def __clf__main__(trainer_class=Trainer):
# generate parser / parse parameters
parser = get_parser()
parser = add_argument(parser)
params = parser.parse_args()
params = from_config_file(params)
if params.device not in ["cpu", "cuda"]:
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
else:
params.device = torch.device(params.device)
set_seeds(params.random_seed)
params.pretrain = os.path.isfile(params.pretrain_config)
if params.pretrain:
params_pretrain = from_config_file(copy.deepcopy(params),
config_file=params.pretrain_config)
__main__(params_pretrain)
else:
params_pretrain = None
# check parameters
check_parameters(params)
# run experiment
main(params, params_pretrain, trainer_class=trainer_class)
import os
import random
from sklearn.preprocessing import normalize
import numpy as np
import utils
import torchfile
from params import get_parser
parser = get_parser()
params = parser.parse_args()
random.seed(params.seed)
DATA_ROOT = params.test_feats
partition = params.partition
img_embs = partition + '_img_embs.t7'
instr_embs = partition + '_instr_embs.t7'
test_ids = partition + '_ids.t7'
im_vecs = np.array(torchfile.load(os.path.join(DATA_ROOT, img_embs)))
instr_vecs = np.array(torchfile.load(os.path.join(DATA_ROOT, instr_embs)))
names = np.array(torchfile.load(os.path.join(DATA_ROOT, test_ids)))
# Sort based on names to always pick same samples for medr
names_str = []
for i in range(names.shape[0]):
names_str.append(''.join(chr(k) for k in names[i]).split('\x00')[0])
names = np.array(names_str)
idxs = np.argsort(names)
names = names[idxs]
im_vecs = normalize(im_vecs)[idxs]
IMPATH = '../../data/recipe800k/images/'
def read_image(impath):
im = Image.open(impath)
im = im.resize((224,224))
return im
def load_layer(json_file):
with open(json_file) as f_layer:
return json.load(f_layer)
ch = {'sushi':[],'pie':[],'pizza':[],'lasagna':[],'soup':[],'burger':[],
'pasta':[],'salad':[],'smoothie':[],'cookie':[]}
parser = get_parser()
params = parser.parse_args()
#random.seed(params.seed)
DATA_ROOT = params.test_feats
IMPATH = os.path.join(params.dataset,'images')
partition = params.partition
img_embs = partition + '_img_embs.t7'
instr_embs = partition + '_instr_embs.t7'
test_ids = partition + '_ids.t7'
im_vecs = np.array(torchfile.load(os.path.join(DATA_ROOT,img_embs)))
instr_vecs = np.array(torchfile.load(os.path.join(DATA_ROOT,instr_embs)))
names = np.array(torchfile.load(os.path.join(DATA_ROOT,test_ids)))
for eval_task in params.eval_tasks:
end_of_epoch(params=eval_trainers[eval_task]["params"],
logger=logger,
trainer=eval_trainers[eval_task]['trainer'],
evaluator=eval_trainers[eval_task]['evaluator'],
eval_task=eval_task)
# our
logger.info("============ garbage collector collecting %d ..." %
gc.collect())
if __name__ == '__main__':
# generate parser / parse parameters
params = get_parser().parse_args()
params = from_config_file(params)
set_seeds(params.random_seed)
if params.device not in ["cpu", "cuda"]:
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
else:
params.device = torch.device(params.device)
__main__(params)
# run experiment
main(params)
import os
import numpy as np
import torch
from transformers import BertTokenizer, BertModel
import logging
import simplejson as json
from tqdm import tqdm
import pickle
# =============================================================================
import params
parser = params.get_parser()
opts = parser.parse_args()
data_path, results_path, logdir = params.show_bert_opts(opts)
# =============================================================================
if opts.view_emb:
from torch.utils.tensorboard import SummaryWriter
pre_writer = SummaryWriter(os.path.join(logdir, "pre"))
# =============================================================================
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
### model の ロード
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Load pre-trained model tokenizer (vocabulary)
model = BertModel.from_pretrained('bert-base-uncased', output_hidden_states = True) # Load pre-trained model (weights)
model.to(device)
model.eval() # Put the model in "evaluation" mode, meaning feed-forward operation.
print("Model loaded")
### json のロード
def load(file):
