ExplainableVQA / predownload_models.py

Add files using upload-large-folder tool

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"""
Pre-download all HuggingFace model repos used by:
  - MedEvalKit/eval_scripts/grounding/diff_region/*.sh (grounding diff-region analysis)
Downloads go to a custom HuggingFace cache directory
(/home/jinhua/ExplainableVQA/checkpoint/models) instead of the default
~/.cache/huggingface. As long as HF_HOME points to this same directory at
load time, `from_pretrained(repo_id, ...)` will transparently hit this cache
instead of re-downloading - it behaves exactly like the normal HF cache,
just relocated.
Each repo is retried several times with resume (snapshot_download skips files
already fully downloaded and resumes partial ones), since mirrors like
hf-mirror.com often time out near the end of large files.
Usage:
    python predownload_models_to_checkpoint.py
"""

import os
import time

# Custom cache location - from_pretrained(repo_id) will read from here as
# long as HF_HOME is set to the same path when the model is actually loaded.
#os.environ.setdefault("HF_HOME", "/home/jinhua/ExplainableVQA/checkpoint/models")

# Use the hf-mirror.com mirror (much faster from China than huggingface.co directly).
#os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")

# Raise the default 10s timeout - hf-mirror.com often stalls on the final
# etag/HEAD check for large files.
os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", "60")

# Disable the Xet backend: it generates a fresh random temp file on every
# resume instead of continuing the previous .incomplete file, so interrupted
# downloads restart from 0 instead of resuming.
os.environ.setdefault("HF_HUB_DISABLE_XET", "1")

from huggingface_hub import snapshot_download

REPO_IDS = [
    # ---- MedEvalKit/eval_scripts/grounding/diff_region/*.sh ----
    # "Qwen/Qwen2.5-VL-3B-Instruct",                 # Qwen2.5VL-3B
    # "Qwen/Qwen2.5-VL-7B-Instruct",                 # Qwen2.5VL-7B
    # "Qwen/Qwen2.5-VL-32B-Instruct",                # Qwen2.5VL-32B
    # "llava-hf/llava-1.5-7b-hf",                    # LLaVA-7B
    # "llava-hf/llava-1.5-13b-hf",                   # LLaVA-13B
    # "microsoft/llava-med-v1.5-mistral-7b",         # LLaVA-Med-7B
    # "FreedomIntelligence/HuatuoGPT-Vision-7B",     # HuatuoGPT-V-7B
    # "FreedomIntelligence/HuatuoGPT-Vision-34B",    # HuatuoGPT-V-34B
    # "google/medgemma-4b-it",                       # MedGemma-4B-IT
    # "google/medgemma-27b-it",                      # MedGemma-27B-IT (gated repo, multimodal variant)
    "Qwen/Qwen3-VL-2B-Instruct",                     # Qwen3VL-2B
    "Qwen/Qwen3-VL-4B-Instruct",                     # Qwen3VL-4B
    "Qwen/Qwen3-VL-8B-Instruct",                     # Qwen3VL-8B
]

DATASET_REPO_IDS = []

MAX_RETRIES = 10
RETRY_DELAY_SECONDS = 5


def download_with_retries(repo_id, repo_type="model"):
    for attempt in range(1, MAX_RETRIES + 1):
        try:
            snapshot_download(repo_id=repo_id, repo_type=repo_type, resume_download=True)
            return True
        except Exception as e:
            print(f"    attempt {attempt}/{MAX_RETRIES} failed: {e}")
            if attempt < MAX_RETRIES:
                time.sleep(RETRY_DELAY_SECONDS)
    return False


def main():
    #print(f"HF_HOME = {os.environ['HF_HOME']}")

    failed = []
    for repo_id in REPO_IDS:
        print(f"==> Downloading {repo_id}")
        if not download_with_retries(repo_id):
            failed.append(repo_id)
            print(f"    GAVE UP: {repo_id}")

    for repo_id in DATASET_REPO_IDS:
        print(f"==> Downloading dataset {repo_id}")
        if not download_with_retries(repo_id, repo_type="dataset"):
            failed.append(repo_id)
            print(f"    GAVE UP: {repo_id}")

    print("\n==== Summary ====")
    if failed:
        print("Failed repos (re-run this script to resume them):")
        for repo_id in failed:
            print(f"  - {repo_id}")
    else:
        print("All repos downloaded successfully.")


if __name__ == "__main__":
    main()

	"""
	Pre-download all HuggingFace model repos used by:
	- MedEvalKit/eval_scripts/grounding/diff_region/*.sh (grounding diff-region analysis)

	Downloads go to a custom HuggingFace cache directory
	(/home/jinhua/ExplainableVQA/checkpoint/models) instead of the default
	~/.cache/huggingface. As long as HF_HOME points to this same directory at
	load time, `from_pretrained(repo_id, ...)` will transparently hit this cache
	instead of re-downloading - it behaves exactly like the normal HF cache,
	just relocated.

	Each repo is retried several times with resume (snapshot_download skips files
	already fully downloaded and resumes partial ones), since mirrors like
	hf-mirror.com often time out near the end of large files.

	Usage:
	python predownload_models_to_checkpoint.py
	"""

	import os
	import time

	# Custom cache location - from_pretrained(repo_id) will read from here as
	# long as HF_HOME is set to the same path when the model is actually loaded.
	#os.environ.setdefault("HF_HOME", "/home/jinhua/ExplainableVQA/checkpoint/models")

	# Use the hf-mirror.com mirror (much faster from China than huggingface.co directly).
	#os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")

	# Raise the default 10s timeout - hf-mirror.com often stalls on the final
	# etag/HEAD check for large files.
	os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", "60")

	# Disable the Xet backend: it generates a fresh random temp file on every
	# resume instead of continuing the previous .incomplete file, so interrupted
	# downloads restart from 0 instead of resuming.
	os.environ.setdefault("HF_HUB_DISABLE_XET", "1")

	from huggingface_hub import snapshot_download

	REPO_IDS = [
	# ---- MedEvalKit/eval_scripts/grounding/diff_region/*.sh ----
	# "Qwen/Qwen2.5-VL-3B-Instruct", # Qwen2.5VL-3B
	# "Qwen/Qwen2.5-VL-7B-Instruct", # Qwen2.5VL-7B
	# "Qwen/Qwen2.5-VL-32B-Instruct", # Qwen2.5VL-32B
	# "llava-hf/llava-1.5-7b-hf", # LLaVA-7B
	# "llava-hf/llava-1.5-13b-hf", # LLaVA-13B
	# "microsoft/llava-med-v1.5-mistral-7b", # LLaVA-Med-7B
	# "FreedomIntelligence/HuatuoGPT-Vision-7B", # HuatuoGPT-V-7B
	# "FreedomIntelligence/HuatuoGPT-Vision-34B", # HuatuoGPT-V-34B
	# "google/medgemma-4b-it", # MedGemma-4B-IT
	# "google/medgemma-27b-it", # MedGemma-27B-IT (gated repo, multimodal variant)
	"Qwen/Qwen3-VL-2B-Instruct", # Qwen3VL-2B
	"Qwen/Qwen3-VL-4B-Instruct", # Qwen3VL-4B
	"Qwen/Qwen3-VL-8B-Instruct", # Qwen3VL-8B
	]

	DATASET_REPO_IDS = []

	MAX_RETRIES = 10
	RETRY_DELAY_SECONDS = 5


	def download_with_retries(repo_id, repo_type="model"):
	for attempt in range(1, MAX_RETRIES + 1):
	try:
	snapshot_download(repo_id=repo_id, repo_type=repo_type, resume_download=True)
	return True
	except Exception as e:
	print(f" attempt {attempt}/{MAX_RETRIES} failed: {e}")
	if attempt < MAX_RETRIES:
	time.sleep(RETRY_DELAY_SECONDS)
	return False


	def main():
	#print(f"HF_HOME = {os.environ['HF_HOME']}")

	failed = []
	for repo_id in REPO_IDS:
	print(f"==> Downloading {repo_id}")
	if not download_with_retries(repo_id):
	failed.append(repo_id)
	print(f" GAVE UP: {repo_id}")

	for repo_id in DATASET_REPO_IDS:
	print(f"==> Downloading dataset {repo_id}")
	if not download_with_retries(repo_id, repo_type="dataset"):
	failed.append(repo_id)
	print(f" GAVE UP: {repo_id}")

	print("\n==== Summary ====")
	if failed:
	print("Failed repos (re-run this script to resume them):")
	for repo_id in failed:
	print(f" - {repo_id}")
	else:
	print("All repos downloaded successfully.")


	if __name__ == "__main__":
	main()