def Create_Input_Files(): # Create input files (along with word map) create_input_files( dataset='coco', karpathy_json_path= '/Users/tangruixiang/Desktop/Fairness_Dataset/code/COCO/Karpathy_split/coco/dataset.json', image_folder='/Volumes/Yuening\ Passport/rxtang/coco/images', captions_per_image=5, min_word_freq=5, output_folder='processed_data', max_len=50)
def run_model(input_summary_file, output_summary_file, out_crop_path, result_output, gl_epochs, crop_type=None, file_name=None, run_path_absolute=r'C:\DSSAT47', glue_flag=1, simulation_model='B'): utils.create_input_files(input_summary_file, output_summary_file) utils.create_xfile(os.path.join(output_summary_file, 'xfile.json'), out_crop_path, crop_type, file_name) for fn in os.listdir(out_crop_path): if os.path.splitext(fn)[-1] in list(SUFFIXES.values()): dssat = DSSAT(os.path.join(out_crop_path, fn), run_path_absolute) dssat(result_output, gl_epochs, glue_flag, simulation_model)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='flickr8k', karpathy_json_path='/scratch/nikolai/dataset_flickr8k.json', image_folder='/scratch/nikolai/Flickr8K', captions_per_image=5, min_word_freq=5, output_folder='/scratch/nikolai/out_data', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files aling with word map create_input_files(dataset = 'coco', karpathy_json_path = './caption_datasets/dataset_coco.json', \ image_folder = './images', captions_per_image = 5, min_word_freq = 5, \ output_folder = './data', max_len = 50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='coco', karpathy_json_path='../splits/dataset_coco.json', image_folder='/datasets/COCO-2015', captions_per_image=5, min_word_freq=5, output_folder='/datasets/home/50/650/agokhale/285project/a-PyTorch-Tutorial-to-Image-Captioning/data_generated', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='coco', karpathy_json_path='caption_data/dataset_coco.json', image_folder='/caption data/', captions_per_image=5, min_word_freq=5, output_folder='caption_data/', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='flickr8kzh', karpathy_json_path='../data/flickr8kzh.json', image_folder='../data/flickr8k_images/', captions_per_image=5, min_word_freq=5, output_folder='../prepared_data/', max_len=50, char_based=False)
from utils import create_input_files, train_word2vec_model if __name__ == '__main__': create_input_files( csv_folder='./data', output_folder='./outdata', # sentence_limit=15, # word_limit=20, # min_word_count=5) sentence_limit=40, word_limit=200, min_word_count=3, label_columns=[ 1, 2, 3, 4, 5 ] # # 'news', 'is_relevant', 'Armed Assault', 'Bombing/Explosion', 'Kidnapping', 'Other' ) train_word2vec_model(data_folder='./outdata', algorithm='skipgram')
from utils import txt_2_json, create_input_files from config import * import os.path if __name__ == '__main__': # convert custom txt caption to karpathy json format, only once if not os.path.isfile(caption_json_path): txt_2_json() # Create input files (along with word map) create_input_files()
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='coco', karpathy_json_path='./crawlers/output/JSON.json', image_folder='', captions_per_image=5, min_word_freq=3, output_folder='./output', max_len=50)
help="caption datasets type,i.e.,'coco', 'flickr8k', 'flickr30k' ") parser.add_argument("--karpathy_json_path", default='data/dataset_coco.json', type=str, required=True, help="annotation json file' ") parser.add_argument( "--image_folder", default='data/', type=str, required=True, help="the directory containing the train2014 and val2014 image folders" ) parser.add_argument("--output_folder", default='data/', type=str, required=True, help="path for saving processed data") parser.add_argument("--captions_per_image", default=5, type=int) parser.add_argument("--min_word_freq", default=5, type=int) parser.add_argument("--max_len", default=50, type=int) args = parser.parse_args() create_input_files(dataset=args.dataset, karpathy_json_path=args.karpathy_json_path, image_folder=args.image_folder, captions_per_image=args.captions_per_image, min_word_freq=args.min_word_freq, output_folder=args.output_folder, max_len=args.max_len)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='coco', karpathy_json_path='scratch/project/dataset_coco.json', image_folder='scratch/project/', captions_per_image=5, min_word_freq=5, output_folder='scratch/project/preprocessed_data/', max_len=50)
from utils import create_input_files, train_word2vec_model if __name__ == '__main__': create_input_files(csv_folder='./yahoo_answers_csv', output_folder='./data', sentence_limit=15, word_limit=20, min_word_count=5) train_word2vec_model(data_folder='./data', algorithm='skipgram')
from utils import create_input_files import _config if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset=_config.dataset, karpathy_json_path=_config.karpathy_json_path, image_folder=_config.image_foldr_path, captions_per_image=_config.captions_per_image, min_word_freq=_config.min_word_freq, output_folder=_config.output_folder, max_len=_config.max_sentence_length)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) address = '' create_input_files(dataset='coco', karpathy_json_path=address + 'karpathy/dataset_coco.json', image_folder=address, captions_per_image=5, min_word_freq=5, output_folder=address + 'out', max_len=50)
from utils import create_input_files from imageio import imread if __name__ == '__main__': # img = imread('./datasets/Flicker8k_Dataset/2513260012_03d33305cf.jpg') create_input_files('flickr8k', './datasets/dataset_flickr8k.json', './datasets/Flicker8k_Dataset', captions_per_image=5, min_word_freq=5, output_folder='./datasets/caption_data', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) trainCaptionPath = "../datasets/coco2014/trainval_coco2014_captions/captions_train2014.json" valCaptionPath = "../datasets/coco2014/trainval_coco2014_captions/captions_val2014.json" trainImagePath = "../datasets/coco2014/" valImagePath = "../datasets/coco2014/2014/" captionPath = "../scratch/dataset_coco.json" create_input_files(dataset='coco', karpathy_json_path=captionPath, image_folder=trainImagePath, captions_per_image=5, min_word_freq=5, output_folder='../scratch/caption data/', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='coco', karpathy_json_path= '/home/work/jiangshuai/image_caption/data/dataset_coco.json', image_folder='/home/work/jiangshuai/image_caption/data/', captions_per_image=5, min_word_freq=5, output_folder='./data/', max_len=50)
from utils import create_input_files max_caption_length = 50 if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='flickr8k', karpathy_json_path="data/Flicker8k_Dataset/karpathy_flickr8k.json", image_folder='data/Flicker8k_Dataset/Flicker8k_images/', captions_per_image=5, min_word_freq=5, output_folder='data/', max_len=max_caption_length, bert_model_name='bert-base-cased')
from utils import create_input_files import os envget = os.environ.get if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='coco', karpathy_json_path=os.path.join(envget('HOME'), 'data/captiondata/dataset_coco.json'), image_folder=os.path.join(envget('HOME'), 'data/captiondata/'), captions_per_image=5, min_word_freq=5, output_folder=os.path.join(envget('HOME'), 'data/captiondata/'), max_len=50)
#train = rd.read_input_file(c.TRAIN_PATH, c.EMOTION_HEADER, c.ATTRIBUTES, c.CLEAN) #test = rd.read_input_file(c.TEST_PATH, c.EMOTION_HEADER, c.ATTRIBUTES, c.CLEAN) train = pd.read_csv("./data/nlp_train.csv") test = pd.read_csv("./data/nlp_test.csv") train = train.dropna() test = test.dropna() train_temp = train.loc[:, ['anger', 'body']] test_temp = test.loc[:, ['anger', 'body']] train_temp.to_csv('./data/train.csv', index=False, header=False) test_temp.to_csv('./data/test.csv', index=False, header=False) create_input_files(csv_folder='./data', output_folder='./outdata', # sentence_limit=15, # word_limit=20, # min_word_count=5) sentence_limit=30, word_limit=100, min_word_count=10) train_word2vec_model(data_folder='./outdata', algorithm='skipgram') file1 = open("label.txt", "a") file2 = open("result.txt", "a") file2.close() for i in c.LABELS: print(i) file1.write(i) file1.write('\n') os.system('python3 train.py') os.system('python3 eval.py')
from utils import create_input_files if __name__ == '__main__': create_input_files(dataset = 'flickr8k', karpathy_json_path = '../../Datasets/train_valid_test_splits/dataset_flickr8k.json', image_folder = '../../Datasets/Flickr8K/Flicker8k_Dataset/', captions_per_image = 5, min_word_freq = 5, ouput_folder = './data') # This will be in /netscratch/deshmukh
from utils import create_input_files import os if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='flickr8k', karpathy_json_path=os.path.join( "dataset_flickr8k.json" ), #'caption_datasets/dataset_flicker8k.json', image_folder=os.path.join( "Flicker8k_Dataset"), #'Flicker8k_Dataset/Flicker8k_Dataset/', captions_per_image=5, min_word_freq=5, output_folder=os.path.join("Flicker8k_Dataset"), max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) json_path = '/home/jithin/datasets/imageCaptioning/captions/dataset_flickr8k.json' img_folder = '/home/jithin/datasets/imageCaptioning/flicker8k/Flicker8k_Dataset' output_folder = './pre_processed' create_input_files(dataset='flickr8k', karpathy_json_path=json_path, image_folder=img_folder, captions_per_image=5, min_word_freq=5, output_folder=output_folder, max_len=50)
from utils import create_input_files from macro import IMAGE_FOLDER, OUTPUT_FOLDER if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='flickr30k', # 'coco', karpathy_json_path= 'karpathy_split_json_path/dataset_flickr30k.json', # where data split is divided image_folder=IMAGE_FOLDER, # '/media/ssd/caption data/', captions_per_image=5, min_word_freq=5, output_folder=OUTPUT_FOLDER, # ''/media/ssd/caption data/', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='flickr30k', karpathy_json_path='../data/dataset_flickr30k.json', image_folder='../data/flickr30k_images/flickr30k_images/', captions_per_image=5, min_word_freq=5, output_folder='../data', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='atlas', karpathy_json_path='../../dataset/atlas_dataset.json', image_folder='../../dataset/', captions_per_image=1, min_word_freq=1, output_folder='output', max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files(dataset='flickr8k', karpathy_json_path='dataset_flickr8k.json', image_folder='Flickr8k_Dataset/gaussian_0.13', captions_per_image=5, min_word_freq=5, output_folder='dataset_gaussian_0.13', max_len=50)
from utils import create_input_files import argparse import os if __name__ == '__main__': parser = argparse.ArgumentParser(description='Create input files') parser.add_argument('--dataset', '-d', default='coco', help='dataset') parser.add_argument('--img_folder', '-i', default='image_folder', help='path to image') parser.add_argument('--caption_folder', '-cf', default='caption_datasets', help='path to captions') args = parser.parse_args() # Create input files (along with word map) create_input_files(dataset=args.dataset, karpathy_json_path=os.path.join( args.caption_folder, 'dataset_{:s}.json'.format(args.dataset)), image_folder=args.img_folder, captions_per_image=5, min_word_freq=5, output_folder='{:s}_folder'.format(args.dataset), max_len=50)
from utils import create_input_files if __name__ == '__main__': # Create input files (along with word map) create_input_files( dataset='face2text', karpathy_json_path='./caption data/dataset_face2text.json', image_folder='./caption data/dataset-face2text/', captions_per_image=2, min_word_freq=2, output_folder='./caption data/', max_len=50)