rwightman

rwightman

import torch
from torch import nn
import torch.nn.functional as F
from collections import OrderedDict
import math

def pdist(v):
    dist = torch.norm(v[:, None] - v, dim=2, p=2)
    return dist

rwightman

''' Two sample RNN (1d CNN + LSTM) networks used in the Kaggle 
QuickDraw Challenge (https://www.kaggle.com/c/quickdraw-doodle-recognition)

Both of these networks expect a tuple input with first element being the sequences
and second being the sequence lengths (typical sorted packed format). The sequence tensor
should adhere to the following shape: (batch_size, channels, seq_len).
Where channels consists of stroke [x, y, t, end]. End indicates whether the 
stroke is the last in a segment (pen up). It could easily be changed to start
(pen down) or combo of both.

rwightman

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair, _quadruple


class MedianPool2d(nn.Module):
    """ Median pool (usable as median filter when stride=1) module.