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/aosp_15_r20/external/pytorch/functorch/op_analysis/
H A Dannotated_ops39 as_strided, view/reshape
44 atleast_1d, view/reshape
45 atleast_2d, view/reshape
46 atleast_3d, view/reshape
62 broadcast_tensors, view/reshape
63 broadcast_to, view/reshape
64 cat, view/reshape
65 block_diag, view/reshape
68 unsafe_chunk, view/reshape
69 chunk, view/reshape
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/
H A Ddynamic_update_slice_test.cc108 reshape.23 = s32[1]{0} reshape(slice.18) in XLA_TEST_F()
109 reshape.4 = s32[4]{0} reshape(dynamic-slice) in XLA_TEST_F()
110 slice.19 = s32[3]{0} slice(reshape.4), slice={[1:4]} in XLA_TEST_F()
112 concatenate.1 = s32[5]{0} concatenate(reshape.23, slice.19, constant.6), dimensions={0} in XLA_TEST_F()
120 reshape.24 = s32[] reshape(slice.18) in XLA_TEST_F()
121 slice.26 = s32[1]{0} slice(reshape.4), slice={[1:2]} in XLA_TEST_F()
122 reshape.10 = s32[] reshape(slice.26) in XLA_TEST_F()
123 slice.27 = s32[1]{0} slice(reshape.4), slice={[2:3]} in XLA_TEST_F()
124 reshape.11 = s32[] reshape(slice.27) in XLA_TEST_F()
125 slice.28 = s32[1]{0} slice(reshape.4), slice={[3:4]} in XLA_TEST_F()
[all …]
H A Dreshape_mover_test.cc58 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
63 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
76 // Verifies that the reshape is not moved, since rng0 is trivially reshapable
101 op::Add(op::Reshape(rng0), const1));
106 op::Add(op::Reshape(rng0), const1));
127 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
133 op::Add(op::Reshape(op::Parameter()), op::Reshape(op::Parameter()))); in TEST_F()
154 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
158 op::Reshape(op::Add(param0, param1))); in TEST_F()
213 op::Reshape(op::Select(op::Reshape(const0), param1, param2)));
[all …]
H A Dreshape_mover.cc21 // %reshape.A = NewShape reshape(%param.A)
22 // %reshape.B = NewShape reshape(%param.B)
23 // %instruction = NewShape instruction(%reshape.A, %reshape.B)
28 // %reshape = NewShape reshape(%instruction)
57 // NOTE: Technically a sequence of reshape(reshape(constant)) is also in CanTriviallyChangeShape()
61 // But it's not that simple. E.g. reshape(reshape(rng)) is only trivially in CanTriviallyChangeShape()
63 // reshape(scalar) isn't trivial at all if the reshape itself isn't scalar. in CanTriviallyChangeShape()
71 // A constant can trivially reshape the literal it holds. in CanTriviallyChangeShape()
93 // Returns true iff `instruction` is a reshape/transpose instruction for which
101 // Finds the first operand of an instruction that is a non-trivial reshape or
[all …]
H A Ddynamic_padder.cc316 HloInstruction* reshape, int64_t input_dim, in GenerateBinaryMask() argument
321 split_input ? reshape->operand(0)->shape() : reshape->shape(); in GenerateBinaryMask()
323 split_input ? reshape->shape() : reshape->operand(0)->shape(); in GenerateBinaryMask()
328 HloInstruction* pred_true = reshape->AddInstruction( in GenerateBinaryMask()
330 HloInstruction* input_shape_pred_mask = reshape->AddInstruction( in GenerateBinaryMask()
335 reshape->AddInstruction(HloInstruction::CreateIota(mask_input_shape, 0)); in GenerateBinaryMask()
369 HloInstruction* static_output_dim_size = reshape->AddInstruction( in GenerateBinaryMask()
373 reshape->AddInstruction(HloInstruction::CreateBroadcast( in GenerateBinaryMask()
378 reshape->AddInstruction(HloInstruction::CreateBinary( in GenerateBinaryMask()
381 HloInstruction* broadcasted_effective_size = reshape->AddInstruction( in GenerateBinaryMask()
[all …]
H A Dconditional_code_motion_test.cc50 %reshape.8493 = f32[2,512,364]{2,1,0} reshape(f32[93184,4]{1,0} %get-tuple-element.1) in TEST_F()
51 %convert.2894 = bf16[2,512,364]{2,1,0} convert(f32[2,512,364]{2,1,0} %reshape.8493) in TEST_F()
52 …ROOT %tuple.1 = ( bf16[2,512,364]{2,1,0}, f32[2,512,364]{2,1,0}) tuple(%convert.2894, %reshape.849… in TEST_F()
58 %reshape.9717 = f32[2,512,364]{2,1,0} reshape(f32[93184,4]{1,0} %get-tuple-element.3) in TEST_F()
59 …dd = f32[2,512,364]{2,1,0} add(f32[2,512,364]{2,1,0} %reshape.9717, f32[2,512,364]{2,1,0} %reshape… in TEST_F()
60 …%convert.3604 = bf16[2,512,364]{2,1,0} convert(f32[2,512,364]{2,1,0} %reshape.9717), metadata={op_… in TEST_F()
136 %reshape.8493 = f32[2,512,364]{2,1,0} reshape(f32[93184,4]{1,0} %get-tuple-element.1) in TEST_F()
137 …93 = f32[2,512,364]{2,1,0} add(f32[2,512,364]{2,1,0} %reshape.8493, f32[2,512,364]{2,1,0} %reshape… in TEST_F()
145 %reshape.9717 = f32[2,512,364]{2,1,0} reshape(f32[93184,4]{1,0} %get-tuple-element.3) in TEST_F()
146 …93 = f32[2,512,364]{2,1,0} add(f32[2,512,364]{2,1,0} %reshape.9717, f32[2,512,364]{2,1,0} %reshape… in TEST_F()
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/mlir/tensorflow/tests/
H A Dunroll-batch-matmul.mlir17 …// CHECK: %[[LHS_RESHAPED:.*]] = "tf.Reshape"(%arg0, %[[LHS_RESHAPED_SHAPE]]) : (tensor<2x3x4x5xf3…
19 …// CHECK: %[[LHS_1:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#0, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
20 …// CHECK: %[[LHS_2:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#1, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
21 …// CHECK: %[[LHS_3:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#2, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
22 …// CHECK: %[[LHS_4:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#3, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
23 …// CHECK: %[[LHS_5:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#4, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
24 …// CHECK: %[[LHS_6:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#5, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
26 …// CHECK: %[[RHS_RESHAPED:.*]] = "tf.Reshape"(%arg1, %[[RHS_RESHAPED_SHAPE]]) : (tensor<2x3x5x6xf3…
28 …// CHECK: %[[RHS_1:.*]] = "tf.Reshape"(%[[RHS_SPLIT]]#0, %[[MATMUL_RHS_SHAPE]]) : (tensor<1x5x6xf3…
29 …// CHECK: %[[RHS_2:.*]] = "tf.Reshape"(%[[RHS_SPLIT]]#1, %[[MATMUL_RHS_SHAPE]]) : (tensor<1x5x6xf3…
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/array_ops/
H A Dreshape_op_test.py35 np_ans = x.reshape(y)
36 tf_ans = array_ops.reshape(x, y)
43 tf_ans = array_ops.reshape(x, y64)
50 y = array_ops.reshape(x, shape)
56 y = array_ops.reshape(x, shape64)
65 x = np.arange(1., 7.).reshape([1, 6]) > 3
69 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
73 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
77 x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
81 x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
[all …]
H A Dweights_broadcast_test.py29 return np.reshape(np.cumsum(np.ones(shape), dtype=np.int32), newshape=shape)
58 weights=np.asarray((5,)).reshape((1, 1, 1)),
64 weights=np.asarray((5, 7, 11, 3)).reshape((1, 1, 4)),
70 weights=np.asarray((5, 11)).reshape((1, 2, 1)),
76 weights=np.asarray((5, 7, 11, 3, 2, 13, 7, 5)).reshape((1, 2, 4)),
82 weights=np.asarray((5, 7, 11)).reshape((3, 1, 1)),
89 5, 7, 11, 3, 2, 12, 7, 5, 2, 17, 11, 3)).reshape((3, 1, 4)),
97 2, 17, 11, 3, 5, 7, 11, 3, 2, 12, 7, 5)).reshape((3, 2, 4)),
122 weights=np.asarray((5,)).reshape((1, 1)),
128 weights=np.asarray((5, 7, 11, 3, 2, 12)).reshape((3, 2)),
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/gpu/tests/
H A Dgpu_reduce_scatter_creator_test.cc82 %reshape = s32[] reshape(%id) in TEST_F()
84 %offset = s32[] multiply(%reshape, %slice_size) in TEST_F()
120 %reshape = s32[] reshape(%id) in TEST_F()
122 %offset = s32[] multiply(%reshape, %slice_size) in TEST_F()
124 %reshape.1 = f32[32,16,64] reshape(%all-reduce) in TEST_F()
125 ROOT %dynamic-slice = f32[4,16,64] dynamic-slice(%reshape.1, %offset, %zero, %zero), in TEST_F()
135 op::Reshape(op::ReduceScatter(op::Parameter(0)))); in TEST_F()
157 %reshape.1 = f32[4,84,1024] reshape(%all-reduce) in TEST_F()
158 ROOT %dynamic-slice = f32[4,84,128] dynamic-slice(%reshape.1, %zero, %zero, %offset), in TEST_F()
168 op::Reshape(op::ReduceScatter(op::Parameter(0)))); in TEST_F()
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/mlir/xla/tests/translate/
H A Dfully_connected_reference_model.hlotxt14 // CHECK-NEXT: %[[VAL_2:.*]] = mhlo.reshape %[[VAL_0]] : (tensor<1x300xf32>) -> tensor<1x300xf32>
15 %reshape.3 = f32[1,300] reshape(%arg0.1)
18 %transpose.27 = f32[300,1] transpose(%reshape.3), dimensions={1,0}
20 …// CHECK-NEXT: %[[VAL_4:.*]] = mhlo.reshape %[[VAL_3]] : (tensor<300x1xf32>) -> tensor<300x1x1xf32>
21 %reshape.28 = f32[300,1,1] reshape(%transpose.27)
23 …// CHECK-NEXT: %[[VAL_5:.*]] = mhlo.reshape %[[VAL_4]] : (tensor<300x1x1xf32>) -> tensor<300x1xf32>
24 %reshape.29 = f32[300,1] reshape(%reshape.28)
27 %broadcast.30 = f32[300,1,5] broadcast(%reshape.29), dimensions={0,1}
62 …// CHECK-NEXT: %[[VAL_18:.*]] = mhlo.reshape %[[VAL_17]] : (tensor<1x300x3x1xf32>) -> tensor<1x300…
63 %reshape.4 = f32[1,300,3,1] reshape(%copy.1)
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/tests/
H A Dconcat_test.cc536 reshape.723 = f32[4,2,1]{2,1,0} reshape(parameter.38) in XLA_TEST_F()
537 reshape.724 = f32[4,2,1]{2,1,0} reshape(parameter.38) in XLA_TEST_F()
538 concatenate.42 = f32[4,2,2]{2,1,0} concatenate(reshape.723, reshape.724), dimensions={2} in XLA_TEST_F()
540 reshape.1058 = f32[4,2]{1,0} reshape(slice.351) in XLA_TEST_F()
541 slice.352 = f32[4,1]{1,0} slice(reshape.1058), slice={[0:4], [1:2]} in XLA_TEST_F()
542 reshape.1059 = f32[4]{0} reshape(slice.352) in XLA_TEST_F()
544 reshape.1060 = f32[4]{0} reshape(slice.353) in XLA_TEST_F()
545 add.124 = f32[4]{0} add(reshape.1059, reshape.1060) in XLA_TEST_F()
546 slice.354 = f32[4,1]{1,0} slice(reshape.1058), slice={[0:4], [0:1]} in XLA_TEST_F()
547 reshape.1061 = f32[4]{0} reshape(slice.354) in XLA_TEST_F()
[all …]
H A Dptxas_bug_120501638.cc41 reshape.2 = f32[2,5,2]{2,1,0} reshape(arg0.1) in TEST_F()
43 pad.4 = f32[2,6,2]{2,1,0} pad(reshape.2, constant.3), padding=0_0x0_1x0_0 in TEST_F()
44 reshape.5 = f32[2,3,2,2]{3,2,1,0} reshape(pad.4) in TEST_F()
45 transpose.6 = f32[2,2,3,2]{3,0,2,1} transpose(reshape.5), dimensions={2,0,1,3} in TEST_F()
46 reshape.7 = f32[4,3,2]{2,1,0} reshape(transpose.6) in TEST_F()
47 reshape.8 = f32[4,1,3,2]{3,2,1,0} reshape(reshape.7) in TEST_F()
48 transpose.9 = f32[4,2,1,3]{1,3,2,0} transpose(reshape.8), dimensions={0,3,1,2} in TEST_F()
60 reshape.24 = f32[4,3,2]{2,1,0} reshape(transpose.23) in TEST_F()
61 reshape.25 = f32[2,2,3,2]{3,2,1,0} reshape(reshape.24) in TEST_F()
62 transpose.26 = f32[2,3,2,2]{3,1,0,2} transpose(reshape.25), dimensions={1,2,0,3} in TEST_F()
[all …]
H A Dreshape_test.cc107 auto reshape = Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, in XLA_TEST_P() local
109 auto new_shape = builder.GetShape(reshape).value(); in XLA_TEST_P()
125 Reshape(/*operand=*/a, /*dimensions=*/{}, /*new_sizes=*/{1}); in XLA_TEST_P()
211 Reshape(/*operand=*/parameter, /*dimensions=*/{0}, in XLA_TEST_P()
227 Reshape(/*operand=*/parameter, /*dimensions=*/{0}, in XLA_TEST_P()
243 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, in XLA_TEST_P()
259 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, in XLA_TEST_P()
277 Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, in XLA_TEST_P()
326 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, in XLA_TEST_P()
341 Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2, 3}, in XLA_TEST_P()
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/python/training/
H A Dcheckpoint_ops_test.py45 np.reshape(np.linspace(0.0, 79, 5 * 16), (5, 16)))
109 np.reshape([18, 34, 50, self.init_val, self.init_val], [5, 1]),
110 np.reshape([16, 32, 48, self.init_val, self.init_val], [5, 1]),
111 np.reshape([self.init_val] * 5, [5, 1]),
112 np.reshape([17, 33, 49, self.init_val, self.init_val], [5, 1]),
113 np.reshape([self.init_val] * 5, [5, 1])
141 np.reshape([2, 18, 34, 50, self.init_val, self.init_val], [6, 1]),
142 np.reshape([0, 16, 32, 48, self.init_val, self.init_val], [6, 1]),
143 np.reshape([self.init_val] * 6, [6, 1]),
144 np.reshape([1, 17, 33, 49, self.init_val, self.init_val], [6, 1]),
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/core/kernels/
H A Dtraining_ops_gpu.cu.cc269 var.device(d) -= lr.reshape(single).broadcast(bcast) * grad; in operator ()()
401 var.device(d) -= lr.reshape(single).broadcast(bcast) * grad * accum.rsqrt(); in operator ()()
424 grad / (accum.sqrt() + epsilon.reshape(single).broadcast(bcast)); in operator ()()
425 var.device(d) -= lr.reshape(single).broadcast(bcast) * update; in operator ()()
474 auto lr_bcast = lr.reshape(single).broadcast(bcast); in operator ()()
475 auto l1_bcast = l1.reshape(single).broadcast(bcast); in operator ()()
476 auto l2_bcast = l2.reshape(single).broadcast(bcast); in operator ()()
532 accum.device(d) = accum * rho.reshape(single).broadcast(bcast) + in operator ()()
534 rho.reshape(single).broadcast(bcast)); in operator ()()
536 (accum_update + epsilon.reshape(single).broadcast(bcast)).sqrt() * in operator ()()
[all …]
H A Dbatch_norm_op.h46 output.reshape(rest_by_depth).device(d) = in operator()
47 (input.reshape(rest_by_depth) - in operator()
48 mean.reshape(one_by_depth).broadcast(rest_by_one)) * in operator()
51 .reshape(one_by_depth) in operator()
53 beta.reshape(one_by_depth).broadcast(rest_by_one); in operator()
55 output.reshape(rest_by_depth).device(d) = in operator()
56 (input.reshape(rest_by_depth) - in operator()
57 mean.reshape(one_by_depth).broadcast(rest_by_one)) * in operator()
60 .reshape(one_by_depth) in operator()
62 beta.reshape(one_by_depth).broadcast(rest_by_one); in operator()
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/spmd/
H A Dcanonicalize_all_gather_for_cse_test.cc59 resh = s32[1,8]{1,0} reshape(param0) in TEST_F()
66 const HloInstruction* const reshape = in TEST_F() local
68 EXPECT_THAT(reshape, in TEST_F()
69 AllOf(op::Reshape(op::AllGather(_)), op::Shape("s32[2,8]"))); in TEST_F()
78 resh = s32[1,8]{1,0} reshape(param0) in TEST_F()
79 resh2 = s32[1,8,1,1]{3,2,1,0} reshape(resh) in TEST_F()
86 const HloInstruction* const reshape = in TEST_F() local
88 EXPECT_THAT(reshape, op::Reshape(op::AllGather(op::Parameter()))); in TEST_F()
97 resh = s32[8,1,1]{2,1,0} reshape(param0) in TEST_F()
98 resh2 = s32[1,8,1,1]{3,2,1,0} reshape(resh) in TEST_F()
[all …]
H A Dspmd_partitioner_test.cc63 // might create reshape/transposes around it. in PartitionComputation()
181 op::Reshape(op::DynamicSlice(op::Constant(), op::PartitionId())), in TEST_F()
203 op::Reshape(op::DynamicSlice(op::Constant(), op::PartitionId())), in TEST_F()
225 op::Reshape(op::DynamicSlice(op::Constant(), op::PartitionId())), in TEST_F()
245 AllOf(op::Copy(op::Reshape(op::Transpose(op::AllToAll(AllOf( in TEST_F()
246 op::Reshape(op::Parameter()), op::Shape("s32[4,2,1]")))))), in TEST_F()
265 AllOf(op::Copy(op::Slice(op::Reshape(AllOf(op::Transpose(op::AllToAll( in TEST_F()
266 op::Reshape(AllOf(op::Pad(), op::Shape("f32[8,16,128]"))))))))))); in TEST_F()
320 op::Reshape(op::DynamicSlice(op::Constant(), op::PartitionId())); in TEST_F()
350 op::Reshape(op::DynamicSlice(op::Constant(), op::PartitionId())), in TEST_F()
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/math_ops/
H A Dtranspose_op_test.py142 vector = np.arange(0, 2).reshape((1, 1, 1, 2, 1))
164 1, total_size + 1, dtype=datatype).reshape(input_shape)
187 1, total_size + 1, dtype=np.float32).reshape(input_shape)
224 1, total_size + 1, dtype=np.float32).reshape(input_shape)
249 1, total_size + 1, dtype=datatype).reshape(input_shape)
272 1, total_size + 1, dtype=np.float32).reshape(input_shape)
336 self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
340 np.arange(0, 8).reshape([2, 4]).astype(np.float32),
346 x = np.arange(0, 8).reshape([2, 4]).astype(np.float32)
358 self._compare(np.arange(0, 21).reshape([3, 7]).astype(np.float16))
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/service/gpu/
H A Dcudnn_vectorize_convolutions_test.cc73 m::Reshape(m::GetTupleElement( in TEST_F()
75 m::Reshape(m::Parameter(0)) in TEST_F()
77 m::Reshape(m::Parameter(1)) in TEST_F()
148 m::Reshape(m::GetTupleElement( in TEST_F()
150 m::Reshape(m::Parameter(0)) in TEST_F()
152 m::Reshape(m::Parameter(1)) in TEST_F()
201 m::Reshape(m::GetTupleElement( in TEST_F()
203 m::Reshape(m::Parameter(0)) in TEST_F()
205 m::Reshape(m::Parameter(1)) in TEST_F()
254 m::Reshape(m::GetTupleElement( in TEST_F()
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/canonicalize/
H A Dreshape.mlir7 %0 = "mhlo.reshape"(%cst) : (tensor<1x1xi32>) -> tensor<i32>
18 %0 = "mhlo.reshape"(%cst) : (tensor<1x2xi32>) -> tensor<2xi32>
29 %0 = "mhlo.reshape"(%cst) : (tensor<i32>) -> tensor<1xi32>
40 %0 = "mhlo.reshape"(%cst) : (tensor<4x4xi64>) -> tensor<16xi64>
51 %0 = "mhlo.reshape"(%cst) : (tensor<4x4xi64>) -> tensor<16xi64>
62 %0 = "mhlo.reshape"(%cst) : (tensor<3x2xi32>) -> tensor<6xi32>
75 %0 = "mhlo.reshape"(%cst) : (tensor<6xi32>) -> tensor<2x3xi32>
86 %0 = "mhlo.reshape"(%cst) : (tensor<4x4xf64>) -> tensor<16xf64>
97 %0 = "mhlo.reshape"(%arg) : (tensor<2x3xi32>) -> tensor<2x3xi32>
106 // CHECK-NEXT: mhlo.reshape [[ARG]] : (tensor<2x3xi32>) -> tensor<3x2xi32>
[all …]
/aosp_15_r20/cts/apps/CameraITS/utils/
H A Dimage_processing_utils.py228 return numpy.array(lsc_map).reshape(lsc_map_h, lsc_map_w, _NUM_RAW_CHANNELS)
353 cap['data'] = unpack_raw10_image(cap['data'].reshape(h, w * 5 // 4))
378 msbs = msbs.reshape(h, w)
380 lsbs = img[::, 4::5].reshape(h, w // 4)
382 numpy.packbits(numpy.unpackbits(lsbs).reshape((h, w // 4, 4, 2)), 3), 6)
384 lsbs = lsbs.reshape(h, w // 4, 4)[:, :, ::-1]
385 lsbs = lsbs.reshape(h, w)
387 img16 = numpy.bitwise_or(msbs, lsbs).reshape(h, w)
406 cap['data'] = unpack_raw12_image(cap['data'].reshape(h, w * 3 // 2))
431 msbs = msbs.reshape(h, w)
[all …]
/aosp_15_r20/external/tensorflow/tensorflow/python/compiler/tensorrt/test/
H A Dreshape_transpose_test.py32 # reshape with scalar input will be filtered out of the segment before
38 incompatible_reshape = array_ops.reshape(inp, shape)
39 reshape_back = array_ops.reshape(incompatible_reshape, orig_shape)
43 compatible_reshape = array_ops.reshape(
44 inp, [-1, 24 * 24, 2], name="reshape-0")
45 compatible_reshape = array_ops.reshape(
46 compatible_reshape, [100, 24, -1], name="reshape-1")
47 compatible_reshape = array_ops.reshape(
48 compatible_reshape, [100, 24 * 2, 24], name="reshape-2")
49 compatible_reshape = array_ops.reshape(
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/mlir/lite/tests/
H A Dcanonicalize.mlir21 // Checks that tfl.reshape shape operand is converted to a vector if it is possible
25 // expected-error @+1 {{'tfl.reshape' op requires 'shape' to be rank 1, but got 2}}
26 %1 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<1x2xi32>) -> tensor<16x4xf32>
32 // Checks that tfl.reshape should be removed if its output's only user is
33 // another tfl.reshape
38 %0 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<2xi32>) -> tensor<16x4xf32>
39 %1 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32>
44 // CHECK: %[[RESHAPE:.*]] = "tfl.reshape"(%arg0, %[[CST]]) : (tensor<4x4x4xf32>, tensor<1xi32>) ->…
45 // CHECK: return %[[RESHAPE]]
48 // Checks that tfl.reshape should be removed if its output has more than one
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