/frameworks/base/core/tests/coretests/src/com/android/internal/http/multipart/ |
H A D | MultipartTest.java | 60 StringBuffer output = new StringBuffer(); 62 output.append("--"); 63 output.append(boundry); 64 output.append(CRLF); 66 output.append("Content-Disposition: form-data; name=\"stringpart\""); 67 output.append(CRLF); 68 output.append("Content-Type: text/plain; charset=US-ASCII"); 69 output.append(CRLF); 70 output.append("Content-Transfer-Encoding: 8bit"); 71 output [all...] |
/frameworks/base/tests/Camera2Tests/SmartCamera/SimpleCamera/src/androidx/media/filterfw/ |
H A D | ColorSpace.java | 33 * YUV to RGB conversion is done using the ITU-R BT.601 transformation. The output buffer must 37 * @param output buffer to hold RGBA8888 data. 42 ByteBuffer input, ByteBuffer output, int width, int height) { 44 expectOutputSize(output, width * height * 4); 45 nativeYuv420pToRgba8888(input, output, width, height); 51 * The input data is expected to be encoded in 8-bit interleaved ARGB channels. The output 52 * buffer must be large enough to hold the data. The output buffer may be the same as the 56 * @param output buffer to hold RGBA8888 data. 61 ByteBuffer input, ByteBuffer output, int width, int height) { 63 expectOutputSize(output, widt 41 convertYuv420pToRgba8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 60 convertArgb8888ToRgba8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 79 convertRgba8888ToHsva8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 98 convertRgba8888ToYcbcra8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 113 expectOutputSize(ByteBuffer output, int expectedSize) argument 121 nativeYuv420pToRgba8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 124 nativeArgb8888ToRgba8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 127 nativeRgba8888ToHsva8888( ByteBuffer input, ByteBuffer output, int width, int height) argument 130 nativeRgba8888ToYcbcra8888( ByteBuffer input, ByteBuffer output, int width, int height) argument [all...] |
/frameworks/ml/nn/runtime/test/specs/V1_0/ |
H A D | depth_to_space_float_1.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 2, 2, 2}") variable 6 model = model.Operation("DEPTH_TO_SPACE", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | depth_to_space_float_2.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 4, 4, 1}") variable 6 model = model.Operation("DEPTH_TO_SPACE", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | depth_to_space_float_3.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 4, 4, 2}") variable 6 model = model.Operation("DEPTH_TO_SPACE", i1, block).To(output) 16 output0 = {output: # output 0
|
H A D | depth_to_space_quant8_1.mod.py | 4 output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 2}, 0.5f, 0") variable 6 model = model.Operation("DEPTH_TO_SPACE", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | depth_to_space_quant8_2.mod.py | 4 output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 4, 4, 1}, 0.5f, 0") variable 6 model = model.Operation("DEPTH_TO_SPACE", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | local_response_norm_float_1.mod.py | 7 output = Output("output", "TENSOR_FLOAT32", "{1, 1, 1, 6}") variable 9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output) 15 output0 = {output: # output 0
|
H A D | local_response_norm_float_2.mod.py | 7 output = Output("output", "TENSOR_FLOAT32", "{1, 1, 1, 6}") variable 9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output) 15 output0 = {output: # output 0
|
H A D | local_response_norm_float_3.mod.py | 7 output = Output("output", "TENSOR_FLOAT32", "{1, 1, 1, 6}") variable 9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output) 15 output0 = {output: # output 0
|
H A D | local_response_norm_float_4.mod.py | 7 output = Output("output", "TENSOR_FLOAT32", "{1, 1, 1, 6}") variable 9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output) 15 output0 = {output: # output 0
|
H A D | softmax_float_1.mod.py | 6 output = Output("output", "TENSOR_FLOAT32", "{1, 4}") variable 9 model = model.Operation("SOFTMAX", i1, beta).To(output) 14 output0 = {output: [.25, .25, .25, .25]}
|
H A D | softmax_float_2.mod.py | 6 output = Output("output", "TENSOR_FLOAT32", "{2, 5}") variable 9 model = model.Operation("SOFTMAX", i1, beta).To(output) 16 output0 = {output:
|
H A D | softmax_quant8_1.mod.py | 6 output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 4}, 0.00390625f, 0") variable 9 model = model.Operation("SOFTMAX", i1, beta).To(output) 14 output0 = {output: [64, 64, 64, 64]}
|
H A D | softmax_quant8_2.mod.py | 6 output = Output("output", "TENSOR_QUANT8_ASYMM", "{2, 5}, 0.00390625f, 0") variable 9 model = model.Operation("SOFTMAX", i1, beta).To(output) 16 output0 = {output:
|
H A D | space_to_depth_float_1.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 1, 1, 8}") variable 6 model = model.Operation("SPACE_TO_DEPTH", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | space_to_depth_float_2.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 2, 2, 4}") variable 6 model = model.Operation("SPACE_TO_DEPTH", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | space_to_depth_float_3.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 2, 2, 8}") variable 6 model = model.Operation("SPACE_TO_DEPTH", i1, block).To(output) 15 output0 = {output: # output 0
|
H A D | space_to_depth_quant8_1.mod.py | 4 output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 1, 1, 8}, 0.5f, 0") variable 6 model = model.Operation("SPACE_TO_DEPTH", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | space_to_depth_quant8_2.mod.py | 4 output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 4}, 0.5f, 0") variable 6 model = model.Operation("SPACE_TO_DEPTH", i1, block).To(output) 12 output0 = {output: # output 0
|
/frameworks/ml/nn/runtime/test/specs/V1_1/ |
H A D | batch_to_space.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 2, 2, 2}") variable 6 model = model.Operation("BATCH_TO_SPACE_ND", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | batch_to_space_float_1.mod.py | 4 output = Output("output", "TENSOR_FLOAT32", "{1, 4, 4, 1}") variable 6 model = model.Operation("BATCH_TO_SPACE_ND", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | batch_to_space_float_1_relaxed.mod.py | 21 output = Output("output", "TENSOR_FLOAT32", "{1, 4, 4, 1}") variable 23 model = model.Operation("BATCH_TO_SPACE_ND", i1, block).To(output) 30 output0 = {output: # output 0
|
H A D | batch_to_space_quant8_1.mod.py | 4 output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 4, 4, 1}, 1.0, 0") variable 6 model = model.Operation("BATCH_TO_SPACE_ND", i1, block).To(output) 12 output0 = {output: # output 0
|
H A D | batch_to_space_relaxed.mod.py | 20 output = Output("output", "TENSOR_FLOAT32", "{1, 2, 2, 2}") variable 22 model = model.Operation("BATCH_TO_SPACE_ND", i1, block).To(output) 29 output0 = {output: # output 0
|