28 lines
887 B
Python
28 lines
887 B
Python
import torch
|
|
import numpy as np
|
|
from models.swin_transformer import SwinTransformer
|
|
# 构建输入
|
|
input_data = np.random.rand(1, 3, 224, 224).astype("float32")
|
|
|
|
|
|
swin_model_cfg_map = {
|
|
"swin_tiny_patch4_window7_224": {
|
|
"EMBED_DIM": 96,
|
|
"DEPTHS": [ 2, 2, 6, 2 ],
|
|
"NUM_HEADS": [ 3, 6, 12, 24 ],
|
|
"WINDOW_SIZE": 7,
|
|
}
|
|
}
|
|
|
|
model_name = "swin_tiny_patch4_window7_224"
|
|
torch_module = SwinTransformer(**swin_model_cfg_map[model_name])
|
|
torch_state_dict = torch.load("/home/andy/data/pretrained_models/{}.pth".format(model_name))["model"]
|
|
torch_module.load_state_dict(torch_state_dict)
|
|
# 设置为eval模式
|
|
torch_module.eval()
|
|
# 进行转换
|
|
from x2paddle.convert import pytorch2paddle
|
|
pytorch2paddle(torch_module,
|
|
save_dir="pd_{}".format(model_name),
|
|
jit_type="trace",
|
|
input_examples=[torch.tensor(input_data)]) |