Recreate Towhee image embedding with timm

[orion512]

In order to fully understand how Towhee works I wanted to recreate image embeddings with timm.
However I can't seem to get the same result and Towhee is built in a way unfamiliar to me thus I can't find the source code I am missing.

EXAMPLE_IMG = "path to some image"

Towhee code

towhee.glob(*[EXAMPLE_IMG]).exception_safe() \
                    .image_decode() \
                    .image_embedding.timm(model_name="resnet50") \
                    .drop_empty() \
                    .tensor_normalize() \
                    .to_list()

This gives me a result like below

[array([0.        , 0.01364289, 0.00837596, ..., 0.        , 0.        ,
        0.        ], dtype=float32)]

While running towhee I looked into ~/.cache/torch/hub/checkpoint for the model weights that were downloaded so that I could use the same.

resnet50_a1_0-14fe96d1.pth

Timm code

Now I tried to recreating the same result purely in Timm.

import timm
import torch

# read image
read_image = PILImage.open(EXAMPLE_IMG)
arr_image = cv2.cvtColor(np.array(read_image), cv2.COLOR_RGB2BGR).astype('float32')
image = torch.as_tensor(arr_image).transpose(2,0)[None]

PATH_RES50_WEIGHTS = "/my_path/resnet50_a1_0-14fe96d1.pth"

model_timm = timm.create_model('resnet50', pretrained=False, checkpoint_path=PATH_RES50_WEIGHTS)
model_timm.reset_classifier(0)
output_cust = model_timm(image)

# same as towhee tensor_normalize()
x = np.array(output_cust.tolist()[0])
gen_vector = x / np.linalg.norm(x, axis=0)

gen_vector

The result I get however is different

array([0.03503343, 0.01141444, 0.01016041, ..., 0.00766325, 0.02729872,
       0.00292635])

I would like to understand how I can recreate the same result as produce by Towhee (the same vector).

[orion512]

Answer here:
towhee-io/towhee#2215