_id = detail.get('id') + bbox = detail.get('bbox', []) + + # Process bbox if valid + if bbox and len(bbox) == 4: + bbox = self.resize_and_crop_bbox(bbox, image_size, 512) + bbox = self.normalize_bbox(bbox, [512, 512]) + else: + bbox = [] + + tokens_positive = detail.get('tokens_positive', []) + + sample = { + 'uid': uid, + 'phrase': phrase, + 'caption_id': object_id, + 'bbox': bbox, + 'tokens_positive': tokens_positive, + 'full_caption': caption, + 'sample_type': 'short_caption' + } + samples.append(sample) + + # # Process dense caption + # if grounding_data['dense_caption']: + # dense_caption = grounding_data['dense_caption'] + # caption = dense_caption.get('caption', '') + # for detail in dense_caption.get('details', []): + # phrase = detail['phrase'] + # object_ids = detail.get('ids', []) + # bboxes = detail.get('bbox', []) + # tokens_positive = detail.get('tokens_positive', []) + + # sample = { + # 'uid': uid, + # 'phrase': phrase, + # 'object_ids': object_ids, + # 'bboxes': bboxes, + # 'tokens_positive': tokens_positive, + # 'full_caption': caption, + # 'sample_type': 'dense_caption' + # } + # samples.append(sample) + + return samples + + def process_tar_file(self, rgb_caption_tar): + """Process a single tar file and create corresponding parquet""" + + # Extract base name (e.g., 'sa_000000' from 'sa_000000.tar') + base_name = os.path.splitext(os.path.basename(rgb_caption_tar))[0] + + # Construct paths for corresponding depth, normal, det_seg, grounding, canny, dino tar files + depth_tar = os.path.join(self.depth_dir, f"{base_name}.tar") + normal_tar = os.path.join(self.normal_dir, f"{base_name}.tar") + det_seg_tar = os.path.join(self.det_seg_dir, f"{base_name}.tar") + grounding_tar = os.path.join(self.grounding_dir, f"{base_name}.tar") + canny_tar = os.path.join(self.canny_dir, f"{base_name}.tar") + dino_tar = os.path.join(self.dino_dir, f"{base_name}.tar") + + logger.info(f"Processing {base_name}") + + # Check if corresponding files exist + if not os.path.exists(depth_tar): + logger.warning(f"Depth tar file not found: {depth_tar}") + return None + if not os.path.exists(normal_tar): + logger.warning(f"Normal tar file not found: {normal_tar}") + return None + if not os.path.exists(det_seg_tar): + logger.warning(f"Det seg tar file not found: {det_seg_tar}") + return None + if not os.path.exists(canny_tar): + logger.warning(f"Canny tar file not found: {canny_tar}") + return None + if not os.path.exists(dino_tar): + logger.warning(f"DINOv2 tar file not found: {dino_tar}") + return None + # Initialize grounding_data + grounding_data = {} + + if not os.path.exists(grounding_tar): + logger.warning(f"Grounding tar file not found: {grounding_tar} - will process without grounding data") + else: + logger.info(f"Extracting from grounding: {grounding_tar}") + grounding_data = self.extract_from_tar(grounding_tar, 'grounding') + + # Extract data from all tar files + logger.info(f"Extracting from rgb_caption: {rgb_caption_tar}") + rgb_caption_data = self.extract_from_tar(rgb_caption_tar, 'rgb_caption') + + logger.info(f"Extracting from depth: {depth_tar}") + depth_data = self.extract_from_tar(depth_tar, 'depth') + + logger.info(f"Extracting from normal: {normal_tar}") + normal_data = self.extract_from_tar(normal_tar, 'normal') + + logger.info(f"Extracting from det_seg: {det_seg_tar}") + det_seg_data = self.extract_from_tar(det_seg_tar, 'det_seg') + + logger.info(f"Extracting from canny: {canny_tar}") + canny_data = self.extract_from_tar(canny_tar, 'canny') + + logger.info(f"Extracting from dino: {dino_tar}") + dino_data = self.extract_from_tar(dino_tar, 'dino') + + # Find common UIDs (grounding is optional) + common_uids = set(rgb_caption_data.keys()) & set(depth_data.keys()) & set(normal_data.keys()) & set(det_seg_data.keys()) & set(canny_data.keys()) & set(dino_data.keys()) + + if not common_uids: + logger.warning(f"No common UIDs found for {base_name}") + return None + + logger.info(f"Found {len(common_uids)} common UIDs") + + # Sort UIDs for consistent ordering + sorted_uids = sorted(common_uids) + + # Create samples for each UID + all_samples = [] + + for uid in sorted_uids: + rgb_info = rgb_caption_data[uid] + + # Skip if missing image or caption + if rgb_info['image'] is None or rgb_info['caption'] is None: + continue + + # Create base sample with all modalities + sample = { + 'uid': uid, + 'rgb': rgb_caption_data[uid]['image'], + 'caption': rgb_caption_data[uid]['caption'], + 'depth': depth_data[uid], + 'normal': normal_data[uid], + 'det_seg': det_seg_data[uid], + 'canny': canny_data[uid], + 'dino': dino_data[uid] + } + + # Add grounding data if available + if uid in grounding_data: + extracted_grounding = self.extract_grounding_data(grounding_data[uid]) + if extracted_grounding: + raw_image = Image.open(io.BytesIO(rgb_caption_data[uid]['image'])) + raw_image_size = raw_image.size + assert raw_image_size == (512, 512), f"Raw image size is {raw_image_size} but expected (512, 512) for {uid}" + grounding_samples = self.create_grounding_samples(extracted_grounding, uid) + if grounding_samples: + sample['grounding'] = grounding_samples + + # visualize_segmentation = sample['grounding'][1]['segmentation'] + # self.visualize_rle_mask(visualize_segmentation,save_path=f"visualize_{uid}_1.png") + all_samples.append(sample) + + if not all_samples: + logger.warning(f"No samples found for {base_name}") + return None + + # Create DataFrame + df = pd.DataFrame(all_samples) + + # Debug information + logger.info(f"DataFrame shape: {df.shape}") + if not df.empty: + logger.info(f"Sample UIDs: {df['uid'].head().tolist()}") + # Count grounding samples + grounding_counts = [] + for _, row in df.iterrows(): + if 'grounding' in row and row['grounding'] is not None and isinstance(row['grounding'], list): + grounding_counts.append(len(row['grounding'])) + else: + grounding_counts.append(0) + if grounding_counts: + logger.info(f"Grounding samples per row: {np.mean(grounding_counts):.2f} avg, {max(grounding_counts)} max") + else: + logger.info("No grounding samples found") + + # Count DINOv2 features + dino_counts = [] + for _, row in df.iterrows(): + if 'dino' in row and row['dino'] is not None: + dino_counts.append(1) + else: + dino_counts.append(0) + if dino_counts: + logger.info(f"DINOv2 features loaded: {sum(dino_counts)}/{len(dino_counts)} samples") + else: + logger.info("No DINOv2 features found") + + # Save to parquet + output_path = os.path.join(self.parquet_dir, f"{base_name}.parquet") + + df.to_parquet( + output_path, + index=False, + engine='pyarrow', + compression='snappy', + row_group_size=1000 + ) + logger.info(f"Saved {len(df)} grounding samples to {output_path}") + + return output_path + + def process_all_tars(self): + """Process all tar files and create corresponding parquet files""" + tar_files = self.get_tar_files() + + if not tar_files: + logger.error("No tar files found in rgb_caption directory") + return + + tar_files = tar_files[1373:] + logger.info(f"Found {len(tar_files)} tar files to process") + + processed_files = [] + for tar_file in tar_files: + try: + output_path = self.process_tar_file(tar_file) + if output_path: + processed_files.append(output_path) + except Exception as e: + logger.error(f"Error processing {tar_file}: {e}") + + logger.info(f"Successfully processed {len(processed_files)} files") + return processed_files + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='Generate parquet files with grounding data') + parser.add_argument('--base_dir', type=str, default='./datasets/blip3o', + help='Base directory (default: ./datasets/blip3o)') + parser.add_argument('--visualize_mask', action='store_true', + help='Visualize RLE masks for debugging') + parser.add_argument('--visualize_tar', type=str, default=None, + help='Specific tar file to visualize (e.g., sa_000001.tar)') + parser.add_argument('--visualize_uid', type=str, default=None, + help='Specific UID to visualize (e.g., sa_1)') + parser.add_argument('--visualize_object_id', type=int, default=0, + help='Object ID to visualize (default: 0)') + + args = parser.parse_args() + + # Set the base directory + base_dir = args.base_dir + + # Create processor + processor = GroundingProcessor(base_dir) + + # Visualize mask if requested + if args.visualize_mask and args.visualize_tar: + logger.info(f"Visualizing mask for {args.visualize_tar}, object {args.visualize_object_id}") + + # Find the grounding tar file for this UID + grounding_tar = os.path.join(processor.grounding_dir, f"{args.visualize_tar}.tar") + if os.path.exists(grounding_tar): + grounding_data = processor.extract_from_tar(grounding_tar, 'grounding') + if args.visualize_uid in grounding_data: + extracted_grounding = processor.extract_grounding_data(grounding_data[args.visualize_uid]) + if extracted_grounding: + save_path = f"mask_visualization_{args.visualize_uid}.png" + processor.visualize_object_mask(extracted_grounding, args.visualize_uid, + args.visualize_object_id, save_path) + else: + logger.error(f"No grounding data found for {args.visualize_uid}") + else: + logger.error(f"UID {args.visualize_uid} not found in grounding tar") + else: + logger.error(f"Grounding tar not found: {grounding_tar}") + return + + # Process all tar files + processed_files = processor.process_all_tars() + + if processed_files: + logger.info(f"Successfully created {len(processed_files)} grounding parquet files:") + for file in processed_files: + logger.info(f" - {file}") + else: + logger.error("No grounding parquet files were created") + +if __name__ == "__main__": + main() diff --git a/data/any2any_preprocess/generate_parquet_json.py b/data/any2any_preprocess/generate_parquet_json.py new file mode 100644 index 0000000000000000000000000000000000000000..4d9e5a8dbc719bd760487553467e379fd8bd12cc --- /dev/null +++ b/data/any2any_preprocess/generate_parquet_json.py @@ -0,0 +1,69 @@ +import os +import json +import argparse +import pyarrow.parquet as pq +from pathlib import Path + +def get_parquet_info(file_path): + """Get information about a parquet file""" + try: + parquet_file = pq.ParquetFile(file_path) + num_row_groups = parquet_file.num_row_groups + + # Calculate total number of rows + total_rows = 0 + for i in range(num_row_groups): + total_rows += parquet_file.metadata.row_group(i).num_rows + + return { + "num_row_groups": num_row_groups, + "num_rows": total_rows + } + except Exception as e: + print(f"Error reading {file_path}: {e}") + return None + +def generate_parquet_info(parquet_dir=None, output_file=None): + """Generate information for all parquet files in the parquet folder""" + parquet_dir = Path(parquet_dir) if parquet_dir is not None else Path("./datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind") + output_file = Path(output_file) if output_file is not None else Path("./datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json") + + # Create output directory if it doesn't exist + output_file.parent.mkdir(parents=True, exist_ok=True) + + # Get all parquet files + parquet_files = list(parquet_dir.glob("*.parquet")) + print(f"Found {len(parquet_files)} parquet files") + + # Process each file + parquet_info = {} + + for i, parquet_file in enumerate(parquet_files): + print(f"Processing {i+1}/{len(parquet_files)}: {parquet_file.name}") + + # Get relative path from current directory using parquet_dir + relative_path = str((parquet_dir / parquet_file.name)) + if not relative_path.startswith("./"): + relative_path = f"./{relative_path}" + + info = get_parquet_info(parquet_file) + if info: + parquet_info[relative_path] = info + print(f"Processed {parquet_file.name} successfully") + print(info) + else: + print(f"Failed to process {parquet_file.name}") + + # Save to JSON file + with open(output_file, 'w') as f: + json.dump(parquet_info, f, indent=2, sort_keys=True) + + print(f"Saved parquet information to {output_file}") + print(f"Processed {len(parquet_info)} files successfully") + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--parquet_dir", default=None) + parser.add_argument("--output_file", default=None) + args = parser.parse_args() + generate_parquet_info(parquet_dir=args.parquet_dir, output_file=args.output_file) diff --git a/data/any2any_preprocess/generate_parquet_vlm_sft.py b/data/any2any_preprocess/generate_parquet_vlm_sft.py new file mode 100644 index 0000000000000000000000000000000000000000..8f00a377ec09be11ec5542218bedfb0516b47d27 --- /dev/null +++ b/data/any2any_preprocess/generate_parquet_vlm_sft.py @@ -0,0 +1,278 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +"""Bundle vlm_sft (llava-onevision style jsonl + loose image files) into +parquet shards loadable by SftParquetIterableDataset (data/vlm_dataset.py). + +Why: llava-onevision is ~4M small files, impractical to upload to / stream +from S3. The shards follow the blip3o convention: few large parquet files + +a parquet_info JSON, sharded by (file, row_group) at load time. + +Equivalence: the jsonl loader selects samples per subset via + rng = random.Random(); rng.seed(shuffle_seed); rng.shuffle(lines) + lines = lines[:num_used_data] +(SftJSONLIterableDataset.get_data_paths). This script replicates that +selection EXACTLY (taking shuffle_lines / shuffle_seed / num_used_data from +the same training yaml), so the parquet rows are precisely the records the +jsonl loader would have used, in the same shuffled order. + +Row schema: + record: str original jsonl line, verbatim + image_bytes: list raw image files (no re-encode), in the order + the jsonl loader's valid_paths would produce + +Skipped at conversion (mirroring the jsonl loader's runtime skips): + - records with 'video' (frame sampling can't be baked into bytes) + - records whose 'image' field resolves to zero existing files + +Usage (single process, all subsets of the group): + python3 data/any2any_preprocess/generate_parquet_vlm_sft.py \ + --config data/configs/modus_stage1.yaml --group vlm_sft \ + --out-dir ./datasets/llava_onevision_vqa_parquet + +Parallelize across subsets with disjoint --subsets lists (e.g. sbatch array); +each invocation only rewrites its own subsets' shards and merge-updates the +shared parquet_info JSON. Already-converted subsets are skipped unless +--overwrite is given. + +After conversion, point the training config at the new group: + vlm_sft_parquet: + dataset_names: + - llava_onevision_vqa + image_transform_args: { ...same as the old vlm_sft block... } + is_mandatory: true + weight: + (no num_used_data / shuffle_lines needed — selection is baked in) +""" + +import argparse +import json +import os +import random +import re +import sys +from concurrent.futures import ThreadPoolExecutor + +import pyarrow as pa +import pyarrow.parquet as pq +import yaml + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..')) +from data.dataset_info import DATASET_INFO # noqa: E402 +from data.parquet_utils import apply_data_root_override, read_file_bytes # noqa: E402 + +SCHEMA = pa.schema([ + pa.field('record', pa.string()), + pa.field('image_bytes', pa.list_(pa.binary())), +]) + + +def sanitize(name): + # subset names contain '(', ',', ')' etc. — make a filesystem-safe stem + return re.sub(r'[^0-9A-Za-z_.-]+', '_', name).strip('_') + + +def _read_image_bytes(path): + if path.startswith('s3://'): + return read_file_bytes(path) + with open(path, 'rb') as f: + return f.read() + + +def _load_record_images(args): + """Worker: (raw_line, image_dir) -> (raw_line, image_bytes list, status). + status: 'ok' | 'skip_video' | 'skip_missing' | 'skip_badjson'.""" + raw_line, image_dir = args + try: + data_item = json.loads(raw_line) + except Exception: + return raw_line, None, 'skip_badjson' + if 'video' in data_item: + return raw_line, None, 'skip_video' + image_bytes = [] + if 'image' in data_item: + images = data_item['image'] if isinstance(data_item['image'], list) else [data_item['image']] + for img_name in images: + if not (isinstance(img_name, str) and img_name.strip() != ''): + continue + img_path = os.path.join(image_dir, img_name) + try: + image_bytes.append(_read_image_bytes(img_path)) + except (OSError, IOError): + continue # mirrors the jsonl loader dropping invalid paths + if len(image_bytes) == 0: + return raw_line, None, 'skip_missing' + return raw_line, image_bytes, 'ok' + + +def select_lines(jsonl_path, num_used_data, shuffle_lines, shuffle_seed): + """Replicates SftJSONLIterableDataset.get_data_paths line selection.""" + jsonl_path = apply_data_root_override(jsonl_path) + if jsonl_path.startswith('s3://'): + raw_data = read_file_bytes(jsonl_path).decode('utf-8').splitlines(keepends=True) + else: + with open(jsonl_path, 'r') as f: + raw_data = f.readlines() + if shuffle_lines: + rng = random.Random() + rng.seed(shuffle_seed) + rng.shuffle(raw_data) + return raw_data[:num_used_data] + + +def convert_subset(subset, jsonl_path, image_dir, num_used_data, shuffle_lines, + shuffle_seed, train_dir, rows_per_file, row_group_size, + io_threads, info_key_dir): + """Convert one subset to parquet shards. Returns (shard_info, stats): + shard_info: {info_key: {'num_row_groups': int, 'num_rows': int}}.""" + image_dir = apply_data_root_override(image_dir) + lines = select_lines(jsonl_path, num_used_data, shuffle_lines, shuffle_seed) + print(f'[convert] {subset}: selected {len(lines)} lines (quota {num_used_data})') + + stem = sanitize(subset) + stats = {'ok': 0, 'skip_video': 0, 'skip_missing': 0, 'skip_badjson': 0} + shard_info = {} + part = 0 + records, images = [], [] + + def flush(): + nonlocal part, records, images + if not records: + return + fname = f'{stem}__part{part:04d}.parquet' + out_path = os.path.join(train_dir, fname) + table = pa.Table.from_arrays( + [pa.array(records, type=pa.string()), + pa.array(images, type=pa.list_(pa.binary()))], + schema=SCHEMA, + ) + pq.write_table(table, out_path, compression='snappy', + row_group_size=row_group_size) + meta = pq.ParquetFile(out_path).metadata + shard_info[os.path.join(info_key_dir, fname)] = { + 'num_row_groups': meta.num_row_groups, + 'num_rows': meta.num_rows, + } + part += 1 + records, images = [], [] + + with ThreadPoolExecutor(max_workers=io_threads) as ex: + # batch the submissions: Executor.map submits everything up front and + # holds completed results until iterated, so mapping the whole subset + # at once would keep every image of the subset in memory. + for start in range(0, len(lines), rows_per_file): + chunk = lines[start:start + rows_per_file] + for raw_line, image_bytes, status in ex.map( + _load_record_images, ((l, image_dir) for l in chunk) + ): + stats[status] += 1 + if status != 'ok': + continue + records.append(raw_line) + images.append(image_bytes) + if len(records) >= rows_per_file: + flush() + flush() + return shard_info, stats + + +def main(): + # DATASET_INFO uses './datasets/...' paths relative to the repo root; + # run from there regardless of the caller's cwd (e.g. container default + # workdir). Pass absolute --config/--out-dir or repo-root-relative ones. + os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))) + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--config', required=True, + help='training data yaml (e.g. data/configs/modus_stage1.yaml)') + parser.add_argument('--group', default='vlm_sft', + help='dataset group in the yaml / DATASET_INFO') + parser.add_argument('--out-dir', default='./datasets/llava_onevision_vqa_parquet') + parser.add_argument('--subsets', nargs='*', default=None, + help='only convert these subsets (default: all in the yaml)') + parser.add_argument('--rows-per-file', type=int, default=5000) + parser.add_argument('--row-group-size', type=int, default=200) + parser.add_argument('--io-threads', type=int, default=16) + parser.add_argument('--overwrite', action='store_true', + help='re-convert subsets whose shards already exist') + args = parser.parse_args() + + with open(args.config, 'r') as f: + cfg = yaml.safe_load(f)[args.group] + dataset_names = cfg['dataset_names'] + num_used_data = cfg['num_used_data'] + shuffle_lines = cfg.get('shuffle_lines', False) + shuffle_seed = cfg.get('shuffle_seed', 0) + if len(dataset_names) != len(num_used_data): + # The jsonl loader zips the two lists, silently truncating the longer + # one (this happens in the real configs: subsets commented out of + # dataset_names leave stale num_used_data rows). Replicate that + # pairing exactly — equivalence with current training behavior. + print(f'[warn] dataset_names ({len(dataset_names)}) and num_used_data ' + f'({len(num_used_data)}) differ; zip-truncating like the jsonl loader') + + stems = [sanitize(s) for s in dataset_names] + assert len(set(stems)) == len(stems), 'sanitized subset names collide' + + train_dir = os.path.join(args.out_dir, 'train') + info_dir = os.path.join(args.out_dir, 'parquet_info') + os.makedirs(train_dir, exist_ok=True) + os.makedirs(info_dir, exist_ok=True) + info_path = os.path.join(info_dir, 'llava_onevision_vqa.json') + # info keys use the configured-style relative path; loaders fall back to + # basename matching anyway, so the prefix only matters cosmetically. + info_key_dir = train_dir + + selected = args.subsets if args.subsets else dataset_names + unknown = [s for s in selected if s not in dataset_names] + assert not unknown, f'subsets not in the yaml group: {unknown}' + + totals = {'ok': 0, 'skip_video': 0, 'skip_missing': 0, 'skip_badjson': 0} + for subset, n_used in zip(dataset_names, num_used_data): + if subset not in selected: + continue + stem = sanitize(subset) + # A subset counts as done only if its shards are in the info JSON, + # which is merge-updated AFTER the whole subset finishes. Files on + # disk without an info entry are partial output of an interrupted + # run — delete and re-convert. + done_in_info = set() + if os.path.exists(info_path) and not args.overwrite: + with open(info_path, 'r') as f: + done_in_info = {os.path.basename(k) for k in json.load(f) + if os.path.basename(k).startswith(f'{stem}__part')} + existing = [f for f in os.listdir(train_dir) + if f.startswith(f'{stem}__part') and f.endswith('.parquet')] + if done_in_info and set(existing) >= done_in_info: + print(f'[skip] {subset}: {len(done_in_info)} shard(s) already converted') + continue + for f in existing: + os.remove(os.path.join(train_dir, f)) + + meta_info = DATASET_INFO[args.group][subset] + shard_info, stats = convert_subset( + subset, meta_info['jsonl_path'], meta_info['data_dir'], n_used, + shuffle_lines, shuffle_seed, train_dir, args.rows_per_file, + args.row_group_size, args.io_threads, info_key_dir, + ) + for k in totals: + totals[k] += stats[k] + print(f'[done] {subset}: {stats}') + + # merge-update the shared parquet_info JSON after each subset so + # partial runs / parallel invocations stay resumable. + merged = {} + if os.path.exists(info_path): + with open(info_path, 'r') as f: + merged = json.load(f) + merged.update(shard_info) + tmp_path = info_path + f'.tmp.{os.getpid()}' + with open(tmp_path, 'w') as f: + json.dump(merged, f, indent=1) + os.replace(tmp_path, info_path) + + print(f'[all done] totals: {totals}') + print(f'parquet_info: {info_path}') + + +if __name__ == '__main__': + main() diff --git a/data/any2any_preprocess/hf_upload.py b/data/any2any_preprocess/hf_upload.py new file mode 100644 index 0000000000000000000000000000000000000000..a06881e4110fc1f55fc6928ec4022eb339393365 --- /dev/null +++ b/data/any2any_preprocess/hf_upload.py @@ -0,0 +1,26 @@ +"""Upload a folder to a HuggingFace dataset repo. + +Token is read from the file at $HF_TOKEN_FILE (never passed on the command line +so it does not appear in logs / process listings). + +Usage: + HF_TOKEN_FILE=/users/mye/.hf_token python hf_upload.py +""" +import os +import sys + +from huggingface_hub import HfApi + +repo_id = sys.argv[1] +folder = sys.argv[2] +token = open(os.environ['HF_TOKEN_FILE']).read().strip() + +api = HfApi(token=token) +api.create_repo(repo_id, repo_type='dataset', exist_ok=True) +api.upload_folder( + repo_id=repo_id, + repo_type='dataset', + folder_path=folder, + commit_message='Add MODUS 16-mod preview (500 SA-1B samples + hero + card)', +) +print(f'DONE: https://huggingface.co/datasets/{repo_id}', flush=True) diff --git a/data/any2any_preprocess/parquet_visualize.py b/data/any2any_preprocess/parquet_visualize.py new file mode 100644 index 0000000000000000000000000000000000000000..cd9c0a9ea7a3b170584f9d9e4b3f385b55332511 --- /dev/null +++ b/data/any2any_preprocess/parquet_visualize.py @@ -0,0 +1,408 @@ +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq +import os +import numpy as np +from pathlib import Path +from PIL import Image, ImageDraw, ImageFont +import json +import io +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from matplotlib.patches import Rectangle +import cv2 + +def make_json_serializable(obj): + """ + Convert numpy arrays and other non-serializable objects to JSON-serializable format. + """ + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, dict): + return {key: make_json_serializable(value) for key, value in obj.items()} + elif isinstance(obj, list): + return [make_json_serializable(item) for item in obj] + elif isinstance(obj, (np.integer, np.floating)): + return obj.item() + elif hasattr(obj, 'tolist'): # Handle other numpy-like objects + return obj.tolist() + elif hasattr(obj, 'item'): # Handle other numpy scalar types + return obj.item() + else: + return obj + +def decode_rle_to_mask(rle_counts, size): + """ + Decode RLE (Run Length Encoding) to binary mask. + This is a simplified RLE decoder for COCO format. + + Args: + rle_counts (str): RLE encoded string + size (list): [width, height] of the mask + + Returns: + numpy.ndarray: Binary mask of shape (height, width) + """ + try: + # Try to use pycocotools if available + import pycocotools.mask as mask_util + + # Create RLE dict + rle = { + 'counts': rle_counts, + 'size': size + } + + # Decode to binary mask + binary_mask = mask_util.decode(rle) + return binary_mask + except ImportError: + # Fallback: create a simple mask visualization without decoding + print("Warning: pycocotools not available, creating placeholder mask") + height, width = size[1], size[0] # size is [width, height] + return np.zeros((height, width), dtype=np.uint8) + +def simple_rle_decode(rle_counts, size): + """ + Simple RLE decoder for COCO format masks. + This is a basic implementation that should work for most cases. + """ + try: + height, width = size[1], size[0] # size is [width, height] + mask = np.zeros(height * width, dtype=np.uint8) + + # Parse the RLE counts + counts = [] + current_num = "" + for char in rle_counts: + if char.isdigit() or char in '-+': + current_num += char + else: + if current_num: + counts.append(int(current_num)) + current_num = "" + if current_num: + counts.append(int(current_num)) + + # Decode RLE + pos = 0 + for i, count in enumerate(counts): + if i % 2 == 0: # Even indices are background (0) + pos += count + else: # Odd indices are foreground (1) + mask[pos:pos+count] = 1 + pos += count + + # Reshape to 2D + mask = mask.reshape((height, width)) + return mask + except Exception as e: + print(f"Error in simple RLE decode: {e}") + height, width = size[1], size[0] + return np.zeros((height, width), dtype=np.uint8) + +def visualize_detection_data(det_data, rgb_image, output_dir, filename_base): + """ + Visualize detection data with bounding boxes and masks overlaid on RGB image. + + Args: + det_data (dict): Detection data containing instances + rgb_image (PIL.Image): RGB image to overlay on + output_dir (str): Output directory + filename_base (str): Base filename for saving + """ + try: + # Make a deep copy of the original data to avoid modifying it + import copy + original_det_data = make_json_serializable(copy.deepcopy(det_data)) + # Convert PIL image to numpy array + img_array = np.array(rgb_image) + height, width = img_array.shape[:2] + + # Create figure with subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 6)) + + # Original image + axes[0].imshow(img_array) + axes[0].set_title('Original Image') + axes[0].axis('off') + + # Image with bounding boxes + axes[1].imshow(img_array) + axes[1].set_title('Bounding Boxes') + axes[1].axis('off') + + # Image with masks + axes[2].imshow(img_array) + axes[2].set_title('Segmentation Masks') + axes[2].axis('off') + + # Colors for different categories + colors = ['red', 'blue', 'green', 'yellow', 'purple', 'orange', 'cyan', 'magenta'] + + # Process each instance + for i, instance in enumerate(det_data.get('instances', [])): + color = colors[i % len(colors)] + category = instance.get('category', 'unknown') + score = instance.get('score', 0.0) + bbox = instance.get('bbox', []) + + # Draw bounding box + if len(bbox) == 4: + x1, y1, x2, y2 = bbox + rect = Rectangle((x1, y1), x2-x1, y2-y1, + linewidth=2, edgecolor=color, facecolor='none') + axes[1].add_patch(rect) + + # Add label + label = f"{category}: {score:.2f}" + axes[1].text(x1, y1-5, label, color=color, fontsize=8, + bbox=dict(boxstyle="round,pad=0.3", facecolor='white', alpha=0.7)) + + # Draw segmentation mask + segmentation = instance.get('segmentation', {}) + mask_drawn = False + + if 'counts' in segmentation and 'size' in segmentation: + # Try multiple decoding methods + mask = None + + # Method 1: Try pycocotools + try: + mask = decode_rle_to_mask(segmentation['counts'], segmentation['size']) + if mask.size > 0 and np.any(mask): + print(f"Successfully decoded mask for {category} using pycocotools") + except: + pass + + # Method 2: Try simple RLE decoder + if mask is None or not np.any(mask): + try: + mask = simple_rle_decode(segmentation['counts'], segmentation['size']) + if mask.size > 0 and np.any(mask): + print(f"Successfully decoded mask for {category} using simple decoder") + except Exception as e: + print(f"Simple RLE decode failed for {category}: {e}") + + # If we have a valid mask, visualize it + if mask is not None and mask.size > 0 and np.any(mask): + # Create a more visible mask visualization + # Method 1: Colored overlay + mask_colored = np.zeros((height, width, 4)) + color_rgb = np.array(plt.cm.colors.to_rgb(color)) * 255 + mask_colored[:, :, :3] = color_rgb + mask_colored[:, :, 3] = mask * 0.6 # More opaque + + # Apply mask overlay + axes[2].imshow(mask_colored.astype(np.uint8), alpha=0.7) + + # Method 2: Also draw contour + from matplotlib.patches import Polygon + contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + for contour in contours: + if len(contour) > 2: + contour = contour.reshape(-1, 2) + polygon = Polygon(contour, fill=False, edgecolor=color, linewidth=2) + axes[2].add_patch(polygon) + + mask_drawn = True + + # Fallback: if no mask was drawn, create a simple mask from bounding box + if not mask_drawn and len(bbox) == 4: + x1, y1, x2, y2 = bbox + # Create a simple rectangular mask + mask = np.zeros((height, width), dtype=np.uint8) + x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) + # Ensure coordinates are within bounds + x1, y1 = max(0, x1), max(0, y1) + x2, y2 = min(width, x2), min(height, y2) + + if x2 > x1 and y2 > y1: + mask[y1:y2, x1:x2] = 1 + + # Create colored mask overlay + mask_colored = np.zeros((height, width, 4)) + color_rgb = np.array(plt.cm.colors.to_rgb(color)) * 255 + mask_colored[:, :, :3] = color_rgb + mask_colored[:, :, 3] = mask * 0.4 # Semi-transparent + + # Apply mask overlay + axes[2].imshow(mask_colored.astype(np.uint8), alpha=0.6) + + # Also draw the bounding box outline + rect = Rectangle((x1, y1), x2-x1, y2-y1, + linewidth=2, edgecolor=color, facecolor='none') + axes[2].add_patch(rect) + + # Save the visualization + output_path = os.path.join(output_dir, f"{filename_base}_detection_visualization.png") + plt.tight_layout() + plt.savefig(output_path, dpi=150, bbox_inches='tight') + plt.close() + + print(f" Saved detection visualization: {filename_base}_detection_visualization.png") + + # Also save the JSON data for reference + json_path = os.path.join(output_dir, f"{filename_base}_det_seg.json") + try: + with open(json_path, 'w') as f: + json.dump(original_det_data, f, indent=2) + print(f" Saved detection JSON: {filename_base}_det_seg.json") + except Exception as json_error: + print(f" JSON save error: {json_error}") + # Try to save a simplified version + try: + simplified_data = { + 'image_id': original_det_data.get('image_id', 'unknown'), + 'instances': [] + } + for instance in original_det_data.get('instances', []): + simplified_instance = { + 'category': instance.get('category', 'unknown'), + 'score': float(instance.get('score', 0.0)), + 'bbox': [float(x) for x in instance.get('bbox', [])], + 'area': float(instance.get('area', 0.0)) + } + simplified_data['instances'].append(simplified_instance) + + with open(json_path, 'w') as f: + json.dump(simplified_data, f, indent=2) + print(f" Saved simplified detection JSON: {filename_base}_det_seg.json") + except Exception as e2: + print(f" Failed to save even simplified JSON: {e2}") + + except Exception as e: + print(f" Error visualizing detection data: {e}") + +def visualize_parquet_examples(input_file, output_dir, max_rows=None): + """ + Visualize examples from a parquet file by saving each row as separate files. + Saves images as PNG and text data as TXT files with uid_column_name naming format. + + Args: + input_file (str): Path to the input parquet file + output_dir (str): Directory to save the visualization files + max_rows (int): Maximum number of rows to process (None for all rows) + """ + + # Create output directory if it doesn't exist + os.makedirs(output_dir, exist_ok=True) + + print(f"Processing {input_file}...") + + # Read the parquet file + df = pd.read_parquet(input_file) + print(f"Total rows in file: {len(df)}") + print(f"Columns: {list(df.columns)}") + + # Limit rows if max_rows is specified + if max_rows is not None: + df = df.head(max_rows) + print(f"Processing only first {max_rows} rows") + + # Display first few rows to understand the data structure + print("\nFirst few rows:") + print(df.head(3)) + + # Process each row + for index, row in df.iterrows(): + print(f"Processing row {index}...") + + filename_base = None + + # Process each column in the row + for col_name, value in row.items(): + if col_name == 'uid': + filename_base = value + elif col_name in ['rgb', 'depth', 'normal'] and filename_base is not None: + # Handle image data (binary) + if value is not None and isinstance(value, bytes): + try: + # Convert binary data to PIL Image + image = Image.open(io.BytesIO(value)) + + # Create filename + filename = f"{filename_base}_{col_name}.png" + filepath = os.path.join(output_dir, filename) + + # Save as PNG + image.save(filepath, 'PNG') + print(f" Saved {col_name} image: {filename}") + + except Exception as e: + print(f" Error processing {col_name} image: {e}") + + elif col_name == 'caption' and filename_base is not None: + # Handle text data + if value is not None: + try: + filename = f"{filename_base}_captions.txt" + filepath = os.path.join(output_dir, filename) + + # Save text data + with open(filepath, 'w', encoding='utf-8') as f: + if isinstance(value, str): + f.write(value) + else: + f.write(str(value)) + print(f" Saved caption: {filename}") + + except Exception as e: + print(f" Error processing caption: {e}") + elif col_name == 'det_seg' and filename_base is not None: + # Handle detection maps (JSON format) + if value is not None: + try: + # Get the RGB image for visualization + rgb_image = None + if 'rgb' in row and row['rgb'] is not None: + rgb_image = Image.open(io.BytesIO(row['rgb'])) + + # Visualize detection data with bounding boxes and masks + if rgb_image is not None: + visualize_detection_data(value, rgb_image, output_dir, filename_base) + else: + # If no RGB image, just save the JSON + filename = f"{filename_base}_det_seg.json" + filepath = os.path.join(output_dir, filename) + with open(filepath, 'w') as f: + serializable_data = make_json_serializable(value) + json.dump(serializable_data, f, indent=2) + print(f" Saved detection JSON: {filename}") + + except Exception as e: + print(f" Error processing detection data: {e}") + + + + print(f"\nVisualization completed! Files saved to: {output_dir}") + + # List the created files + created_files = os.listdir(output_dir) + print(f"Created {len(created_files)} files:") + for file in sorted(created_files)[:10]: # Show first 10 files + print(f" {file}") + if len(created_files) > 10: + print(f" ... and {len(created_files) - 10} more files") + +def main(): + # Configuration + input_file = "./datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg/sa_000002.parquet" + output_dir = "./datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_visualize" + max_rows = 30 # Process only first 30 rows + + # Check if input file exists + if not os.path.exists(input_file): + print(f"Error: Input file {input_file} not found!") + return + + # Create output directory + os.makedirs(output_dir, exist_ok=True) + + # Visualize the parquet examples + visualize_parquet_examples(input_file, output_dir, max_rows) + + print("\nVisualization completed successfully!") + +if __name__ == "__main__": + main() diff --git a/data/any2any_preprocess/process_grounding.py b/data/any2any_preprocess/process_grounding.py new file mode 100644 index 0000000000000000000000000000000000000000..3cc5406a8d3defdab0b2145e6e53bae4e9d0b80d --- /dev/null +++ b/data/any2any_preprocess/process_grounding.py @@ -0,0 +1,346 @@ +import os +import tarfile +import json +import glob +import io +from tqdm import tqdm +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +class GroundingProcessor: + def __init__(self, base_dir): + self.base_dir = base_dir + self.rgb_caption_dir = os.path.join(base_dir, 'datasets/blip3o/datasets') + self.grounding_source_dir = os.path.join(base_dir, 'datasets/glamm') + self.grounding_output_dir = os.path.join(base_dir, 'datasets/blip3o/datasets_grounding') + self.grounding_cache_dir = os.path.join(base_dir, 'datasets/blip3o/grounding_cache') + + # Create output directories if they don't exist + os.makedirs(self.grounding_output_dir, exist_ok=True) + os.makedirs(self.grounding_cache_dir, exist_ok=True) + + def get_rgb_tar_files(self): + """Get all RGB tar files from datasets directory""" + tar_files = glob.glob(os.path.join(self.rgb_caption_dir, '*.tar')) + return sorted(tar_files) + + def get_grounding_tar_files(self): + """Get all grounding tar files from part directories""" + grounding_tars = [] + for part in ['part_1', 'part_2', 'part_3', 'part_4']: + part_dir = os.path.join(self.grounding_source_dir, part) + if os.path.exists(part_dir): + part_tars = glob.glob(os.path.join(part_dir, '*.tar.gz')) + grounding_tars.extend(part_tars) + return sorted(grounding_tars) + + def extract_uids_from_rgb_tar(self, tar_path): + """Extract all UIDs from RGB tar file""" + uids = set() + + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if member.isfile(): + filename = member.name + uid = os.path.splitext(filename)[0] # Remove extension + uids.add(uid) + except Exception as e: + logger.error(f"Error extracting UIDs from {tar_path}: {e}") + return set() + + return uids + + def build_grounding_index(self): + """Build an index of all grounding data: uid -> (tar_path, member_name)""" + logger.info("Building grounding data index...") + grounding_index = {} + + grounding_tars = self.get_grounding_tar_files() + + for tar_path in tqdm(grounding_tars, desc="Indexing grounding tars"): + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if member.isfile() and member.name.endswith('.json'): + uid = os.path.splitext(member.name)[0] + grounding_index[uid] = (tar_path, member.name) + except Exception as e: + logger.warning(f"Error indexing {tar_path}: {e}") + continue + + logger.info(f"Built index with {len(grounding_index)} grounding entries") + return grounding_index + + def pre_extract_all_grounding_data_to_disk(self): + """Pre-extract all grounding data to disk cache for fastest access""" + logger.info("Pre-extracting all grounding data to disk cache...") + + grounding_tars = self.get_grounding_tar_files() + cache_index = {} + + for tar_path in tqdm(grounding_tars, desc="Pre-extracting grounding data to disk"): + try: + with tarfile.open(tar_path, 'r') as tar: + for member in tar.getmembers(): + if member.isfile() and member.name.endswith('.json'): + uid = os.path.splitext(member.name)[0] + + # Extract JSON content + f = tar.extractfile(member) + if f is not None: + content = f.read() + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + + # Save to disk cache + cache_file = os.path.join(self.grounding_cache_dir, f"{uid}.json") + with open(cache_file, 'w') as cache_f: + json.dump(json_data, cache_f, indent=2) + + cache_index[uid] = cache_file + except Exception as e: + logger.warning(f"Error pre-extracting from {tar_path}: {e}") + continue + + # Save index file + index_file = os.path.join(self.grounding_cache_dir, "index.json") + with open(index_file, 'w') as f: + json.dump(cache_index, f, indent=2) + + logger.info(f"Pre-extracted {len(cache_index)} grounding entries to disk cache") + return cache_index + + def load_grounding_cache_index(self): + """Load the grounding cache index""" + index_file = os.path.join(self.grounding_cache_dir, "index.json") + if os.path.exists(index_file): + with open(index_file, 'r') as f: + return json.load(f) + return {} + + def load_grounding_data_from_cache(self, uids): + """Load grounding data from disk cache for specific UIDs""" + uid_to_grounding = {} + + for uid in uids: + cache_file = os.path.join(self.grounding_cache_dir, f"{uid}.json") + if os.path.exists(cache_file): + try: + with open(cache_file, 'r') as f: + json_data = json.load(f) + uid_to_grounding[uid] = json_data + except Exception as e: + logger.warning(f"Error loading {cache_file}: {e}") + continue + + return uid_to_grounding + + def find_grounding_data_for_uids_fast(self, uids, grounding_data): + """Find grounding JSON data for given UIDs using pre-extracted data - FASTEST""" + uid_to_grounding = {} + + for uid in uids: + if uid in grounding_data: + uid_to_grounding[uid] = grounding_data[uid] + + return uid_to_grounding + + def find_grounding_data_for_uids(self, uids, grounding_index): + """Find grounding JSON data for given UIDs using pre-built index with batch processing""" + uid_to_grounding = {} + + # Group UIDs by tar_path to minimize tar file opens + tar_to_uids = {} + for uid in uids: + if uid in grounding_index: + tar_path, member_name = grounding_index[uid] + if tar_path not in tar_to_uids: + tar_to_uids[tar_path] = [] + tar_to_uids[tar_path].append((uid, member_name)) + + # Process each tar file once, extracting all needed UIDs + for tar_path, uid_members in tar_to_uids.items(): + try: + with tarfile.open(tar_path, 'r') as tar: + for uid, member_name in uid_members: + try: + member = tar.getmember(member_name) + f = tar.extractfile(member) + if f is not None: + content = f.read() + json_str = content.decode('utf-8') + json_data = json.loads(json_str) + uid_to_grounding[uid] = json_data + except Exception as e: + logger.warning(f"Error extracting {uid} from {tar_path}: {e}") + continue + except Exception as e: + logger.warning(f"Error opening {tar_path}: {e}") + continue + + return uid_to_grounding + + def create_grounding_tar(self, rgb_tar_path, uid_to_grounding): + """Create a grounding tar file with the same structure as RGB tar""" + + # Extract base name (e.g., 'sa_000000' from 'sa_000000.tar') + base_name = os.path.splitext(os.path.basename(rgb_tar_path))[0] + output_tar_path = os.path.join(self.grounding_output_dir, f"{base_name}.tar") + + logger.info(f"Creating grounding tar: {output_tar_path}") + + try: + with tarfile.open(output_tar_path, 'w') as output_tar: + for uid, grounding_data in uid_to_grounding.items(): + # Create JSON content + json_str = json.dumps(grounding_data, indent=2) + json_bytes = json_str.encode('utf-8') + + # Add to tar with same naming convention as RGB + tarinfo = tarfile.TarInfo(name=f"{uid}.json") + tarinfo.size = len(json_bytes) + output_tar.addfile(tarinfo, io.BytesIO(json_bytes)) + + logger.info(f"Created grounding tar with {len(uid_to_grounding)} UIDs: {output_tar_path}") + return output_tar_path + + except Exception as e: + logger.error(f"Error creating grounding tar {output_tar_path}: {e}") + return None + + def process_single_rgb_tar(self, rgb_tar_path, grounding_data=None, grounding_index=None, use_cache=False): + """Process a single RGB tar file and create corresponding grounding tar""" + + # Extract base name + base_name = os.path.splitext(os.path.basename(rgb_tar_path))[0] + + logger.info(f"Processing {base_name}") + + # Extract UIDs from RGB tar + uids = self.extract_uids_from_rgb_tar(rgb_tar_path) + + if not uids: + logger.warning(f"No UIDs found in {rgb_tar_path}") + return None + + logger.info(f"Found {len(uids)} UIDs in {base_name}") + + # Find grounding data for these UIDs + if use_cache: + # Use disk cache (fastest and memory efficient) + uid_to_grounding = self.load_grounding_data_from_cache(uids) + elif grounding_data is not None: + # Use pre-extracted data (fastest) + uid_to_grounding = self.find_grounding_data_for_uids_fast(uids, grounding_data) + elif grounding_index is not None: + # Use index-based lookup (faster than original) + uid_to_grounding = self.find_grounding_data_for_uids(uids, grounding_index) + else: + logger.error("No grounding data or index provided") + return None + + if not uid_to_grounding: + logger.warning(f"No grounding data found for UIDs in {base_name}") + return None + + logger.info(f"Found grounding data for {len(uid_to_grounding)}/{len(uids)} UIDs") + + # Create grounding tar file + output_path = self.create_grounding_tar(rgb_tar_path, uid_to_grounding) + + return output_path + + def process_all_rgb_tars(self, use_cache=True, pre_extract_cache=False): + """Process all RGB tar files and create corresponding grounding tars""" + rgb_tars = self.get_rgb_tar_files() + + if not rgb_tars: + logger.error("No RGB tar files found") + return + + logger.info(f"Found {len(rgb_tars)} RGB tar files to process") + + if use_cache: + # Check if cache exists + cache_index = self.load_grounding_cache_index() + + if not cache_index: + if pre_extract_cache: + logger.info("Pre-extracting grounding data to disk cache...") + self.pre_extract_all_grounding_data_to_disk() + else: + logger.error("No cache found. Use --pre_extract_cache to create cache first.") + return + + logger.info("Using disk cache method (fastest and memory efficient)") + processed_files = [] + for rgb_tar in tqdm(rgb_tars, desc="Processing RGB tars"): + try: + output_path = self.process_single_rgb_tar(rgb_tar, use_cache=True) + if output_path: + processed_files.append(output_path) + except Exception as e: + logger.error(f"Error processing {rgb_tar}: {e}") + else: + # Use index-based method (faster than original, less memory) + logger.info("Using index-based method (faster, less memory)") + grounding_index = self.build_grounding_index() + + processed_files = [] + for rgb_tar in tqdm(rgb_tars, desc="Processing RGB tars"): + try: + output_path = self.process_single_rgb_tar(rgb_tar, grounding_index=grounding_index) + if output_path: + processed_files.append(output_path) + except Exception as e: + logger.error(f"Error processing {rgb_tar}: {e}") + + logger.info(f"Successfully processed {len(processed_files)} grounding tar files") + return processed_files + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='Process grounding data') + parser.add_argument('--use_cache', action='store_true', default=True, + help='Use disk cache method (fastest and memory efficient)') + parser.add_argument('--pre_extract_cache', action='store_true', + help='Pre-extract grounding data to disk cache') + parser.add_argument('--base_dir', type=str, default='./', + help='Base directory (default: ./)') + + args = parser.parse_args() + + # Set the base directory + base_dir = args.base_dir + + # Create processor + processor = GroundingProcessor(base_dir) + + # Process all RGB tar files + processed_files = processor.process_all_rgb_tars( + use_cache=args.use_cache, + pre_extract_cache=args.pre_extract_cache + ) + + if processed_files: + logger.info(f"Successfully created {len(processed_files)} grounding tar files:") + for file in processed_files: + logger.info(f" - {file}") + else: + logger.error("No grounding tar files were created") + +if __name__ == "__main__": + main() + + + +# This will take time but only needs to be done once +# python data/any2any_preprocess/process_grounding.py --pre_extract_cache + +# This will be extremely fast +# python data/any2any_preprocess/process_grounding.py --use_cache \ No newline at end of file diff --git a/data/any2any_preprocess/recompress_normal_jpeg.py b/data/any2any_preprocess/recompress_normal_jpeg.py new file mode 100644 index 0000000000000000000000000000000000000000..e38be021beaffc9076808e62cab2e79761b05cce --- /dev/null +++ b/data/any2any_preprocess/recompress_normal_jpeg.py @@ -0,0 +1,90 @@ +"""Recompress the `normal` column of the 16-mod parquet from PNG to JPEG q95. + +The `normal` column stores raw image bytes (Marigold normal maps, pass-through +PNG) and accounts for ~65% of on-disk size while being ~6x larger than the +JPEG-encoded `rgb` column. Re-encoding normal as JPEG q95 shrinks it to +roughly rgb size with negligible quality loss. + +Every other column is passed through untouched; the Arrow schema is identical +to the source, so downstream dataloader / HF viewer see no structural change. + +Usage: + python recompress_normal_jpeg.py --out_dir file1.parquet [file2 ...] +""" +import argparse +import io +import os + +import pyarrow as pa +import pyarrow.parquet as pq +from PIL import Image + +JPEG_QUALITY = 95 + + +def recompress_normal_bytes(b): + if b is None: + return None + if isinstance(b, memoryview): + b = b.tobytes() + img = Image.open(io.BytesIO(b)).convert('RGB') + out = io.BytesIO() + img.save(out, format='JPEG', quality=JPEG_QUALITY) + return out.getvalue() + + +def process_file(src, out_dir): + dst = os.path.join(out_dir, os.path.basename(src)) + pf = pq.ParquetFile(src) + schema = pf.schema_arrow + assert 'normal' in schema.names, f'no normal column in {src}' + norm_idx = schema.names.index('normal') + + reported_fmt = False + writer = pq.ParquetWriter(dst, schema, compression='snappy') + try: + for rg in range(pf.num_row_groups): + table = pf.read_row_group(rg) + normal_col = table.column('normal').to_pylist() + + if not reported_fmt: + for b in normal_col: + if b is not None: + bb = b.tobytes() if isinstance(b, memoryview) else b + fmt = Image.open(io.BytesIO(bb)).format + print(f' source normal format = {fmt}', flush=True) + reported_fmt = True + break + + new_normal = [recompress_normal_bytes(b) for b in normal_col] + new_col = pa.array(new_normal, type=schema.field('normal').type) + table = table.set_column(norm_idx, schema.field('normal'), new_col) + writer.write_table(table) + finally: + writer.close() + + s0, s1 = os.path.getsize(src), os.path.getsize(dst) + print(f' {os.path.basename(src)}: {s0/1e9:.2f} GB -> {s1/1e9:.2f} GB ' + f'({100*(1-s1/s0):.0f}% smaller)', flush=True) + return s0, s1 + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--out_dir', required=True) + ap.add_argument('files', nargs='+') + args = ap.parse_args() + os.makedirs(args.out_dir, exist_ok=True) + + tot0 = tot1 = 0 + for f in args.files: + print(f'[{os.path.basename(f)}]', flush=True) + s0, s1 = process_file(f, args.out_dir) + tot0 += s0 + tot1 += s1 + print(f'TOTAL: {tot0/1e9:.2f} GB -> {tot1/1e9:.2f} GB ' + f'({100*(1-tot1/tot0):.0f}% smaller)', flush=True) + + +if __name__ == '__main__': + main() diff --git a/data/any2any_preprocess/run_full_rebuild.py b/data/any2any_preprocess/run_full_rebuild.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb6761cbec7984a49fc2b9d6d725db4b885c115 --- /dev/null +++ b/data/any2any_preprocess/run_full_rebuild.py @@ -0,0 +1,82 @@ +"""Slurm driver: rebuild a strided slice of the full dataset. + +Robust against memory leaks: each small chunk of files is processed by a FRESH +subprocess (build_full_release.py --workers 1) that exits afterwards, so the OS +reclaims all memory — no accumulation across the run. `concurrency` such +subprocesses run at once. Resumable via process_one's skip. +""" +import argparse +import os +import subprocess +import sys +from concurrent.futures import ThreadPoolExecutor, as_completed + +SRC = ('datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding' + '_canny_dino_global_clip448_imagebind_samseg_samedge_cocodet') +OUT = 'datasets/blip3o/modus_full' +WORKER = 'data/any2any_preprocess/build_full_release.py' +CHUNK = 4 + + +def config_of(fname): + if fname.startswith('sa_'): + return 'sa1b' + if fname.startswith('webdataset_JDB_'): + return 'journeydb' + if fname.startswith('webdataset_shard_'): + return 'cc12m' + return None + + +def run_chunk(cfg, files): + cmd = [sys.executable, WORKER, '--config', cfg, + '--out_dir', os.path.join(OUT, cfg), '--workers', '1'] + files + r = subprocess.run(cmd, capture_output=True, text=True) + return cfg, len(files), r.returncode, (r.stderr or '')[-300:] + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--num-tasks', type=int, required=True) + ap.add_argument('--task-id', type=int, required=True) + ap.add_argument('--concurrency', type=int, default=48) + args = ap.parse_args() + + for cfg in ('sa1b', 'journeydb', 'cc12m'): + os.makedirs(os.path.join(OUT, cfg), exist_ok=True) + + items = [] + for fn in sorted(os.listdir(SRC)): + if fn.endswith('.parquet') and config_of(fn): + items.append((config_of(fn), os.path.join(SRC, fn))) + mine = items[args.task_id::args.num_tasks] + + # group into per-config chunks + chunks = [] + bycfg = {} + for cfg, src in mine: + bycfg.setdefault(cfg, []).append(src) + for cfg, fs in bycfg.items(): + for i in range(0, len(fs), CHUNK): + chunks.append((cfg, fs[i:i + CHUNK])) + + print(f'task {args.task_id}/{args.num_tasks}: {len(mine)} files in ' + f'{len(chunks)} chunks, concurrency={args.concurrency}', flush=True) + done_files = fail = 0 + with ThreadPoolExecutor(max_workers=args.concurrency) as ex: + futs = [ex.submit(run_chunk, cfg, fs) for cfg, fs in chunks] + for i, fu in enumerate(as_completed(futs), 1): + cfg, n, rc, err = fu.result() + if rc == 0: + done_files += n + else: + fail += n + print(f' CHUNK FAIL {cfg} rc={rc}: {err}', flush=True) + if i % 20 == 0: + print(f'[{i}/{len(chunks)} chunks] files_ok={done_files} ' + f'fail={fail}', flush=True) + print(f'TASK COMPLETE: {done_files} files ok, {fail} failed', flush=True) + + +if __name__ == '__main__': + main() diff --git a/data/any2any_preprocess/upload_full.py b/data/any2any_preprocess/upload_full.py new file mode 100644 index 0000000000000000000000000000000000000000..e36fc9c1632afdc5ff5ec559cc91f1c19fd22fe4 --- /dev/null +++ b/data/any2any_preprocess/upload_full.py @@ -0,0 +1,26 @@ +"""Resumable upload of the full-release folder to the HF dataset repo. + +Uses upload_large_folder (xet, multi-commit, resumable). Uploads folder_path +contents to the repo root, preserving the sa1b/ journeydb/ cc12m/ structure. +Re-running skips already-uploaded files. + +Usage: + HF_TOKEN_FILE=/users/mye/.hf_token python upload_full.py [allow_glob] +""" +import os +import sys + +from huggingface_hub import HfApi + +repo_id = sys.argv[1] +folder = sys.argv[2] +allow = list(sys.argv[3:]) # zero or more allow_patterns +token = open(os.environ['HF_TOKEN_FILE']).read().strip() + +api = HfApi(token=token) +api.create_repo(repo_id, repo_type='dataset', exist_ok=True) +kw = dict(repo_id=repo_id, repo_type='dataset', folder_path=folder, print_report=True) +if allow: + kw['allow_patterns'] = allow +api.upload_large_folder(**kw) +print(f'UPLOAD PASS DONE: {folder} {allow} -> {repo_id}', flush=True) diff --git a/data/any2any_preprocess/vqa_convert_parquet_to_jsonl.py b/data/any2any_preprocess/vqa_convert_parquet_to_jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..024c3631ea72b57e7ba290d02b177d466e4887b1 --- /dev/null +++ b/data/any2any_preprocess/vqa_convert_parquet_to_jsonl.py @@ -0,0 +1,312 @@ +#!/usr/bin/env python3 +""" +Convert LLaVA OneVision parquet files to JSONL format matching llava_ov_si.jsonl + +Single-folder usage (backwards compatible): + python vqa_convert_parquet_to_jsonl.py --image-dir [--max-rows N] + +Multi-folder usage (process all immediate subfolders of ): + python vqa_convert_parquet_to_jsonl.py dummy.jsonl --process-all --output-root [--max-rows N] + +Notes: +- In multi-folder mode, outputs are written per subfolder under //{images,label/labels.jsonl} +- The script prints the sorted folder index and name, and a global image counter every N (default 1000) +""" + +import argparse +import json +import os +import glob +from pathlib import Path +import pandas as pd +from PIL import Image +import io +from tqdm import tqdm +def _save_pil_image_as_jpeg(pil_image: Image.Image, image_path: str): + """ + Ensure the PIL image is compatible with JPEG (no alpha), then save. + """ + try: + # Convert to RGB if image has alpha channel or unsupported mode for JPEG + if pil_image.mode not in ("RGB", "L"): + pil_image = pil_image.convert("RGB") + pil_image.save(image_path, format="JPEG") + except Exception: + # As a fallback, force convert to RGB and retry + pil_image = pil_image.convert("RGB") + pil_image.save(image_path, format="JPEG") + + + +def convert_parquet_to_jsonl( + parquet_dir: str, + output_jsonl: str, + image_dir: str = None, + max_rows: int = None, + sample_id_offset: int = 0, + progress: dict | None = None, +): + """ + Convert parquet files to JSONL format compatible with llava_ov_si.jsonl + + Args: + parquet_dir: Directory containing parquet files (can be subdirectory) + output_jsonl: Output JSONL file path + image_dir: Directory to save images (optional, if None saves with dataset) + max_rows: Maximum number of rows to process (for testing) + sample_id_offset: Starting ID offset (for merging multiple datasets) + progress: Optional dict to track global progress across folders. Expected keys: + {"count": int, "next_milestone": int, "interval": int} + """ + + # Find all parquet files in the directory + parquet_pattern = os.path.join(parquet_dir, "train-*.parquet") + parquet_files = sorted(glob.glob(parquet_pattern)) + + if not parquet_files: + print(f"Error: No parquet files found in {parquet_dir}") + return + + print(f"Found {len(parquet_files)} parquet file(s)") + + # Create image directory if specified + if image_dir: + os.makedirs(image_dir, exist_ok=True) + + all_entries = [] + current_id = sample_id_offset + + for parquet_file in parquet_files: + print(f"\nProcessing {parquet_file}...") + df = pd.read_parquet(parquet_file) + + for idx, row in tqdm(df.iterrows(), total=len(df)): + if max_rows and len(all_entries) >= max_rows: + break + + # Extract conversations - handle numpy array conversion + conversations = row["conversations"] + if hasattr(conversations, "tolist"): + conversations = conversations.tolist() + + # Handle image - extract from binary data and save + image_filename = None + if row["image"] is not None: + try: + # The image data is stored as binary bytes + image_data = row["image"] + + # Generate filename from ID + image_filename = f"{current_id}.jpg" + + if image_dir: + image_path = os.path.join(image_dir, image_filename) + else: + # Save in same directory as parquet file + image_dir_local = os.path.dirname(parquet_file) + image_path = os.path.join( + image_dir_local, image_filename + ) + + # Convert image data to PIL Image and save + if isinstance(image_data, dict): + # Image is stored as a dict with 'bytes' key containing image data + if ( + "bytes" in image_data + and image_data["bytes"] is not None + ): + image_bytes = image_data["bytes"] + pil_image = Image.open(io.BytesIO(image_bytes)) + _save_pil_image_as_jpeg(pil_image, image_path) + else: + print( + f"Warning: Dict image data doesn't contain 'bytes' key for row {idx}" + ) + continue + elif isinstance(image_data, bytes): + # Try to open as PIL Image from bytes + pil_image = Image.open(io.BytesIO(image_data)) + _save_pil_image_as_jpeg(pil_image, image_path) + elif isinstance(image_data, str): + # If it's a string, try to decode it + image_bytes = image_data.encode( + "latin-1" + ) # Convert string to bytes + pil_image = Image.open(io.BytesIO(image_bytes)) + _save_pil_image_as_jpeg(pil_image, image_path) + else: + print( + f"Warning: Unknown image data type {type(image_data)} for row {idx}" + ) + continue + + except Exception as e: + print(f"Error processing image for row {idx}: {e}") + continue + + # Create JSONL entry matching llava_ov_si.jsonl format + entry = { + "id": current_id, + "image": image_filename, + "conversations": conversations, + } + + all_entries.append(entry) + current_id += 1 + + # Update global progress if provided + if progress is not None: + progress["count"] += 1 + if progress["count"] >= progress.get("next_milestone", 1000): + print( + f"[global] Processed {progress['count']} images total" + ) + interval = progress.get("interval", 1000) + progress["next_milestone"] = progress["count"] + interval + + if max_rows and len(all_entries) >= max_rows: + break + + if max_rows and len(all_entries) >= max_rows: + break + + # Write JSONL file + print(f"\nWriting {len(all_entries)} entries to {output_jsonl}...") + with open(output_jsonl, "w", encoding="utf-8") as f: + for entry in all_entries: + f.write(json.dumps(entry, ensure_ascii=False) + "\n") + + print(f"✓ Converted {len(all_entries)} samples to {output_jsonl}") + if image_dir: + print(f"✓ Images saved to {image_dir}") + + +def main(): + parser = argparse.ArgumentParser( + description="Convert LLaVA OneVision parquet files to JSONL format" + ) + parser.add_argument( + "parquet_dir", + help="Directory containing parquet files, or a root directory with subfolders", + ) + parser.add_argument("output_jsonl", help="Output JSONL file path (single-folder mode)") + parser.add_argument( + "--image-dir", + default=None, + help="Directory to save images (default: same as parquet directory)", + ) + parser.add_argument( + "--max-rows", + type=int, + default=None, + help="Maximum number of rows to process (for testing)", + ) + parser.add_argument( + "--id-offset", + type=int, + default=0, + help="Starting ID offset (for merging datasets)", + ) + parser.add_argument( + "--process-all", + action="store_true", + help="Process all immediate subfolders in parquet_dir and write per-folder outputs under --output-root", + ) + parser.add_argument( + "--output-root", + default=None, + help="Base output directory when using --process-all", + ) + parser.add_argument( + "--print-every", + type=int, + default=1000, + help="Global image progress interval for multi-folder mode", + ) + parser.add_argument( + "--start-index", + type=int, + default=1, + help="1-based index of subfolder to start processing from (resume)", + ) + + args = parser.parse_args() + + if args.process_all: + if not args.output_root: + raise SystemExit("--output-root is required when using --process-all") + + # Discover immediate subfolders and sort + if not os.path.isdir(args.parquet_dir): + raise SystemExit(f"Input root '{args.parquet_dir}' is not a directory") + + subfolders = [ + d + for d in sorted(os.listdir(args.parquet_dir)) + if os.path.isdir(os.path.join(args.parquet_dir, d)) + ] + + # Filter to only those having parquet files + filtered_subfolders = [] + for d in subfolders: + pattern = os.path.join(args.parquet_dir, d, "train-*.parquet") + if glob.glob(pattern): + filtered_subfolders.append(d) + subfolders = filtered_subfolders + + total = len(subfolders) + print(f"Found {total} subfolder(s) to process under {args.parquet_dir}") + + # Compute starting index (1-based) and slice subfolders for resume + start_index = max(1, int(args.start_index)) if args.start_index else 1 + if start_index > total: + print(f"Start index {start_index} is beyond total {total}; nothing to do.") + return + subfolders_to_process = subfolders[start_index - 1 :] + + # Prepare global progress + progress = { + "count": 0, + "next_milestone": args.print_every, + "interval": args.print_every, + } + # When resuming at folder index 41, hardcode the global counter baseline + if start_index == 41: + progress["count"] = 1380000 + progress["next_milestone"] = progress["count"] + progress["interval"] + + for idx, sub in enumerate(subfolders_to_process, start=start_index): + print(f"\n[{idx}/{total}] Processing folder: {sub}") + sub_in = os.path.join(args.parquet_dir, sub) + sub_out_images = os.path.join(args.output_root, sub, "images") + sub_out_label_dir = os.path.join(args.output_root, sub, "label") + os.makedirs(sub_out_images, exist_ok=True) + os.makedirs(sub_out_label_dir, exist_ok=True) + sub_out_jsonl = os.path.join(sub_out_label_dir, "labels.jsonl") + + convert_parquet_to_jsonl( + parquet_dir=sub_in, + output_jsonl=sub_out_jsonl, + image_dir=sub_out_images, + max_rows=args.max_rows, + sample_id_offset=args.id_offset, + progress=progress, + ) + else: + convert_parquet_to_jsonl( + parquet_dir=args.parquet_dir, + output_jsonl=args.output_jsonl, + image_dir=args.image_dir, + max_rows=args.max_rows, + sample_id_offset=args.id_offset, + ) + + +if __name__ == "__main__": + main() + + +# python data/any2any_preprocess/vqa_convert_parquet_to_jsonl.py datasets/llava_onevision/ai2d\(cauldron\,llava_format\)/ datasets/llava_onevision_vqa/label/ai2d_converted.jsonl --image-dir datasets/llava_onevision_vqa/images + +# python data/any2any_preprocess/vqa_convert_parquet_to_jsonl.py datasets/llava_onevision dummy.jsonl --process-all --output-root datasets/llava_onevision_vqa +# python data/any2any_preprocess/vqa_convert_parquet_to_jsonl.py datasets/llava_onevision dummy.jsonl --process-all --output-root datasets/llava_onevision_vqa --start-index 41 diff --git a/data/bundled_parquet_info/README.md b/data/bundled_parquet_info/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f4ea6abeef51e717eb3a157b23a118a73eecaf5a --- /dev/null +++ b/data/bundled_parquet_info/README.md @@ -0,0 +1,43 @@ +# bundled_parquet_info — repo-tracked parquet metadata (auto-fallback) + +Repo-tracked copies of the parquet-info JSONs used by BAGEL training. +**The code reads this directory automatically**: `data/dataset_base.py` +falls back to `data/bundled_parquet_info/` whenever the +configured live path (`./datasets/blip3o/parquet_info/…`, a symlink chain +that only exists on the original cluster) is missing. When the live path +exists it still wins — behavior on the original cluster is unchanged. + +## Files + +| File | Files listed | Rows | Used by | +|---|---|---|---| +| `…_grounding_canny_dino_global_clip448_imagebind.json` | 2891 (1000 `sa_*` + 1891 `webdataset_*`) | 29.2M | 13mod: all target groups except grounding (any2rgb / depth / normal / caption / canny / dino / dinolocal / clip / imagebind / imagebindlocal) | +| `…_grounding2_canny_dino_global_clip448_imagebind.json` | 1000 (`sa_*` only) | 11.1M | 13mod: `unified_any2grounding` only | +| `…_clip448_imagebind_samseg_samedge_cocodet.json` | — | — | 16mod runs (adds cocodet / samseg / samedge) | +| `llava_onevision_vqa.json` | — | — | VLM-SFT (llava-onevision) | + +**Why grounding has two JSONs:** grounding annotations (GLaMM phrase+bbox, +matched by UID) only exist for the SA-1B portion of the parquet set. The +`webdataset_*` shards have no `grounding` column at all (verified via +parquet footer schema). So when grounding is the generation TARGET, the +loader must restrict itself to the `sa_*` files — that restricted file +list is the `grounding2` JSON. Both JSONs point into the SAME `data_dir`. + +## Setting up on a new machine + +1. Get the parquet data itself (≈ several TB, NOT in this repo): + `parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind/` + (2891 parquet files). +2. Place (or symlink) it at + `datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind/` + relative to the repo root. +3. parquet-info JSONs: **no action needed** — the loader automatically + falls back to this directory when the live + `datasets/blip3o/parquet_info/` path is absent + (`data/dataset_base.py`, "Fall back to the copy bundled in the repo"). +4. Note the JSON keys are relative paths + (`./datasets/blip3o/parquet_.../sa_000000.parquet`), so always launch + training from the repo root. + +If the parquet set ever changes, regenerate with +`data/any2any_preprocess/generate_parquet_json.py` and refresh these copies. diff --git a/data/bundled_parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding2_canny_dino_global_clip448_imagebind.json b/data/bundled_parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding2_canny_dino_global_clip448_imagebind.json new file mode 100644 index 0000000000000000000000000000000000000000..0a0c84013027548f866bd341801284da86acadd1 --- /dev/null +++ b/data/bundled_parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding2_canny_dino_global_clip448_imagebind.json @@ -0,0 +1,4002 @@ +{ + "./datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind/sa_000000.parquet": { + "num_row_groups": 12, + "num_rows": 11119 + }, + 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"num_rows": 308 + }, + "./datasets/llava_onevision_vqa_parquet/train/vsr_cauldron_llava_format__part0000.parquet": { + "num_row_groups": 11, + "num_rows": 2152 + }, + "./datasets/llava_onevision_vqa_parquet/train/websight_cauldron__part0000.parquet": { + "num_row_groups": 25, + "num_rows": 5000 + }, + "./datasets/llava_onevision_vqa_parquet/train/websight_cauldron__part0001.parquet": { + "num_row_groups": 25, + "num_rows": 4995 + } +} \ No newline at end of file diff --git a/data/configs/ablation_any2any_only.yaml b/data/configs/ablation_any2any_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8982538d6fc7e2303642909cf26947dee3de7dbf --- /dev/null +++ b/data/configs/ablation_any2any_only.yaml @@ -0,0 +1,130 @@ +# Dataset ablation: any-to-any only (MMMU lower bound). +# Keeps all unified_any2X groups from BLIP3O; vlm_sft removed. + +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2canny: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 1 diff --git a/data/configs/ablation_vqa_only.yaml b/data/configs/ablation_vqa_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f3ab6a4423c6ee58009f1f18d0413b7c4305e0e --- /dev/null +++ b/data/configs/ablation_vqa_only.yaml @@ -0,0 +1,193 @@ +# Dataset ablation: VQA-only (MMMU upper bound). +# Keeps only vlm_sft; all unified_any2X groups removed. +# Total samples: ~3,938,627 + +# Total samples across all vlm_sft subsets: 3,938,627. +vlm_sft: + dataset_names: + - ai2d(cauldron,llava_format) + - ai2d(gpt4v) + - ai2d(internvl) + - allava_instruct_laion4v + - allava_instruct_vflan4v + - aokvqa(cauldron,llava_format) + - chart2text(cauldron) + - chartqa(cauldron,llava_format) + - chrome_writting + - clevr(cauldron,llava_format) + - CLEVR-Math(MathV360K) + - diagram_image_to_text(cauldron) + - dvqa(cauldron,llava_format) + - figureqa(cauldron,llava_format) + - FigureQA(MathV360K) + - geo170k(align) + - geo170k(qa) + - geo3k + - Geometry3K(MathV360K) + - geomverse(cauldron) + - GeoQA+(MathV360K) + - GEOS(MathV360K) + - hateful_memes(cauldron,llava_format) + - hitab(cauldron,llava_format) + - hme100k + - iam(cauldron) + - iconqa(cauldron,llava_format) + - IconQA(MathV360K) + - iiit5k + - image_textualization(filtered) + - infographic(gpt4v) + - infographic_vqa + - infographic_vqa_llava_format + - intergps(cauldron,llava_format) + - k12_printing + - llavar_gpt4_20k + - llava_wild_4v_12k_filtered + - llava_wild_4v_39k_filtered + - lrv_chart + - lrv_normal(filtered) + - mapqa(cauldron,llava_format) + - MapQA(MathV360K) + - MathV360K_TQA + - MathV360K_VQA-AS + - MathV360K_VQA-RAD + - mavis_math_metagen + - mavis_math_rule_geo + - multihiertt(cauldron) + - orand_car_a + - PMC-VQA(MathV360K) + - raven(cauldron) + - rendered_text(cauldron) + - robut_sqa(cauldron) + - robut_wikisql(cauldron) + - robut_wtq(cauldron,llava_format) + - scienceqa(cauldron,llava_format) + - scienceqa(nona_context) + - screen2words(cauldron) + - sharegpt4o + - sharegpt4v(coco) + - sharegpt4v(knowledge) + - sharegpt4v(llava) + - sharegpt4v(sam) + - sroie + - st_vqa(cauldron,llava_format) + - Super-CLEVR(MathV360K) + - tabmwp(cauldron) + - TabMWP(MathV360K) + - tallyqa(cauldron,llava_format) + - textcaps + - textocr(gpt4v) + - tqa(cauldron,llava_format) + - UniGeo(MathV360K) + - ureader_cap + - ureader_ie + - vision_flan(filtered) + - vistext(cauldron) + - visual7w(cauldron,llava_format) + - visualmrc(cauldron) + - VisualWebInstruct(filtered) + - VizWiz(MathV360K) + - vqarad(cauldron,llava_format) + - vsr(cauldron,llava_format) + - websight(cauldron) + image_transform_args: + image_stride: 14 + max_image_size: 980 + min_image_size: 378 + max_pixels: 2_007_040 + frame_sampler_args: + max_num_frames: 12 + min_num_frames: 8 + is_mandatory: true + shuffle_lines: True + shuffle_seed: 0 + num_used_data: + - 2429 + - 4864 + - 12403 + - 49990 + - 19990 + - 16534 + - 26956 + - 18260 + - 8825 + - 69995 + - 5280 + - 295 + - 199995 + - 142985 + - 99995 + - 17587 + - 60242 + - 67823 + - 2091 + - 9724 + - 9298 + - 17162 + - 498 + - 8495 + - 2495 + - 74492 + - 5658 + - 27302 + - 22589 + - 1990 + - 99573 + - 1982 + - 4394 + - 2113 + - 1275 + - 256636 + - 19790 + - 15124 + - 39362 + - 1776 + - 10490 + - 299988 + - 299990 + - 299982 + - 37412 + - 5225 + - 29827 + - 10176 + - 5902 + - 2125 + - 87348 + - 99990 + - 7614 + - 1999 + - 35948 + - 41995 + - 9995 + - 8509 + - 74984 + - 38241 + - 4971 + - 19208 + - 15725 + - 57284 + - 50017 + - 1988 + - 29990 + - 8990 + - 33616 + - 17242 + - 8642 + - 22717 + - 22452 + - 98675 + - 21942 + - 25104 + - 27302 + - 11949 + - 91434 + - 17322 + - 186060 + - 9964 + - 14361 + - 3022 + - 263584 + - 6604 + - 308 + - 2152 + - 9995 + weight: 1 diff --git a/data/configs/any2any.yaml b/data/configs/any2any.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b389ca052fe930a50e84404be3696155a8d871c1 --- /dev/null +++ b/data/configs/any2any.yaml @@ -0,0 +1,64 @@ +unified_any2rgb: + dataset_names: + - blip3o + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 \ No newline at end of file diff --git a/data/configs/any2any_8modalities_stage1.yaml b/data/configs/any2any_8modalities_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..befc4addf968bfb1d770badd95aa9b39aa15bcbf --- /dev/null +++ b/data/configs/any2any_8modalities_stage1.yaml @@ -0,0 +1,49 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 10 diff --git a/data/configs/any2any_9modalities_stage1.yaml b/data/configs/any2any_9modalities_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f581a11ef684c633c5b90b75d5438c5529333918 --- /dev/null +++ b/data/configs/any2any_9modalities_stage1.yaml @@ -0,0 +1,65 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 5 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 \ No newline at end of file diff --git a/data/configs/any2any_9modalities_stage1_toy.yaml b/data/configs/any2any_9modalities_stage1_toy.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e648f36f7fbdc44eb156a8de5c036c7dd42df0fb --- /dev/null +++ b/data/configs/any2any_9modalities_stage1_toy.yaml @@ -0,0 +1,65 @@ +unified_any2rgb: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 1 + + +unified_any2grounding: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 5 + +unified_any2dino: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 5 \ No newline at end of file diff --git a/data/configs/any2any_9modalities_stage1_toy_debug.yaml b/data/configs/any2any_9modalities_stage1_toy_debug.yaml new file mode 100644 index 0000000000000000000000000000000000000000..59b0262066df30968cbdb5fadfa37481f2f68d15 --- /dev/null +++ b/data/configs/any2any_9modalities_stage1_toy_debug.yaml @@ -0,0 +1,31 @@ +# unified_any2rgb: +# dataset_names: +# - blip3o_example_toydataset +# image_transform_args: +# image_stride: 16 +# max_image_size: 1024 +# min_image_size: 512 +# vit_image_transform_args: +# image_stride: 14 +# max_image_size: 518 +# min_image_size: 224 +# is_mandatory: true +# num_used_data: +# - 1 +# weight: 1 + +unified_any2dino: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 5 \ No newline at end of file diff --git a/data/configs/any2any_mandatory_rgb_caption_depth_normal_det.yaml b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7a5210e95ebd5b43ee2470ebcc4aa2f15a275226 --- /dev/null +++ b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det.yaml @@ -0,0 +1,82 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2det: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + diff --git a/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg.yaml b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e6b27e2d004f1dd0f5cdd9ebe48c4d2b5b0f81d8 --- /dev/null +++ b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg.yaml @@ -0,0 +1,98 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2det: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + diff --git a/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg_grounding.yaml b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg_grounding.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bb6a5787ebd3918712a85d9715bf91383cef1d18 --- /dev/null +++ b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg_grounding.yaml @@ -0,0 +1,97 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 1 \ No newline at end of file diff --git a/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg_grounding_canny_dino.yaml b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg_grounding_canny_dino.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8520c8332d09b9534228920e562476a60528ee13 --- /dev/null +++ b/data/configs/any2any_mandatory_rgb_caption_depth_normal_det_seg_grounding_canny_dino.yaml @@ -0,0 +1,130 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 1 + + +unified_any2canny: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 \ No newline at end of file diff --git a/data/configs/any2any_rgb_caption_depth_normal_det_seg.yaml b/data/configs/any2any_rgb_caption_depth_normal_det_seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..896719549fcd463350d157067271ba6c6dd55270 --- /dev/null +++ b/data/configs/any2any_rgb_caption_depth_normal_det_seg.yaml @@ -0,0 +1,98 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + + +unified_any2det: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + diff --git a/data/configs/bagel_stage3_exact3cond.yaml b/data/configs/bagel_stage3_exact3cond.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1842355e52d083de493a2f86239ba1c675c067c3 --- /dev/null +++ b/data/configs/bagel_stage3_exact3cond.yaml @@ -0,0 +1,111 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 5 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 + +unified_any2dinolocal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 + +unified_any2clip: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 + +unified_any2imagebind: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 + +unified_any2imagebindlocal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 diff --git a/data/configs/example.yaml b/data/configs/example.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7834e7bd0c3704a8a0edd966d91f410a669ebdcc --- /dev/null +++ b/data/configs/example.yaml @@ -0,0 +1,45 @@ +# t2i_pretrain: +# dataset_names: +# - t2i +# image_transform_args: +# image_stride: 16 +# max_image_size: 1024 +# min_image_size: 512 +# is_mandatory: true +# num_used_data: # The sum should be larger that NUM_GPUS x NUM_WORKERS +# - 10 +# weight: 1 + +# unified_edit: +# dataset_names: +# - seedxedit_multi +# image_transform_args: +# image_stride: 16 +# max_image_size: 1024 +# min_image_size: 512 +# vit_image_transform_args: +# image_stride: 14 +# max_image_size: 518 +# min_image_size: 224 +# is_mandatory: false +# num_used_data: +# - 10 +# weight: 1 + +vlm_sft: + dataset_names: + - llava_ov + image_transform_args: + image_stride: 14 + max_image_size: 980 + min_image_size: 378 + max_pixels: 2_007_040 + frame_sampler_args: + max_num_frames: 12 + min_num_frames: 8 + is_mandatory: true + shuffle_lines: True + shuffle_seed: 0 + num_used_data: + - 1000 + weight: 1 diff --git a/data/configs/example_any2any.yaml b/data/configs/example_any2any.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a2041ed1d82c4156c94a2b3144d9abcd5c110f7 --- /dev/null +++ b/data/configs/example_any2any.yaml @@ -0,0 +1,64 @@ +unified_any2rgb: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 1 + + +unified_any2caption: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_example_toydataset + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1 + weight: 1 \ No newline at end of file diff --git a/data/configs/modus_stage1.yaml b/data/configs/modus_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..57b3c6150c7a68ebba30d0ac16a3fc5568797ec3 --- /dev/null +++ b/data/configs/modus_stage1.yaml @@ -0,0 +1,245 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +# Total samples across all vlm_sft subsets: 3,938,627. +vlm_sft: + dataset_names: + - ai2d(cauldron,llava_format) + - ai2d(gpt4v) + - ai2d(internvl) + - allava_instruct_laion4v + - allava_instruct_vflan4v + - aokvqa(cauldron,llava_format) + - chart2text(cauldron) + - chartqa(cauldron,llava_format) + - chrome_writting + - clevr(cauldron,llava_format) + - CLEVR-Math(MathV360K) + - diagram_image_to_text(cauldron) + - dvqa(cauldron,llava_format) + # - Evol-Instruct-GPT4-Turbo + - figureqa(cauldron,llava_format) + - FigureQA(MathV360K) + - geo170k(align) + - geo170k(qa) + - geo3k + - Geometry3K(MathV360K) + - geomverse(cauldron) + - GeoQA+(MathV360K) + - GEOS(MathV360K) + - hateful_memes(cauldron,llava_format) + - hitab(cauldron,llava_format) + - hme100k + - iam(cauldron) + - iconqa(cauldron,llava_format) + - IconQA(MathV360K) + - iiit5k + - image_textualization(filtered) + - infographic(gpt4v) + - infographic_vqa + - infographic_vqa_llava_format + - intergps(cauldron,llava_format) + - k12_printing + - llavar_gpt4_20k + - llava_wild_4v_12k_filtered + - llava_wild_4v_39k_filtered + - lrv_chart + - lrv_normal(filtered) + # - magpie_pro(l3_80b_mt) + # - magpie_pro(l3_80b_st) + # - magpie_pro(qwen2_72b_st) + - mapqa(cauldron,llava_format) + - MapQA(MathV360K) + # - mathqa + - MathV360K_TQA + - MathV360K_VQA-AS + - MathV360K_VQA-RAD + - mavis_math_metagen + - mavis_math_rule_geo + - multihiertt(cauldron) + - orand_car_a + - PMC-VQA(MathV360K) + - raven(cauldron) + - rendered_text(cauldron) + - robut_sqa(cauldron) + - robut_wikisql(cauldron) + - robut_wtq(cauldron,llava_format) + - scienceqa(cauldron,llava_format) + - scienceqa(nona_context) + - screen2words(cauldron) + - sharegpt4o + - sharegpt4v(coco) + - sharegpt4v(knowledge) + - sharegpt4v(llava) + - sharegpt4v(sam) + - sroie + - st_vqa(cauldron,llava_format) + - Super-CLEVR(MathV360K) + - tabmwp(cauldron) + - TabMWP(MathV360K) + - tallyqa(cauldron,llava_format) + - textcaps + - textocr(gpt4v) + - tqa(cauldron,llava_format) + - UniGeo(MathV360K) + - ureader_cap + - ureader_ie + - vision_flan(filtered) + - vistext(cauldron) + - visual7w(cauldron,llava_format) + - visualmrc(cauldron) + - VisualWebInstruct(filtered) + - VizWiz(MathV360K) + - vqarad(cauldron,llava_format) + - vsr(cauldron,llava_format) + - websight(cauldron) + image_transform_args: + image_stride: 14 + max_image_size: 980 + min_image_size: 378 + max_pixels: 2_007_040 + frame_sampler_args: + max_num_frames: 12 + min_num_frames: 8 + is_mandatory: true + shuffle_lines: True + shuffle_seed: 0 + num_used_data: + - 2429 + - 4864 + - 12403 + - 49990 + - 19990 + - 16534 + - 26956 + - 18260 + - 8825 + - 69995 + - 5280 + - 295 + - 199995 + - 99995 + - 17587 + - 60242 + - 67823 + - 2091 + - 9724 + - 9298 + - 17162 + - 498 + - 8495 + - 2495 + - 74492 + - 5658 + - 27302 + - 22589 + - 1990 + - 99573 + - 1982 + - 4394 + - 2113 + - 1275 + - 256636 + - 19790 + - 15124 + - 39362 + - 1776 + - 10490 + - 37412 + - 5225 + - 10176 + - 5902 + - 2125 + - 87348 + - 99990 + - 7614 + - 1999 + - 35948 + - 41995 + - 9995 + - 8509 + - 74984 + - 38241 + - 4971 + - 19208 + - 15725 + - 57284 + - 50017 + - 1988 + - 29990 + - 8990 + - 33616 + - 17242 + - 8642 + - 22717 + - 22452 + - 98675 + - 21942 + - 25104 + - 27302 + - 11949 + - 91434 + - 17322 + - 186060 + - 9964 + - 14361 + - 3022 + - 263584 + - 6604 + - 308 + - 2152 + - 9995 + # 5 text-only subsets (Evol-Instruct/magpie_pro x3/mathqa) are + # excluded from dataset_names; their stale quotas were removed + # here too (they previously shifted every later subset's quota). + weight: 1 + +# ==== VQA Consistency Check ==== +# Datasets: 89 | Records: 3938627 | Image refs: 3938627 | Missing images: 1072772 | Video refs: 0 | Missing videos: 0 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 5 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 5 diff --git a/data/configs/modus_stage2.yaml b/data/configs/modus_stage2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..67a4f62fce17f3b202275ad2beb5c678fc151b40 --- /dev/null +++ b/data/configs/modus_stage2.yaml @@ -0,0 +1,329 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +# Total samples across all vlm_sft subsets: 3,938,627. +vlm_sft: + dataset_names: + - ai2d(cauldron,llava_format) + - ai2d(gpt4v) + - ai2d(internvl) + - allava_instruct_laion4v + - allava_instruct_vflan4v + - aokvqa(cauldron,llava_format) + - chart2text(cauldron) + - chartqa(cauldron,llava_format) + - chrome_writting + - clevr(cauldron,llava_format) + - CLEVR-Math(MathV360K) + - diagram_image_to_text(cauldron) + - dvqa(cauldron,llava_format) + # - Evol-Instruct-GPT4-Turbo + - figureqa(cauldron,llava_format) + - FigureQA(MathV360K) + - geo170k(align) + - geo170k(qa) + - geo3k + - Geometry3K(MathV360K) + - geomverse(cauldron) + - GeoQA+(MathV360K) + - GEOS(MathV360K) + - hateful_memes(cauldron,llava_format) + - hitab(cauldron,llava_format) + - hme100k + - iam(cauldron) + - iconqa(cauldron,llava_format) + - IconQA(MathV360K) + - iiit5k + - image_textualization(filtered) + - infographic(gpt4v) + - infographic_vqa + - infographic_vqa_llava_format + - intergps(cauldron,llava_format) + - k12_printing + - llavar_gpt4_20k + - llava_wild_4v_12k_filtered + - llava_wild_4v_39k_filtered + - lrv_chart + - lrv_normal(filtered) + # - magpie_pro(l3_80b_mt) + # - magpie_pro(l3_80b_st) + # - magpie_pro(qwen2_72b_st) + - mapqa(cauldron,llava_format) + - MapQA(MathV360K) + # - mathqa + - MathV360K_TQA + - MathV360K_VQA-AS + - MathV360K_VQA-RAD + - mavis_math_metagen + - mavis_math_rule_geo + - multihiertt(cauldron) + - orand_car_a + - PMC-VQA(MathV360K) + - raven(cauldron) + - rendered_text(cauldron) + - robut_sqa(cauldron) + - robut_wikisql(cauldron) + - robut_wtq(cauldron,llava_format) + - scienceqa(cauldron,llava_format) + - scienceqa(nona_context) + - screen2words(cauldron) + - sharegpt4o + - sharegpt4v(coco) + - sharegpt4v(knowledge) + - sharegpt4v(llava) + - sharegpt4v(sam) + - sroie + - st_vqa(cauldron,llava_format) + - Super-CLEVR(MathV360K) + - tabmwp(cauldron) + - TabMWP(MathV360K) + - tallyqa(cauldron,llava_format) + - textcaps + - textocr(gpt4v) + - tqa(cauldron,llava_format) + - UniGeo(MathV360K) + - ureader_cap + - ureader_ie + - vision_flan(filtered) + - vistext(cauldron) + - visual7w(cauldron,llava_format) + - visualmrc(cauldron) + - VisualWebInstruct(filtered) + - VizWiz(MathV360K) + - vqarad(cauldron,llava_format) + - vsr(cauldron,llava_format) + - websight(cauldron) + image_transform_args: + image_stride: 14 + max_image_size: 980 + min_image_size: 378 + max_pixels: 2_007_040 + frame_sampler_args: + max_num_frames: 12 + min_num_frames: 8 + is_mandatory: true + shuffle_lines: True + shuffle_seed: 0 + num_used_data: + - 2429 + - 4864 + - 12403 + - 49990 + - 19990 + - 16534 + - 26956 + - 18260 + - 8825 + - 69995 + - 5280 + - 295 + - 199995 + - 99995 + - 17587 + - 60242 + - 67823 + - 2091 + - 9724 + - 9298 + - 17162 + - 498 + - 8495 + - 2495 + - 74492 + - 5658 + - 27302 + - 22589 + - 1990 + - 99573 + - 1982 + - 4394 + - 2113 + - 1275 + - 256636 + - 19790 + - 15124 + - 39362 + - 1776 + - 10490 + - 37412 + - 5225 + - 10176 + - 5902 + - 2125 + - 87348 + - 99990 + - 7614 + - 1999 + - 35948 + - 41995 + - 9995 + - 8509 + - 74984 + - 38241 + - 4971 + - 19208 + - 15725 + - 57284 + - 50017 + - 1988 + - 29990 + - 8990 + - 33616 + - 17242 + - 8642 + - 22717 + - 22452 + - 98675 + - 21942 + - 25104 + - 27302 + - 11949 + - 91434 + - 17322 + - 186060 + - 9964 + - 14361 + - 3022 + - 263584 + - 6604 + - 308 + - 2152 + - 9995 + # 5 text-only subsets (Evol-Instruct/magpie_pro x3/mathqa) are + # excluded from dataset_names; their stale quotas were removed + # here too (they previously shifted every later subset's quota). + weight: 1 + +# ==== VQA Consistency Check ==== +# Datasets: 89 | Records: 3938627 | Image refs: 3938627 | Missing images: 1072772 | Video refs: 0 | Missing videos: 0 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 1000 + weight: 1 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +unified_any2canny: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 diff --git a/data/configs/modus_stage3.yaml b/data/configs/modus_stage3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f61222bed0c184410bcee5c71e3b950e6cff261d --- /dev/null +++ b/data/configs/modus_stage3.yaml @@ -0,0 +1,329 @@ +unified_any2rgb: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 + + +# Total samples across all vlm_sft subsets: 3,938,627. +vlm_sft: + dataset_names: + - ai2d(cauldron,llava_format) + - ai2d(gpt4v) + - ai2d(internvl) + - allava_instruct_laion4v + - allava_instruct_vflan4v + - aokvqa(cauldron,llava_format) + - chart2text(cauldron) + - chartqa(cauldron,llava_format) + - chrome_writting + - clevr(cauldron,llava_format) + - CLEVR-Math(MathV360K) + - diagram_image_to_text(cauldron) + - dvqa(cauldron,llava_format) + # - Evol-Instruct-GPT4-Turbo + - figureqa(cauldron,llava_format) + - FigureQA(MathV360K) + - geo170k(align) + - geo170k(qa) + - geo3k + - Geometry3K(MathV360K) + - geomverse(cauldron) + - GeoQA+(MathV360K) + - GEOS(MathV360K) + - hateful_memes(cauldron,llava_format) + - hitab(cauldron,llava_format) + - hme100k + - iam(cauldron) + - iconqa(cauldron,llava_format) + - IconQA(MathV360K) + - iiit5k + - image_textualization(filtered) + - infographic(gpt4v) + - infographic_vqa + - infographic_vqa_llava_format + - intergps(cauldron,llava_format) + - k12_printing + - llavar_gpt4_20k + - llava_wild_4v_12k_filtered + - llava_wild_4v_39k_filtered + - lrv_chart + - lrv_normal(filtered) + # - magpie_pro(l3_80b_mt) + # - magpie_pro(l3_80b_st) + # - magpie_pro(qwen2_72b_st) + - mapqa(cauldron,llava_format) + - MapQA(MathV360K) + # - mathqa + - MathV360K_TQA + - MathV360K_VQA-AS + - MathV360K_VQA-RAD + - mavis_math_metagen + - mavis_math_rule_geo + - multihiertt(cauldron) + - orand_car_a + - PMC-VQA(MathV360K) + - raven(cauldron) + - rendered_text(cauldron) + - robut_sqa(cauldron) + - robut_wikisql(cauldron) + - robut_wtq(cauldron,llava_format) + - scienceqa(cauldron,llava_format) + - scienceqa(nona_context) + - screen2words(cauldron) + - sharegpt4o + - sharegpt4v(coco) + - sharegpt4v(knowledge) + - sharegpt4v(llava) + - sharegpt4v(sam) + - sroie + - st_vqa(cauldron,llava_format) + - Super-CLEVR(MathV360K) + - tabmwp(cauldron) + - TabMWP(MathV360K) + - tallyqa(cauldron,llava_format) + - textcaps + - textocr(gpt4v) + - tqa(cauldron,llava_format) + - UniGeo(MathV360K) + - ureader_cap + - ureader_ie + - vision_flan(filtered) + - vistext(cauldron) + - visual7w(cauldron,llava_format) + - visualmrc(cauldron) + - VisualWebInstruct(filtered) + - VizWiz(MathV360K) + - vqarad(cauldron,llava_format) + - vsr(cauldron,llava_format) + - websight(cauldron) + image_transform_args: + image_stride: 14 + max_image_size: 980 + min_image_size: 378 + max_pixels: 2_007_040 + frame_sampler_args: + max_num_frames: 12 + min_num_frames: 8 + is_mandatory: true + shuffle_lines: True + shuffle_seed: 0 + num_used_data: + - 2429 + - 4864 + - 12403 + - 49990 + - 19990 + - 16534 + - 26956 + - 18260 + - 8825 + - 69995 + - 5280 + - 295 + - 199995 + - 99995 + - 17587 + - 60242 + - 67823 + - 2091 + - 9724 + - 9298 + - 17162 + - 498 + - 8495 + - 2495 + - 74492 + - 5658 + - 27302 + - 22589 + - 1990 + - 99573 + - 1982 + - 4394 + - 2113 + - 1275 + - 256636 + - 19790 + - 15124 + - 39362 + - 1776 + - 10490 + - 37412 + - 5225 + - 10176 + - 5902 + - 2125 + - 87348 + - 99990 + - 7614 + - 1999 + - 35948 + - 41995 + - 9995 + - 8509 + - 74984 + - 38241 + - 4971 + - 19208 + - 15725 + - 57284 + - 50017 + - 1988 + - 29990 + - 8990 + - 33616 + - 17242 + - 8642 + - 22717 + - 22452 + - 98675 + - 21942 + - 25104 + - 27302 + - 11949 + - 91434 + - 17322 + - 186060 + - 9964 + - 14361 + - 3022 + - 263584 + - 6604 + - 308 + - 2152 + - 9995 + # 5 text-only subsets (Evol-Instruct/magpie_pro x3/mathqa) are + # excluded from dataset_names; their stale quotas were removed + # here too (they previously shifted every later subset's quota). + weight: 1 + +# ==== VQA Consistency Check ==== +# Datasets: 89 | Records: 3938627 | Image refs: 3938627 | Missing images: 1072772 | Video refs: 0 | Missing videos: 0 + + +unified_any2grounding: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 1000 + weight: 1 + +unified_any2dino: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + + + + +unified_any2caption: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + +unified_any2seg: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 + + +unified_any2canny: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: false + num_used_data: + - 2891 + weight: 1 diff --git a/data/configs/single_task_depth.yaml b/data/configs/single_task_depth.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80c0e80df2a0c619aae251e7407a7b23308f1dda --- /dev/null +++ b/data/configs/single_task_depth.yaml @@ -0,0 +1,15 @@ +unified_any2depth: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 diff --git a/data/configs/single_task_normal.yaml b/data/configs/single_task_normal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77b1a98b2b36b601c89ecc14c14700b3f0ca9223 --- /dev/null +++ b/data/configs/single_task_normal.yaml @@ -0,0 +1,15 @@ +unified_any2normal: + dataset_names: + - blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino + image_transform_args: + image_stride: 16 + max_image_size: 1024 + min_image_size: 512 + vit_image_transform_args: + image_stride: 14 + max_image_size: 518 + min_image_size: 224 + is_mandatory: true + num_used_data: + - 2891 + weight: 1 diff --git a/data/data_utils.py b/data/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9dc69d5345a78a621f2bfa9923bb8627b3799c62 --- /dev/null +++ b/data/data_utils.py @@ -0,0 +1,509 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + + +import math +import random +from typing import Any, Dict, List, Optional, Tuple +from PIL import Image + +import torch +from torch.nn.attention.flex_attention import or_masks, and_masks + + +def create_sparse_mask(document_lens, split_lens, attn_modes, device): + def causal_mask(b, h, q_idx, kv_idx): + return q_idx >= kv_idx + + def full_and_noise_mask(b, h, q_idx, kv_idx): + return (full_and_noise_seq_id[q_idx] == full_and_noise_seq_id[kv_idx]) & (full_and_noise_seq_id[q_idx] >= 0) + + def remove_noise_mask(b, h, q_idx, kv_idx): + return (~((noise_seq_id[kv_idx] >= 0) & (noise_seq_id[q_idx] != noise_seq_id[kv_idx]))) + + def sample_mask(b, h, q_idx, kv_idx): + return document_id[q_idx] == document_id[kv_idx] + + full_and_noise_tmp = [] + noise_tmp = [] + + for i, (length, model) in enumerate(zip(split_lens, attn_modes)): + value = i if model in ['full', 'noise'] else -1 + full_and_noise_tmp.extend([value] * length) + value_noise = i if model == 'noise' else -1 + noise_tmp.extend([value_noise] * length) + + full_and_noise_seq_id = torch.Tensor(full_and_noise_tmp).to(device) + noise_seq_id = torch.Tensor(noise_tmp).to(device) + + document_id = torch.cat([torch.full((l,), i) for i, l in enumerate(document_lens, start=1)]).to(device) + + return and_masks(or_masks(causal_mask, full_and_noise_mask), remove_noise_mask, sample_mask) + + +def patchify(image, patch_size): + p = patch_size + c, h, w = image.shape + assert h % p == 0 and w % p == 0 + image = image.reshape(c, h // p, p, w // p, p) + image = torch.einsum("chpwq->hwpqc", image) + image = image.reshape(-1, p**2 * c) + return image + + +def get_flattened_position_ids_extrapolate(img_h, img_w, patch_size, max_num_patches_per_side): + num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size + coords_h = torch.arange(0, num_patches_h) + coords_w = torch.arange(0, num_patches_w) + pos_ids = (coords_h[:, None] * max_num_patches_per_side + coords_w).flatten() + return pos_ids + + +def get_flattened_position_ids_interpolate(img_h, img_w, patch_size, max_num_patches_per_side): + num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size + boundaries = torch.arange(1 / max_num_patches_per_side, 1.0, 1 / max_num_patches_per_side) + fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / num_patches_h) + fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / num_patches_w) + bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) + bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) + pos_ids = (bucket_coords_h[:, None] * max_num_patches_per_side + bucket_coords_w).flatten() + return pos_ids + + +def prepare_attention_mask_per_sample(split_lens, attn_modes, device="cpu"): + """ + nested_split_lens: A list of N lists of ints. Each int indicates the length of a split within + a sample, where each sample contains multiple splits with different attn modes. + nested_attn_modes: whether to use full attn in each split. + """ + sample_len = sum(split_lens) + attention_mask = torch.zeros((sample_len, sample_len), dtype=torch.bool, device=device) + + csum = 0 + for s, attn_mode in zip(split_lens, attn_modes): + assert attn_mode in ['causal', 'full', 'noise'] + if attn_mode == "causal": + attention_mask[csum:csum + s, csum:csum + s] = torch.ones((s, s), device=device).tril() + attention_mask[csum:csum + s, :csum] = 1 + else: + attention_mask[csum:csum + s, csum:csum + s] = torch.ones((s, s)) + attention_mask[csum:csum + s, :csum] = 1 + csum += s + + csum = 0 + for s, attn_mode in zip(split_lens, attn_modes): + if attn_mode == "noise": + attention_mask[:, csum : csum + s] = torch.zeros((sample_len, s)) + attention_mask[csum : csum + s, csum : csum + s] = torch.ones((s, s)) + csum += s + + attention_mask = torch.zeros_like(attention_mask, dtype=torch.float).masked_fill_( + ~attention_mask, float("-inf") + ) + + return attention_mask + + +def split_integer_exp_decay(S, ng_sample_decay=1.0): + if ng_sample_decay == 1.0: + N = random.randint(1, S) + else: + base = (1 - ng_sample_decay) / (1 - math.pow(ng_sample_decay, S)) + p = [base * math.pow(ng_sample_decay, i) for i in range(S)] + N = random.choices(list(range(1, S + 1)), p, k=1)[0] + cumsum = [0] + sorted(random.sample(range(1, S), N - 1)) + [S] + result = [cumsum[i+1] - cumsum[i] for i in range(len(cumsum) - 1)] + return result, cumsum + + +def pil_img2rgb(image): + if image.mode == "RGBA" or image.info.get("transparency", None) is not None: + image = image.convert("RGBA") + white = Image.new(mode="RGB", size=image.size, color=(255, 255, 255)) + white.paste(image, mask=image.split()[3]) + image = white + else: + image = image.convert("RGB") + + return image + + +def add_special_tokens(tokenizer): + all_special_tokens = [] + for k, v in tokenizer.special_tokens_map.items(): + if isinstance(v, str): + all_special_tokens.append(v) + elif isinstance(v, list): + all_special_tokens += v + + new_tokens = [] + + if '<|im_start|>' not in all_special_tokens: + new_tokens.append('<|im_start|>') + + if '<|im_end|>' not in all_special_tokens: + new_tokens.append('<|im_end|>') + + if '<|vision_start|>' not in all_special_tokens: + new_tokens.append('<|vision_start|>') + + if '<|vision_end|>' not in all_special_tokens: + new_tokens.append('<|vision_end|>') + + if '<|caption_start|>' not in all_special_tokens: + new_tokens.append('<|caption_start|>') + if '<|caption_end|>' not in all_special_tokens: + new_tokens.append('<|caption_end|>') + + if '<|depth_start|>' not in all_special_tokens: + new_tokens.append('<|depth_start|>') + if '<|depth_end|>' not in all_special_tokens: + new_tokens.append('<|depth_end|>') + + if '<|normal_start|>' not in all_special_tokens: + new_tokens.append('<|normal_start|>') + if '<|normal_end|>' not in all_special_tokens: + new_tokens.append('<|normal_end|>') + + num_new_tokens = tokenizer.add_tokens(new_tokens) + bos_token_id = tokenizer.convert_tokens_to_ids('<|im_start|>') + eos_token_id = tokenizer.convert_tokens_to_ids('<|im_end|>') + start_of_image = tokenizer.convert_tokens_to_ids('<|vision_start|>') + end_of_image = tokenizer.convert_tokens_to_ids('<|vision_end|>') + + start_of_caption = tokenizer.convert_tokens_to_ids('<|caption_start|>') + end_of_caption = tokenizer.convert_tokens_to_ids('<|caption_end|>') + start_of_depth = tokenizer.convert_tokens_to_ids('<|depth_start|>') + end_of_depth = tokenizer.convert_tokens_to_ids('<|depth_end|>') + start_of_normal = tokenizer.convert_tokens_to_ids('<|normal_start|>') + end_of_normal = tokenizer.convert_tokens_to_ids('<|normal_end|>') + + new_token_ids = dict( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + start_of_image=start_of_image, + end_of_image=end_of_image, + start_of_caption=start_of_caption, + end_of_caption=end_of_caption, + start_of_depth=start_of_depth, + end_of_depth=end_of_depth, + start_of_normal=start_of_normal, + end_of_normal=end_of_normal, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def _unique_preserve_order(tokens: List[str]) -> List[str]: + seen = set() + out: List[str] = [] + for t in tokens: + if t in seen: + continue + seen.add(t) + out.append(t) + return out + + +def _format_tokens(token_format: str, start: int, end: int) -> List[str]: + # Inclusive. + return [token_format.format(i=i) for i in range(int(start), int(end) + 1)] + + +def add_codebook_tokens( + tokenizer, + *, + token_groups: List[Dict[str, Any]], + delimiter_tokens: List[str], + extra_tokens: Optional[List[str]] = None, +) -> Tuple[Any, Dict[str, List[int]], Dict[str, int], int]: + """ + Generic helper to add codebook-style tokens (DET/DINO/DINOLOCAL/custom) in one format. + + Returns: + tokenizer, + group_token_ids: dict[group_name] -> list[token_id] aligned to (start..end), + delimiter_token_ids: dict[token_str] -> token_id, + num_new_tokens + """ + extra_tokens = extra_tokens or [] + + group_tokens: Dict[str, List[str]] = {} + all_code_tokens: List[str] = [] + for g in token_groups: + name = str(g["name"]) + fmt = str(g["token_format"]) + start = int(g.get("start", 0)) + end = g.get("end") + if end is None: + raise ValueError(f"token_groups entry '{name}' missing 'end'") + end = int(end) + toks = _format_tokens(fmt, start, end) + group_tokens[name] = toks + all_code_tokens.extend(toks) + + tokens_to_add = _unique_preserve_order(all_code_tokens + list(delimiter_tokens) + list(extra_tokens)) + num_new_tokens = tokenizer.add_tokens(tokens_to_add) + + group_token_ids: Dict[str, List[int]] = { + name: [int(tokenizer.convert_tokens_to_ids(t)) for t in toks] + for name, toks in group_tokens.items() + } + delimiter_token_ids: Dict[str, int] = { + str(t): int(tokenizer.convert_tokens_to_ids(str(t))) + for t in _unique_preserve_order(list(delimiter_tokens) + list(extra_tokens)) + } + return tokenizer, group_token_ids, delimiter_token_ids, int(num_new_tokens) + + +def add_det_special_tokens(tokenizer): + """ + Add special tokens for detection box coordinates. + Creates tokens for quantized coordinate values (0-999) to make them learnable. + """ + token_groups = [ + {"name": "x1", "token_format": "<|x1_{i:03d}|>", "start": 0, "end": 999}, + {"name": "y1", "token_format": "<|y1_{i:03d}|>", "start": 0, "end": 999}, + {"name": "x2", "token_format": "<|x2_{i:03d}|>", "start": 0, "end": 999}, + {"name": "y2", "token_format": "<|y2_{i:03d}|>", "start": 0, "end": 999}, + {"name": "score", "token_format": "<|score_{i:02d}|>", "start": 0, "end": 99}, + ] + delimiter_tokens = ["<|det_start|>", "<|det_end|>"] + extra_tokens = ["<|box_start|>", "<|box_end|>"] + + tokenizer, group_ids, delim_ids, num_new_tokens = add_codebook_tokens( + tokenizer, + token_groups=token_groups, + delimiter_tokens=delimiter_tokens, + extra_tokens=extra_tokens, + ) + + coord_token_map: Dict[str, Dict[int, int]] = {} + for coord_type in ["x1", "y1", "x2", "y2"]: + coord_token_map[coord_type] = {i: int(tok_id) for i, tok_id in enumerate(group_ids[coord_type])} + + score_token_map: Dict[int, int] = {i: int(tok_id) for i, tok_id in enumerate(group_ids["score"])} + + det_delimiter_map = { + "det_start": int(delim_ids["<|det_start|>"]), + "det_end": int(delim_ids["<|det_end|>"]), + "box_start": int(delim_ids["<|box_start|>"]), + "box_end": int(delim_ids["<|box_end|>"]), + } + + new_token_ids = dict( + coord_token_map=coord_token_map, + score_token_map=score_token_map, + det_delimiter_map=det_delimiter_map, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def add_dino_feature_tokens(tokenizer, vocab_size=8192): + """ + Add special tokens for DINO features. + - 8192 code tokens: <|dino_0000|> ... <|dino_8191|> + - 2 delimiter tokens: <|dino_start|>, <|dino_end|> + Returns mapping dicts similar to add_det_special_tokens. + """ + token_groups = [ + {"name": "dino", "token_format": "<|dino_{i:04d}|>", "start": 0, "end": int(vocab_size) - 1}, + ] + delimiter_tokens = ["<|dino_start|>", "<|dino_end|>"] + tokenizer, group_ids, delim_ids, num_new_tokens = add_codebook_tokens( + tokenizer, + token_groups=token_groups, + delimiter_tokens=delimiter_tokens, + extra_tokens=None, + ) + + dino_token_map = {i: int(tok_id) for i, tok_id in enumerate(group_ids["dino"])} + dino_delimiter_map = { + "dino_start": int(delim_ids["<|dino_start|>"]), + "dino_end": int(delim_ids["<|dino_end|>"]), + } + + new_token_ids = dict( + dino_token_map=dino_token_map, + dino_delimiter_map=dino_delimiter_map, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def add_dinolocal_feature_tokens(tokenizer, vocab_size=8192): + """ + Add special tokens for DINO-Local features. + - vocab_size code tokens: <|dinolocal_0000|> ... <|dinolocal_{vocab_size-1:04d}|> + - 2 delimiter tokens: <|dinolocal_start|>, <|dinolocal_end|> + Returns mapping dicts similar to add_det_special_tokens. + """ + token_groups = [ + {"name": "dinolocal", "token_format": "<|dinolocal_{i:04d}|>", "start": 0, "end": int(vocab_size) - 1}, + ] + delimiter_tokens = ["<|dinolocal_start|>", "<|dinolocal_end|>"] + tokenizer, group_ids, delim_ids, num_new_tokens = add_codebook_tokens( + tokenizer, + token_groups=token_groups, + delimiter_tokens=delimiter_tokens, + extra_tokens=None, + ) + + dinolocal_token_map = {i: int(tok_id) for i, tok_id in enumerate(group_ids["dinolocal"])} + dinolocal_delimiter_map = { + "dinolocal_start": int(delim_ids["<|dinolocal_start|>"]), + "dinolocal_end": int(delim_ids["<|dinolocal_end|>"]), + } + + new_token_ids = dict( + dinolocal_token_map=dinolocal_token_map, + dinolocal_delimiter_map=dinolocal_delimiter_map, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def add_clip_feature_tokens(tokenizer, vocab_size=8192): + """ + Add special tokens for CLIP features. + - vocab_size code tokens: <|clip_0000|> ... <|clip_{vocab_size-1:04d}|> + - 2 delimiter tokens: <|clip_start|>, <|clip_end|> + Returns mapping dicts similar to add_det_special_tokens. + """ + token_groups = [ + {"name": "clip", "token_format": "<|clip_{i:04d}|>", "start": 0, "end": int(vocab_size) - 1}, + ] + delimiter_tokens = ["<|clip_start|>", "<|clip_end|>"] + tokenizer, group_ids, delim_ids, num_new_tokens = add_codebook_tokens( + tokenizer, + token_groups=token_groups, + delimiter_tokens=delimiter_tokens, + extra_tokens=None, + ) + + clip_token_map = {i: int(tok_id) for i, tok_id in enumerate(group_ids["clip"])} + clip_delimiter_map = { + "clip_start": int(delim_ids["<|clip_start|>"]), + "clip_end": int(delim_ids["<|clip_end|>"]), + } + + new_token_ids = dict( + clip_token_map=clip_token_map, + clip_delimiter_map=clip_delimiter_map, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def add_imagebind_feature_tokens(tokenizer, vocab_size=8192): + """ + Add special tokens for ImageBind (global) features. + - vocab_size code tokens: <|imagebind_0000|> ... <|imagebind_{vocab_size-1:04d}|> + - 2 delimiter tokens: <|imagebind_start|>, <|imagebind_end|> + Returns mapping dicts similar to add_det_special_tokens. + """ + token_groups = [ + {"name": "imagebind", "token_format": "<|imagebind_{i:04d}|>", "start": 0, "end": int(vocab_size) - 1}, + ] + delimiter_tokens = ["<|imagebind_start|>", "<|imagebind_end|>"] + tokenizer, group_ids, delim_ids, num_new_tokens = add_codebook_tokens( + tokenizer, + token_groups=token_groups, + delimiter_tokens=delimiter_tokens, + extra_tokens=None, + ) + + imagebind_token_map = {i: int(tok_id) for i, tok_id in enumerate(group_ids["imagebind"])} + imagebind_delimiter_map = { + "imagebind_start": int(delim_ids["<|imagebind_start|>"]), + "imagebind_end": int(delim_ids["<|imagebind_end|>"]), + } + + new_token_ids = dict( + imagebind_token_map=imagebind_token_map, + imagebind_delimiter_map=imagebind_delimiter_map, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def add_imagebindlocal_feature_tokens(tokenizer, vocab_size=8192): + """ + Add special tokens for ImageBind-Local features. + - vocab_size code tokens: <|imagebindlocal_0000|> ... <|imagebindlocal_{vocab_size-1:04d}|> + - 2 delimiter tokens: <|imagebindlocal_start|>, <|imagebindlocal_end|> + Returns mapping dicts similar to add_det_special_tokens. + """ + token_groups = [ + {"name": "imagebindlocal", "token_format": "<|imagebindlocal_{i:04d}|>", "start": 0, "end": int(vocab_size) - 1}, + ] + delimiter_tokens = ["<|imagebindlocal_start|>", "<|imagebindlocal_end|>"] + tokenizer, group_ids, delim_ids, num_new_tokens = add_codebook_tokens( + tokenizer, + token_groups=token_groups, + delimiter_tokens=delimiter_tokens, + extra_tokens=None, + ) + + imagebindlocal_token_map = {i: int(tok_id) for i, tok_id in enumerate(group_ids["imagebindlocal"])} + imagebindlocal_delimiter_map = { + "imagebindlocal_start": int(delim_ids["<|imagebindlocal_start|>"]), + "imagebindlocal_end": int(delim_ids["<|imagebindlocal_end|>"]), + } + + new_token_ids = dict( + imagebindlocal_token_map=imagebindlocal_token_map, + imagebindlocal_delimiter_map=imagebindlocal_delimiter_map, + ) + + return tokenizer, new_token_ids, num_new_tokens + + +def add_special_tokens_text_image(tokenizer): + all_special_tokens = [] + for k, v in tokenizer.special_tokens_map.items(): + if isinstance(v, str): + all_special_tokens.append(v) + elif isinstance(v, list): + all_special_tokens += v + + new_tokens = [] + + if '<|im_start|>' not in all_special_tokens: + new_tokens.append('<|im_start|>') + + if '<|im_end|>' not in all_special_tokens: + new_tokens.append('<|im_end|>') + + if '<|vision_start|>' not in all_special_tokens: + new_tokens.append('<|vision_start|>') + + if '<|vision_end|>' not in all_special_tokens: + new_tokens.append('<|vision_end|>') + + num_new_tokens = tokenizer.add_tokens(new_tokens) + bos_token_id = tokenizer.convert_tokens_to_ids('<|im_start|>') + eos_token_id = tokenizer.convert_tokens_to_ids('<|im_end|>') + start_of_image = tokenizer.convert_tokens_to_ids('<|vision_start|>') + end_of_image = tokenizer.convert_tokens_to_ids('<|vision_end|>') + + new_token_ids = dict( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + start_of_image=start_of_image, + end_of_image=end_of_image, + ) + + return tokenizer, new_token_ids, num_new_tokens + +def len2weight(x, loss_reduction='square'): + if x == 0: + return x + if loss_reduction == 'token': + return 1 + if loss_reduction == 'sample': + return 1 / x + if loss_reduction == 'square': + return 1 / (x ** 0.5) + raise NotImplementedError(loss_reduction) diff --git a/data/dataset_base.py b/data/dataset_base.py new file mode 100644 index 0000000000000000000000000000000000000000..751e254a72798a951b5897b594e19eba6e0fb939 --- /dev/null +++ b/data/dataset_base.py @@ -0,0 +1,1235 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + + +import math +import random +import json +import os +import copy + +import numpy as np +import torch +import torch.nn.functional as F + +from .data_utils import ( + get_flattened_position_ids_interpolate, + get_flattened_position_ids_extrapolate, + len2weight, + patchify, + prepare_attention_mask_per_sample, +) +from .dataset_info import DATASET_INFO, DATASET_REGISTRY +from .interleave_datasets.any2any_dataset import UnifiedAny2AnyIterableDataset +from .transforms import ImageTransform +from .video_utils import FrameSampler + + +class DataConfig: + def __init__( + self, + grouped_datasets, + text_cond_dropout_prob=0.1, + vit_cond_dropout_prob=0.4, + vae_cond_dropout_prob=0.1, + vae_image_downsample=16, + max_latent_size=32, + vit_patch_size=14, + max_num_patch_per_side=70, + grounding_phrase_dropout_prob=0.5, + ): + self.grouped_datasets = grouped_datasets + self.text_cond_dropout_prob = text_cond_dropout_prob + self.vit_cond_dropout_prob = vit_cond_dropout_prob + self.vit_patch_size = vit_patch_size + self.max_num_patch_per_side = max_num_patch_per_side + self.vae_cond_dropout_prob = vae_cond_dropout_prob + self.vae_image_downsample = vae_image_downsample + self.max_latent_size = max_latent_size + self.grounding_phrase_dropout_prob = grounding_phrase_dropout_prob + + +class PackedDataset(torch.utils.data.IterableDataset): + def __init__( + self, + data_config, + tokenizer, + special_tokens, + modality_registry, + local_rank, + world_size, + num_workers, + expected_num_tokens=32768, + max_num_tokens_per_sample=16384, + max_num_tokens=36864, + prefer_buffer_before=16384, + max_buffer_size=50, + interpolate_pos=False, + use_flex=False, + data_status=None, + data_resume_state=None, + use_instruction=False, + use_condition_instruction=True, + use_target_instruction=True, + num_condition_modalities=1, + strict_num_condition_modalities=False, + timestep_sample=None, + mode_scale=None, + timestep_sample_mix_prob=None, + use_det_image=False, + visual_gen=True, + visual_und=True, + tail_n_rows=None, + force_condition_modality=None, + force_condition_modalities=None, + pred_intermediate_dir=None, + gt_restricted_dir=None, + fixed_row_list_path=None, + ): + super().__init__() + self.expected_num_tokens = expected_num_tokens + self.max_num_tokens_per_sample = max_num_tokens_per_sample + self.prefer_buffer_before = prefer_buffer_before + self.max_num_tokens = max_num_tokens + self.max_buffer_size = max_buffer_size + self.tokenizer = tokenizer + self.local_rank = local_rank + self.world_size = world_size + self.num_workers = num_workers + self.use_flex = use_flex + self.data_resume_state = data_resume_state + self.use_instruction = use_instruction + self.use_condition_instruction = use_condition_instruction + self.use_target_instruction = use_target_instruction + self.num_condition_modalities = num_condition_modalities + self.strict_num_condition_modalities = strict_num_condition_modalities + self.timestep_sample = timestep_sample + self.mode_scale = mode_scale + self.timestep_sample_mix_prob = timestep_sample_mix_prob + self.use_det_image = use_det_image + self.visual_gen = visual_gen + self.visual_und = visual_und + self.tail_n_rows = tail_n_rows + self.pred_intermediate_dir = pred_intermediate_dir + self.gt_restricted_dir = gt_restricted_dir + self.fixed_row_list_path = fixed_row_list_path + self.force_condition_modality = force_condition_modality + self.force_condition_modalities = force_condition_modalities + for k, v in special_tokens.items(): + setattr(self, k, v) + self.modality_registry = modality_registry + + # Derive token mappings and modality IDs from the registry. + self.start_token_mapping = { + s.name: s.start_token_key for s in self.modality_registry.specs + } + self.end_token_mapping = { + s.name: s.end_token_key for s in self.modality_registry.specs + } + self.modality_to_id = self.modality_registry.name_to_id() + self.id_to_modality = self.modality_registry.id_to_name() + + self.data_config = data_config + self.total_dataset_samples = 0 + self._seen_parquet_files = set() + self._epoch_tracked_groups = set() + grouped_datasets, is_mandatory, grouped_weights = self.build_datasets( + data_config.grouped_datasets, data_status + ) + self.grouped_datasets = grouped_datasets + self.dataset_iters = [iter(dataset) for dataset in grouped_datasets] + self.is_mandatory = is_mandatory + self.grouped_weights = grouped_weights + self.interpolate_pos = interpolate_pos + if self.interpolate_pos: + self.get_flattened_position_ids = get_flattened_position_ids_interpolate + else: + self.get_flattened_position_ids = get_flattened_position_ids_extrapolate + self._vit_size_warning_emitted = False + self._latent_size_warning_emitted = False + + def _fit_vit_image_tensor(self, image_tensor, modality_type): + patch_size = self.data_config.vit_patch_size + max_num_patch_per_side = self.data_config.max_num_patch_per_side + max_vit_side = patch_size * max_num_patch_per_side + _, img_h, img_w = image_tensor.shape + + def _align_down_to_patch_multiple(size): + return max(patch_size, (size // patch_size) * patch_size) + + needs_resize = img_h > max_vit_side or img_w > max_vit_side + if needs_resize: + scale = min(max_vit_side / img_h, max_vit_side / img_w) + resized_h = _align_down_to_patch_multiple(int(math.floor(img_h * scale))) + resized_w = _align_down_to_patch_multiple(int(math.floor(img_w * scale))) + resized_h = min(resized_h, max_vit_side) + resized_w = min(resized_w, max_vit_side) + else: + resized_h = _align_down_to_patch_multiple(img_h) + resized_w = _align_down_to_patch_multiple(img_w) + + if resized_h == img_h and resized_w == img_w: + return image_tensor + + image_tensor = F.interpolate( + image_tensor.unsqueeze(0), + size=(resized_h, resized_w), + mode="bilinear", + align_corners=False, + ).squeeze(0) + + if not self._vit_size_warning_emitted: + print( + "[JanusFlow][ViTClamp] resized visual-understanding tensor " + f"for modality={modality_type} from {img_h}x{img_w} to {resized_h}x{resized_w} " + f"(patch_size={patch_size}, max_num_patch_per_side={max_num_patch_per_side})", + flush=True, + ) + self._vit_size_warning_emitted = True + + return image_tensor + + def _fit_vae_image_tensor(self, image_tensor, modality_type): + downsample = self.data_config.vae_image_downsample + max_latent_size = self.data_config.max_latent_size + max_vae_side = downsample * max_latent_size + _, img_h, img_w = image_tensor.shape + + def _align_down_to_latent_multiple(size): + return max(downsample, (size // downsample) * downsample) + + needs_resize = img_h > max_vae_side or img_w > max_vae_side + if needs_resize: + scale = min(max_vae_side / img_h, max_vae_side / img_w) + resized_h = _align_down_to_latent_multiple(int(math.floor(img_h * scale))) + resized_w = _align_down_to_latent_multiple(int(math.floor(img_w * scale))) + resized_h = min(resized_h, max_vae_side) + resized_w = min(resized_w, max_vae_side) + else: + resized_h = _align_down_to_latent_multiple(img_h) + resized_w = _align_down_to_latent_multiple(img_w) + + if resized_h == img_h and resized_w == img_w: + return image_tensor + + image_tensor = F.interpolate( + image_tensor.unsqueeze(0), + size=(resized_h, resized_w), + mode="bilinear", + align_corners=False, + ).squeeze(0) + + if not self._latent_size_warning_emitted: + print( + "[JanusFlow][LatentClamp] resized visual-generation tensor " + f"for modality={modality_type} from {img_h}x{img_w} to {resized_h}x{resized_w} " + f"(vae_downsample={downsample}, max_latent_size={max_latent_size})", + flush=True, + ) + self._latent_size_warning_emitted = True + + return image_tensor + + + def build_datasets(self, datasets_metainfo, data_status): + datasets = [] + is_mandatory = [] + grouped_weights = [] + for grouped_dataset_name, dataset_args in datasets_metainfo.items(): + is_mandatory.append(dataset_args.pop('is_mandatory', False)) + grouped_weights.append(dataset_args.pop('weight', 0.0)) + + if 'frame_sampler_args' in dataset_args.keys(): + frame_sampler = FrameSampler(**dataset_args.pop('frame_sampler_args')) + dataset_args['frame_sampler'] = frame_sampler + if 'image_transform_args' in dataset_args.keys(): + transform = ImageTransform(**dataset_args.pop('image_transform_args')) + dataset_args['transform'] = transform + if 'vit_image_transform_args' in dataset_args.keys(): + vit_transform = ImageTransform(**dataset_args.pop('vit_image_transform_args')) + dataset_args['vit_transform'] = vit_transform + + assert 'dataset_names' in dataset_args.keys() + dataset_names = dataset_args.pop('dataset_names') + dataset_args['data_dir_list'] = [] + for item in dataset_names: + if self.local_rank == 0: + print(f'Preparing Dataset {grouped_dataset_name}/{item}') + meta_info = DATASET_INFO[grouped_dataset_name][item] + dataset_args['data_dir_list'].append(meta_info['data_dir']) + + if "parquet_info_path" in meta_info.keys(): + if 'parquet_info' not in dataset_args.keys(): + dataset_args['parquet_info'] = {} + info_path = meta_info['parquet_info_path'] + if not os.path.exists(info_path): + # Fall back to the copy bundled in the repo. The configured + # path lives under datasets/ which may be a symlink that + # isn't present on a fresh clone / a different cluster. + bundled = os.path.join( + os.path.dirname(__file__), 'bundled_parquet_info', + os.path.basename(info_path)) + if os.path.exists(bundled): + info_path = bundled + with open(info_path, 'r') as f: + parquet_info = json.load(f) + dataset_args['parquet_info'].update(parquet_info) + self._epoch_tracked_groups.add(grouped_dataset_name) + for path, info in parquet_info.items(): + if path not in self._seen_parquet_files: + self._seen_parquet_files.add(path) + self.total_dataset_samples += info.get('num_rows', 0) + + if 'json_dir' in meta_info.keys(): + # parquet/tar with json + if 'json_dir_list' not in dataset_args.keys(): + dataset_args['json_dir_list'] = [meta_info['json_dir']] + else: + dataset_args['json_dir_list'].append(meta_info['json_dir']) + + if 'jsonl_path' in meta_info.keys(): + # jsonl with jpeg + if 'jsonl_path_list' not in dataset_args.keys(): + dataset_args['jsonl_path_list'] = [meta_info['jsonl_path']] + else: + dataset_args['jsonl_path_list'].append(meta_info['jsonl_path']) + + resume_data_status = dataset_args.pop('resume_data_status', True) + if data_status is not None and grouped_dataset_name in data_status.keys() and resume_data_status: + data_status_per_group = data_status[grouped_dataset_name] + else: + data_status_per_group = None + # Validation hooks (force_condition_modality, tail_n_rows) only make + # sense for any2any parquet datasets; passing them to e.g. vlm_sft + # (SftJSONLIterableDataset) would raise TypeError on unknown kwargs. + extra_args = {} + if issubclass(DATASET_REGISTRY[grouped_dataset_name], UnifiedAny2AnyIterableDataset): + if self.tail_n_rows is not None: + extra_args['tail_n_rows'] = self.tail_n_rows + if self.force_condition_modality is not None: + extra_args['force_condition_modality'] = self.force_condition_modality + if self.force_condition_modalities is not None: + extra_args['force_condition_modalities'] = self.force_condition_modalities + if self.pred_intermediate_dir is not None: + extra_args['pred_intermediate_dir'] = self.pred_intermediate_dir + if self.gt_restricted_dir is not None: + extra_args['gt_restricted_dir'] = self.gt_restricted_dir + if self.fixed_row_list_path is not None: + extra_args['fixed_row_list_path'] = self.fixed_row_list_path + dataset = DATASET_REGISTRY[grouped_dataset_name]( + dataset_name=grouped_dataset_name, + tokenizer=self.tokenizer, + local_rank=self.local_rank, + world_size=self.world_size, + num_workers=self.num_workers, + data_status=data_status_per_group, + use_instruction=self.use_instruction, + use_condition_instruction=self.use_condition_instruction, + use_target_instruction=self.use_target_instruction, + num_condition_modalities=self.num_condition_modalities, + strict_num_condition_modalities=self.strict_num_condition_modalities, + use_det_image=self.use_det_image, + modality_registry=self.modality_registry, + **extra_args, + **dataset_args, + ) + dataset.grounding_phrase_dropout_prob = getattr( + self.data_config, 'grounding_phrase_dropout_prob', 0.0 + ) + datasets.append(dataset) + + return datasets, is_mandatory, grouped_weights + + def set_epoch(self, seed): + for dataset in self.grouped_datasets: + dataset.set_epoch(seed) + + def set_sequence_status(self): + sequence_status = dict( + curr = 0, + sample_lens = list(), + packed_position_ids = list(), + nested_attention_masks = list(), + split_lens = list(), + attn_modes = list(), + packed_text_ids = list(), + packed_text_indexes = list(), + packed_label_ids = list(), + ce_loss_indexes = list(), + ce_loss_weights = list(), + ce_loss_modality_ids = list(), # Track modality ID for each CE loss token + vae_image_tensors = list(), + vae_image_modality_types = list(), + packed_latent_position_ids = list(), + vae_latent_shapes = list(), + packed_vae_token_indexes = list(), + packed_timesteps = list(), + mse_loss_indexes = list(), + mse_loss_modality_ids = list(), # Track modality ID for each MSE loss token + mse_loss_route_ids = list(), # Route tag for rgb target monitoring + mse_loss_image_ids = list(), # Per-token image id for per-sample MSE logging + mse_loss_timesteps = list(), # Per-token sampled timestep for MSE tokens + packed_vit_tokens = list(), + vit_token_seqlens = list(), + vit_image_modality_types = list(), + packed_vit_position_ids = list(), + packed_vit_token_indexes = list(), + # (token_h, token_w) per ViT-input image; consumed by model_wrapper.forward + # to build 2D rope_image_info for the ViT-input image segment, matching the + # pretrained Hunyuan convention (build_batch_rope_image_info, cond_vit_image). + vit_image_token_shapes = list(), + target_modality_types = list(), + next_mse_image_id = 0, + ) + return sequence_status + + def to_tensor(self, sequence_status): + data = dict( + sequence_length=sum(sequence_status['sample_lens']), + num_samples=len(sequence_status['sample_lens']), + sample_lens=sequence_status['sample_lens'], + target_modality_types=sequence_status['target_modality_types'], + vae_image_modality_types=sequence_status['vae_image_modality_types'], + packed_text_ids=torch.tensor(sequence_status['packed_text_ids']), + packed_text_indexes=torch.tensor(sequence_status['packed_text_indexes']), + packed_position_ids=torch.tensor(sequence_status['packed_position_ids']), + ) + if not self.use_flex: + data['nested_attention_masks'] = sequence_status['nested_attention_masks'] + else: + sequence_len = data['sequence_length'] + pad_len = self.max_num_tokens - sequence_len + data['split_lens'] = sequence_status['split_lens'] + [pad_len] + data['attn_modes'] = sequence_status['attn_modes'] + ['causal'] + data['sample_lens'] += [pad_len] + + # if the model has a convnet vae (e.g., as visual tokenizer) + if len(sequence_status['vae_image_tensors']) > 0: + image_tensors = sequence_status.pop('vae_image_tensors') + image_sizes = [item.shape for item in image_tensors] + max_image_size = [max(item) for item in list(zip(*image_sizes))] + padded_images = torch.zeros(size=(len(image_tensors), *max_image_size)) + for i, image_tensor in enumerate(image_tensors): + padded_images[i, :, :image_tensor.shape[1], :image_tensor.shape[2]] = image_tensor + + data['padded_images'] = padded_images + data['patchified_vae_latent_shapes'] = sequence_status['vae_latent_shapes'] + data['packed_latent_position_ids'] = torch.cat(sequence_status['packed_latent_position_ids'], dim=0) + data['packed_vae_token_indexes'] = torch.tensor(sequence_status['packed_vae_token_indexes']) + + # if the model has a vit (e.g., as visual tokenizer) + if len(sequence_status['packed_vit_tokens']) > 0: + data['packed_vit_tokens'] = torch.cat(sequence_status['packed_vit_tokens'], dim=0) + data['packed_vit_position_ids'] = torch.cat(sequence_status['packed_vit_position_ids'], dim=0) + data['packed_vit_token_indexes'] = torch.tensor(sequence_status['packed_vit_token_indexes']) + data['vit_image_token_shapes'] = list(sequence_status['vit_image_token_shapes']) + data['vit_token_seqlens'] = torch.tensor(sequence_status['vit_token_seqlens']) + data['vit_image_modality_types'] = sequence_status['vit_image_modality_types'] + + # if the model is required to perform visual generation + if len(sequence_status['packed_timesteps']) > 0: + data['packed_timesteps'] = torch.tensor(sequence_status['packed_timesteps']) + data['mse_loss_indexes'] = torch.tensor(sequence_status['mse_loss_indexes'], dtype=torch.long) + data['mse_loss_modality_ids'] = torch.tensor(sequence_status['mse_loss_modality_ids'], dtype=torch.long) + data['mse_loss_route_ids'] = torch.tensor(sequence_status['mse_loss_route_ids'], dtype=torch.long) + data['mse_loss_image_ids'] = torch.tensor(sequence_status['mse_loss_image_ids'], dtype=torch.long) + data['mse_loss_timesteps'] = torch.tensor(sequence_status['mse_loss_timesteps']) + + # if the model is required to perform text generation + if len(sequence_status['packed_label_ids']) > 0: + data['packed_label_ids'] = torch.tensor(sequence_status['packed_label_ids'], dtype=torch.long) + data['ce_loss_indexes'] = torch.tensor(sequence_status['ce_loss_indexes'], dtype=torch.long) + data['ce_loss_weights'] = torch.tensor(sequence_status['ce_loss_weights']) + data['ce_loss_modality_ids'] = torch.tensor(sequence_status['ce_loss_modality_ids'], dtype=torch.long) + + return data + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + worker_id = worker_info.id if worker_info is not None else 0 + total_weights = sum(self.grouped_weights) + assert total_weights > 0.0 + group_cumprobs = [sum(self.grouped_weights[:i + 1]) / total_weights + for i in range(len(self.grouped_weights))] + sequence_status = self.set_sequence_status() + batch_data_indexes = [] + + buffer = [] + if self.data_resume_state is not None: + worker_resume_state = self.data_resume_state.get(worker_id, self.data_resume_state) + buffer = copy.deepcopy(worker_resume_state.get("buffer", [])) + py_state = worker_resume_state.get("python_random_state") + np_state = worker_resume_state.get("numpy_random_state") + torch_state = worker_resume_state.get("torch_random_state") + if py_state is not None: + random.setstate(py_state) + if np_state is not None: + np.random.set_state(np_state) + if torch_state is not None: + torch.random.set_rng_state(torch_state) + # Consume once. Future epochs in the same process should proceed normally. + self.data_resume_state = None + while True: + # Ensure at least one sample from each group unless explicitly disabled. + # Disabling this can drastically reduce packed sequence length and + # avoid pathological long-sequence attention OOM/NaN behavior. + _disable_mandatory = os.environ.get("HUNYUAN_DISABLE_MANDATORY_WARMSTART", "0") == "1" + if sequence_status['curr'] == 0 and (not _disable_mandatory): + _max_mandatory_trials = int(os.environ.get("HUNYUAN_MANDATORY_MAX_TRIALS", "2000")) + for group_index, group_iter in enumerate(self.dataset_iters): + if self.is_mandatory[group_index]: + _trials = 0 + while True: + sample = next(group_iter) + # Tentative pack on a cloned status: this gives the + # true token expansion for visual branches. + trial_status = self.pack_sequence( + sample, self._clone_sequence_status(sequence_status) + ) + num_tokens = trial_status['curr'] - sequence_status['curr'] + if ( + num_tokens < self.max_num_tokens_per_sample + and trial_status['curr'] <= self.max_num_tokens + ): + sequence_status = trial_status + batch_data_indexes.append(sample['data_indexes']) + break + else: + _trials += 1 + if _max_mandatory_trials > 0 and _trials >= _max_mandatory_trials: + print( + f"[DataPack] mandatory group {group_index} exceeded " + f"{_max_mandatory_trials} trials under token constraints " + f"(max_per_sample={self.max_num_tokens_per_sample}, max_total={self.max_num_tokens}); " + "skipping mandatory warmstart for this group in this batch" + ) + break + continue + + if sequence_status['curr'] < self.prefer_buffer_before and len(buffer) > 0: + sample = buffer.pop(0) + sample_from_buffer = True + else: + # sample normally across all groups + n = random.random() + group_index = 0 + for i, cumprob in enumerate(group_cumprobs): + if n < cumprob: + group_index = i + break + sample = next(self.dataset_iters[group_index]) + sample_from_buffer = False + + trial_status = self.pack_sequence( + sample, self._clone_sequence_status(sequence_status) + ) + num_tokens = trial_status['curr'] - sequence_status['curr'] + if num_tokens > self.max_num_tokens_per_sample: + # print(f"skip a sample with length {num_tokens}") + continue + + if trial_status['curr'] > self.max_num_tokens: + if len(buffer) < self.max_buffer_size and not sample_from_buffer: + buffer.append(sample) + else: + if sequence_status['curr'] > 0: + data = self.to_tensor(sequence_status) + data['batch_data_indexes'] = batch_data_indexes + data['num_epoch_samples'] = sum( + 1 for idx in batch_data_indexes + if idx["dataset_name"] in self._epoch_tracked_groups + ) + data['data_resume_state'] = self._build_resume_state(buffer, worker_id) + yield data + sequence_status = self.set_sequence_status() + batch_data_indexes = [] + continue + + sequence_status = trial_status + batch_data_indexes.append(sample['data_indexes']) + + if sequence_status['curr'] >= self.expected_num_tokens: + data = self.to_tensor(sequence_status) + data['batch_data_indexes'] = batch_data_indexes + data['num_epoch_samples'] = sum( + 1 for idx in batch_data_indexes + if idx["dataset_name"] in self._epoch_tracked_groups + ) + data['data_resume_state'] = self._build_resume_state(buffer, worker_id) + yield data + sequence_status = self.set_sequence_status() + batch_data_indexes = [] + + def _build_resume_state(self, buffer, worker_id): + """Capture the iterable packer's next-step resume state. + + This stores only CPU-side iterator state. Sub-dataset row cursors are + still tracked separately via ``data_status`` in the training loop. + """ + return { + "worker_id": worker_id, + "version": 1, + "buffer": copy.deepcopy(buffer), + "python_random_state": random.getstate(), + "numpy_random_state": np.random.get_state(), + "torch_random_state": torch.random.get_rng_state().cpu(), + } + + def _clone_sequence_status(self, sequence_status): + """Clone mutable sequence status containers for tentative packing.""" + cloned = {} + for k, v in sequence_status.items(): + if isinstance(v, list): + cloned[k] = v.copy() + else: + cloned[k] = v + return cloned + + def _sample_timestep(self): + """Sample a final timestep value in (0, 1] for one image chunk. + + The sampling strategy is controlled by ``self.timestep_sample``: + + * ``logit_norm`` – logistic-distribution sample (sigmoid of a normal). + * ``mode`` – uniform sample optionally warped by a cosine mode + schedule controlled by ``self.mode_scale``. + * ``linear`` – inverse-CDF sample giving P(t)=2t (higher density + near t=1). + * ``pure_noise`` – constant ``1.0`` (pure-noise target). + * ``mix`` – with probability ``timestep_sample_mix_prob`` draw a + uniform sample, otherwise ``1.0``. + + Returns a ``float`` ready to be used as the flow-matching timestep. + """ + eps = 1e-6 + strategy = self.timestep_sample + + if strategy == 'logit_norm': + # Sigmoid of a standard-normal sample → logistic distribution on (0,1). + t = 1.0 / (1.0 + math.exp(-np.random.randn())) + elif strategy == 'mode': + t = float(np.random.rand()) + if self.mode_scale != 0.0: + t = ( + 1.0 - t + - self.mode_scale + * (math.cos(math.pi * t / 2) ** 2 - 1.0 + t) + ) + t = max(eps, min(1.0 - eps, t)) + elif strategy == 'linear': + t = math.sqrt(float(np.random.rand())) + t = max(eps, min(1.0 - eps, t)) + elif strategy == 'pure_noise': + t = 1.0 + elif strategy == 'mix': + if np.random.rand() < self.timestep_sample_mix_prob: + t = float(np.random.rand()) + else: + t = 1.0 + else: + raise ValueError(f"Unknown timestep_sample strategy: {strategy}") + + return t + + def _native_token_id(self, token): + token_id = self.tokenizer.convert_tokens_to_ids(token) + if token_id is None or token_id < 0: + raise ValueError(f"Native Hunyuan token is not in tokenizer: {token}") + return int(token_id) + + def pack_sequence(self, sample, sequence_status): + image_tensor_list = list(sample['image_tensor_list']) + text_ids_list = list(sample['text_ids_list']) + sequence_plan = sample['sequence_plan'] + + split_lens, attn_modes = list(), list() + curr = sequence_status['curr'] + curr_rope_id = 0 + sample_lens = 0 + last_loss_modality_type = None + + for item in sequence_plan: + split_start = item.get('split_start', True) + if split_start: + curr_split_len = 0 + + if item['type'] == 'raw_text': + text_ids = text_ids_list.pop(0) + if item['enable_cfg'] == 1 and random.random() < self.data_config.text_cond_dropout_prob: + continue + if item['loss'] == 1: + raise NotImplementedError("raw_text sequence items are for no-loss chat scaffolding only") + + sequence_status['packed_text_ids'].extend(text_ids) + sequence_status['packed_text_indexes'].extend(range(curr, curr + len(text_ids))) + curr += len(text_ids) + curr_split_len += len(text_ids) + + attn_modes.append("causal") + sequence_status['packed_position_ids'].extend(range(curr_rope_id, curr_rope_id + curr_split_len)) + curr_rope_id += curr_split_len + + elif item['type'] == 'text': + start_token_attr = self.start_token_mapping[item['modality_type']] + end_token_attr = self.end_token_mapping[item['modality_type']] + text_ids = text_ids_list.pop(0) + if item['enable_cfg'] == 1 and random.random() < self.data_config.text_cond_dropout_prob: + continue + + + shifted_text_ids = [getattr(self, start_token_attr)] + text_ids + sequence_status['packed_text_ids'].extend(shifted_text_ids) + sequence_status['packed_text_indexes'].extend(range(curr, curr + len(shifted_text_ids))) + if item['loss'] == 1: + last_loss_modality_type = item['modality_type'] + sequence_status['ce_loss_indexes'].extend(range(curr, curr + len(shifted_text_ids))) + sequence_status['ce_loss_weights'].extend( + [len2weight(len(shifted_text_ids))] * len(shifted_text_ids) + ) + sequence_status['ce_loss_modality_ids'].extend( + [self.modality_to_id[item['modality_type']]] * len(shifted_text_ids) + ) + sequence_status['packed_label_ids'].extend(text_ids + [getattr(self, end_token_attr)]) + curr += len(shifted_text_ids) + curr_split_len += len(shifted_text_ids) + + # add a <|im_end|> token + sequence_status['packed_text_ids'].append(getattr(self, end_token_attr)) + sequence_status['packed_text_indexes'].append(curr) + if item['special_token_loss'] == 1: # <|im_end|> may have loss + sequence_status['ce_loss_indexes'].append(curr) + sequence_status['ce_loss_weights'].append(1.0) + sequence_status['packed_label_ids'].append(item['special_token_label']) + curr += 1 + curr_split_len += 1 + + # update sequence status + attn_modes.append("causal") + sequence_status['packed_position_ids'].extend(range(curr_rope_id, curr_rope_id + curr_split_len)) + curr_rope_id += curr_split_len + + elif item['type'] == 'vit_image': + start_token_attr = self.start_token_mapping[item['modality_type']] + end_token_attr = self.end_token_mapping[item['modality_type']] + if not self.visual_und: + curr_rope_id += 1 + continue + image_tensor = image_tensor_list.pop(0) + # Config-driven conditioning: allow modalities to opt out of ViT conditioning in BOTH train/infer. + # Only applies to conditioning branches (loss==0). Target branches are kept intact. + if self.modality_registry is not None and int(item.get('loss', 0)) == 0: + try: + spec = self.modality_registry.get(item['modality_type']) + if not spec.represent_vit: + curr_rope_id += 1 + continue + except Exception: + # Unknown modality -> keep legacy behavior + pass + if item['enable_cfg'] == 1 and random.random() < self.data_config.vit_cond_dropout_prob: + curr_rope_id += 1 + continue + + # add a <|startofimage|> token + + sequence_status['packed_text_ids'].append(getattr(self, start_token_attr)) + sequence_status['packed_text_indexes'].append(curr) + curr += 1 + curr_split_len += 1 + + # preprocess image + image_tensor = self._fit_vit_image_tensor(image_tensor, item['modality_type']) + vit_tokens = patchify(image_tensor, self.data_config.vit_patch_size) + num_img_tokens = vit_tokens.shape[0] + sequence_status['packed_vit_token_indexes'].extend(range(curr, curr + num_img_tokens)) + curr += num_img_tokens + curr_split_len += num_img_tokens + + sequence_status['packed_vit_tokens'].append(vit_tokens) + sequence_status['vit_token_seqlens'].append(num_img_tokens) + sequence_status['vit_image_modality_types'].append(item['modality_type']) + sequence_status['vit_image_token_shapes'].append(( + image_tensor.size(1) // self.data_config.vit_patch_size, + image_tensor.size(2) // self.data_config.vit_patch_size, + )) + vit_position_ids = self.get_flattened_position_ids( + image_tensor.size(1), image_tensor.size(2), + self.data_config.vit_patch_size, + max_num_patches_per_side=self.data_config.max_num_patch_per_side + ) + max_vit_position_id = self.data_config.max_num_patch_per_side ** 2 - 1 + if vit_position_ids.numel() > 0 and int(vit_position_ids.max().item()) > max_vit_position_id: + raise ValueError( + "ViT position id out of range after transform: " + f"modality={item['modality_type']}, " + f"image_hw={tuple(image_tensor.shape[-2:])}, " + f"patch_size={self.data_config.vit_patch_size}, " + f"max_num_patch_per_side={self.data_config.max_num_patch_per_side}, " + f"max_position_id={int(vit_position_ids.max().item())}, " + f"allowed_max={max_vit_position_id}" + ) + sequence_status['packed_vit_position_ids'].append(vit_position_ids) + # import pdb; pdb.set_trace() + + # add a <|endofimage|> token + sequence_status['packed_text_ids'].append(getattr(self, end_token_attr)) + sequence_status['packed_text_indexes'].append(curr) + if item['special_token_loss'] == 1: # <|endofimage|> may have loss + sequence_status['ce_loss_indexes'].append(curr) + sequence_status['ce_loss_weights'].append(1.0) + sequence_status['packed_label_ids'].append(item['special_token_label']) + curr += 1 + curr_split_len += 1 + + # update sequence status + attn_modes.append("full") + sequence_status['packed_position_ids'].extend([curr_rope_id] * curr_split_len) + curr_rope_id += 1 + + elif item['type'] == 'vae_image': + start_token_attr = self.start_token_mapping[item['modality_type']] + end_token_attr = self.end_token_mapping[item['modality_type']] + if not self.visual_gen: + curr_rope_id += 1 + continue + image_tensor = image_tensor_list.pop(0) + # Config-driven conditioning: allow modalities to opt out of VAE conditioning in BOTH train/infer. + # Only applies to conditioning branches (loss==0). Target branches are kept intact. + if self.modality_registry is not None and int(item.get('loss', 0)) == 0: + try: + spec = self.modality_registry.get(item['modality_type']) + if not spec.represent_vae: + curr_rope_id += 1 + continue + except Exception: + # Unknown modality -> keep legacy behavior + pass + if item['enable_cfg'] == 1 and random.random() < self.data_config.vae_cond_dropout_prob: + # FIXME fix vae dropout in video2video setting. + curr_rope_id += 1 + continue + + # add a <|startofimage|> token + + sequence_status['packed_text_ids'].append(getattr(self, start_token_attr)) + sequence_status['packed_text_indexes'].append(curr) + curr += 1 + curr_split_len += 1 + + # preprocess image + image_tensor = self._fit_vae_image_tensor(image_tensor, item['modality_type']) + sequence_status['vae_image_tensors'].append(image_tensor) + sequence_status['vae_image_modality_types'].append(item['modality_type']) + latent_position_ids = self.get_flattened_position_ids( + image_tensor.size(1), image_tensor.size(2), + self.data_config.vae_image_downsample, + max_num_patches_per_side=self.data_config.max_latent_size + ) + max_latent_position_id = self.data_config.max_latent_size ** 2 - 1 + if ( + latent_position_ids.numel() > 0 + and int(latent_position_ids.max().item()) > max_latent_position_id + ): + raise ValueError( + "Latent position id out of range after transform: " + f"modality={item['modality_type']}, " + f"image_hw={tuple(image_tensor.shape[-2:])}, " + f"vae_downsample={self.data_config.vae_image_downsample}, " + f"max_latent_size={self.data_config.max_latent_size}, " + f"max_position_id={int(latent_position_ids.max().item())}, " + f"allowed_max={max_latent_position_id}" + ) + sequence_status['packed_latent_position_ids'].append(latent_position_ids) + H, W = image_tensor.shape[1:] + h = H // self.data_config.vae_image_downsample + w = W // self.data_config.vae_image_downsample + sequence_status['vae_latent_shapes'].append((h, w)) + + num_img_tokens = w * h + sequence_status['packed_vae_token_indexes'].extend(range(curr, curr + num_img_tokens)) + if item['loss'] == 1: + last_loss_modality_type = item['modality_type'] + mse_image_id = sequence_status['next_mse_image_id'] + sequence_status['next_mse_image_id'] += 1 + sequence_status['mse_loss_indexes'].extend(range(curr, curr + num_img_tokens)) + sequence_status['mse_loss_modality_ids'].extend( + [self.modality_to_id[item['modality_type']]] * num_img_tokens + ) + if split_start: + timestep = self._sample_timestep() + route_name = sample.get('rgb_loss_route', None) + if item['modality_type'] == 'rgb' and route_name is not None: + route_to_id = {'caption2rgb': 1, 'grounding2rgb': 2, 'dinolocal2rgb': 3} + route_id = route_to_id.get(route_name, 0) + else: + route_id = 0 + sequence_status['mse_loss_route_ids'].extend([route_id] * num_img_tokens) + sequence_status['mse_loss_image_ids'].extend([mse_image_id] * num_img_tokens) + sequence_status['mse_loss_timesteps'].extend([timestep] * num_img_tokens) + else: + timestep = 0.0 + + sequence_status['packed_timesteps'].extend([timestep] * num_img_tokens) + curr += num_img_tokens + curr_split_len += num_img_tokens + + # add a <|endofimage|> token + sequence_status['packed_text_ids'].append(getattr(self, end_token_attr)) + sequence_status['packed_text_indexes'].append(curr) + # <|endofimage|> may have loss + if item['special_token_loss'] == 1: + sequence_status['ce_loss_indexes'].append(curr) + sequence_status['ce_loss_weights'].append(1.0) + sequence_status['packed_label_ids'].append(item['special_token_label']) + curr += 1 + curr_split_len += 1 + + # update sequence status + if split_start: + if item['loss'] == 1 and 'frame_delta' not in item.keys(): + attn_modes.append("noise") + else: + attn_modes.append("full") + sequence_status['packed_position_ids'].extend([curr_rope_id] * (num_img_tokens + 2)) + if 'frame_delta' in item.keys(): + curr_rope_id += item['frame_delta'] + elif item['loss'] == 0: + curr_rope_id += 1 + + elif item['type'] == 'native_gen_image': + if not self.visual_gen: + curr_rope_id += 1 + continue + image_tensor = image_tensor_list.pop(0) + + prefix_ids = [ + self._native_token_id(''), + self._native_token_id(f""), + self._native_token_id(f""), + self._native_token_id(''), + ] + eoi_id = self._native_token_id('') + + sequence_status['packed_text_ids'].extend(prefix_ids) + sequence_status['packed_text_indexes'].extend(range(curr, curr + len(prefix_ids))) + curr += len(prefix_ids) + curr_split_len += len(prefix_ids) + + image_tensor = self._fit_vae_image_tensor(image_tensor, item['modality_type']) + sequence_status['vae_image_tensors'].append(image_tensor) + sequence_status['vae_image_modality_types'].append(item['modality_type']) + latent_position_ids = self.get_flattened_position_ids( + image_tensor.size(1), image_tensor.size(2), + self.data_config.vae_image_downsample, + max_num_patches_per_side=self.data_config.max_latent_size + ) + sequence_status['packed_latent_position_ids'].append(latent_position_ids) + H, W = image_tensor.shape[1:] + h = H // self.data_config.vae_image_downsample + w = W // self.data_config.vae_image_downsample + sequence_status['vae_latent_shapes'].append((h, w)) + + num_img_tokens = w * h + sequence_status['packed_vae_token_indexes'].extend(range(curr, curr + num_img_tokens)) + last_loss_modality_type = item['modality_type'] + mse_image_id = sequence_status['next_mse_image_id'] + sequence_status['next_mse_image_id'] += 1 + sequence_status['mse_loss_indexes'].extend(range(curr, curr + num_img_tokens)) + sequence_status['mse_loss_modality_ids'].extend( + [self.modality_to_id[item['modality_type']]] * num_img_tokens + ) + timestep = self._sample_timestep() + route_name = sample.get('rgb_loss_route', None) + if item['modality_type'] == 'rgb' and route_name is not None: + route_to_id = {'caption2rgb': 1, 'grounding2rgb': 2, 'dinolocal2rgb': 3} + route_id = route_to_id.get(route_name, 0) + else: + route_id = 0 + sequence_status['mse_loss_route_ids'].extend([route_id] * num_img_tokens) + sequence_status['mse_loss_image_ids'].extend([mse_image_id] * num_img_tokens) + sequence_status['mse_loss_timesteps'].extend([timestep] * num_img_tokens) + sequence_status['packed_timesteps'].extend([timestep] * num_img_tokens) + curr += num_img_tokens + curr_split_len += num_img_tokens + + sequence_status['packed_text_ids'].append(eoi_id) + sequence_status['packed_text_indexes'].append(curr) + curr += 1 + curr_split_len += 1 + + if split_start: + attn_modes.append("noise") + sequence_status['packed_position_ids'].extend( + [curr_rope_id] * (len(prefix_ids) + num_img_tokens + 1) + ) + + elif item['type'] == 'native_cond_joint_image': + if not (self.visual_gen or self.visual_und): + curr_rope_id += 1 + continue + vae_image_tensor = image_tensor_list.pop(0) + vit_image_tensor = image_tensor_list.pop(0) + if item['enable_cfg'] == 1 and random.random() < self.data_config.vae_cond_dropout_prob: + curr_rope_id += 1 + continue + + boi_id = self._native_token_id('') + sep_id = self._native_token_id('') + eoi_id = self._native_token_id('') + + sequence_status['packed_text_ids'].append(boi_id) + sequence_status['packed_text_indexes'].append(curr) + curr += 1 + curr_split_len += 1 + + vae_image_tensor = self._fit_vae_image_tensor(vae_image_tensor, item['modality_type']) + sequence_status['vae_image_tensors'].append(vae_image_tensor) + sequence_status['vae_image_modality_types'].append(item['modality_type']) + latent_position_ids = self.get_flattened_position_ids( + vae_image_tensor.size(1), vae_image_tensor.size(2), + self.data_config.vae_image_downsample, + max_num_patches_per_side=self.data_config.max_latent_size + ) + sequence_status['packed_latent_position_ids'].append(latent_position_ids) + H, W = vae_image_tensor.shape[1:] + h = H // self.data_config.vae_image_downsample + w = W // self.data_config.vae_image_downsample + num_vae_tokens = w * h + sequence_status['vae_latent_shapes'].append((h, w)) + sequence_status['packed_vae_token_indexes'].extend(range(curr, curr + num_vae_tokens)) + sequence_status['packed_timesteps'].extend([0.0] * num_vae_tokens) + curr += num_vae_tokens + curr_split_len += num_vae_tokens + + sequence_status['packed_text_ids'].append(sep_id) + sequence_status['packed_text_indexes'].append(curr) + curr += 1 + curr_split_len += 1 + + vit_image_tensor = self._fit_vit_image_tensor(vit_image_tensor, item['modality_type']) + vit_tokens = patchify(vit_image_tensor, self.data_config.vit_patch_size) + num_vit_tokens = vit_tokens.shape[0] + sequence_status['packed_vit_token_indexes'].extend(range(curr, curr + num_vit_tokens)) + curr += num_vit_tokens + curr_split_len += num_vit_tokens + sequence_status['packed_vit_tokens'].append(vit_tokens) + sequence_status['vit_token_seqlens'].append(num_vit_tokens) + sequence_status['vit_image_modality_types'].append(item['modality_type']) + sequence_status['vit_image_token_shapes'].append(( + vit_image_tensor.size(1) // self.data_config.vit_patch_size, + vit_image_tensor.size(2) // self.data_config.vit_patch_size, + )) + vit_position_ids = self.get_flattened_position_ids( + vit_image_tensor.size(1), vit_image_tensor.size(2), + self.data_config.vit_patch_size, + max_num_patches_per_side=self.data_config.max_num_patch_per_side + ) + sequence_status['packed_vit_position_ids'].append(vit_position_ids) + + sequence_status['packed_text_ids'].append(eoi_id) + sequence_status['packed_text_indexes'].append(curr) + curr += 1 + curr_split_len += 1 + + if split_start: + attn_modes.append("full") + sequence_status['packed_position_ids'].extend([curr_rope_id] * curr_split_len) + curr_rope_id += 1 + + if item.get('split_end', True): + split_lens.append(curr_split_len) + sample_lens += curr_split_len + + sequence_status['curr'] = curr + sequence_status['sample_lens'].append(sample_lens) + sequence_status['target_modality_types'].append(last_loss_modality_type or item['modality_type']) + # prepare attention mask + if not self.use_flex: + sequence_status['nested_attention_masks'].append( + prepare_attention_mask_per_sample(split_lens, attn_modes) + ) + else: + sequence_status['split_lens'].extend(split_lens) + sequence_status['attn_modes'].extend(attn_modes) + + return sequence_status + + +class SimpleCustomBatch: + def __init__(self, batch): + data = batch[0] + self.batch_data_indexes = data['batch_data_indexes'] + self.data_resume_state = data.get("data_resume_state") + self.sequence_length = data["sequence_length"] + self.num_samples = data["num_samples"] + self.num_epoch_samples = data.get("num_epoch_samples", data["num_samples"]) + self.sample_lens = data["sample_lens"] + self.target_modality_types = data["target_modality_types"] + self.vae_image_modality_types = data["vae_image_modality_types"] + self.packed_text_ids = data["packed_text_ids"] + self.packed_text_indexes = data["packed_text_indexes"] + self.packed_position_ids = data["packed_position_ids"] + + self.use_flex = "nested_attention_masks" not in data.keys() + + if self.use_flex: + self.split_lens = data["split_lens"] + self.attn_modes = data["attn_modes"] + else: + self.nested_attention_masks = data["nested_attention_masks"] + + if "padded_images" in data.keys(): + self.padded_images = data["padded_images"] + self.patchified_vae_latent_shapes = data["patchified_vae_latent_shapes"] + self.packed_latent_position_ids = data["packed_latent_position_ids"] + self.packed_vae_token_indexes = data["packed_vae_token_indexes"] + + if "packed_vit_tokens" in data.keys(): + self.packed_vit_tokens = data["packed_vit_tokens"] + self.packed_vit_position_ids = data["packed_vit_position_ids"] + self.packed_vit_token_indexes = data["packed_vit_token_indexes"] + self.vit_token_seqlens = data["vit_token_seqlens"] + self.vit_image_modality_types = data["vit_image_modality_types"] + # Per-image (token_h, token_w) for 2D rope_image_info on ViT inputs (B fix) + self.vit_image_token_shapes = data.get("vit_image_token_shapes", []) + + if "packed_timesteps" in data.keys(): + self.packed_timesteps = data["packed_timesteps"] + self.mse_loss_indexes = data["mse_loss_indexes"] + self.mse_loss_modality_ids = data["mse_loss_modality_ids"] + self.mse_loss_route_ids = data["mse_loss_route_ids"] + self.mse_loss_image_ids = data["mse_loss_image_ids"] + self.mse_loss_timesteps = data["mse_loss_timesteps"] + + if "packed_label_ids" in data.keys(): + self.packed_label_ids = data["packed_label_ids"] + self.ce_loss_indexes = data["ce_loss_indexes"] + self.ce_loss_weights = data["ce_loss_weights"] + self.ce_loss_modality_ids = data["ce_loss_modality_ids"] + + def pin_memory(self): + self.packed_text_ids = self.packed_text_ids.pin_memory() + self.packed_text_indexes = self.packed_text_indexes.pin_memory() + self.packed_position_ids = self.packed_position_ids.pin_memory() + + if not self.use_flex: + self.nested_attention_masks = [item.pin_memory() for item in self.nested_attention_masks] + + if hasattr(self, 'padded_images'): + self.padded_images = self.padded_images.pin_memory() + self.packed_vae_token_indexes = self.packed_vae_token_indexes.pin_memory() + self.packed_latent_position_ids = self.packed_latent_position_ids.pin_memory() + + if hasattr(self, 'packed_timesteps'): + self.packed_timesteps = self.packed_timesteps.pin_memory() + self.mse_loss_indexes = self.mse_loss_indexes.pin_memory() + self.mse_loss_modality_ids = self.mse_loss_modality_ids.pin_memory() + self.mse_loss_route_ids = self.mse_loss_route_ids.pin_memory() + self.mse_loss_image_ids = self.mse_loss_image_ids.pin_memory() + self.mse_loss_timesteps = self.mse_loss_timesteps.pin_memory() + + if hasattr(self, 'packed_vit_tokens'): + self.packed_vit_tokens = self.packed_vit_tokens.pin_memory() + self.packed_vit_position_ids = self.packed_vit_position_ids.pin_memory() + self.packed_vit_token_indexes = self.packed_vit_token_indexes.pin_memory() + self.vit_token_seqlens = self.vit_token_seqlens.pin_memory() + + if hasattr(self, 'packed_label_ids'): + self.packed_label_ids = self.packed_label_ids.pin_memory() + self.ce_loss_indexes = self.ce_loss_indexes.pin_memory() + self.ce_loss_weights = self.ce_loss_weights.pin_memory() + self.ce_loss_modality_ids = self.ce_loss_modality_ids.pin_memory() + + return self + + def cuda(self, device): + self.packed_text_ids = self.packed_text_ids.to(device) + self.packed_text_indexes = self.packed_text_indexes.to(device) + self.packed_position_ids = self.packed_position_ids.to(device) + + if not self.use_flex: + self.nested_attention_masks = [item.to(device) for item in self.nested_attention_masks] + + if hasattr(self, 'padded_images'): + self.padded_images = self.padded_images.to(device) + self.packed_vae_token_indexes = self.packed_vae_token_indexes.to(device) + self.packed_latent_position_ids = self.packed_latent_position_ids.to(device) + + if hasattr(self, 'packed_timesteps'): + self.packed_timesteps = self.packed_timesteps.to(device) + self.mse_loss_indexes = self.mse_loss_indexes.to(device) + self.mse_loss_modality_ids = self.mse_loss_modality_ids.to(device) + self.mse_loss_route_ids = self.mse_loss_route_ids.to(device) + self.mse_loss_image_ids = self.mse_loss_image_ids.to(device) + self.mse_loss_timesteps = self.mse_loss_timesteps.to(device) + + if hasattr(self, 'packed_vit_tokens'): + self.packed_vit_tokens = self.packed_vit_tokens.to(device) + self.packed_vit_position_ids = self.packed_vit_position_ids.to(device) + self.packed_vit_token_indexes = self.packed_vit_token_indexes.to(device) + self.vit_token_seqlens = self.vit_token_seqlens.to(device) + + if hasattr(self, 'packed_label_ids'): + self.packed_label_ids = self.packed_label_ids.to(device) + self.ce_loss_indexes = self.ce_loss_indexes.to(device) + self.ce_loss_weights = self.ce_loss_weights.to(device) + self.ce_loss_modality_ids = self.ce_loss_modality_ids.to(device) + + return self + + def to_dict(self): + data = dict( + sequence_length = self.sequence_length, + num_samples = self.num_samples, + num_epoch_samples = self.num_epoch_samples, + sample_lens = self.sample_lens, + target_modality_types = self.target_modality_types, + vae_image_modality_types = self.vae_image_modality_types, + packed_text_ids = self.packed_text_ids, + packed_text_indexes = self.packed_text_indexes, + packed_position_ids = self.packed_position_ids, + batch_data_indexes = self.batch_data_indexes, + data_resume_state = self.data_resume_state, + ) + + if not self.use_flex: + data['nested_attention_masks'] = self.nested_attention_masks + else: + data['split_lens'] = self.split_lens + data['attn_modes'] = self.attn_modes + + if hasattr(self, 'padded_images'): + data['padded_images'] = self.padded_images + data['patchified_vae_latent_shapes'] = self.patchified_vae_latent_shapes + data['packed_latent_position_ids'] = self.packed_latent_position_ids + data['packed_vae_token_indexes'] = self.packed_vae_token_indexes + + if hasattr(self, 'packed_vit_tokens'): + data['packed_vit_tokens'] = self.packed_vit_tokens + data['packed_vit_position_ids'] = self.packed_vit_position_ids + data['packed_vit_token_indexes'] = self.packed_vit_token_indexes + data['vit_token_seqlens'] = self.vit_token_seqlens + data['vit_image_modality_types'] = self.vit_image_modality_types + data['vit_image_token_shapes'] = getattr(self, 'vit_image_token_shapes', []) + + if hasattr(self, 'packed_timesteps'): + data['packed_timesteps'] = self.packed_timesteps + data['mse_loss_indexes'] = self.mse_loss_indexes + data['mse_loss_modality_ids'] = self.mse_loss_modality_ids + data['mse_loss_route_ids'] = self.mse_loss_route_ids + data['mse_loss_image_ids'] = self.mse_loss_image_ids + data['mse_loss_timesteps'] = self.mse_loss_timesteps + + if hasattr(self, 'packed_label_ids'): + data['packed_label_ids'] = self.packed_label_ids + data['ce_loss_indexes'] = self.ce_loss_indexes + data['ce_loss_weights'] = self.ce_loss_weights + data['ce_loss_modality_ids'] = self.ce_loss_modality_ids + + return data + + +def collate_wrapper(): + def collate_fn(batch): + return SimpleCustomBatch(batch) + return collate_fn diff --git a/data/dataset_info.py b/data/dataset_info.py new file mode 100644 index 0000000000000000000000000000000000000000..6706f11ca97846dd893266c49a1fcf71803af61b --- /dev/null +++ b/data/dataset_info.py @@ -0,0 +1,1019 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +from .interleave_datasets import UnifiedEditIterableDataset +from .t2i_dataset import T2IIterableDataset +from .vlm_dataset import SftJSONLIterableDataset, SftParquetIterableDataset +from .interleave_datasets.any2any_dataset import UnifiedAny2AnyIterableDataset +from .interleave_datasets.hunyuan_native_chat_any2any_dataset import ( + HunyuanNativeChatAny2AnyIterableDataset, +) +import torch +import os + + +Any2AnyDatasetClass = ( + HunyuanNativeChatAny2AnyIterableDataset + if os.environ.get("HUNYUAN_NATIVE_CHAT_SERIALIZATION", "0") == "1" + else UnifiedAny2AnyIterableDataset +) + +DATASET_REGISTRY = { + 't2i_pretrain': T2IIterableDataset, + 'vlm_sft': SftJSONLIterableDataset, + 'vlm_sft_parquet': SftParquetIterableDataset, + 'unified_edit': UnifiedEditIterableDataset, + 'unified_any2rgb': Any2AnyDatasetClass, + 'unified_any2depth': Any2AnyDatasetClass, + 'unified_any2normal': Any2AnyDatasetClass, + 'unified_any2caption': Any2AnyDatasetClass, + 'unified_any2det': Any2AnyDatasetClass, + 'unified_any2seg': Any2AnyDatasetClass, + 'unified_any2grounding': Any2AnyDatasetClass, + 'unified_any2canny': Any2AnyDatasetClass, + 'unified_any2dino': Any2AnyDatasetClass, + 'unified_any2dinolocal': Any2AnyDatasetClass, + 'unified_any2clip': Any2AnyDatasetClass, + 'unified_any2imagebind': Any2AnyDatasetClass, + 'unified_any2imagebindlocal': Any2AnyDatasetClass, + 'unified_any2cocodet': Any2AnyDatasetClass, + 'unified_any2samseg': Any2AnyDatasetClass, + 'unified_any2samedge': Any2AnyDatasetClass, +} + + +DATASET_INFO = { + 't2i_pretrain': { + 't2i': { + 'data_dir': './datasets/bagel_example/t2i', # path of the parquet files + 'num_files': 10, # number of data units to be sharded across all ranks and workers + 'num_total_samples': 1000, # number of total samples in the dataset + }, + }, + 'unified_edit':{ + 'seedxedit_multi': { + 'data_dir': './datasets/bagel_example/editing/seedxedit_multi', + 'num_files': 10, + 'num_total_samples': 1000, + "parquet_info_path": './datasets/bagel_example/editing/parquet_info/seedxedit_multi.json', # information of the parquet files + }, + }, + 'vlm_sft': { + 'text_only_benchmark': { + 'data_dir': './datasets/text_only_benchmark/images', + 'jsonl_path': './datasets/text_only_benchmark/label/labels.jsonl', + }, + 'ai2d(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/ai2d(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/ai2d(cauldron,llava_format)/label/labels.jsonl', + }, + 'ai2d(gpt4v)': { + 'data_dir': './datasets/llava_onevision_vqa/ai2d(gpt4v)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/ai2d(gpt4v)/label/labels.jsonl', + }, + 'ai2d(internvl)': { + 'data_dir': './datasets/llava_onevision_vqa/ai2d(internvl)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/ai2d(internvl)/label/labels.jsonl', + }, + 'allava_instruct_laion4v': { + 'data_dir': './datasets/llava_onevision_vqa/allava_instruct_laion4v/images', + 'jsonl_path': './datasets/llava_onevision_vqa/allava_instruct_laion4v/label/labels.jsonl', + }, + 'allava_instruct_vflan4v': { + 'data_dir': './datasets/llava_onevision_vqa/allava_instruct_vflan4v/images', + 'jsonl_path': './datasets/llava_onevision_vqa/allava_instruct_vflan4v/label/labels.jsonl', + }, + 'aokvqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/aokvqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/aokvqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'chart2text(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/chart2text(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/chart2text(cauldron)/label/labels.jsonl', + }, + 'chartqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/chartqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/chartqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'chrome_writting': { + 'data_dir': './datasets/llava_onevision_vqa/chrome_writting/images', + 'jsonl_path': './datasets/llava_onevision_vqa/chrome_writting/label/labels.jsonl', + }, + 'CLEVR-Math(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/CLEVR-Math(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/CLEVR-Math(MathV360K)/label/labels.jsonl', + }, + 'clevr(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/clevr(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/clevr(cauldron,llava_format)/label/labels.jsonl', + }, + 'diagram_image_to_text(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/diagram_image_to_text(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/diagram_image_to_text(cauldron)/label/labels.jsonl', + }, + 'dvqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/dvqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/dvqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'Evol-Instruct-GPT4-Turbo': { + 'data_dir': './datasets/llava_onevision_vqa/Evol-Instruct-GPT4-Turbo/images', + 'jsonl_path': './datasets/llava_onevision_vqa/Evol-Instruct-GPT4-Turbo/label/labels.jsonl', + }, + 'figureqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/figureqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/figureqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'FigureQA(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/FigureQA(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/FigureQA(MathV360K)/label/labels.jsonl', + }, + 'geo170k(align)': { + 'data_dir': './datasets/llava_onevision_vqa/geo170k(align)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/geo170k(align)/label/labels.jsonl', + }, + 'geo170k(qa)': { + 'data_dir': './datasets/llava_onevision_vqa/geo170k(qa)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/geo170k(qa)/label/labels.jsonl', + }, + 'geo3k': { + 'data_dir': './datasets/llava_onevision_vqa/geo3k/images', + 'jsonl_path': './datasets/llava_onevision_vqa/geo3k/label/labels.jsonl', + }, + 'Geometry3K(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/Geometry3K(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/Geometry3K(MathV360K)/label/labels.jsonl', + }, + 'geomverse(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/geomverse(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/geomverse(cauldron)/label/labels.jsonl', + }, + 'GeoQA+(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/GeoQA+(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/GeoQA+(MathV360K)/label/labels.jsonl', + }, + 'GEOS(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/GEOS(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/GEOS(MathV360K)/label/labels.jsonl', + }, + 'hateful_memes(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/hateful_memes(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/hateful_memes(cauldron,llava_format)/label/labels.jsonl', + }, + 'hitab(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/hitab(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/hitab(cauldron,llava_format)/label/labels.jsonl', + }, + 'hme100k': { + 'data_dir': './datasets/llava_onevision_vqa/hme100k/images', + 'jsonl_path': './datasets/llava_onevision_vqa/hme100k/label/labels.jsonl', + }, + 'iam(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/iam(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/iam(cauldron)/label/labels.jsonl', + }, + 'iconqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/iconqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/iconqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'IconQA(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/IconQA(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/IconQA(MathV360K)/label/labels.jsonl', + }, + 'iiit5k': { + 'data_dir': './datasets/llava_onevision_vqa/iiit5k/images', + 'jsonl_path': './datasets/llava_onevision_vqa/iiit5k/label/labels.jsonl', + }, + 'image_textualization(filtered)': { + 'data_dir': './datasets/llava_onevision_vqa/image_textualization(filtered)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/image_textualization(filtered)/label/labels.jsonl', + }, + 'infographic_vqa': { + 'data_dir': './datasets/llava_onevision_vqa/infographic_vqa/images', + 'jsonl_path': './datasets/llava_onevision_vqa/infographic_vqa/label/labels.jsonl', + }, + 'infographic_vqa_llava_format': { + 'data_dir': './datasets/llava_onevision_vqa/infographic_vqa_llava_format/images', + 'jsonl_path': './datasets/llava_onevision_vqa/infographic_vqa_llava_format/label/labels.jsonl', + }, + 'infographic(gpt4v)': { + 'data_dir': './datasets/llava_onevision_vqa/infographic(gpt4v)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/infographic(gpt4v)/label/labels.jsonl', + }, + 'intergps(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/intergps(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/intergps(cauldron,llava_format)/label/labels.jsonl', + }, + 'k12_printing': { + 'data_dir': './datasets/llava_onevision_vqa/k12_printing/images', + 'jsonl_path': './datasets/llava_onevision_vqa/k12_printing/label/labels.jsonl', + }, + 'llava_wild_4v_12k_filtered': { + 'data_dir': './datasets/llava_onevision_vqa/llava_wild_4v_12k_filtered/images', + 'jsonl_path': './datasets/llava_onevision_vqa/llava_wild_4v_12k_filtered/label/labels.jsonl', + }, + 'llava_wild_4v_39k_filtered': { + 'data_dir': './datasets/llava_onevision_vqa/llava_wild_4v_39k_filtered/images', + 'jsonl_path': './datasets/llava_onevision_vqa/llava_wild_4v_39k_filtered/label/labels.jsonl', + }, + 'llavar_gpt4_20k': { + 'data_dir': './datasets/llava_onevision_vqa/llavar_gpt4_20k/images', + 'jsonl_path': './datasets/llava_onevision_vqa/llavar_gpt4_20k/label/labels.jsonl', + }, + 'lrv_chart': { + 'data_dir': './datasets/llava_onevision_vqa/lrv_chart/images', + 'jsonl_path': './datasets/llava_onevision_vqa/lrv_chart/label/labels.jsonl', + }, + 'lrv_normal(filtered)': { + 'data_dir': './datasets/llava_onevision_vqa/lrv_normal(filtered)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/lrv_normal(filtered)/label/labels.jsonl', + }, + 'magpie_pro(l3_80b_mt)': { + 'data_dir': './datasets/llava_onevision_vqa/magpie_pro(l3_80b_mt)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/magpie_pro(l3_80b_mt)/label/labels.jsonl', + }, + 'magpie_pro(l3_80b_st)': { + 'data_dir': './datasets/llava_onevision_vqa/magpie_pro(l3_80b_st)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/magpie_pro(l3_80b_st)/label/labels.jsonl', + }, + 'magpie_pro(qwen2_72b_st)': { + 'data_dir': './datasets/llava_onevision_vqa/magpie_pro(qwen2_72b_st)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/magpie_pro(qwen2_72b_st)/label/labels.jsonl', + }, + 'mapqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/mapqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/mapqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'MapQA(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/MapQA(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/MapQA(MathV360K)/label/labels.jsonl', + }, + 'mathqa': { + 'data_dir': './datasets/llava_onevision_vqa/mathqa/images', + 'jsonl_path': './datasets/llava_onevision_vqa/mathqa/label/labels.jsonl', + }, + 'MathV360K_TQA': { + 'data_dir': './datasets/llava_onevision_vqa/MathV360K_TQA/images', + 'jsonl_path': './datasets/llava_onevision_vqa/MathV360K_TQA/label/labels.jsonl', + }, + 'MathV360K_VQA-AS': { + 'data_dir': './datasets/llava_onevision_vqa/MathV360K_VQA-AS/images', + 'jsonl_path': './datasets/llava_onevision_vqa/MathV360K_VQA-AS/label/labels.jsonl', + }, + 'MathV360K_VQA-RAD': { + 'data_dir': './datasets/llava_onevision_vqa/MathV360K_VQA-RAD/images', + 'jsonl_path': './datasets/llava_onevision_vqa/MathV360K_VQA-RAD/label/labels.jsonl', + }, + 'mavis_math_metagen': { + 'data_dir': './datasets/llava_onevision_vqa/mavis_math_metagen/images', + 'jsonl_path': './datasets/llava_onevision_vqa/mavis_math_metagen/label/labels.jsonl', + }, + 'mavis_math_rule_geo': { + 'data_dir': './datasets/llava_onevision_vqa/mavis_math_rule_geo/images', + 'jsonl_path': './datasets/llava_onevision_vqa/mavis_math_rule_geo/label/labels.jsonl', + }, + 'multihiertt(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/multihiertt(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/multihiertt(cauldron)/label/labels.jsonl', + }, + 'orand_car_a': { + 'data_dir': './datasets/llava_onevision_vqa/orand_car_a/images', + 'jsonl_path': './datasets/llava_onevision_vqa/orand_car_a/label/labels.jsonl', + }, + 'PMC-VQA(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/PMC-VQA(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/PMC-VQA(MathV360K)/label/labels.jsonl', + }, + 'raven(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/raven(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/raven(cauldron)/label/labels.jsonl', + }, + 'rendered_text(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/rendered_text(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/rendered_text(cauldron)/label/labels.jsonl', + }, + 'robut_sqa(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/robut_sqa(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/robut_sqa(cauldron)/label/labels.jsonl', + }, + 'robut_wikisql(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/robut_wikisql(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/robut_wikisql(cauldron)/label/labels.jsonl', + }, + 'robut_wtq(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/robut_wtq(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/robut_wtq(cauldron,llava_format)/label/labels.jsonl', + }, + 'scienceqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/scienceqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/scienceqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'scienceqa(nona_context)': { + 'data_dir': './datasets/llava_onevision_vqa/scienceqa(nona_context)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/scienceqa(nona_context)/label/labels.jsonl', + }, + 'screen2words(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/screen2words(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/screen2words(cauldron)/label/labels.jsonl', + }, + 'sharegpt4o': { + 'data_dir': './datasets/llava_onevision_vqa/sharegpt4o/images', + 'jsonl_path': './datasets/llava_onevision_vqa/sharegpt4o/label/labels.jsonl', + }, + 'sharegpt4v(coco)': { + 'data_dir': './datasets/llava_onevision_vqa/sharegpt4v(coco)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/sharegpt4v(coco)/label/labels.jsonl', + }, + 'sharegpt4v(knowledge)': { + 'data_dir': './datasets/llava_onevision_vqa/sharegpt4v(knowledge)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/sharegpt4v(knowledge)/label/labels.jsonl', + }, + 'sharegpt4v(llava)': { + 'data_dir': './datasets/llava_onevision_vqa/sharegpt4v(llava)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/sharegpt4v(llava)/label/labels.jsonl', + }, + 'sharegpt4v(sam)': { + 'data_dir': './datasets/llava_onevision_vqa/sharegpt4v(sam)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/sharegpt4v(sam)/label/labels.jsonl', + }, + 'sroie': { + 'data_dir': './datasets/llava_onevision_vqa/sroie/images', + 'jsonl_path': './datasets/llava_onevision_vqa/sroie/label/labels.jsonl', + }, + 'st_vqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/st_vqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/st_vqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'Super-CLEVR(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/Super-CLEVR(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/Super-CLEVR(MathV360K)/label/labels.jsonl', + }, + 'tabmwp(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/tabmwp(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/tabmwp(cauldron)/label/labels.jsonl', + }, + 'TabMWP(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/TabMWP(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/TabMWP(MathV360K)/label/labels.jsonl', + }, + 'tallyqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/tallyqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/tallyqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'textcaps': { + 'data_dir': './datasets/llava_onevision_vqa/textcaps/images', + 'jsonl_path': './datasets/llava_onevision_vqa/textcaps/label/labels.jsonl', + }, + 'textocr(gpt4v)': { + 'data_dir': './datasets/llava_onevision_vqa/textocr(gpt4v)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/textocr(gpt4v)/label/labels.jsonl', + }, + 'tqa(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/tqa(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/tqa(cauldron,llava_format)/label/labels.jsonl', + }, + 'UniGeo(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/UniGeo(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/UniGeo(MathV360K)/label/labels.jsonl', + }, + 'ureader_cap': { + 'data_dir': './datasets/llava_onevision_vqa/ureader_cap/images', + 'jsonl_path': './datasets/llava_onevision_vqa/ureader_cap/label/labels.jsonl', + }, + 'ureader_ie': { + 'data_dir': './datasets/llava_onevision_vqa/ureader_ie/images', + 'jsonl_path': './datasets/llava_onevision_vqa/ureader_ie/label/labels.jsonl', + }, + 'vision_flan(filtered)': { + 'data_dir': './datasets/llava_onevision_vqa/vision_flan(filtered)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/vision_flan(filtered)/label/labels.jsonl', + }, + 'vistext(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/vistext(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/vistext(cauldron)/label/labels.jsonl', + }, + 'visual7w(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/visual7w(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/visual7w(cauldron,llava_format)/label/labels.jsonl', + }, + 'visualmrc(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/visualmrc(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/visualmrc(cauldron)/label/labels.jsonl', + }, + 'VisualWebInstruct(filtered)': { + 'data_dir': './datasets/llava_onevision_vqa/VisualWebInstruct(filtered)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/VisualWebInstruct(filtered)/label/labels.jsonl', + }, + 'VizWiz(MathV360K)': { + 'data_dir': './datasets/llava_onevision_vqa/VizWiz(MathV360K)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/VizWiz(MathV360K)/label/labels.jsonl', + }, + 'vqarad(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/vqarad(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/vqarad(cauldron,llava_format)/label/labels.jsonl', + }, + 'vsr(cauldron,llava_format)': { + 'data_dir': './datasets/llava_onevision_vqa/vsr(cauldron,llava_format)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/vsr(cauldron,llava_format)/label/labels.jsonl', + }, + 'websight(cauldron)': { + 'data_dir': './datasets/llava_onevision_vqa/websight(cauldron)/images', + 'jsonl_path': './datasets/llava_onevision_vqa/websight(cauldron)/label/labels.jsonl', + }, + }, + # vlm_sft bundled into parquet shards (streaming-friendly, S3-ready). + # Written by data/any2any_preprocess/generate_parquet_vlm_sft.py; sample + # selection (shuffle_lines + num_used_data) is baked in at conversion. + 'vlm_sft_parquet': { + 'llava_onevision_vqa': { + 'data_dir': './datasets/llava_onevision_vqa_parquet/train', + 'parquet_info_path': './datasets/llava_onevision_vqa_parquet/parquet_info/llava_onevision_vqa.json', + }, + }, + 'unified_any2rgb': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o':{ + 'data_dir': './datasets/blip3o/parquet', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2depth': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o':{ + 'data_dir': './datasets/blip3o/parquet', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2normal': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o':{ + 'data_dir': './datasets/blip3o/parquet', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2caption': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o':{ + 'data_dir': './datasets/blip3o/parquet', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2det': { + 'blip3o_rgb_caption_depth_normal_det_seg':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg.json', + } + }, + 'unified_any2seg': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2grounding': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding', + 'num_files': 1000, + 'num_total_samples': 11_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding2.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 1000, + 'num_total_samples': 11_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding2_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 1000, + 'num_total_samples': 11_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding2_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2canny': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2dino': { + 'blip3o_example_toydataset': { + 'data_dir': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global/train/blip3o_example_toydataset.json', + }, + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2dinolocal': { + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2clip': { + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2imagebind': { + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + }, + 'unified_any2imagebindlocal': { + 'blip3o_example_toydataset_13modality_clip448_imagebind': { + 'data_dir': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train', + 'num_files': 1, + 'num_total_samples': 1000, + 'parquet_info_path': './datasets/blip3o/parquet_toy_13modality_clip448_imagebind/train/blip3o_example_toydataset.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind', + 'num_files': 2891, + 'num_total_samples': 25_000_000, + 'parquet_info_path': './datasets/blip3o/parquet_info/blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind.json', + }, + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind_aligned':{ + 'data_dir': './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned', + 'num_files': 1000, + 'num_total_samples': 3_899_886, + 'parquet_info_path': './datasets/blip3o/parquet_info/parquet_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global_clip448_imagebind_aligned.json', + } + } +} + + +# ── 16-modality parquet (13 old + samseg/samedge/cocodet). Superset of the old +# parquet → registered under every modality key the new run uses. Additive: does +# not touch existing dataset entries. ──────────────────────────────────────── +_PARQUET_16MOD_NAME = ( + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_clip448_imagebind' + '_samseg_samedge_cocodet' +) +_PARQUET_16MOD_INFO = { + 'data_dir': ( + './datasets/blip3o/parquet_rgb_caption_depth_normal_det_seg_grounding' + '_canny_dino_global_clip448_imagebind_samseg_samedge_cocodet' + ), + 'num_files': 2888, # 2891 minus 3 held-out val files (parquet_16mod_VAL_heldout) + 'num_total_samples': 25_000_000, + 'parquet_info_path': ( + './datasets/blip3o/parquet_info/' + 'blip3o_rgb_caption_depth_normal_det_seg_grounding_canny_dino_global' + '_clip448_imagebind_samseg_samedge_cocodet.json' + ), +} +for _mod_key in [ + 'unified_any2rgb', 'unified_any2caption', 'unified_any2depth', + 'unified_any2normal', 'unified_any2seg', 'unified_any2canny', + 'unified_any2grounding', 'unified_any2dino', 'unified_any2dinolocal', + 'unified_any2clip', 'unified_any2imagebind', 'unified_any2imagebindlocal', + 'unified_any2cocodet', 'unified_any2samseg', 'unified_any2samedge', +]: + DATASET_INFO.setdefault(_mod_key, {})[_PARQUET_16MOD_NAME] = dict(_PARQUET_16MOD_INFO) + + +# MODALITY_STATS = { +# "rgb": { +# "mean": [ +# 0.09998548775911331, -0.25365808606147766, 0.02537657506763935, 0.15525108575820923, +# -0.13307523727416992, 0.18412069976329803, -0.3696898818016052, 0.1216680109500885, +# 0.07324755936861038, 0.011612555012106895, 0.17749321460723877, 0.009583432227373123, +# 0.006619213614612818, -0.1372869461774826, -0.34581223130226135, -0.08381273597478867 +# ], +# "std": [ +# 0.6712213754653931, 1.1496732234954834, 0.6890255212783813, 0.48065587878227234, +# 0.5058963894844055, 0.5045602917671204, 0.964796781539917, 0.7499234676361084, +# 0.60835862159729, 0.8583494424819946, 0.5590784549713135, 0.6568386554718018, +# 0.8276281356811523, 0.8293890357017517, 0.7076113820075989, 0.8054326176643372 +# ] +# }, +# "depth": { +# "mean": [ +# 0.3062567710876465, -0.9071211814880371, -0.5659458041191101, -0.027933871373534203, +# 0.13497579097747803, 1.6215254068374634, -1.7268935441970825, 1.1100306510925293, +# 0.06933767348527908, -0.5322779417037964, -0.2097727358341217, 0.07714101672172546, +# -1.3442918062210083, -0.46783745288848877, -0.31114235520362854, 0.9852880239486694 +# ], +# "std": [ +# 0.4548855125904083, 2.0455949306488037, 0.40026405453681946, 0.34950074553489685, +# 0.40104442834854126, 0.47336098551750183, 1.658141851425171, 1.2504311800003052, +# 0.3990429639816284, 1.1091442108154297, 0.724875271320343, 0.8045597672462463, +# 1.4909602403640747, 1.501410961151123, 1.1055831909179688, 1.3423223495483398 +# ] +# }, +# "normal": { +# "mean": [ +# 1.1839842796325684, 0.8041657209396362, -0.391467422246933, 0.5322199463844299, +# -0.5196242928504944, 0.537630021572113, 0.4950917363166809, -0.8294451236724854, +# 1.2865995168685913, -1.2402032613754272, 0.2119874656200409, 0.4859861135482788, +# 0.43453630805015564, -1.550497055053711, -0.6797669529914856, -0.09787978231906891 +# ], +# "std": [ +# 0.6379154920578003, 0.5921596884727478, 1.1566283702850342, 0.6052563786506653, +# 0.6098771095275879, 0.5771185755729675, 0.5548882484436035, 0.42952394485473633, +# 0.7510039806365967, 1.1233150959014893, 0.7662065625190735, 0.6668922305107117, +# 0.5062761306762695, 1.2029993534088135, 0.9624990820884705, 0.5272090435028076 +# ] +# } +# } + + +MODALITY_STATS = { + "depth": { + "mean": [ + 0.28762707114219666, -1.011340856552124, -0.5465618371963501, -0.01756225898861885, + 0.12532611191272736, 1.56802499294281, -1.7659778594970703, 1.1437808275222778, + 0.0657510980963707, -0.5864744186401367, -0.2622603476047516, 0.04382546991109848, + -1.4009149074554443, -0.35514819622039795, -0.2363666445016861, 1.04072904586792 + ], + "std": [ + 0.4532972276210785, 2.055128812789917, 0.397210031747818, 0.34771019220352173, + 0.4028947949409485, 0.47736671566963196, 1.6445382833480835, 1.2453844547271729, + 0.4030587077140808, 1.1071559190750122, 0.7346603870391846, 0.809998095035553, + 1.4902814626693726, 1.511523723602295, 1.1147352457046509, 1.346556544303894 + ] + }, + "normal": { + "mean": [ + 1.1103582382202148, 0.7585429549217224, -0.4633699059486389, 0.4460155665874481, + -0.4614908993244171, 0.5055513978004456, 0.5129532217979431, -0.8317145705223083, + 1.1523557901382446, -1.0965044498443604, 0.29184550046920776, 0.3881677985191345, + 0.41409650444984436, -1.3409103155136108, -0.7599201202392578, -0.14071156084537506 + ], + "std": [ + 0.658498227596283, 0.6019635200500488, 1.2136141061782837, 0.644258439540863, + 0.656324028968811, 0.5987396836280823, 0.5700253844261169, 0.4413583278656006, + 0.8087592124938965, 1.2170354127883911, 0.7942348718643188, 0.6947795748710632, + 0.5312002897262573, 1.2880762815475464, 1.0215877294540405, 0.5333302021026611 + ] + } +} + + +def normalize_latents_by_modality(latents, modality_types, device): + """ + Normalize VAE latents by modality type. + Supports rgb, depth, and normal modalities. + + Args: + latents: torch.Tensor of shape [B, C, H, W] + modality_types: List[str] of length B indicating modality for each sample + device: torch device + + Returns: + Normalized latents tensor + """ + if not MODALITY_STATS: + return latents + + normalized_latents = latents.clone() + + for i, modality in enumerate(modality_types): + if modality in MODALITY_STATS: + # Get mean and std for this modality + mean = torch.tensor(MODALITY_STATS[modality]["mean"], device=device, dtype=latents.dtype) + std = torch.tensor(MODALITY_STATS[modality]["std"], device=device, dtype=latents.dtype) + + # Reshape to [C, 1, 1] for broadcasting + mean = mean.view(-1, 1, 1) + std = std.view(-1, 1, 1) + + # Normalize: (x - mean) / std + normalized_latents[i] = (latents[i] - mean) / std + + return normalized_latents + +def denormalize_latents_by_modality(latents, modality_type, device): + """ + Denormalize VAE latents by modality type. + Reverses the normalization applied during training for rgb, depth, and normal. + + Args: + latents: torch.Tensor of shape [B, C, H, W] - normalized latents from model + modality_type: str indicating modality for the sample + device: torch device + + Returns: + Denormalized latents tensor ready for VAE decoder + """ + if not MODALITY_STATS or modality_type not in MODALITY_STATS: + return latents + + # Get mean and std for this modality + mean = torch.tensor(MODALITY_STATS[modality_type]["mean"], device=device, dtype=latents.dtype) + std = torch.tensor(MODALITY_STATS[modality_type]["std"], device=device, dtype=latents.dtype) + + # Reshape to [C, 1, 1] for broadcasting + mean = mean.view(-1, 1, 1) + std = std.view(-1, 1, 1) + + # Denormalize: x * std + mean (reverse of normalization) + denormalized_latents = latents * std + mean + + return denormalized_latents diff --git a/data/distributed_iterable_dataset.py b/data/distributed_iterable_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..65668a4b97b7824caee2a6f02ce1e0999238b39c --- /dev/null +++ b/data/distributed_iterable_dataset.py @@ -0,0 +1,58 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import random +import torch + + +class DistributedIterableDataset(torch.utils.data.IterableDataset): + def __init__(self, dataset_name, local_rank=0, world_size=1, num_workers=8): + self.dataset_name = dataset_name + self.local_rank = local_rank + self.world_size = world_size + self.num_workers = num_workers + self.rng = random.Random() + self.data_paths = None + + def get_data_paths(self, *args, **kwargs): + raise NotImplementedError + + def set_epoch(self, seed=42): + if self.data_paths is None: + return + + if isinstance(self.data_paths[0], tuple): + data_paths = sorted(self.data_paths, key=lambda x: (x[0], x[1])) + elif isinstance(self.data_paths[0], str): + data_paths = sorted(self.data_paths) + else: + raise ValueError(f"Unknown data_paths type: {type(self.data_paths[0])}") + + self.rng.seed(seed) + self.rng.shuffle(data_paths) + + num_files_per_rank = len(data_paths) // self.world_size + local_start = self.local_rank * num_files_per_rank + local_end = (self.local_rank + 1) * num_files_per_rank + self.num_files_per_rank = num_files_per_rank + self.data_paths_per_rank = data_paths[local_start:local_end] + + def get_data_paths_per_worker(self): + if self.data_paths is None: + return None + + info = torch.utils.data.get_worker_info() + if info is None: + # Single worker: Use all files assigned to the rank + return self.data_paths_per_rank, 0 + + worker_id = info.id + num_files_per_worker = self.num_files_per_rank // info.num_workers + start = num_files_per_worker * worker_id + end = num_files_per_worker * (worker_id + 1) + data_paths_per_worker = self.data_paths_per_rank[start:end] + + return data_paths_per_worker[::-1], worker_id + + def __iter__(self): + raise NotImplementedError diff --git a/data/generate_text_benchmark.py b/data/generate_text_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..2de2ca29630b1feec42c5c0061d06c31df786fe5 --- /dev/null +++ b/data/generate_text_benchmark.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +"""Generate a synthetic text-only JSONL benchmark dataset. + +Usage: + python data/generate_text_benchmark.py + python data/generate_text_benchmark.py --num-samples 200000 --output datasets/text_only_benchmark/label/labels.jsonl + +Each record has no 'image' key so SftJSONLIterableDataset treats it as pure text, +which is what we want for text-only throughput benchmarking. +""" + +import argparse +import json +import os +import random + +# Topic pool for varied question/answer generation +TOPICS = [ + ("mathematics", "Explain how gradient descent works in machine learning.", + "Gradient descent is an optimization algorithm used to minimize a loss function by iteratively moving in the direction of steepest descent. Starting from an initial parameter vector, we compute the gradient of the loss with respect to each parameter, then update the parameters by subtracting a small multiple (the learning rate) of the gradient. This process repeats until convergence. Variants include stochastic gradient descent, which uses a single sample per update, and mini-batch gradient descent, which uses a small subset. Adaptive methods like Adam adjust the learning rate per parameter using estimates of first and second moments of the gradients."), + ("science", "What is the relationship between entropy and information?", + "Entropy in information theory, introduced by Claude Shannon, quantifies the average uncertainty or information content of a random variable. For a discrete distribution with probabilities p_i, Shannon entropy is H = -sum(p_i log p_i). High entropy means the outcomes are more unpredictable and each observation carries more information. This parallels thermodynamic entropy, which measures the number of microstates compatible with a macrostate. The connection runs deep: both measure the spread or disorder of a distribution. In data compression, entropy gives a lower bound on the average number of bits needed to encode messages from a source."), + ("history", "Describe the main causes of the First World War.", + "The First World War emerged from a complex interplay of factors. Long-term causes included the system of European alliances dividing the continent into two armed camps, intense nationalism especially in the Balkans, imperial competition among European powers, and a massive arms race particularly in naval power between Britain and Germany. The immediate trigger was the assassination of Archduke Franz Ferdinand of Austria-Hungary in Sarajevo on June 28, 1914, by a Bosnian Serb nationalist. Austria-Hungary's ultimatum to Serbia, Russia's mobilization in defense of Serbia, Germany's declaration of war on Russia, and the subsequent chain of alliance obligations drew all major European powers into conflict within weeks."), + ("technology", "How does a transformer neural network process sequences?", + "Transformer networks process sequences through self-attention mechanisms that allow each token to attend to every other token in the input. The input is first converted to embeddings and augmented with positional encodings to preserve order information. Each transformer layer applies multi-head self-attention, which computes query, key, and value projections for all tokens simultaneously, then computes attention weights as scaled dot products between queries and keys, applies softmax normalization, and aggregates values weighted by these scores. A feed-forward network follows each attention block. Residual connections and layer normalization stabilize training. This parallel processing of all positions simultaneously makes transformers highly efficient on modern hardware compared to recurrent architectures."), + ("philosophy", "What is the hard problem of consciousness?", + "The hard problem of consciousness, articulated by philosopher David Chalmers, concerns why physical processes in the brain give rise to subjective experience. The easy problems of consciousness involve explaining cognitive functions like attention, memory, and behavioral control, which seem tractable through neuroscience. The hard problem asks why there is something it is like to be a conscious creature at all. Why does neural activity produce qualia, the felt quality of experiences like the redness of red or the painfulness of pain? Physicalist approaches attempt to reduce consciousness to brain states, while dualists argue mind is irreducible to matter. Illusionists claim our intuitions about qualia are themselves mistaken."), + ("biology", "Explain the process of DNA replication.", + "DNA replication is the process by which a cell copies its genome before division. It begins at specific sequences called origins of replication. The enzyme helicase unwinds the double helix by breaking hydrogen bonds between base pairs, creating a replication fork. Single-strand binding proteins stabilize the unwound strands. Primase synthesizes short RNA primers to provide a starting point for DNA polymerase. DNA polymerase III then extends the new strand in the 5' to 3' direction, reading the template 3' to 5'. Since both strands must be replicated but polymerase can only work in one direction, one strand is synthesized continuously (leading strand) and the other discontinuously in Okazaki fragments (lagging strand). RNA primers are later replaced with DNA and fragments joined by ligase."), + ("economics", "What is the efficient market hypothesis?", + "The efficient market hypothesis (EMH), developed by Eugene Fama, states that asset prices fully reflect all available information. In the weak form, prices incorporate all historical price data, making technical analysis unprofitable. In the semi-strong form, prices reflect all publicly available information including financial statements and news, making fundamental analysis unable to generate consistent excess returns. In the strong form, prices even incorporate private insider information. EMH implies that consistently beating the market through stock picking is impossible without taking on additional risk. Critics point to documented anomalies like momentum effects, value premiums, and market bubbles as evidence against strict EMH. Behavioral finance offers alternative explanations based on investor psychology and cognitive biases."), + ("physics", "How does quantum entanglement work?", + "Quantum entanglement occurs when two or more particles become correlated in such a way that the quantum state of each particle cannot be described independently of the others, regardless of the distance separating them. When particles interact or are created together under certain conditions, they can form an entangled state. Measuring a property of one particle instantly determines the corresponding property of its entangled partner. Einstein called this spooky action at a distance and argued it implied quantum mechanics was incomplete. Bell's theorem showed that correlations in entangled systems cannot be explained by local hidden variables, and subsequent experiments confirmed quantum mechanical predictions. Entanglement does not allow faster-than-light communication because the measurement outcomes are random; only by comparing results classically can the correlation be seen."), + ("literature", "What are the major themes in Dostoevsky's Crime and Punishment?", + "Crime and Punishment explores several interconnected themes. The central theme is the psychological burden of guilt and the impossibility of escaping moral consequences. Raskolnikov's theory that extraordinary men are above conventional morality and may commit crimes for higher purposes is systematically dismantled through his suffering. The novel examines nihilism and its failure as a guiding philosophy. Redemption through suffering is another major theme, influenced by Orthodox Christian values. The contrast between rational theories and human emotional reality runs throughout. Social conditions in St. Petersburg, poverty, and class inequality provide the backdrop and partially explain characters' desperation. The novel also explores compassion as a transformative force, embodied in Sonya, whose faith and love ultimately reach Raskolnikov."), + ("computer science", "Explain the CAP theorem in distributed systems.", + "The CAP theorem, proved by Eric Brewer, states that a distributed data store can provide at most two of three guarantees simultaneously: Consistency (every read receives the most recent write or an error), Availability (every request receives a non-error response, though it may not be the most recent), and Partition tolerance (the system continues operating despite network partitions dropping messages between nodes). Since network partitions are inevitable in real distributed systems, the practical choice is between consistency and availability during a partition. CP systems (like HBase, Zookeeper) choose consistency, potentially refusing requests during partitions. AP systems (like Cassandra, CouchDB) remain available but may return stale data. The theorem applies only during partition events; normal operation can achieve both consistency and availability."), +] + +ANSWER_EXTENSIONS = [ + " Furthermore, it is worth noting that this concept has significant practical implications in modern applications.", + " Research in this area continues to evolve, with recent advances challenging some earlier assumptions.", + " Understanding this requires familiarity with related concepts that form the theoretical foundation.", + " Empirical evidence from numerous studies supports the theoretical framework outlined here.", + " The historical development of this idea reflects broader intellectual trends of the period.", + " Practical applications of this principle can be found across many domains and disciplines.", + " Critics have raised important objections that have led to refinements in the standard account.", + " The mathematical formalization of this concept provides precision lacking in intuitive descriptions.", + " Cross-disciplinary connections to adjacent fields have proven particularly fruitful.", + " Ongoing debates in the field center on questions that remain open to investigation.", +] + + +def generate_sample(idx: int, rng: random.Random) -> dict: + topic_idx = idx % len(TOPICS) + _, question, base_answer = TOPICS[topic_idx] + + # Vary questions slightly + question_prefix = rng.choice(["", "Please ", "Can you ", ""]) + question_suffix = rng.choice(["", " Please be detailed.", " Explain thoroughly.", ""]) + human_text = question_prefix + question + question_suffix + + # Vary answers by appending extensions to hit ~400-600 tokens + answer = base_answer + num_extensions = rng.randint(1, 3) + extensions = rng.sample(ANSWER_EXTENSIONS, min(num_extensions, len(ANSWER_EXTENSIONS))) + answer = answer + "".join(extensions) + + return { + "id": idx, + "conversations": [ + {"from": "human", "value": human_text}, + {"from": "gpt", "value": answer}, + ], + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--num-samples", type=int, default=200000) + parser.add_argument( + "--output", + type=str, + default="datasets/text_only_benchmark/label/labels.jsonl", + ) + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + + os.makedirs(os.path.dirname(args.output), exist_ok=True) + # Also create the images dir (required field in dataset_info but never used for text-only) + images_dir = os.path.join(os.path.dirname(os.path.dirname(args.output)), "images") + os.makedirs(images_dir, exist_ok=True) + + rng = random.Random(args.seed) + written = 0 + with open(args.output, "w") as f: + for i in range(args.num_samples): + sample = generate_sample(i, rng) + f.write(json.dumps(sample, ensure_ascii=False) + "\n") + written += 1 + if written % 10000 == 0: + print(f"Written {written}/{args.num_samples} samples...") + + print(f"Done. {written} samples written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/data/interleave_datasets/__init__.py b/data/interleave_datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..528e72ab18a4d86179409d5834d19f34466f8c66 --- /dev/null +++ b/data/interleave_datasets/__init__.py @@ -0,0 +1,5 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +from .edit_dataset import UnifiedEditIterableDataset + diff --git a/data/interleave_datasets/any2any_dataset.py b/data/interleave_datasets/any2any_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..286cf64729c72c5f46486f320cbf1ed4d5db3525 --- /dev/null +++ b/data/interleave_datasets/any2any_dataset.py @@ -0,0 +1,1391 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import io +import os +import json +import random +from PIL import Image, ImageFile, PngImagePlugin, ImageDraw, ImageFont +import numpy as np +import pycocotools.mask as mask_util +import re + +from .interleave_t2i_dataset import InterleavedBaseIterableDataset, ParquetStandardIterableDataset +from ..data_utils import pil_img2rgb + + +Image.MAX_IMAGE_PIXELS = 200000000 +ImageFile.LOAD_TRUNCATED_IMAGES = True +MaximumDecompressedSize = 1024 +MegaByte = 2 ** 20 +PngImagePlugin.MAX_TEXT_CHUNK = MaximumDecompressedSize * MegaByte + + +class PredIntermediateMissing(Exception): + """Raised in pred-intermediate mode when the saved PNG for this row is + missing. Caller (interleave_t2i_dataset.__iter__) catches it silently + to skip the row without falling back to GT.""" + pass + + +class UnifiedAny2AnyIterableDataset(InterleavedBaseIterableDataset, ParquetStandardIterableDataset): + + IMAGE_LIKE_TARGETS = {'rgb', 'depth', 'normal', 'canny', 'seg', 'samseg', 'samedge'} + FEATURE_TARGETS = {'dino', 'dinolocal', 'clip', 'imagebind', 'imagebindlocal'} + # New direct-PNG image modalities → parquet column name mapping. + SAM_IMAGE_COLUMN = {'samseg': 'sam_seg', 'samedge': 'sam_edge'} + PRED_INTERMEDIATE_IMAGE_MODS = {'depth', 'normal', 'canny', 'seg'} + PRED_INTERMEDIATE_TEXT_MODS = {'caption'} + PRED_INTERMEDIATE_DINO_MODS = {'dino'} + + def _load_pred_text(self, modality): + """Load Phase-1 generated text (caption) keyed by row provenance. + Opt-in: only when pred_intermediate_dir is set. Returns str or None.""" + if getattr(self, 'pred_intermediate_dir', None) is None: + return None + if modality not in self.PRED_INTERMEDIATE_TEXT_MODS: + return None + parquet_path = getattr(self, '_current_parquet_path', None) + rg_id = getattr(self, '_current_rg_id', None) + row_idx = getattr(self, '_current_row_idx', None) + if parquet_path is None or rg_id is None or row_idx is None: + return None + basename = os.path.splitext(os.path.basename(parquet_path))[0] + path = os.path.join( + self.pred_intermediate_dir, modality, + f"{basename}_rg{rg_id}_row{int(row_idx)}_pred.txt", + ) + if not os.path.exists(path): + return None + with open(path, 'r') as f: + return f.read().strip() + + def _load_pred_dino(self, modality): + """Load Phase-1 generated dino feature (.npy) keyed by row provenance. + Returns np.ndarray or None.""" + if getattr(self, 'pred_intermediate_dir', None) is None: + return None + if modality not in self.PRED_INTERMEDIATE_DINO_MODS: + return None + parquet_path = getattr(self, '_current_parquet_path', None) + rg_id = getattr(self, '_current_rg_id', None) + row_idx = getattr(self, '_current_row_idx', None) + if parquet_path is None or rg_id is None or row_idx is None: + return None + basename = os.path.splitext(os.path.basename(parquet_path))[0] + path = os.path.join( + self.pred_intermediate_dir, modality, + f"{basename}_rg{rg_id}_row{int(row_idx)}_pred.npy", + ) + if not os.path.exists(path): + return None + arr = np.load(path) + # Dino tokens are codebook ints; if Phase 1 saved as float (older + # runs) round to nearest int to avoid float16 precision loss. + if arr.dtype.kind == 'f': + arr = np.rint(arr).astype(np.int64) + # Phase 1 saves with leading batch dim (1, 16); _add_dino expects + # a flat (16,) array. Squeeze any singleton leading dims. + while arr.ndim > 1 and arr.shape[0] == 1: + arr = arr.squeeze(0) + return arr + + def _load_pred_pil(self, modality): + """Load a model-generated intermediate image keyed by + (parquet_basename, rg_id, row_idx). Returns PIL or None. + Opt-in: only active when pred_intermediate_dir is set. + Filename convention written by + scripts/paper/modus_stage3/generate_chained_intermediates.py: + //_rg_row_pred.png + """ + if getattr(self, 'pred_intermediate_dir', None) is None: + return None + if modality not in self.PRED_INTERMEDIATE_IMAGE_MODS: + return None + parquet_path = getattr(self, '_current_parquet_path', None) + rg_id = getattr(self, '_current_rg_id', None) + row_idx = getattr(self, '_current_row_idx', None) + if parquet_path is None or rg_id is None or row_idx is None: + return None + basename = os.path.splitext(os.path.basename(parquet_path))[0] + path = os.path.join( + self.pred_intermediate_dir, modality, + f"{basename}_rg{rg_id}_row{int(row_idx)}_pred.png", + ) + if not os.path.exists(path): + return None + return pil_img2rgb(Image.open(path)) + NATIVE_CHAT_SYSTEM_PROMPT = ( + "You are an advanced multimodal model. Given the user's text, visual, " + "or structured inputs, follow the user instruction and produce the " + "requested output modality accurately." + ) + + def _native_aligned_serialization(self): + return os.environ.get("HUNYUAN_NATIVE_ALIGNED_SERIALIZATION", "0") == "1" + + def _native_chat_serialization(self): + return os.environ.get("HUNYUAN_NATIVE_CHAT_SERIALIZATION", "0") == "1" + + def _add_native_chat_preamble(self, data): + data = self._add_text( + data, + self.NATIVE_CHAT_SYSTEM_PROMPT, + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + return self._add_text( + data, + "User:", + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + + def _add_native_chat_assistant_prefix(self, data): + return self._add_text( + data, + "Assistant: ", + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + + def _target_instruction_text_native_chat(self, target_modality): + if target_modality == 'rgb': + return 'Generate an image.' + if target_modality == 'caption': + return 'Describe the image.' + if target_modality in self.IMAGE_LIKE_TARGETS: + return f'Generate the {target_modality} image.' + if target_modality == 'det': + return 'Detect the requested bounding boxes.' + if target_modality == 'grounding': + return 'Ground the requested phrase.' + if target_modality in self.FEATURE_TARGETS: + return f'Extract {target_modality} features.' + return f'Generate the {target_modality}.' + + def _target_instruction_text(self, target_modality): + if not self._native_aligned_serialization(): + return ( + f'[start {target_modality} {target_modality} {target_modality} ' + f'{target_modality} {target_modality} {target_modality} ' + f'{target_modality} {target_modality} {target_modality} {target_modality}]' + ) + if target_modality == 'rgb': + return 'Generate an image.' + if target_modality in self.IMAGE_LIKE_TARGETS: + return f'Generate the {target_modality} image.' + if target_modality == 'caption': + return 'Describe the image.' + return f'Generate the {target_modality}.' + + def parse_row(self, row): + # Parse dataset name to get target modality + # Format: "any2rgb", "any2caption", etc. + target_modality = self.dataset_name.split("2")[-1] + if target_modality == 'grounding' and ('grounding' not in row.index or row['grounding'] is None): + return None + + enable_cfg = False if target_modality in ('caption', 'det', 'cocodet') else True + + # cocodet target: skip rows with no detections (would be an empty + # target sequence). is_mandatory: false lets DataPack pick another row. + if target_modality == 'cocodet': + cd_dets, _ = self._parse_coco_det(row) + if not cd_dets: + return None + + # Get all available modalities except uid + available_modalities = [col for col in row.index if col != 'uid'] + if 'grounding' in available_modalities: + if row['grounding'] is None: + available_modalities.remove('grounding') + + + if 'det_seg' in available_modalities: + available_modalities.remove('det_seg') + # available_modalities.append('det') + available_modalities.append('seg') + + if 'dino' in available_modalities: + available_modalities.remove('dino') + available_modalities.append('dinolocal') + + if 'dino_global' in available_modalities: + available_modalities.remove('dino_global') + available_modalities.append('dino') + + # Remap parquet column 'clip448' to modality name 'clip' + if 'clip448' in available_modalities: + available_modalities.remove('clip448') + available_modalities.append('clip') + + if 'imagebind' in available_modalities: + available_modalities.remove('imagebind') + available_modalities.append('imagebindlocal') + + if 'imagebind_global' in available_modalities: + available_modalities.remove('imagebind_global') + available_modalities.append('imagebind') + + # New modalities: remap parquet column names → modality names. + if 'sam_seg' in available_modalities: + available_modalities.remove('sam_seg') + available_modalities.append('samseg') + if 'sam_edge' in available_modalities: + available_modalities.remove('sam_edge') + available_modalities.append('samedge') + if 'coco_det' in available_modalities: + available_modalities.remove('coco_det') + # Only offer cocodet as a condition when there are detections. + cd_dets, _ = self._parse_coco_det(row) + if cd_dets: + available_modalities.append('cocodet') + + # Remove target modality from available modalities for conditioning + condition_modalities = [mod for mod in available_modalities if mod != target_modality] + condition_sampling_probs = None + + # Drop modalities the active registry doesn't recognise. This codepath + # matters when the parquet predates a remap added later (e.g. the + # `dino` column was renamed to `dinolocal` in cbba109/2026-01-20, but + # a stage3 ckpt trained at 0a337b1/2025-11-06 has no `dinolocal` + # modality). Without this guard the dataset feeds an unrecognised + # modality_type into pack_sequence and triggers KeyError on + # self.modality_to_id[...]. + if self.modality_registry is not None: + known = set(self.modality_registry.name_to_id().keys()) + # `grounding` is a dataset-internal conditioning alias (phrase+bbox). + # Its emitted tokens are tagged `modality_type='det'`/`'text'` (see + # `_add_grounding`), so the registry KeyError that motivated this + # filter doesn't apply. Keep it as a usable selection name. + known.add('grounding') + condition_modalities = [mod for mod in condition_modalities if mod in known] + + # Filter conditions via config-driven modality registry. + if self.modality_registry is not None: + allowed = self.modality_registry.conditions_for(target_modality) + if allowed is not None: + condition_modalities = [mod for mod in condition_modalities if mod in allowed] + configured_probs = self.modality_registry.condition_probs_for(target_modality) + if configured_probs is not None: + prob_by_modality = { + cond: prob for cond, prob in zip(allowed, configured_probs) + } + condition_sampling_probs = [ + prob_by_modality[mod] + for mod in condition_modalities + if mod in prob_by_modality + ] + + if len(condition_modalities) == 0: + return None + + # Validation path: force a single fixed condition modality so the loss + # can be bucketed into a (cond, tgt) matrix. Skip rows where the + # requested modality is not available for this target. + if getattr(self, 'force_condition_modalities', None) is not None: + required = list(self.force_condition_modalities) + if not all(m in condition_modalities for m in required): + return None + selected_conditions = required + elif getattr(self, 'force_condition_modality', None) is not None: + if self.force_condition_modality not in condition_modalities: + return None + selected_conditions = [self.force_condition_modality] + elif self.num_condition_modalities == 0: + num_conditions = random.randint(1, len(condition_modalities)) + selected_conditions = None # set below + else: + if self.strict_num_condition_modalities: + if len(condition_modalities) < self.num_condition_modalities: + return None + num_conditions = self.num_condition_modalities + else: + max_conditions = min(self.num_condition_modalities, len(condition_modalities)) + if max_conditions == 1: + num_conditions = max_conditions + else: + num_conditions = random.randint(1, max_conditions) + selected_conditions = None # set below + + if selected_conditions is None: + if condition_sampling_probs is None: + selected_conditions = random.sample(condition_modalities, num_conditions) + else: + probs = np.array(condition_sampling_probs, dtype=np.float64) + probs_sum = float(probs.sum()) + if probs_sum <= 0.0: + probs = np.full(len(condition_modalities), 1.0 / len(condition_modalities)) + else: + probs = probs / probs_sum + selected_indices = np.random.choice( + len(condition_modalities), + size=num_conditions, + replace=False, + p=probs, + ) + selected_conditions = [condition_modalities[i] for i in selected_indices] + + # Optional monitoring tag for route-specific rgb generation logging. + # Only tag unambiguous single-condition rgb targets. + if target_modality == 'rgb' and len(selected_conditions) == 1: + cond = selected_conditions[0] + if cond in ('caption', 'grounding', 'dinolocal'): + data_route = f"{cond}2rgb" + else: + data_route = None + else: + data_route = None + + + # if num_conditions > 1: + # print("selected conditions:", selected_conditions) + # print("num conditions:", num_conditions) + # print('target modality:', target_modality) + # if 'grounding' in selected_conditions: + # print("grounding instances:", row['grounding']) + data = self._init_data() + if data_route is not None: + data['rgb_loss_route'] = data_route + + native_chat = self._native_chat_serialization() + if native_chat: + data = self._add_native_chat_preamble(data) + + # Add condition modalities in random order + random.shuffle(selected_conditions) + + for modality in selected_conditions: + if self.use_instruction and self.use_condition_instruction: + data = self._add_text(data, f'[start {modality}]', need_loss=False, enable_cfg=enable_cfg, modality_type='text') + if modality == 'caption': + # Pred caption: load from disk if Phase 1 wrote it; else + # use parquet GT. + pred_caption = self._load_pred_text('caption') + if pred_caption is not None: + caption_text = pred_caption + elif (getattr(self, 'pred_intermediate_dir', None) is not None + and 'caption' in self.PRED_INTERMEDIATE_TEXT_MODS): + raise PredIntermediateMissing( + 'no pred TXT for caption at this row') + else: + caption_text = row[modality] + data = self._add_text(data, caption_text, need_loss=False, enable_cfg=enable_cfg, modality_type=modality) + elif modality in ['rgb', 'depth', 'normal', 'canny']: + # Handle image modalities. In pred-intermediate mode, depth/ + # normal/canny conditions come from disk (Phase 1 outputs); + # rgb is always real. If pred mode is on and the file for + # this row is missing, skip the row entirely — do NOT + # silently fall back to GT. + pred_pil = self._load_pred_pil(modality) + if pred_pil is not None: + image = pred_pil + elif (getattr(self, 'pred_intermediate_dir', None) is not None + and modality in self.PRED_INTERMEDIATE_IMAGE_MODS): + raise PredIntermediateMissing( + f'no pred PNG for {modality} at this row') + else: + image = pil_img2rgb(Image.open(io.BytesIO(row[modality]))) + data = self._add_image( + data, + image, + need_loss=False, + enable_cfg=enable_cfg, + need_vae=True, + need_vit=True, + modality_type=modality, + ) + elif modality in ['samseg', 'samedge']: + # Direct-PNG SAM image modalities (samseg is RGBA → pil_img2rgb). + image = pil_img2rgb(Image.open(io.BytesIO(row[self.SAM_IMAGE_COLUMN[modality]]))) + data = self._add_image( + data, + image, + need_loss=False, + enable_cfg=enable_cfg, + need_vae=True, + need_vit=True, + modality_type=modality, + ) + elif modality in ['cocodet']: + cd_dets, cd_size = self._parse_coco_det(row) + data = self._add_cocodet( + data, + detections=cd_dets, + image_size=cd_size, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality, + ) + elif modality in ['det']: + det_seg_instances = row['det_seg']['instances'] + det_seg_raw_size = row['det_seg']['size'] + det_seg_prompts = row['det_seg']['prompt'].split(' .') + data = self._add_det( + data, + det_seg_instances=det_seg_instances, + det_seg_raw_size=det_seg_raw_size, + det_seg_prompts=det_seg_prompts, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + + elif modality in ['seg']: + # Pred-mode seg: if Phase 1 saved a pred seg PNG, use it + # via _add_image directly (skip _add_seg's RLE rendering). + pred_seg = self._load_pred_pil('seg') + if pred_seg is not None: + data = self._add_image( + data, + pred_seg, + need_loss=False, + enable_cfg=enable_cfg, + need_vae=True, + need_vit=True, + modality_type='seg', + ) + continue + elif (getattr(self, 'pred_intermediate_dir', None) is not None + and 'seg' in self.PRED_INTERMEDIATE_IMAGE_MODS): + raise PredIntermediateMissing( + 'no pred PNG for seg at this row') + det_seg_instances = row['det_seg']['instances'] + det_seg_raw_size = row['det_seg']['size'] + det_seg_prompts = row['det_seg']['prompt'].split(' .') + data = self._add_seg( + data, + det_seg_instances=det_seg_instances, + det_seg_raw_size=det_seg_raw_size, + det_seg_prompts=det_seg_prompts, + need_loss=False, + enable_cfg=enable_cfg, + need_vae=True, + need_vit=True, + modality_type=modality + ) + + elif modality in ['grounding']: + grounding_instances = row['grounding'] + data = self._add_grounding( + data, + grounding_instances=grounding_instances, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + elif modality in ['dino']: + # Pred dino: load .npy from disk if Phase 1 wrote it. + pred_dino = self._load_pred_dino('dino') + if pred_dino is not None: + dino_tokens = pred_dino + elif (getattr(self, 'pred_intermediate_dir', None) is not None + and 'dino' in self.PRED_INTERMEDIATE_DINO_MODS): + raise PredIntermediateMissing( + 'no pred NPY for dino at this row') + else: + dino_tokens = row['dino_global'] + data = self._add_dino( + data, + dino_tokens=dino_tokens, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + elif modality in ['dinolocal']: + dino_tokens = row['dino'] + data = self._add_dinolocal( + data, + dino_tokens=dino_tokens, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + elif modality in ['clip']: + clip_tokens = row['clip448'] + data = self._add_clip( + data, + clip_tokens=clip_tokens, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + elif modality in ['imagebind']: + imagebind_tokens = row['imagebind_global'] + data = self._add_imagebind( + data, + imagebind_tokens=imagebind_tokens, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + elif modality in ['imagebindlocal']: + imagebind_tokens = row['imagebind'] + data = self._add_imagebindlocal( + data, + imagebind_tokens=imagebind_tokens, + need_loss=False, + enable_cfg=enable_cfg, + modality_type=modality + ) + + if native_chat: + if target_modality not in ('det', 'seg', 'grounding'): + if self.use_instruction and self.use_target_instruction: + data = self._add_text( + data, + self._target_instruction_text_native_chat(target_modality), + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + data = self._add_native_chat_assistant_prefix(data) + elif self.use_instruction and self.use_target_instruction: + if target_modality == 'det' or target_modality == 'seg' or target_modality == 'grounding': + pass # separate instruction in add_det() function + else: + data = self._add_text( + data, + self._target_instruction_text(target_modality), + need_loss=False, + enable_cfg=enable_cfg, + modality_type='text', + ) + # Add target modality at the end with loss=True + if target_modality == 'caption': + # Handle text target + # data = self._add_text(data, row[target_modality], enable_cfg=enable_cfg, need_loss=True, modality_type=target_modality) + caption_text = row[target_modality] + if native_chat: + caption_text = f"{caption_text}" + data = self._add_text(data, caption_text, enable_cfg=enable_cfg, need_loss=True, modality_type=target_modality) + elif target_modality in ['rgb', 'depth', 'normal', 'canny']: + # Handle image target + image = pil_img2rgb(Image.open(io.BytesIO(row[target_modality]))) + data = self._add_image( + data, + image, + need_loss=True, + need_vae=False, + need_vit=False, + modality_type=target_modality, + ) + elif target_modality in ['samseg', 'samedge']: + # Direct-PNG SAM image target (samseg is RGBA → pil_img2rgb). + image = pil_img2rgb(Image.open(io.BytesIO(row[self.SAM_IMAGE_COLUMN[target_modality]]))) + data = self._add_image( + data, + image, + need_loss=True, + need_vae=False, + need_vit=False, + modality_type=target_modality, + ) + elif target_modality == 'cocodet': + cd_dets, cd_size = self._parse_coco_det(row) + data = self._add_cocodet( + data, + detections=cd_dets, + image_size=cd_size, + need_loss=True, + enable_cfg=enable_cfg, + modality_type=target_modality, + ) + elif target_modality == 'det': + det_seg_instances = row['det_seg']['instances'] + det_seg_raw_size = row['det_seg']['size'] + det_seg_prompts = row['det_seg']['prompt'].split(' .') + data = self._add_det( + data, + det_seg_instances=det_seg_instances, + det_seg_raw_size=det_seg_raw_size, + det_seg_prompts=det_seg_prompts, + need_loss=True, + enable_cfg=enable_cfg, + modality_type=target_modality + ) + elif target_modality == 'seg': + det_seg_instances = row['det_seg']['instances'] + det_seg_raw_size = row['det_seg']['size'] + det_seg_prompts = row['det_seg']['prompt'].split(' .') + data = self._add_seg( + data, + det_seg_instances=det_seg_instances, + det_seg_raw_size=det_seg_raw_size, + det_seg_prompts=det_seg_prompts, + need_loss=True, + enable_cfg=enable_cfg, + need_vae=False, + need_vit=False, + modality_type=target_modality + ) + elif target_modality == 'grounding': + grounding_instances = row['grounding'] + data = self._add_grounding( + data, + grounding_instances=grounding_instances, + need_loss=True, + enable_cfg=False, + modality_type=target_modality + ) + elif target_modality == 'dino': + dino_tokens = row['dino_global'] + data = self._add_dino( + data, + dino_tokens=dino_tokens, + need_loss=True, + enable_cfg=False, + modality_type=target_modality + ) + elif target_modality == 'dinolocal': + dino_tokens = row['dino'] + data = self._add_dinolocal( + data, + dino_tokens=dino_tokens, + need_loss=True, + enable_cfg=False, + modality_type=target_modality + ) + elif target_modality == 'clip': + clip_tokens = row['clip448'] + data = self._add_clip( + data, + clip_tokens=clip_tokens, + need_loss=True, + enable_cfg=False, + modality_type=target_modality + ) + elif target_modality == 'imagebind': + imagebind_tokens = row['imagebind_global'] + data = self._add_imagebind( + data, + imagebind_tokens=imagebind_tokens, + need_loss=True, + enable_cfg=False, + modality_type=target_modality + ) + elif target_modality == 'imagebindlocal': + imagebind_tokens = row['imagebind'] + data = self._add_imagebindlocal( + data, + imagebind_tokens=imagebind_tokens, + need_loss=True, + enable_cfg=False, + modality_type=target_modality + ) + + return data + + + def _add_det(self, data, det_seg_instances, det_seg_raw_size, det_seg_prompts, need_loss, enable_cfg, modality_type): + + if not need_loss: + is_all_sampled = False + num_instances = len(det_seg_instances) + num_selected = random.randint(1, num_instances) + # Sample by indices to support containers that aren't proper sequences + selected_indices = random.sample(range(num_instances), num_selected) + selected_instances = [det_seg_instances[i] for i in selected_indices] + is_all_sampled = (num_selected == num_instances) + else: + unique_categories = sorted(set(inst.get('category') for inst in det_seg_instances)) + num_categories = len(unique_categories) + + # num_selected_categories = random.randint(1, num_categories) + num_selected_categories = 1 + selected_categories = random.sample(unique_categories, num_selected_categories) + + selected_instances = [] + for inst in det_seg_instances: + if inst.get('category') in selected_categories: + selected_instances.append(inst) + + # Size assumed as [width, height] + width, height = det_seg_raw_size[0], det_seg_raw_size[1] + width = max(float(width), 1.0) + height = max(float(height), 1.0) + + normalized_dets = '' + # selected_instances = sort_instances_by_distance_to_origin(selected_instances) + selected_instances = sort_instances_by_raster(selected_instances) + + for inst in selected_instances: + bbox = inst.get('bbox') + category = inst.get('category') + score = inst.get('score', None) + + if bbox is None or len(bbox) != 4: + continue + + # bbox assumed [x1, y1, x2, y2] in raw pixel coords + x1, y1, x2, y2 = float(bbox[0]), float(bbox[1]), float(bbox[2]), float(bbox[3]) + + # Normalize to [0,1] + x1_n = x1 / width + y1_n = y1 / height + x2_n = x2 / width + y2_n = y2 / height + + # Clamp + x1_n = min(max(x1_n, 0.0), 0.999) + y1_n = min(max(y1_n, 0.0), 0.999) + x2_n = min(max(x2_n, 0.0), 0.999) + y2_n = min(max(y2_n, 0.0), 0.999) + + # Quantize: keep 3 digits by *1000 and round + def q(v, max_digits=3): + return int(round(v * 10 ** max_digits)) + + bbox_q = [q(x1_n), q(y1_n), q(x2_n), q(y2_n)] + score_q = q(score, max_digits=2) + + coord_types = ['x1', 'y1', 'x2', 'y2'] + coord_tokens = [f'<|{coord_types[i]}_{bbox_q[i]:03d}|>' for i in range(4)] + score_token = f'<|score_{score_q:02d}|>' + # bbox_str = f'{"".join(coord_tokens)}{category}{score_token}' + # bbox_str = f'{"".join(coord_tokens)} || {category} ||{score_token}' + # bbox_str = f'{"".join(coord_tokens)}{category}' + + + bbox_str = f'{"".join(coord_tokens)}' + + # bbox_str = f'{" ".join(coord_tokens)} {category}' + # bbox_str = f'<|box_start|>{"".join(coord_tokens)}<|box_end|>{category},score={score_q}' + + # bbox_str = f'x1={bbox_q[0]},y1={bbox_q[1]},x2={bbox_q[2]},y2={bbox_q[3]},{category},score={score_q}' + + # normalized_dets = normalized_dets + bbox_str + ',' + normalized_dets = normalized_dets + bbox_str + + if need_loss: + # if len(selected_categories) == len(unique_categories): + # instruction_text = "start detect the box of everything" + # else: + # instruction_text = f"start detect the box of {', '.join(selected_categories)}" + if self._native_chat_serialization(): + instruction_text = f"Detect the box of {', '.join(selected_categories)}." + elif self._native_aligned_serialization(): + instruction_text = f"Detect the box of {', '.join(selected_categories)}." + else: + instruction_text = f"start detect the box of {', '.join(selected_categories)}" + data = self._add_text(data, instruction_text, need_loss=False, enable_cfg=enable_cfg, modality_type='text') + if self._native_chat_serialization(): + data = self._add_native_chat_assistant_prefix(data) + # print("det instruction text:", instruction_text) + else: + prefix = "bounding boxes (all): " if is_all_sampled else "bounding boxes (partial): " + # normalized_dets = prefix + normalized_dets + + if self.use_det_image: + # det_image = get_det_image(normalized_dets, det_seg_raw_size) + det_image = get_det_image_new(normalized_dets, det_seg_raw_size) + data = self._add_image(data, det_image, need_loss=False, enable_cfg=enable_cfg, need_vae=True, need_vit=True, modality_type='rgb') + + + data = self._add_text(data, prefix, need_loss=False, enable_cfg=enable_cfg, modality_type='text') + + + data = self._add_text(data, normalized_dets, need_loss=need_loss, enable_cfg=enable_cfg, modality_type=modality_type) + # print("det normalized_dets:", normalized_dets) + + return data + + + def _parse_coco_det(self, row): + """Parse the `coco_det` JSON string column. + + Returns (detections_list, image_size). On missing/blank/malformed + input returns ([], None) so callers can treat it as 'no detections'. + """ + raw = row['coco_det'] if 'coco_det' in row.index else None + if raw is None: + return [], None + try: + obj = json.loads(raw) + except (TypeError, ValueError): + return [], None + dets = obj.get('detections') or [] + size = obj.get('image_size') or None + return dets, size + + def _add_cocodet(self, data, detections, image_size, need_loss, enable_cfg, modality_type): + """Pix2seq-style detection serialization (cocodet). + + Per box: <|x1_q|><|y1_q|><|x2_q|><|y2_q|><|coco_cls_label|>, boxes sorted + by distance-to-origin. Coords reuse det's quantized coordinate tokens; + class is a dedicated token aligned to the COCO id. No score, no separators. + The start/end delimiters are added by the packing layer (modality_type). + """ + width = max(float(image_size[0]), 1.0) if image_size else 1.0 + height = max(float(image_size[1]), 1.0) if image_size else 1.0 + + instances = sort_instances_by_distance_to_origin(detections or []) + + def q(v): + return min(max(int(round(v * 1000)), 0), 999) + + seq = '' + for inst in instances: + bbox = inst.get('bbox') + if bbox is None or len(bbox) != 4: + continue + label = inst.get('label') + if label is None: + continue + label = int(label) + if label < 0 or label > 90: # COCO id range; out-of-range → skip + continue + x1, y1, x2, y2 = (float(bbox[0]), float(bbox[1]), + float(bbox[2]), float(bbox[3])) + x1q, y1q = q(x1 / width), q(y1 / height) + x2q, y2q = q(x2 / width), q(y2 / height) + seq += (f'<|x1_{x1q:03d}|><|y1_{y1q:03d}|>' + f'<|x2_{x2q:03d}|><|y2_{y2q:03d}|><|coco_cls_{label:02d}|>') + + # Empty sequence is valid (e.g. as a "no objects" condition); the + # packing layer still wraps it with the cocodet start/end delimiters. + data = self._add_text(data, seq, need_loss=need_loss, + enable_cfg=enable_cfg, modality_type=modality_type) + return data + + def _add_grounding(self, data, grounding_instances, need_loss, enable_cfg, modality_type): + + selected_grounding = random.choice(grounding_instances) + phrase = selected_grounding['phrase'] + bbox = selected_grounding['bbox'] + + + if need_loss: + if self._native_chat_serialization(): + instruction_text = f"Ground the phrase: {phrase}" + elif self._native_aligned_serialization(): + instruction_text = f"Ground the phrase: {phrase}" + else: + instruction_text = f"[start grounding the phrase] {phrase}" + data = self._add_text(data, instruction_text, need_loss=False, enable_cfg=enable_cfg, modality_type='text') + if self._native_chat_serialization(): + data = self._add_native_chat_assistant_prefix(data) + elif random.random() >= self.grounding_phrase_dropout_prob: + data = self._add_text(data, phrase, need_loss=False, enable_cfg=enable_cfg, modality_type='text') + + coord_types = ['x1', 'y1', 'x2', 'y2'] + coord_tokens = [f'<|{coord_types[i]}_{bbox[i]:03d}|>' for i in range(4)] + bbox_str = f'{"".join(coord_tokens)}' + data = self._add_text(data, bbox_str, need_loss=need_loss, enable_cfg=enable_cfg, modality_type='det') + + return data + + def _add_seg(self, data, det_seg_instances, det_seg_raw_size, det_seg_prompts, need_loss, enable_cfg, need_vae, need_vit, modality_type): + # Helper: decode COCO RLE to binary mask; fallback to empty mask if not available + def decode_rle_to_mask(rle_counts, size): + try: + rle = {'counts': rle_counts, 'size': [int(size[0]), int(size[1])]} + mask = mask_util.decode(rle) + if mask is None: + return None + # pycocotools returns (H,W,1) sometimes; squeeze to (H,W) + if hasattr(mask, 'shape') and len(mask.shape) == 3: + mask = mask[:, :, 0] + return mask.astype('uint8') + except Exception: + try: + height, width = int(size[1]), int(size[0]) + return np.zeros((height, width), dtype='uint8') + except Exception: + return None + + width, height = float(det_seg_raw_size[0]), float(det_seg_raw_size[1]) + width = max(width, 1.0) + height = max(height, 1.0) + + mask_images = get_mask_image(det_seg_instances, det_seg_raw_size) + # Build mask(s) + if not need_loss: + # Conditioning: select at most 2 categories from mask_images, add category name and mask image + if len(mask_images) > 0: + k = random.randint(1, min(2, len(mask_images))) + selected_categories = random.sample(list(mask_images.keys()), k) + else: + selected_categories = [] + + if not selected_categories: + return data + + for category in selected_categories: + # Use pre-computed merged mask for this category + pil_mask = mask_images[category] + + # Add category text + data = self._add_text( + data, + f"mask category: {category}", + need_loss=False, + enable_cfg=enable_cfg, + modality_type='text', + ) + + data = self._add_image( + data, + pil_mask, + need_loss=False, + enable_cfg=enable_cfg, + need_vae=need_vae, + need_vit=need_vit, + modality_type=modality_type, + ) + + return data + else: + # Supervision: select one category, add instruction, then add mask image with loss + if len(mask_images) == 0: + return data + + # Select one category + category = random.choice(list(mask_images.keys())) + + # Create instruction text + if self._native_chat_serialization(): + instruction_text = f"Generate the segmentation mask of {category}." + elif self._native_aligned_serialization(): + instruction_text = f"Generate the segmentation mask of {category}." + else: + instruction_text = f"start segment the mask of {category}" + + data = self._add_text(data, instruction_text, need_loss=False, enable_cfg=enable_cfg, modality_type='text') + if self._native_chat_serialization(): + data = self._add_native_chat_assistant_prefix(data) + + # Use pre-computed mask image for this category + pil_mask = mask_images[category] + + data = self._add_image( + data, + pil_mask, + need_loss=True, + enable_cfg=enable_cfg, + need_vae=False, + need_vit=False, + modality_type=modality_type, + ) + + return data + + + def _add_dino(self, data, dino_tokens, need_loss, enable_cfg, modality_type): + text_ids = dino_tokens + self.tokenizer.encode('<|dino_0000|>')[0] + text_ids = text_ids.tolist() + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'text', + 'enable_cfg': int(enable_cfg), + 'loss': int(need_loss), + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + def _add_dinolocal(self, data, dino_tokens, need_loss, enable_cfg, modality_type): + text_ids = dino_tokens + self.tokenizer.encode('<|dinolocal_0000|>')[0] + text_ids = text_ids.tolist() + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'text', + 'enable_cfg': int(enable_cfg), + 'loss': int(need_loss), + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + def _add_clip(self, data, clip_tokens, need_loss, enable_cfg, modality_type): + text_ids = clip_tokens + self.tokenizer.encode('<|clip_0000|>')[0] + text_ids = text_ids.tolist() + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'text', + 'enable_cfg': int(enable_cfg), + 'loss': int(need_loss), + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + def _add_imagebind(self, data, imagebind_tokens, need_loss, enable_cfg, modality_type): + text_ids = imagebind_tokens + self.tokenizer.encode('<|imagebind_0000|>')[0] + text_ids = text_ids.tolist() + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'text', + 'enable_cfg': int(enable_cfg), + 'loss': int(need_loss), + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + def _add_imagebindlocal(self, data, imagebind_tokens, need_loss, enable_cfg, modality_type): + text_ids = imagebind_tokens + self.tokenizer.encode('<|imagebindlocal_0000|>')[0] + text_ids = text_ids.tolist() + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'text', + 'enable_cfg': int(enable_cfg), + 'loss': int(need_loss), + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + +def get_det_image(normalized_dets, det_seg_raw_size): + """ + Create an image with bounding boxes drawn on it based on normalized detections. + + Args: + normalized_dets (str): String containing normalized bounding box information + det_seg_raw_size (list): [width, height] of the image in pixels + + Returns: + PIL.Image: Image with bounding boxes drawn in random colors + """ + + # Extract image dimensions + width, height = int(det_seg_raw_size[0]), int(det_seg_raw_size[1]) + + # Create a blank white image + image = Image.new('RGB', (width, height), color='black') + draw = ImageDraw.Draw(image) + + # Define a palette of random colors for bounding boxes + colors = [ + (255, 0, 0), # Red + (0, 255, 0), # Green + (0, 0, 255), # Blue + (255, 255, 0), # Yellow + (255, 0, 255), # Magenta + (0, 255, 255), # Cyan + (255, 128, 0), # Orange + (128, 0, 255), # Purple + (255, 192, 203), # Pink + (0, 128, 0), # Dark Green + (128, 128, 0), # Olive + (0, 128, 128), # Teal + ] + + # Dictionary to map categories to colors + category_colors = {} + + # Parse the normalized_dets string + if not normalized_dets or normalized_dets.strip() == '': + return image + + # Split by comma to get individual bounding box strings + bbox_strings = [s.strip() for s in normalized_dets.split(',') if s.strip()] + + for i, bbox_str in enumerate(bbox_strings): + try: + # Parse the bounding box string format: "<|x1_xxx|><|y1_xxx|><|x2_xxx|><|y2_xxx|> || category ||<|score_xx|>" + # We need to be more careful with the splitting since category names might contain "||" + + # First, find the score part at the end + score_match = re.search(r'<\|score_\d+\|>$', bbox_str) + if not score_match: + continue + score_part = score_match.group(0) + + # Remove the score part to get the rest + remaining = bbox_str[:-len(score_part)] + + # Split by " || " to separate coordinates from category + if ' || ' not in remaining: + continue + coord_part, category = remaining.split(' || ', 1) + + # Clean up the category name (remove any trailing "||" that might be left) + category = category.strip().rstrip('||').strip() + + # Extract coordinates from tokens like <|x1_123|><|y1_456|><|x2_789|><|y2_012|> + coord_pattern = r'<\|([xy][12])_(\d+)\|>' + coord_matches = re.findall(coord_pattern, coord_part) + + if len(coord_matches) != 4: + continue + + # Convert to dictionary for easier access + coords = {} + for coord_type, value in coord_matches: + coords[coord_type] = int(value) + + # Convert quantized coordinates back to pixel coordinates + # The coordinates are quantized by multiplying by 1000, so divide by 1000 + x1 = int((coords['x1'] / 1000.0) * width) + y1 = int((coords['y1'] / 1000.0) * height) + x2 = int((coords['x2'] / 1000.0) * width) + y2 = int((coords['y2'] / 1000.0) * height) + + # Clamp coordinates to image bounds + x1 = max(0, min(x1, width)) + y1 = max(0, min(y1, height)) + x2 = max(0, min(x2, width)) + y2 = max(0, min(y2, height)) + + # Skip invalid bounding boxes + if x1 >= x2 or y1 >= y2: + continue + + # Assign color based on category - same category gets same color + if category not in category_colors: + # Assign next available color to this category + color_index = len(category_colors) % len(colors) + category_colors[category] = colors[color_index] + color = category_colors[category] + + # Draw the bounding box rectangle + draw.rectangle([x1, y1, x2, y2], outline=color, width=3) + # Draw the bounding box rectangle (filled) + # draw.rectangle([x1, y1, x2, y2], fill=color) + + # Extract score if available + score_match = re.search(r'<\|score_(\d+)\|>', score_part) + score = float(score_match.group(1)) / 100.0 if score_match else 0.0 + + # Draw label with category and score + label = f"{category}: {score:.2f}" + + # Calculate text position (above the bounding box with better spacing) + text_y = max(5, y1 - 15) + + # Draw text background rectangle aligned to left edge of bounding box + text_bbox = draw.textbbox((x1, text_y), label) + text_x1, text_y1, text_x2, text_y2 = text_bbox + # Keep left edge aligned with bounding box, add padding only on right/bottom + draw.rectangle([x1, text_y1-2, text_x2+4, text_y2+2], fill=color, outline=color) + + font = ImageFont.load_default() + draw.text((x1, text_y), label, fill=(255, 255, 255), font=font) + + except Exception as e: + # Skip malformed bounding box strings + print(f"Warning: Could not parse bounding box string '{bbox_str}': {e}") + continue + + return image + + +def get_det_image_new(normalized_dets, det_seg_raw_size): + """ + Create an image with bounding boxes drawn based on the NEW detections format: + "<|x1_100|><|y1_100|><|x2_499|><|y2_499|>person,<|x1_700|>...>apple," + + Categories can include commas, so we must NOT split by comma. We instead + locate each coordinate token group and take the category text that follows + it up to the start of the next coordinate group (or end of string). + + Args: + normalized_dets (str): Detection string in the new format + det_seg_raw_size (list): [width, height] + + Returns: + PIL.Image: Image with bounding boxes drawn + """ + + width, height = int(det_seg_raw_size[0]), int(det_seg_raw_size[1]) + image = Image.new('RGB', (width, height), color='black') + draw = ImageDraw.Draw(image) + + colors = [ + (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), + (0, 255, 255), (255, 128, 0), (128, 0, 255), (255, 192, 203), + (0, 128, 0), (128, 128, 0), (0, 128, 128), + ] + category_colors = {} + + if not normalized_dets or normalized_dets.strip() == '': + return image + + # Find all coordinate token groups and their spans + group_pattern = re.compile(r"<\|x1_(\d+)\|><\|y1_(\d+)\|><\|x2_(\d+)\|><\|y2_(\d+)\|>") + matches = list(group_pattern.finditer(normalized_dets)) + + for idx, m in enumerate(matches): + try: + x1_q = int(m.group(1)) + y1_q = int(m.group(2)) + x2_q = int(m.group(3)) + y2_q = int(m.group(4)) + + # Determine category slice: after this match to before next match (or end) + start_cat = m.end() + end_cat = matches[idx + 1].start() if idx + 1 < len(matches) else len(normalized_dets) + category = normalized_dets[start_cat:end_cat] + # Strip a single trailing comma and whitespace + category = category.rstrip() + if category.endswith(','): + category = category[:-1] + category = category.strip() + if category == '': + category = 'object' + + # De-quantize from [0,1000) back to pixel coords + x1 = int((x1_q / 1000.0) * width) + y1 = int((y1_q / 1000.0) * height) + x2 = int((x2_q / 1000.0) * width) + y2 = int((y2_q / 1000.0) * height) + + # Clamp + x1 = max(0, min(x1, width)) + y1 = max(0, min(y1, height)) + x2 = max(0, min(x2, width)) + y2 = max(0, min(y2, height)) + + if x1 >= x2 or y1 >= y2: + continue + + if category not in category_colors: + color_index = len(category_colors) % len(colors) + category_colors[category] = colors[color_index] + color = category_colors[category] + + # Draw box + draw.rectangle([x1, y1, x2, y2], outline=color, width=3) + + # Label (category only; no score in new format) + label = f"{category}" + text_y = max(5, y1 - 15) + text_bbox = draw.textbbox((x1, text_y), label) + tb_x1, tb_y1, tb_x2, tb_y2 = text_bbox + draw.rectangle([x1, tb_y1 - 2, tb_x2 + 4, tb_y2 + 2], fill=color, outline=color) + font = ImageFont.load_default() + draw.text((x1, text_y), label, fill=(255, 255, 255), font=font) + + except Exception as e: + print(f"Warning: Could not parse detection entry around index {m.start()}: {e}") + continue + + return image + +def get_mask_image(det_seg_instances, det_seg_raw_size): + """ + Create mask images for each category with different colors for different instances. + + Args: + det_seg_instances: List of detection/segmentation instances + det_seg_raw_size: Raw size of the image [width, height] + + Returns: + dict: Dictionary mapping category_name to PIL Image (RGB mode) + """ + # Helper: decode COCO RLE to binary mask; fallback to empty mask if not available + def decode_rle_to_mask(rle_counts, size): + try: + rle = {'counts': rle_counts, 'size': [int(size[0]), int(size[1])]} + mask = mask_util.decode(rle) + if mask is None: + return None + # pycocotools returns (H,W,1) sometimes; squeeze to (H,W) + if hasattr(mask, 'shape') and len(mask.shape) == 3: + mask = mask[:, :, 0] + return mask.astype('uint8') + except Exception: + try: + height, width = int(size[1]), int(size[0]) + return np.zeros((height, width), dtype='uint8') + except Exception: + return None + + width, height = float(det_seg_raw_size[0]), float(det_seg_raw_size[1]) + width = max(width, 1.0) + height = max(height, 1.0) + + # Group instances by category + category_groups = {} + for inst in det_seg_instances: + category = inst.get('category', 'object') + if category not in category_groups: + category_groups[category] = [] + category_groups[category].append(inst) + + # Define colors for different instances (20 colors) - matplotlib colorblind-friendly palette + colors = [ + (31, 119, 180), # C1 - Blue + (174, 199, 232), # C2 - Light Blue + (255, 127, 14), # C3 - Orange + (255, 187, 120), # C4 - Light Orange + (44, 160, 44), # C5 - Green + (152, 223, 138), # C6 - Light Green + (214, 39, 40), # C7 - Red + (255, 152, 150), # C8 - Light Red + (148, 103, 189), # C9 - Purple + (197, 176, 213), # C10 - Light Purple + (140, 86, 75), # C11 - Brown + (196, 156, 148), # C12 - Light Brown + (227, 119, 194), # C13 - Pink + (247, 182, 210), # C14 - Light Pink + (127, 127, 127), # C15 - Gray + (199, 199, 199), # C16 - Light Gray + (188, 189, 34), # C17 - Olive + (219, 219, 141), # C18 - Light Olive + (23, 190, 207), # C19 - Cyan + (158, 218, 229), # C20 - Light Cyan + ] + + # Create mask images for each category + category_masks = {} + for category, instances in category_groups.items(): + # Initialize merged mask for this category + merged_mask = np.zeros((int(height), int(width), 3), dtype='uint8') + + # Randomly shuffle colors for this category to avoid bias + available_colors = colors.copy() + random.shuffle(available_colors) + + for i, inst in enumerate(instances): + seg = inst.get('segmentation', {}) or {} + rle_counts = seg.get('counts', None) + size = seg.get('size', [int(width), int(height)]) + + mask = None + if rle_counts is not None and size is not None: + mask = decode_rle_to_mask(rle_counts, size) + + if mask is not None: + # Get color for this instance (randomly selected, no overlap within category) + color = available_colors[i % len(available_colors)] + + # Apply color to mask regions + for c in range(3): # RGB channels + merged_mask[:, :, c] = np.where(mask > 0, color[c], merged_mask[:, :, c]) + + # Convert to PIL Image + category_masks[category] = Image.fromarray(merged_mask, mode='RGB') + + return category_masks + +def sort_instances_by_distance_to_origin(instances): + return sorted(instances, key=lambda x: x.get('bbox')[0]**2 + x.get('bbox')[1]**2) + +def sort_instances_by_raster(instances): + return sorted(instances, key=lambda obj: (obj['bbox'][1], obj['bbox'][0])) diff --git a/data/interleave_datasets/edit_dataset.py b/data/interleave_datasets/edit_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ec72e2e4490855b3b20a099fb340c3b107358c91 --- /dev/null +++ b/data/interleave_datasets/edit_dataset.py @@ -0,0 +1,72 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import io +import random +from PIL import Image, ImageFile, PngImagePlugin + +from .interleave_t2i_dataset import InterleavedBaseIterableDataset, ParquetStandardIterableDataset +from ..data_utils import pil_img2rgb + + +Image.MAX_IMAGE_PIXELS = 200000000 +ImageFile.LOAD_TRUNCATED_IMAGES = True +MaximumDecompressedSize = 1024 +MegaByte = 2 ** 20 +PngImagePlugin.MAX_TEXT_CHUNK = MaximumDecompressedSize * MegaByte + + +class UnifiedEditIterableDataset(InterleavedBaseIterableDataset, ParquetStandardIterableDataset): + + def parse_row(self, row): + image_num = len(row["image_list"]) + # randomly choose start and end, return [0, 1] when only two images + start_idx = random.choice(range(image_num - 1)) + max_end = min(start_idx + 3, image_num) + end_idx = random.choice(range(start_idx + 1, max_end)) + + data = self._init_data() + data = self._add_image( + data, + pil_img2rgb(Image.open(io.BytesIO(row["image_list"][start_idx]))), + need_loss=False, + need_vae=True, + need_vit=True, + ) + + if end_idx - start_idx > 1 and random.random() < 0.5: # concat multiple insturction + if end_idx == image_num - 1: + end_idx -= 1 + + instruction = "" + for idx in range(start_idx + 1, end_idx + 1): + instruction += random.choice(row["instruction_list"][idx-1]) + ". " + data = self._add_text(data, instruction.rstrip(), need_loss=False) + data = self._add_image( + data, + pil_img2rgb(Image.open(io.BytesIO(row["image_list"][end_idx]))), + need_loss=True, + need_vae=False, + need_vit=False, + ) + else: + for idx in range(start_idx + 1, end_idx + 1): + instruction = random.choice(row["instruction_list"][idx-1]) + data = self._add_text(data, instruction, need_loss=False) + if idx != end_idx: + data = self._add_image( + data, + pil_img2rgb(Image.open(io.BytesIO(row["image_list"][idx]))), + need_loss=True, + need_vae=True, + need_vit=True, + ) + else: + data = self._add_image( + data, + pil_img2rgb(Image.open(io.BytesIO(row["image_list"][idx]))), + need_loss=True, + need_vae=False, + need_vit=False, + ) + return data diff --git a/data/interleave_datasets/hunyuan_native_chat_any2any_dataset.py b/data/interleave_datasets/hunyuan_native_chat_any2any_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ae8eaba467b356ba350f6f3fa33e7adc4c48ab30 --- /dev/null +++ b/data/interleave_datasets/hunyuan_native_chat_any2any_dataset.py @@ -0,0 +1,257 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +"""Hunyuan-native chat serialization for MODUS any2any parquet datasets. + +This file is intentionally separate from ``any2any_dataset.py`` so v59 can +change the Hunyuan training contract without changing the default MODUS/BAGEL +dataloader class. +""" + +import os + +from .any2any_dataset import UnifiedAny2AnyIterableDataset + + +class HunyuanNativeChatAny2AnyIterableDataset(UnifiedAny2AnyIterableDataset): + """v59 dataset: native chat shell plus MODUS modality blocks. + + Text that belongs to the chat template is emitted as raw tokenizer ids, so + it is not wrapped by MODUS text BOS/EOS tokens. Supervised text targets keep + the legacy ``text`` item type because the shared packer already implements + shifted CE labels for that path. + """ + + def _native_chat_serialization(self): + return True + + def _native_aligned_serialization(self): + return True + + def _strict_native_image_block(self): + return os.environ.get("HUNYUAN_STRICT_NATIVE_IMAGE_BLOCK", "0") == "1" + + # --- native multi-resolution helpers --------------------------------- + # Generation-target size tiers (base_size), capped at 1024 so a 512 image + # keeps 512 instead of being forced to a single fixed 1024 tier. Each tier + # is a real token the model was pretrained with. + _NATIVE_SIZE_TIERS = [256, 512, 768, 1024] + + def _native_reso_groups(self): + groups = getattr(self, "_native_reso_groups_cache", None) + if groups is None: + from modeling.hunyuan_image_3.src.tokenization_hunyuan_image_3 import ResolutionGroup + stride = self.transform.stride + groups = {b: ResolutionGroup(base_size=b, align=stride) for b in self._NATIVE_SIZE_TIERS} + self._native_reso_groups_cache = groups + return groups + + def _pick_native_size_ratio(self, width, height): + """Map an actual (per-image, <=1024) gen-target tensor to the nearest + native (size-tier, aspect-ratio) bucket, so the emitted + / tokens honestly describe the latent instead of + a hardcoded 1024. Bucket dims are stride-aligned -> exact token count. + Returns (base_size, ratio_idx, bucket_w, bucket_h).""" + import numpy as np + groups = self._native_reso_groups() + eff = (width * height) ** 0.5 + base_size = min(self._NATIVE_SIZE_TIERS, key=lambda b: abs(b - eff)) + group = groups[base_size] + ratio_idx = int(np.argmin(np.abs(group.ratio - (height / width)))) + reso = group.data[ratio_idx] + return base_size, ratio_idx, reso.w, reso.h + + def _add_raw_text(self, data, text, need_loss=False, enable_cfg=True, modality_type='text'): + if need_loss: + raise NotImplementedError("raw_text is only used for no-loss chat scaffolding") + text_ids = self.tokenizer.encode(text) + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'raw_text', + 'enable_cfg': int(enable_cfg), + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + def _add_native_chat_preamble(self, data): + data = self._add_raw_text( + data, + f"{self.NATIVE_CHAT_SYSTEM_PROMPT}\n\nUser:\n", + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + return data + + def _add_native_chat_assistant_prefix(self, data): + return self._add_raw_text( + data, + "\nAssistant: ", + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + + def _add_answer_suffix(self, data): + return self._add_raw_text( + data, + "", + need_loss=False, + enable_cfg=False, + modality_type='text', + ) + + def _feature_boundary(self, modality_type, close=False): + if close: + return f"" + return f"" + + def _add_text(self, data, text, need_loss, enable_cfg=True, modality_type=None): + if not need_loss: + return self._add_raw_text( + data, + f"{text}\n", + need_loss=False, + enable_cfg=enable_cfg, + modality_type='text', + ) + + if modality_type in ('caption', 'det', 'grounding') and not str(text).endswith(""): + text = f"{text}" + return super()._add_text(data, text, need_loss, enable_cfg, modality_type) + + def _add_image(self, data, image, need_loss, need_vae, need_vit, enable_cfg=True, modality_type=None): + if self._strict_native_image_block() and need_loss and not need_vae and not need_vit: + image_tensor = self.transform(image) + height, width = image_tensor.shape[1:] + # Native multi-resolution: snap the per-image gen target to the + # nearest (size-tier, aspect-ratio) bucket (tiers capped at 1024) so + # the / metadata honestly matches the latent + # token count -- a 512 image emits , not a fixed 1024. + base_size, ratio_idx, bw, bh = self._pick_native_size_ratio(width, height) + if (width, height) != (bw, bh): + import torch.nn.functional as _F + image_tensor = _F.interpolate( + image_tensor.unsqueeze(0), size=(bh, bw), + mode='bilinear', align_corners=False, + ).squeeze(0) + height, width = bh, bw + data['num_tokens'] += width * height // self.transform.stride ** 2 + 5 + data['image_tensor_list'].append(image_tensor) + data['sequence_plan'].append( + { + 'type': 'native_gen_image', + 'enable_cfg': 0, + 'loss': 1, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + 'base_size': base_size, + 'ratio_idx': ratio_idx, + } + ) + data = self._add_answer_suffix(data) + return data + + if self._strict_native_image_block() and (not need_loss) and need_vae and need_vit: + vae_image_tensor = self.transform(image) + vit_image_tensor = self.vit_transform(image) + vh, vw = vae_image_tensor.shape[1:] + th, tw = vit_image_tensor.shape[1:] + data['num_tokens'] += ( + vh * vw // self.transform.stride ** 2 + + th * tw // self.vit_transform.stride ** 2 + + 3 + ) + data['image_tensor_list'].append(vae_image_tensor) + data['image_tensor_list'].append(vit_image_tensor) + data['sequence_plan'].append( + { + 'type': 'native_cond_joint_image', + 'enable_cfg': int(enable_cfg), + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + data = super()._add_image( + data, + image, + need_loss=need_loss, + need_vae=need_vae, + need_vit=need_vit, + enable_cfg=enable_cfg, + modality_type=modality_type, + ) + if need_loss: + data = self._add_answer_suffix(data) + return data + + def _add_dino(self, data, dino_tokens, need_loss, enable_cfg, modality_type): + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"{self._feature_boundary(modality_type)}\n", False, False, 'text') + data = super()._add_dino(data, dino_tokens, need_loss, enable_cfg, modality_type) + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"\n{self._feature_boundary(modality_type, close=True)}", False, False, 'text') + if need_loss: + data = self._add_answer_suffix(data) + return data + + def _add_dinolocal(self, data, dino_tokens, need_loss, enable_cfg, modality_type): + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"{self._feature_boundary(modality_type)}\n", False, False, 'text') + data = super()._add_dinolocal(data, dino_tokens, need_loss, enable_cfg, modality_type) + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"\n{self._feature_boundary(modality_type, close=True)}", False, False, 'text') + if need_loss: + data = self._add_answer_suffix(data) + return data + + def _add_clip(self, data, clip_tokens, need_loss, enable_cfg, modality_type): + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"{self._feature_boundary(modality_type)}\n", False, False, 'text') + data = super()._add_clip(data, clip_tokens, need_loss, enable_cfg, modality_type) + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"\n{self._feature_boundary(modality_type, close=True)}", False, False, 'text') + if need_loss: + data = self._add_answer_suffix(data) + return data + + def _add_imagebind(self, data, imagebind_tokens, need_loss, enable_cfg, modality_type): + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"{self._feature_boundary(modality_type)}\n", False, False, 'text') + data = super()._add_imagebind(data, imagebind_tokens, need_loss, enable_cfg, modality_type) + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"\n{self._feature_boundary(modality_type, close=True)}", False, False, 'text') + if need_loss: + data = self._add_answer_suffix(data) + return data + + def _add_imagebindlocal(self, data, imagebind_tokens, need_loss, enable_cfg, modality_type): + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"{self._feature_boundary(modality_type)}\n", False, False, 'text') + data = super()._add_imagebindlocal(data, imagebind_tokens, need_loss, enable_cfg, modality_type) + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"\n{self._feature_boundary(modality_type, close=True)}", False, False, 'text') + if need_loss: + data = self._add_answer_suffix(data) + return data + + def _add_cocodet(self, data, detections, image_size, need_loss, enable_cfg, modality_type): + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"{self._feature_boundary(modality_type)}\n", False, False, 'text') + data = super()._add_cocodet(data, detections, image_size, need_loss, enable_cfg, modality_type) + if self._strict_native_image_block(): + data = self._add_raw_text(data, f"\n{self._feature_boundary(modality_type, close=True)}", False, False, 'text') + if need_loss: + data = self._add_answer_suffix(data) + return data diff --git a/data/interleave_datasets/interleave_t2i_dataset.py b/data/interleave_datasets/interleave_t2i_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..4738fe0274766e86c66ee5931c91722e5e212406 --- /dev/null +++ b/data/interleave_datasets/interleave_t2i_dataset.py @@ -0,0 +1,376 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import json +import os + +import pyarrow.parquet as pq + +from ..distributed_iterable_dataset import DistributedIterableDataset +from ..parquet_utils import get_parquet_data_paths, init_arrow_pf_fs, strip_s3_scheme + + +class InterleavedBaseIterableDataset(DistributedIterableDataset): + + def _init_data(self): + data = { + 'sequence_plan': [], + 'text_ids_list': [], + 'image_tensor_list': [], + 'num_tokens': 0, + } + return data + + def _add_text(self, data, text, need_loss, enable_cfg=True, modality_type=None): + text_ids = self.tokenizer.encode(text) + data['num_tokens'] += len(text_ids) + data['text_ids_list'].append(text_ids) + data['sequence_plan'].append( + { + 'type': 'text', + 'enable_cfg': int(enable_cfg), + 'loss': int(need_loss), + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + return data + + def _add_image(self, data, image, need_loss, need_vae, need_vit, enable_cfg=True, modality_type=None): + assert need_loss or need_vae or need_vit + + if need_loss: + data['sequence_plan'].append( + { + 'type': 'vae_image', + 'enable_cfg': 0, + 'loss': 1, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + + image_tensor = self.transform(image) + height, width = image_tensor.shape[1:] + data['num_tokens'] += width * height // self.transform.stride ** 2 + data['image_tensor_list'].append(image_tensor) + + if need_vae: + data['sequence_plan'].append( + { + 'type': 'vae_image', + 'enable_cfg': int(enable_cfg), + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + } + ) + + image_tensor = self.transform(image) + height, width = image_tensor.shape[1:] + data['num_tokens'] += width * height // self.transform.stride ** 2 + data['image_tensor_list'].append(image_tensor.clone()) + + if need_vit: + data['sequence_plan'].append( + { + 'type': 'vit_image', + 'enable_cfg': int(enable_cfg), + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': modality_type, + }, + ) + vit_image_tensor = self.vit_transform(image) + height, width = vit_image_tensor.shape[1:] + data['num_tokens'] += width * height // self.vit_transform.stride ** 2 + data['image_tensor_list'].append(vit_image_tensor) + + return data + + def _add_video(self, data, frames, frame_indexes, need_loss, need_vae, enable_cfg=True): + assert int(need_loss) + int(need_vae) == 1 + + if need_loss: + for idx, (image, frame_idx) in enumerate(zip(frames, frame_indexes)): + current_sequence_plan = { + 'type': 'vae_image', + 'enable_cfg': 0, + 'loss': 1, + 'special_token_loss': 0, + 'special_token_label': None, + 'split_start': idx == 0, + 'split_end': idx == len(frames) - 1, + } + if idx < len(frame_indexes) - 1: + current_sequence_plan['frame_delta'] = frame_indexes[idx + 1] - frame_idx + data['sequence_plan'].append(current_sequence_plan) + image_tensor = self.transform(image) + height, width = image_tensor.shape[1:] + data['image_tensor_list'].append(image_tensor) + data['num_tokens'] += width * height // self.transform.stride ** 2 + + elif need_vae: + for idx, (image, frame_idx) in enumerate(zip(frames, frame_indexes)): + current_sequence_plan = { + 'type': 'vae_image', + 'enable_cfg': int(enable_cfg), + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + 'split_start': idx == 0, + 'split_end': idx == len(frames) - 1, + } + if idx < len(frame_indexes) - 1: + current_sequence_plan['frame_delta'] = frame_indexes[idx + 1] - frame_idx + data['sequence_plan'].append(current_sequence_plan) + image_tensor = self.transform(image) + height, width = image_tensor.shape[1:] + data['image_tensor_list'].append(image_tensor) + data['num_tokens'] += width * height // self.transform.stride ** 2 + + return data + + +class ParquetStandardIterableDataset(DistributedIterableDataset): + + def __init__( + self, dataset_name, transform, tokenizer, vit_transform, + data_dir_list, num_used_data, parquet_info, + local_rank=0, world_size=1, num_workers=8, data_status=None, + use_instruction=False, use_condition_instruction=True, use_target_instruction=True, + num_condition_modalities=1, + strict_num_condition_modalities=False, + use_det_image=False, + modality_registry=None, + tail_n_rows=None, + force_condition_modality=None, + force_condition_modalities=None, + pred_intermediate_dir=None, + gt_restricted_dir=None, + fixed_row_list_path=None, + ): + """ + data_dir_list: list of data directories contains parquet files + num_used_data: list of number of sampled data paths for each data directory + vit_transform: input transform for vit model. + tail_n_rows: if set, only the last N rows of each row-group are yielded + (held-out validation slice; default None preserves training behavior). + force_condition_modality: if set, every sample uses exactly this single + condition modality (used by the validation matrix script). Default + None preserves training behavior (random sampling). + """ + super().__init__(dataset_name, local_rank, world_size, num_workers) + self.transform = transform + self.vit_transform = vit_transform + self.tokenizer = tokenizer + self.data_status = data_status + self.data_paths = self.get_data_paths(data_dir_list, num_used_data, parquet_info) + # Pred-intermediate mode: keep only (file, rg) pairs that have at + # least one Phase-1-saved PNG. Otherwise distributed ranks whose + # data slice contains no pred PNGs would iterate forever skipping + # every row and deadlock at NCCL all-reduce. Done BEFORE set_epoch + # so the shuffle+chunk operates on the filtered set. + # + # gt_restricted_dir is the apples-to-apples sibling: applies the + # SAME pair filter (so the GT matrix runs on the exact same 8 pairs + # PRED uses) but does NOT substitute pred PNGs — the intermediate + # condition is still loaded from parquet GT. Used to compare GT vs + # PRED on identical sample slices. + self.pred_intermediate_dir = pred_intermediate_dir + self.gt_restricted_dir = gt_restricted_dir + _filter_dir = pred_intermediate_dir or gt_restricted_dir + if _filter_dir is not None: + self.data_paths = self._filter_data_paths_by_pred( + self.data_paths, _filter_dir) + + # Fixed val set mode: V_fixed.json lists exact (basename, rg, row) + # tuples to use. Each row is keyed (basename, rg, row_idx) → + # global_idx for deterministic rank striping. Pairs are filtered + # to only those touched by V_fixed. + self.fixed_row_list_path = fixed_row_list_path + self._fixed_rows_by_pair = None # (basename, rg) -> set(row_idx) + self._fixed_global_idx = None # (basename, rg, row_idx) -> global_idx + if fixed_row_list_path is not None: + with open(fixed_row_list_path, "r") as _f: + _payload = json.load(_f) + _rows = _payload.get("rows", _payload) # accept bare list too + _by_pair = {} + _gidx = {} + for r in _rows: + key_pair = (r["parquet_basename"], int(r["rg_id"])) + _by_pair.setdefault(key_pair, set()).add(int(r["row_idx"])) + _gidx[(r["parquet_basename"], int(r["rg_id"]), int(r["row_idx"]))] = ( + int(r.get("global_idx", len(_gidx)))) + self._fixed_rows_by_pair = _by_pair + self._fixed_global_idx = _gidx + # Filter data_paths to only the (file, rg) pairs touched by V_fixed. + _keep = set(_by_pair.keys()) + _filtered = [] + for path, rg in self.data_paths: + base = os.path.splitext(os.path.basename(path))[0] + if (base, int(rg)) in _keep: + _filtered.append((path, rg)) + print(f"[fixed-row] V_fixed has {len(_rows)} rows across " + f"{len(_by_pair)} pairs; kept {len(_filtered)} matching " + f"data_paths (out of {len(self.data_paths)})") + self.data_paths = _filtered + + self.set_epoch() + self.use_instruction = use_instruction + self.use_condition_instruction = use_condition_instruction + self.use_target_instruction = use_target_instruction + self.num_condition_modalities = num_condition_modalities + self.strict_num_condition_modalities = strict_num_condition_modalities + self.use_det_image = use_det_image + self.modality_registry = modality_registry + self.tail_n_rows = tail_n_rows + self.force_condition_modality = force_condition_modality + self.force_condition_modalities = force_condition_modalities + print(f"use_instruction: {self.use_instruction}") + + def _filter_data_paths_by_pred(self, data_paths, pred_intermediate_dir): + """Keep only (parquet_path, rg_id) pairs covered by Phase-1 outputs. + Phase 1 writes files as + //_rg_row*_pred.png + We consider a pair 'covered' if ANY modality subdir has at least + one PNG matching `_rg_row*_pred.png`. + Backward compat: only invoked when pred_intermediate_dir is set. + """ + import glob as _glob + covered = set() + if not os.path.isdir(pred_intermediate_dir): + print(f'[pred-filter] WARN: pred_intermediate_dir not found: {pred_intermediate_dir}') + return [] + # Scan each modality subdir, harvest (basename, rg_id) keys. + for sub in os.listdir(pred_intermediate_dir): + sub_dir = os.path.join(pred_intermediate_dir, sub) + if not os.path.isdir(sub_dir): + continue + for fn in os.listdir(sub_dir): + if not fn.endswith('_pred.png'): + continue + # filename: _rg_row_pred.png + # split from the right: ['_pred.png'] then row, rg, basename + stem = fn[:-len('_pred.png')] + # find last '_row' before number + idx_row = stem.rfind('_row') + if idx_row < 0: + continue + pre_row = stem[:idx_row] # _rg + idx_rg = pre_row.rfind('_rg') + if idx_rg < 0: + continue + basename = pre_row[:idx_rg] + try: + rg_id = int(pre_row[idx_rg + len('_rg'):]) + except ValueError: + continue + covered.add((basename, rg_id)) + filtered = [] + for path, rg in data_paths: + base = os.path.splitext(os.path.basename(path))[0] + if (base, rg) in covered: + filtered.append((path, rg)) + print(f'[pred-filter] kept {len(filtered)}/{len(data_paths)} (file, rg) pairs ' + f'covered by pred_intermediate_dir={pred_intermediate_dir}') + return filtered + + def get_data_paths(self, data_dir_list, num_used_data, parquet_info): + # Index parquet_info by basename too, so the lookup is independent of the + # data_dir prefix (local path vs s3://...). Basenames are unique within a + # dataset's parquet_info, so this is equivalent to exact-path matching for + # existing local runs (forward-compatible) while letting an s3:// data_dir + # reuse the same JSON unchanged. + info_by_base = {os.path.basename(k): v for k, v in parquet_info.items()} + row_groups = [] + for data_dir, num_data_path in zip(data_dir_list, num_used_data): + data_paths = get_parquet_data_paths([data_dir], [num_data_path]) + for data_path in data_paths: + info = parquet_info.get(data_path) or info_by_base.get(os.path.basename(data_path)) + if info is not None: + num_row_groups = info['num_row_groups'] + for rg_idx in range(num_row_groups): + row_groups.append((data_path, rg_idx)) + return row_groups + + def parse_row(self, row): + raise NotImplementedError + + def __iter__(self): + file_paths_per_worker, worker_id = self.get_data_paths_per_worker() + if self.data_status is not None: + global_row_group_start_id = self.data_status[worker_id][0] + row_start_id = self.data_status[worker_id][1] + 1 + else: + global_row_group_start_id = 0 + row_start_id = 0 + + print( + f"rank-{self.local_rank} worker-{worker_id} dataset-{self.dataset_name}: " + f"resuming data at global_rg#{global_row_group_start_id}, row#{row_start_id}" + ) + + while True: + file_paths_per_worker_ = file_paths_per_worker[global_row_group_start_id:] + for global_row_group_idx, (parquet_file_path, row_group_id) in enumerate( + file_paths_per_worker_, start=global_row_group_start_id + ): + fs = init_arrow_pf_fs(parquet_file_path) + with fs.open_input_file(strip_s3_scheme(parquet_file_path)) as f: + try: + fr = pq.ParquetFile(f) + df = fr.read_row_group(row_group_id).to_pandas() + if self.tail_n_rows is not None and self.tail_n_rows > 0: + df = df.iloc[-int(self.tail_n_rows):] + df = df.iloc[row_start_id:] + except Exception as e: + print(f'Error {e} in rg#{row_group_id}, {parquet_file_path}') + continue + + # Fixed val set: pre-compute which row_idxs of THIS + # (file, rg) pair are in V_fixed. Skip rows not in the + # set. No-op when fixed_row_list_path is None. + _fixed_set_for_pair = None + if self._fixed_rows_by_pair is not None: + _basename = os.path.splitext( + os.path.basename(parquet_file_path))[0] + _fixed_set_for_pair = self._fixed_rows_by_pair.get( + (_basename, int(row_group_id)), set()) + + for row_idx, row in df.iterrows(): + try: + if _fixed_set_for_pair is not None and int(row_idx) not in _fixed_set_for_pair: + continue + # Expose per-row parquet provenance to subclasses + # via transient attrs (subclasses ignore if unused). + # Backward compatible — old code paths don't read these. + self._current_parquet_path = parquet_file_path + self._current_rg_id = row_group_id + self._current_row_idx = row_idx + data = self.parse_row(row) + if data is None or len(data) == 0: + continue + data['data_indexes'] = { + "data_indexes": [global_row_group_idx, row_idx], + "worker_id": worker_id, + "dataset_name": self.dataset_name, + } + except Exception as e: + # Silently skip rows whose pred-intermediate + # PNG is missing — that's the expected case + # outside Phase 1's generated subset, not an + # error. + if type(e).__name__ == 'PredIntermediateMissing': + continue + print(f'Error {e} in rg#{row_group_id}, {parquet_file_path}') + continue + yield data + + row_start_id = 0 + global_row_group_start_id = 0 + print(f"{self.dataset_name} repeat in rank-{self.local_rank} worker-{worker_id}") diff --git a/data/parquet_utils.py b/data/parquet_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..aabd5d3cbbac1081bc95e35bc1748a181bc05594 --- /dev/null +++ b/data/parquet_utils.py @@ -0,0 +1,153 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + + +import os +import subprocess +import logging + +import pyarrow.fs as pf +import torch.distributed as dist + +logger = logging.getLogger(__name__) + + +def apply_data_root_override(path): + # If MODUS_DATA_ROOT is set, replace the local 'datasets/' prefix with it, + # preserving the sub-path. This mirrors the datasets/ tree under the root and + # works for BOTH parquet dirs (datasets/blip3o/parquet_...) and vlm_sft + # image/jsonl paths (datasets/llava_onevision_vqa//...). Switching + # local<->S3 — and 13mod<->16mod<->Hunyuan — becomes a pure env/config change + # with NO edit to dataset_info.py. Paths not under datasets/ and the unset + # case are returned unchanged (forward-compatible). + root = os.environ.get("MODUS_DATA_ROOT") + if not root: + return path + for prefix in ("./datasets/", "datasets/"): + if path.startswith(prefix): + return root.rstrip("/") + "/" + path[len(prefix):] + return path + + +def read_file_bytes(path): + # Read a whole file as bytes, scheme-selected (local / hdfs / s3) via the same + # filesystem logic as parquet. Used for S3 streaming of vlm_sft jsonl + images. + fs = init_arrow_pf_fs(path) + with fs.open_input_file(strip_s3_scheme(path)) as f: + return f.read() + + +def get_parquet_data_paths(data_dir_list, num_sampled_data_paths, rank=0, world_size=1): + data_dir_list = [apply_data_root_override(d) for d in data_dir_list] + num_data_dirs = len(data_dir_list) + if world_size > 1: + chunk_size = (num_data_dirs + world_size - 1) // world_size + start_idx = rank * chunk_size + end_idx = min(start_idx + chunk_size, num_data_dirs) + local_data_dir_list = data_dir_list[start_idx:end_idx] + local_num_sampled_data_paths = num_sampled_data_paths[start_idx:end_idx] + else: + local_data_dir_list = data_dir_list + local_num_sampled_data_paths = num_sampled_data_paths + + local_data_paths = [] + for data_dir, num_data_path in zip(local_data_dir_list, local_num_sampled_data_paths): + if data_dir.startswith("hdfs://"): + files = hdfs_ls_cmd(data_dir) + data_paths_per_dir = [ + file for file in files if file.endswith(".parquet") + ] + elif data_dir.startswith("s3://"): + fs = init_arrow_pf_fs(data_dir) + selector = pf.FileSelector(strip_s3_scheme(data_dir), recursive=False) + data_paths_per_dir = [ + "s3://" + info.path + for info in fs.get_file_info(selector) + if info.path.endswith(".parquet") + ] + else: + files = os.listdir(data_dir) + data_paths_per_dir = [ + os.path.join(data_dir, name) + for name in files + if name.endswith(".parquet") + ] + if num_data_path is None: + # None → use every parquet file in the dir exactly once + # (no repeat/truncate). Used by vlm_sft_parquet where the + # sample selection is already baked in at conversion time. + local_data_paths.extend(data_paths_per_dir) + continue + repeat = num_data_path // len(data_paths_per_dir) + data_paths_per_dir = data_paths_per_dir * (repeat + 1) + local_data_paths.extend(data_paths_per_dir[:num_data_path]) + + if world_size > 1: + gather_list = [None] * world_size + dist.all_gather_object(gather_list, local_data_paths) + + combined_chunks = [] + for chunk_list in gather_list: + if chunk_list is not None: + combined_chunks.extend(chunk_list) + else: + combined_chunks = local_data_paths + + return combined_chunks + + +# NOTE: cumtomize this function for your cluster +def get_hdfs_host(): + return "hdfs://xxx" + + +# NOTE: cumtomize this function for your cluster +def get_hdfs_block_size(): + return 134217728 + + +# NOTE: cumtomize this function for your cluster +def get_hdfs_extra_conf(): + return None + + +def strip_s3_scheme(path): + # pyarrow's S3FileSystem operates on 'bucket/key' (no scheme). Local/hdfs + # paths are returned unchanged, so their behavior is untouched. + if path.startswith("s3://"): + return path[len("s3://"):] + return path + + +def init_arrow_pf_fs(parquet_file_path): + if parquet_file_path.startswith("hdfs://"): + fs = pf.HadoopFileSystem( + host=get_hdfs_host(), + port=0, + buffer_size=get_hdfs_block_size(), + extra_conf=get_hdfs_extra_conf(), + ) + elif parquet_file_path.startswith("s3://"): + # Credentials + endpoint come from the environment so no secrets live in + # code/config. Standard AWS_* names work; MODUS_S3_ENDPOINT/REGION/SCHEME + # cover custom (non-AWS) S3 endpoints (e.g. Apple conductor, SCITAS). + s3_kwargs = dict( + access_key=os.environ.get("AWS_ACCESS_KEY_ID"), + secret_key=os.environ.get("AWS_SECRET_ACCESS_KEY"), + scheme=os.environ.get("MODUS_S3_SCHEME", "https"), + ) + if os.environ.get("MODUS_S3_ENDPOINT"): + s3_kwargs["endpoint_override"] = os.environ["MODUS_S3_ENDPOINT"] + if os.environ.get("MODUS_S3_REGION"): + s3_kwargs["region"] = os.environ["MODUS_S3_REGION"] + if os.environ.get("AWS_SESSION_TOKEN"): + s3_kwargs["session_token"] = os.environ["AWS_SESSION_TOKEN"] + fs = pf.S3FileSystem(**s3_kwargs) + else: + fs = pf.LocalFileSystem() + return fs + + +def hdfs_ls_cmd(dir): + result = subprocess.run(["hdfs", "dfs", "ls", dir], capture_output=True, text=True).stdout + return ['hdfs://' + i.split('hdfs://')[-1].strip() for i in result.split('\n') if 'hdfs://' in i] diff --git a/data/t2i_dataset.py b/data/t2i_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0d2cce17563f6805abd785dcdecf1b47f7db6c0b --- /dev/null +++ b/data/t2i_dataset.py @@ -0,0 +1,131 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import io +import json +import pyarrow.parquet as pq +import random +from PIL import Image + +from .data_utils import pil_img2rgb +from .distributed_iterable_dataset import DistributedIterableDataset +from .parquet_utils import get_parquet_data_paths, init_arrow_pf_fs, strip_s3_scheme + +Image.MAX_IMAGE_PIXELS = 20_000_000 + + +class T2IIterableDataset(DistributedIterableDataset): + def __init__( + self, dataset_name, transform, tokenizer, data_dir_list, num_used_data, + local_rank=0, world_size=1, num_workers=8, data_status=None, use_instruction=False, + use_condition_instruction=False, use_target_instruction=False, num_condition_modalities=0, + strict_num_condition_modalities=False, + use_det_image=False, modality_registry=None, + ): + """ + data_dir_list: list of data directories contains parquet files + num_used_data: list of number of sampled data paths for each data directory + """ + super().__init__(dataset_name, local_rank, world_size, num_workers) + self.transform = transform + self.tokenizer = tokenizer + self.data_status = data_status + self.data_paths = self.get_data_paths(data_dir_list, num_used_data) + self.set_epoch() + + def get_data_paths(self, data_dir_list, num_used_data): + return get_parquet_data_paths(data_dir_list, num_used_data) + + def __iter__(self): + data_paths_per_worker, worker_id = self.get_data_paths_per_worker() + if self.data_status is not None: + parquet_start_id = self.data_status[worker_id][0] + row_group_start_id = self.data_status[worker_id][1] + row_start_id = self.data_status[worker_id][2] + 1 + else: + parquet_start_id = 0 + row_group_start_id = 0 + row_start_id = 0 + transform_stride = self.transform.stride + + print( + f"rank-{self.local_rank} worker-{worker_id} dataset-{self.dataset_name}: " + f"resuming data at parquet#{parquet_start_id}, rg#{row_group_start_id}, row#{row_start_id}" + ) + + while True: + data_paths_per_worker_ = data_paths_per_worker[parquet_start_id:] + for parquet_idx, parquet_file_path in enumerate(data_paths_per_worker_, start=parquet_start_id): + fs = init_arrow_pf_fs(parquet_file_path) + with fs.open_input_file(strip_s3_scheme(parquet_file_path)) as f: + fr = pq.ParquetFile(f) + row_group_ids = list(range(fr.num_row_groups)) + row_group_ids_ = row_group_ids[row_group_start_id:] + + for row_group_id in row_group_ids_: + df = fr.read_row_group(row_group_id).to_pandas() + df = df.iloc[row_start_id:] + + for row_idx, row in df.iterrows(): + num_tokens = 0 + try: + image_byte = row['image'] + image = pil_img2rgb(Image.open(io.BytesIO(image_byte))) + except Exception as e: + print(f'Error: {e} in rg#{row_group_id}, {parquet_file_path}') + continue + image_tensor = self.transform(image) + height, width = image_tensor.shape[1:] + num_tokens += width * height // transform_stride ** 2 + + try: + caption_dict = row['captions'] + caption_dict = json.loads(caption_dict) + except Exception as e: + print(f'Error: {e} in rg#{row_group_id}, {parquet_file_path}') + continue + + caps_token = [self.tokenizer.encode(v) for _, v in caption_dict.items()] + if len(caps_token) == 0: + print(f'no caption in rg#{row_group_id}, {parquet_file_path}') + caption_token = self.tokenizer.encode(' ') + else: + caption_token = random.choice(caps_token) + + sequence_plan, text_ids_list = [], [] + text_ids = caption_token + num_tokens += len(caption_token) + text_ids_list.append(text_ids) + sequence_plan.append({ + 'type': 'text', + 'enable_cfg': 1, + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + }) + + sequence_plan.append({ + 'type': 'vae_image', + 'enable_cfg': 0, + 'loss': 1, + 'special_token_loss': 0, + 'special_token_label': None, + }) + + sample = dict( + image_tensor_list=[image_tensor], + text_ids_list=text_ids_list, + num_tokens=num_tokens, + sequence_plan=sequence_plan, + data_indexes={ + "data_indexes": [parquet_idx, row_group_id, row_idx], + "worker_id": worker_id, + "dataset_name": self.dataset_name, + } + ) + yield sample + + row_start_id = 0 + row_group_start_id = 0 + parquet_start_id = 0 + print(f"{self.dataset_name} repeat in rank-{self.local_rank} worker-{worker_id}") diff --git a/data/transforms.py b/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..89953b8bd5d6738fef7f611951e41d40941844aa --- /dev/null +++ b/data/transforms.py @@ -0,0 +1,322 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import random +from PIL import Image + +import cv2 +import numpy as np +import torch +import torch.nn.functional as _F + +# torchvision-free replacements: the HF ZeroGPU base image ships a custom torch +# with no matching torchvision wheel, and installing one downgrades torch and +# breaks ZeroGPU. For PIL inputs these are numerically identical to torchvision +# (F.resize on a PIL image delegates to PIL.Image.resize; ToTensor/Normalize are +# plain arithmetic). Set MODUS_FORCE_SDPA_ATTN-style parity was validated on-cluster. +_PIL_RESAMPLE = {"bicubic": Image.BICUBIC, "bilinear": Image.BILINEAR, "nearest": Image.NEAREST} + + +class InterpolationMode: + BICUBIC = "bicubic" + BILINEAR = "bilinear" + NEAREST = "nearest" + + +def _resize(img, size, interpolation="bicubic", antialias=True): + new_h, new_w = size + if isinstance(img, torch.Tensor): + x = img if img.dim() == 4 else img.unsqueeze(0) + aa = antialias and interpolation in ("bicubic", "bilinear") + x = _F.interpolate(x.float(), size=(new_h, new_w), mode=interpolation, + align_corners=False if interpolation != "nearest" else None, + antialias=aa) + return x if img.dim() == 4 else x.squeeze(0) + return img.resize((new_w, new_h), resample=_PIL_RESAMPLE[interpolation]) + + +def _to_tensor(img): + """PIL image -> C×H×W float tensor in [0, 1] (matches transforms.ToTensor).""" + arr = np.asarray(img if img.mode == "RGB" else img.convert("RGB"), dtype=np.uint8) + return torch.from_numpy(arr).permute(2, 0, 1).contiguous().float().div_(255.0) + + +def _normalize(t, mean, std): + m = torch.as_tensor(mean, dtype=t.dtype, device=t.device).view(-1, 1, 1) + s = torch.as_tensor(std, dtype=t.dtype, device=t.device).view(-1, 1, 1) + return t.sub_(m).div_(s) + + +class MaxLongEdgeMinShortEdgeResize(torch.nn.Module): + """Resize the input image so that its longest side and shortest side are within a specified range, + ensuring that both sides are divisible by a specified stride. + + Args: + max_size (int): Maximum size for the longest edge of the image. + min_size (int): Minimum size for the shortest edge of the image. + stride (int): Value by which the height and width of the image must be divisible. + max_pixels (int): Maximum pixels for the full image. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR``, and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g., ``PIL.Image.BILINEAR`` are also accepted. + antialias (bool, optional): Whether to apply antialiasing (default is True). + """ + + def __init__( + self, + max_size: int, + min_size: int, + stride: int, + max_pixels: int, + interpolation=InterpolationMode.BICUBIC, + antialias=True + ): + super().__init__() + self.max_size = max_size + self.min_size = min_size + self.stride = stride + self.max_pixels = max_pixels + self.interpolation = interpolation + self.antialias = antialias + + def _make_divisible(self, value, stride): + """Ensure the value is divisible by the stride.""" + return max(stride, int(round(value / stride) * stride)) + + def _apply_scale(self, width, height, scale): + new_width = round(width * scale) + new_height = round(height * scale) + new_width = self._make_divisible(new_width, self.stride) + new_height = self._make_divisible(new_height, self.stride) + return new_width, new_height + + def forward(self, img, img_num=1): + """ + Args: + img (PIL Image): Image to be resized. + img_num (int): Number of images, used to change max_tokens. + Returns: + PIL Image or Tensor: Rescaled image with divisible dimensions. + """ + if isinstance(img, torch.Tensor): + height, width = img.shape[-2:] + else: + width, height = img.size + + scale = min(self.max_size / max(width, height), 1.0) + scale = max(scale, self.min_size / min(width, height)) + new_width, new_height = self._apply_scale(width, height, scale) + + # Ensure the number of pixels does not exceed max_pixels + if new_width * new_height > self.max_pixels / img_num: + scale = self.max_pixels / img_num / (new_width * new_height) + new_width, new_height = self._apply_scale(new_width, new_height, scale) + + # Ensure longest edge does not exceed max_size + if max(new_width, new_height) > self.max_size: + scale = self.max_size / max(new_width, new_height) + new_width, new_height = self._apply_scale(new_width, new_height, scale) + + return _resize(img, (new_height, new_width), self.interpolation, antialias=self.antialias) + + +class ImageTransform: + def __init__( + self, + max_image_size, + min_image_size, + image_stride, + max_pixels=14*14*9*1024, + image_mean=[0.5, 0.5, 0.5], + image_std=[0.5, 0.5, 0.5] + ): + self.stride = image_stride + + self.resize_transform = MaxLongEdgeMinShortEdgeResize( + max_size=max_image_size, + min_size=min_image_size, + stride=image_stride, + max_pixels=max_pixels, + ) + self.to_tensor_transform = _to_tensor + self.normalize_transform = lambda t: _normalize(t, image_mean, image_std) + + def __call__(self, img, img_num=1): + img = self.resize_transform(img, img_num=img_num) + img = self.to_tensor_transform(img) + img = self.normalize_transform(img) + return img + + +def decolorization(image): + gray_image = image.convert('L') + return Image.merge(image.mode, [gray_image] * 3) if image.mode in ('RGB', 'L') else gray_image + + +def downscale(image, scale_factor): + new_width = int(round(image.width * scale_factor)) + new_height = int(round(image.height * scale_factor)) + new_width = max(1, new_width) + new_height = max(1, new_height) + return image.resize((new_width, new_height), resample=Image.BICUBIC) + + +def crop(image, crop_factors): + target_h, target_w = crop_factors + img_w, img_h = image.size + + if target_h > img_h or target_w > img_w: + raise ValueError("Crop size exceeds image dimensions") + + x = random.randint(0, img_w - target_w) + y = random.randint(0, img_h - target_h) + + return image.crop((x, y, x + target_w, y + target_h)), [[x, y], [x + target_w, y + target_h]] + + +def motion_blur_opencv(image, kernel_size=15, angle=0): + # 线性核 + kernel = np.zeros((kernel_size, kernel_size), dtype=np.float32) + kernel[kernel_size // 2, :] = np.ones(kernel_size, dtype=np.float32) + + # 旋转核 + center = (kernel_size / 2 - 0.5, kernel_size / 2 - 0.5) + M = cv2.getRotationMatrix2D(center, angle, 1) + rotated_kernel = cv2.warpAffine(kernel, M, (kernel_size, kernel_size)) + + # 归一化核 + rotated_kernel /= rotated_kernel.sum() if rotated_kernel.sum() != 0 else 1 + + img = np.array(image) + if img.ndim == 2: + blurred = cv2.filter2D(img, -1, rotated_kernel, borderType=cv2.BORDER_REFLECT) + else: + # 对于彩色图像，各通道独立卷积 + blurred = np.zeros_like(img) + for c in range(img.shape[2]): + blurred[..., c] = cv2.filter2D(img[..., c], -1, rotated_kernel, borderType=cv2.BORDER_REFLECT) + + return Image.fromarray(blurred.astype(np.uint8)) + + +def shuffle_patch(image, num_splits, gap_size=2): + """将图像分割为块（允许尺寸不整除），随机打乱后拼接，块间保留间隙""" + h_splits, w_splits = num_splits + img_w, img_h = image.size + + base_patch_h = img_h // h_splits + patch_heights = [base_patch_h] * (h_splits - 1) + patch_heights.append(img_h - sum(patch_heights)) + + base_patch_w = img_w // w_splits + patch_widths = [base_patch_w] * (w_splits - 1) + patch_widths.append(img_w - sum(patch_widths)) + + patches = [] + current_y = 0 + for i in range(h_splits): + current_x = 0 + patch_h = patch_heights[i] + for j in range(w_splits): + patch_w = patch_widths[j] + patch = image.crop((current_x, current_y, current_x + patch_w, current_y + patch_h)) + patches.append(patch) + current_x += patch_w + current_y += patch_h + + random.shuffle(patches) + + total_width = sum(patch_widths) + (w_splits - 1) * gap_size + total_height = sum(patch_heights) + (h_splits - 1) * gap_size + new_image = Image.new(image.mode, (total_width, total_height), color=(255, 255, 255)) + + current_y = 0 # 当前行的起始 Y 坐标 + patch_idx = 0 # 当前处理的块索引 + for i in range(h_splits): + current_x = 0 # 当前列的起始 X 坐标 + patch_h = patch_heights[i] # 当前行块的高度 + for j in range(w_splits): + # 取出打乱后的块 + patch = patches[patch_idx] + patch_w = patch_widths[j] # 当前列块的宽度 + # 粘贴块（左上角坐标为 (current_x, current_y)） + new_image.paste(patch, (current_x, current_y)) + # 更新 X 坐标（下一个块的起始位置 = 当前块宽度 + 间隙） + current_x += patch_w + gap_size + patch_idx += 1 + # 更新 Y 坐标（下一行的起始位置 = 当前行高度 + 间隙） + current_y += patch_h + gap_size + + return new_image + + +def inpainting(image, num_splits, blank_ratio=0.3, blank_color=(255, 255, 255)): + """ + 图像分割后随机空白部分patch，用于inpainting任务 + + 参数： + image: PIL.Image 输入图像（RGB模式） + h_splits: int 行分割数（垂直方向分割块数） + w_splits: int 列分割数（水平方向分割块数） + blank_ratio: float 空白patch的比例（0~1） + blank_color: tuple 空白区域的颜色（RGB，如白色(255,255,255)） + + 返回： + PIL.Image 处理后拼接的图像 + """ + h_splits, w_splits = num_splits + img_w, img_h = image.size + + base_patch_h = img_h // h_splits + patch_heights = [base_patch_h] * (h_splits - 1) + patch_heights.append(img_h - sum(patch_heights)) + + base_patch_w = img_w // w_splits + patch_widths = [base_patch_w] * (w_splits - 1) + patch_widths.append(img_w - sum(patch_widths)) + + patches = [] + current_y = 0 + for i in range(h_splits): + current_x = 0 + patch_h = patch_heights[i] + for j in range(w_splits): + patch_w = patch_widths[j] + patch = image.crop((current_x, current_y, current_x + patch_w, current_y + patch_h)) + patches.append(patch) + current_x += patch_w + current_y += patch_h + + total_patches = h_splits * w_splits + num_blank = int(total_patches * blank_ratio) + num_blank = max(0, min(num_blank, total_patches)) + blank_indices = random.sample(range(total_patches), num_blank) + + processed_patches = [] + for idx, patch in enumerate(patches): + if idx in blank_indices: + blank_patch = Image.new("RGB", patch.size, color=blank_color) + processed_patches.append(blank_patch) + else: + processed_patches.append(patch) + + # 创建结果图像（尺寸与原图一致） + result_image = Image.new("RGB", (img_w, img_h)) + current_y = 0 + patch_idx = 0 + for i in range(h_splits): + current_x = 0 + patch_h = patch_heights[i] + for j in range(w_splits): + # 取出处理后的patch + patch = processed_patches[patch_idx] + patch_w = patch_widths[j] + # 粘贴到原位置 + result_image.paste(patch, (current_x, current_y)) + current_x += patch_w + patch_idx += 1 + current_y += patch_h + + return result_image diff --git a/data/video_utils.py b/data/video_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9b72fc5096207e69333a81388c82609c24e7f48a --- /dev/null +++ b/data/video_utils.py @@ -0,0 +1,165 @@ +# Copyright (c) 2023 OpenGVLab +# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: MIT +# +# This file has been modified by ByteDance Ltd. and/or its affiliates. on 2025-05-20. +# +# Original file was released under MIT, with the full license text +# available at https://github.com/OpenGVLab/InternVL/blob/main/LICENSE. +# +# This modified file is released under the same license. + + +import io +import os +import random +import re + +import numpy as np +import decord +from PIL import Image + + +def get_frame_indices(num_frames, vlen, sample='rand', fix_start=None, input_fps=1, max_num_frames=-1): + if sample in ['rand', 'middle']: # uniform sampling + acc_samples = min(num_frames, vlen) + # split the video into `acc_samples` intervals, and sample from each interval. + intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int) + ranges = [] + for idx, interv in enumerate(intervals[:-1]): + ranges.append((interv, intervals[idx + 1] - 1)) + if sample == 'rand': + try: + frame_indices = [random.choice(range(x[0], x[1])) for x in ranges] + except: + frame_indices = np.random.permutation(vlen)[:acc_samples] + frame_indices.sort() + frame_indices = list(frame_indices) + elif fix_start is not None: + frame_indices = [x[0] + fix_start for x in ranges] + elif sample == 'middle': + frame_indices = [(x[0] + x[1]) // 2 for x in ranges] + else: + raise NotImplementedError + + if len(frame_indices) < num_frames: # padded with last frame + padded_frame_indices = [frame_indices[-1]] * num_frames + padded_frame_indices[:len(frame_indices)] = frame_indices + frame_indices = padded_frame_indices + elif 'fps' in sample: # fps0.5, sequentially sample frames at 0.5 fps + output_fps = float(sample[3:]) + duration = float(vlen) / input_fps + delta = 1 / output_fps # gap between frames, this is also the clip length each frame represents + frame_seconds = np.arange(0 + delta / 2, duration + delta / 2, delta) + frame_indices = np.around(frame_seconds * input_fps).astype(int) + frame_indices = [e for e in frame_indices if e < vlen] + if max_num_frames > 0 and len(frame_indices) > max_num_frames: + frame_indices = frame_indices[:max_num_frames] + else: + raise ValueError + return frame_indices + + +def read_frames_decord(video_path, num_frames, sample='rand', fix_start=None, clip=None, min_num_frames=4): + video_reader = decord.VideoReader(video_path, num_threads=1) + vlen = len(video_reader) + fps = video_reader.get_avg_fps() + duration = vlen / float(fps) + if clip: + start, end = clip + duration = end - start + vlen = int(duration * fps) + start_index = int(start * fps) + + t_num_frames = np.random.randint(min_num_frames, num_frames + 1) + + frame_indices = get_frame_indices( + t_num_frames, vlen, sample=sample, fix_start=fix_start, + input_fps=fps + ) + if clip: + frame_indices = [f + start_index for f in frame_indices] + frames = video_reader.get_batch(frame_indices).asnumpy() # (T, H, W, C), np.uint8 + frames = [Image.fromarray(frames[i]) for i in range(frames.shape[0])] + return frames + + +def extract_frame_number(filename): + # Extract the numeric part from the filename using regular expressions + match = re.search(r'_(\d+).jpg$', filename) + return int(match.group(1)) if match else -1 + + +def sort_frames(frame_paths): + # Extract filenames from each path and sort by their numeric part + return sorted(frame_paths, key=lambda x: extract_frame_number(os.path.basename(x))) + + +def read_frames_folder(video_path, num_frames, sample='rand', fix_start=None, min_num_frames=4): + image_list = sort_frames(list(os.listdir(video_path))) + frames = [] + for image in image_list: + fp = os.path.join(video_path, image) + frame = Image.open(fp).convert('RGB') + frames.append(frame) + vlen = len(frames) + + t_num_frames = np.random.randint(min_num_frames, num_frames + 1) + + if vlen > t_num_frames: + frame_indices = get_frame_indices( + t_num_frames, vlen, sample=sample, fix_start=fix_start + ) + frames = [frames[i] for i in frame_indices] + return frames + + +class FrameSampler: + def __init__(self, max_num_frames=-1, min_num_frames=8, sample='rand'): + self.max_num_frames = max_num_frames + self.min_num_frames = min_num_frames + self.sample = sample + + def __call__(self, file_name): + fn = read_frames_folder if file_name.endswith('/') else read_frames_decord + frames = fn(file_name, num_frames=self.max_num_frames, min_num_frames=self.min_num_frames, sample=self.sample) + return frames + + +def decode_video_byte(video_bytes): + video_stream = io.BytesIO(video_bytes) + vr = decord.VideoReader(video_stream) + return vr + + +def sample_mp4_frames(mp4_p, n_frames=None, fps=None, return_frame_indices=False, random_sample=False): + if isinstance(mp4_p, str): + vr = decord.VideoReader(mp4_p, num_threads=1) + elif isinstance(mp4_p, decord.video_reader.VideoReader): + vr = mp4_p + video_fps = vr.get_avg_fps() # 获取视频的帧率 + video_duration = len(vr) / video_fps + if n_frames is not None: + if random_sample: + frame_indices = sorted(random.sample(range(len(vr)), n_frames)) + else: + frame_indices = np.linspace(0, len(vr)-1, n_frames, dtype=int).tolist() + else: + frame_indices = [int(i) for i in np.arange(0, len(vr)-1, video_fps/fps)] + frames = vr.get_batch(frame_indices).asnumpy() # 转换为 numpy 数组 + frames = [Image.fromarray(frame).convert("RGB") for frame in frames] + if not return_frame_indices: + return frames, video_duration + else: + return frames, video_duration, frame_indices + + +def sample_mp4_frames_by_indices(mp4_p, frame_indices: list): + if isinstance(mp4_p, str): + vr = decord.VideoReader(mp4_p, num_threads=1) + elif isinstance(mp4_p, decord.video_reader.VideoReader): + vr = mp4_p + # sample the frames in frame_indices + frames = vr.get_batch(frame_indices).asnumpy() # 转换为 numpy 数组 + frames = [Image.fromarray(frame).convert("RGB") for frame in frames] + return frames \ No newline at end of file diff --git a/data/vlm_dataset.py b/data/vlm_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..472ab5b5a6f21cc2680501920947e1595486594b --- /dev/null +++ b/data/vlm_dataset.py @@ -0,0 +1,361 @@ +# Copyright 2025 Bytedance Ltd. and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +import io +import json +import os +import traceback +from PIL import Image, ImageFile, PngImagePlugin + +import pyarrow.parquet as pq + +from .data_utils import pil_img2rgb +from .distributed_iterable_dataset import DistributedIterableDataset +from .parquet_utils import ( + apply_data_root_override, + get_parquet_data_paths, + init_arrow_pf_fs, + read_file_bytes, + strip_s3_scheme, +) + + +def _open_image(path): + # Local Image.open, or read bytes from S3 first (vlm_sft S3 streaming). + if path.startswith("s3://"): + return Image.open(io.BytesIO(read_file_bytes(path))) + return Image.open(path) + + +Image.MAX_IMAGE_PIXELS = 200000000 +ImageFile.LOAD_TRUNCATED_IMAGES = True +MaximumDecompressedSize = 1024 +MegaByte = 2 ** 20 +PngImagePlugin.MAX_TEXT_CHUNK = MaximumDecompressedSize * MegaByte + + +class SftJSONLIterableDataset(DistributedIterableDataset): + def __init__( + self, dataset_name, transform, tokenizer, frame_sampler=None, + jsonl_path_list=None, data_dir_list=None, num_used_data=None, + local_rank=0, world_size=1, num_workers=8, data_status=None, + shuffle_lines=False, shuffle_seed=0, use_instruction=False, + use_condition_instruction=False, use_target_instruction=False, num_condition_modalities=0, + strict_num_condition_modalities=False, + use_det_image=False, modality_registry=None, + ): + """ + jsonl_path_list: list of jsonl file paths + data_dir_list: list of image directories containing the images of each jsonl file + num_used_data: list of number of sampled data points for each jsonl + """ + super().__init__(dataset_name, local_rank, world_size, num_workers) + self.transform = transform + self.tokenizer = tokenizer + self.frame_sampler = frame_sampler + self.data_status = data_status + self.data_paths = self.get_data_paths( + jsonl_path_list, + data_dir_list, + num_used_data, + shuffle_lines, + shuffle_seed, + ) + self.set_epoch() + + def get_data_paths( + self, + jsonl_path_list, + data_dir_list, + num_used_data, + shuffle_lines, + shuffle_seed, + ): + data_paths = [] + for jsonl_path, image_dir, num_data_point in zip( + jsonl_path_list, data_dir_list, num_used_data + ): + # MODUS_DATA_ROOT redirects these to S3 (mirrors datasets/); unset → local. + jsonl_path = apply_data_root_override(jsonl_path) + image_dir = apply_data_root_override(image_dir) + if jsonl_path.startswith("s3://"): + raw_data = read_file_bytes(jsonl_path).decode("utf-8").splitlines(keepends=True) + else: + with open(jsonl_path, 'r') as f: + raw_data = f.readlines() + if shuffle_lines: + self.rng.seed(shuffle_seed) + self.rng.shuffle(raw_data) + raw_data = raw_data[:num_data_point] + data_paths.extend([(json_data, image_dir) for json_data in raw_data]) + return data_paths + + def change_format(self, data, num_images): + elements = [] + for conversation in data['conversations']: + if conversation['from'] == 'human': + if '' not in conversation['value']: + elements.append({ + 'type': 'text', + 'has_loss': 0, + 'text': conversation['value'], + }) + else: + text_list = conversation['value'].split('') + for idx, text in enumerate(text_list): + if text.strip() != '': + elements.append({ + 'type': 'text', + 'has_loss': 0, + 'text': text.strip(), + }) + if (idx != len(text_list) - 1) and (idx < num_images): + elements.append({'type': 'image',}) + elif conversation['from'] == 'gpt': + elements.append({ + 'type': 'text', + 'has_loss': 1, + 'text': conversation['value'], + }) + return elements + + def _build_sample(self, data_item, raw_images): + # Shared record→sample logic for the jsonl and parquet backends. + # Returns the sample dict (without data_indexes) or None when the + # record produces no loss tokens. + num_tokens = 0 + image_tensor_list = [] + text_ids_list = [] + sequence_plan = [] + + if raw_images: + for raw_image in raw_images: + image_tensor = self.transform(raw_image, img_num=len(raw_images)) + image_tensor_list.append(image_tensor) + height, width = image_tensor.shape[1:] + num_tokens += width * height // self.transform.stride ** 2 + + elements = self.change_format(data_item, len(image_tensor_list)) + + for item in elements: + if item['type'] == 'text': + text_data = item['text'] + text_ids = self.tokenizer.encode(text_data) + if len(text_ids) > 0: + text_ids_list.append(text_ids) + num_tokens += len(text_ids) + current_plan = { + 'type': 'text', + 'enable_cfg': 0, + 'loss': item['has_loss'], + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': 'text', + } + sequence_plan.append(current_plan) + elif item['type'] == 'image': + current_plan = { + 'type': 'vit_image', + 'enable_cfg': 0, + 'loss': 0, + 'special_token_loss': 0, + 'special_token_label': None, + 'modality_type': 'rgb', + } + sequence_plan.append(current_plan) + # NOTE: add VAE or not? + # firstly just ViT and check result, then maybe VAE but not very important for now. train two versions maybe. + + has_loss = [item['loss'] for item in sequence_plan] + if sum(has_loss) == 0: + print(f'No loss defined, skipped.') + return None + + return dict( + image_tensor_list=image_tensor_list, + text_ids_list=text_ids_list, + sequence_plan=sequence_plan, + num_tokens=num_tokens, + ) + + def __iter__(self): + data_paths_per_worker, worker_id = self.get_data_paths_per_worker() + if self.data_status is not None: + row_start_id = self.data_status[worker_id] + 1 + else: + row_start_id = 0 + + print( + f"rank-{self.local_rank} worker-{worker_id} dataset-{self.dataset_name}: " + f"resuming data at row#{row_start_id}" + ) + + while True: + data_paths_per_worker_ = data_paths_per_worker[row_start_id:] + for row_idx, (data, image_dir) in enumerate(data_paths_per_worker_, start=row_start_id): + try: + data_item = json.loads(data) + raw_images = None + if 'image' in data_item: + images = data_item['image'] if isinstance(data_item['image'], list) else [data_item['image']] + valid_paths = [] + for img_name in images: + if isinstance(img_name, str) and img_name.strip() != '': + img_path = os.path.join(image_dir, img_name) + # S3 paths can't be cheaply stat'd; include them and + # let the read below (in the try/except) drop a record + # whose image is missing. + if img_path.startswith("s3://") or os.path.isfile(img_path): + valid_paths.append(img_path) + if len(valid_paths) == 0: + raise FileNotFoundError("No valid image files found for this record.") + raw_images = [pil_img2rgb(_open_image(p)) for p in valid_paths] + elif 'video' in data_item: + vid_name = data_item['video'] + if not (isinstance(vid_name, str) and vid_name.strip() != ''): + raise ValueError("Invalid video name in record.") + vid_path = os.path.join(image_dir, vid_name) + if not os.path.isfile(vid_path): + raise FileNotFoundError("Video file not found for this record.") + raw_images = self.frame_sampler(vid_path) + special_tokens = '' * len(raw_images) + for item in data_item['conversations']: + if '