/frameworks/native/opengl/libagl/ |
H A D | dxt.cpp | 102 int b1 = (x >> 16) & 0xff; local 106 return (uint32_t)((b3 << 24) | (b2 << 16) | (b1 << 8) | b0); 167 // bits: b31 b30 b29 ... b3 b2 b1 b0 168 // bits >> 1: b31 b31 b30 ... b4 b3 b2 b1 169 // &: b31 (b31 & b30) (b29 & b28) ... (b2 & b1) (b1 & b0) 170 // & 0x55..: 0 (b31 & b30) 0 ... 0 (b1 & b0) 241 int b1 = blue(color1); local 245 c[1] = (r1 << 11) | ((g1 >> 1) << 6) | (b1 << 1) | 0x1; 261 b2 = avg23(b0, b1); 371 int b1 = blue(color1); local 528 int b1 = blue(color1); local [all...] |
H A D | matrix.h | 111 GLfixed a1, GLfixed b1, 125 "%r"(a1), "r"(b1), 134 int64_t(a1)*b1)>>16) + c; 140 GLfixed a1, GLfixed b1, 156 "%r"(a1), "r"(b1), 169 "madd %[a1],%[b1] \r\n" 178 : [a0] "r" (a0),[b0] "r" (b0),[a1] "r" (a1),[b1] "r" (b1),[a2] "r" (a2),[b2] "r" (b2),[c] "r" (c) 186 int64_t(a1)*b1 + 192 // b0, b1, b 110 mla2a( GLfixed a0, GLfixed b0, GLfixed a1, GLfixed b1, GLfixed c) argument 139 mla3a( GLfixed a0, GLfixed b0, GLfixed a1, GLfixed b1, GLfixed a2, GLfixed b2, GLfixed c) argument 316 mla3( GLfixed a0, GLfixed b0, GLfixed a1, GLfixed b1, GLfixed a2, GLfixed b2) argument 347 mla4( GLfixed a0, GLfixed b0, GLfixed a1, GLfixed b1, GLfixed a2, GLfixed b2, GLfixed a3, GLfixed b3) argument [all...] |
/frameworks/native/opengl/libs/ETC1/ |
H A D | etc1.cpp | 203 int r1, r2, g1, g2, b1, b2; local 213 b1 = convert5To8(bBase); 221 b1 = convert4To8(high >> 12); 229 decode_subblock(pOut, r1, g1, b1, tableA, low, false, flipped); 379 int r1, g1, b1, r2, g2, b2; // 8 bit base colors for sub-blocks local 391 b1 = convert5To8(b51); 417 b1 = convert4To8(b41); 426 pBaseColors[2] = b1;
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/frameworks/ml/nn/common/operations/internal/optimized/ |
H A D | depthwiseconv_uint8.h | 1314 const int32x4_t b1 = vld1q_s32(bias_data + 4); local 1317 vst1q_s32(acc_buffer + 8 * i + 4, b1); 1319 vst1q_s32(acc_buffer + 8 * i + 12, b1); 1323 vst1q_s32(acc_buffer + 8 * i + 4, b1); 1327 const int32x4_t b1 = vld1q_s32(bias_data + 4); local 1332 vst1q_s32(acc_buffer + 16 * i + 4, b1);
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H A D | optimized_ops.h | 241 auto b1 = vld1q_f32(bias_data + i + 4); local 249 auto x1 = vaddq_f32(a1, b1);
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/frameworks/ml/nn/runtime/test/specs/ |
H A D | conv_float.mod.py | 20 b1 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output)
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H A D | conv_float_channels.mod.py | 20 b1 = Parameter("op3", "TENSOR_FLOAT32", "{3}", [0., 0., 0.]) variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output)
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H A D | conv_float_channels_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_FLOAT32", "{3}") variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 37 b1:
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H A D | conv_float_large.mod.py | 20 b1 = Parameter("op3", "TENSOR_FLOAT32", "{3}", [0., 0., 0.]) variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output)
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H A D | conv_float_large_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_FLOAT32", "{3}") variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 38 b1:
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H A D | conv_float_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_FLOAT32", "{1}") variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 35 b1:
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H A D | conv_quant8.mod.py | 22 b1 = Parameter("op3", "TENSOR_INT32", "{1}, 0.25f, 0", [4]) variable 30 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, 38 # (i1 (conv) f1) + b1
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H A D | conv_quant8_channels.mod.py | 20 b1 = Parameter("op3", "TENSOR_INT32", "{3}, 0.25, 0", [0, 0, 0]) variable 26 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output)
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H A D | conv_quant8_channels_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_INT32", "{3}, 0.25, 0") variable 26 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 35 b1:
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H A D | conv_quant8_large.mod.py | 20 b1 = Parameter("op3", "TENSOR_INT32", "{3}, 0.25, 0", [0, 0, 0]) variable 26 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output)
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H A D | conv_quant8_large_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_INT32", "{3}, 0.25, 0") variable 26 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 36 b1:
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H A D | conv_quant8_overflow.mod.py | 20 b1 = Parameter("op3", "TENSOR_INT32", "{3}, 0.25, 0", [0, 0, 0]) variable 26 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output)
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H A D | conv_quant8_overflow_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_INT32", "{3}, 0.25, 0") variable 26 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 36 b1:
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H A D | conv_quant8_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_INT32", "{1}, 0.25f, 0") variable 28 model = model.Operation("CONV_2D", i1, f1, b1, pad0, pad0, pad0, pad0, stride, stride, act).To(output) 35 b1: 37 # (i1 (conv) f1) + b1
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H A D | depthwise_conv2d_float.mod.py | 20 b1 = Parameter("op3", "TENSOR_FLOAT32", "{4}", [1, 2, 3, 4]) variable 28 i1, f1, b1, 38 # (i1 (conv) f1) + b1
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H A D | depthwise_conv2d_float_large.mod.py | 20 b1 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [100, 200]) # depth_out = 2 variable 28 i1, f1, b1, 39 # (i1 (conv) f1) + b1
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H A D | depthwise_conv2d_float_large_2.mod.py | 20 b1 = Parameter("op3", "TENSOR_FLOAT32", "{4}", [600000, 700000, 800000, 900000]) # depth_out = 4 variable 28 i1, f1, b1, 41 # (i1 (conv) f1) + b1
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H A D | depthwise_conv2d_float_large_2_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_FLOAT32", "{4}") # depth_out = 4 variable 28 i1, f1, b1, 45 b1: 48 # (i1 (conv) f1) + b1
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H A D | depthwise_conv2d_float_large_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_FLOAT32", "{2}") # depth_out = 2 variable 28 i1, f1, b1, 41 b1: 44 # (i1 (conv) f1) + b1
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H A D | depthwise_conv2d_float_weights_as_inputs.mod.py | 20 b1 = Input("op3", "TENSOR_FLOAT32", "{4}") variable 28 i1, f1, b1, 43 b1: 45 # (i1 (conv) f1) + b1
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