gustavla

# use the same data augmentation scheme as the FlowNet2 paper
layer {
  name: "img0"
  type: "CustomData"
  top: "img0"
  top: "img1"
  top: "flow_gt"
  top: "aux"
  include {
    phase: TRAIN

gustavla

Supporting __format__

Reference: Format Specification Mini-Language.

Overview

Quick demo of desired behavior and motivation:

>>> x = np.array([100.002, 1.2])

gustavla

from __future__ import division, print_function, absolute_import
import os
import struct
from array import array
import numpy as np


def load_mnist(section="training", offset=0, count=None, ret='xy',
               x_dtype=np.float64, y_dtype=np.int64, path=None):
    """

gustavla

from __future__ import division, print_function, absolute_import
import os
import struct
from array import array
import numpy as np


def load_mnist(section="training", offset=0, count=None, ret='xy',
               x_dtype=np.float64, y_dtype=np.int64, path=None):
    """

gustavla

# Author: Nelle Varoquaux, Andrew Tulloch

# Uses the pool adjacent violators algorithm (PAVA), with the
# enhancement of searching for the longest decreasing subsequence to
# pool at each step.

import numpy as np
cimport numpy as np
cimport cython