import torch
import numpy as np
from baseline.utils import lookup_sentence, get_version
from torch.autograd import Variable
import torch.autograd
import torch.nn as nn
import torch.nn.functional
import math
import copy
PYT_MAJOR_VERSION = get_version(torch)
def sequence_mask(lengths):
lens = lengths.cpu()
max_len = torch.max(lens)
# 1 x T
row = torch.arange(0, max_len.item()).type_as(lens).view(1, -1)
# B x 1
col = lens.view(-1, 1)
# Broadcast to B x T, compares increasing number to max
mask = row < col
return mask
def classify_bt(model, batch_time):
tensor = torch.from_numpy(batch_time) if type(batch_time) == np.ndarray else batch_time
probs = model(torch.autograd.Variable(tensor, requires_grad=False).cuda()).exp().data
probs.div_(torch.sum(probs))
results = []
batchsz = probs.size(0)
import math
import copy
import numpy as np
import torch
import torch.autograd
import torch.nn as nn
import torch.nn.functional as F
from baseline.utils import lookup_sentence, get_version, Offsets
PYT_MAJOR_VERSION = get_version(torch)
def sequence_mask(lengths, max_len=-1):
lens = lengths.cpu()
if max_len < 0:
max_len = torch.max(lens).item()
# 1 x T
row = torch.arange(0, max_len).type_as(lens).view(1, -1)
# B x 1
col = lens.view(-1, 1)
# Broadcast to B x T, compares increasing number to max
mask = row < col
return mask
class VariationalDropout(nn.Module):
"""Inverted dropout that applies the same mask at each time step."""
def __init__(self, p=0.5):
"""Variational Dropout
