Traceback (most recent call last):
File "/root/autodl-tmp/EVA/EVA-CLIP/rei/test.py", line 2, in
from eva_clip import create_model_and_transforms, get_tokenizer
File "/root/autodl-tmp/EVA/EVA-CLIP/rei/eva_clip/init.py", line 2, in
from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer
File "/root/autodl-tmp/EVA/EVA-CLIP/rei/eva_clip/factory.py", line 12, in
from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,
File "/root/autodl-tmp/EVA/EVA-CLIP/rei/eva_clip/model.py", line 21, in
from .eva_vit_model import EVAVisionTransformer
File "/root/autodl-tmp/EVA/EVA-CLIP/rei/eva_clip/eva_vit_model.py", line 15, in
from .transformer import PatchDropout
File "/root/autodl-tmp/EVA/EVA-CLIP/rei/eva_clip/transformer.py", line 31, in
import xformers.ops as xops
File "/root/miniconda3/lib/python3.12/site-packages/xformers/ops/init.py", line 8, in
from .fmha import (
File "/root/miniconda3/lib/python3.12/site-packages/xformers/ops/fmha/init.py", line 10, in
from . import (
File "/root/miniconda3/lib/python3.12/site-packages/xformers/ops/fmha/triton_splitk.py", line 548, in
_get_splitk_kernel(num_groups)
File "/root/miniconda3/lib/python3.12/site-packages/xformers/ops/fmha/triton_splitk.py", line 503, in _get_splitk_kernel
_fwd_kernel_splitK_unrolled = unroll_varargs(_fwd_kernel_splitK, N=num_groups)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/xformers/triton/vararg_kernel.py", line 166, in unroll_varargs
jitted_fn = triton.jit(fn)
^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/triton/runtime/jit.py", line 882, in jit
return decorator(fn)
^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/triton/runtime/jit.py", line 871, in decorator
return JITFunction(
^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/triton/runtime/jit.py", line 704, in init
self.starting_line_number = inspect.getsourcelines(fn)[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/inspect.py", line 1270, in getsourcelines
return getblock(lines[lnum:]), lnum + 1
^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/inspect.py", line 1237, in getblock
for _token in tokens:
File "/root/miniconda3/lib/python3.12/tokenize.py", line 582, in _generate_tokens_from_c_tokenizer
raise TokenError(msg, (e.lineno, e.offset)) from None
tokenize.TokenError: ('unterminated string literal (detected at line 1122)', (1122, 1))
import torch
from eva_clip import create_model_and_transforms, get_tokenizer
from PIL import Image
model_name = "EVA02-CLIP-B-16"
pretrained = "eva_clip" # or "/path/to/EVA02_CLIP_B_psz16_s8B.pt"
image_path = "./cat.jpg"
caption = ["a diagram", "a dog", "a cat"]
device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = create_model_and_transforms(model_name, pretrained, force_custom_clip=True)
tokenizer = get_tokenizer(model_name)
model = model.to(device)
image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
text = tokenizer(["a diagram", "a dog", "a cat"]).to(device)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs) # prints: [[0.8275, 0.1372, 0.0352]]
I got RuntimeError: linalg.vector_norm: Expected a floating point or complex tensor as input. Got Long
baaivision
