Datasets:
jblitzar
/
github-python-corpus

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def forward(self, x, y):
scale, bias = self.embed(y.unsqueeze(-1)).chunk(2, dim=-1)
return x * scale[:, :, None, None] + bias[:, :, None, None]
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, *args, **kwargs):
return
# <FILESEP>
"""
Run evaluation with saved models.
"""
import os
import random
import argparse
import pickle
import torch
import torch.nn as nn
import torch.optim as optim
from global_random_seed import RANDOM_SEED
from data.loader import DataLoader
from model.relation_model import RelationModel
from utils import torch_utils, scorer, constant, helper
from utils.vocab import Vocab
parser = argparse.ArgumentParser()
parser.add_argument(
'--model_dir', type=str, help='Directory of the model.',
default="saved_models/tmp5/"
)
# parser.add_argument('--model', type=str, default='best_model.pt', help='Name of the model file.')
parser.add_argument('--model', type=str, default='best_model.pt', help='Name of the model file.')
parser.add_argument('--data_dir', type=str, default='dataset/tacred')
parser.add_argument('--dataset', type=str, default='test', help="Evaluate on dev or test.")
parser.add_argument('--out', type=str,
default="saved_models/out/test_6.pkl",
help="Save model predictions to this dir."
)
parser.add_argument('--seed', type=int, default=RANDOM_SEED)
parser.add_argument('--cuda', type=bool, default=torch.cuda.is_available())
parser.add_argument('--cpu', action='store_true')
args = parser.parse_args()
with open('global_random_seed.py', 'w') as the_file:
the_file.write('RANDOM_SEED = '+str(args.seed))
# set top level random seeds
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cpu:
args.cuda = False
elif args.cuda:
# set random seed for cuda as well
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# load opt
model_file = args.model_dir + '/' + args.model
print("Loading model from {}".format(model_file))
opt = torch_utils.load_config(model_file)
# TODO: are we using dropout in testing??
# opt["dropout"] = 0.0
# opt["scaled_dropout"] = 0.0
model = RelationModel(opt)
model.load(model_file)
# load vocab
vocab_file = args.model_dir + '/vocab.pkl'
vocab = Vocab(vocab_file, load=True)
assert opt['vocab_size'] == vocab.size, "Vocab size must match that in the saved model."
# load data
data_file = opt['data_dir'] + '/{}.json'.format(args.dataset)
print("Loading data from {} with batch size {}...".format(data_file, opt['batch_size']))
batch = DataLoader(data_file, opt['batch_size'], opt, vocab, evaluation=True)
helper.print_config(opt)
id2label = dict([(v,k) for k,v in constant.LABEL_TO_ID.items()])
predictions = []
all_probs = []
with torch.no_grad():
for i, b in enumerate(batch):
preds, probs, _ = model.predict(b)
predictions += preds
all_probs += probs