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update #56

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c7a63a1
update
59SaPhire95 Jun 11, 2018
96023b4
Update test_cpu.py
59SaPhire95 Jun 11, 2018
c9c68a8
Update test_gpu.py
59SaPhire95 Jun 11, 2018
dbde83a
Update test_cpu.py
59SaPhire95 Jun 11, 2018
315bb2d
Update test_gpu.py
59SaPhire95 Jun 11, 2018
982ba1c
Update test_cpu.py
59SaPhire95 Jun 11, 2018
095143c
Update test_gpu.py
59SaPhire95 Jun 11, 2018
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3 changes: 2 additions & 1 deletion pytorch_binding/setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,8 @@
from torch.utils.ffi import create_extension
import torch

extra_compile_args = ['-std=c++11', '-fPIC']
#extra_compile_args = ['-std=c++11', '-fPIC']
extra_compile_args = ['-std=c99', '-fPIC']
warp_ctc_path = "../build"

if torch.cuda.is_available() or "CUDA_HOME" in os.environ:
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7 changes: 6 additions & 1 deletion pytorch_binding/src/binding.cpp
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Expand Up @@ -20,6 +20,8 @@ extern "C" int cpu_ctc(THFloatTensor *probs,
THIntTensor *label_sizes,
THIntTensor *sizes,
int minibatch_size,
int blanklabel_index,
int num_threads,
THFloatTensor *costs) {

float *probs_ptr = probs->storage->data + probs->storageOffset;
Expand All @@ -38,7 +40,8 @@ extern "C" int cpu_ctc(THFloatTensor *probs,
ctcOptions options;
memset(&options, 0, sizeof(options));
options.loc = CTC_CPU;
options.num_threads = 0; // will use default number of threads
options.blank_label = blanklabel_index;
options.num_threads = num_threads; // will use given number of threads

#if defined(CTC_DISABLE_OMP) || defined(APPLE)
// have to use at least one
Expand Down Expand Up @@ -68,6 +71,7 @@ extern "C" int cpu_ctc(THFloatTensor *probs,
THIntTensor *label_sizes,
THIntTensor *sizes,
int minibatch_size,
int blanklabel_index,
THFloatTensor *costs) {

float *probs_ptr = probs->storage->data + probs->storageOffset;
Expand All @@ -86,6 +90,7 @@ extern "C" int cpu_ctc(THFloatTensor *probs,
ctcOptions options;
memset(&options, 0, sizeof(options));
options.loc = CTC_GPU;
options.blank_label = blanklabel_index;
options.stream = THCState_getCurrentStream(state);

size_t gpu_size_bytes;
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6 changes: 6 additions & 0 deletions pytorch_binding/tests/test_cpu.py
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Expand Up @@ -17,6 +17,8 @@ def test_simple():
label_sizes,
sizes,
minibatch_size,
0,
0,
costs)
print('CPU_cost: %f' % costs.sum())

Expand All @@ -40,6 +42,8 @@ def test_medium(multiplier):
label_sizes,
sizes,
minibatch_size,
0,
0,
costs)
print('CPU_cost: %f' % costs.sum())

Expand All @@ -62,6 +66,8 @@ def test_empty_label():
label_sizes,
sizes,
minibatch_size,
0,
0,
costs)
print('CPU_cost: %f' % costs.sum())

Expand Down
9 changes: 9 additions & 0 deletions pytorch_binding/tests/test_gpu.py
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Expand Up @@ -18,6 +18,8 @@ def test_simple():
label_sizes,
sizes,
minibatch_size,
0,
0,
costs)
print('CPU_cost: %f' % costs.sum())
probs = probs.clone().cuda()
Expand All @@ -29,6 +31,7 @@ def test_simple():
label_sizes,
sizes,
minibatch_size,
0,
costs)
print('GPU_cost: %f' % costs.sum())
print(grads.view(grads.size(0) * grads.size(1), grads.size(2)))
Expand All @@ -54,6 +57,8 @@ def test_medium(multiplier):
label_sizes,
sizes,
minibatch_size,
0,
0,
costs)
print('CPU_cost: %f' % costs.sum())
probs = probs.clone().cuda()
Expand All @@ -65,6 +70,7 @@ def test_medium(multiplier):
label_sizes,
sizes,
minibatch_size,
0,
costs)
print('GPU_cost: %f' % costs.sum())
print(grads.view(grads.size(0) * grads.size(1), grads.size(2)))
Expand All @@ -89,6 +95,8 @@ def test_empty_label():
label_sizes,
sizes,
minibatch_size,
0,
0,
costs)
print('CPU_cost: %f' % costs.sum())
probs = probs.clone().cuda()
Expand All @@ -100,6 +108,7 @@ def test_empty_label():
label_sizes,
sizes,
minibatch_size,
0,
costs)
print('GPU_cost: %f' % costs.sum())
print(grads.view(grads.size(0) * grads.size(1), grads.size(2)))
Expand Down
16 changes: 6 additions & 10 deletions pytorch_binding/warpctc_pytorch/__init__.py
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Expand Up @@ -9,22 +9,18 @@

class _CTC(Function):
@staticmethod
def forward(ctx, acts, labels, act_lens, label_lens, size_average=False,
def forward(ctx, acts, labels, act_lens, label_lens, blank_label=0, num_threads=0, size_average=False,
length_average=False):
is_cuda = True if acts.is_cuda else False
acts = acts.contiguous()
loss_func = warp_ctc.gpu_ctc if is_cuda else warp_ctc.cpu_ctc
grads = torch.zeros(acts.size()).type_as(acts)
minibatch_size = acts.size(1)
costs = torch.zeros(minibatch_size).cpu()
loss_func(acts,
grads,
labels,
label_lens,
act_lens,
minibatch_size,
costs)

if is_cuda:
# num_threads will be negeleted in GPU mode
warp_ctc.gpu_ctc(acts, grads,labels, label_lens, act_lens, minibatch_size, blank_label, costs)
else:
warp_ctc.cpu_ctc(acts, grads,labels, label_lens, act_lens, minibatch_size, blank_label, num_threads, costs)
costs = torch.FloatTensor([costs.sum()])

if length_average:
Expand Down