text stringlengths 1 93.6k |
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def text_prompt_openai_pasta_pool_4part_ucla():
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print("Use text prompt openai pasta pool ucla")
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text_dict = {}
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num_text_aug = 5
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for ii in range(num_text_aug):
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if ii == 0:
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text_dict[ii] = torch.cat([clip.tokenize((pasta_list[ii])) for pasta_list in ucla_paste_text_map1])
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elif ii == 1:
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text_dict[ii] = torch.cat([clip.tokenize((','.join(pasta_list[0:2]))) for pasta_list in ucla_paste_text_map1])
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elif ii == 2:
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text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0] +','.join(pasta_list[2:4]))) for pasta_list in ucla_paste_text_map1])
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elif ii == 3:
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text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0] +','+ pasta_list[4])) for pasta_list in ucla_paste_text_map1])
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else:
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text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0]+','+','.join(pasta_list[5:]))) for pasta_list in ucla_paste_text_map1])
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classes = torch.cat([v for k, v in text_dict.items()])
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return classes, num_text_aug, text_dict
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# <FILESEP>
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# -*- coding: utf-8 -*-
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"""
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Class definition of YOLO_v3 style detection model on image and video
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"""
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import colorsys
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import os
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from timeit import default_timer as timer
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import numpy as np
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from keras import backend as K
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from keras.models import load_model
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from keras.layers import Input
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from PIL import Image, ImageFont, ImageDraw
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from yolo4.model import yolo_eval, yolo4_body
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from yolo4.utils import letterbox_image
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import os
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from keras.utils import multi_gpu_model
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class YOLO(object):
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_defaults = {
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"model_path": 'model_data/yolo4.h5',
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"anchors_path": 'model_data/yolo_anchors.txt',
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"classes_path": 'model_data/voc_classes.txt',
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"score" : 0.5,
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"iou" : 0.45,
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"model_image_size" : (608, 608),
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"gpu_num" : 1,
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}
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@classmethod
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def get_defaults(cls, n):
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if n in cls._defaults:
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return cls._defaults[n]
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else:
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return "Unrecognized attribute name '" + n + "'"
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def __init__(self, **kwargs):
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self.__dict__.update(self._defaults) # set up default values
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self.__dict__.update(kwargs) # and update with user overrides
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self.class_names = self._get_class()
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self.anchors = self._get_anchors()
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self.sess = K.get_session()
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self.boxes, self.scores, self.classes = self.generate()
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def _get_class(self):
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classes_path = os.path.expanduser(self.classes_path)
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with open(classes_path) as f:
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class_names = f.readlines()
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class_names = [c.strip() for c in class_names]
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return class_names
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def _get_anchors(self):
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anchors_path = os.path.expanduser(self.anchors_path)
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with open(anchors_path) as f:
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anchors = f.readline()
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anchors = [float(x) for x in anchors.split(',')]
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return np.array(anchors).reshape(-1, 2)
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def generate(self):
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model_path = os.path.expanduser(self.model_path)
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assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
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# Load model, or construct model and load weights.
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num_anchors = len(self.anchors)
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num_classes = len(self.class_names)
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is_tiny_version = num_anchors==6 # default setting
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try:
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self.yolo_model = load_model(model_path, compile=False)
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except:
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self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
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if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
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self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
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else:
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