| /external/tensorflow/tensorflow/lite/testing/nnapi_tflite_zip_tests/ |
| D | not_supported.txt | 9 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
10 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
11 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.i…
12 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.i…
13 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
14 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
15 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.i…
16 arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.i…
17 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
18 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
[all …]
|
| D | test_manifest.txt | 17 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[…
18 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[…
19 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[…
20 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,15,14,1],ksize=[1,1,1,1],padding='SAME',strides…
21 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,15,14,1],ksize=[1,1,1,1],padding='SAME',strides…
22 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,15,14,1],ksize=[1,1,1,1],padding='SAME',strides…
23 avg_pool/avg_pool_data_format='NHWC',input_shape=[3,15,14,3],ksize=[1,1,1,1],padding='SAME',strides…
24 avg_pool/avg_pool_data_format='NHWC',input_shape=[3,15,14,3],ksize=[1,1,1,1],padding='SAME',strides…
25 avg_pool/avg_pool_data_format='NHWC',input_shape=[3,15,14,3],ksize=[1,1,1,1],padding='SAME',strides…
26 avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='VALID',strides=…
[all …]
|
| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | conv_utils_test.py | 30 def _get_const_output_shape(input_shape, dim): argument
31 return tuple([min(d, dim) for d in input_shape])
166 def test_conv_kernel_mask_fc(self, *input_shape): argument
168 kernel_shape = input_shape
169 ndims = len(input_shape)
171 output_shape = _get_const_output_shape(input_shape, dim=1)
172 mask = np.ones(input_shape + output_shape, np.bool)
176 input_shape,
183 def test_conv_kernel_mask_diag(self, *input_shape): argument
184 ndims = len(input_shape)
[all …]
|
| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| D | transpose_utils_test.cc | 24 RuntimeShape input_shape({9}); in TEST() local
31 transpose_utils::RemoveOneSizeDimensions(&input_shape, &output_shape, in TEST()
34 EXPECT_EQ(input_shape, RuntimeShape({9})); in TEST()
42 RuntimeShape input_shape({9, 3}); in TEST() local
50 transpose_utils::RemoveOneSizeDimensions(&input_shape, &output_shape, in TEST()
53 EXPECT_EQ(input_shape, RuntimeShape({9, 3})); in TEST()
62 RuntimeShape input_shape({9, 1}); in TEST() local
70 transpose_utils::RemoveOneSizeDimensions(&input_shape, &output_shape, in TEST()
73 EXPECT_EQ(input_shape, RuntimeShape({9})); in TEST()
81 RuntimeShape input_shape({4, 3, 8}); in TEST() local
[all …]
|
| D | resize_nearest_neighbor_test.cc | 30 const RuntimeShape& input_shape, const std::vector<T>& input_data, in TestReferenceResizeNearestNeighbor() argument
40 op_params, input_shape, input_data.data(), output_size_shape, in TestReferenceResizeNearestNeighbor()
49 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
55 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
60 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
66 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
72 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
79 input_shape, input_data, output_size_data, output_shape, output_data, in TEST()
84 RuntimeShape input_shape = {1, 2, 2, 1}; in TEST() local
90 TestReferenceResizeNearestNeighbor(input_shape, input_data, output_size_data, in TEST()
[all …]
|
| D | transpose_utils.cc | 21 const RuntimeShape& input_shape, int* dim0, in IsTranspose2DApplicable() argument
23 const int dims_cnt = input_shape.DimensionsCount(); in IsTranspose2DApplicable()
26 *dim0 = input_shape.Dims(0); in IsTranspose2DApplicable()
27 *dim1 = input_shape.Dims(1); in IsTranspose2DApplicable()
45 *dim0 *= input_shape.Dims(i); in IsTranspose2DApplicable()
47 *dim1 *= input_shape.Dims(i); in IsTranspose2DApplicable()
53 void RemoveOneSizeDimensions(RuntimeShape* input_shape, in RemoveOneSizeDimensions() argument
56 const int dims_cnt = input_shape->DimensionsCount(); in RemoveOneSizeDimensions()
61 if (input_shape->Dims(i) == 1) { in RemoveOneSizeDimensions()
71 if (input_shape->FlatSize() == 1) { in RemoveOneSizeDimensions()
[all …]
|
| /external/tensorflow/tensorflow/python/keras/applications/ |
| D | imagenet_utils.py | 296 def obtain_input_shape(input_shape, argument
322 if weights != 'imagenet' and input_shape and len(input_shape) == 3:
324 if input_shape[0] not in {1, 3}:
327 str(input_shape[0]) + ' input channels.')
328 default_shape = (input_shape[0], default_size, default_size)
330 if input_shape[-1] not in {1, 3}:
333 str(input_shape[-1]) + ' input channels.')
334 default_shape = (default_size, default_size, input_shape[-1])
341 if input_shape is not None:
342 if input_shape != default_shape:
[all …]
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | pool_test.py | 148 def _test(self, input_shape, **kwargs): argument
152 np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
162 input_shape=[1, 1, 10, 1],
173 for input_shape in [[2, 9, 2], [2, 10, 2]]:
178 input_shape=input_shape,
188 input_shape=input_shape,
199 for input_shape in [[2, 9, 10, 2], [2, 10, 9, 2]]:
204 input_shape=input_shape,
214 input_shape=input_shape,
225 for input_shape in [[2, 9, 10, 11, 2], [2, 10, 9, 11, 2]]:
[all …]
|
| /external/tensorflow/tensorflow/lite/micro/kernels/ |
| D | strided_slice_test.cc | 59 void TestStridedSliceFloat(const int* input_shape, const int* begin_shape, in TestStridedSliceFloat() argument
67 TfLiteIntArray* input_dims = IntArrayFromInts(input_shape); in TestStridedSliceFloat()
91 const int* input_shape, const int* begin_shape, const int* end_shape, in TestStridedSliceQuantized() argument
96 TfLiteIntArray* input_dims = IntArrayFromInts(input_shape); in TestStridedSliceQuantized()
127 const int input_shape[] = {5, 2, 2, 2, 2, 2}; in TF_LITE_MICRO_TEST() local
142 input_shape, begin_shape, end_shape, strides_shape, &builtin_data, in TF_LITE_MICRO_TEST()
148 const int input_shape[] = {1, 4}; in TF_LITE_MICRO_TEST() local
163 input_shape, begin_shape, end_shape, strides_shape, &builtin_data, in TF_LITE_MICRO_TEST()
169 const int input_shape[] = {1, 4}; in TF_LITE_MICRO_TEST() local
184 input_shape, begin_shape, end_shape, strides_shape, &builtin_data, in TF_LITE_MICRO_TEST()
[all …]
|
| D | pooling_test.cc | 185 const int input_shape[] = {4, 1, 2, 4, 1}; in TF_LITE_MICRO_TEST() local
195 input_shape, input_values, filter_height, filter_width, stride_height, in TF_LITE_MICRO_TEST()
201 const int input_shape[] = {4, 1, 2, 4, 1}; in TF_LITE_MICRO_TEST() local
216 input_shape, input_values, input_scale, input_zero_point, filter_height, in TF_LITE_MICRO_TEST()
223 const int input_shape[] = {4, 1, 2, 4, 1}; in TF_LITE_MICRO_TEST() local
238 input_shape, input_values, input_scale, input_zero_point, filter_height, in TF_LITE_MICRO_TEST()
245 const int input_shape[] = {4, 1, 2, 4, 1}; in TF_LITE_MICRO_TEST() local
260 input_shape, input_values, input_scale, input_zero_point, filter_height, in TF_LITE_MICRO_TEST()
268 const int input_shape[] = {4, 1, 2, 4, 1}; in TF_LITE_MICRO_TEST() local
283 input_shape, input_values, input_scale, input_zero_point, filter_height, in TF_LITE_MICRO_TEST()
[all …]
|
| /external/tensorflow/tensorflow/lite/kernels/internal/reference/ |
| D | strided_slice.h | 45 const RuntimeShape input_shape = in StridedSlice() local
54 const int start_0 = StartForAxis(params_copy, input_shape, 0); in StridedSlice()
55 const int stop_0 = StopForAxis(params_copy, input_shape, 0, start_0); in StridedSlice()
56 const int start_1 = StartForAxis(params_copy, input_shape, 1); in StridedSlice()
57 const int stop_1 = StopForAxis(params_copy, input_shape, 1, start_1); in StridedSlice()
58 const int start_2 = StartForAxis(params_copy, input_shape, 2); in StridedSlice()
59 const int stop_2 = StopForAxis(params_copy, input_shape, 2, start_2); in StridedSlice()
60 const int start_3 = StartForAxis(params_copy, input_shape, 3); in StridedSlice()
61 const int stop_3 = StopForAxis(params_copy, input_shape, 3, start_3); in StridedSlice()
62 const int start_4 = StartForAxis(params_copy, input_shape, 4); in StridedSlice()
[all …]
|
| D | pooling.h | 27 const RuntimeShape& input_shape, in AveragePool() argument
30 TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); in AveragePool()
32 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in AveragePool()
33 const int depth = MatchingDim(input_shape, 3, output_shape, 3); in AveragePool()
34 const int input_height = input_shape.Dims(1); in AveragePool()
35 const int input_width = input_shape.Dims(2); in AveragePool()
65 input_data[Offset(input_shape, batch, in_y, in_x, channel)]; in AveragePool()
80 const RuntimeShape& input_shape, in AveragePool() argument
86 TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); in AveragePool()
88 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in AveragePool()
[all …]
|
| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | split_op.cc | 39 const TensorShape input_shape = ctx->InputShape(1); in Compile() local
48 int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input_shape.dims() in Compile()
50 OP_REQUIRES(ctx, 0 <= split_dim && split_dim < input_shape.dims(), in Compile()
51 errors::InvalidArgument("-input rank(-", input_shape.dims(), in Compile()
53 input_shape.dims(), "), but got ", in Compile()
62 ctx, input_shape.dim_size(split_dim) % num_split == 0, in Compile()
66 split_dim_orig, " (size = ", input_shape.dim_size(split_dim), ") ", in Compile()
71 const int32 slice_size = input_shape.dim_size(split_dim) / num_split; in Compile()
75 std::vector<int64> begin(input_shape.dims(), 0); in Compile()
76 std::vector<int64> limits(input_shape.dims()); in Compile()
[all …]
|
| D | unpack_op.cc | 47 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
50 if (axis < 0) axis += input_shape.dims(); in Compile()
52 OP_REQUIRES(ctx, 0 <= axis && axis < input_shape.dims(), in Compile()
54 -input_shape.dims(), ", ", in Compile()
55 input_shape.dims(), ")")); in Compile()
58 ctx, input_shape.dims() > 0 && input_shape.dim_size(axis) == num, in Compile()
60 ", got shape ", input_shape.DebugString())); in Compile()
62 auto output_shape = input_shape; in Compile()
67 std::vector<int64> start_indices(input_shape.dims(), 0); in Compile()
68 std::vector<int64> limit_indices(input_shape.dims()); in Compile()
[all …]
|
| D | shape_op.cc | 45 const TensorShape input_shape = ctx->InputShape(0); in Compile() local
47 const int rank = input_shape.dims(); in Compile()
59 Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()})); in Compile()
60 OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant)); in Compile()
77 const TensorShape input_shape = ctx->InputShape("input"); in Compile() local
86 input_shape.DebugString())); in Compile()
89 ctx, input_shape.dims() == 0, in Compile()
92 input_shape.DebugString())); in Compile()
153 const TensorShape input_shape = ctx->InputShape(i); in Compile() local
156 const int rank = input_shape.dims(); in Compile()
[all …]
|
| D | spacetodepth_op.cc | 60 absl::Span<const int64> input_shape = in Compile() local
62 int input_rank = input_shape.size(); in Compile()
81 OP_REQUIRES(ctx, input_shape[1 + i] % block_size_ == 0, in Compile()
83 "input shape[", 1 + i, "]=", input_shape[1 + i], in Compile()
88 reshaped_shape.push_back(input_shape[0]); in Compile()
90 reshaped_shape.push_back(input_shape[1 + i] / block_size_); in Compile()
93 reshaped_shape.push_back(input_shape[feature_dim]); in Compile()
104 output_shape.push_back(input_shape[0]); in Compile()
106 output_shape.push_back(input_shape[1 + i] / block_size_); in Compile()
108 output_shape.push_back(input_shape[feature_dim] * block_elems); in Compile()
[all …]
|
| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | convolutional.py | 187 def build(self, input_shape): argument
188 input_shape = tensor_shape.TensorShape(input_shape)
189 input_channel = self._get_input_channel(input_shape)
195 input_shape))
246 input_shape = inputs.shape
274 out_shape = self.compute_output_shape(input_shape)
292 def compute_output_shape(self, input_shape): argument
293 input_shape = tensor_shape.TensorShape(input_shape).as_list()
294 batch_rank = len(input_shape) - self.rank - 1
297 input_shape[:batch_rank]
[all …]
|
| D | pooling.py | 83 def compute_output_shape(self, input_shape): argument
84 input_shape = tensor_shape.TensorShape(input_shape).as_list()
86 steps = input_shape[2]
87 features = input_shape[1]
89 steps = input_shape[1]
90 features = input_shape[2]
96 return tensor_shape.TensorShape([input_shape[0], features, length])
98 return tensor_shape.TensorShape([input_shape[0], length, features])
303 def compute_output_shape(self, input_shape): argument
304 input_shape = tensor_shape.TensorShape(input_shape).as_list()
[all …]
|
| D | pooling_test.py | 38 keras.layers.pooling.GlobalMaxPooling1D, input_shape=(3, 4, 5))
42 input_shape=(3, 4, 5))
44 keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
48 input_shape=(3, 4, 5))
52 model.add(keras.layers.Masking(mask_value=0., input_shape=(None, 4)))
73 masking = keras.layers.Masking(mask_value=0., input_shape=(3, 2))(inputs)
118 input_shape=(3, 4, 5, 6))
122 input_shape=(3, 5, 6, 4))
126 input_shape=(3, 4, 5, 6))
130 input_shape=(3, 5, 6, 4))
[all …]
|
| D | merge.py | 90 def build(self, input_shape): argument
92 if not isinstance(input_shape[0], tuple):
94 if len(input_shape) < 2:
97 'Got ' + str(len(input_shape)) + ' inputs.')
98 batch_sizes = {s[0] for s in input_shape if s} - {None}
102 'batch sizes. Got tensors with shapes : ' + str(input_shape))
103 if input_shape[0] is None:
106 output_shape = input_shape[0][1:]
107 for i in range(1, len(input_shape)):
108 if input_shape[i] is None:
[all …]
|
| /external/tensorflow/tensorflow/lite/delegates/xnnpack/ |
| D | reshape_test.cc | 38 const std::vector<int32_t> input_shape{ variable
40 std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
44 .InputShape(input_shape)
59 const std::vector<int32_t> input_shape{ variable
61 std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
65 .InputShape(input_shape)
80 const std::vector<int32_t> input_shape{ variable
82 std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
86 .InputShape(input_shape)
101 const std::vector<int32_t> input_shape{ variable
[all …]
|
| /external/tensorflow/tensorflow/lite/delegates/hexagon/builders/tests/ |
| D | transpose_test.cc | 75 const RuntimeShape input_shape = ::tflite::GetTensorShape(shape); in ComputeExpectedTransposeResult() local
78 output_shape.SetDim(i, input_shape.Dims(perms[i])); in ComputeExpectedTransposeResult()
87 reference_ops::Transpose<integer_type>(params, input_shape, input->data(), in ComputeExpectedTransposeResult()
95 std::vector<int> input_shape = {3}; in TEST() local
96 ComputeExpectedTransposeResult(input_shape, {0}, &input, &expected_output); in TEST()
98 TransposeOpModel model({TensorType_UINT8, input_shape, -10, 10}, {1}, {0}, in TEST()
108 std::vector<int> input_shape = {3}; in TEST() local
109 ComputeExpectedTransposeResult(input_shape, {0}, &input, &expected_output); in TEST()
111 TransposeOpModel model({TensorType_INT8, input_shape, -10, 10}, {1}, {0}, in TEST()
120 std::vector<int> input_shape = {3, 2}; in TEST() local
[all …]
|
| /external/tensorflow/tensorflow/compiler/xla/client/lib/ |
| D | sorting.cc | 31 TF_ASSIGN_OR_RETURN(Shape input_shape, builder->GetShape(input)); in TopK()
32 int last_dim = input_shape.dimensions_size() - 1; in TopK()
33 int64 last_dim_size = input_shape.dimensions(last_dim); in TopK()
49 ShapeUtil::MakeShape(S32, AsInt64Slice(input_shape.dimensions())); in TopK()
51 for (int64 i = 0; i < input_shape.rank(); ++i) { in TopK()
52 if (input_shape.is_dynamic_dimension(i)) { in TopK()
57 auto input_dims = input_shape.dimensions(); in TopK()
60 CreateScalarGtComputation({input_shape.element_type(), S32}, in TopK()
63 std::vector<int64> start_indices(input_shape.dimensions_size(), 0); in TopK()
66 std::vector<int64> strides(input_shape.dimensions_size(), 1); in TopK()
[all …]
|
| /external/tensorflow/tensorflow/lite/kernels/internal/reference/integer_ops/ |
| D | pooling.h | 25 const RuntimeShape& input_shape, in AveragePool() argument
30 TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); in AveragePool()
32 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in AveragePool()
33 const int depth = MatchingDim(input_shape, 3, output_shape, 3); in AveragePool()
34 const int input_height = input_shape.Dims(1); in AveragePool()
35 const int input_width = input_shape.Dims(2); in AveragePool()
65 input_data[Offset(input_shape, batch, in_y, in_x, channel)]; in AveragePool()
82 inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, in MaxPool() argument
91 TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); in MaxPool()
93 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in MaxPool()
[all …]
|
| /external/tensorflow/tensorflow/python/ops/ |
| D | concat_benchmark.py | 35 def build_graph(device, input_shape, variable, num_inputs, axis, grad): argument
51 inputs = [array_ops.zeros(input_shape) for _ in range(num_inputs)]
56 input_shape[0],
57 random.randint(max(1, input_shape[1] - 5), input_shape[1] + 5)
63 random.randint(max(1, input_shape[0] - 5), input_shape[0] + 5),
64 input_shape[1]
81 def _run_graph(self, device, input_shape, variable, num_inputs, axis, grad, argument
99 outputs = build_graph(device, input_shape, variable, num_inputs, axis,
112 "GB/sec" % (device, input_shape[0], input_shape[1], variable,
114 num_inputs * input_shape[0] * input_shape[1] * 4 * 2 *
[all …]
|