def main():
# get hyperparameters from input args
opt = ArgParser().get_options()
# deterministic seed across numpy, torch and cuda
# store as variable due to garbage collecting
seeder = Seeder(opt.seed)
seeder.activate()
# data from ./zoo.py
opt.model_class = models[opt.model_name]
opt.initializer = initializers[opt.initializer]
opt.optimizer = optimizers[opt.optimizer]
# data from ./config.yml
opt.dataset_file = config['datasets'][opt.dataset]
opt.model_inputs = config['model_inputs'][opt.model_name]
# run on gpu if available
device = opt.device
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
opt.device = torch.device(device)
runner = ModelRunner(opt)
runner.run()
def __init__(self, port=80, threaded=True, debug=True):
self.port = port
self.threaded = threaded
self.debug = debug
self.app = Flask(__name__.split('.')[0])
self.app.config['SECRET_KEY'] = str(
UUID(int=random.randint(0, 10000000000)))
self.app.add_url_rule('/',
'index',
self.index,
methods=['GET', 'POST'])
self.app.add_url_rule('/players/<p1>/<p2>',
'players',
self.players,
methods=['GET', 'POST'])
self.app.add_url_rule('/bets', 'bets', self.bets)
self.app.add_url_rule('/rankings', 'rankings', self.rankings)
self.app.add_url_rule('/recent', 'recent', self.recentMatches)
helper.fillRegionDict()
model = MLP(max_epochs=10000,
batch_size=128,
learning_rate=5e-3,
width=50,
layers=1)
print('Model Created')
self.runner = ModelRunner(model,
"data/matchResults_aligulac.csv",
trainRatio=0.8,
testRatio=0.2,
lastGameId="302054",
keepPercent=1.0,
decay=False)
print(datetime.now(), 'Model Runner Created')
print(self.runner.getLastId())
self.runner.loadProfiles()
self.runner.model.loadBackup()
self.runner.clearMemory()
print("Memory Cleared")
rankingsList = self.runner.generateRanking(20)
rank = 1
print(
"Rank, Name, Race, Country, Project W/R, Elo, Glicko, MatchExpAvg")
for [rate, profile] in rankingsList:
print(rank, profile.name, profile.race, profile.country, rate,
profile.elo, profile.glickoRating, profile.expOverall)
rank += 1
self.liveThread = threading.Thread(target=self.runner.getLive)
self.liveThread.start()
def main():
parser = argparse.ArgumentParser(description="-----[IMDB-classifier]-----")
parser.add_argument("--sample",
default=False,
action='store_true',
help="flag whether use sample dataset")
parser.add_argument(
"--mode",
default="train",
help="train: train (with test) a model / test: test saved models")
parser.add_argument("--model",
default="simple-gru",
help="available models: simple-gru, ...")
parser.add_argument("--epoch",
default=10,
type=int,
help="number of max epoch")
parser.add_argument("--learning_rate",
default=0.001,
type=float,
help="learning rate")
parser.add_argument("--batch_size",
default=32,
type=int,
help="batch size")
options = parser.parse_args()
params = {
'sample': options.sample,
'model': options.model,
'mode': options.mode,
'batch_size': options.batch_size,
'epoch': options.epoch,
'learning_rate': options.learning_rate
}
modelRunner = ModelRunner(params)
if options.mode == 'train':
print("=" * 20 + "TRAINING STARTED" + "=" * 20)
modelRunner.train()
elif options.mode == 'test':
print("=" * 20 + "TESTING STARTED" + "=" * 20)
modelRunner.load_model()
modelRunner.test()
def download_model_from_storage(self):
if len(self.model_url) == 0:
return
try:
self.logger.info("request for downloading model file: %s",
self.model_url)
r = requests.get(self.model_url, stream=True)
self.logger.info("request status for downloading model file: %d",
r.status_code)
filepath = 'ml_model'
with open(filepath, 'wb') as fd:
for chunk in r.iter_content(2000):
fd.write(chunk)
self.modelrunner = ModelRunner(filepath)
except Exception as e:
self.logger.info("model file cannot be downloaded: %s", str(e))
def main(args):
# create dataloader
data_loader = DataLoaderFactory()
train_data_loader = data_loader.get_input_for_ccf(file_path,
batch_size,
max_seq_length,
shuffle,
drop_last)
test_data_loader = data_loader.get_input_for_ccf(file_path,
batch_size,
max_seq_length,
shuffle,
drop_last)
dev_data_loader = data_loader.get_input_for_ccf(file_path,
batch_size,
max_seq_length,
shuffle,
drop_last)
# 设置task model
task_model = TaskModel(num_labels=len(args.label_to_id), dropout_prob=args.dropout_prob,
bret_pretrainded_path=args.bert_pretrain_path)
# 重新加载模型参数
# print("从test_model.bin 加载参数")
# task_model.load_state_dict(torch.load(os.path.join(os.path.dirname(curr_path), "model_save", "model_89.15.bin"),
# "cuda" if torch.cuda.is_available() else None))
# 设置优化器
optimizer = get_optim(task_model.parameters(), args)
# print config
print("args", args)
# 开始模型训练
cls_app = ModelRunner(task_type="cls", is_bert=False, label_to_id=args.label_to_id)
cls_app.train(total_step=args.total_step, eval_per_step=args.eval_per_step, task_model=task_model,
model_save_path=args.model_save_path, optimizer=optimizer,
train_data_loader=train_data_loader, dev_data_loader=dev_data_loader,
eval_label_list=list(args.label_to_id.values()), compare_param="f1",
eval_file_save_name=args.eval_file_save_name)
cls_app.predict_for_ccf(dataiter=dev_data_loader, model=task_model,
save_file_path="test_predict_out.txt",
model_path=args.model_save_path, load_from_onnx=False)
print("模型运行完成")
def main(argv):
data_loader = BaseLoader.get_loader_from_flags(FLAGS.data_set)
train_set, valid_set, test_set = data_loader.load_dataset(FLAGS.num_steps, FLAGS.shuffle_train)
model = DualStageRNN(encoder_dim=FLAGS.encoder_dim,
decoder_dim=FLAGS.decoder_dim,
num_steps=FLAGS.num_steps,
num_series=data_loader.num_series,
use_cur_exg=FLAGS.use_cur_exg)
save_path = 'data/data_nasdaq'
model_runner = ModelRunner(model, data_loader.label_scaler, FLAGS,save_path)
model_runner.train(train_set, valid_set, test_set, FLAGS.max_epoch)
# model_runner.restore('logs/ModelWrapper/lr-0.001_encoder-32_decoder-32/20190922-125838/saved_model')
model_runner.evaluate(test_set, plot=FLAGS.plot_prediction)
return
def prep_trial(self, input_params, grid_logger, grid_logger_avg):
runners = []
for it in range(input_params['iterations']):
# train and test split
if Train_test_split == 'bipartite':
person_data = np.delete(np.arange(len(self._labels[0])), np.arange(Bipartite_products))
rand_test_indices = np.random.choice(person_data, round(len(person_data) * 0.9), replace=False)
rand_train_indices = np.delete(np.arange(len(self._labels[0])), rand_test_indices)
else:
rand_test_indices = np.random.choice(len(self._labels[0]), round(len(self._labels[0]) * 0.9), replace=False)
rand_train_indices = np.delete(np.arange(len(self._labels[0])), rand_test_indices)
train = [[k for k in rand_train_indices if self._labels[j][k] != -1] for j in range(len(self._labels))]
test = [[k for k in rand_test_indices if self._labels[j][k] != -1] for j in range(len(self._labels))]
test_labels = [torch.tensor([self._labels[j][k] for k in rand_test_indices if self._labels[j][k] != -1],
dtype=torch.double).to(self._device) for j in range(input_params['time_inds'])]
train_labels = [torch.tensor([self._labels[j][k] for k in rand_train_indices if self._labels[j][k] != -1],
dtype=torch.double).to(self._device) for j in range(input_params['time_inds'])]
input_params['it_num'] = it
input_params['activation'] = torch.nn.functional.relu
input_params['train_person'] = rand_train_indices
input_params['test_person'] = rand_test_indices
input_params['training_inds'] = train
input_params['test_inds'] = test
input_params['training_labels'] = train_labels
input_params['test_labels'] = test_labels
input_params['adj_matrices'] = self._adjacency_matrices
input_params['feature_matrices'] = self._feature_matrices
dumping_name = ""
logger = self.fix_logger(dumping_name)
runner = ModelRunner(input_params, logger=logger)
runners.append(runner)
execute_runners(runners, grid_logger, grid_logger_avg, is_nni=self._nni)
import logging
# pylint: disable-msg=F0401
# pylint: disable=no-name-in-module
from app_config import mlflow_config, service_config
from model_runner import ModelRunner
model_handler = ModelRunner(mlflow_config, service_config)
if __name__ == '__main__':
logging_level = service_config['log_level']
log = logging.getLogger('werkzeug')
log.setLevel(logging_level)
model_handler.run_flask_server(port=service_config['port'], host='0.0.0.0')
def main() -> None:
device = f"cuda:{gpu_number}" if torch.cuda.is_available() else torch.device('cpu')
print(device)
train_boxes_df = pd.read_csv(META_TRAIN)
train_boxes_df = preprocess_boxes(train_boxes_df)
train_images_df = pd.read_csv('folds/orig_alex_folds.csv')
print(f'\nTotal images: {len(train_images_df.image_id.unique())}')
# Leave only images with bboxes
image_id_column = 'image_id'
print('Leave only train images with boxes')
with_boxes_filter = train_images_df[image_id_column].isin(train_boxes_df[image_id_column].unique())
# train/val images
images_val = train_images_df.loc[
(train_images_df['fold'] == fold) & with_boxes_filter, image_id_column].values
images_train = train_images_df.loc[
(train_images_df['fold'] != fold) & with_boxes_filter, image_id_column].values
print(f'\nTrain images:{len(images_train)}, validation images {len(images_val)}')
# get datasets
train_dataset = WheatDataset(
image_ids = images_train[:16],
image_dir = TRAIN_DIR,
#train_box_callback,
labels_df = train_boxes_df,
transforms = get_train_transforms(image_size),
is_test = False
)
valid_dataset = WheatDataset(
image_ids = images_val[:16],
image_dir = TRAIN_DIR,
labels_df = train_boxes_df,
#train_box_callback,
transforms=get_valid_transforms(image_size),
is_test=True
)
# get dataloaders
train_data_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
collate_fn=collate_fn
)
valid_data_loader = DataLoader(
valid_dataset,
batch_size=inf_batch_size,
shuffle=False,
num_workers=num_workers,
collate_fn=collate_fn
)
# efficientdet config
config = get_efficientdet_config(f'tf_efficientdet_d{model_name[-1]}')
config.num_classes = 1
config.image_size = image_size
net = EfficientDet(config, pretrained_backbone=False)
net.class_net = HeadNet(config, num_outputs=config.num_classes, norm_kwargs=dict(eps=.001, momentum=.01))
weights_file = f'{experiment_name}.pth'
# If resume training
if os.path.exists(weights_file):
print(f'Continue training, loading weights from: {weights_file}')
load_weights(net, weights_file)
else:
print(f'Use coco pretrain')
pretrain = get_effdet_pretrain_names(model_name)
load_weights(net, '../../timm-efficientdet-pytorch/{pretrain}')
model = DetBenchTrain(net, config)
runner = ModelRunner(model, device)
weights_file = f'{experiment_name}.pth'
# add tags
neptune.log_text('save checkpoints as', weights_file[:-4])
# run training
runner.run_train(train_data_loader, valid_data_loader, n_epoches=n_epochs, weights_file=weights_file,
factor=factor, start_lr=start_lr, min_lr=min_lr, lr_patience=lr_patience, overall_patience=overall_patience, loss_delta=loss_delta)
neptune.stop()
n_items,
iter=iter)
X_neg_sppmi = convert_to_SPPMI_matrix(X_neg,
max_row=n_items,
shifted_K=SHIFTED_K_VALUE)
Y_neg_sppmi = None
t2 = time.time()
print 'Time : %d seconds' % (t2 - t1)
# build the model
print 'build the model...'
t1 = time.time()
runner = ModelRunner(train_data,
vad_data,
None,
X_sppmi,
X_neg_sppmi,
Y_sppmi,
None,
save_dir=save_dir)
U, V, ndcg100 = runner.run("rme",
n_jobs=1,
lam=lam,
lam_emb=lam_emb,
n_components=n_components,
ret_params_only=1)
t2 = time.time()
print 'Time : %d seconds' % (t2 - t1)
print '*************************************ITER %d ******************************************' % iter
print 'NDCG@100 at this iter:', ndcg100
#
if best_ndcg100 < ndcg100:
def main() -> None:
device = f"cuda:{gpu_number}" if torch.cuda.is_available(
) else torch.device('cpu')
print(device)
train_boxes_df = pd.read_csv(META_TRAIN)
train_boxes_df = preprocess_boxes(train_boxes_df)
print(train_boxes_df.head())
#train_images_df = pd.read_csv('orig_alex_folds.csv')
image_id_column = 'image_id'
train_images_df = pd.read_csv('folds/train_alex_folds.csv')
print(f'\nTotal images: {len(train_images_df[image_id_column].unique())}')
# Leave only images with bboxes
#print('Leave only train images with boxes (all)')
img_list = train_boxes_df[image_id_column].unique()
print(len(img_list))
with_boxes_filter = train_images_df[image_id_column].isin(img_list)
fold = 0
# val images
images_val = img_list
#images_val = train_images_df.loc[
# (train_images_df['fold'] == fold) & with_boxes_filter, image_id_column].values
print(f'\nValidation images {len(images_val)}')
# get dataset
valid_dataset = WheatDataset(
image_ids=images_val[:16],
image_dir=TRAIN_DIR,
labels_df=train_boxes_df,
#train_box_callback,
transforms=get_valid_transforms(image_size),
is_test=True)
valid_data_loader = DataLoader(valid_dataset,
batch_size=inf_batch_size,
shuffle=False,
num_workers=num_workers,
collate_fn=collate_fn)
# efficientdet config
config = get_efficientdet_config(f'tf_efficientdet_d{model_name[-1]}')
config.num_classes = 1
config.image_size = image_size
net = EfficientDet(config, pretrained_backbone=False)
net.class_net = HeadNet(config,
num_outputs=config.num_classes,
norm_kwargs=dict(eps=.001, momentum=.01))
weights_file = f'{experiment_name}.pth'
if os.path.exists(weights_file):
print(f'Loading weights from: {weights_file}')
load_weights(net, weights_file)
else:
print(f'No {weights_file} checkpoint')
model = DetBenchTrain(net, config)
# get predictions
manager = ModelRunner(model, device)
true_boxes, pred_boxes, pred_scores = manager.predict(valid_data_loader)
nms_thresholds = np.linspace(min_thres,
max_thres,
num=points,
endpoint=False)
best_metric = 0
for thr in nms_thresholds:
print('thr', thr)
cur_metric = competition_metric(true_boxes, pred_boxes, pred_scores,
thr)
if cur_metric > best_metric:
best_thr = thr
best_metric = cur_metric
print(f'best_metric: {best_metric}, best thr: {best_thr}')
print(f"lang: {LANG}",
f"epoch: {epoch}",
f"train_loss: {train_loss}",
f"test_loss: {test_loss}",
sep='\t')
logs.append({"train": train_loss, "test": test_loss, "epoch": epoch})
# if epoch % 1 == 0:
# liveloss.send()
if epoch % 10 == 0:
subprocess.call(["bash", "-c", "rm *{prediction,balanced.tsv}"])
if epoch % 20 == 0:
model_runner = ModelRunner(model, char2index, device)
# model_runner.run_model_on_word("bare")
datasets = build_datasets(model_runner,
model_filename="turmodel_size_6_epoch_" +
str(epoch))
for task_fn in datasets:
for hash in map(str, range(1)):
with_agg_v_vals = (True, False)
with_agg_v_vals = (False, )
for with_agg_v in with_agg_v_vals:
probing_metainfo = dt_probe_dataset(task_fn,
hash + 'TREE' +
str(with_agg_v),
with_agg=with_agg_v,
tree=True)
def start_processing(cls, path_to_base_folder):
start = time()
Utilities.prepare_properties_dictionary()
if not os.path.exists(
os.path.join(
path_to_base_folder,
Utilities.get_prop_value(Utilities.BOOK_DESCRIPTOR_KEY))):
print("Please provide the book descriptor file")
return
if not os.path.exists(
os.path.join(path_to_base_folder,
Utilities.get_prop_value(
Utilities.BOOK_REPO_KEY))):
print("Please provide the book folder")
return
if not os.path.exists(
os.path.join(
os.getcwd(),
Utilities.get_prop_value(Utilities.DATA_POINT_KEY))):
data_start = time()
DataPointSelector.select_datapoints(path_to_base_folder)
data_end = time()
print("Data Selection took : {} minutes".format(
(data_end - data_start) / 60))
else:
print(
"Data Point CSV found in directory, continuing to Feature Extraction"
)
if not os.path.exists(
os.path.join(
os.getcwd(),
Utilities.get_prop_value(Utilities.PYTHON_FEATURE_CSV))):
py_start = time()
extractor = FeatureExtractor(
base_folder_address=path_to_base_folder)
extractor.extract_features()
py_end = time()
print("Python Extractor took : {} minutes".format(
(py_end - py_start) / 60))
else:
print(
"Python Feature Vector CSV found in directory, continuing to run Java project"
)
if not os.path.exists(
os.path.join(
os.getcwd(),
Utilities.get_prop_value(Utilities.JAVA_FEATURE_CSV))):
bat_file_name = r'command.bat'
folder_path = os.path.join(
path_to_base_folder,
Utilities.get_prop_value(Utilities.BOOK_REPO_KEY))
output_file_name = ".\\" + Utilities.get_prop_value(
Utilities.JAVA_FEATURE_CSV)
book_descriptor_file_name = ".\\" + Utilities.get_prop_value(
Utilities.BOOK_DESCRIPTOR_KEY)
data_points_file_name = ".\\" + Utilities.get_prop_value(
Utilities.DATA_POINT_KEY)
java_start = time()
x = subprocess.call([
bat_file_name, folder_path, output_file_name,
book_descriptor_file_name, data_points_file_name
])
java_end = time()
print("Java Project took : {} minutes".format(
(java_end - java_start) / 60))
else:
print(
"Java output Feature Vector CSV found in directory, continuing to Model Runner"
)
runner = ModelRunner(path_to_base_folder)
runner.drive_model_runner()
end = time()
total = end - start
print("Total Time for the whole process : {} minutes".format(
(end - start) / 60))
class API:
def __init__(self, port=80, threaded=True, debug=True):
self.port = port
self.threaded = threaded
self.debug = debug
self.app = Flask(__name__.split('.')[0])
self.app.config['SECRET_KEY'] = str(
UUID(int=random.randint(0, 10000000000)))
self.app.add_url_rule('/',
'index',
self.index,
methods=['GET', 'POST'])
self.app.add_url_rule('/players/<p1>/<p2>',
'players',
self.players,
methods=['GET', 'POST'])
self.app.add_url_rule('/bets', 'bets', self.bets)
self.app.add_url_rule('/rankings', 'rankings', self.rankings)
self.app.add_url_rule('/recent', 'recent', self.recentMatches)
helper.fillRegionDict()
model = MLP(max_epochs=10000,
batch_size=128,
learning_rate=5e-3,
width=50,
layers=1)
print('Model Created')
self.runner = ModelRunner(model,
"data/matchResults_aligulac.csv",
trainRatio=0.8,
testRatio=0.2,
lastGameId="302054",
keepPercent=1.0,
decay=False)
print(datetime.now(), 'Model Runner Created')
print(self.runner.getLastId())
self.runner.loadProfiles()
self.runner.model.loadBackup()
self.runner.clearMemory()
print("Memory Cleared")
rankingsList = self.runner.generateRanking(20)
rank = 1
print(
"Rank, Name, Race, Country, Project W/R, Elo, Glicko, MatchExpAvg")
for [rate, profile] in rankingsList:
print(rank, profile.name, profile.race, profile.country, rate,
profile.elo, profile.glickoRating, profile.expOverall)
rank += 1
self.liveThread = threading.Thread(target=self.runner.getLive)
self.liveThread.start()
def start(self):
http_server = WSGIServer(('', self.port), self.app)
http_server.serve_forever()
# self.app.run(debug=self.debug, port=self.port, threaded=self.threaded)
def index(self):
form = PlayersForm()
if form.validate_on_submit():
return redirect(
url_for('players', p1=form.player1.data, p2=form.player2.data))
elif request.method == 'GET':
return render_template('index.html', form=form)
def players(self, p1, p2):
p1List = self.runner.profiles[p1.lower()]
p2List = self.runner.profiles[p2.lower()]
firstKeyPairs = [(i, str(p1List[i])) for i in range(len(p1List))]
secondKeyPairs = [(i, str(p2List[i])) for i in range(len(p2List))]
cform = ChooseForm()
cform.choice1.choices = firstKeyPairs
cform.choice2.choices = secondKeyPairs
if request.method == 'POST':
bestOf = cform.bo.data
profile1 = p1List[int(cform.choice1.data)]
profile2 = p2List[int(cform.choice2.data)]
seriesPred = self.runner.predictSeries(player1=profile1.name,
p1Race=profile1.race,
p1Country=profile1.country,
player2=profile2.name,
p2Race=profile2.race,
p2Country=profile2.country,
bestOf=bestOf)
tableList = []
tableList.append(
"<table><tr><th>Probability</th><th>Score</th><th>Score</th><th>Probability</th></tr>"
)
keySize = len(seriesPred[0].keys())
numKeys = 0
t1 = 0
t2 = 0
for key in sorted(seriesPred[0].keys(), reverse=True):
numKeys += 2
seriesPred[0][key] = seriesPred[0][key].item()
t1 += seriesPred[0][key]
backwardsKey = key[::-1]
seriesPred[0][backwardsKey] = seriesPred[0][backwardsKey].item(
)
t2 += seriesPred[0][backwardsKey]
tableList.append("<tr><td>")
tableList.append("%.4f" % round(seriesPred[0][key], 4))
tableList.append("</td>")
tableList.append("<td>")
tableList.append(key)
tableList.append("</td>")
tableList.append("<td>")
tableList.append(backwardsKey)
tableList.append("</td>")
tableList.append("<td>")
tableList.append("%.4f" %
round(seriesPred[0][backwardsKey], 4))
tableList.append("</td>")
tableList.append("</tr>")
if numKeys >= keySize:
break
tableList.append("<tr><td>")
tableList.append("%.4f" % round(t1, 4))
tableList.append("</td>")
tableList.append("<td>")
tableList.append("Winner (")
tableList.append(profile1.name)
tableList.append(")</td><td>")
tableList.append("Winner (")
tableList.append(profile2.name)
tableList.append(")</td>")
tableList.append("<td>")
tableList.append("%.4f" % round(t2, 4))
tableList.append("</td></tr></table>")
tableString = "".join(tableList)
style = "<style>* {margin: 0;font-family: Arial, Helvetica, sans-serif;}table {font-family: arial, sans-serif;border-collapse: collapse;width: 100%;}td, th {border: 1px solid #dddddd;text-align: left;padding: 8px;}tr:nth-child(even) {background-color: #dddddd;}</style>"
return "{6}<h2>Best of {4}: {0} {1} vs {3} {2}</h2><br><h3>{5}</h3>".format(
profile1.name, "%.4f" % round(seriesPred[1].item(), 4),
profile2.name, "%.4f" % round(seriesPred[2].item(), 4), bestOf,
tableString, style)
return render_template('players.html', form=cform)
def bets(self):
graph_url = ""
try:
graph_url = self.runner.graphBalance()
except NoBetsYetExceptions:
print("Not enough data points in balance history")
return render_template("bets.html", graph=graph_url)
def rankings(self):
rankingsList = self.runner.generateRanking(20)
ranks = []
rates = []
names = []
races = []
countries = []
elos = []
glickos = []
rank = 1
for [rate, profile] in rankingsList:
ranks.append(rank)
rates.append("%.4f" % round(rate, 4))
names.append(profile.name)
races.append(profile.race)
countries.append(profile.country)
elos.append("%.2f" % round(profile.elo, 2))
glickos.append("%.2f" % round(profile.glickoRating, 2))
rank += 1
return render_template("rankings.html",
ranks=ranks,
rates=rates,
names=names,
races=races,
countries=countries,
elos=elos,
glickos=glickos)
def recentMatches(self):
matchList = reversed(self.runner.recentMatches)
return render_template("recent.html", matches=matchList)
from model_runner import ModelRunner runner = ModelRunner() runner.train_and_save() runner.load_model() runner.test()
