def __init__(self,
num,
batch_size=1,
progress_bar=False,
log_epoch=10,
get_fn=None,
cycle=False,
shuffle=True,
stagnant=False,
seed=2,
dataseed=2,
num_batches=-1):
"""Construct a batch iterator.
Args:
data: numpy.ndarray, (N, D), N is the number of examples, D is the
feature dimension.
labels: numpy.ndarray, (N), N is the number of examples.
batch_size: int, batch size.
"""
self._num = num
self._batch_size = batch_size
self._step = 0
self._num_steps = int(np.ceil(self._num / float(batch_size)))
if num_batches > 0:
self._num_steps = min(self._num_steps, num_batches)
self._pb = None
self._variables = None
self._get_fn = get_fn
self.get_fn = get_fn
self._cycle = cycle
self._shuffle_idx = np.arange(self._num)
self._shuffle = shuffle
self._random = np.random.RandomState(seed)
if shuffle:
self._random.shuffle(self._shuffle_idx)
self._shuffle_flag = False
self._stagnant = stagnant
self._log_epoch = log_epoch
self._log = get_logger()
self._epoch = 0
if progress_bar:
self._pb = pb.get(self._num_steps)
pass
self._mutex = threading.Lock()
pass
from fewshot.configs.episode_config_pb2 import EpisodeConfig
from fewshot.configs.experiment_config_pb2 import ExperimentConfig
from fewshot.experiments.build_model import build_pretrain_net
from fewshot.experiments.get_data_iter import get_dataiter_continual
from fewshot.experiments.get_data_iter import get_dataiter_sim
from fewshot.experiments.get_stats import get_stats
from fewshot.experiments.get_stats import log_results
from fewshot.experiments.utils import get_config
from fewshot.experiments.utils import get_data_fs
from fewshot.experiments.utils import latest_file
from fewshot.utils.logger import get as get_logger
from fewshot.models.model_factory import get_module
from fewshot.models.modules.rnn_encoder import RNNEncoder
from fewshot.models.nets.rnn_encoder_net import RNNEncoderNet
log = get_logger()
def eval_step(x, y, ys, K, T):
"""Leave one out at each step."""
assert x.shape[0] == 1
y = y
ys = ys
T2 = x.shape[1]
x = x[:, :T]
y = y[:, :T]
ys = ys[:, :T]
ypred = np.zeros([1, T2, K + 1], dtype=np.float32)
ypred_id = np.zeros([1, T2], dtype=np.int64)
ys[ys == K] = -1
