File size: 4,405 Bytes
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import os
import sys
from PIL import Image
import torch
from huggingface_hub import login
def load_data(token, dataset_id="smmile/SMMILE-050525"):
print(f"Loading dataset '{dataset_id}'...")
try:
dataset = datasets.load_dataset(dataset_id, token=token)['train']
except Exception as e:
print(f"Error loading dataset. Make sure HF_TOKEN is valid and you have accepted the terms for the dataset if necessary.")
print(f"Error details: {e}")
try:
login(token=token)
dataset = datasets.load_dataset(dataset_id, token=token)['train']
except:
print("direct login also didn't work!")
sys.exit(1)
# Try to load images for each example
for idx, example in enumerate(dataset):
if example['image'] is None:
print(f'Image missing in the dataset. Please update the HuggingFace dataset.')
print(example['problem_id'], example['image_url'])
# Group examples by problem_id
problems_by_id = {}
for example in dataset:
pid = example['problem_id']
if pid not in problems_by_id:
problems_by_id[pid] = []
problems_by_id[pid].append(example)
for pid in problems_by_id:
problems_by_id[pid] = sorted(problems_by_id[pid], key=lambda x: x['order'])
return dataset, problems_by_id
def process_data(image_dir, token, dataset_id="smmile/SMMILE-050525"):
"""Loads dataset, handles missing images, and groups by problem_id."""
dataset, problems_by_id = load_data(token, dataset_id)
# Remove problem sets that do not have all images (either original or manually loaded)
icl_questions_with_images = []
skipped_problems = []
for problem_id, examples in problems_by_id.items():
has_all_images = True
missing_image_count = 0
for example in examples:
if example['image'] is None:
has_all_images = False
missing_image_count += 1
break
if has_all_images:
icl_questions_with_images.append(examples)
else:
skipped_problems.append(problem_id)
print(f'Total problems in dataset: {len(problems_by_id)}')
print(f'Number of problems included (all images present): {len(icl_questions_with_images)}')
print(f'Number of problems skipped (missing images): {len(skipped_problems)}')
return icl_questions_with_images
def check_device(device):
if device == "cpu":
print("WARNING: CUDA is not available. Running on CPU which will be very slow!")
else:
print(f"Using GPU: {torch.cuda.get_device_name(0)}")
print(f"CUDA Version: {torch.version.cuda}")
print(f"GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
# Optional: monitor GPU memory usage
def print_gpu_memory():
if torch.cuda.is_available():
print(f"GPU Memory allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
print(f"GPU Memory reserved: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
print_gpu_memory() # Initial memory usage
def print_gpu_memory(stage=""):
"""Prints current GPU memory usage."""
if torch.cuda.is_available():
print(f"--- GPU Memory Usage ({stage}) ---")
for i in range(torch.cuda.device_count()):
allocated = torch.cuda.memory_allocated(i) / 1e9
reserved = torch.cuda.memory_reserved(i) / 1e9
total = torch.cuda.get_device_properties(i).total_memory / 1e9
print(f"GPU {i}: Allocated: {allocated:.2f} GB, Reserved: {reserved:.2f} GB, Total: {total:.2f} GB")
print("-------------------------------")
else:
print("CUDA not available, cannot print GPU memory usage.")
def check_environment():
print("--- Environment Check ---")
print(f"Python version: {sys.version}")
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"Pytorch CUDA version: {torch.version.cuda}")
print(f"Number of GPUs available: {torch.cuda.device_count()}")
for i in range(torch.cuda.device_count()):
print(f" GPU {i}: {torch.cuda.get_device_name(i)}")
else:
print("CUDA available: False")
print("-------------------------") |