/frameworks/base/libs/hwui/tests/common/scenes/ |
H A D | SaveLayer2Animation.cpp | 40 int padding = smallRectHeight / 4; variable 44 mBluePaint.setTextSize(padding); 46 mGreenPaint.setTextSize(padding); 50 canvas.saveLayer(bounds.fLeft, top, bounds.fRight, top + padding, &mBluePaint, 56 top + padding); 59 canvas.drawRect(bounds.fLeft, top + padding, bounds.fRight, 60 top + smallRectHeight - padding, mBluePaint); 63 top + smallRectHeight - padding);
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/frameworks/base/tests/UiBench/src/com/android/test/uibench/ |
H A D | SaveLayerInterleaveActivity.java | 62 int padding = smallRectHeight / 4; 65 mBluePaint.setTextSize(padding); 67 mGreenPaint.setTextSize(padding); 71 canvas.saveLayer(bounds.left, top, bounds.right, top + padding, 74 canvas.drawText("offscreen line "+ i, bounds.left, top + padding, 78 Rect partX = new Rect(bounds.left, top + padding, 79 bounds.right,top + smallRectHeight - padding); 82 top + smallRectHeight - padding, mGreenPaint);
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/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | avg_pool_float_2.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | avg_pool_float_2_relaxed.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | avg_pool_float_3.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | avg_pool_float_3_relaxed.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | avg_pool_float_4.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
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H A D | avg_pool_float_4_relaxed.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
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H A D | avg_pool_quant8_2.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | avg_pool_quant8_3.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_float_2.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_float_2_relaxed.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_float_3.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
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H A D | max_pool_float_3_relaxed.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
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H A D | max_pool_quant8_2.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_quant8_3.model.cpp | 10 auto padding = model->addOperand(&type1); local 19 model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu1_activation}, {output});
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/frameworks/ml/nn/runtime/test/specs/V1_0/ |
H A D | avg_pool_float_2.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
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H A D | avg_pool_float_3.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
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H A D | avg_pool_float_4.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act3).To(output)
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H A D | avg_pool_quant8_2.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
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H A D | avg_pool_quant8_3.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
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H A D | max_pool_float_2.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
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H A D | max_pool_float_3.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act3).To(output)
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H A D | max_pool_quant8_2.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
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H A D | max_pool_quant8_3.mod.py | 33 padding = Int32Scalar("padding", pad) variable 42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act2).To(output)
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