def __init__(self, trainable=False, batch_size=32, epochs=3, **kwargs):
super(TFMobileNet, self).__init__(**kwargs)
self.image_width, self.image_height = 224, 224
self.trainable = trainable
self.batch_size = batch_size
self.epochs = epochs
self.feature_extractor_layer = hub.KerasLayer(
feature_extractor_model,
input_shape=(self.image_width, self.image_height, 3),
trainable=trainable)
self.from_name, self.to_name, self.value, self.labels_in_config = get_single_tag_keys(
self.parsed_label_config, 'Choices', 'Image')
self.labels = tf.convert_to_tensor(sorted(self.labels_in_config))
num_classes = len(self.labels_in_config)
self.model = tf.keras.Sequential(
[self.feature_extractor_layer,
tf.keras.layers.Dense(num_classes)])
self.model.summary()
if self.train_output:
model_file = self.train_output['model_file']
logger.info('Restore model from ' + model_file)
# Restore previously saved weights
self.model.load_weights(self.train_output['model_file'])
def __init__(self, freeze_extractor=False, **kwargs):
super(ImageClassifierAPI, self).__init__(**kwargs)
self.from_name, self.to_name, self.value, self.classes = get_single_tag_keys(
self.parsed_label_config, 'Choices', 'Image')
self.freeze_extractor = freeze_extractor
if self.train_output:
self.classes = self.train_output['classes']
self.model = ImageClassifier(len(self.classes), freeze_extractor)
self.model.load(self.train_output['model_path'])
else:
self.model = ImageClassifier(len(self.classes), freeze_extractor)
def __init__(self, freeze_extractor=False, **kwargs):
# don't forget to call base class constructor
super(animalClassifier, self).__init__(**kwargs)
self.from_name, self.to_name, self.value, self.classes = get_single_tag_keys(
self.parsed_label_config, 'Choices', 'Image')
if self.train_output:
self.classes = self.train_output['classes']
self.model = ImageClassifier(len(self.classes))
self.model.load(self.train_output['model_path']
) # save the checkpoint of pretrained weight
else:
self.model = ImageClassifier(len(self.classes))
def __init__(self,
config_file,
checkpoint_file,
labels_file=None,
score_threshold=0.3,
device="cpu",
**kwargs):
"""
Load MMDetection model from config and checkpoint into memory.
(Check https://mmdetection.readthedocs.io/en/v1.2.0/GETTING_STARTED.html#high-level-apis-for-testing-images)
Optionally set mappings from COCO classes to target labels
:param config_file: Absolute path to MMDetection config file (e.g. /home/user/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py)
:param checkpoint_file: Absolute path MMDetection checkpoint file (e.g. /home/user/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth)
:param labels_file: file with mappings from COCO labels to custom labels {"airplane": "Boeing"}
:param score_threshold: score threshold to wipe out noisy results
:param device: device (cpu, cuda:0, cuda:1, ...)
:param kwargs:
"""
super(MMDetection, self).__init__(**kwargs)
self.config_file = config_file
self.checkpoint_file = checkpoint_file
self.labels_file = labels_file
if self.labels_file and os.path.exists(self.labels_file):
self.label_map = json_load(self.labels_file)
else:
self.label_map = {}
(
self.from_name,
self.to_name,
self.value,
self.labels_in_config,
) = get_single_tag_keys(self.parsed_label_config, "RectangleLabels",
"Image")
schema = list(self.parsed_label_config.values())[0]
self.labels_in_config = set(self.labels_in_config)
# Collect label maps from `predicted_values="airplane,car"` attribute in <Label> tag
self.labels_attrs = schema.get("labels_attrs")
if self.labels_attrs:
for label_name, label_attrs in self.labels_attrs.items():
for predicted_value in label_attrs.get("predicted_values",
"").split(","):
self.label_map[predicted_value] = label_name
print("Load new model from: ", config_file, checkpoint_file)
self.model = init_detector(config_file, checkpoint_file, device=device)
self.score_thresh = score_threshold
def __init__(self, num_responses=5, model='microsoft/DialoGPT-small', **kwargs):
super(DialoGPTSimpleGenerator, self).__init__(**kwargs)
self.num_return_sequences = num_responses
self.top_k = num_responses
self.model_name = model
self.from_name, self.to_name, self.value, self.labels_in_config = get_single_tag_keys(
self.parsed_label_config, 'TextArea', 'Paragraphs')
config = etree.fromstring(self.label_config)
paragraphs = config.find('.//Paragraphs')
self.name_key = paragraphs.get('nameKey') or paragraphs.get('namekey') or 'author'
self.text_key = paragraphs.get('textKey') or paragraphs.get('textkey') or 'text'
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
def __init__(self, freeze_extractor=False, **kwargs):
super(ObjectDetectionAPI, self).__init__(**kwargs)
self.from_name, self.to_name, self.value, self.classes =\
get_single_tag_keys(self.parsed_label_config, 'RectangleLabels', 'Image')
self.freeze_extractor = freeze_extractor
self.model = Detectron2LayoutModel(
config_path=
"https://www.dropbox.com/s/ta4777i1g1jjj18/config.yml?dl=1",
model_path=
"https://www.dropbox.com/s/f261qar6f75b9c0/model_final.pth?dl=1",
label_map={
1: "title",
2: "address",
3: "text",
4: "number"
},
extra_config=[
"TEST.DETECTIONS_PER_IMAGE", 150,
"MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.5,
"MODEL.ROI_HEADS.NMS_THRESH_TEST", 0.75
])
