-
Notifications
You must be signed in to change notification settings - Fork 2
/
save_model.py
60 lines (51 loc) · 1.85 KB
/
save_model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
from __future__ import annotations
import PIL.Image
import PIL.ImageOps
import transformers
import bentoml
def download_model() -> int:
test_path = "./samples/NORMAL2-IM-1427-0001.jpeg"
extractor = transformers.ViTImageProcessor.from_pretrained(
"nickmuchi/vit-finetuned-chest-xray-pneumonia"
)
model = transformers.AutoModelForImageClassification.from_pretrained(
"nickmuchi/vit-finetuned-chest-xray-pneumonia"
)
# preprocess image
im = PIL.Image.open(test_path)
im = PIL.ImageOps.exif_transpose(im).convert("RGB")
outputs = model(**extractor(images=im, return_tensors="pt"))
top_k = len(model.config.id2label) # there are two classes, normal and pneumonia
probs = outputs.logits.softmax(-1)[0]
scores, ids = probs.topk(top_k)
print(
"Prediction:",
[
{"score": score, "label": model.config.id2label[id_]}
for score, id_ in zip(scores.tolist(), ids.tolist())
],
)
try:
bmodel = bentoml.transformers.get("vit-model-pneumonia")
print("Model already saved to model store:", bmodel)
except bentoml.exceptions.NotFound:
print(
"Saved model:",
bentoml.transformers.save_model(
"vit-model-pneumonia",
model,
metadata={"top_k": len(model.config.id2label)},
custom_objects={"id2label": model.config.id2label},
),
)
try:
bextractor = bentoml.transformers.get("vit-extractor-pneumonia")
print("Extractor already saved to model store:", bextractor)
except bentoml.exceptions.NotFound:
print(
"Saved model:",
bentoml.transformers.save_model("vit-extractor-pneumonia", extractor),
)
return 0
if __name__ == "__main__":
raise SystemExit(download_model())