1 # 2 # Copyright (C) 2019 The Android Open Source Project 3 # 4 # Licensed under the Apache License, Version 2.0 (the "License"); 5 # you may not use this file except in compliance with the License. 6 # You may obtain a copy of the License at 7 # 8 # http://www.apache.org/licenses/LICENSE-2.0 9 # 10 # Unless required by applicable law or agreed to in writing, software 11 # distributed under the License is distributed on an "AS IS" BASIS, 12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 # See the License for the specific language governing permissions and 14 # limitations under the License. 15 # 16 17 layout = BoolScalar("layout", False) # NHWC 18 19 # Operation 1, GENERATE_PROPOSALS 20 scores = Input("scores", "TENSOR_FLOAT32", "{1, 1, 1, 1}") 21 deltas = Input("deltas", "TENSOR_FLOAT32", "{1, 1, 1, 4}") 22 anchors = Input("anchors", "TENSOR_FLOAT32", "{1, 4}") 23 image = Input("imageInfo", "TENSOR_FLOAT32", "{1, 2}") 24 scoresOut_1 = Output("scores", "TENSOR_FLOAT32", "{0}") 25 roiOut_1 = Internal("roi", "TENSOR_FLOAT32", "{0, 4}") 26 batchOut_1 = Internal("batches", "TENSOR_INT32", "{0}") 27 model = Model("zero_sized").Operation("GENERATE_PROPOSALS", scores, deltas, anchors, image, 1.0, 1.0, -1, -1, 0.3, 10.0, layout).To(scoresOut_1, roiOut_1, batchOut_1) 28 29 # Operation 2, ROI_ALIGN 30 feature = Input("featureMap", "TENSOR_FLOAT32", "{1, 1, 1, 1}") 31 featureOut_2 = Internal("scores", "TENSOR_FLOAT32", "{0, 2, 2, 1}") 32 model = model.Operation("ROI_ALIGN", feature, roiOut_1, batchOut_1, 2, 2, 1.0, 1.0, 4, 4, layout).To(featureOut_2) 33 34 # Operation 3, FULLY_CONNECTED 35 weights_3 = Parameter("weights", "TENSOR_FLOAT32", "{8, 4}", [1] * 32) 36 bias_3 = Parameter("bias", "TENSOR_FLOAT32", "{8}", [1] * 8) 37 deltaOut_3 = Internal("delta", "TENSOR_FLOAT32", "{0, 8}") 38 model = model.Operation("FULLY_CONNECTED", featureOut_2, weights_3, bias_3, 0).To(deltaOut_3) 39 40 # Operation 4, FULLY_CONNECTED 41 weights_4 = Parameter("weights", "TENSOR_FLOAT32", "{2, 4}", [1] * 8) 42 bias_4 = Parameter("bias", "TENSOR_FLOAT32", "{2}", [1] * 2) 43 scoresOut_4 = Internal("scores", "TENSOR_FLOAT32", "{0, 2}") 44 model = model.Operation("FULLY_CONNECTED", featureOut_2, weights_4, bias_4, 0).To(scoresOut_4) 45 46 # Operation 5, AXIS_ALIGNED_BBOX_TRANSFORM 47 roiOut_5 = Internal("roi", "TENSOR_FLOAT32", "{0, 8}") 48 model = model.Operation("AXIS_ALIGNED_BBOX_TRANSFORM", roiOut_1, deltaOut_3, batchOut_1, image).To(roiOut_5) 49 50 # Operation 6, BOX_WITH_NMS_LIMIT 51 scoresOut_6 = Output("scores", "TENSOR_FLOAT32", "{0}") 52 roiOut_6 = Output("roi", "TENSOR_FLOAT32", "{0, 4}") 53 classOut_6 = Output("classes", "TENSOR_INT32", "{0}") 54 batchOut_6 = Output("batches", "TENSOR_INT32", "{0}") 55 model = model.Operation("BOX_WITH_NMS_LIMIT", scoresOut_4, roiOut_5, batchOut_1, 0.1, -1, 0, 0.3, 1.0, 0.1).To(scoresOut_6, roiOut_6, classOut_6, batchOut_6) 56 57 quant8 = DataTypeConverter().Identify({ 58 scores: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 59 deltas: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 60 anchors: ("TENSOR_QUANT16_SYMM", 0.125, 0), 61 image: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 62 scoresOut_1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 63 roiOut_1: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 64 feature: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 65 featureOut_2: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 66 weights_3: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 67 bias_3: ("TENSOR_INT32", 0.01, 0), 68 deltaOut_3: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 69 weights_4: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 70 bias_4: ("TENSOR_INT32", 0.01, 0), 71 scoresOut_4: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 72 roiOut_5: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 73 scoresOut_6: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 74 roiOut_6: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 75 }) 76 77 Example({ 78 79 # Inputs that will lead to zero-sized output of GENERATE_PROPOSALS 80 scores: [0.5], 81 deltas: [0, 0, -10, -10], 82 anchors: [0, 0, 10, 10], 83 image: [32, 32], 84 feature: [1], 85 86 # Dummy outputs 87 scoresOut_1: [0], 88 scoresOut_6: [0], 89 roiOut_6: [0], 90 classOut_6: [0], 91 batchOut_6: [0], 92 93 }).AddVariations("relaxed", "float16", quant8) 94
