1 # 2 # Copyright (C) 2018 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 # TEST 1: ROI_ALIGN_1, outputShape = [2, 2], spatialScale = [0.5, 0.5], samplingRatio = [4, 4] 20 i1 = Input("in", "TENSOR_FLOAT32", "{1, 4, 4, 1}") 21 roi1 = Input("roi", "TENSOR_FLOAT32", "{4, 4}") 22 o1 = Output("out", "TENSOR_FLOAT32", "{4, 2, 2, 1}") 23 Model().Operation("ROI_ALIGN", i1, roi1, [0, 0, 0, 0], 2, 2, 2.0, 2.0, 4, 4, layout).To(o1) 24 25 quant8 = DataTypeConverter().Identify({ 26 i1: ("TENSOR_QUANT8_ASYMM", 0.25, 128), 27 roi1: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 28 o1: ("TENSOR_QUANT8_ASYMM", 0.0625, 128) 29 }) 30 31 # Instantiate an example 32 Example({ 33 i1: [ 34 -10, -1, 4, -5, 35 -8, -2, 9, 1, 36 7, -2, 3, -7, 37 -2, 10, -3, 5 38 ], 39 roi1: [ 40 2, 2, 4, 4, 41 0, 0, 8, 8, 42 2, 0, 4, 8, 43 0, 2, 8, 4 44 ], 45 o1: [ 46 0.375, 5.125, -0.375, 2.875, 47 -0.5, -0.3125, 3.1875, 1.125, 48 0.25, 4.25, 4.875, 0.625, 49 -0.1875, 1.125, 0.9375, -2.625 50 ] 51 }).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16") 52 53 54 # TEST 2: ROI_ALIGN_2, outputShape = [2, 3], spatialScale = [0.25, 0.25], samplingRatio = [4, 4] 55 i2 = Input("in", "TENSOR_FLOAT32", "{4, 4, 8, 2}") 56 roi2 = Input("roi", "TENSOR_FLOAT32", "{4, 4}") 57 o2 = Output("out", "TENSOR_FLOAT32", "{4, 2, 3, 2}") 58 Model().Operation("ROI_ALIGN", i2, roi2, [0, 0, 3, 3], 2, 3, 4.0, 4.0, 4, 4, layout).To(o2) 59 60 quant8 = DataTypeConverter().Identify({ 61 i2: ("TENSOR_QUANT8_ASYMM", 0.04, 0), 62 roi2: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 63 o2: ("TENSOR_QUANT8_ASYMM", 0.03125, 10) 64 }) 65 66 # Instantiate an example 67 Example({ 68 i2: [ 69 8.84, 8.88, 7.41, 5.60, 9.95, 4.37, 0.10, 7.64, 6.50, 9.47, 70 7.55, 3.00, 0.89, 3.01, 6.30, 4.40, 1.64, 6.74, 6.16, 8.60, 71 5.85, 3.17, 7.12, 6.79, 5.77, 6.62, 5.13, 8.44, 5.08, 7.12, 72 2.84, 1.19, 8.37, 0.90, 7.86, 9.69, 1.97, 1.31, 4.42, 9.89, 73 0.18, 9.00, 9.30, 0.44, 5.05, 6.47, 1.09, 9.50, 1.30, 2.18, 74 2.05, 7.74, 7.66, 0.65, 4.18, 7.14, 5.35, 7.90, 1.04, 1.47, 75 9.01, 0.95, 4.07, 0.65, 76 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 77 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 78 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 79 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 80 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 81 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 82 0.00, 0.00, 0.00, 0.00, 83 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 84 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 85 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 86 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 87 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 88 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 89 0.00, 0.00, 0.00, 0.00, 90 5.47, 2.64, 0.86, 4.86, 2.38, 2.45, 8.77, 0.06, 3.60, 9.28, 91 5.84, 8.97, 6.89, 1.43, 3.90, 5.91, 7.40, 9.25, 3.12, 4.92, 92 1.87, 3.22, 9.50, 6.73, 2.07, 7.30, 3.07, 4.97, 0.24, 8.91, 93 1.09, 0.27, 7.29, 6.94, 2.31, 6.88, 4.33, 1.37, 0.86, 0.46, 94 6.07, 3.81, 0.86, 6.99, 4.36, 1.92, 8.19, 3.57, 7.90, 6.78, 95 4.64, 6.82, 6.18, 9.63, 2.63, 2.33, 1.36, 2.70, 9.99, 9.85, 96 8.06, 4.80, 7.80, 5.43 97 ], 98 roi2: [ 99 4, 4, 28, 12, 100 4, 4, 32, 16, 101 7, 1, 29, 15, # test rounding 102 1, 7, 9, 11 # test roi with shape smaller than output 103 ], 104 o2: [ 105 5.150000, 5.491250, 4.733750, 7.100000, 4.827500, 106 5.843750, 4.721250, 4.797500, 3.750000, 6.592500, 107 5.452500, 3.362500, 108 4.899396, 5.861696, 4.941504, 5.979741, 3.182904, 109 6.111551, 5.141833, 4.631891, 3.903325, 4.627793, 110 5.537240, 1.356019, 111 4.845915, 3.618338, 3.301958, 6.250566, 2.930461, 112 4.269676, 3.642174, 4.201423, 5.008657, 5.735293, 113 7.426004, 4.819665, 114 4.518229, 6.887344, 2.952656, 5.565781, 3.952786, 115 2.552812, 5.191667, 6.854167, 3.920000, 6.512500, 116 4.886250, 5.497708 117 ] 118 }).AddNchw(i2, o2, layout).AddVariations("relaxed", quant8, "float16") 119 120 121 # TEST 3: ROI_ALIGN_3, outputShape = [2, 3], spatialScale = [0.25, 0.25], samplingRatio = [0, 0] 122 i3 = Input("in", "TENSOR_FLOAT32", "{2, 4, 8, 2}") 123 roi3 = Input("roi", "TENSOR_FLOAT32", "{4, 4}") 124 o3 = Output("out", "TENSOR_FLOAT32", "{4, 2, 3, 2}") 125 Model().Operation("ROI_ALIGN", i3, roi3, [0, 0, 1, 1], 2, 3, 4.0, 4.0, 0, 0, layout).To(o3) 126 127 quant8 = DataTypeConverter().Identify({ 128 i3: ("TENSOR_QUANT8_ASYMM", 0.04, 0), 129 roi3: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 130 o3: ("TENSOR_QUANT8_ASYMM", 0.03125, 10) 131 }) 132 133 # Instantiate an example 134 Example({ 135 i3: [ 136 8.84, 8.88, 7.41, 5.60, 9.95, 4.37, 0.10, 7.64, 6.50, 9.47, 137 7.55, 3.00, 0.89, 3.01, 6.30, 4.40, 1.64, 6.74, 6.16, 8.60, 138 5.85, 3.17, 7.12, 6.79, 5.77, 6.62, 5.13, 8.44, 5.08, 7.12, 139 2.84, 1.19, 8.37, 0.90, 7.86, 9.69, 1.97, 1.31, 4.42, 9.89, 140 0.18, 9.00, 9.30, 0.44, 5.05, 6.47, 1.09, 9.50, 1.30, 2.18, 141 2.05, 7.74, 7.66, 0.65, 4.18, 7.14, 5.35, 7.90, 1.04, 1.47, 142 9.01, 0.95, 4.07, 0.65, 143 5.47, 2.64, 0.86, 4.86, 2.38, 2.45, 8.77, 0.06, 3.60, 9.28, 144 5.84, 8.97, 6.89, 1.43, 3.90, 5.91, 7.40, 9.25, 3.12, 4.92, 145 1.87, 3.22, 9.50, 6.73, 2.07, 7.30, 3.07, 4.97, 0.24, 8.91, 146 1.09, 0.27, 7.29, 6.94, 2.31, 6.88, 4.33, 1.37, 0.86, 0.46, 147 6.07, 3.81, 0.86, 6.99, 4.36, 1.92, 8.19, 3.57, 7.90, 6.78, 148 4.64, 6.82, 6.18, 9.63, 2.63, 2.33, 1.36, 2.70, 9.99, 9.85, 149 8.06, 4.80, 7.80, 5.43 150 ], 151 roi3: [ 152 4, 4, 28, 12, 153 4, 4, 32, 16, 154 7, 1, 29, 15, # test rounding 155 1, 7, 9, 11 # test roi with shape smaller than output 156 ], 157 o3: [ 158 5.150000, 5.491250, 4.733750, 7.100000, 4.827500, 159 5.843750, 4.721250, 4.797500, 3.750000, 6.592500, 160 5.452500, 3.362500, 161 4.869884, 5.908148, 4.941701, 5.955718, 3.113403, 162 6.341898, 5.156389, 4.604016, 3.881782, 4.616123, 163 5.690694, 1.237153, 164 5.028047, 3.560944, 3.157656, 6.395469, 2.896243, 165 4.336576, 3.563021, 4.057767, 5.053437, 6.028906, 166 7.396966, 4.668906, 167 4.385000, 6.905000, 2.815000, 5.502500, 4.161667, 168 1.829167, 5.191667, 6.854167, 3.920000, 6.512500, 169 5.106667, 5.612500 170 ] 171 }).AddNchw(i3, o3, layout).AddVariations("relaxed", quant8, "float16") 172 173 174 # TEST 4: ROI_ALIGN_4, outputShape = [2, 2], spatialScale = [0.5, 1.0], samplingRatio = [0, 4] 175 i4 = Input("in", "TENSOR_FLOAT32", "{4, 4, 4, 1}") 176 roi4 = Input("roi", "TENSOR_FLOAT32", "{5, 4}") 177 o4 = Output("out", "TENSOR_FLOAT32", "{5, 2, 2, 1}") 178 Model().Operation("ROI_ALIGN", i4, roi4, [2, 2, 2, 2, 2], 2, 2, 2.0, 1.0, 0, 4, layout).To(o4) 179 180 quant8 = DataTypeConverter().Identify({ 181 i4: ("TENSOR_QUANT8_ASYMM", 0.25, 128), 182 roi4: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 183 o4: ("TENSOR_QUANT8_ASYMM", 0.0625, 128) 184 }) 185 186 # Instantiate an example 187 Example({ 188 i4: [ 189 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 190 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 191 -10, -1, 4, -5, 192 -8, -2, 9, 1, 193 7, -2, 3, -7, 194 -2, 10, -3, 5, 195 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 196 ], 197 roi4: [ 198 1, 2, 2, 4, 199 0, 0, 4, 8, 200 1, 0, 2, 8, 201 0, 2, 4, 4, 202 0, 0, 0, 0 203 ], 204 o4: [ 205 0.375, 5.125, -0.375, 2.875, 206 -0.5, -0.3125, 3.1875, 1.125, 207 0.25, 4.25, 4.875, 0.625, 208 -0.1875, 1.125, 0.9375, -2.625, 209 -7.4375, -3.3125, -6.8125, -3.4375 210 ] 211 }).AddNchw(i4, o4, layout).AddVariations("relaxed", quant8, "float16") 212 213 214 # TEST 5: ROI_ALIGN_zero_sized 215 216 # Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates. 217 p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores 218 p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi 219 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out 220 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out 221 tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out 222 tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out 223 model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3, -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2) 224 225 # ROI_ALIGN op with numRois = 0. 226 i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 1}") 227 zero_sized = Output("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 1}") 228 model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized) 229 230 quant8 = DataTypeConverter().Identify({ 231 p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 232 p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 233 o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 234 tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 235 i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128), 236 zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128) 237 }) 238 239 # Create test case with dummy values. 240 Example({ 241 i1: [0], 242 o1: [0], 243 o2: [0], 244 zero_sized: [0], 245 }).AddNchw(i1, zero_sized, layout).AddVariations("relaxed", quant8, "float16") 246 247 248 # TEST 6: ROI_ALIGN_6, hanging issue 249 i4 = Input("in", "TENSOR_FLOAT32", "{1, 512, 8, 1}") 250 roi4 = Input("roi", "TENSOR_FLOAT32", "{1, 4}") 251 o4 = Output("out", "TENSOR_FLOAT32", "{1, 128, 4, 1}") 252 Model().Operation("ROI_ALIGN", i4, roi4, [0], 128, 4, 1.0, 64.0, 10, 10, layout).To(o4) 253 254 quant8 = DataTypeConverter().Identify({ 255 i4: ("TENSOR_QUANT8_ASYMM", 0.25, 128), 256 roi4: ("TENSOR_QUANT16_ASYMM", 0.125, 0), 257 o4: ("TENSOR_QUANT8_ASYMM", 0.0625, 128) 258 }) 259 260 # Instantiate an example 261 Example({ 262 i4: [0] * (512 * 8), 263 roi4: [450, 500, 466, 508], 264 o4: [0] * (128 * 4) 265 }).AddNchw(i4, o4, layout).AddVariations("relaxed", quant8, "float16") 266
