def forward(self, img, volatile=False):
# BDWH -> HBWD -> HBsD
b, d, w, h = img.size()
seq = img.permute(3, 0, 2, 1).contiguous().view(h, b * w, d)
bs = b * w
h0 = Variable(helpers.typeas(torch.zeros(1, bs, self.noutput), img),
volatile=volatile)
c0 = Variable(helpers.typeas(torch.zeros(1, bs, self.noutput), img),
volatile=volatile)
# HBsD -> HBsD
assert seq.size() == (h, b * w, d), (seq.size(), (h, b * w, d))
post_lstm, _ = self.lstm(seq, (h0, c0))
assert post_lstm.size() == (h, b * w, self.noutput), (post_lstm.size(),
(h, b * w,
self.noutput))
# HBsD -> BsD -> BWD
final = post_lstm.select(0, h - 1).view(b, w, self.noutput)
assert final.size() == (b, w, self.noutput), (final.size(),
(b, w, self.noutput))
# BWD -> BDW
final = final.permute(0, 2, 1).contiguous()
assert final.size() == (b, self.noutput, w), (final.size(),
(b, self.noutput,
self.noutput))
return final
def forward(self, seq, volatile=False):
seq = bdl2lbd(seq)
l, bs, d = seq.size()
assert d == self.ninput, seq.size()
h0 = Variable(helpers.typeas(torch.zeros(self.ndir, bs, self.noutput),
seq),
volatile=volatile)
c0 = Variable(helpers.typeas(torch.zeros(self.ndir, bs, self.noutput),
seq),
volatile=volatile)
post_lstm, _ = self.lstm(seq, (h0, c0))
return lbd2bdl(post_lstm)
def forward(self, seq):
volatile = not isinstance(seq, Variable) or seq.volatile
seq = bdl2lbd(seq)
l, b, d = seq.size()
assert d == self.ninput, (d, self.ninput)
h0 = Variable(helpers.typeas(torch.zeros(1, b, self.noutput), seq),
volatile=volatile)
c0 = Variable(helpers.typeas(torch.zeros(1, b, self.noutput), seq),
volatile=volatile)
assert seq.size() == (l, b, d)
post_lstm, _ = self.lstm(seq, (h0, c0))
assert post_lstm.size() == (l, b, self.noutput)
final = post_lstm.select(0, l - 1).view(b, self.noutput)
return final
def forward(self, img):
volatile = not isinstance(img, Variable) or img.volatile
b, d, h, w = img.size()
# BDHW -> WHBD -> WB'D
seq = img.permute(3, 2, 0, 1).contiguous().view(w, h * b, d)
# WB'D
h0 = helpers.typeas(torch.zeros(self.ndir, h * b, self.noutput), img)
c0 = helpers.typeas(torch.zeros(self.ndir, h * b, self.noutput), img)
h0 = Variable(h0, volatile=volatile)
c0 = Variable(c0, volatile=volatile)
seqresult, _ = self.lstm(seq, (h0, c0))
# WB'D' -> BD'HW
result = seqresult.view(w, h, b,
self.noutput * self.ndir).permute(2, 3, 1, 0)
return result
def ctc_align(prob, target):
"""Perform CTC alignment on torch sequence batches (using ocrolstm).
Inputs are in BDL format.
"""
import cctc
assert dlh.sequence_is_normalized(prob), prob
assert dlh.sequence_is_normalized(target), target
# inputs are BDL
prob_ = dlh.novar(prob).permute(0, 2, 1).cpu().contiguous()
target_ = dlh.novar(target).permute(0, 2, 1).cpu().contiguous()
# prob_ and target_ are both BLD now
assert prob_.size(0) == target_.size(0), (prob_.size(), target_.size())
assert prob_.size(2) == target_.size(2), (prob_.size(), target_.size())
assert prob_.size(1) >= target_.size(1), (prob_.size(), target_.size())
result = torch.rand(1)
cctc.ctc_align_targets_batch(result, prob_, target_)
return dlh.typeas(result.permute(0, 2, 1).contiguous(), prob)