showgood163

from botorch.utils.sampling import manual_seed
import warnings
from ax.utils.common.typeutils import not_none
from ax.modelbridge.modelbridge_utils import get_pending_observation_features
from ax.utils.common.logger import _round_floats_for_logging, get_logger
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from typing import Any, Dict, List, Optional, Tuple, Union
from ax.core.generator_run import GeneratorRun
from ax.core.types import TParameterization
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy

showgood163

# NOTE
# 1. 5 dropouts inside the network
# 2. The `raw_data` feeding into the `ax.complete_trial` here are 10 accs + 1 average acc + 1 loss, I actually use 4 accs, 4 F1s, 4 unweithged F1s, 3 averages (of acc, F1 and unweighted F1) and 1 loss instead.

import torch
import numpy as np

from ax.service.ax_client import AxClient
from ax.plot.contour import plot_contour
from ax.plot.trace import optimization_trace_single_method

showgood163

def decode(serialized_example, sess):
    '''
    Given a serialized example in which the frames are stored as
    compressed JPG images 'frames/0001', 'frames/0002' etc., this
    function samples SEQ_NUM_FRAMES from the frame list, decodes them from
    JPG into a tensor and packs them to obtain a tensor of shape (N,H,W,3).
    Returns the the tuple (frames, class_label (tf.int64)

    :param serialized_example: serialized example from tf.data.TFRecordDataset
    :return: tuple: (frames (tf.uint8), class_label (tf.int64)