def rest():
config.parse_args(sys.argv)
logging.setup("cloudemulator")
launcher = service.process_launcher()
server = service.WSGIService('rest', use_ssl=False,
max_url_len=16384)
launcher.launch_service(server, workers=server.workers or 1)
launcher.wait()
def main():
try:
config.parse_args()
log.setup('glance')
server = wsgi.Server()
server.start(config.load_paste_app('glance-api'), default_port=9292)
server.wait()
except exception.WorkerCreationFailure as e:
fail(2, e)
except RuntimeError as e:
fail(1, e)
def pytest_funcarg__config_a(request, test_corp):
conf = config.parse_args(raw_args = [
'--mode', 'train',
'--model', 'v2',
'--embed', '128',
'--hidden', '64',
'--minbatch', '33',
'--lr', '0.3',
'--train_file', 'data/test.html'
])
conf.corpus = test_corp
return conf
def runtest(args, **kwargs):
parser = config.create_parser('make concurrent requests')
# receive push, -p is ideal but we have --port option
parser.add_argument('-r', '--receive-push', action='store_true', default=False,
help='receive push message')
parser.add_argument('-c', '--count', action='store', type=int, default=1,
help='concurrent count')
ns = config.parse_args(args, parser = parser, **kwargs)
case = ConcurrentCase(ns.count, ns.message.values())
try:
case.run(ns.address, ns.port)
except KeyboardInterrupt:
case.print_result()
def infer():
args = parse_args()
print(args)
place = fluid.CPUPlace()
inference_scope = fluid.Scope()
test_valid_files = [
os.path.join(args.test_valid_data_dir, fname)
for fname in next(os.walk(args.test_valid_data_dir))[2]
]
test_files = random.sample(test_valid_files,
int(len(test_valid_files) * 0.5))
if not test_files:
test_files = test_valid_files
print('test files num {}'.format(len(test_files)))
criteo_dataset = CriteoDataset()
criteo_dataset.setup(args.vocab_dir)
test_reader = criteo_dataset.test_reader(test_files, args.batch_size, 100)
startup_program = fluid.framework.Program()
test_program = fluid.framework.Program()
cur_model_path = os.path.join(args.model_output_dir,
'epoch_' + args.test_epoch, "checkpoint")
with fluid.scope_guard(inference_scope):
with fluid.framework.program_guard(test_program, startup_program):
cat_feat_dims_dict = OrderedDict()
for line in open(args.cat_feat_num):
spls = line.strip().split()
assert len(spls) == 2
cat_feat_dims_dict[spls[0]] = int(spls[1])
dcn_model = DCN(args.cross_num, args.dnn_hidden_units,
args.l2_reg_cross, args.use_bn, args.clip_by_norm,
cat_feat_dims_dict, args.is_sparse)
dcn_model.build_network(is_test=True)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(
feed_list=dcn_model.data_list, place=place)
exe.run(startup_program)
fluid.load(fluid.default_main_program(), cur_model_path)
for var in dcn_model.auc_states: # reset auc states
set_zero(var.name, scope=inference_scope, place=place)
loss_all = 0
num_ins = 0
for batch_id, data_test in enumerate(test_reader()):
loss_val, auc_val = exe.run(test_program,
feed=feeder.feed(data_test),
fetch_list=[
dcn_model.avg_logloss.name,
dcn_model.auc_var.name
])
# num_ins += len(data_test)
num_ins += 1
loss_all += loss_val
logger.info('TEST --> batch: {} loss: {} auc_val: {}'.format(
batch_id, loss_all / num_ins, auc_val))
print(
'The last log info is the total Logloss and AUC for all test data. '
)
import itertools
from BiLSTM_CRF import BiLSTM_CRF_S, BiLSTM_CRF_L
from config import parse_args
import torch.nn as nn
from evaluate import evaluate, evaluate_with_perl, predict_write, evaluate_by_file
def complete_dict(label2idx, add_label2idx):
for label in add_label2idx:
if label not in label2idx:
label2idx[label] = len(label2idx)
if __name__ == '__main__':
args = parse_args()
model_name = '{:}_{:}_{:}_{:.2f}_{:}_{:}_{:.3f}'.format(
args.batch_size, args.hidden_size, args.embedding_dim, args.drop_out,
args.layers, args.update, args.lr)
args.checkpoint_dir = os.path.join(args.checkpoint_dir, model_name)
args.log_path = os.path.join(args.checkpoint_dir, 'log')
# 不然最后面会带一个\n!
args.load_check_point = args.load_check_point.strip()
# 创建checkpoint目录
if not os.path.exists(args.checkpoint_dir):
print('Making dir %s' % args.checkpoint_dir)
os.makedirs(args.checkpoint_dir)
if args.gpu >= 0 and torch.cuda.is_available():
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from routes import items
import config
from constants import *
config.parse_args()
app = FastAPI(
title="API",
description="API boilerplate",
version="1.0.0",
openapi_tags=API_TAGS_METADATA,
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(items.router)
@app.get("/")
async def root():
import tensorflow as tf
from tqdm import tqdm
from config import parse_args, FLAGS
from tfsolver import TFSolver
from dataset import DatasetFactory
from network_completion import CompletionResnet
from ocnn import l2_regularizer
sys.path.append('..')
from libs import octree_scan, octree_batch, normalize_points
# flags
FLAGS.DATA.train.camera = '_' # used to generate partial scans
FLAGS.MODEL.skip_connections = True
FLAGS = parse_args()
# the dataset
class NormalizePoints:
def __call__(self, points):
radius = 64.0
center = (64.0, 64.0, 64.0)
points = normalize_points(points, radius, center)
return points
class PointDataset:
def __init__(self, parse_example, normalize_points, transform_points, points2octree):
self.parse_example = parse_example
self.normalize_points = normalize_points
def train():
opt = parse_args()
os.makedirs("images/%s" % (opt.dataset), exist_ok=True)
os.makedirs("checkpoints/%s" % (opt.dataset), exist_ok=True)
cuda = True if torch.cuda.is_available() else False
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# get dataloader
train_loader = celeba_loader(opt, mode='train')
val_loader = celeba_loader(opt, mode='val')
# Dimensionality
c_dim = len(opt.selected_attrs)
# Initialize generator and discriminator
generator = Generator(opt.channels, opt.residual_blocks, c_dim)
discriminator = Discriminator(opt.channels, opt.img_height, c_dim)
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Loss function
cycle_loss = torch.nn.L1Loss()
if cuda:
generator = generator.cuda()
discriminator = discriminator.cuda()
cycle_loss.cuda()
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
# ------------
# Training
# ------------
prev_time = time.time()
for epoch in range(opt.epochs):
for i, (imgs, labels) in enumerate(train_loader):
# Model inputs
imgs = Variable(imgs.type(FloatTensor))
labels = Variable(labels.type(FloatTensor))
# Sample label as generator inputs and Generate fake batch of images
sampled_c = Variable(
FloatTensor(np.random.randint(0, 2, (imgs.size(0), c_dim))))
fake_imgs = generator(imgs, sampled_c)
# ----------------------
# Train Discriminator
# ----------------------
optimizer_D.zero_grad()
real_validity, pred_cls = discriminator(imgs)
fake_validity, _ = discriminator(fake_imgs.detach())
gradient_penalty = compute_gradient_penalty(
discriminator, imgs.data, fake_imgs.data, FloatTensor)
d_adv_loss = -torch.mean(real_validity) + torch.mean(
fake_validity) + opt.lambda_gp * gradient_penalty
d_cls_loss = criterion_cls(pred_cls, labels)
D_loss = d_adv_loss + opt.lambda_cls * d_cls_loss
D_loss.backward()
optimizer_D.step()
# -----------------------------
# Train Generators
# -----------------------------
optimizer_G.zero_grad()
if i % opt.n_critic == 0:
gen_imgs = generator(imgs, sampled_c)
recov_imgs = generator(gen_imgs, labels)
fake_validity, pred_cls = discriminator(gen_imgs)
g_adv_loss = -torch.mean(fake_validity)
g_cls_loss = criterion_cls(pred_cls, sampled_c)
g_rec_loss = cycle_loss(recov_imgs, imgs)
G_loss = g_adv_loss + opt.lambda_cls * g_cls_loss + opt.lambda_rec * g_rec_loss
G_loss.backward()
optimizer_G.step()
# ------------------
# Log Information
# ------------------
batches_done = epoch * len(train_loader) + i
batches_left = opt.epochs * len(train_loader) - batches_done
time_left = datetime.timedelta(seconds=batches_left *
(time.time() - prev_time))
prev_time = time.time()
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f, aux: %f] [G loss: %f, aux: %f, cycle: %f] ETA: %s"
% (epoch, opt.epochs, i, len(train_loader), D_loss.item(),
d_cls_loss.item(), G_loss.item(), g_cls_loss.item(),
g_rec_loss, time_left))
if batches_done % opt.sample_interval == 0:
save_sample(opt.dataset, val_loader, batches_done,
generator, FloatTensor)
if batches_done % opt.checkpoint_interval == 0:
torch.save(
Generator.state_dict(),
"checkpoints/%s/G_%d.pth" % (opt.dataset, epoch))
torch.save(Generator.state_dict(),
"checkpoints/%s/shared_E_done.pth" % opt.dataset)
print("Training Process has been Done!")
import requests
import logging
import config as c
opts = c.parse_args([c.DBS])
db = opts.db
list_of_tweets = []
def query(url):
r = requests.get(url)
if r.status_code != 200:
return True
else:
logging.info("Tweet still exists")
def read_database(db):
cur = db.getTweets()
for tweet in cur:
list_of_tweets.append(tweet)
logging.info(tweet)
return list_of_tweets
def check_tweet():
for tweet in read_database(db):
if query(tweet[3]) is True:
db.markDeleted(tweet[4])
def main():
args = parse_args()
args.pretrain = False
print("Using GPU: {}".format(args.local_rank))
root_path = 'exps/exp_{}'.format(args.exp)
if args.local_rank == 0 and not os.path.exists(root_path):
os.mkdir(root_path)
os.mkdir(os.path.join(root_path, "log"))
os.mkdir(os.path.join(root_path, "model"))
base_lr = args.lr # base learning rate
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_dataset, val_dataset = build_dataset(args.dataset, args.data_root,
args.train_list)
args.world_size = len(args.gpu.split(","))
if args.world_size > 1:
os.environ['MASTER_PORT'] = args.port
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group('nccl')
device = torch.device('cuda:{}'.format(args.local_rank))
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=len(args.gpu.split(",")),
rank=args.local_rank)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=args.num_workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
num_workers=args.num_workers,
pin_memory=True)
model = VNet(args.n_channels, args.n_classes).cuda(args.local_rank)
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=0.0005)
#scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.7)
if args.world_size > 1:
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
model.train()
print("Loaded weights")
logger = Logger(root_path)
saver = Saver(root_path)
for epoch in range(args.start_epoch, args.epochs):
train(model, train_loader, optimizer, logger, args, epoch)
validate(model, val_loader, optimizer, logger, saver, args, epoch)
adjust_learning_rate(args, optimizer, epoch)
def main():
global args
args = parse_args()
# global logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("[%(asctime)s] %(levelname)s:%(name)s:%(message)s")
# file logger
fh = logging.FileHandler(os.path.join(args.save, args.expname)+'.log', mode='w')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
# console logger
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
# argument validation
args.cuda = args.cuda and torch.cuda.is_available()
device = torch.device("cuda:0" if args.cuda else "cpu")
if args.sparse and args.wd != 0:
logger.error('Sparsity and weight decay are incompatible, pick one!')
exit()
logger.debug(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
train_dir = os.path.join(args.data, 'train/')
test_dir = os.path.join(args.data, 'test/')
# get vocab object from vocab file previously written
vocab_toks = Vocab(filename=os.path.join(args.data, 'vocab_toks.txt'), data=[Constants.PAD_WORD, Constants.UNK_WORD, Constants.BOS_WORD, Constants.EOS_WORD])
vocab_chars = Vocab(filename=os.path.join(args.data, 'vocab_chars.txt'))
vocab_pos = Vocab(filename=os.path.join(args.data, 'vocab_pos.txt'))
vocab_rels = Vocab(filename=os.path.join(args.data, 'vocab_rels.txt'))
vocab_output = Vocab(filename=os.path.join(args.data, 'vocab_output.txt'))
# Set number of classes based on vocab_output
args.num_classes = vocab_output.size()
logger.debug('==> LC-QUAD vocabulary toks size : %d ' % vocab_toks.size())
logger.debug('==> LC-QUAD vocabulary chars size : %d ' % vocab_chars.size())
logger.debug('==> LC-QUAD vocabulary pos size : %d ' % vocab_pos.size())
logger.debug('==> LC-QUAD vocabulary rels size : %d ' % vocab_rels.size())
logger.debug('==> LC-QUAD output vocabulary size : %d ' % vocab_output.size())
# load LC_QUAD dataset splits
train_file = os.path.join(args.data, 'pth/lc_quad_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = LC_QUAD_Dataset(train_dir, vocab_toks, vocab_pos, vocab_rels, args.num_classes)
torch.save(train_dataset, train_file)
logger.debug('==> Size of train data : %d ' % len(train_dataset))
test_file = os.path.join(args.data, 'pth/lc_quad_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = LC_QUAD_Dataset(test_dir, vocab_toks, vocab_pos, vocab_rels, args.num_classes)
torch.save(test_dataset, test_file)
logger.debug('==> Size of test data : %d ' % len(test_dataset))
criterion = nn.NLLLoss()
input_dim = EMBEDDING_DIM + vocab_pos.size() + vocab_rels.size() + vocab_chars.size()
model = TreeLSTM(
input_dim,
args.mem_dim,
args.num_classes,
criterion,
vocab_output,
dropout=True
)
toks_embedding_model = nn.Embedding(vocab_toks.size(), EMBEDDING_DIM)
chars_embedding_model = nn.Embedding(vocab_chars.size(), vocab_chars.size())
pos_embedding_model = nn.Embedding(vocab_pos.size(), vocab_pos.size())
rels_embedding_model = nn.Embedding(vocab_rels.size(), vocab_rels.size())
toks_emb = generate_embeddings(vocab_toks, os.path.join(args.data, 'pth/lc_quad_toks_embed.pth'))
chars_emb = generate_one_hot_vectors(vocab_chars)
pos_emb = generate_one_hot_vectors(vocab_pos)
rels_emb = generate_one_hot_vectors(vocab_rels)
# plug these into embedding matrix inside model
chars_embedding_model.state_dict()['weight'].copy_(chars_emb)
toks_embedding_model.state_dict()['weight'].copy_(toks_emb)
pos_embedding_model.state_dict()['weight'].copy_(pos_emb)
rels_embedding_model.state_dict()['weight'].copy_(rels_emb)
model.to(device), criterion.to(device)
if args.optim == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad([
{'params': model.parameters(), 'lr': args.lr}
], lr=args.lr, weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
metrics = Metrics()
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.25)
# create trainer object for training and testing
trainer = Trainer(args, model, {'toks': toks_embedding_model, 'pos': pos_embedding_model, 'rels': rels_embedding_model, 'chars': chars_embedding_model}, {'toks': vocab_toks, 'chars': vocab_chars, 'output': vocab_output}, criterion, optimizer)
file_name = "analysis/expname={},input_dim={},mem_dim={},lr={},emblr={},wd={},epochs={}".format(args.expname, input_dim, args.mem_dim, args.lr, args.emblr, args.wd, args.epochs)
for epoch in range(args.epochs):
print("\n" * 5)
scheduler.step()
# Train Model
trainer.train(train_dataset)
# Test Model on Training Dataset
train_loss, train_pred = trainer.test(train_dataset)
train_acc = metrics.accuracy_score(train_pred, train_dataset.labels, vocab_output)
print('==> Train loss : %f \t' % train_loss, end="")
print('Epoch ', str(epoch + 1), 'train percentage ', train_acc)
write_analysis_file(file_name, epoch, train_pred, train_dataset.labels, "train_acc", train_acc, train_loss, vocab_output)
# Test Model on Testing Dataset
test_loss, test_pred = trainer.test(test_dataset)
test_acc = metrics.accuracy_score(test_pred, test_dataset.labels, vocab_output)
print('==> Test loss : %f \t' % test_loss, end="")
print('Epoch ', str(epoch + 1), 'test percentage ', test_acc)
write_analysis_file(file_name, epoch, test_pred, test_dataset.labels, "test_acc", test_acc, test_loss, vocab_output)
checkpoint_filename = '%s.pt' % os.path.join(args.save, args.expname + ',epoch={},test_acc={}'.format(epoch + 1, test_acc))
checkpoint = {'trainer': trainer, 'test_accuracy': test_acc, 'scheduler': scheduler}
torch.save(checkpoint, checkpoint_filename)
if updates is not None:
checkpoint_path = "checkpoints_{}_{}.pt".format(epoch, updates)
else:
checkpoint_path = "checkpoints_{}.pt".format(epoch)
checkpoint_path = os.path.join(args.save, checkpoint_path)
torch.save(model.state_dict(), checkpoint_path)
## save best
if score > best_scores:
save_checkpoint.best_scores = score
logging.info("save best checkpoint ::best scores {}, epoch {} updates {}".format(score, epoch, updates))
checkpoint_path = os.path.join(args.save, "checkpoints_best.pt")
torch.save(model.state_dict(), checkpoint_path)
if __name__ == "__main__":
args = config.parse_args()
main(args)
def main():
config.parse_args(sys.argv)
opts.register_opts()
conf = cfg.CONF
app.run(host=conf.server.addr, port=conf.server.port)
import os
import os.path as osp
import sys
import numpy as np
from scipy.io import loadmat
sys.path.insert(0, '../')
import config as cfg
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--mat_dir', default='slices_mat_64x64', type=str)
parser.add_argument('--npy_dir', default='slices_npy_64x64', type=str)
args = cfg.parse_args(parser)
args.mat_path = osp.join(args.data_root, args.mat_dir)
args.npy_path = osp.join(args.data_root, args.npy_dir)
for date in sorted(os.listdir(args.mat_path)):
date_mat_path = osp.join(args.mat_path, date)
date_npy_path = osp.join(args.npy_path, date)
for session in sorted(os.listdir(date_mat_path)):
session_mat_path = osp.join(date_mat_path, session)
session_npy_path = osp.join(date_npy_path, session)
if not osp.isdir(session_npy_path):
os.makedirs(session_npy_path)
for file in sorted(os.listdir(session_mat_path)):
file_mat_path = osp.join(session_mat_path, file)
def main():
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.enabled = True
args = parse_args()
print("Model input size: ", args.model_input_size)
print("Batch size: ", args.batch_size)
print('Arch: ', args.arch)
print('Optimizer: ', args.optim)
print('Weighted loss: ', args.use_weighted_loss)
dir_path = os.path.dirname(__file__)
print('Loading dataset and dataloader..')
train_percentage = args.train_percentage
if args.validate and args.train_percentage == 1:
train_percentage = 0.9
print(
'Warning: train percentage was given 1 with validation enabled, train percentage dropped to 0.9'
)
train_set, train_loader = data_fact.get_dataloader(
args, 'train', train_percentage=train_percentage)
if args.validate:
val_set, val_loader = data_fact.get_dataloader(
args, 'val', train_percentage=train_percentage)
if args.test:
test_set, test_loader = data_fact.get_dataloader(args, 'test')
num_classes = len(train_set.classes)
class_weights = [1.0] * num_classes
if args.use_weighted_loss:
class_weights = train_set.class_weights
print("Class weights: ", class_weights)
class_weights = torch.Tensor(class_weights)
criterion = nn.CrossEntropyLoss(class_weights)
if args.use_cuda:
criterion = criterion.cuda()
best_perf1 = 0
best_perf5 = 0
begin_epoch = 0
best_epoch = 0
state_dict = None # won't be None if resuming from a trained model
optimizer_dict = None # won't be None if resuming from a trained model
scheduler_steps = 2 # [30, 60, 90]
scheduler_decay = 0.9 # 0.1
if args.resume_path:
print('Loading finetuned model from {}..', args.resume_path)
checkpoint = torch.load(args.resume_path)
begin_epoch = checkpoint['epoch']
best_epoch = begin_epoch
# best_epoch = checkpoint['best_epoch']
best_perf1 = checkpoint['perf1']
# best_perf5 = checkpoint['perf5']
args.arch = checkpoint['arch']
num_classes = checkpoint['num_classes']
state_dict = checkpoint['state_dict']
optimizer_dict = checkpoint['optimizer']
print('Begin epoch: ', begin_epoch)
print('Best Acc@1 at epoch {}: {}'.format(best_epoch, best_perf1))
# scheduler.load_state_dict(checkpoint['scheduler'])
model = model_factory.generate_model(args.arch, num_classes, state_dict,
args.use_cuda)
optimizer = get_optimizer(args, model, optimizer_dict)
print('Learning rate: {:1.5f}', optimizer.param_groups[0]['lr'])
if args.train:
for epoch in range(begin_epoch, args.num_epochs):
print('Epoch: {} / {}'.format(epoch + 1, args.num_epochs))
perf_indicator1, perf_indicator5 = train_epoch(
epoch, train_loader, model, criterion, optimizer,
args.use_cuda)
if args.validate:
perf_indicator1, perf_indicator5 = validate_epoch(
val_loader, model, criterion, args.use_cuda)
if perf_indicator1 >= best_perf1:
best_perf1 = perf_indicator1
best_perf5 = perf_indicator5
best_epoch = epoch
checkpoint_file = '{}_{}_{}_{}{}{}'.format(
args.model_input_size, args.arch, args.optim,
args.batch_size, '_subset' if args.subset_finetune else '',
'_weightedloss' if args.use_weighted_loss else '')
save_checkpoint(
{
'epoch': epoch + 1,
'best_epoch': best_epoch + 1,
'perf1': best_perf1,
'perf5': best_perf5,
'arch': args.arch,
'num_classes': model.num_classes,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
# 'scheduler': scheduler.state_dict(),
},
dir_path,
is_best=True,
filename=checkpoint_file)
if (epoch + 1) % 5 == 0: # save model every 5 epochs
checkpoint_file = 'Epoch{}_{}_{}_{}_{}{}{}'.format(
epoch + 1, args.model_input_size, args.arch, args.optim,
args.batch_size, '_subset' if args.subset_finetune else '',
'_weightedloss' if args.use_weighted_loss else '')
save_checkpoint(
{
'epoch': epoch + 1,
'best_epoch': best_epoch + 1,
'perf1': best_perf1,
'perf5': best_perf5,
'arch': args.arch,
'num_classes': model.num_classes,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
# 'scheduler': scheduler.state_dict(),
},
dir_path,
filename=checkpoint_file)
print('Epoch {} perf acc@1: {}, perf acc@5: {}'.format(
epoch + 1, perf_indicator1, perf_indicator5))
print('Best perf acc@1: {}, perf acc@5: {} at epoch {}'.format(
best_perf1, best_perf5, best_epoch + 1))
# scheduler.step(perf_indicator1)
if epoch + 1 < 100:
adjust_learning_rate(optimizer,
epoch + 1,
args,
steps=scheduler_steps,
dec_rate=scheduler_decay)
if args.test:
test_cassava(test_loader, model, train_set.classes, args)
def main():
args = parse_args()
args.pretrain = True
print("Using GPU: {}".format(args.local_rank))
base_lr = args.lr # base learning rate
batch_size = 1
max_iterations = 20000
cell_size = 96 # size of volume we crop patch from
patch_size = 64
puzzle_config = 3 # 2 or 3 for 2X2X2 or 3X3X3 puzzle
puzzle_num = puzzle_config**3
feature_len = 256 #
iter_num = 0
sr_feature_size = 32
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_dataset, val_dataset = build_dataset(args)
args.world_size = len(args.gpu.split(","))
if args.world_size > 1:
os.environ['MASTER_PORT'] = args.port
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group('nccl')
device = torch.device('cuda:{}'.format(args.local_rank))
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=len(args.gpu.split(",")),
rank=args.local_rank)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=args.num_workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
num_workers=args.num_workers,
pin_memory=True)
model = VNet(args.n_channels, args.n_classes, input_size=64,
pretrain=True).cuda(args.local_rank)
model_ema = VNet(args.n_channels,
args.n_classes,
input_size=64,
pretrain=True).cuda(args.local_rank)
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=0.0005)
#scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.7)
parallel_state_dict = torch.load(args.load_path)['state_dict']
new_state_dict = {}
for key in parallel_state_dict.keys():
new_state_dict[key[7:]] = parallel_state_dict[key]
model.load_state_dict(new_state_dict)
model.eval()
print("Loaded weights")
print("Using Dataset: {}".format(type(train_dataset)))
features = []
for i, batch in enumerate(tqdm(train_loader)):
volume = batch['image'].cuda(args.local_rank, non_blocking=True)
volume = volume.view((-1, ) + volume.shape[2:])
with torch.no_grad():
q = model(volume, pretrain=True)
features.append(q)
if i > 100:
break
features = torch.cat(features, 0)
pickle.dump(features.cpu().numpy(), open("features.pkl", 'wb'))
def main():
global args
args = parse_args()
# global logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("[%(asctime)s] %(levelname)s:%(name)s:%(message)s")
# file logger
fh = logging.FileHandler(os.path.join(args.save, args.expname)+'.log', mode='w')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
# console logger
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
# argument validation
args.cuda = args.cuda and torch.cuda.is_available()
if args.sparse and args.wd != 0:
logger.error('Sparsity and weight decay are incompatible, pick one!')
exit()
logger.debug(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# write unique words from all token files
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
if not os.path.isfile(sick_vocab_file):
token_files_b = [os.path.join(split, 'b.toks') for split in [train_dir, dev_dir, test_dir]]
token_files_a = [os.path.join(split, 'a.toks') for split in [train_dir, dev_dir, test_dir]]
token_files = token_files_a + token_files_b
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
build_vocab(token_files, sick_vocab_file)
# get vocab object from vocab file previously written
vocab = Vocab(filename=sick_vocab_file, data=[Constants.PAD_WORD, Constants.UNK_WORD, Constants.BOS_WORD, Constants.EOS_WORD])
logger.debug('==> SICK vocabulary size : %d ' % vocab.size())
# load SICK dataset splits
train_file = os.path.join(args.data, 'sick_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = SICKDataset(train_dir, vocab, args.num_classes)
torch.save(train_dataset, train_file)
logger.debug('==> Size of train data : %d ' % len(train_dataset))
dev_file = os.path.join(args.data, 'sick_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = SICKDataset(dev_dir, vocab, args.num_classes)
torch.save(dev_dataset, dev_file)
logger.debug('==> Size of dev data : %d ' % len(dev_dataset))
test_file = os.path.join(args.data, 'sick_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = SICKDataset(test_dir, vocab, args.num_classes)
torch.save(test_dataset, test_file)
logger.debug('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
model = SimilarityTreeLSTM(
vocab.size(),
args.input_dim,
args.mem_dim,
args.hidden_dim,
args.num_classes,
args.sparse,
args.freeze_embed)
criterion = nn.KLDivLoss()
if args.cuda:
model.cuda(), criterion.cuda()
if args.optim == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
emb_file = os.path.join(args.data, 'sick_embed.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else:
# load glove embeddings and vocab
glove_vocab, glove_emb = load_word_vectors(os.path.join(args.glove, 'glove.840B.300d'))
logger.debug('==> GLOVE vocabulary size: %d ' % glove_vocab.size())
emb = torch.Tensor(vocab.size(), glove_emb.size(1)).normal_(-0.05, 0.05)
# zero out the embeddings for padding and other special words if they are absent in vocab
for idx, item in enumerate([Constants.PAD_WORD, Constants.UNK_WORD, Constants.BOS_WORD, Constants.EOS_WORD]):
emb[idx].zero_()
for word in vocab.labelToIdx.keys():
if glove_vocab.getIndex(word):
emb[vocab.getIndex(word)] = glove_emb[glove_vocab.getIndex(word)]
torch.save(emb, emb_file)
# plug these into embedding matrix inside model
if args.cuda:
emb = emb.cuda()
model.emb.weight.data.copy_(emb)
# create trainer object for training and testing
trainer = Trainer(args, model, criterion, optimizer)
best = -float('inf')
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
dev_loss, dev_pred = trainer.test(dev_dataset)
test_loss, test_pred = trainer.test(test_dataset)
train_pearson = metrics.pearson(train_pred, train_dataset.labels)
train_mse = metrics.mse(train_pred, train_dataset.labels)
logger.info('==> Epoch {}, Train \tLoss: {}\tPearson: {}\tMSE: {}'.format(epoch, train_loss, train_pearson, train_mse))
dev_pearson = metrics.pearson(dev_pred, dev_dataset.labels)
dev_mse = metrics.mse(dev_pred, dev_dataset.labels)
logger.info('==> Epoch {}, Dev \tLoss: {}\tPearson: {}\tMSE: {}'.format(epoch, dev_loss, dev_pearson, dev_mse))
test_pearson = metrics.pearson(test_pred, test_dataset.labels)
test_mse = metrics.mse(test_pred, test_dataset.labels)
logger.info('==> Epoch {}, Test \tLoss: {}\tPearson: {}\tMSE: {}'.format(epoch, test_loss, test_pearson, test_mse))
if best < test_pearson:
best = test_pearson
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'pearson': test_pearson, 'mse': test_mse,
'args': args, 'epoch': epoch
}
logger.debug('==> New optimum found, checkpointing everything now...')
torch.save(checkpoint, '%s.pt' % os.path.join(args.save, args.expname))
close failed: [Errno 9] Bad file descriptor
'''
if __name__ == '__main__':
parser = config.default_parser(defaults)
parser.add_option("--profile", default="",
dest="profile", help="unit to profile doctest [default: %default]")
parser.add_option('--psyco', dest='psyco', default='',
help="specialized python compiler for speed without debugging")
import sys
(options, args) = config.parse_args(parser, sys.argv)
configuration.set(options.__dict__)
configuration.subprocess_gateway = eval(configuration.subprocess_gateway)
configuration.setup_client = eval(configuration.setup_client)
configuration.globe_class = eval(configuration.globe_class)
config.setup_logging(configuration.verbose)
if setup_flash_master == configuration.setup_client:
# TODO: Master client class
set_property = slave_set_property
dispatch_event = slave_dispatch_event
mouse_down_and_sleep = slave_mouse_down_and_sleep
mouse_down_and_news = slave_mouse_down_and_news
#from optparse import OptionParser
#parser = OptionParser()
def segment_data(dfs, col_names):
"""Segment the given dataframes into EDUs, add the EDUs into the dataframes and return"""
args = parse_args()
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
# Logging
logger = logging.getLogger("SegEDU")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
# Loading
rst_data = RSTData()
logger.info('Loading vocab...')
with open(args.word_vocab_path, 'rb') as fin:
word_vocab = pickle.load(fin)
logger.info('Word vocab size: {}'.format(word_vocab.size()))
rst_data.word_vocab = word_vocab
logger.info('Loading the model...')
model = AttnSegModel(args, word_vocab)
model.restore('best', args.model_dir)
if model.use_ema:
model.sess.run(model.ema_backup_op)
model.sess.run(model.ema_assign_op)
spacy_nlp = spacy.load('en', disable=['parser', 'ner', 'textcat'])
for df, col_name in zip(dfs, col_names):
edu_results = {}
for idx, row in tqdm(df.iterrows(), total=len(df.index)):
try:
# logger.info('Segmenting example {}...'.format(idx))
raw_sents = [row[col_name]]
samples = []
for sent in spacy_nlp.pipe(raw_sents, batch_size=1000, n_threads=5):
samples.append({'words': [token.text for token in sent],
'words_ws': [token.text_with_ws for token in sent],
'edu_seg_indices': []})
rst_data.test_samples = samples
data_batches = rst_data.gen_mini_batches(args.batch_size, test=True, shuffle=False)
edus = []
for batch in data_batches:
batch_pred_segs = model.segment(batch)
for sample, pred_segs in zip(batch['raw_data'], batch_pred_segs):
one_edu_words = []
for word_idx, word in enumerate(sample['words_ws']):
if word_idx in pred_segs:
edus.append(''.join(one_edu_words))
one_edu_words = []
one_edu_words.append(word)
if one_edu_words:
edus.append(''.join(one_edu_words))
edu_results[idx] = edus
except:
logger.info("Crashed while segmenting {}.".format(idx))
edu_results[idx] = []
continue
df['edus'] = pd.Series(edu_results)
merged = pd.concat(dfs).reset_index(drop=True)
merged = merged[merged['edus'].map(lambda x: len(x)) > 0] # Remove rows with unsegmentable EDUs
return merged
def main():
# export CUDA_VISIBLE_DEVICES=3
global args
args = parse_args()
# global logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("[%(asctime)s] %(levelname)s:%(name)s:%(message)s")
# file logger
fh = logging.FileHandler(os.path.join(args.save, args.expname)+'.log', mode='w')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
# console logger
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
# argument validation
args.cuda = False
device = torch.device("cuda:0" if args.cuda else "cpu")
if args.sparse and args.wd != 0:
logger.error('Sparsity and weight decay are incompatible, pick one!')
exit()
logger.debug(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
# some settings
if args.fine_grain:
args.num_classes = 5
else:
args.num_classes = 3
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# 准备目录
vocab_file = os.path.join(args.data, 'vocab-cased.txt') # use vocab-cased
# NO, DO NOT BUILD VOCAB, USE OLD VOCAB
# get vocab object from vocab file previously written
print(vocab_file)
vocab = Vocab(filename=vocab_file,
data=[Constants.PAD_WORD, Constants.UNK_WORD,
Constants.BOS_WORD, Constants.EOS_WORD])
print('==> SST vocabulary size : %d ' % vocab.size())
# let program turn off after preprocess data
is_preprocessing_data = False
# train
train_file = os.path.join(args.data, 'sst_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = SSTDataset(train_dir, vocab, args.num_classes, args.fine_grain)
torch.save(train_dataset, train_file)
# is_preprocessing_data = True
logger.debug('==> Size of train data : %d ' % len(train_dataset))
# dev
dev_file = os.path.join(args.data,'sst_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = SSTDataset(dev_dir, vocab, args.num_classes, args.fine_grain)
torch.save(dev_dataset, dev_file)
# is_preprocessing_data = True
logger.debug('==> Size of dev data : %d ' % len(dev_dataset))
# test
test_file = os.path.join(args.data,'sst_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = SSTDataset(test_dir, vocab, args.num_classes, args.fine_grain)
torch.save(test_dataset, test_file)
# is_preprocessing_data = True
logger.debug('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
criterion = nn.NLLLoss()
model = SentimentTreeLSTM(
vocab.size(),
args.input_dim,
args.mem_dim,
args.num_classes,
args.freeze_embed,
criterion,
device,
args.dropout,
args.n
)
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
emb_file = os.path.join(args.data, 'sst_embed.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else:
# load glove embeddings and vocab
glove_vocab, glove_emb = utils.load_word_vectors(
os.path.join(args.glove, 'glove.840B.300d'))
logger.debug('==> GLOVE vocabulary size: %d ' % glove_vocab.size())
emb = torch.zeros(vocab.size(), glove_emb.size(1), dtype=torch.float, device=device)
emb.normal_(0, 0.05)
# zero out the embeddings for padding and other special words if they are absent in vocab
for idx, item in enumerate([Constants.PAD_WORD, Constants.UNK_WORD,
Constants.BOS_WORD, Constants.EOS_WORD]):
emb[idx].zero_()
for word in vocab.labelToIdx.keys():
if glove_vocab.getIndex(word):
emb[vocab.getIndex(word)] = glove_emb[glove_vocab.getIndex(word)]
# is_preprocessing_data = True
torch.save(emb, emb_file)
if is_preprocessing_data:
print ('done preprocessing data, quit program to prevent memory leak.')
print ('please run again.')
quit()
# plug these into embedding matrix inside model
# python原地操作的后缀为 _，处理高维数据时可帮助减少内存
model.emb.weight.data.copy_(emb)
model.to(device), criterion.to(device)
if args.optim == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# create trainer object for training and testing
trainer = SentimentTrainer(args, model, criterion, optimizer, device)
best = -float('inf')
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
dev_loss, dev_pred = trainer.test(dev_dataset)
#test_loss, test_pred = trainer.test(test_dataset)
train_acc = metrics.sentiment_accuracy_score(train_pred, train_dataset.labels)
dev_acc = metrics.sentiment_accuracy_score(dev_pred, dev_dataset.labels)
#test_acc = metrics.sentiment_accuracy_score(test_pred, test_dataset.labels)
logger.info('==> Epoch {}, Train \tLoss: {} \tAccuracy: {}'.format(
epoch, train_loss, train_acc))
logger.info('==> Epoch {}, Dev \tLoss: {} \tAccuracy: {}'.format(
epoch, dev_loss, dev_acc))
#logger.info('==> Epoch {}, Test \tLoss: {}\tAccuracy: {}'.format(
#epoch, test_loss, test_acc))
if best < dev_acc:
best = dev_acc
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'train_acc': train_acc, 'dev_acc': dev_acc,
'args': args, 'epoch': epoch
}
logger.debug('==> New optimum found, checkpointing everything now...')
torch.save(checkpoint, '%s.pt' % os.path.join(args.save, args.expname))
generator.cuda()
discriminator.cuda()
summary(generator, (opt.channels, opt.img_height, opt.img_width))
summary(discriminator, [(opt.channels, opt.img_height, opt.img_width),
(opt.channels, opt.img_height, opt.img_width)])
def infer(opt):
cuda = True if torch.cuda.is_available() else False
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
generator = Generator()
generator.load_state_dict(torch.load(opt.load_model))
if cuda:
generator.cuda()
sample = load_img(opt)
sample = Variable(sample.unsqueeze(0).type(FloatTensor))
gen_img = generator(sample)
sample = torch.cat((sample.data, gen_img.data), -1)
save_image(sample, "images/infer.png", nrow=1, normalize=True)
if __name__ == '__main__':
opt = parse_args()
infer(opt)
# display_network(opt)
def train():
os.makedirs("images", exist_ok=True)
os.makedirs("checkpoints", exist_ok=True)
cuda = True if torch.cuda.is_available() else False
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# get configs and dataloader
opt = parse_args()
data_loader = mnist_loader(opt)
# Initialize generator and discriminator
generator = Generator(opt)
discriminator = Discriminator(opt)
if cuda:
generator.cuda()
discriminator.cuda()
# Optimizers
optimizer_G = torch.optim.RMSprop(generator.parameters(), lr=opt.lr)
optimizer_D = torch.optim.RMSprop(discriminator.parameters(), lr=opt.lr)
for epoch in range(opt.epochs):
for i, (imgs, _) in enumerate(data_loader):
# Configure input
z = Variable(
Tensor(np.random.normal(0, 1,
(imgs.shape[0], opt.latent_dim))))
gen_imgs = generator(z)
real_imgs = Variable(imgs.type(Tensor))
# ------------------
# Train Discriminator
# ------------------
optimizer_D.zero_grad()
d_loss = -torch.mean(discriminator(real_imgs)) + torch.mean(
discriminator(gen_imgs.detach()))
d_loss.backward()
optimizer_D.step()
# Clip weights of discriminator
for p in discriminator.parameters():
p.data.clamp_(-opt.clip_value, opt.clip_value)
# ------------------
# Train Generator
# ------------------
if i % opt.n_critic == 0:
optimizer_G.zero_grad()
g_loss = -torch.mean(discriminator(gen_imgs))
g_loss.backward()
optimizer_G.step()
# ------------------
# Log Information
# ------------------
print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" %
(epoch, opt.epochs, i, len(data_loader), d_loss.item(),
g_loss.item()))
batches_done = epoch * len(data_loader) + i
if batches_done % opt.sample_interval == 0:
save_image(gen_imgs.data[:25],
"images/%d.png" % batches_done,
nrow=5,
normalize=True)
if batches_done % opt.checkpoint_interval == 0:
torch.save(generator.state_dict(),
"checkpoints/generator_%d.pth" % epoch)
# torch.save(discriminator.state_dict(), "checkpoints/discriminator_%d.pth" % epoch)
torch.save(generator.state_dict(), "checkpoints/generator_done.pth")
print("Training Process has been Done!")
def main():
global args
args = parse_args()
# global logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("[%(asctime)s] %(levelname)s:%(name)s:%(message)s")
# file logger
fh = logging.FileHandler(os.path.join(args.save, args.expname)+'.log', mode='w')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
# console logger
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
if not torch.cuda.is_available() and args.cuda:
args.cuda = False
logger.info("CUDA is unavailable, convert to cpu mode")
if args.sparse and args.wd != 0:
logger.error('Sparsity and weight decay are incompatible, pick one!')
exit()
logger.debug(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
# set directory
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# load vocabulary
vocab_path = os.path.join(args.data, "vocab.npy")
vocab = Vocab(
filename=vocab_path,
labels=[constants.PAD_WORD, constants.UNK_WORD, constants.BOS_WORD, constants.EOS_WORD]
)
logger.debug('==> vocabulary size : %d ' % len(vocab))
# load train dataset
train_file = os.path.join(train_dir, "ERdata.pt")
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = ERDataset(train_dir, vocab, 2)
torch.save(train_dataset, train_file)
logger.debug('==> train data size: %d' % len(train_dataset))
# load dev dataset
dev_file = os.path.join(dev_dir, "ERdata.pt")
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = ERDataset(dev_dir, vocab, 2)
torch.save(dev_dataset, dev_file)
logger.debug('==> dev data size: %d' % len(dev_dataset))
# load test dataset
test_file = os.path.join(test_dir, "ERdata.pt")
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = ERDataset(test_dir, vocab, 2)
torch.save(test_dataset, test_file)
logger.debug('==> test data size: %d' % len(test_dataset))
# trainer:
# tree model
model = TreeModel(
len(vocab),
args.input_dim,
args.mem_dim,
2, # 0-1 prediction
args.sparse,
args.freeze_embed
)
# criterion
criterion = nn.KLDivLoss()
if args.cuda:
model.cuda(), criterion.cuda()
# optimizer
if args.optim == 'adam':
optimizer = optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=args.wd
)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(
filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=args.wd
)
elif args.optim == 'sgd':
optimizer = optim.SGD(
filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=args.wd
)
else:
raise Exception("Unknown optimizer")
# metrics
metrics = Metrics(2) # 0-1 prediction
# embeddings
sent_emb_path = os.path.join(args.data, "sent_emb.pt")
raw_sent_emb_path = os.path.join(args.glove, 'glove.840B.300d.txt')
sent_emb = load_word_vectors(sent_emb_path, vocab, raw_sent_emb_path)
logger.debug('==> sentence embedding size: %d * %d' % (sent_emb.size()[0], sent_emb.size()[1]))
if args.cuda:
sent_emb.cuda()
model.sent_emb.weight.data.copy_(sent_emb)
trainer = Trainer(args, model, criterion, optimizer)
# train and test
best = float("-inf")
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
dev_loss, dev_pred = trainer.test(dev_dataset)
test_loss, test_pred = trainer.test(test_dataset)
train_pearson = metrics.pearson(train_pred, train_dataset.labels)
train_mse = metrics.mse(train_pred, train_dataset.labels)
logger.info('==> Epoch {}, Train \tLoss: {}\tPearson: {}\tMSE: {}'.format(epoch, train_loss, train_pearson, train_mse))
dev_pearson = metrics.pearson(dev_pred, dev_dataset.labels)
dev_mse = metrics.mse(dev_pred, dev_dataset.labels)
logger.info('==> Epoch {}, Dev \tLoss: {}\tPearson: {}\tMSE: {}'.format(epoch, dev_loss, dev_pearson, dev_mse))
test_pearson = metrics.pearson(test_pred, test_dataset.labels)
test_mse = metrics.mse(test_pred, test_dataset.labels)
logger.info('==> Epoch {}, Test \tLoss: {}\tPearson: {}\tMSE: {}'.format(epoch, test_loss, test_pearson, test_mse))
if best < dev_pearson:
best = dev_pearson
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'pearson': dev_pearson, 'mse': dev_mse,
'args': args, 'epoch': epoch
}
logger.debug('==> New optimum found, checkpointing everything now...')
torch.save(checkpoint, '%s.pt' % os.path.join(args.save, args.expname))
'',
'steps':
1 + info.splits['train_examples'] // params.training.batch_size
})
model.callbacks.append(progress)
params.logdir = os.path.join(params.logdir, 'multi_mnist')
print('config:', params)
model_dir = os.path.join(params.logdir, model.name)
ckpt = tf.train.Checkpoint(optimizer=model.optimizer, net=model)
manager = tf.train.CheckpointManager(ckpt, model_dir, max_to_keep=3)
ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
print("Restored from {}".format(manager.latest_checkpoint))
init_epoch = params.training.batch_size * model.optimizer.iterations.numpy(
) // info.splits['train_examples']
else:
print("Initializing from scratch.")
init_epoch = 0
if args.train:
train(model, model_log, manager, init_epoch, train_set, test_set)
else:
evaluate(model, model_log, test_set)
if __name__ == "__main__":
args, params = parse_args()
main(args, params)
def prepare_to_train(data=None, glove=None):
args = parse_args()
if data is not None:
args.data = data
if glove is not None:
args.glove = glove
args.input_dim, args.mem_dim = 300, 150
args.hidden_dim, args.num_classes = 50, 5
args.cuda = args.cuda and torch.cuda.is_available()
if args.sparse and args.wd != 0:
print('Sparsity and weight decay are incompatible, pick one!')
exit()
print(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
numpy.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# write unique words from all token files
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
if not os.path.isfile(sick_vocab_file):
token_files_a = [
os.path.join(split, 'a.toks')
for split in [train_dir, dev_dir, test_dir]
]
token_files_b = [
os.path.join(split, 'b.toks')
for split in [train_dir, dev_dir, test_dir]
]
token_files = token_files_a + token_files_b
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
build_vocab(token_files, sick_vocab_file)
# get vocab object from vocab file previously written
vocab = Vocab(filename=sick_vocab_file,
data=[
Constants.PAD_WORD, Constants.UNK_WORD,
Constants.BOS_WORD, Constants.EOS_WORD
])
print('==> SICK vocabulary size : %d ' % vocab.size())
# load SICK dataset splits
train_file = os.path.join(args.data, 'sick_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = SICKDataset(train_dir, vocab, args.num_classes)
torch.save(train_dataset, train_file)
print('==> Size of train data : %d ' % len(train_dataset))
dev_file = os.path.join(args.data, 'sick_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = SICKDataset(dev_dir, vocab, args.num_classes)
torch.save(dev_dataset, dev_file)
print('==> Size of dev data : %d ' % len(dev_dataset))
test_file = os.path.join(args.data, 'sick_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = SICKDataset(test_dir, vocab, args.num_classes)
torch.save(test_dataset, test_file)
print('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
model = SimilarityTreeLSTM(args.cuda, vocab.size(), args.input_dim,
args.mem_dim, args.hidden_dim, args.num_classes,
args.sparse)
criterion = nn.KLDivLoss()
if args.cuda:
model.cuda(), criterion.cuda()
if args.optim == 'adam':
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
emb_file = os.path.join(args.data, 'sick_embed.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else:
# load glove embeddings and vocab
glove_vocab, glove_emb = load_word_vectors(
os.path.join(args.glove, 'glove.840B.300d'))
print('==> GLOVE vocabulary size: %d ' % glove_vocab.size())
emb = torch.Tensor(vocab.size(),
glove_emb.size(1)).normal_(-0.05, 0.05)
# zero out the embeddings for padding and other special words if they are absent in vocab
for idx, item in enumerate([
Constants.PAD_WORD, Constants.UNK_WORD, Constants.BOS_WORD,
Constants.EOS_WORD
]):
emb[idx].zero_()
for word in vocab.labelToIdx.keys():
if glove_vocab.get_index(word):
emb[vocab.get_index(word)] = glove_emb[glove_vocab.get_index(
word)]
torch.save(emb, emb_file)
# plug these into embedding matrix inside model
if args.cuda:
emb = emb.cuda()
model.childsumtreelstm.emb.state_dict()['weight'].copy_(emb)
# create trainer object for training and testing
#trainer = Trainer(args, model, criterion, optimizer)
best = -float('inf')
return (args, best, train_dataset, dev_dataset, test_dataset, metrics,
optimizer, criterion, model)
def main():
global args
args = parse_args()
# global logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter(
"[%(asctime)s] %(levelname)s:%(name)s:%(message)s")
# file logger
fh = logging.FileHandler(os.path.join(args.save, args.expname) + '.log',
mode='w')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
# console logger
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
# argument validation
args.cuda = args.cuda and torch.cuda.is_available()
device = torch.device("cuda:0" if args.cuda else "cpu")
if args.sparse and args.wd != 0:
logger.error('Sparsity and weight decay are incompatible, pick one!')
exit()
logger.debug(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# write unique words from all token files
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
if not os.path.isfile(sick_vocab_file):
token_files_b = [
os.path.join(split, 'b.toks')
for split in [train_dir, dev_dir, test_dir]
]
token_files_a = [
os.path.join(split, 'a.toks')
for split in [train_dir, dev_dir, test_dir]
]
token_files = token_files_a + token_files_b
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
utils.build_vocab(token_files, sick_vocab_file)
# get vocab object from vocab file previously written, create dictionary from list of unique words
vocab = Vocab(filename=sick_vocab_file,
data=[
Constants.PAD_WORD, Constants.UNK_WORD,
Constants.BOS_WORD, Constants.EOS_WORD
])
logger.debug('==> SICK vocabulary size : %d ' % vocab.size())
# load SICK dataset splits
train_file = os.path.join(args.data, 'sick_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = SICKDataset(train_dir, vocab, args.num_classes)
torch.save(train_dataset, train_file)
logger.debug('==> Size of train data : %d ' % len(train_dataset))
dev_file = os.path.join(args.data, 'sick_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = SICKDataset(dev_dir, vocab, args.num_classes)
torch.save(dev_dataset, dev_file)
logger.debug('==> Size of dev data : %d ' % len(dev_dataset))
test_file = os.path.join(args.data, 'sick_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = SICKDataset(test_dir, vocab, args.num_classes)
torch.save(test_dataset, test_file)
logger.debug('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
model = SimilarityTreeLSTM(vocab.size(), args.input_dim, args.mem_dim,
args.hidden_dim, args.num_classes, args.sparse,
args.freeze_embed)
criterion = nn.KLDivLoss()
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
emb_file = os.path.join(args.data, 'sick_embed.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else:
# load glove embeddings and vocab
glove_vocab, glove_emb = utils.load_word_vectors(
os.path.join(args.glove, 'glove.840B.300d'))
logger.debug('==> GLOVE vocabulary size: %d ' % glove_vocab.size())
emb = torch.zeros(vocab.size(),
glove_emb.size(1),
dtype=torch.float,
device=device)
emb.normal_(0, 0.05)
# zero out the embeddings for padding and other special words if they are absent in vocab
for idx, item in enumerate([
Constants.PAD_WORD, Constants.UNK_WORD, Constants.BOS_WORD,
Constants.EOS_WORD
]):
emb[idx].zero_()
for word in vocab.labelToIdx.keys():
if glove_vocab.getIndex(word):
emb[vocab.getIndex(word)] = glove_emb[glove_vocab.getIndex(
word)]
torch.save(emb, emb_file)
# plug these into embedding matrix inside model
model.emb.weight.data.copy_(emb)
model.to(device), criterion.to(device)
if args.optim == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# create trainer object for training and testing
trainer = Trainer(args, model, criterion, optimizer, device)
best = -float('inf')
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
dev_loss, dev_pred = trainer.test(dev_dataset)
test_loss, test_pred = trainer.test(test_dataset)
train_pearson = metrics.pearson(train_pred, train_dataset.labels)
train_mse = metrics.mse(train_pred, train_dataset.labels)
logger.info(
'==> Epoch {}, Train \tLoss: {}\tPearson: {}\tMSE: {}'.format(
epoch, train_loss, train_pearson, train_mse))
dev_pearson = metrics.pearson(dev_pred, dev_dataset.labels)
dev_mse = metrics.mse(dev_pred, dev_dataset.labels)
logger.info(
'==> Epoch {}, Dev \tLoss: {}\tPearson: {}\tMSE: {}'.format(
epoch, dev_loss, dev_pearson, dev_mse))
test_pearson = metrics.pearson(test_pred, test_dataset.labels)
test_mse = metrics.mse(test_pred, test_dataset.labels)
logger.info(
'==> Epoch {}, Test \tLoss: {}\tPearson: {}\tMSE: {}'.format(
epoch, test_loss, test_pearson, test_mse))
if best < test_pearson:
best = test_pearson
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'pearson': test_pearson,
'mse': test_mse,
'args': args,
'epoch': epoch
}
logger.debug(
'==> New optimum found, checkpointing everything now...')
torch.save(checkpoint,
'%s.pt' % os.path.join(args.save, args.expname))
dev_loss, dev_pred, _ = trainer.test(dev_dataset)
test_loss, test_pred, _ = trainer.test(test_dataset)
train_acc = metrics.sentiment_accuracy_score(
train_pred, train_dataset.labels)
dev_acc = metrics.sentiment_accuracy_score(dev_pred,
dev_dataset.labels)
test_acc = metrics.sentiment_accuracy_score(
test_pred, test_dataset.labels)
print('==> Train loss : %f \t' % train_loss, end="")
print('Epoch ', epoch, 'train percentage ', train_acc)
print('Epoch ', epoch, 'dev percentage ', dev_acc)
print('Epoch ', epoch, 'test percentage ', test_acc)
if __name__ == "__main__":
args = parse_args(type=1)
# log to console and file
logger1 = log_util.create_logger(os.path.join('logs', args.name),
print_console=True)
logger1.info("LOG_FILE") # log using loggerba
# attach log to stdout (print function)
s1 = log_util.StreamToLogger(logger1)
sys.stdout = s1
print(
'_________________________________start___________________________________'
)
main()
log_link = log_util.up_gist(os.path.join('logs', args.name + '.log'),
args.name, __file__)
print(log_link)
import sys
import traceback
import random
import config
import utils
from model import Model
from dataloader import Dataloader
from checkpoints import Checkpoints
import torch
from itertools import combinations
import numpy as np
import pdb
args, config_file = config.parse_args()
# Data Loading
if args.train == 'face_cls':
from test_cls import Tester
from train_cls import Trainer
if args.train == 'face_margin':
from test_margin import Tester
from train_margin import Trainer
if args.dataset_train == 'ClassSamplesDataLoader':
from train_classload import Trainer
def main():
# parse the arguments
def main():
global args
args = parse_args(type=1)
print(args.name)
print(args.model_name)
args.input_dim = 300
if args.mem_dim == 0:
if args.model_name == 'dependency':
args.mem_dim = 168
elif args.model_name == 'constituency':
args.mem_dim = 150
elif args.model_name == 'lstm':
args.mem_dim = 168
elif args.model_name == 'bilstm':
args.mem_dim = 168
if args.num_classes == 0:
if args.fine_grain:
args.num_classes = 5 # 0 1 2 3 4
else:
args.num_classes = 3 # 0 1 2 (1 neutral)
elif args.num_classes == 2:
# assert False # this will not work
assert not args.fine_grain
args.cuda = args.cuda and torch.cuda.is_available()
# args.cuda = False
print(args)
# torch.manual_seed(args.seed)
# if args.cuda:
# torch.cuda.manual_seed(args.seed)
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# write unique words from all token files
token_files = [
os.path.join(split, 'sents.toks')
for split in [train_dir, dev_dir, test_dir]
]
vocab_file = os.path.join(args.data, 'vocab-cased.txt') # use vocab-cased
# build_vocab(token_files, vocab_file) NO, DO NOT BUILD VOCAB, USE OLD VOCAB
# get vocab object from vocab file previously written
vocab = Vocab(filename=vocab_file)
print('==> SST vocabulary size : %d ' % vocab.size())
# Load SST dataset splits
is_preprocessing_data = False # let program turn off after preprocess data
# train
train_file = os.path.join(args.data, 'sst_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = SSTDataset(train_dir, vocab, args.num_classes,
args.fine_grain, args.model_name)
torch.save(train_dataset, train_file)
is_preprocessing_data = True
# dev
dev_file = os.path.join(args.data, 'sst_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = SSTDataset(dev_dir, vocab, args.num_classes,
args.fine_grain, args.model_name)
torch.save(dev_dataset, dev_file)
is_preprocessing_data = True
# test
test_file = os.path.join(args.data, 'sst_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = SSTDataset(test_dir, vocab, args.num_classes,
args.fine_grain, args.model_name)
torch.save(test_dataset, test_file)
is_preprocessing_data = True
criterion = nn.NLLLoss()
# initialize model, criterion/loss_function, optimizer
model = DMNWraper(args.cuda, args.input_dim, args.mem_dim, criterion,
args.train_subtrees, args.num_classes, args.embdrop)
embedding_model = nn.Embedding(vocab.size(), args.input_dim)
if args.cuda:
embedding_model = embedding_model.cuda()
if args.cuda:
model.cuda(), criterion.cuda()
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
if args.embedding == 'glove':
emb_torch = 'sst_embed.pth'
emb_vector = 'glove.840B.300d'
emb_vector_path = os.path.join(args.glove, emb_vector)
assert os.path.isfile(emb_vector_path + '.txt')
elif args.embedding == 'paragram':
emb_torch = 'sst_embed_paragram.pth'
emb_vector = 'paragram_300_sl999'
emb_vector_path = os.path.join(args.paragram, emb_vector)
assert os.path.isfile(emb_vector_path + '.txt')
elif args.embedding == 'paragram_xxl':
emb_torch = 'sst_embed_paragram_xxl.pth'
emb_vector = 'paragram-phrase-XXL'
emb_vector_path = os.path.join(args.paragram, emb_vector)
assert os.path.isfile(emb_vector_path + '.txt')
else:
assert False
emb_file = os.path.join(args.data, emb_torch)
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else:
# load glove embeddings and vocab
glove_vocab, glove_emb = load_word_vectors(emb_vector_path)
print('==> Embedding vocabulary size: %d ' % glove_vocab.size())
emb = torch.zeros(vocab.size(), glove_emb.size(1))
for word in vocab.labelToIdx.keys():
if glove_vocab.getIndex(word):
emb[vocab.getIndex(word)] = glove_emb[glove_vocab.getIndex(
word)]
else:
emb[vocab.getIndex(word)] = torch.Tensor(
emb[vocab.getIndex(word)].size()).normal_(-0.05, 0.05)
torch.save(emb, emb_file)
is_preprocessing_data = True # flag to quit
print('done creating emb, quit')
if is_preprocessing_data:
print('quit program')
quit()
# plug these into embedding matrix inside model
if args.cuda:
emb = emb.cuda()
embedding_model.state_dict()['weight'].copy_(emb)
if args.optim == 'adam':
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'adagrad':
# optimizer = optim.Adagrad(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wd)
optimizer = optim.Adagrad(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'adam_combine':
optimizer = optim.Adam([{
'params': model.parameters(),
'lr': args.lr,
'weight_decay': args.wd
}, {
'params': embedding_model.parameters(),
'lr': args.emblr,
'weight_decay': args.embwd
}])
args.manually_emb = 0
elif args.optim == 'adagrad_combine':
optimizer = optim.Adagrad([{
'params': model.parameters(),
'lr': args.lr,
'weight_decay': args.wd
}, {
'params': embedding_model.parameters(),
'lr': args.emblr,
'weight_decay': args.embwd
}])
args.manually_emb = 0
elif args.optim == 'adam_combine_v2':
model.embedding_model = embedding_model
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
args.manually_emb = 0
metrics = Metrics(args.num_classes)
utils.count_param(model)
trainer = SentimentTrainer(args, model, embedding_model, criterion,
optimizer)
trainer.set_initial_emb(emb)
question_idx = vocab.labelToIdx['sentiment']
question_idx = torch.Tensor([question_idx])
trainer.set_question(question_idx)
# trainer = SentimentTrainer(args, model, embedding_model ,criterion, optimizer)
mode = args.mode
if mode == 'DEBUG':
for epoch in range(args.epochs):
# print a tree
tree, sent, label = dev_dataset[3]
utils.print_span(tree, sent, vocab)
quit()
dev_loss = trainer.train(dev_dataset)
dev_loss, dev_pred, _ = trainer.test(dev_dataset)
test_loss, test_pred, _ = trainer.test(test_dataset)
dev_acc = metrics.sentiment_accuracy_score(dev_pred,
dev_dataset.labels)
test_acc = metrics.sentiment_accuracy_score(
test_pred, test_dataset.labels)
print('==> Dev loss : %f \t' % dev_loss, end="")
print('Epoch ', epoch, 'dev percentage ', dev_acc)
elif mode == "PRINT_TREE":
for i in range(0, 10):
ttree, tsent, tlabel = dev_dataset[i]
utils.print_tree(ttree, 0)
print('_______________')
print('break')
quit()
elif mode == 'EVALUATE':
filename = args.name + '.pth'
epoch = args.epochs
model_name = str(epoch) + '_model_' + filename
embedding_name = str(epoch) + '_embedding_' + filename
model = torch.load(os.path.join(args.saved, model_name))
embedding_model = torch.load(os.path.join(args.saved, embedding_name))
trainer = SentimentTrainer(args, model, embedding_model, criterion,
optimizer)
trainer.set_question(question_idx)
test_loss, test_pred, subtree_metrics = trainer.test(test_dataset)
test_acc = metrics.sentiment_accuracy_score(
test_pred, test_dataset.labels, num_classes=args.num_classes)
print('Epoch with max dev:' + str(epoch) + ' |test percentage ' +
str(test_acc))
print('____________________' + str(args.name) + '___________________')
print_list = subtree_metrics.print_list
torch.save(print_list,
os.path.join(args.saved, args.name + 'printlist.pth'))
utils.print_trees_file(args,
vocab,
test_dataset,
print_list,
name='tree')
elif mode == "EXPERIMENT":
# dev_loss, dev_pred = trainer.test(dev_dataset)
# dev_acc = metrics.sentiment_accuracy_score(dev_pred, dev_dataset.labels, num_classes=args.num_classes)
max_dev = 0
max_dev_epoch = 0
filename = args.name + '.pth'
for epoch in range(args.epochs):
# train_loss, train_pred, _ = trainer.test(train_dataset)
train_loss_while_training = trainer.train(train_dataset)
train_loss, train_pred, _ = trainer.test(train_dataset)
dev_loss, dev_pred, _ = trainer.test(dev_dataset)
dev_acc = metrics.sentiment_accuracy_score(
dev_pred, dev_dataset.labels, num_classes=args.num_classes)
train_acc = metrics.sentiment_accuracy_score(
train_pred, train_dataset.labels, num_classes=args.num_classes)
print('==> Train loss : %f \t' % train_loss_while_training,
end="")
print('Epoch ', epoch, 'dev percentage ', dev_acc)
print('Epoch %d dev percentage %f ' % (epoch, dev_acc))
print('Train acc %f ' % (train_acc))
if dev_acc > max_dev:
print('update best dev acc %f ' % (dev_acc))
max_dev = dev_acc
max_dev_epoch = epoch
utils.mkdir_p(args.saved)
torch.save(
model,
os.path.join(args.saved,
str(epoch) + '_model_' + filename))
torch.save(
embedding_model,
os.path.join(args.saved,
str(epoch) + '_embedding_' + filename))
gc.collect()
print('epoch ' + str(max_dev_epoch) + ' dev score of ' + str(max_dev))
print('eva on test set ')
model = torch.load(
os.path.join(args.saved,
str(max_dev_epoch) + '_model_' + filename))
embedding_model = torch.load(
os.path.join(args.saved,
str(max_dev_epoch) + '_embedding_' + filename))
trainer = SentimentTrainer(args, model, embedding_model, criterion,
optimizer)
trainer.set_question(question_idx)
test_loss, test_pred, _ = trainer.test(test_dataset)
test_acc = metrics.sentiment_accuracy_score(
test_pred, test_dataset.labels, num_classes=args.num_classes)
print('Epoch with max dev:' + str(max_dev_epoch) +
' |test percentage ' + str(test_acc))
print('____________________' + str(args.name) + '___________________')
else:
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred, _ = trainer.test(train_dataset)
dev_loss, dev_pred, _ = trainer.test(dev_dataset)
test_loss, test_pred, _ = trainer.test(test_dataset)
train_acc = metrics.sentiment_accuracy_score(
train_pred, train_dataset.labels)
dev_acc = metrics.sentiment_accuracy_score(dev_pred,
dev_dataset.labels)
test_acc = metrics.sentiment_accuracy_score(
test_pred, test_dataset.labels)
print('==> Train loss : %f \t' % train_loss, end="")
print('Epoch ', epoch, 'train percentage ', train_acc)
print('Epoch ', epoch, 'dev percentage ', dev_acc)
print('Epoch ', epoch, 'test percentage ', test_acc)
def pytest_funcarg__test_conf(request):
args = "--mode train".split(" ")
return config.parse_args(raw_args = args)
def main():
global args
args = parse_args()
args.input_dim, args.mem_dim = 200, 150
args.hidden_dim, args.num_classes = 50, 2
args.cuda = args.cuda and torch.cuda.is_available()
if args.sparse and args.wd != 0:
print('Sparsity and weight decay are incompatible!')
exit()
print(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
numpy.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
word_vectors_path = os.path.join(
args.glove, '/data1/qspace/yananlu/embedding/huge.readable')
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# write unique words from all token files
xianer_vocab_file = os.path.join(args.data, 'xianer.vocab')
if not os.path.isfile(xianer_vocab_file):
token_files_a = [
os.path.join(split, 'a.toks')
for split in [train_dir, dev_dir, test_dir]
]
token_files_b = [
os.path.join(split, 'b.toks')
for split in [train_dir, dev_dir, test_dir]
]
token_files = token_files_a + token_files_b
xianer_vocab_file = os.path.join(args.data, 'xianer.vocab')
build_vocab(token_files, xianer_vocab_file)
# get vocab object from vocab file previously written
vocab = Vocab(filename=xianer_vocab_file,
data=[
config.PAD_WORD, config.UNK_WORD, config.BOS_WORD,
config.EOS_WORD
])
print('==> Xianer vocabulary size : %d ' % vocab.size())
# load Xianer dataset splits
train_file = os.path.join(args.data, 'xianer_train.pth') # quora_train.pth
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = QuoraDataset(train_dir, vocab, args.num_classes)
torch.save(train_dataset, train_file)
print('==> Size of train data : %d ' % len(train_dataset))
dev_file = os.path.join(args.data, 'xianer_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = QuoraDataset(dev_dir, vocab, args.num_classes)
torch.save(dev_dataset, dev_file)
print('==> Size of dev data : %d ' % len(dev_dataset))
test_file = os.path.join(args.data, 'xianer_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = QuoraDataset(test_dir, vocab, args.num_classes)
torch.save(test_dataset, test_file)
print('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
model = SimilarityTreeLSTM(
args.cuda,
vocab, # vocab.size()
args.input_dim,
args.mem_dim,
args.hidden_dim,
args.num_classes,
args.sparse)
# criterion = nn.KLDivLoss()
criterion = nn.CrossEntropyLoss() # nn.MSELoss()
if args.cuda:
model.cuda(), criterion.cuda()
if args.optim == 'adam':
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
emb_file = os.path.join(args.data, 'xianer_embed.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else: # load glove embeddings and vocab
glove_vocab, glove_emb = load_word_vectors(word_vectors_path)
print('==> GLOVE vocabulary size: %d ' % glove_vocab.size())
emb = torch.Tensor(vocab.size(),
glove_emb.size(1)).normal_(-0.05, 0.05)
# zero out the embeddings for padding and other special words if they are absent in vocab
for idx, item in enumerate([
config.PAD_WORD, config.UNK_WORD, config.BOS_WORD,
config.EOS_WORD
]):
emb[idx].zero_()
for word in vocab.labelToIdx.keys():
if glove_vocab.getIndex(word):
emb[vocab.getIndex(word)] = glove_emb[glove_vocab.getIndex(
word)]
torch.save(emb, emb_file)
# plug these into embedding matrix inside model
if args.cuda:
emb = emb.cuda()
model.childsumtreelstm.emb.state_dict()['weight'].copy_(emb)
# create trainer object for training and testing
trainer = Trainer(args, model, criterion, optimizer)
best = -float('inf')
for epoch in range(args.epochs):
_ = trainer.train(train_dataset)
train_loss, train_pred, train_score = trainer.test(train_dataset,
plot_flag=False)
train_pearson = metrics.pearson(train_pred, train_dataset.labels)
train_mse = metrics.mse(train_pred, train_dataset.labels)
train_accu = metrics.accuracy(train_pred, train_dataset.labels)
train_f1 = metrics.f1(train_pred, train_dataset.labels) # GEOFF
print('==> Train Loss: {}\tPearson: {}\tMSE: {}\tAccu: {}\tF1: {}'.
format(train_loss, train_pearson, train_mse, train_accu,
train_f1))
dev_loss, dev_pred, dev_score = trainer.test(dev_dataset,
plot_flag=False)
dev_pearson = metrics.pearson(dev_pred, dev_dataset.labels)
dev_mse = metrics.mse(dev_pred, dev_dataset.labels)
dev_accu = metrics.accuracy(dev_pred, dev_dataset.labels)
dev_f1 = metrics.f1(dev_pred, dev_dataset.labels)
print(
'==> Dev Loss: {}\tPearson: {}\tMSE: {}\tAccu: {}\tF1: {}'.format(
dev_loss, dev_pearson, dev_mse, dev_accu, dev_f1))
test_loss, test_pred, test_score = trainer.test(test_dataset,
plot_flag=False)
test_pearson = metrics.pearson(test_pred, test_dataset.labels)
test_mse = metrics.mse(test_pred, test_dataset.labels)
test_accu = metrics.accuracy(test_pred, test_dataset.labels)
test_f1 = metrics.f1(test_pred, test_dataset.labels)
print(
'==> Test Loss: {}\tPearson: {}\tMSE: {}\tAccu: {}\tF1: {}'.format(
test_loss, test_pearson, test_mse, test_accu, test_f1))
if best < dev_f1: # xianer use dev
best = dev_f1
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'pearson': dev_pearson,
'mse': dev_mse,
'f1': dev_f1,
'accuracy': dev_accu,
'args': args,
'epoch': epoch
}
print('==> New optimum found.')
torch.save(
checkpoint,
'%s.pt' % os.path.join(args.save, args.expname + '.pth'))
remove_dirs() # clear attention dir
trainer.test(dev_dataset, plot_flag=True) # plot attention
numpy.savetxt(os.path.join(args.data, 'dev/dev.predict'),
dev_score.cpu().numpy()) # save predict result
numpy.savetxt(os.path.join(args.data, 'train/train.predict'),
train_score.cpu().numpy())
numpy.savetxt(os.path.join(args.data, 'test/test.predict'),
test_score.cpu().numpy())
def parse_conf(model, test_corp):
args = "--mode train --embed 50 --hidden 30 --minbatch 15".split(" ")
args += ["--model", model]
conf = config.parse_args(raw_args = args)
conf.corpus = test_corp
return conf
# 00471 20160317 002
#
# 01332 20150102 101 1:10
# 01332 20150105 002 1 - 1
# 01332 20150520 002 4 - 1
# 16.75
request_5519.start_time = 0
request_5519.req_num = 1
request_5519.restore_type = message.RESTORE_RIGHT_AFTER
m = self.client.send_and_receive(request_5519)
self.assertEqual(len(m.data), 1)
def tearDown(self):
self.client.close()
def runtest(ns):
if ns.message.keys() == all_messages.keys():
suite = unittest.TestLoader().loadTestsFromTestCase(TestMessage)
else:
suite = unittest.TestSuite(map(TestMessage,
('test_message_{}'.format(x) for x in ns.message.keys())))
unittest.TextTestRunner(verbosity=ns.verbosity).run(suite)
if __name__ == '__main__':
import sys
ns = config.parse_args(sys.argv[1:], receive_push=False)
runtest(ns)
def pytest_funcarg__test_conf_v2(request):
args = "--mode train --embed 50 --hidden 30 --minbatch 2 --model v2 --epoch 2 --minor_word 0".split(" ")
conf = config.parse_args(raw_args = args)
conf.corpus = corpus.open(test_file)
return conf
#!/usr/bin/env python
import os
import logging
import config
def convert_plist_file():
plist_file_path = os.path.realpath(args.plist_file_path)
logger.info(plist_file_path)
logger.debug(config.OUTPUT_DIRECTORY)
if __name__ == '__main__':
args = config.parse_args()
logging.basicConfig(filename=config.LOGGING_FILE, level=config.LOGGING_LEVEL)
logger = logging.getLogger(__name__)
convert_plist_file()
def main():
args=parse_args()
print(args)
num_classes = 7
data_dir = args.data_dir #,'train_texts.blk')
train_file=os.path.join(data_dir,'train_data.pth')
#val_dir = args.val_data #'val_texts.blk')
val_file= os.path.join(data_dir,'val_data.pth')
vocab_file="../data/vocab.txt"
vocab = Vocab(filename=vocab_file)
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = WebKbbDataset(vocab, num_classes,os.path.join(data_dir,'train_texts.blk'),os.path.join(data_dir,'train_labels.blk'))
torch.save(train_dataset, train_file)
if os.path.isfile(val_file):
val_dataset = torch.load(val_file)
else:
val_dataset = WebKbbDataset(vocab, num_classes,os.path.join(data_dir,'val_texts.blk'),os.path.join(data_dir,'val_labels.blk'))
torch.save(val_dataset, val_file)
vocab_size=vocab.size()
in_dim=200
mem_dim=200
hidden_dim=200
num_classes=7
sparsity=True
freeze=args.freeze_emb
epochs=args.epochs
lr=args.lr
pretrain=args.pretrain
cuda_flag=True
if not torch.cuda.is_available():
cuda_flag=False
model = DomTreeLSTM(vocab_size,in_dim, mem_dim, hidden_dim, num_classes, sparsity, freeze)
criterion = nn.CrossEntropyLoss()
if pretrain:
emb_file = os.path.join('../data', 'emb.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
print(emb.size())
print("Embedding weights loaded")
else:
print("Embedding file not found")
model.emb.weight.data.copy_(emb)
optimizer = optimizer = optim.Adagrad(filter(lambda p: p.requires_grad, model.parameters()), lr=lr)
trainer = Trainer(model, criterion, optimizer,train_dataset,val_dataset,cuda_flag=cuda_flag)
for epoch in range(epochs):
trainer.train()
trainer.test()
arg.augm_scal)
coord, vector = make_input_tps_param(tps_param_dic)
coord, vector = coord.to(device), vector.to(device)
image_spatial_t, _ = ThinPlateSpline(original, coord, vector,
original.shape[3], device)
image_appearance_t = K.ColorJitter(arg.brightness,
arg.contrast,
arg.saturation,
arg.hue)(original)
image, reconstruction, mu, shape_stream_parts, heat_map = model(
original, image_spatial_t, image_appearance_t, coord,
vector)
if __name__ == '__main__':
arg = DotMap(vars(parse_args()))
main(arg)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
self.beta = nn.Parameter(torch.zeros(num_features))
def forward(self, x):
counts = np.array([ np.float32(v) for v in row.split() ])
return counts
def log_softmax(output):
if output.owner.op == T.nnet.softmax_op:
x = output.owner.inputs[0]
k = T.max(x,axis=1,keepdims=True)
sum_x = T.log(T.sum(T.exp(x - k),axis=1,keepdims=True)) + k
print >> sys.stderr, "Stable log softmax"
return x - sum_x
else:
return T.log(softmax)
if __name__ == "__main__":
config.parse_args()
X = T.matrix('X')
P = Parameters()
predict = model.build(P)
_,outputs = predict(X)
counts = load_counts()
predict = theano.function(
inputs = [X],
outputs = log_softmax(outputs) - T.log(counts/T.sum(counts))
)
if predict != None:
stream = data_io.context(ark_io.parse_binary(sys.stdin),left=5,right=5)
for name,frames in stream:
import os
from dataloder import Dataset
from solver import Solver
from config import parse_args, read_conf_file
args_cmd = parse_args()
args_yml = read_conf_file(args_cmd.config)
data = Dataset(args_yml)
if __name__ == '__main__':
module = args_cmd.module
os.environ['CUDA_VISIBLE_DEVICES'] = args_cmd.GPU
if module == 'test_dataset':
data.test_dataset()
elif module == 'create_dataset':
data.create_dataset()
elif module == 'train':
solver = Solver(args_yml)
solver.train()
elif module == 'test_dir':
solver = Solver(args_cmd)
solver.test_dir()
for i in range(len(devList)):
strLabel = devList[i]
fpTuple = getfpTuple(strLabel, x_day_dir)
label2Data[i] = fpTuple
oneData, oneLabel = getOneDevData(fpTuple, i, params)
allData.extend(oneData)
allLabel.extend(oneLabel)
splitRatio = params['splitRatio']
trainData, trainLabels, valData, valLabels, testData, testLabels = splitData(opts, splitRatio, allData, allLabel)
return trainData, trainLabels, valData, valLabels, testData, testLabels
def test_read_one_data(opts):
allDataSize = 1000
splitRatio = {'train': 0.7, 'val': 0.2, 'test': 0.1}
train_ind, val_ind, test_ind = getSplitIndex(allDataSize, splitRatio)
# Load a dataset
x_day_dir = opts.input
trainData, trainLabels, valData, valLabels, testData, testLabels = getData(opts, x_day_dir)
print(trainData.shape, valData.shape, testData.shape)
print(trainLabels.shape)
if __name__ == "__main__":
opts = config.parse_args(sys.argv)
test_read_one_data(opts)
print('all test passed!')
"""
from config import parse_args
from iterator import DiscoveryIterator, SmartIterator
from keras.optimizers import RMSprop
from models import ReferringRelationshipsModel
from utils.eval_utils import format_results_eval
from utils.visualization_utils import objdict
from utils.eval_utils import get_metrics
from utils.train_utils import get_loss_func
import json
import os
if __name__ == '__main__':
# Parse command line arguments.
args = parse_args(evaluation=True)
models_dir = os.path.dirname(args.model_checkpoint)
params = objdict(
json.load(open(os.path.join(models_dir, "args.json"), "r")))
params.batch_size = args.batch_size
params.discovery = args.discovery
params.shuffle = False
# If the dataset does exists, alert the user.
if not os.path.isdir(args.data_dir):
raise ValueError('The directory %s doesn\'t exist. '
'Exiting evaluation!' % args.data_dir)
# Make sure the dataset and images exist.
for hdf5_file in [
os.path.join(args.data_dir, 'images.hdf5'),