5ea4960e65
Backup état complet après enregistrement vidéo démo de bout en bout. À utiliser comme point de référence pour la consolidation post-démo. Changements majeurs de la session 18-19 mai : - AIVA-URGENCE : page autonome avec preset URL + auto-focus chain - Workflow Demo_urgence_3_db : merge linux_db + steps AIVA + pause humaine NoMachine - Bypass LLM (static_result / static_text) dans replay_engine pour démos déterministes sans appel Ollama - Fix api_stream:3013 — replay_paused au premier polling /next - dag_execute : lift duration_ms vers top-level pour wait runtime - NPM bypass auth /aiva-urgence/ via location ^~ (proxy_host/10.conf hors git) - scripts/cancel-replays.sh — workaround Stop VWB qui ne purge pas la queue Anchors visuels (468) forcés dans le commit pour garantir restorabilité. DB workflows actuelle + ~12 .bak DB de la journée incluses. Sujets identifiés pour consolidation post-démo (TODO) : 1. Bug VWB recapture anchor ne régénère pas le PNG 2. Léa client accumule état mémoire (restart périodique requis) 3. Stop VWB ne purge pas la queue serveur (lien manquant vers /replay/cancel) 4. Bug coord client mss tronqué 2560x60 → mapping Y cassé 5. delay_before/delay_after ignorés au runtime (fix partiel duration_ms) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
"""
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Dump runtime des attributs et comportement effectif du processor
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Qwen3-VL-8B-Instruct. Script jetable, à supprimer après usage.
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Usage : python tools/probe_qwen3vl_processor.py
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"""
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from transformers import AutoProcessor
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from PIL import Image
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import torch
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MODEL_ID = "Qwen/Qwen3-VL-8B-Instruct"
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FIXTURE = "data/training/live_sessions/bg_DESKTOP-58D5CAC_windows/shots/heartbeat_1773792436.png"
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print("=" * 70)
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print("DUMP PROCESSOR :", MODEL_ID)
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print("=" * 70)
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proc = AutoProcessor.from_pretrained(MODEL_ID)
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ip = proc.image_processor
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# Section 1 — Attributs bruts
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print("\n--- ATTRIBUTS BRUTS ---")
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print("class:", type(ip).__name__)
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print("size:", ip.size)
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print("patch_size:", ip.patch_size)
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print("merge_size:", ip.merge_size)
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for attr in ['min_pixels', 'max_pixels', 'temporal_patch_size',
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'image_mean', 'image_std', 'do_resize', 'do_rescale',
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'rescale_factor', 'do_normalize', 'do_convert_rgb']:
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print(f"{attr}:", getattr(ip, attr, '<absent>'))
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# Section 2 — Comportement effectif sur fixture
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print("\n--- COMPORTEMENT EFFECTIF SUR FIXTURE ---")
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img = Image.open(FIXTURE)
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print(f"Image source : {img.size} (W×H)")
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out = ip(images=img, return_tensors='pt')
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print(f"Keys retournées : {list(out.keys())}")
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print(f"pixel_values shape : {out['pixel_values'].shape}")
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print(f"image_grid_thw : {out.get('image_grid_thw')}")
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# Section 3 — Reconstruction des dimensions resize
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print("\n--- RECONSTRUCTION DIMS RESIZE ---")
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grid = out.get('image_grid_thw')
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if grid is not None:
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grid = grid[0].tolist() # [t, h, w]
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factor = ip.patch_size * ip.merge_size
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H_resized = grid[1] * factor
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W_resized = grid[2] * factor
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print(f"grid_thw : t={grid[0]}, h={grid[1]}, w={grid[2]}")
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print(f"factor calculé (patch_size × merge_size) : {factor}")
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print(f"Dims resize reconstruites : {W_resized}×{H_resized} (W×H)")
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print(f"Dims source : {img.size}")
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print(f"Ratio resize : {W_resized / img.size[0]:.4f} (W), "
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f"{H_resized / img.size[1]:.4f} (H)")
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# Section 4 — Test borne haute pour comprendre min/max_pixels
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print("\n--- TEST BORNE HAUTE (image grande) ---")
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big_img = Image.new('RGB', (4096, 2560), color='white')
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big_out = ip(images=big_img, return_tensors='pt')
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big_grid = big_out['image_grid_thw'][0].tolist()
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factor = ip.patch_size * ip.merge_size
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print(f"Image source : {big_img.size}")
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print(f"grid_thw : {big_grid}")
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print(f"Dims resize : {big_grid[2] * factor}×{big_grid[1] * factor}")
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print(f"Pixels totaux après resize : "
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f"{big_grid[1] * factor * big_grid[2] * factor}")
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# Section 5 — Test borne basse (image petite)
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print("\n--- TEST BORNE BASSE (image petite) ---")
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small_img = Image.new('RGB', (128, 64), color='white')
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small_out = ip(images=small_img, return_tensors='pt')
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small_grid = small_out['image_grid_thw'][0].tolist()
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print(f"Image source : {small_img.size}")
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print(f"grid_thw : {small_grid}")
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print(f"Dims resize : {small_grid[2] * factor}×{small_grid[1] * factor}")
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print("\n" + "=" * 70)
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print("FIN DUMP")
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print("=" * 70)
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