| /dports/math/py-autograd/autograd-1.3/autograd/numpy/ |
| H A D | numpy_vjps.py | 280 new_shape = onp.array(shape)
281 new_shape[axis] = 1
285 return anp.reshape(g, new_shape) + onp.zeros(shape, dtype=dtype), num_reps
287 def grad_broadcast_to(ans, x, new_shape): argument
289 assert anp.shape(ans) == new_shape
290 assert len(old_shape) == len(new_shape), "Can't handle extra leading dims"
293 onp.array(new_shape) > 1))[0])
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| /dports/science/py-pyscf/pyscf-2.0.1/pyscf/pbc/lib/ |
| H A D | kpts_helper.py | 211 new_shape = [shape[i] for i, x in enumerate(kijkab)
213 kconserv = kconserv.reshape(new_shape)
|
| /dports/graphics/vigra/vigra-8acd73a/vigranumpy/lib/ |
| H A D | tagged_array.py | 296 def resize(self, new_shape, refcheck=True, order=False): argument
297 res = numpy.ndarray.reshape(self, new_shape, refcheck, order)
|
| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/executor/ |
| H A D | graph_executor.cc | 923 const mxnet::TShape& new_shape = shape_vec[idx.entry_id(nid, 0)]; in Reshape() local
927 if (partial_shaping || provided_arg_shapes.count(name) || new_shape == arr.shape()) { in Reshape()
928 if (new_shape.Size() > arr.shape().Size()) { in Reshape()
934 in_args->emplace_back(new_shape, arr.ctx(), false, arr.dtype()); in Reshape()
937 arg_grads->emplace_back(new_shape, darr.ctx(), false, darr.dtype()); in Reshape()
944 in_args->push_back(arr.Reshape(new_shape)); in Reshape()
947 arg_grads->push_back(darr.Reshape(new_shape)); in Reshape()
962 if (partial_shaping || new_shape == arr.shape()) { in Reshape()
963 if (new_shape.Size() > arr.shape().Size()) { in Reshape()
969 aux_states->emplace_back(new_shape, arr.ctx(), false, arr.dtype()); in Reshape()
[all …]
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/src/executor/ |
| H A D | graph_executor.cc | 923 const mxnet::TShape& new_shape = shape_vec[idx.entry_id(nid, 0)]; in Reshape() local
927 if (partial_shaping || provided_arg_shapes.count(name) || new_shape == arr.shape()) { in Reshape()
928 if (new_shape.Size() > arr.shape().Size()) { in Reshape()
934 in_args->emplace_back(new_shape, arr.ctx(), false, arr.dtype()); in Reshape()
937 arg_grads->emplace_back(new_shape, darr.ctx(), false, darr.dtype()); in Reshape()
944 in_args->push_back(arr.Reshape(new_shape)); in Reshape()
947 arg_grads->push_back(darr.Reshape(new_shape)); in Reshape()
962 if (partial_shaping || new_shape == arr.shape()) { in Reshape()
963 if (new_shape.Size() > arr.shape().Size()) { in Reshape()
969 aux_states->emplace_back(new_shape, arr.ctx(), false, arr.dtype()); in Reshape()
[all …]
|
| /dports/devel/swig/swig-4.0.2/Examples/ocaml/shapes/ |
| H A D | runme.ml | 65 new_shape
|
| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/tensor/ |
| H A D | elemwise_binary_op-inl.h | 243 mxnet::TShape new_shape = output.aux_shape(rowsparse::kIdx); in RspRspOp()
244 CHECK_LE(iter_out, new_shape.Size()); in RspRspOp()
247 new_shape[0] -= num_common_rows; in RspRspOp()
248 output.set_aux_shape(rowsparse::kIdx, new_shape); in RspRspOp()
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/tensor/ |
| H A D | elemwise_binary_op-inl.h | 243 mxnet::TShape new_shape = output.aux_shape(rowsparse::kIdx); in RspRspOp()
244 CHECK_LE(iter_out, new_shape.Size()); in RspRspOp()
247 new_shape[0] -= num_common_rows; in RspRspOp()
248 output.set_aux_shape(rowsparse::kIdx, new_shape); in RspRspOp()
|
| /dports/science/py-nibabel/nibabel-3.2.1/nibabel/tests/ |
| H A D | test_image_api.py | 500 new_shape = (shape[0] + 1,) + shape[1:]
501 hdr.set_data_shape(new_shape)
503 assert img.header.get_data_shape() == new_shape
|
| /dports/math/py-yt/yt-4.0.1/yt/data_objects/index_subobjects/ |
| H A D | octree_subset.py | 91 new_shape = (nz, nz, nz, n_oct)
93 new_shape = (nz, nz, nz, n_oct, 3)
100 arr = arr.reshape(new_shape, order="F")
|
| /dports/math/py-numpy/numpy-1.20.3/numpy/lib/ |
| H A D | arraypad.py | 109 new_shape = tuple(
114 padded = np.empty(new_shape, dtype=array.dtype, order=order)
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/symbol/numpy/ |
| H A D | _symbol.py | 112 new_shape = ()
122 new_shape += (-2,)
132 new_shape += (-3,)
136 new_shape += (-4,)
138 return _npi.reshape(sliced, new_shape)
6128 def resize(a, new_shape): argument
6176 return _npi.resize_fallback(a, new_shape=new_shape)
|
| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/symbol/numpy/ |
| H A D | _symbol.py | 112 new_shape = ()
122 new_shape += (-2,)
132 new_shape += (-3,)
136 new_shape += (-4,)
138 return _npi.reshape(sliced, new_shape)
6128 def resize(a, new_shape): argument
6176 return _npi.resize_fallback(a, new_shape=new_shape)
|
| /dports/devel/py-pythran/pythran-0.11.0/pythran/pythonic/include/types/ |
| H A D | numpy_iexpr.hpp | 276 auto reshape(pS const &new_shape) const -> numpy_iexpr< in reshape()
287 fixed_new_shape, new_shape, in reshape()
|
| /dports/science/py-cirq-aqt/Cirq-0.12.0/cirq-core/cirq/qis/ |
| H A D | states.py | 701 new_shape = np.prod([shape[i] for i in indices], dtype=np.int64)
703 return rho.reshape((new_shape, new_shape))
|
| /dports/science/py-cirq-pasqal/Cirq-0.13.1/cirq-core/cirq/qis/ |
| H A D | states.py | 701 new_shape = np.prod([shape[i] for i in indices], dtype=np.int64)
703 return rho.reshape((new_shape, new_shape))
|
| /dports/science/py-cirq-core/Cirq-0.13.1/cirq-core/cirq/qis/ |
| H A D | states.py | 701 new_shape = np.prod([shape[i] for i in indices], dtype=np.int64)
703 return rho.reshape((new_shape, new_shape))
|
| /dports/science/py-cirq-google/Cirq-0.13.0/cirq-core/cirq/qis/ |
| H A D | states.py | 701 new_shape = np.prod([shape[i] for i in indices], dtype=np.int64)
703 return rho.reshape((new_shape, new_shape))
|
| /dports/science/py-cirq-ionq/Cirq-0.13.1/cirq-core/cirq/qis/ |
| H A D | states.py | 701 new_shape = np.prod([shape[i] for i in indices], dtype=np.int64)
703 return rho.reshape((new_shape, new_shape))
|
| /dports/science/py-h5py/h5py-3.6.0/docs/ |
| H A D | swmr.rst | 69 new_shape = ((i+1) * len(arr), )
70 dset.resize( new_shape )
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/autotvm/graph_tuner/ |
| H A D | base_graph_tuner.py | 204 new_shape = tuple([val.value for val in node_entry["types"][0].shape])
207 + (("TENSOR", new_shape, dtype),)
|
| /dports/misc/tvm/incubator-tvm-0.6.1/python/tvm/autotvm/graph_tuner/ |
| H A D | base_graph_tuner.py | 180 new_shape = tuple([val.value for val in node_entry["types"][0].shape])
182 ((new_shape + (dtype,)),) + input_workload[2:]
|
| /dports/misc/py-tvm/incubator-tvm-0.6.1/python/tvm/autotvm/graph_tuner/ |
| H A D | base_graph_tuner.py | 180 new_shape = tuple([val.value for val in node_entry["types"][0].shape])
182 ((new_shape + (dtype,)),) + input_workload[2:]
|
| /dports/math/py-numpy/numpy-1.20.3/numpy/core/src/multiarray/ |
| H A D | nditer_constr.c | 2510 npy_intp new_shape[NPY_MAXDIMS], strides[NPY_MAXDIMS]; in npyiter_new_temp_array() local
2563 new_shape[i] = 1; in npyiter_new_temp_array()
2566 new_shape[i] = NAD_SHAPE(axisdata); in npyiter_new_temp_array()
2568 stride *= new_shape[i]; in npyiter_new_temp_array()
2614 new_shape[i] = NAD_SHAPE(axisdata); in npyiter_new_temp_array()
2615 stride *= new_shape[i]; in npyiter_new_temp_array()
2627 shape = new_shape; in npyiter_new_temp_array()
|
| /dports/games/exult/exult-snapshot-v1.7.0.20211128/mapedit/ |
| H A D | shapelst.h | 177 void new_shape();
|