def fit(self, data):
"""Fit the model to the given data.
Args:
data(pandas.DataFrame): dataset to fit the model.
Returns:
None
"""
self.preprocessor = Preprocessor(
continuous_columns=self.continuous_columns)
data = self.preprocessor.fit_transform(data)
self.metadata = self.preprocessor.metadata
dataflow = TGANDataFlow(data, self.metadata)
batch_data = BatchData(dataflow, self.batch_size)
input_queue = QueueInput(batch_data)
self.model = self.get_model(training=True)
from tensorpack.callbacks import CometMLMonitor
trainer = SeparateGANTrainer(
model=self.model,
input_queue=input_queue,
g_period=6,
)
self.restore_path = os.path.join(self.model_dir, 'checkpoint')
if os.path.isfile(self.restore_path) and self.restore_session:
session_init = SaverRestore(self.restore_path)
with open(os.path.join(self.log_dir, 'stats.json')) as f:
starting_epoch = json.load(f)[-1]['epoch_num'] + 1
else:
session_init = None
starting_epoch = 1
action = 'k' if self.restore_session else 'd'
# logger.set_logger_dir(self.log_dir, action=action)
callbacks = []
monitors = []
if self.save_checkpoints:
callbacks.append(ModelSaver(checkpoint_dir=self.model_dir))
callbacks.append(MergeAllSummaries(period=10))
if self.experiment is not None:
monitors.append(CometMLMonitor(experiment=self.experiment))
trainer.train_with_defaults(callbacks=callbacks,
monitors=monitors,
steps_per_epoch=self.steps_per_epoch,
max_epoch=self.max_epoch,
session_init=session_init,
starting_epoch=starting_epoch)
self.prepare_sampling()
def init(args=None, is_running=0, pt=None):
global ckpt2
# 网络
model = Net2()
if is_running == 1:
if pt == "":
ckpt2 = tf.train.latest_checkpoint(logdir2)
else:
ckpt2 = '{}/{}'.format(logdir2, pt)
else:
ckpt2 = '{}/{}'.format(
logdir2,
args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
pred_conf = PredictConfig(
model=model,
input_names=['x_ppgs', 'x_mfccs', 'y_spec', 'y_mel'],
output_names=['pred_spec', "ppgs"],
session_init=ChainInit(session_inits))
global predictor
predictor = OfflinePredictor(pred_conf)
if is_running == 1:
return jsonify({"code": 0, "ckpt": ckpt2})
def __init__(self, config):
"""Create a quality that uses nomagic_net as a quality function. """
from nomagic_submission import ConvNetModel
from tensorpack.predict.config import PredictConfig
from tensorpack import SaverRestore
from tensorpack.predict import OfflinePredictor
# store parameters
self._model_path = config['gqcnn_model']
self._batch_size = config['batch_size']
self._crop_height = config['crop_height']
self._crop_width = config['crop_width']
self._im_height = config['im_height']
self._im_width = config['im_width']
self._num_channels = config['num_channels']
self._pose_dim = config['pose_dim']
self._gripper_mode = config['gripper_mode']
# init config
model = ConvNetModel()
self._config = PredictConfig(model=model,
session_init=SaverRestore(
self._model_path),
output_names=['prob'])
self._predictor = OfflinePredictor(self._config)
def __init__(self, config):
"""Create a quality that uses `nomagic_net` as a quality function."""
from nomagic_submission import ConvNetModel
from tensorpack import SaverRestore
from tensorpack.predict import OfflinePredictor
from tensorpack.predict.config import PredictConfig
GraspQualityFunction.__init(self)
# Store parameters.
self._model_path = config["gqcnn_model"]
self._batch_size = config["batch_size"]
self._crop_height = config["crop_height"]
self._crop_width = config["crop_width"]
self._im_height = config["im_height"]
self._im_width = config["im_width"]
self._num_channels = config["num_channels"]
self._pose_dim = config["pose_dim"]
self._gripper_mode = config["gripper_mode"]
self._data_mean = config["data_mean"]
self._data_std = config["data_std"]
# Init config.
model = ConvNetModel()
self._config = PredictConfig(model=model,
session_init=SaverRestore(
self._model_path),
output_names=["prob"])
self._predictor = OfflinePredictor(self._config)
def fit(self, data):
"""Fit the model to the given data.
Args:
data(pandas.DataFrame): dataset to fit the model.
Returns:
None
"""
self.preprocessor = Preprocessor(
continuous_columns=self.continuous_columns)
data = self.preprocessor.fit_transform(data)
self.metadata = self.preprocessor.metadata
dataflow = TGANDataFlow(data, self.metadata)
batch_data = BatchData(dataflow, self.batch_size)
input_queue = QueueInput(batch_data)
self.model = self.get_model(training=True)
if self.trainer == 'GANTrainer':
trainer = GANTrainer(model=self.model, input_queue=input_queue)
elif self.trainer == 'SeparateGANTrainer':
trainer = SeparateGANTrainer(model=self.model,
input_queue=input_queue)
else:
raise ValueError(
'Incorrect trainer name. Use GANTrainer or SeparateGANTrainer')
# trainer = SeparateGANTrainer(model=self.model, input_queue=input_queue)
self.restore_path = os.path.join(self.model_dir, 'checkpoint')
if os.path.isfile(self.restore_path) and self.restore_session:
session_init = SaverRestore(self.restore_path)
with open(os.path.join(self.log_dir, 'stats.json')) as f:
starting_epoch = json.load(f)[-1]['epoch_num'] + 1
else:
session_init = None
starting_epoch = 1
action = 'k' if self.restore_session else None
logger.set_logger_dir(self.log_dir, action=action)
callbacks = []
if self.save_checkpoints:
callbacks.append(ModelSaver(checkpoint_dir=self.model_dir))
trainer.train_with_defaults(callbacks=callbacks,
steps_per_epoch=self.steps_per_epoch,
max_epoch=self.max_epoch,
session_init=session_init,
starting_epoch=starting_epoch)
self.prepare_sampling()
def prepare_sampling(self):
"""Prepare model for generate samples."""
if self.model is None:
self.model = self.get_model(training=False)
else:
self.model.training = False
predict_config = PredictConfig(
session_init=SaverRestore(self.restore_path),
model=self.model,
input_names=['z'],
output_names=['gen/gen', 'z'],
)
self.simple_dataset_predictor = SimpleDatasetPredictor(
predict_config, RandomZData((self.batch_size, self.z_dim)))
parser = argparse.ArgumentParser()
parser.add_argument('image_path')
parser.add_argument('--model_path', default='log/checkpoint')
parser.add_argument('--output_path', default='figures/')
parser.add_argument('--size', type=int, default=32)
args = parser.parse_args()
np.random.seed(0)
# initialize the model
predict_func = OfflinePredictor(
PredictConfig(inputs_desc=[
InputDesc(tf.float32, [None, INPUT_SIZE, INPUT_SIZE, 2],
'input_image')
],
tower_func=model.feedforward,
session_init=SaverRestore(args.model_path),
input_names=['input_image'],
output_names=['prob']))
# simulate suda's gridworld input
image = cv2.imread(
args.image_path,
cv2.IMREAD_GRAYSCALE) # 0 if obstacle, 255 if free space
h, w = image.shape[:2]
obj = img2obj(image) # list containing row major indices of objects
# specify position is recent memory
radius = 6
#s = [340/2, 110/2] # needs to be a list
s = [131, 147, 162]
min_distance = 1 # minimum distance between each station (smaller means more locations are returned)
