| /external/XNNPACK/test/ |
| D | f32-dwconv-spchw.cc | 21 .input_width(4) in TEST()
32 for (size_t input_width = 1; input_width < 4; input_width++) { in TEST() local
36 .input_width(input_width) in TEST()
48 for (size_t input_width = 5; input_width < 8; input_width++) { in TEST() local
52 .input_width(input_width) in TEST()
64 for (size_t input_width = 8; input_width < 32; input_width += 4) { in TEST() local
68 .input_width(input_width) in TEST()
80 for (size_t input_width = 1; input_width < 32; input_width += 3) { in TEST() local
84 .input_width(input_width) in TEST()
97 for (size_t input_width = 1; input_width < 32; input_width += 5) { in TEST() local
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| D | f32-conv-hwc.cc | 25 .input_width(4) in TEST()
32 for (size_t input_width = 8; input_width <= 32; input_width += 12) { in TEST() local
40 .input_width(input_width) in TEST()
48 for (size_t input_width = 1; input_width < 4; input_width++) { in TEST() local
56 .input_width(input_width) in TEST()
64 for (size_t input_width = 5; input_width < 8; input_width++) { in TEST() local
72 .input_width(input_width) in TEST()
81 for (size_t input_width = 1; input_width < 32; input_width += 7) { in TEST() local
89 .input_width(input_width) in TEST()
99 for (size_t input_width = 1; input_width < 32; input_width += 7) { in TEST() local
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| D | f32-conv-hwc2spchw.cc | 25 .input_width(4) in TEST()
32 for (size_t input_width = 8; input_width <= 32; input_width += 12) { in TEST() local
40 .input_width(input_width) in TEST()
48 for (size_t input_width = 1; input_width < 4; input_width++) { in TEST() local
56 .input_width(input_width) in TEST()
64 for (size_t input_width = 5; input_width < 8; input_width++) { in TEST() local
72 .input_width(input_width) in TEST()
81 for (size_t input_width = 1; input_width < 32; input_width += 7) { in TEST() local
89 .input_width(input_width) in TEST()
99 for (size_t input_width = 1; input_width < 32; input_width += 7) { in TEST() local
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| D | resize-bilinear-nhwc.cc | 25 for (size_t input_width = 1; input_width <= 3; input_width++) { in TEST() local
26 for (size_t output_width = input_width + 1; output_width < 15; output_width *= 3) { in TEST()
28 .input_size(1, input_width) in TEST()
65 for (size_t input_width = output_width + 1; input_width < 15; input_width *= 3) { in TEST() local
67 .input_size(1, input_width) in TEST()
78 for (size_t input_width = 3; input_width <= 5; input_width += 2) { in TEST() local
80 .input_size(input_height, input_width) in TEST()
179 for (size_t input_width = 1; input_width <= 3; input_width++) { in TEST() local
180 for (size_t output_width = input_width + 1; output_width < 15; output_width *= 3) { in TEST()
183 .input_size(1, input_width) in TEST()
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| D | max-pooling-nhwc.cc | 23 .input_width(pool_size + 2) in TEST()
41 .input_width(pool_size + 2) in TEST()
61 .input_width(pool_size + 4) in TEST()
78 .input_width(2 * pool_size + 1) in TEST()
95 .input_width(3) in TEST()
113 .input_width(3) in TEST()
133 .input_width(3) in TEST()
150 .input_width(3) in TEST()
167 .input_width(3) in TEST()
176 .input_width(pool_size + 2) in TEST()
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| D | average-pooling-nhwc.cc | 23 .input_width(pool_size + 2) in TEST()
41 .input_width(pool_size + 2) in TEST()
61 .input_width(pool_size + 4) in TEST()
78 .input_width(3) in TEST()
96 .input_width(3) in TEST()
116 .input_width(3) in TEST()
133 .input_width(3) in TEST()
142 .input_width(pool_size + 2) in TEST()
159 .input_width(3) in TEST()
168 .input_width(pool_size + 2) in TEST()
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| D | convolution-nhwc.cc | 468 for (size_t input_width = 13; input_width <= 14; input_width++) { variable
470 .input_size(input_height, input_width)
496 for (size_t input_width = 13; input_width <= 14; input_width++) { variable
498 .input_size(input_height, input_width)
524 for (size_t input_width = 13; input_width <= 14; input_width++) { variable
526 .input_size(input_height, input_width)
608 for (size_t input_width = 14; input_width <= 15; input_width++) { in TEST() local
610 .input_size(input_height, input_width) in TEST()
634 for (size_t input_width = 14; input_width <= 15; input_width++) { in TEST() local
636 .input_size(input_height, input_width) in TEST()
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| D | argmax-pooling-nhwc.cc | 36 .input_width(pool_size + 2) in TEST()
54 .input_width(pool_size + 2) in TEST()
74 .input_width(3) in TEST()
92 .input_width(3) in TEST()
112 .input_width(3) in TEST()
121 .input_width(pool_size + 2) in TEST()
138 .input_width(3) in TEST()
147 .input_width(pool_size + 2) in TEST()
164 .input_width(3) in TEST()
173 .input_width(pool_size + 2) in TEST()
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| D | unpooling-nhwc.cc | 18 .input_width(7) in TEST()
36 .input_width(7) in TEST()
56 .input_width(1) in TEST()
74 .input_width(1) in TEST()
94 .input_width(4) in TEST()
113 .input_width(4) in TEST()
137 .input_width(7) in TEST()
152 .input_width(7) in TEST()
167 .input_width(7) in TEST()
186 .input_width(7) in TEST()
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| D | convolution-nchw.cc | 39 for (size_t input_width = 25; input_width <= 31; input_width++) { variable
41 .input_size(input_width, 29)
159 for (size_t input_width = 25; input_width <= 31; input_width++) { in TEST() local
162 .input_size(input_width, 29) in TEST()
300 for (size_t input_width = 25; input_width <= 31; input_width++) { variable
302 .input_size(input_width, 29)
407 for (size_t input_width = 25; input_width <= 31; input_width++) { in TEST() local
410 .input_size(input_width, 29) in TEST()
543 for (size_t input_width = 25; input_width <= 31; input_width++) { in TEST() local
545 .input_size(input_width, 29) in TEST()
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| D | resize-bilinear-operator-tester.h | 24 inline ResizeBilinearOperatorTester& input_size(size_t input_height, size_t input_width) { in input_size() argument
26 assert(input_width >= 1); in input_size()
28 this->input_width_ = input_width; in input_size()
42 inline ResizeBilinearOperatorTester& input_width(size_t input_width) { in input_width() argument
43 assert(input_width >= 1); in input_width()
44 this->input_width_ = input_width; in input_width()
48 inline size_t input_width() const { in input_width() function
90 return float(input_width() - 1) / float(output_width() - 1); in width_scale()
92 return float(input_width()) / float(output_width()); in width_scale()
176 return input_width(); in next_input_width()
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| D | unpooling-operator-tester.h | 88 inline UnpoolingOperatorTester& input_size(size_t input_height, size_t input_width) { in input_size() argument
90 assert(input_width >= 1); in input_size()
92 this->input_width_ = input_width; in input_size()
106 inline UnpoolingOperatorTester& input_width(size_t input_width) { in input_width() function
107 assert(input_width >= 1); in input_width()
108 this->input_width_ = input_width; in input_width()
112 inline size_t input_width() const { in input_width() function
178 return std::max<size_t>(input_width() * pooling_width(), padding_width) - padding_width; in output_width()
241 return input_width(); in next_input_width()
286 …std::vector<uint32_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stri… in TestX32()
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| D | dwconv-spchw-microkernel-tester.h | 77 inline DWConvSpCHWMicrokernelTester& input_width(uint32_t input_width) { in input_width() function
78 assert(input_width >= 1); in input_width()
79 this->input_width_ = input_width; in input_width()
83 inline uint32_t input_width() const { in input_width() function
132 const uint32_t padded_input_width = padding_left() + input_width() + padding_right(); in output_width()
176 … return (this->input_width() + input_tuple_size() - 1) / input_tuple_size() * input_tuple_size(); in input_width_stride()
232 …(input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tup…
250 if (ix < input_width()) {
272 output_params = xnn_init_f32_spchw_params(input_width(), output_min, output_max);
275 output_params = xnn_init_scalar_f32_spchw_params(input_width(), output_min, output_max);
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| /external/XNNPACK/src/ |
| D | indirection.c | 28 const size_t input_width = op->input_width; in xnn_indirection_init_conv2d() local
59 if (input_x < input_width) { in xnn_indirection_init_conv2d()
61 ((uintptr_t) input + (input_y * input_width + input_x) * input_pixel_stride); in xnn_indirection_init_conv2d()
91 const size_t input_width = op->input_width; in xnn_indirection_init_dwconv2d() local
112 if (input_x < input_width) { in xnn_indirection_init_dwconv2d()
114 … void*) ((uintptr_t) input + ((batch_index * input_height + input_y) * input_width + input_x) * in… in xnn_indirection_init_dwconv2d()
143 const size_t input_width = op->input_width; in xnn_indirection_init_deconv2d() local
177 …de_height == y && input_y < input_height && input_x * stride_width == x && input_x < input_width) { in xnn_indirection_init_deconv2d()
178 …indirection_buffer[index] = (const void*) ((uintptr_t) input + (input_y * input_width + input_x) *… in xnn_indirection_init_deconv2d()
199 const size_t input_width = op->input_width; in xnn_indirection_init_subconv2d() local
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| D | unpooling-nhwc.c | 129 size_t input_width, in xnn_setup_unpooling2d_nhwc_x32() argument
146 if (input_width == 0 || input_height == 0) { in xnn_setup_unpooling2d_nhwc_x32()
149 input_width, input_height); in xnn_setup_unpooling2d_nhwc_x32()
160 unpooling_op->input_width = input_width; in xnn_setup_unpooling2d_nhwc_x32()
167 input_width, unpooling_op->padding_left + unpooling_op->padding_right, in xnn_setup_unpooling2d_nhwc_x32()
174 input_width == unpooling_op->last_input_width) in xnn_setup_unpooling2d_nhwc_x32()
188 …const size_t indirection_buffer_size = sizeof(void*) * (batch_size * input_height * input_width * … in xnn_setup_unpooling2d_nhwc_x32()
203 .input_height_stride = input_width * input_pixel_stride_in_bytes, in xnn_setup_unpooling2d_nhwc_x32()
206 .index_height_stride = input_width * channels * sizeof(uint32_t), in xnn_setup_unpooling2d_nhwc_x32()
209 .indirect_output_height_stride = input_width * pooling_size * sizeof(void*), in xnn_setup_unpooling2d_nhwc_x32()
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| D | resize-bilinear-nhwc.c | 93 size_t input_width, in xnn_setup_resize_bilinear2d_nhwc_f32() argument
111 if (input_width == 0 || input_height == 0) { in xnn_setup_resize_bilinear2d_nhwc_f32()
114 input_width, input_height); in xnn_setup_resize_bilinear2d_nhwc_f32()
118 if (max(input_width, input_height) >= 16777216) { in xnn_setup_resize_bilinear2d_nhwc_f32()
122 input_width, input_height); in xnn_setup_resize_bilinear2d_nhwc_f32()
167 input_width != resize_op->last_input_width || in xnn_setup_resize_bilinear2d_nhwc_f32()
174 input_height, input_width, in xnn_setup_resize_bilinear2d_nhwc_f32()
182 resize_op->last_input_width = input_width; in xnn_setup_resize_bilinear2d_nhwc_f32()
192 .input_batch_stride = input_pixel_stride_in_bytes * input_height * input_width, in xnn_setup_resize_bilinear2d_nhwc_f32()
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | depthtospace_op_gpu.cu.cc | 36 const int input_height, const int input_width, in D2S_NHWC() argument
56 in_d + input_depth * (in_w + input_width * (in_h + input_height * b)); in D2S_NHWC()
66 const int block_size, const int input_width, in D2S_NCHW() argument
77 const int n_bY_bX_oC_iY = input_idx / input_width; in D2S_NCHW()
78 const int iX = input_idx - n_bY_bX_oC_iY * input_width; in D2S_NCHW()
92 (iX + input_width * in D2S_NCHW()
103 const int input_width, const int output_width, in D2S_NCHW_LOOP() argument
119 const int n_oC_iY = thread_idx / input_width; in D2S_NCHW_LOOP()
120 const int iX = thread_idx - n_oC_iY * input_width; in D2S_NCHW_LOOP()
152 const int input_width = input.dimension(2); in operator ()() local
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| D | spacetodepth_op_gpu.cu.cc | 35 const int input_height, const int input_width, in S2D_NHWC() argument
43 const int w = inp_idx2 % input_width; in S2D_NHWC()
44 const int inp_idx3 = inp_idx2 / input_width; in S2D_NHWC()
104 const int output_width, const int input_width, in S2D_NCHW_LOOP() argument
126 auto input_ptr = input + (n_iC_oY * input_width + oX) * block_size; in S2D_NCHW_LOOP()
135 ldg(input_ptr + bY * input_width + bX); in S2D_NCHW_LOOP()
149 const int input_width = input.dimension(2); in operator ()() local
156 batch_size * input_height * input_width * input_depth; in operator ()()
164 input_height, input_width, input_depth, output_height, output_width, in operator ()()
188 const int input_width = input.dimension(3); in operator ()() local
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| D | conv_ops_using_gemm.cc | 89 int input_batches, int input_height, int input_width, in operator ()() argument
112 ((output_width - 1) * stride_cols + filter_width - input_width + 1) / in operator ()()
119 ((output_width - 1) * stride_cols + filter_width - input_width) / 2; in operator ()()
164 if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && in operator ()()
167 input_data[(batch * input_height * input_width * in operator ()()
169 (in_y * input_width * input_depth) + in operator ()()
212 int input_batches, int input_height, int input_width, in operator ()() argument
217 if ((input_batches <= 0) || (input_width <= 0) || (input_height <= 0) || in operator ()()
221 << input_width << ", " << input_depth; in operator ()()
241 const int m = input_batches * input_height * input_width; in operator ()()
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| D | quantized_activation_ops_test.cc | 47 const int input_width = 2; in TEST_F() local
49 Tensor input_float(DT_FLOAT, {input_height, input_width}); in TEST_F()
53 Tensor expected_float(DT_FLOAT, {input_height, input_width}); in TEST_F()
78 const int input_width = 2; in TEST_F() local
80 Tensor input_float(DT_FLOAT, {input_height, input_width}); in TEST_F()
84 Tensor expected_float(DT_FLOAT, {input_height, input_width}); in TEST_F()
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| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| D | resize_bilinear_test.cc | 30 int batch, int depth, int input_width, in TestOneResizeBilinear() argument
33 RuntimeShape input_dims_inference({batch, input_height, input_width, depth}); in TestOneResizeBilinear()
89 const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); in TEST_P() local
94 TestOneResizeBilinear<uint8>(op_params, batch, depth, input_width, in TEST_P()
108 const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); in TEST_P() local
110 const int output_width = input_width * 2; in TEST_P()
119 TestOneResizeBilinear<uint8>(op_params, batch, depth, input_width, in TEST_P()
133 const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); in TEST_P() local
144 TestOneResizeBilinear<float>(op_params, batch, depth, input_width, in TEST_P()
158 const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); in TEST_P() local
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| D | averagepool_quantized_test.cc | 72 const int input_width = input_width_offset + filter_width; in CreateDataAndRunAveragePool() local
75 padding_same ? (input_width + stride_width - 1) / stride_width in CreateDataAndRunAveragePool()
76 : (input_width - filter_width + stride_width) / stride_width; in CreateDataAndRunAveragePool()
83 RuntimeShape({batch, input_height, input_width, input_depth}); in CreateDataAndRunAveragePool()
105 compute_padding(stride_width, input_width, filter_width, output_width); in CreateDataAndRunAveragePool()
138 const int input_width = input_width_offset + filter_width; in CreateExtremalDataAndRunAveragePool() local
141 padding_same ? (input_width + stride_width - 1) / stride_width in CreateExtremalDataAndRunAveragePool()
142 : (input_width - filter_width + stride_width) / stride_width; in CreateExtremalDataAndRunAveragePool()
149 RuntimeShape({batch, input_height, input_width, input_depth}); in CreateExtremalDataAndRunAveragePool()
168 compute_padding(stride_width, input_width, filter_width, output_width); in CreateExtremalDataAndRunAveragePool()
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| D | resize_nearest_neighbor_test.cc | 152 void TestOptimizedResizeNearestNeighbor(int batch, int depth, int input_width, in TestOptimizedResizeNearestNeighbor() argument
158 RuntimeShape input_shape({batch, input_height, input_width, depth}); in TestOptimizedResizeNearestNeighbor()
184 bool is_valid_scale(int input_width, int input_height, int output_width, in is_valid_scale() argument
189 static_cast<float>(input_width) / output_width; in is_valid_scale()
192 int32 width_scale_int = (input_width << 16) / output_width + 1; in is_valid_scale()
205 input_width - 1); in is_valid_scale()
206 int32 in_x_int = std::min((x * width_scale_int) >> 16, input_width - 1); in is_valid_scale()
221 const int input_width = ExponentialRandomPositiveInt(0.9f, 20, 200); in TEST() local
226 if (is_valid_scale(input_width, input_height, output_width, in TEST()
229 batch, depth, input_width, input_height, output_width, output_height); in TEST()
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| /external/tensorflow/tensorflow/examples/label_image/ |
| D | label_image.py | 40 input_width=299, argument
59 resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
81 input_width = 299 variable
107 if args.input_width:
108 input_width = args.input_width variable
122 input_width=input_width,
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| /external/XNNPACK/bench/ |
| D | max-pooling.cc | 25 const size_t input_width = state.range(2); in max_pooling_u8() local
36 const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1; in max_pooling_u8()
38 std::vector<uint8_t> input(batch_size * input_height * input_width * channels); in max_pooling_u8()
65 batch_size, input_height, input_width, in max_pooling_u8()
91 …batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(uint8… in max_pooling_u8()
98 const size_t input_width = state.range(2); in max_pooling_f32() local
109 const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1; in max_pooling_f32()
111 std::vector<float> input(batch_size * input_height * input_width * channels); in max_pooling_f32()
138 batch_size, input_height, input_width, in max_pooling_f32()
164 …batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float… in max_pooling_f32()
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