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/dports/misc/mxnet/incubator-mxnet-1.9.0/benchmark/python/sparse/
H A Dsparse_end2end.py154 num_batch = args.num_batch variable
166 … num_batch = datasets[dataset]['lc'] / batch_size if num_batch == MAX_NUM_BATCH else num_batch variable
169 … num_batch = datasets[dataset]['lc'] / batch_size if num_batch == MAX_NUM_BATCH else num_batch variable
279 if nbatch == num_batch:
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/contrib/
H A Dbounding_box-inl.cuh194 int num_batch, int topk, in NMSApply() argument
210 nms_apply_kernel<DType, N, true><<<num_batch, block_size, 0, stream>>>(topk, in NMSApply()
224 int blocks = blocks_per_batch * num_batch; in NMSApply()
242 nms_apply_kernel<DType, N, false><<<num_batch, block_size, 0, stream>>>(topk, in NMSApply()
256 int blocks = blocks_per_batch * num_batch; in NMSApply()
281 int num_batch) { in nms_calculate_batch_start_kernel() argument
297 for (int32_t current = my + 1; current <= num_batch; ++current) { in nms_calculate_batch_start_kernel()
307 int num_batch) { in NMSCalculateBatchStart() argument
318 num_batch); in NMSCalculateBatchStart()
H A Dbounding_box-inl.h248 int num_batch, int topk, in NMSApply() argument
263 Kernel<nms_impl, cpu>::Launch(s, num_batch * num_worker, in NMSApply()
274 int num_batch) { in NMSCalculateBatchStart() argument
278 for (int b = 0; b < num_batch + 1; b++) { in NMSCalculateBatchStart()
364 Shape<1> sort_index_shape = Shape1(num_batch * num_elem); in BoxNMSForward()
366 Shape<1> batch_start_shape = Shape1(num_batch + 1); in BoxNMSForward()
413 all_sorted_index = range<int32_t>(0, num_batch * num_elem); in BoxNMSForward()
441 if (num_valid < num_batch * num_elem) { in BoxNMSForward()
462 Kernel<compute_area, xpu>::Launch(s, num_batch * topk, in BoxNMSForward()
467 mxnet::op::NMSApply(s, num_batch, topk, &sorted_index, in BoxNMSForward()
[all …]
H A Dbounding_box.cu513 TempWorkspace<DType> GetWorkspace(const index_t num_batch, in GetWorkspace() argument
625 int num_batch = indim <= 2? 1 : in_shape.ProdShape(0, indim - 2); in BoxNMSForwardGPU_notemp() local
631 .get_with_shape<gpu, 3, DType>(Shape3(num_batch, num_elem, width_elem), s); in BoxNMSForwardGPU_notemp()
633 .get_with_shape<gpu, 3, DType>(Shape3(num_batch, num_elem, width_elem), s); in BoxNMSForwardGPU_notemp()
645 const auto& workspace = GetWorkspace<DType>(num_batch, num_elem, in BoxNMSForwardGPU_notemp()
651 Shape1(num_batch * num_elem), s); in BoxNMSForwardGPU_notemp()
654 Shape1(num_batch * num_elem), s); in BoxNMSForwardGPU_notemp()
658 Shape3(num_batch, num_elem, width_elem), s); in BoxNMSForwardGPU_notemp()
661 topk * num_batch), in BoxNMSForwardGPU_notemp()
663 indices = mshadow::expr::range<index_t>(0, num_batch * num_elem); in BoxNMSForwardGPU_notemp()
[all …]
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/benchmark/python/sparse/
H A Dsparse_end2end.py154 num_batch = args.num_batch variable
166 … num_batch = datasets[dataset]['lc'] / batch_size if num_batch == MAX_NUM_BATCH else num_batch variable
169 … num_batch = datasets[dataset]['lc'] / batch_size if num_batch == MAX_NUM_BATCH else num_batch variable
279 if nbatch == num_batch:
/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/contrib/
H A Dbounding_box-inl.cuh194 int num_batch, int topk, in NMSApply() argument
210 nms_apply_kernel<DType, N, true><<<num_batch, block_size, 0, stream>>>(topk, in NMSApply()
224 int blocks = blocks_per_batch * num_batch; in NMSApply()
242 nms_apply_kernel<DType, N, false><<<num_batch, block_size, 0, stream>>>(topk, in NMSApply()
256 int blocks = blocks_per_batch * num_batch; in NMSApply()
281 int num_batch) { in nms_calculate_batch_start_kernel() argument
297 for (int32_t current = my + 1; current <= num_batch; ++current) { in nms_calculate_batch_start_kernel()
307 int num_batch) { in NMSCalculateBatchStart() argument
318 num_batch); in NMSCalculateBatchStart()
H A Dbounding_box-inl.h248 int num_batch, int topk, in NMSApply() argument
263 Kernel<nms_impl, cpu>::Launch(s, num_batch * num_worker, in NMSApply()
274 int num_batch) { in NMSCalculateBatchStart() argument
278 for (int b = 0; b < num_batch + 1; b++) { in NMSCalculateBatchStart()
364 Shape<1> sort_index_shape = Shape1(num_batch * num_elem); in BoxNMSForward()
366 Shape<1> batch_start_shape = Shape1(num_batch + 1); in BoxNMSForward()
413 all_sorted_index = range<int32_t>(0, num_batch * num_elem); in BoxNMSForward()
441 if (num_valid < num_batch * num_elem) { in BoxNMSForward()
462 Kernel<compute_area, xpu>::Launch(s, num_batch * topk, in BoxNMSForward()
467 mxnet::op::NMSApply(s, num_batch, topk, &sorted_index, in BoxNMSForward()
[all …]
H A Dbounding_box.cu513 TempWorkspace<DType> GetWorkspace(const index_t num_batch, in GetWorkspace() argument
625 int num_batch = indim <= 2? 1 : in_shape.ProdShape(0, indim - 2); in BoxNMSForwardGPU_notemp() local
631 .get_with_shape<gpu, 3, DType>(Shape3(num_batch, num_elem, width_elem), s); in BoxNMSForwardGPU_notemp()
633 .get_with_shape<gpu, 3, DType>(Shape3(num_batch, num_elem, width_elem), s); in BoxNMSForwardGPU_notemp()
645 const auto& workspace = GetWorkspace<DType>(num_batch, num_elem, in BoxNMSForwardGPU_notemp()
651 Shape1(num_batch * num_elem), s); in BoxNMSForwardGPU_notemp()
654 Shape1(num_batch * num_elem), s); in BoxNMSForwardGPU_notemp()
658 Shape3(num_batch, num_elem, width_elem), s); in BoxNMSForwardGPU_notemp()
661 topk * num_batch), in BoxNMSForwardGPU_notemp()
663 indices = mshadow::expr::range<index_t>(0, num_batch * num_elem); in BoxNMSForwardGPU_notemp()
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/ssd/evaluate/
H A Devaluate_net.py34 def evaluate_net(net, path_imgrec, num_classes, num_batch, mean_pixels, data_shape, argument
115 num = num_batch * batch_size
126 results = mod.score(eval_iter, metric, num_batch=num_batch)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/ssd/evaluate/
H A Devaluate_net.py34 def evaluate_net(net, path_imgrec, num_classes, num_batch, mean_pixels, data_shape, argument
115 num = num_batch * batch_size
126 results = mod.score(eval_iter, metric, num_batch=num_batch)
/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/module/
H A Dbase_module.py198 def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None, argument
250 if num_batch is not None and nbatch == num_batch:
278 def iter_predict(self, eval_data, num_batch=None, reset=True, sparse_row_id_fn=None): argument
309 if num_batch is not None and nbatch == num_batch:
318 def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True, argument
382 if num_batch is not None and nbatch == num_batch:
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/module/
H A Dbase_module.py198 def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None, argument
250 if num_batch is not None and nbatch == num_batch:
278 def iter_predict(self, eval_data, num_batch=None, reset=True, sparse_row_id_fn=None): argument
309 if num_batch is not None and nbatch == num_batch:
318 def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True, argument
382 if num_batch is not None and nbatch == num_batch:
/dports/misc/mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/lib/AI/MXNet/Module/
H A DBase.pm258 Maybe[Int] :$num_batch=,
277 last if (defined $num_batch and $nbatch == $num_batch);
325 method iter_predict(AI::MXNet::DataIter $eval_data, Maybe[Int] :$num_batch=, Bool :$reset=1)
336 last if defined $num_batch and $nbatch == $num_batch;
385 Maybe[Int] :$num_batch=, Bool :$merge_batches=1, Bool :$reset=1, Bool :$always_output_list=0
403 last if defined $num_batch and $nbatch == $num_batch;
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/lib/AI/MXNet/Module/
H A DBase.pm258 Maybe[Int] :$num_batch=,
277 last if (defined $num_batch and $nbatch == $num_batch);
325 method iter_predict(AI::MXNet::DataIter $eval_data, Maybe[Int] :$num_batch=, Bool :$reset=1)
336 last if defined $num_batch and $nbatch == $num_batch;
385 Maybe[Int] :$num_batch=, Bool :$merge_batches=1, Bool :$reset=1, Bool :$always_output_list=0
403 last if defined $num_batch and $nbatch == $num_batch;
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tools/coreml/test/
H A Dtest_mxnet_image.py88 num_batch = 0
106 num_batch += 1
107 if (num_batch == 5):
/dports/misc/mxnet/incubator-mxnet-1.9.0/tools/coreml/test/
H A Dtest_mxnet_image.py88 num_batch = 0
106 num_batch += 1
107 if (num_batch == 5):
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/python/unittest/
H A Dtest_gluon_event_handler.py204 self.num_batch = 0
209 self.num_batch = 0
213 self.num_batch += 1
214 if self.num_batch == self.batch_stop:
233 assert custom_handler.num_batch == 3
237 assert custom_handler.num_batch == 5 * 4
/dports/misc/mxnet/incubator-mxnet-1.9.0/tests/python/unittest/
H A Dtest_gluon_event_handler.py204 self.num_batch = 0
209 self.num_batch = 0
213 self.num_batch += 1
214 if self.num_batch == self.batch_stop:
233 assert custom_handler.num_batch == 3
237 assert custom_handler.num_batch == 5 * 4
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tools/caffe_translator/src/main/resources/templates/
H A Dmetrics_classes.st91 def score_and_print(self, module, itr, num_batch):
94 module.score(itr, metric, num_batch=num_batch)
/dports/misc/py-xgboost/xgboost-1.5.1/tests/cpp/data/
H A Dtest_adapter.cc155 int num_batch = 0; in TEST() local
158 ++num_batch; in TEST()
160 ASSERT_EQ(num_batch, 1); in TEST()
/dports/misc/xgboost/xgboost-1.5.1/tests/cpp/data/
H A Dtest_adapter.cc155 int num_batch = 0; in TEST() local
158 ++num_batch; in TEST()
160 ASSERT_EQ(num_batch, 1); in TEST()
/dports/misc/mxnet/incubator-mxnet-1.9.0/tools/caffe_translator/src/main/resources/templates/
H A Dmetrics_classes.st91 def score_and_print(self, module, itr, num_batch):
94 module.score(itr, metric, num_batch=num_batch)
/dports/misc/mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/t/
H A Dtest_io.t130 my $num_batch = 0;
133 $num_batch += 1;
136 ok($num_batch == int($num_rows / $batch_size));
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/t/
H A Dtest_io.t130 my $num_batch = 0;
133 $num_batch += 1;
136 ok($num_batch == int($num_rows / $batch_size));
/dports/misc/py-gluoncv/gluon-cv-0.9.0/docs/tutorials/classification/
H A Dtransfer_learning_minc.py251 num_batch = len(train_data) variable
277 train_loss /= num_batch

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