ncnn
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20 hours ago •
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设置use_vulkan_compute=true后MemoryData拿到的数据全为0
expectation | 诉求 | 期待する
设置use_vulkan_compute=true后MemoryData拿到的数据全为0
model | 模型 | モデル
- model.param and model.bin 模型param 7767517 308 380 Input in0 0 1 in0 Convolution convrelu_0 1 1 in0 1 0=64 1=7 11=7 12=1 13=2 14=3 2=1 3=2 4=3 5=1 6=9408 9=1 Pooling maxpool2d_125 1 1 1 2 0=0 1=3 11=3 12=2 13=1 2=2 3=1 5=1 Split splitncnn_0 1 2 2 3 4 Convolution convrelu_1 1 1 4 5 0=64 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=4096 9=1 Convolution convrelu_2 1 1 5 6 0=64 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=36864 9=1 Convolution conv_43 1 1 6 7 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=16384 Convolution conv_42 1 1 3 8 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=16384 BinaryOp add_0 2 1 7 8 9 0=0 ReLU relu_129 1 1 9 10 Split splitncnn_1 1 2 10 11 12 Convolution convrelu_3 1 1 12 13 0=64 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=16384 9=1 Convolution convrelu_4 1 1 13 14 0=64 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=36864 9=1 Convolution conv_46 1 1 14 15 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=16384 BinaryOp add_1 2 1 15 11 16 0=0 ReLU relu_132 1 1 16 17 Split splitncnn_2 1 2 17 18 19 Convolution convrelu_5 1 1 19 20 0=64 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=16384 9=1 Convolution convrelu_6 1 1 20 21 0=64 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=36864 9=1 Convolution conv_49 1 1 21 22 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=16384 BinaryOp add_2 2 1 22 18 23 0=0 ReLU relu_135 1 1 23 24 Split splitncnn_3 1 2 24 25 26 MemoryData backbone.layers.1.0.conv2 0 1 27 0=128 MemoryData pnnx_unique_6 0 1 28 0=3 1=3 11=128 2=128 Convolution convrelu_7 1 1 26 29 0=128 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=32768 9=1 Split splitncnn_4 1 2 29 30 31 Convolution conv_51 1 1 31 32 0=27 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=31104 Slice chunk_0 1 3 32 33 34 35 -23300=3,-233,-233,-233 1=0 Concat cat_0 2 1 33 34 36 0=0 Sigmoid sigmoid_5 1 1 35 37 DCNv2 pnnx_35 5 1 30 28 27 36 37 38 10=1 11=1 12=1 13=1 5=3 6=3 7=2 8=2 9=1 BatchNorm bn_26 1 1 38 39 0=128 1=1.000000e-05 ReLU relu_137 1 1 39 40 Convolution conv_53 1 1 40 41 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 Convolution conv_52 1 1 25 42 0=512 1=1 11=1 12=1 13=2 14=0 2=1 3=2 4=0 5=1 6=131072 BinaryOp add_3 2 1 41 42 43 0=0 ReLU relu_138 1 1 43 44 Split splitncnn_5 1 2 44 45 46 MemoryData backbone.layers.1.1.conv2 0 1 47 0=128 MemoryData pnnx_unique_9 0 1 48 0=3 1=3 11=128 2=128 Convolution convrelu_8 1 1 46 49 0=128 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 9=1 Split splitncnn_6 1 2 49 50 51 Convolution conv_55 1 1 51 52 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=31104 Slice chunk_1 1 3 52 53 54 55 -23300=3,-233,-233,-233 1=0 Concat cat_1 2 1 53 54 56 0=0 Sigmoid sigmoid_6 1 1 55 57 DCNv2 pnnx_55 5 1 50 48 47 56 57 58 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_27 1 1 58 59 0=128 1=1.000000e-05 ReLU relu_140 1 1 59 60 Convolution conv_56 1 1 60 61 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 BinaryOp add_4 2 1 61 45 62 0=0 ReLU relu_141 1 1 62 63 Split splitncnn_7 1 2 63 64 65 MemoryData backbone.layers.1.2.conv2 0 1 66 0=128 MemoryData pnnx_unique_12 0 1 67 0=3 1=3 11=128 2=128 Convolution convrelu_9 1 1 65 68 0=128 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 9=1 Split splitncnn_8 1 2 68 69 70 Convolution conv_58 1 1 70 71 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=31104 Slice chunk_2 1 3 71 72 73 74 -23300=3,-233,-233,-233 1=0 Concat cat_2 2 1 72 73 75 0=0 Sigmoid sigmoid_7 1 1 74 76 DCNv2 pnnx_75 5 1 69 67 66 75 76 77 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_28 1 1 77 78 0=128 1=1.000000e-05 ReLU relu_143 1 1 78 79 Convolution conv_59 1 1 79 80 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 BinaryOp add_5 2 1 80 64 81 0=0 ReLU relu_144 1 1 81 82 Split splitncnn_9 1 2 82 83 84 MemoryData backbone.layers.1.3.conv2 0 1 85 0=128 MemoryData pnnx_unique_15 0 1 86 0=3 1=3 11=128 2=128 Convolution convrelu_10 1 1 84 87 0=128 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 9=1 Split splitncnn_10 1 2 87 88 89 Convolution conv_61 1 1 89 90 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=31104 Slice chunk_3 1 3 90 91 92 93 -23300=3,-233,-233,-233 1=0 Concat cat_3 2 1 91 92 94 0=0 Sigmoid sigmoid_8 1 1 93 95 DCNv2 pnnx_95 5 1 88 86 85 94 95 96 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_29 1 1 96 97 0=128 1=1.000000e-05 ReLU relu_146 1 1 97 98 Convolution conv_62 1 1 98 99 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=65536 BinaryOp add_6 2 1 99 83 100 0=0 ReLU relu_147 1 1 100 101 Split splitncnn_11 1 3 101 102 103 104 MemoryData backbone.layers.2.0.conv2 0 1 105 0=256 MemoryData pnnx_unique_18 0 1 106 0=3 1=3 11=256 2=256 Convolution convrelu_11 1 1 104 107 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=131072 9=1 Split splitncnn_12 1 2 107 108 109 Convolution conv_64 1 1 109 110 0=27 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=62208 Slice chunk_4 1 3 110 111 112 113 -23300=3,-233,-233,-233 1=0 Concat cat_4 2 1 111 112 114 0=0 Sigmoid sigmoid_9 1 1 113 115 DCNv2 pnnx_115 5 1 108 106 105 114 115 116 10=1 11=1 12=1 13=1 5=3 6=3 7=2 8=2 9=1 BatchNorm bn_30 1 1 116 117 0=256 1=1.000000e-05 ReLU relu_149 1 1 117 118 Convolution conv_66 1 1 118 119 0=1024 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 Convolution conv_65 1 1 103 120 0=1024 1=1 11=1 12=1 13=2 14=0 2=1 3=2 4=0 5=1 6=524288 BinaryOp add_7 2 1 119 120 121 0=0 ReLU relu_150 1 1 121 122 Split splitncnn_13 1 2 122 123 124 MemoryData backbone.layers.2.1.conv2 0 1 125 0=256 MemoryData pnnx_unique_21 0 1 126 0=3 1=3 11=256 2=256 Convolution convrelu_12 1 1 124 127 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 9=1 Split splitncnn_14 1 2 127 128 129 Convolution conv_68 1 1 129 130 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=62208 Slice chunk_5 1 3 130 131 132 133 -23300=3,-233,-233,-233 1=0 Concat cat_5 2 1 131 132 134 0=0 Sigmoid sigmoid_10 1 1 133 135 DCNv2 pnnx_135 5 1 128 126 125 134 135 136 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_31 1 1 136 137 0=256 1=1.000000e-05 ReLU relu_152 1 1 137 138 Convolution conv_69 1 1 138 139 0=1024 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 BinaryOp add_8 2 1 139 123 140 0=0 ReLU relu_153 1 1 140 141 Split splitncnn_15 1 2 141 142 143 MemoryData backbone.layers.2.2.conv2 0 1 144 0=256 MemoryData pnnx_unique_24 0 1 145 0=3 1=3 11=256 2=256 Convolution convrelu_13 1 1 143 146 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 9=1 Split splitncnn_16 1 2 146 147 148 Convolution conv_71 1 1 148 149 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=62208 Slice chunk_6 1 3 149 150 151 152 -23300=3,-233,-233,-233 1=0 Concat cat_6 2 1 150 151 153 0=0 Sigmoid sigmoid_11 1 1 152 154 DCNv2 pnnx_155 5 1 147 145 144 153 154 155 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_32 1 1 155 156 0=256 1=1.000000e-05 ReLU relu_155 1 1 156 157 Convolution conv_72 1 1 157 158 0=1024 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 BinaryOp add_9 2 1 158 142 159 0=0 ReLU relu_156 1 1 159 160 Split splitncnn_17 1 2 160 161 162 MemoryData backbone.layers.2.3.conv2 0 1 163 0=256 MemoryData pnnx_unique_27 0 1 164 0=3 1=3 11=256 2=256 Convolution convrelu_14 1 1 162 165 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 9=1 Split splitncnn_18 1 2 165 166 167 Convolution conv_74 1 1 167 168 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=62208 Slice chunk_7 1 3 168 169 170 171 -23300=3,-233,-233,-233 1=0 Concat cat_7 2 1 169 170 172 0=0 Sigmoid sigmoid_12 1 1 171 173 DCNv2 pnnx_175 5 1 166 164 163 172 173 174 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_33 1 1 174 175 0=256 1=1.000000e-05 ReLU relu_158 1 1 175 176 Convolution conv_75 1 1 176 177 0=1024 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 BinaryOp add_10 2 1 177 161 178 0=0 ReLU relu_159 1 1 178 179 Split splitncnn_19 1 2 179 180 181 MemoryData backbone.layers.2.4.conv2 0 1 182 0=256 MemoryData pnnx_unique_30 0 1 183 0=3 1=3 11=256 2=256 Convolution convrelu_15 1 1 181 184 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 9=1 Split splitncnn_20 1 2 184 185 186 Convolution conv_77 1 1 186 187 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=62208 Slice chunk_8 1 3 187 188 189 190 -23300=3,-233,-233,-233 1=0 Concat cat_8 2 1 188 189 191 0=0 Sigmoid sigmoid_13 1 1 190 192 DCNv2 pnnx_195 5 1 185 183 182 191 192 193 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_34 1 1 193 194 0=256 1=1.000000e-05 ReLU relu_161 1 1 194 195 Convolution conv_78 1 1 195 196 0=1024 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 BinaryOp add_11 2 1 196 180 197 0=0 ReLU relu_162 1 1 197 198 Split splitncnn_21 1 2 198 199 200 MemoryData backbone.layers.2.5.conv2 0 1 201 0=256 MemoryData pnnx_unique_33 0 1 202 0=3 1=3 11=256 2=256 Convolution convrelu_16 1 1 200 203 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 9=1 Split splitncnn_22 1 2 203 204 205 Convolution conv_80 1 1 205 206 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=62208 Slice chunk_9 1 3 206 207 208 209 -23300=3,-233,-233,-233 1=0 Concat cat_9 2 1 207 208 210 0=0 Sigmoid sigmoid_14 1 1 209 211 DCNv2 pnnx_215 5 1 204 202 201 210 211 212 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_35 1 1 212 213 0=256 1=1.000000e-05 ReLU relu_164 1 1 213 214 Convolution conv_81 1 1 214 215 0=1024 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 BinaryOp add_12 2 1 215 199 216 0=0 ReLU relu_165 1 1 216 217 Split splitncnn_23 1 3 217 218 219 220 MemoryData backbone.layers.3.0.conv2 0 1 221 0=512 MemoryData pnnx_unique_36 0 1 222 0=3 1=3 11=512 2=512 Convolution convrelu_17 1 1 220 223 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=524288 9=1 Split splitncnn_24 1 2 223 224 225 Convolution conv_83 1 1 225 226 0=27 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=124416 Slice chunk_10 1 3 226 227 228 229 -23300=3,-233,-233,-233 1=0 Concat cat_10 2 1 227 228 230 0=0 Sigmoid sigmoid_15 1 1 229 231 DCNv2 pnnx_235 5 1 224 222 221 230 231 232 10=1 11=1 12=1 13=1 5=3 6=3 7=2 8=2 9=1 BatchNorm bn_36 1 1 232 233 0=512 1=1.000000e-05 ReLU relu_167 1 1 233 234 Convolution conv_85 1 1 234 235 0=2048 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=1048576 Convolution conv_84 1 1 219 236 0=2048 1=1 11=1 12=1 13=2 14=0 2=1 3=2 4=0 5=1 6=2097152 BinaryOp add_13 2 1 235 236 237 0=0 ReLU relu_168 1 1 237 238 Split splitncnn_25 1 2 238 239 240 MemoryData backbone.layers.3.1.conv2 0 1 241 0=512 MemoryData pnnx_unique_39 0 1 242 0=3 1=3 11=512 2=512 Convolution convrelu_18 1 1 240 243 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=1048576 9=1 Split splitncnn_26 1 2 243 244 245 Convolution conv_87 1 1 245 246 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=124416 Slice chunk_11 1 3 246 247 248 249 -23300=3,-233,-233,-233 1=0 Concat cat_11 2 1 247 248 250 0=0 Sigmoid sigmoid_16 1 1 249 251 DCNv2 pnnx_255 5 1 244 242 241 250 251 252 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_37 1 1 252 253 0=512 1=1.000000e-05 ReLU relu_170 1 1 253 254 Convolution conv_88 1 1 254 255 0=2048 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=1048576 BinaryOp add_14 2 1 255 239 256 0=0 ReLU relu_171 1 1 256 257 Split splitncnn_27 1 2 257 258 259 MemoryData backbone.layers.3.2.conv2 0 1 260 0=512 MemoryData pnnx_unique_42 0 1 261 0=3 1=3 11=512 2=512 Convolution convrelu_19 1 1 259 262 0=512 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=1048576 9=1 Split splitncnn_28 1 2 262 263 264 Convolution conv_90 1 1 264 265 0=27 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=124416 Slice chunk_12 1 3 265 266 267 268 -23300=3,-233,-233,-233 1=0 Concat cat_12 2 1 266 267 269 0=0 Sigmoid sigmoid_17 1 1 268 270 DCNv2 pnnx_275 5 1 263 261 260 269 270 271 10=1 11=1 12=1 13=1 5=3 6=3 7=1 8=1 9=1 BatchNorm bn_38 1 1 271 272 0=512 1=1.000000e-05 ReLU relu_173 1 1 272 273 Convolution conv_91 1 1 273 274 0=2048 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=1048576 BinaryOp add_15 2 1 274 258 275 0=0 ReLU relu_174 1 1 275 276 Convolution conv_92 1 1 276 277 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=524288 Split splitncnn_29 1 2 277 278 279 Interp upsample_24 1 1 278 280 0=2 3=35 4=35 6=0 Convolution conv_93 1 1 218 281 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=262144 BinaryOp add_16 2 1 280 281 282 0=0 Split splitncnn_30 1 2 282 283 284 Interp upsample_25 1 1 283 285 0=2 3=69 4=69 6=0 Convolution conv_94 1 1 102 286 0=256 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=131072 BinaryOp add_17 2 1 285 286 287 0=0 Convolution convrelu_20 1 1 279 288 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_31 1 2 288 289 290 Convolution convrelu_21 1 1 287 291 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_32 1 2 291 292 293 Convolution convrelu_22 1 1 293 294 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Convolution convrelu_23 1 1 294 295 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Convolution convrelu_24 1 1 295 296 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Interp interpolate_0 1 1 296 297 0=2 1=2.000000e+00 2=2.000000e+00 6=0 ReLU relu_178 1 1 297 298 Convolution convrelu_25 1 1 292 299 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_33 1 3 299 300 301 302 Convolution conv_106 1 1 302 303 0=36 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=82944 Convolution conv_107 1 1 301 304 0=729 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=1679616 Convolution conv_108 1 1 300 305 0=288 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=663552 Convolution convrelu_26 1 1 284 306 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Convolution convrelu_27 1 1 306 307 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_34 1 3 307 308 309 310 Convolution conv_110 1 1 310 311 0=36 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=82944 Convolution conv_111 1 1 309 312 0=729 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=1679616 Convolution conv_112 1 1 308 313 0=288 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=663552 Convolution convrelu_28 1 1 289 314 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_35 1 3 314 315 316 317 Convolution conv_114 1 1 317 318 0=36 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=82944 Convolution conv_115 1 1 316 319 0=729 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=1679616 Convolution conv_116 1 1 315 320 0=288 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=663552 Convolution conv_98 1 1 290 321 0=256 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=589824 Split splitncnn_36 1 2 321 322 323 Convolution convrelu_29 1 1 322 324 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_37 1 3 324 325 326 327 Convolution conv_118 1 1 327 328 0=36 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=82944 Convolution conv_119 1 1 326 329 0=729 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=1679616 Convolution conv_120 1 1 325 330 0=288 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=663552 Convolution conv_99 1 1 323 331 0=256 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=589824 Convolution convrelu_30 1 1 331 332 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Split splitncnn_38 1 3 332 333 334 335 Convolution conv_122 1 1 335 336 0=36 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=82944 Convolution conv_123 1 1 334 337 0=729 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=1679616 Convolution conv_124 1 1 333 338 0=288 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=663552 Permute permute_202 1 1 304 339 0=3 Reshape reshape_195 1 1 339 340 0=81 1=-1 Permute permute_205 1 1 312 341 0=3 Reshape reshape_194 1 1 341 342 0=81 1=-1 Permute permute_208 1 1 319 343 0=3 Reshape reshape_193 1 1 343 344 0=81 1=-1 Permute permute_211 1 1 329 345 0=3 Reshape reshape_192 1 1 345 346 0=81 1=-1 Permute permute_214 1 1 337 347 0=3 Reshape reshape_191 1 1 347 348 0=81 1=-1 Concat cat_13 5 1 340 342 344 346 348 349 0=0 Permute permute_215 1 1 338 350 0=3 Reshape reshape_190 1 1 350 351 0=32 1=-1 TanH tanh_23 1 1 351 352 Permute permute_212 1 1 330 353 0=3 Reshape reshape_189 1 1 353 354 0=32 1=-1 TanH tanh_22 1 1 354 355 Permute permute_209 1 1 320 356 0=3 Reshape reshape_188 1 1 356 357 0=32 1=-1 TanH tanh_21 1 1 357 358 Permute permute_206 1 1 313 359 0=3 Reshape reshape_187 1 1 359 360 0=32 1=-1 TanH tanh_20 1 1 360 361 Permute permute_203 1 1 305 362 0=3 Reshape reshape_186 1 1 362 363 0=32 1=-1 TanH tanh_19 1 1 363 364 Concat cat_14 5 1 364 361 358 355 352 out2 0=0 Permute permute_201 1 1 303 366 0=3 Reshape reshape_200 1 1 366 367 0=4 1=-1 Permute permute_204 1 1 311 368 0=3 Reshape reshape_199 1 1 368 369 0=4 1=-1 Permute permute_207 1 1 318 370 0=3 Reshape reshape_198 1 1 370 371 0=4 1=-1 Permute permute_210 1 1 328 372 0=3 Reshape reshape_197 1 1 372 373 0=4 1=-1 Permute permute_213 1 1 336 374 0=3 Reshape reshape_196 1 1 374 375 0=4 1=-1 Concat cat_15 5 1 367 369 371 373 375 out0 0=0 Softmax softmax_18 1 1 349 out1 0=1 1=1 Convolution convrelu_31 1 1 298 378 0=256 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=589824 9=1 Convolution convrelu_32 1 1 378 out3 0=32 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=8192 9=1
detail | 详细描述 | 詳細な説明
平台:VS2019 win10 X64 Intel显卡 yolact++模型 推理部分代码 `static int detect_yolact(const cv::Mat& bgr, std::vector<Object>& objects) { ncnn::Net yolact; printf("detect_yolact\n"); yolact.opt.use_vulkan_compute = true; // line2 if (yolact.load_param("yolactplus100.ncnn.param")) exit(-1); if (yolact.load_model("yolactplus100.ncnn.bin")) exit(-1);
const int target_size = 550;
int img_w = bgr.cols;
int img_h = bgr.rows;
ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, target_size, target_size);
const float mean_vals[3] = {123.68f, 116.78f, 103.94f};
const float norm_vals[3] = {1.0 / 58.40f, 1.0 / 57.12f, 1.0 / 57.38f};
in.substract_mean_normalize(mean_vals, norm_vals);
ncnn::Extractor ex = yolact.create_extractor();
ex.set_num_threads(1);
ex.input("in0", in);
ncnn::Mat maskmaps;
ncnn::Mat location;
ncnn::Mat mask;
ncnn::Mat confidence;
ex.extract("27", confidence); // 81 x 19248
pretty_print(confidence);}`
其中ex.extract("27", confidence); 27是MemoryData输出的数据，通过打印输出，拿到的数据全为 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 而上一层的24的数据打印输出看都是正常，如果use_vulkan_compute=false，则MemoryData输出的数据正常，请问会是什么原因造成
Same issue here. I'm using https://github.com/Tencent/ncnn/releases/download/20220420/ncnn-20220420-windows-vs2019-shared.zip. But my project using two model, when enable vulkan one works well, one get out with full zeros.
