inception_resnet_v2.tf_in1k 模型卡片

from PIL import Image
import timm
from timm.models.efficientnet import _cfg
from openmind import is_torch_npu_available
import torch_npu
import torch
import argparse
from openmind_hub import snapshot_download

if is_torch_npu_available():
    device = "npu:0"
else:
    device = "cpu"

def parse_args():
        parser = argparse.ArgumentParser()
        parser.add_argument( "--model_name_or_path", type=str, default="inception_resnet_v2.tf_in1k", )
        args = parser.parse_args()
        return args
args = parse_args()

if args.model_name_or_path:
	model_path = args.model_name_or_path
else:
	model_path = snapshot_download(
		"CICC/inception_resnet_v2.tf_in1k",
		revision="main",
		resume_download=True,
		ignore_patterns=["*.h5", "*.ot", "	*.msgpack"]
	)
# load tokenizer
img = Image.open('./beignets-task-guide.png')

config = _cfg(url='', file='model.safetensors')
model = timm.create_model("inception_resnet_v2.tf_in1k", pretrained=True, pretrained_cfg=config).to(device)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0).npu())  # unsqueeze single image into batch of 1
print(output)

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 64, 147, 147])
    #  torch.Size([1, 192, 71, 71])
    #  torch.Size([1, 288, 35, 35])
    #  torch.Size([1, 768, 17, 17])
    #  torch.Size([1, 2048, 8, 8])

    print(o.shape)

@article{Szegedy2016Inceptionv4IA,
  title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
  author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alexander A. Alemi},
  journal={ArXiv},
  year={2016},
  volume={abs/1602.07261}
}