Feature Extraction
Transformers
Safetensors
English
spectre
medical-imaging
ct-scan
3d
vision-transformer
self-supervised-learning
foundation-model
radiology
custom_code
Instructions to use cclaess/SPECTRE-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cclaess/SPECTRE-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cclaess/SPECTRE-Large", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cclaess/SPECTRE-Large", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,196 Bytes
8b41845 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import random
from typing import Iterable
from itertools import repeat
import torch
import numpy as np
MONAI_IMPORT_ERROR = None
try:
import monai
except ImportError as e:
monai = None # type: ignore
MONAI_IMPORT_ERROR = e
def fix_random_seeds(seed: int = 31):
"""
Fix random seeds.
"""
if MONAI_IMPORT_ERROR is not None:
raise ImportError(
"MONAI is required to use fix_random_seeds but not installed. "
"Please install MONAI to use this function."
) from MONAI_IMPORT_ERROR
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
monai.utils.set_determinism(seed=seed)
def _ntuple(n: int):
"""
Helper function to create n-tuple.
"""
def parse(x):
if isinstance(x, Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
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