| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| batch_to_space_nd.cc | 40 crops = GetInput(context, node, 2);
45 TfLiteTensor* crops; member in struct:tflite::ops::builtin::batch_to_space_nd::BatchToSpaceNDContext
60 const int* crops = GetTensorData<int32>(op_context->crops); local
66 TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->crops),
69 // TODO(ycling): Add crops as part of calculation. Remove check for a crops
71 TF_LITE_ENSURE_EQ(context, crops[0], 0);
72 TF_LITE_ENSURE_EQ(context, crops[1], 0);
73 TF_LITE_ENSURE_EQ(context, crops[2], 0)
[all...] |
| batch_to_space_nd_test.cc | 51 // Tests case where block_shape and crops are const tensors.
54 // BatchToSpaceNDOpConstModel m(input_shape, block_shape, crops);
61 std::initializer_list<int> crops) {
64 crops_ = AddConstInput(TensorType_INT32, crops, {2, 2});
74 // Tests case where block_shape and crops are non-const tensors.
80 // m.SetPaddings(crops);
|
| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| batchtospace_op.cc | 27 const xla::Literal& crops) {
42 xla::ShapeUtil::Rank(crops.shape()) == 2 &&
43 block_rank == xla::ShapeUtil::GetDimension(crops.shape(), 0) &&
44 2 == xla::ShapeUtil::GetDimension(crops.shape(), 1),
45 errors::InvalidArgument("crops should have shape [", block_rank,
47 xla::ShapeUtil::HumanString(crops.shape())));
118 // `reshaped_permuted` according to `crops` to produce the output of shape:
121 // input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
123 // input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1]
155 xla::Literal crops; variable
175 xla::Literal crops; variable
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| batchtospace_op_test.py | 57 crops = array_ops.zeros((2, 2), dtype=crops_dtype)
58 y1 = self.batch_to_space(x, crops, block_size=block_size)
76 crops = np.zeros((2, 2), dtype=np.int32)
79 _ = self.batch_to_space(x_np, crops, block_size)
84 crops = np.zeros((2, 2), dtype=np.int32)
87 out_tf = self.batch_to_space(x_np, crops, block_size)
93 crops = np.zeros((2, 2), dtype=np.int32)
96 out_tf = self.batch_to_space(x_np, crops, block_size)
102 crops = np.zeros((2, 2), dtype=np.int32)
105 out_tf = self.batch_to_space(x_np, crops, block_size
[all...] |
| spacetobatch_op_test.py | 591 crops):
592 """Checks that `paddings` and `crops` satisfy invariants."""
599 self.assertEqual(crops.shape, (num_block_dims, 2))
607 self.assertEqual(crops[i, 0], 0)
608 self.assertEqual(crops[i, 1], paddings[i, 1] - base_paddings[i, 1])
616 paddings, crops = array_ops.required_space_to_batch_paddings(input_shape,
620 crops_const = tensor_util.constant_value(crops)
|
| atrous_conv2d_test.py | 103 net = batch_to_space(net, crops=pad, block_size=rate)
133 y2 = array_ops.batch_to_space(y2, crops=pad, block_size=rate)
|
| /external/tensorflow/tensorflow/core/kernels/ |
| batchtospace_op.cc | 63 errors::InvalidArgument("crops should have shape [", block_dims,
67 // crops tensors are concurrently modified, we must copy the values.
69 gtl::InlinedVector<int64, 8> crops; local
71 internal::spacetobatch::SubtleMustCopyFlat(orig_crops, &crops);
78 if (crops[2 * dim] != 0 || crops[2 * dim + 1] != 0 ||
90 if (crops[2 * dim] != 0 || crops[2 * dim + 1] != 0 ||
150 const int64 crop_start = crops[2 * block_dim],
151 crop_end = crops[2 * block_dim + 1]
[all...] |
| crop_and_resize_op.h | 35 typename TTypes<float, 4>::Tensor crops);
|
| crop_and_resize_op_gpu.cu.cc | 328 typename TTypes<float, 4>::Tensor crops) {
333 const int num_boxes = crops.dimension(0);
334 const int crop_height = crops.dimension(1);
335 const int crop_width = crops.dimension(2);
336 const int depth = crops.dimension(3);
347 crop_height, crop_width, depth, extrapolation_value, crops.data());
|
| crop_and_resize_op.cc | 208 typename TTypes<float, 4>::Tensor crops) {
213 const int num_boxes = crops.dimension(0);
214 const int crop_height = crops.dimension(1);
215 const int crop_width = crops.dimension(2);
216 const int depth = crops.dimension(3);
246 crops(b, y, x, d) = extrapolation_value;
261 crops(b, y, x, d) = extrapolation_value;
281 crops(b, y, x, d) = top + (bottom - top) * y_lerp;
|
| /external/tensorflow/tensorflow/python/ops/ |
| image_grad_test.py | 266 crops = image_ops.crop_and_resize(
275 self.assertEqual(crops_shape, list(crops.get_shape()))
276 crops = sess.run(crops)
277 self.assertEqual(crops_shape, list(crops.shape))
359 crops = image_ops.crop_and_resize(
368 crops,
|
| nn_ops.py | 239 adjusted_{paddings,crops} are int64 tensors of shape [max(spatial_dims), 2]
243 We first define two int64 tensors `paddings` and `crops` of shape
249 paddings, crops = required_space_to_batch_paddings(
258 paddings, crops = required_space_to_batch_paddings(
267 `crops` in order to be usable with these operations. For a given dimension,
278 adjusted_crops[spatial_dims[i] - 1, :] = crops[i, :]
494 paddings, crops = array_ops.required_space_to_batch_paddings(
502 crops = _with_space_to_batch_adjust(crops, 0, spatial_dims)
509 input=result, block_shape=dilation_rate, crops=crops
[all...] |
| array_ops.py | [all...] |
| /external/tensorflow/tensorflow/core/ops/ |
| array_ops_test.cc | 1452 Tensor crops = test::AsTensor<int32>({1, 1, 0, 1}, {{2, 2}}); local
1460 Tensor crops = test::AsTensor<int32>({1, 1, 0, 0}, {{2, 2}}); local
1495 Tensor crops = test::AsTensor<int32>({3, 2, 0, 0}, {{2, 2}}); local
[all...] |
| /frameworks/native/libs/gui/tests/ |
| SurfaceTextureGL_test.cpp | 140 android_native_rect_t crops[] = { local
149 const android_native_rect_t& crop(crops[i]);
|
| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| sliced_wasserstein_impl.py | 73 res = array_ops.batch_to_space(res, crops=[[0, 0], [0, 0]], block_size=2)
|
| /external/tensorflow/tensorflow/contrib/lite/testing/ |
| generate_examples.py | 98 # const tensors as crops.
99 r"batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\]": "70594634",
[all...] |
| /external/tensorflow/tensorflow/compiler/tests/ |
| randomized_tests.cc | [all...] |
| /external/ImageMagick/www/api/ |
| transform.php | 152 <p>CropImageToTiles() crops a single image, into a possible list of tiles. This may include a single sub-region of the image. This basically applies all the normal geometry flags for Crop.</p>
|
| /prebuilts/go/darwin-x86/src/cmd/internal/obj/ |
| objfile.go | 826 // This scenario crops up when we have references to variables of an
|
| /prebuilts/go/linux-x86/src/cmd/internal/obj/ |
| objfile.go | 826 // This scenario crops up when we have references to variables of an
|
| /external/tensorflow/tensorflow/go/op/ |
| wrappers.go | [all...] |
