/dports/math/py-theano/Theano-1.0.5/theano/gpuarray/c_code/ |
H A D | dnn_sptf_sampler.c | 68 size_t out_dims[4]; in APPLY_SPECIFIC() local 96 out_dims[0] = (size_t) PyGpuArray_DIM(input, 0); // num_images in APPLY_SPECIFIC() 97 out_dims[1] = (size_t) PyGpuArray_DIM(input, 1); // num_channels in APPLY_SPECIFIC() 98 out_dims[2] = (size_t) PyGpuArray_DIM(grid, 1); // grid height in APPLY_SPECIFIC() 99 out_dims[3] = (size_t) PyGpuArray_DIM(grid, 2); // grid width in APPLY_SPECIFIC() 101 desc_dims[0] = (int) out_dims[0]; in APPLY_SPECIFIC() 102 desc_dims[1] = (int) out_dims[1]; in APPLY_SPECIFIC() 103 desc_dims[2] = (int) out_dims[2]; in APPLY_SPECIFIC() 104 desc_dims[3] = (int) out_dims[3]; in APPLY_SPECIFIC() 106 if ( out_dims[0] == 0 || out_dims[1] == 0 || out_dims[2] == 0 || out_dims[3] == 0 ) in APPLY_SPECIFIC() [all …]
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H A D | dnn_sptf_grid.c | 28 PyArrayObject * out_dims, in APPLY_SPECIFIC() 56 if ( PyArray_NDIM( out_dims ) != 1 || PyArray_SIZE( out_dims ) != 4 ) in APPLY_SPECIFIC() 64 num_images = (int) *( (npy_int64 *) PyArray_GETPTR1( out_dims, 0 ) ); in APPLY_SPECIFIC() 65 num_channels = (int) *( (npy_int64 *) PyArray_GETPTR1( out_dims, 1 ) ); in APPLY_SPECIFIC() 66 height = (int) *( (npy_int64 *) PyArray_GETPTR1( out_dims, 2 ) ); in APPLY_SPECIFIC() 67 width = (int) *( (npy_int64 *) PyArray_GETPTR1( out_dims, 3 ) ); in APPLY_SPECIFIC()
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H A D | dnn_sptf_gi.c | 84 int out_dims[4]; in APPLY_SPECIFIC() local 131 out_dims[0] = (int) PyGpuArray_DIM(input, 0); // num_images in APPLY_SPECIFIC() 132 out_dims[1] = (int) PyGpuArray_DIM(input, 1); // num_channels in APPLY_SPECIFIC() 133 out_dims[2] = (int) PyGpuArray_DIM(grid, 1); // grid height in APPLY_SPECIFIC() 134 out_dims[3] = (int) PyGpuArray_DIM(grid, 2); // grid width in APPLY_SPECIFIC() 139 dt, 4, out_dims ); in APPLY_SPECIFIC()
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/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/native/ |
H A D | im2col.cc | 35 const Dims& out_dims, in Im2ColImpl() argument 42 CHAINERX_ASSERT(kKernelNdim == static_cast<int8_t>(out_dims.size())); in Im2ColImpl() 47 Indexer<kKernelNdim> out_dims_indexer{Shape{out_dims.begin(), out_dims.end()}}; in Im2ColImpl() 104 Dims out_dims; // Number of patches along each axis in Im2Col() local 107 CHAINERX_ASSERT(out_dims.back() > 0); in Im2Col() 109 CHAINERX_ASSERT(ndim == static_cast<int8_t>(out_dims.size())); in Im2Col() 116 std::copy(out_dims.begin(), out_dims.end(), std::back_inserter(out_shape)); in Im2Col() 128 … Im2ColImpl<T, 0>(padded_x, out, kernel_size, stride, out_dims, batch_channel_indexer); in Im2Col() 131 … Im2ColImpl<T, 1>(padded_x, out, kernel_size, stride, out_dims, batch_channel_indexer); in Im2Col() 134 … Im2ColImpl<T, 2>(padded_x, out, kernel_size, stride, out_dims, batch_channel_indexer); in Im2Col() [all …]
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/dports/math/py-jax/jax-0.2.9/jax/interpreters/ |
H A D | batching.py | 60 yield out_vals, out_dims 66 out_dims = out_dims_thunk() 67 for od, od_dest in zip(out_dims, out_dim_dests): 80 fun, out_dims = batch_subtrace(fun) 81 return _batch_fun2(fun, in_dims), out_dims 237 fst, out_dims = lu.merge_linear_aux(out_dims1, out_dims2) 239 assert out_dims == out_dims[:len(out_dims) // 2] * 2 240 out_dims = out_dims[:len(out_dims) // 2] 262 out_dims = out_dims[-len(out_vals) % len(out_dims):] 443 for d, inst in zip(out_dims, instantiate)] [all …]
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/dports/science/mpb/mpb-1.11.1/utils/ |
H A D | mpb-data.c | 304 out_dims[i] = 1; in handle_dataset() 306 N *= (out_dims[i] = MAX2(out_dims[i], 1)); in handle_dataset() 309 out_dims2[0] = out_dims[1]; in handle_dataset() 310 out_dims2[1] = out_dims[0]; in handle_dataset() 311 out_dims2[2] = out_dims[2]; in handle_dataset() 314 out_dims2[0] = out_dims[0]; in handle_dataset() 315 out_dims2[1] = out_dims[1]; in handle_dataset() 316 out_dims2[2] = out_dims[2]; in handle_dataset() 449 out_dims[i] = 1; in handle_cvector_dataset() 451 N *= (out_dims[i] = MAX2(out_dims[i], 1)); in handle_cvector_dataset() [all …]
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/dports/math/ideep/ideep-2.0.0-119-gb57539e/python/ideep4py/ |
H A D | __init__.py | 291 def convolution2DParam(out_dims, dy, dx, sy, sx, ph, pw, pd, pr): argument 293 cp.out_dims = intVector() 294 for d in out_dims: 295 cp.out_dims.push_back(d) 303 def pooling2DParam(out_dims, kh, kw, sy, sx, ph, pw, pd, pr, algo): argument 305 pp.out_dims = intVector() 306 for d in out_dims: 307 pp.out_dims.push_back(d)
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/dports/math/ideep/ideep-2.0.0-119-gb57539e/python/ideep4py/py/primitives/ |
H A D | conv_py.h | 52 *(bias->get()), cp->out_dims, dst, in Forward() 59 *(src->get()), *(weights->get()), cp->out_dims, dst, in Forward() 76 *(src->get()), *(grady->get()), cp->out_dims, gW, in BackwardWeights() 93 *(src->get()), *(grady->get()), cp->out_dims, gW, gb, in BackwardWeightsBias() 112 *(grady->get()), *(weights->get()), cp->out_dims, gx, in BackwardData()
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H A D | param.h | 31 std::vector<int> out_dims; member 39 std::vector<int> out_dims; member
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H A D | param.i | 27 std::vector<int> out_dims; member 36 std::vector<int> out_dims; member
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H A D | pooling_py.h | 49 *(src->get()), pp->out_dims, dst, in Forward() 95 src.init({pp->out_dims, grady->get()->get_data_type(), in Backward() 96 engine::default_format(pp->out_dims.size())}, nullptr); in Backward()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/src/relay/transforms/ |
H A D | combine_parallel_dense.cc | 184 auto out_dims = tir::as_const_int(out_shape[out_shape.size() - 1]); in UpdateGroupOutput() local 185 CHECK(out_dims != nullptr); in UpdateGroupOutput() 195 end.push_back(*out_dims); in UpdateGroupOutput() 197 index += *out_dims; in UpdateGroupOutput() 205 int64_t out_dims = 0; in TransformWeight() local 210 out_dims += *tir::as_const_int(weight->type_as<TensorTypeNode>()->shape[0]); in TransformWeight() 213 tir::make_const(DataType::Int(32), out_dims)); in TransformWeight()
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/dports/science/py-chainer-chemistry/chainer-chemistry-0.7.1/chainer_chemistry/models/ |
H A D | rsgcn.py | 45 out_dims = [hidden_channels for _ in range(n_update_layers)] 46 out_dims[n_update_layers - 1] = out_dim 52 *[RSGCNUpdate(in_dims[i], out_dims[i]) 57 out_dims[i]) for i in range(n_update_layers)])
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/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/cuda/ |
H A D | cuda_conv_test.cc | 153 Shape out_dims{5, 3}; in TEST() local 155 std::copy(out_dims.begin(), out_dims.end(), std::back_inserter(out_shape)); in TEST() 169 Shape out_dims{9, 5}; in TEST() local 171 std::copy(out_dims.begin(), out_dims.end(), std::back_inserter(out_shape)); in TEST()
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/dports/math/py-matplotlib/matplotlib-3.4.3/src/ |
H A D | _image_wrapper.cpp | 65 npy_intp out_dims[3]; in _get_transform_mesh() local 67 out_dims[0] = dims[0] * dims[1]; in _get_transform_mesh() 68 out_dims[1] = 2; in _get_transform_mesh() 75 numpy::array_view<double, 2> input_mesh(out_dims); in _get_transform_mesh()
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/dports/math/py-matplotlib2/matplotlib-2.2.4/src/ |
H A D | _image_wrapper.cpp | 69 npy_intp out_dims[3]; in _get_transform_mesh() local 71 out_dims[0] = dims[0] * dims[1]; in _get_transform_mesh() 72 out_dims[1] = 2; in _get_transform_mesh() 80 numpy::array_view<double, 2> input_mesh(out_dims); in _get_transform_mesh()
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/dports/devel/py-qutip/qutip-4.6.2/qutip/ |
H A D | superop_reps.py | 336 out_dims, in_dims = q_oper.dims 337 out_left, out_right = out_dims 371 out_dims, in_dims = q_oper.dims 372 out_left, out_right = out_dims
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/subgraph/mkldnn/ |
H A D | mkldnn_fc.cc | 193 mkldnn::memory::dims out_dims(2); in Forward() local 195 out_dims[0] = static_cast<int>(oshape[0]); in Forward() 196 out_dims[1] = static_cast<int>(oshape[1]); in Forward() 199 out_dims[0] = static_cast<int>(oshape.ProdShape(0, oshape.ndim()-1)); in Forward() 200 out_dims[1] = static_cast<int>(oshape[oshape.ndim()-1]); in Forward() 202 out_dims[0] = static_cast<int>(static_cast<int>(oshape[0])); in Forward() 203 out_dims[1] = static_cast<int>(oshape.ProdShape(1, oshape.ndim())); in Forward() 206 mkldnn::memory::desc out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(output.dtype()), in Forward()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/subgraph/mkldnn/ |
H A D | mkldnn_fc.cc | 193 mkldnn::memory::dims out_dims(2); in Forward() local 195 out_dims[0] = static_cast<int>(oshape[0]); in Forward() 196 out_dims[1] = static_cast<int>(oshape[1]); in Forward() 199 out_dims[0] = static_cast<int>(oshape.ProdShape(0, oshape.ndim()-1)); in Forward() 200 out_dims[1] = static_cast<int>(oshape[oshape.ndim()-1]); in Forward() 202 out_dims[0] = static_cast<int>(static_cast<int>(oshape[0])); in Forward() 203 out_dims[1] = static_cast<int>(oshape.ProdShape(1, oshape.ndim())); in Forward() 206 mkldnn::memory::desc out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(output.dtype()), in Forward()
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/dports/science/hdf5-18/hdf5-1.8.21/test/ |
H A D | tsohm.c | 3237 hsize_t out_dims[2]; in test_sohm_extend_dset_helper() local 3357 VERIFY(out_dims[x], dims2[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3364 VERIFY(out_dims[x], dims1[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3371 VERIFY(out_dims[x], dims1[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3419 VERIFY(out_dims[x], dims2[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3426 VERIFY(out_dims[x], dims2[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3433 VERIFY(out_dims[x], dims1[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3481 VERIFY(out_dims[x], dims2[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3488 VERIFY(out_dims[x], dims2[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() 3495 VERIFY(out_dims[x], dims2[x], "H5Sget_simple_extent_dims"); in test_sohm_extend_dset_helper() [all …]
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/mkldnn/src/gpu/ocl/rnn/ |
H A D | rnn_reorders.cpp | 103 const auto &out_dims = dst_mdw.padded_dims(); in init_kernel_ctx() local 111 (d < dst_mdw.ndims()) ? out_dims[d] : 1); in init_kernel_ctx()
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/dports/math/onednn/oneDNN-2.5.1/src/gpu/ocl/rnn/ |
H A D | rnn_reorders.cpp | 103 const auto &out_dims = dst_mdw.padded_dims(); in init_kernel_ctx() local 111 (d < dst_mdw.ndims()) ? out_dims[d] : 1); in init_kernel_ctx()
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/nn/mkldnn/ |
H A D | mkldnn_fully_connected.cc | 161 mkldnn::memory::dims out_dims{static_cast<int>(oshape.ProdShape(0, oshape.ndim()-1)), in MKLDNNFCFlattenData() local 163 *out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(out_data.dtype()), in MKLDNNFCFlattenData() 167 mkldnn::memory::dims out_dims{static_cast<int>(oshape[0]), in MKLDNNFCFlattenData() local 169 *out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(out_data.dtype()), in MKLDNNFCFlattenData()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/nn/mkldnn/ |
H A D | mkldnn_fully_connected.cc | 161 mkldnn::memory::dims out_dims{static_cast<int>(oshape.ProdShape(0, oshape.ndim()-1)), in MKLDNNFCFlattenData() local 163 *out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(out_data.dtype()), in MKLDNNFCFlattenData() 167 mkldnn::memory::dims out_dims{static_cast<int>(oshape[0]), in MKLDNNFCFlattenData() local 169 *out_md = mkldnn::memory::desc(out_dims, get_mkldnn_type(out_data.dtype()), in MKLDNNFCFlattenData()
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/dports/science/minc2/minc-release-2.2.00/progs/mincresample/ |
H A D | mincresample.c | 1291 int ndims, in_dims[MAX_VAR_DIMS], out_dims[MAX_VAR_DIMS]; in create_output_file() local 1384 out_dims[out_index] = ncdimid(out_file->mincid, dimname); in create_output_file() 1386 dim_exists = (out_dims[out_index] != MI_ERROR); in create_output_file() 1400 (void) ncdimrename(out_file->mincid, out_dims[out_index], string); in create_output_file() 1402 out_dims[out_index] = ncdimdef(out_file->mincid, dimname, in create_output_file() 1406 out_dims[out_index] = ncdimdef(out_file->mincid, dimname, in create_output_file() 1434 ncdiminq(out_file->mincid, out_dims[ndims-1], dimname, NULL); in create_output_file() 1444 out_maxmin_dims[nmaxmin_dims] = out_dims[idim]; in create_output_file() 1518 ndims, out_dims); in create_output_file()
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