dev(narugo): migrate for classify (4214928c) · Commits · git-mirror / Imgutils

imgutils/generic/classify.py

+32 −12

Original line number	Diff line number	Diff line
		@@ -17,12 +17,13 @@ from threading import Lock
		from typing import Tuple, Optional, List, Dict, Callable

		import numpy as np
		import requests
		from PIL import Image
		from hfutils.operate import get_hf_client
		from hfutils.repository import hf_hub_repo_url
		from hfutils.utils import hf_fs_path, hf_normpath
		from huggingface_hub import hf_hub_download, HfFileSystem
		from huggingface_hub.errors import EntryNotFoundError
		from huggingface_hub.errors import EntryNotFoundError, OfflineModeIsEnabled

		from ..data import rgb_encode, ImageTyping, load_image
		from ..preprocess import create_pillow_transforms
		@@ -108,6 +109,7 @@ def _labels_scores_to_topk(labels: np.ndarray, scores: np.ndarray, topk: Optiona


		ImagePreprocessFunc = Callable[[Image.Image], Image.Image]
		_OFFLINE = object()


		class ClassifyModel:
		@@ -181,6 +183,7 @@ class ClassifyModel:
		"""
		with self._global_lock:
		if self._model_names is None:
		try:
		hf_fs = HfFileSystem(token=self._get_hf_token())
		self._model_names = [
		hf_normpath(os.path.dirname(os.path.relpath(item, self.repo_id)))
		@@ -190,6 +193,12 @@ class ClassifyModel:
		filename='*/model.onnx',
		))
		]
		except (
		requests.exceptions.ConnectionError,
		requests.exceptions.Timeout,
		OfflineModeIsEnabled,
		):
		self._model_names = _OFFLINE

		return self._model_names

		@@ -202,7 +211,10 @@ class ClassifyModel:

		:raises ValueError: If model name is not found in repository
		"""
		if model_name not in self.model_names:
		model_list = self.model_names
		if model_list is _OFFLINE:
		return # do not check when in offline mode
		if model_name not in model_list:
		raise ValueError(f'Unknown model {model_name!r} in model repository {self.repo_id!r}, '
		f'models {self.model_names!r} are available.')

		@@ -462,6 +474,10 @@ class ClassifyModel:
		# demo for classifier model
		_check_gradio_env()
		model_list = self.model_names
		if model_list is _OFFLINE and not default_model_name:
		raise EnvironmentError('You are in OFFLINE mode, '
		'you must assign a default model name to make this ui usable.')

		if not default_model_name:
		hf_client = get_hf_client(hf_token=self._get_hf_token())
		selected_model_name, selected_time = None, None
		@@ -481,7 +497,11 @@ class ClassifyModel:
		with gr.Row():
		gr_input_image = gr.Image(type='pil', label='Original Image')
		with gr.Row():
		if model_list is not _OFFLINE:
		gr_model = gr.Dropdown(model_list, value=default_model_name, label='Model')
		else:
		gr_model = gr.Dropdown([default_model_name], value=default_model_name, label='Model',
		interactive=False)
		gr_label_group = gr.Dropdown(list(self._open_label(default_model_name).keys()),
		value='default', label='Label Group')
		with gr.Row():

test/generic/test_classify.py

+32 −1

Original line number	Diff line number	Diff line
		from unittest.mock import patch

		import numpy as np
		import pytest
		from PIL import Image
		from huggingface_hub.utils import reset_sessions

		from imgutils.generic import classify_predict_score
		from imgutils.generic.classify import _open_models_for_repo_id, classify_predict_fmt
		@@ -12,7 +15,18 @@ def _release_model_after_run():
		try:
		yield
		finally:
		_open_models_for_repo_id('deepghs/timms_mobilenet').clear()
		_open_models_for_repo_id.cache_clear()


		@pytest.fixture()
		def clean_session():
		reset_sessions()
		_open_models_for_repo_id.cache_clear()
		try:
		yield
		finally:
		reset_sessions()
		_open_models_for_repo_id.cache_clear()


		@pytest.mark.unittest
		@@ -216,3 +230,20 @@ class TestGenericClassify:
		emb_sims = (emb_1 * emb_2).sum()
		assert emb_sims >= 0.99, 'Direction not match with expected embedding.'
		assert np.linalg.norm(results['embedding']) == pytest.approx(np.linalg.norm(expected_embedding))

		@patch("huggingface_hub.constants.HF_HUB_OFFLINE", True)
		def test_classify_predict_score_top5_offline_mode(self, clean_session):
		image = Image.open(get_testfile('png_640.png'))
		scores = classify_predict_score(
		image,
		repo_id='deepghs/timms_mobilenet',
		model_name='mobilenetv4_hybrid_medium.ix_e550_r384_in1k',
		topk=5,
		)
		assert scores == pytest.approx({
		'n02966687': 0.48493319749832153,
		'n03481172': 0.1228410005569458,
		'n04482393': 0.07170269638299942,
		'n04154565': 0.029927952215075493,
		'n03000684': 0.02070867270231247,
		}, abs=1e-3)