/external/tensorflow/tensorflow/python/keras/engine/ |
D | input_spec.py | 57 ndim=None, argument 63 self.ndim = len(shape) 66 self.ndim = ndim 76 if self.axes and (self.ndim is not None or self.max_ndim is not None): 77 max_dim = (self.ndim if self.ndim else self.max_ndim) - 1 86 ('ndim=' + str(self.ndim)) if self.ndim else '', 96 'ndim': self.ndim, 118 if spec.ndim is None and spec.shape is None: 123 shape = [None] * spec.ndim 160 if (spec.ndim is not None or [all …]
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D | input_spec_test.py | 43 spec = input_spec.InputSpec(ndim=5) 47 spec = input_spec.InputSpec(ndim=0) 51 spec = input_spec.InputSpec(ndim=3, axes={1: 3, -1: 2})
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/external/python/pybind11/include/pybind11/ |
D | buffer_info.h | 22 ssize_t ndim = 0; // Number of dimensions member 28 buffer_info(void *ptr, ssize_t itemsize, const std::string &format, ssize_t ndim, in buffer_info() 30 : ptr(ptr), itemsize(itemsize), size(1), format(format), ndim(ndim), in buffer_info() 32 if (ndim != (ssize_t) shape.size() || ndim != (ssize_t) strides.size()) in buffer_info() 34 for (size_t i = 0; i < (size_t) ndim; ++i) in buffer_info() 50 : buffer_info(view->buf, view->itemsize, view->format, view->ndim, 51 {view->shape, view->shape + view->ndim}, {view->strides, view->strides + view->ndim}) { 68 ndim = rhs.ndim; 83 … buffer_info(private_ctr_tag, void *ptr, ssize_t itemsize, const std::string &format, ssize_t ndim, in buffer_info() 85 : buffer_info(ptr, itemsize, format, ndim, std::move(shape_in), std::move(strides_in)) { } in buffer_info()
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D | numpy.h | 391 ssize_t ndim() const { return dims_; } 400 return std::accumulate(shape_, shape_ + ndim(), (ssize_t) 1, std::multiplies<ssize_t>()); 560 auto ndim = shape->size(); 561 if (ndim != strides->size()) 577 api.PyArray_Type_, descr.release().ptr(), (int) ndim, shape->data(), strides->data(), 619 return std::accumulate(shape(), shape() + ndim(), (ssize_t) 1, std::multiplies<ssize_t>()); 633 ssize_t ndim() const { 649 if (dim >= ndim()) 661 if (dim >= ndim()) 698 if ((ssize_t) sizeof...(index) > ndim()) [all …]
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/external/python/cpython3/Objects/ |
D | memoryobject.c | 253 (view->suboffsets && view->suboffsets[dest->ndim-1] >= 0) 258 assert(dest->ndim > 0 && src->ndim > 0); in last_dim_is_contiguous() 261 dest->strides[dest->ndim-1] == dest->itemsize && in last_dim_is_contiguous() 262 src->strides[src->ndim-1] == src->itemsize); in last_dim_is_contiguous() 296 if (dest->ndim != src->ndim) in equiv_shape() 299 for (i = 0; i < dest->ndim; i++) { in equiv_shape() 359 copy_rec(const Py_ssize_t *shape, Py_ssize_t ndim, Py_ssize_t itemsize, in copy_rec() argument 366 assert(ndim >= 1); in copy_rec() 368 if (ndim == 1) { in copy_rec() 380 copy_rec(shape+1, ndim-1, itemsize, in copy_rec() [all …]
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/external/tensorflow/tensorflow/python/framework/ |
D | fast_tensor_util.pyx | 10 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument 22 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument 30 tensor_proto, np.ndarray[np.float32_t, ndim=1] nparray): argument 38 tensor_proto, np.ndarray[np.float64_t, ndim=1] nparray): argument 46 tensor_proto, np.ndarray[np.int32_t, ndim=1] nparray): argument 53 tensor_proto, np.ndarray[np.uint32_t, ndim=1] nparray): argument 60 tensor_proto, np.ndarray[np.int64_t, ndim=1] nparray): argument 67 tensor_proto, np.ndarray[np.uint64_t, ndim=1] nparray): argument 74 tensor_proto, np.ndarray[np.uint8_t, ndim=1] nparray): argument 82 tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): argument [all …]
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/external/python/cpython3/Modules/ |
D | _testbuffer.c | 154 base->ndim = 1; in ndbuf_new() 267 if (ndbuf->base.ndim == 0) in init_flags() 473 copy_rec(const Py_ssize_t *shape, Py_ssize_t ndim, Py_ssize_t itemsize, in copy_rec() argument 480 assert(ndim >= 1); in copy_rec() 482 if (ndim == 1) { in copy_rec() 506 copy_rec(shape+1, ndim-1, itemsize, in copy_rec() 520 dest->ndim != src->ndim) in cmp_structure() 523 for (i = 0; i < dest->ndim; i++) { in cmp_structure() 541 assert(dest->ndim > 0); in copy_buffer() 549 if ((dest->suboffsets && dest->suboffsets[dest->ndim-1] >= 0) || in copy_buffer() [all …]
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/external/tensorflow/tensorflow/python/layers/ |
D | utils.py | 28 def convert_data_format(data_format, ndim): argument 30 if ndim == 3: 32 elif ndim == 4: 34 elif ndim == 5: 37 raise ValueError('Input rank not supported:', ndim) 39 if ndim == 3: 41 elif ndim == 4: 43 elif ndim == 5: 46 raise ValueError('Input rank not supported:', ndim)
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/external/tensorflow/tensorflow/python/keras/utils/ |
D | conv_utils.py | 28 def convert_data_format(data_format, ndim): argument 30 if ndim == 3: 32 elif ndim == 4: 34 elif ndim == 5: 37 raise ValueError('Input rank not supported:', ndim) 39 if ndim == 3: 41 elif ndim == 4: 43 elif ndim == 5: 46 raise ValueError('Input rank not supported:', ndim) 228 if not 3 <= kernel.ndim <= 5: [all …]
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/external/python/cpython2/Objects/ |
D | memoryobject.c | 11 if (buf->ndim == 0) in get_shape0() 23 if (src->ndim == 1 && src->shape != NULL) { in dup_buffer() 27 if (src->ndim == 1 && src->strides != NULL) { in dup_buffer() 181 if (view->ndim > PY_SSIZE_T_MAX / sizeof(Py_ssize_t)) { in _indirect_copy_nd() 186 indices = (Py_ssize_t *)PyMem_Malloc(sizeof(Py_ssize_t)*view->ndim); in _indirect_copy_nd() 191 for (k=0; k<view->ndim;k++) { in _indirect_copy_nd() 196 for (k=0; k<view->ndim; k++) { in _indirect_copy_nd() 206 func(view->ndim, indices, view->shape); in _indirect_copy_nd() 297 _strided_copy_nd(dest, view->buf, view->ndim, view->shape, in PyMemoryView_GetContiguous() 366 return _IntTupleFromSsizet(self->view.ndim, self->view.shape); in memory_shape_get() [all …]
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/external/tensorflow/tensorflow/python/keras/ |
D | activations_test.py | 70 x = keras.backend.placeholder(ndim=2) 79 x = keras.backend.placeholder(ndim=1) 91 x = keras.backend.placeholder(ndim=2) 109 x = keras.backend.placeholder(ndim=2) 120 x = keras.backend.placeholder(ndim=2) 136 x = keras.backend.placeholder(ndim=2) 149 x = keras.backend.placeholder(ndim=2) 157 x = keras.backend.placeholder(ndim=2) 169 x = keras.backend.placeholder(ndim=2) 181 x = keras.backend.placeholder(ndim=2) [all …]
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D | activations.py | 70 ndim = K.ndim(x) 71 if ndim == 2: 73 elif ndim > 2:
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/external/python/cpython3/Lib/test/ |
D | test_buffer.py | 256 def strides_from_shape(ndim, shape, itemsize, layout): argument 259 if ndim == 0: 263 for i in range(ndim-2, -1, -1): 267 for i in range(1, ndim): 313 def getindex(ndim, ind, strides): argument 316 for i in range(ndim): 325 ndim = len(shape) 326 sstrides = strides_from_shape(ndim, shape, 1, 'C') 327 dstrides = strides_from_shape(ndim, shape[::-1], 1, 'C') 330 fr = getindex(ndim, ind, sstrides) [all …]
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/external/tensorflow/tensorflow/python/ops/ragged/ |
D | ragged_factory_ops.py | 171 if not isinstance(pylist, (list, tuple)) and np.ndim(pylist) == 0: 260 if isinstance(pylist, (list, tuple)) or np.ndim(pylist) != 0: 279 if not isinstance(item, (list, tuple)) and np.ndim(item) == 0: 287 is_nested = isinstance(item, (list, tuple)) or np.ndim(item) != 0 300 isinstance(v, (list, tuple)) or np.ndim(v) != 0 for v in flat_values):
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/external/tensorflow/tensorflow/compiler/xla/python/ |
D | dlpack.cc | 263 dt.ndim = buffer->on_host_shape().dimensions_size(); in BufferToDLPackManagedTensor() 300 if (dlmt->dl_tensor.ndim < 0) { in DLPackManagedTensorToBuffer() 303 dlmt->dl_tensor.ndim); in DLPackManagedTensorToBuffer() 308 reinterpret_cast<int64*>(dlmt->dl_tensor.shape), dlmt->dl_tensor.ndim); in DLPackManagedTensorToBuffer() 316 dlmt->dl_tensor.ndim); in DLPackManagedTensorToBuffer() 319 minor_to_major.resize(dlmt->dl_tensor.ndim); in DLPackManagedTensorToBuffer()
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/external/tensorflow/tensorflow/python/keras/layers/ |
D | merge.py | 125 input_ndims = list(map(K.ndim, inputs)) 132 x_ndim = K.ndim(x) 142 x_ndim = K.ndim(x) 165 y_ndim = K.ndim(y) 558 elif K.ndim(mask_i) < K.ndim(input_i): 686 axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)] 693 axes.append(self.axes[i] % K.ndim(inputs[i]))
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D | local.py | 171 self.input_spec = InputSpec(ndim=3) 256 self.input_spec = InputSpec(ndim=3, axes={1: input_dim}) 258 self.input_spec = InputSpec(ndim=3, axes={-1: input_dim}) 485 self.input_spec = InputSpec(ndim=4) 578 self.input_spec = InputSpec(ndim=4, axes={1: input_filter}) 580 self.input_spec = InputSpec(ndim=4, axes={-1: input_filter}) 711 ndims = int(mask.ndim / 2) 773 kernel = make_2d(kernel, split_dim=K.ndim(kernel) // 2)
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D | convolutional.py | 147 self.input_spec = InputSpec(ndim=self.rank + 2) 174 self.input_spec = InputSpec(ndim=self.rank + 2, 910 self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim}) 988 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) 1203 self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim}) 1277 data_format=conv_utils.convert_data_format(self.data_format, ndim=5), 1289 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) 1460 self.input_spec = InputSpec(ndim=self.rank + 2, 1705 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) 1711 data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) [all …]
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/external/python/pybind11/tests/ |
D | test_numpy_array.cpp | 118 if (r.ndim() != 2) throw std::domain_error("error: ndim != 2"); in auxiliaries() 123 l.append(r.ndim()); in auxiliaries() 160 sm.def("ndim", [](const arr& a) { return a.ndim(); }); in TEST_SUBMODULE() 161 sm.def("shape", [](const arr& a) { return arr(a.ndim(), a.shape()); }); in TEST_SUBMODULE() 163 sm.def("strides", [](const arr& a) { return arr(a.ndim(), a.strides()); }); in TEST_SUBMODULE() 198 {a.shape(), a.shape() + a.ndim()}, in TEST_SUBMODULE() 199 {a.strides(), a.strides() + a.ndim()}, in TEST_SUBMODULE() 325 if (r.ndim() != 2) throw std::domain_error("error: ndim != 2"); in TEST_SUBMODULE() 333 if (r.ndim() != 3) throw std::domain_error("error: ndim != 3"); in TEST_SUBMODULE()
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/external/OpenCL-CTS/test_conformance/gl/ |
D | test_images_read_common.cpp | 297 int ndim = 1; in test_cl_image_read() local 302 ndim = 1; in test_cl_image_read() 310 ndim = 2; in test_cl_image_read() 317 ndim = 3; in test_cl_image_read() 329 local_range = (size_t*)malloc(sizeof(size_t) * ndim); in test_cl_image_read() 333 local_range = (size_t*)malloc(sizeof(size_t) * ndim); in test_cl_image_read() 337 error = clEnqueueNDRangeKernel( queue, kernel, ndim, NULL, global_range, in test_cl_image_read()
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/external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
D | image_preprocessing.py | 73 self.input_spec = InputSpec(ndim=4) 79 self.input_spec = InputSpec(ndim=4, axes={channel_axis: channel_dim}) 126 self.input_spec = InputSpec(ndim=4) 132 self.input_spec = InputSpec(ndim=4, axes={channel_axis: channel_dim}) 205 self.input_spec = InputSpec(ndim=4) 354 self.input_spec = InputSpec(ndim=4) 460 self.input_spec = InputSpec(ndim=4) 689 self.input_spec = InputSpec(ndim=4) 803 self.input_spec = InputSpec(ndim=4) 935 self.input_spec = InputSpec(ndim=4)
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/external/tensorflow/tensorflow/compiler/xla/service/gpu/ |
D | cusolver_rewriter.cc | 56 int ndim = a_shape.dimensions_size(); in CreateCholesky() local 57 CHECK_GE(ndim, 2); in CreateCholesky() 58 int64 n = a_shape.dimensions(ndim - 1); in CreateCholesky()
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/external/python/cpython3/Doc/c-api/ |
D | buffer.rst | 156 .. c:member:: int ndim 169 An array of :c:type:`Py_ssize_t` of length :c:member:`~Py_buffer.ndim` 171 ``shape[0] * ... * shape[ndim-1] * itemsize`` MUST be equal to 182 An array of :c:type:`Py_ssize_t` of length :c:member:`~Py_buffer.ndim` 194 An array of :c:type:`Py_ssize_t` of length :c:member:`~Py_buffer.ndim`. 235 :c:member:`~Py_buffer.len`, :c:member:`~Py_buffer.itemsize`, :c:member:`~Py_buffer.ndim`. 348 :c:member:`~Py_buffer.ndim`, :c:member:`~Py_buffer.shape` and :c:member:`~Py_buffer.strides`. 350 If ``ndim == 0``, the memory location pointed to by :c:member:`~Py_buffer.buf` is 370 def verify_structure(memlen, itemsize, ndim, shape, strides, offset): 384 if ndim <= 0: [all …]
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/external/tensorflow/tensorflow/compiler/xla/python_api/ |
D | xla_shape.py | 120 layout = range(ndarray.ndim) 124 layout = list(reversed(xrange(ndarray.ndim)))
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_DynamicPartition.pbtxt | 17 For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` 24 outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
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