Searched refs:padding (Results 1 - 25 of 159) sorted by relevance

1234567

/frameworks/base/libs/hwui/tests/common/scenes/
H A DSaveLayer2Animation.cpp40 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);
/frameworks/base/tests/UiBench/src/com/android/test/uibench/
H A DSaveLayerInterleaveActivity.java62 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);
/frameworks/ml/nn/runtime/test/generated/models/
H A Davg_pool_float_2.model.cpp10 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});
H A Davg_pool_float_2_relaxed.model.cpp10 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});
H A Davg_pool_float_3.model.cpp10 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});
H A Davg_pool_float_3_relaxed.model.cpp10 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});
H A Davg_pool_float_4.model.cpp10 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});
H A Davg_pool_float_4_relaxed.model.cpp10 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});
H A Davg_pool_quant8_2.model.cpp10 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});
H A Davg_pool_quant8_3.model.cpp10 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});
H A Dmax_pool_float_2.model.cpp10 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});
H A Dmax_pool_float_2_relaxed.model.cpp10 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});
H A Dmax_pool_float_3.model.cpp10 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});
H A Dmax_pool_float_3_relaxed.model.cpp10 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});
H A Dmax_pool_quant8_2.model.cpp10 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});
H A Dmax_pool_quant8_3.model.cpp10 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});
/frameworks/ml/nn/runtime/test/specs/V1_0/
H A Davg_pool_float_2.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
H A Davg_pool_float_3.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
H A Davg_pool_float_4.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act3).To(output)
H A Davg_pool_quant8_2.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
H A Davg_pool_quant8_3.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "AVERAGE_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
H A Dmax_pool_float_2.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
H A Dmax_pool_float_3.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act3).To(output)
H A Dmax_pool_quant8_2.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act0).To(output)
H A Dmax_pool_quant8_3.mod.py33 padding = Int32Scalar("padding", pad) variable
42 "MAX_POOL_2D", i0, padding, padding, padding, padding, stride, stride, filt, filt, act2).To(output)

Completed in 1398 milliseconds

1234567