regnety_032.ra_in1k 的模型卡

import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
import torch
import torch_npu
import argparse
from openmind import pipeline, is_torch_npu_available
from PIL import Image
import requests
import timm

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default=None,
    )
    args = parser.parse_args()
    return args



def main():
    current_path = os.getcwd()
    print("Current path:",current_path)

    args = parse_args()
    model_path = args.model_name_or_path
    
    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"
    
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    img = Image.open(requests.get(url, stream=True).raw)
    
    # model = timm.create_model(model_path, pretrained=True).to(device)
    print("model_path:", model_path)
    model = timm.create_model('regnety_032.ra_in1k', pretrained=True).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).to(device))  # unsqueeze single image into batch of 1
    top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
    print(top5_class_indices)

if __name__=="__main__":
    main()

import torch
import torch_npu
import argparse
from openmind import pipeline, is_torch_npu_available
from PIL import Image
import requests
import timm

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default=None,
    )
    args = parser.parse_args()
    return args



def main():
    args = parse_args()
    model_path = args.model_name_or_path
    
    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"
    
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    img = Image.open(requests.get(url, stream=True).raw)
    
    model = timm.create_model(model_path, pretrained=True).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).to(device))  # unsqueeze single image into batch of 1
    for o in output:
        # print shape of each feature map in output
        # e.g. for swin_base_patch4_window7_224 (NHWC output)
        #  torch.Size([1, 56, 56, 128])
        #  torch.Size([1, 28, 28, 256])
        #  torch.Size([1, 14, 14, 512])
        #  torch.Size([1, 7, 7, 1024])
        # e.g. for swinv2_cr_small_ns_224 (NCHW output)
        #  torch.Size([1, 96, 56, 56]) 
        #  torch.Size([1, 192, 28, 28])
        #  torch.Size([1, 384, 14, 14])
        #  torch.Size([1, 768, 7, 7])
        print(o.shape)

if __name__=="__main__":
    main()

import torch
import torch_npu
import argparse
from openmind import pipeline, is_torch_npu_available
from PIL import Image
import requests
import timm

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default=None,
    )
    args = parser.parse_args()
    return args



def main():
    args = parse_args()
    model_path = args.model_name_or_path
    
    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"
    
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    img = Image.open(requests.get(url, stream=True).raw)
    
    model = timm.create_model(model_path, pretrained=True).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.forward_features(transforms(img).unsqueeze(0).to(device))
    # output is unpooled, a (1, 2240, 7, 7) shaped tensor
    

    output = model.forward_head(output, pre_logits=True)
    # output is a (1, num_features) shaped tensor

@InProceedings{Radosavovic2020,
  title = {Designing Network Design Spaces},
  author = {Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Doll{'a}r},
  booktitle = {CVPR},
  year = {2020}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://modelers.cn/datasets/YunnanAICC/pytorch-image-models}}
}