| /external/tensorflow/tensorflow/compiler/mlir/xla/tests/translate/ |
| D | fully_connected_reference_model.hlotxt | 14 …// CHECK-NEXT: %[[VAL_2:.*]] = "mhlo.reshape"(%[[VAL_0]]) : (tensor<1x300xf32>) -> tensor<1x300xf3…
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<300x1x1x…
21 %reshape.28 = f32[300,1,1] reshape(%transpose.27)
23 …// CHECK-NEXT: %[[VAL_5:.*]] = "mhlo.reshape"(%[[VAL_4]]) : (tensor<300x1x1xf32>) -> tensor<300x1x…
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<1x…
63 %reshape.4 = f32[1,300,3,1] reshape(%copy.1)
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | reshape_op_test.py | 37 np_ans = x.reshape(y)
38 tf_ans = array_ops.reshape(x, y)
45 tf_ans = array_ops.reshape(x, y64)
52 y = array_ops.reshape(x, shape)
58 y = array_ops.reshape(x, shape64)
67 x = np.arange(1., 7.).reshape([1, 6]) > 3
71 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
75 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
79 x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
83 x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
[all …]
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| D | weights_broadcast_test.py | 33 return np.reshape(np.cumsum(np.ones(shape), dtype=np.int32), newshape=shape)
62 weights=np.asarray((5,)).reshape((1, 1, 1)),
68 weights=np.asarray((5, 7, 11, 3)).reshape((1, 1, 4)),
74 weights=np.asarray((5, 11)).reshape((1, 2, 1)),
80 weights=np.asarray((5, 7, 11, 3, 2, 13, 7, 5)).reshape((1, 2, 4)),
86 weights=np.asarray((5, 7, 11)).reshape((3, 1, 1)),
93 5, 7, 11, 3, 2, 12, 7, 5, 2, 17, 11, 3)).reshape((3, 1, 4)),
101 2, 17, 11, 3, 5, 7, 11, 3, 2, 12, 7, 5)).reshape((3, 2, 4)),
126 weights=np.asarray((5,)).reshape((1, 1)),
132 weights=np.asarray((5, 7, 11, 3, 2, 12)).reshape((3, 2)),
[all …]
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| D | transpose_op_test.py | 149 vector = np.arange(0, 2).reshape((1, 1, 1, 2, 1))
171 1, total_size + 1, dtype=datatype).reshape(input_shape)
194 1, total_size + 1, dtype=np.float32).reshape(input_shape)
231 1, total_size + 1, dtype=np.float32).reshape(input_shape)
256 1, total_size + 1, dtype=datatype).reshape(input_shape)
279 1, total_size + 1, dtype=np.float32).reshape(input_shape)
344 self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
349 np.arange(0, 8).reshape([2, 4]).astype(np.float32),
355 x = np.arange(0, 8).reshape([2, 4]).astype(np.float32)
367 self._compare(np.arange(0, 21).reshape([3, 7]).astype(np.float16))
[all …]
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| D | extract_image_patches_op_test.py | 58 image = np.reshape(range(120), [2, 3, 4, 5])
60 patches = np.reshape(range(120), [2, 3, 4, 5])
73 image = np.reshape(range(120), [2, 4, 5, 3])
116 image = np.arange(16).reshape(1, 4, 4, 1).astype(np.float32)
132 np.reshape(range(120), [2, 3, 4, 5]).astype(dtype) +
133 np.reshape(range(120, 240), [2, 3, 4, 5]).astype(dtype) * 1j)
135 np.reshape(range(120), [2, 3, 4, 5]).astype(dtype) +
136 np.reshape(range(120, 240), [2, 3, 4, 5]).astype(dtype) * 1j)
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| D | scan_ops_test.py | 102 x = np.arange(1, 6).reshape([5]).astype(dtype)
112 x = np.arange(1, 6).reshape([5]).astype(dtype)
120 x = np.arange(1, 6).reshape([5]).astype(dtype)
127 x = np.arange(0, 10).reshape([2, 5]).astype(dtype)
134 x = np.arange(0, 20).reshape([2, 2, 5]).astype(dtype)
141 x = np.arange(1, 145).reshape([2, 2, 3, 3, 2, 2]).astype(dtype)
153 x = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
170 x = np.arange(0, 50).reshape(shape).astype(np.float64)
235 x = np.arange(1, 6).reshape([5]).astype(dtype)
245 x = np.arange(1, 6).reshape([5]).astype(dtype)
[all …]
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| D | extract_volume_patches_op_test.py | 59 image = np.arange(2 * 3 * 4 * 5 * 6).reshape([2, 3, 4, 5, 6]) + 1
71 image = np.arange(6 * 2 * 4 * 5 * 3).reshape([6, 2, 4, 5, 3]) + 1
83 image = np.arange(45).reshape([1, 3, 3, 5, 1]) + 1
102 image = np.arange(8).reshape([1, 2, 2, 2, 1]) + 1
113 image = np.arange(8).reshape([1, 2, 2, 2, 1]) + 1
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| /external/tensorflow/tensorflow/python/training/ |
| D | checkpoint_ops_test.py | 49 np.reshape(np.linspace(0.0, 79, 5 * 16), (5, 16)))
113 np.reshape([18, 34, 50, self.init_val, self.init_val], [5, 1]),
114 np.reshape([16, 32, 48, self.init_val, self.init_val], [5, 1]),
115 np.reshape([self.init_val] * 5, [5, 1]),
116 np.reshape([17, 33, 49, self.init_val, self.init_val], [5, 1]),
117 np.reshape([self.init_val] * 5, [5, 1])
145 np.reshape([2, 18, 34, 50, self.init_val, self.init_val], [6, 1]),
146 np.reshape([0, 16, 32, 48, self.init_val, self.init_val], [6, 1]),
147 np.reshape([self.init_val] * 6, [6, 1]),
148 np.reshape([1, 17, 33, 49, self.init_val, self.init_val], [6, 1]),
[all …]
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | space_to_batch_converter_test.cc | 56 auto reshape = root->operand(0)->operand(0); in TEST_F() local
57 EXPECT_THAT(reshape, op::Reshape()); in TEST_F()
58 EXPECT_THAT(reshape->operand(0)->operand(1), op::Convolution()); in TEST_F()
59 const int64 batch_dim = reshape->operand(0) in TEST_F()
64 EXPECT_GT(reshape->operand(0)->shape().dimensions(batch_dim), 1); in TEST_F()
105 auto reshape = root->operand(0)->operand(0); in TEST_F() local
106 EXPECT_THAT(reshape, op::Reshape()); in TEST_F()
107 EXPECT_THAT(reshape->operand(0)->operand(1), op::Convolution()); in TEST_F()
108 const int64 batch_dim = reshape->operand(0) in TEST_F()
113 EXPECT_GT(reshape->operand(0)->shape().dimensions(batch_dim), 4); in TEST_F()
[all …]
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | batch_norm_op.h | 52 output.reshape(rest_by_depth).device(d) = in operator()
53 (input.reshape(rest_by_depth) - in operator()
54 mean.reshape(one_by_depth).broadcast(rest_by_one)) * in operator()
57 .reshape(one_by_depth) in operator()
59 beta.reshape(one_by_depth).broadcast(rest_by_one); in operator()
61 output.reshape(rest_by_depth).device(d) = in operator()
62 (input.reshape(rest_by_depth) - in operator()
63 mean.reshape(one_by_depth).broadcast(rest_by_one)) * in operator()
66 .reshape(one_by_depth) in operator()
68 beta.reshape(one_by_depth).broadcast(rest_by_one); in operator()
[all …]
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| D | training_ops_gpu.cu.cc | 344 var.device(d) -= lr.reshape(single).broadcast(bcast) * grad; in operator ()()
474 var.device(d) -= lr.reshape(single).broadcast(bcast) * grad * accum.rsqrt(); in operator ()()
497 grad / (accum.sqrt() + epsilon.reshape(single).broadcast(bcast)); in operator ()()
498 var.device(d) -= lr.reshape(single).broadcast(bcast) * update; in operator ()()
547 auto lr_bcast = lr.reshape(single).broadcast(bcast); in operator ()()
548 auto l1_bcast = l1.reshape(single).broadcast(bcast); in operator ()()
549 auto l2_bcast = l2.reshape(single).broadcast(bcast); in operator ()()
605 accum.device(d) = accum * rho.reshape(single).broadcast(bcast) + in operator ()()
607 rho.reshape(single).broadcast(bcast)); in operator ()()
609 (accum_update + epsilon.reshape(single).broadcast(bcast)).sqrt() * in operator ()()
[all …]
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| /external/tensorflow/tensorflow/compiler/mlir/hlo/tests/ |
| D | reshape.mlir | 7 %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 …]
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| /external/tensorflow/tensorflow/python/compiler/tensorrt/test/ |
| D | biasadd_matmul_test.py | 51 x2 = gen_array_ops.reshape(x2, [4, -1])
60 x4 = gen_array_ops.reshape(x4, [4, -1])
71 x6 = gen_array_ops.reshape(x, [4, 24, 6])
74 x6 = gen_array_ops.reshape(x6, [4, -1])
76 x7 = gen_array_ops.reshape(x, [4, 12, 4, 3])
79 x7 = gen_array_ops.reshape(x7, [4, -1])
81 x8 = gen_array_ops.reshape(x, [4, 4, 3, 2, 6])
84 x8 = gen_array_ops.reshape(x8, [4, -1])
86 x9 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2])
89 x9 = gen_array_ops.reshape(x9, [4, -1])
[all …]
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| D | reshape_transpose_test.py | 40 incompatible_reshape = array_ops.reshape(inp, shape)
41 reshape_back = array_ops.reshape(incompatible_reshape, [-1, 24, 24, 2])
45 compatible_reshape = array_ops.reshape(
47 compatible_reshape = array_ops.reshape(
49 compatible_reshape = array_ops.reshape(
51 compatible_reshape = array_ops.reshape(
53 compatible_reshape = array_ops.reshape(
55 compatible_reshape = array_ops.reshape(
57 compatible_reshape = array_ops.reshape(
|
| D | annotate_max_batch_sizes_test.py | 103 tensor = array_ops.reshape(tensor, [-1] + self.tensor_shapes[1][1:])
109 tensor = array_ops.reshape(tensor, [-1] + self.tensor_shapes[2][1:])
115 tensor = array_ops.reshape(tensor, [-1] + self.tensor_shapes[3][1:])
129 tensor = array_ops.reshape(tensor, self.tensor_shapes[1])
131 tensor = array_ops.reshape(tensor, self.tensor_shapes[2])
133 tensor = array_ops.reshape(tensor, self.tensor_shapes[3])
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| /external/tensorflow/tensorflow/compiler/xla/service/spmd/ |
| D | canonicalize_all_gather_for_cse_test.cc | 66 const HloInstruction* const reshape = in TEST_F() local
68 EXPECT_THAT(reshape, in TEST_F()
86 const HloInstruction* const reshape = in TEST_F() local
88 EXPECT_THAT(reshape, in TEST_F()
106 const HloInstruction* const reshape = in TEST_F() local
108 EXPECT_THAT(reshape, in TEST_F()
126 const HloInstruction* const reshape = in TEST_F() local
128 EXPECT_THAT(reshape, AllOf(op::AllGather(op::Reshape(op::Reshape(_))), in TEST_F()
146 const HloInstruction* const reshape = in TEST_F() local
148 EXPECT_THAT(reshape, AllOf(op::AllGather(op::Reshape(op::Reshape(_))), in TEST_F()
[all …]
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| /external/llvm-project/mlir/test/Dialect/Tosa/ |
| D | broadcast.mlir | 6 // CHECK-NOT: reshape
14 // CHECK: reshape
22 // CHECK: reshape
30 // CHECK: reshape
38 // CHECK: reshape
46 // CHECK: reshape
54 // CHECK: reshape
62 // CHECK: reshape
70 // CHECK: reshape
78 // CHECK: reshape
[all …]
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | image_preprocessing_test.py | 96 input_image = np.reshape(np.arange(0, 16), (1, 4, 4, 1)).astype(dtype)
106 expected_output = np.reshape(expected_output, (1, 2, 2, 1))
112 input_image = np.reshape(np.arange(0, 4), (1, 2, 2, 1)).astype(dtype)
124 expected_output = np.reshape(expected_output, (1, 4, 4, 1))
355 mock_random = np.reshape(mock_random, [2, 1, 1, 1])
382 mock_random = np.reshape(mock_random, [2, 1, 1, 1])
392 mock_random = np.reshape(mock_random, [2, 1, 1, 1])
412 mock_random = np.reshape(mock_random, [2, 1, 1, 1])
533 input_image = np.reshape(np.arange(0, 25), (1, 5, 5, 1)).astype(dtype)
547 expected_output = np.reshape(expected_output, (1, 5, 5, 1))
[all …]
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_Reshape.pbtxt | 30 reshape(t, [3, 3]) ==> [[1, 2, 3],
37 reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
48 reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
53 reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
56 reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
59 reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
68 reshape(t, []) ==> 7
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| /external/tensorflow/tensorflow/compiler/mlir/lite/tests/ |
| D | canonicalize.mlir | 3 // Checks that tfl.reshape shape operand is converted to a vector if it is possible
7 // expected-error @+1 {{'tfl.reshape' op requires 'shape' to be rank 1, but got 2}}
8 %1 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<1x2xi32>) -> tensor<16x4xf32>
14 // Checks that tfl.reshape should be removed if its output's only user is
15 // another tfl.reshape
20 %0 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<2xi32>) -> tensor<16x4xf32>
21 %1 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32>
26 // CHECK: %[[RESHAPE:.*]] = "tfl.reshape"(%arg0, %[[CST]]) : (tensor<4x4x4xf32>, tensor<1xi32>) ->…
30 // Checks that tfl.reshape should be removed if its output has more than one
31 // user but all users are tfl.reshape
[all …]
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| /external/tensorflow/tensorflow/compiler/mlir/tfr/integration/ |
| D | graph_decompose_test.py | 46 self.assertAllEqual(sq1.numpy().reshape(-1), [1, 2, 3, 4])
47 self.assertAllEqual(sq2.numpy().reshape(-1), [2, 4, 6, 8])
48 self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12])
56 self.assertAllEqual(sq.numpy().reshape(-1), [-3, 0, 5, 12])
64 self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12])
77 self.assertAllClose(sq.numpy().reshape(-1), [-0.950213, 0, 5, 12])
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| D | node_expansion_test.py | 46 self.assertAllEqual(sq1.numpy().reshape(-1), [1, 2, 3, 4])
47 self.assertAllEqual(sq2.numpy().reshape(-1), [2, 4, 6, 8])
48 self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12])
55 self.assertAllEqual(sq.numpy().reshape(-1), [-3, 0, 5, 12])
62 self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12])
74 self.assertAllClose(sq.numpy().reshape(-1), [-0.950213, 0, 5, 12])
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| D | scan_ops_test.py | 102 x = np.arange(1, 6).reshape([5]).astype(dtype)
111 x = np.arange(1, 6).reshape([5]).astype(dtype)
117 x = np.arange(0, 10).reshape([2, 5]).astype(dtype)
123 x = np.arange(0, 20).reshape([2, 2, 5]).astype(dtype)
129 x = np.arange(1, 145).reshape([2, 2, 3, 3, 2, 2]).astype(dtype)
135 x = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
181 x = np.arange(1, 6).reshape([5]).astype(dtype)
190 x = np.arange(1, 6).reshape([5]).astype(dtype)
196 x = np.arange(1, 11).reshape([2, 5]).astype(dtype)
202 x = np.arange(1, 21).reshape([2, 2, 5]).astype(dtype)
[all …]
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| D | function_test.py | 39 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
40 bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32)
65 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
66 bval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
89 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
90 bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32)
139 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
140 bval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
|
| /external/llvm-project/mlir/test/Dialect/Linalg/ |
| D | canonicalize.mlir | 63 %0 = linalg.reshape %arg0 [affine_map<(d0, d1, d2) -> (d0, d1, d2)>] :
65 %1 = linalg.reshape %0 [] : memref<1xf32> into memref<f32>
69 // CHECK: linalg.reshape %{{.*}} []
97 %0 = linalg.reshape %arg0
102 %1 = linalg.reshape %0
111 // CHECK: linalg.reshape %{{.*}} [#[[$MAP0]], #[[$MAP1]]]
112 // CHECK-NOT: linalg.reshape
118 %0 = linalg.reshape %arg0
122 %1 = linalg.reshape %0
132 // CHECK: linalg.reshape %{{.*}} [#[[$MAP0]], #[[$MAP1]]]
[all …]
|