| import paddle |
| import math |
| from x2paddle.op_mapper.pytorch2paddle import pytorch_custom_layer as x2paddle_nn |
|
|
| class DetectionModel(paddle.nn.Layer): |
| def __init__(self): |
| super(DetectionModel, self).__init__() |
| self.conv2d0 = paddle.nn.Conv2D(stride=2, padding=2, out_channels=32, kernel_size=(6, 6), in_channels=3) |
| self.silu0 = paddle.nn.Silu() |
| self.conv2d1 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=64, kernel_size=(3, 3), in_channels=32) |
| self.silu1 = paddle.nn.Silu() |
| self.conv2d2 = paddle.nn.Conv2D(out_channels=32, kernel_size=(1, 1), in_channels=64) |
| self.silu2 = paddle.nn.Silu() |
| self.conv2d3 = paddle.nn.Conv2D(out_channels=32, kernel_size=(1, 1), in_channels=32) |
| self.silu3 = paddle.nn.Silu() |
| self.conv2d4 = paddle.nn.Conv2D(padding=1, out_channels=32, kernel_size=(3, 3), in_channels=32) |
| self.silu4 = paddle.nn.Silu() |
| self.conv2d5 = paddle.nn.Conv2D(out_channels=32, kernel_size=(1, 1), in_channels=64) |
| self.silu5 = paddle.nn.Silu() |
| self.conv2d6 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.silu6 = paddle.nn.Silu() |
| self.conv2d7 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=128, kernel_size=(3, 3), in_channels=64) |
| self.silu7 = paddle.nn.Silu() |
| self.conv2d8 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=128) |
| self.silu8 = paddle.nn.Silu() |
| self.conv2d9 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.silu9 = paddle.nn.Silu() |
| self.conv2d10 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64) |
| self.silu10 = paddle.nn.Silu() |
| self.conv2d11 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.silu11 = paddle.nn.Silu() |
| self.conv2d12 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64) |
| self.silu12 = paddle.nn.Silu() |
| self.conv2d13 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=128) |
| self.silu13 = paddle.nn.Silu() |
| self.conv2d14 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu14 = paddle.nn.Silu() |
| self.conv2d15 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=256, kernel_size=(3, 3), in_channels=128) |
| self.silu15 = paddle.nn.Silu() |
| self.conv2d16 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256) |
| self.silu16 = paddle.nn.Silu() |
| self.conv2d17 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu17 = paddle.nn.Silu() |
| self.conv2d18 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu18 = paddle.nn.Silu() |
| self.conv2d19 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu19 = paddle.nn.Silu() |
| self.conv2d20 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu20 = paddle.nn.Silu() |
| self.conv2d21 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu21 = paddle.nn.Silu() |
| self.conv2d22 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu22 = paddle.nn.Silu() |
| self.conv2d23 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256) |
| self.silu23 = paddle.nn.Silu() |
| self.conv2d24 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256) |
| self.silu24 = paddle.nn.Silu() |
| self.conv2d25 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=512, kernel_size=(3, 3), in_channels=256) |
| self.silu25 = paddle.nn.Silu() |
| self.conv2d26 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512) |
| self.silu26 = paddle.nn.Silu() |
| self.conv2d27 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256) |
| self.silu27 = paddle.nn.Silu() |
| self.conv2d28 = paddle.nn.Conv2D(padding=1, out_channels=256, kernel_size=(3, 3), in_channels=256) |
| self.silu28 = paddle.nn.Silu() |
| self.conv2d29 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512) |
| self.silu29 = paddle.nn.Silu() |
| self.conv2d30 = paddle.nn.Conv2D(out_channels=512, kernel_size=(1, 1), in_channels=512) |
| self.silu30 = paddle.nn.Silu() |
| self.conv2d31 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512) |
| self.silu31 = paddle.nn.Silu() |
| self.pool2d0 = paddle.nn.MaxPool2D(kernel_size=[5, 5], stride=1, padding=2) |
| self.pool2d1 = paddle.nn.MaxPool2D(kernel_size=[5, 5], stride=1, padding=2) |
| self.pool2d2 = paddle.nn.MaxPool2D(kernel_size=[5, 5], stride=1, padding=2) |
| self.conv2d32 = paddle.nn.Conv2D(out_channels=512, kernel_size=(1, 1), in_channels=1024) |
| self.silu32 = paddle.nn.Silu() |
| self.conv2d33 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512) |
| self.silu33 = paddle.nn.Silu() |
| self.conv2d34 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=512) |
| self.silu34 = paddle.nn.Silu() |
| self.conv2d35 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu35 = paddle.nn.Silu() |
| self.conv2d36 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu36 = paddle.nn.Silu() |
| self.conv2d37 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=512) |
| self.silu37 = paddle.nn.Silu() |
| self.conv2d38 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256) |
| self.silu38 = paddle.nn.Silu() |
| self.conv2d39 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256) |
| self.silu39 = paddle.nn.Silu() |
| self.conv2d40 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=256) |
| self.silu40 = paddle.nn.Silu() |
| self.conv2d41 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.silu41 = paddle.nn.Silu() |
| self.conv2d42 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64) |
| self.silu42 = paddle.nn.Silu() |
| self.conv2d43 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=256) |
| self.silu43 = paddle.nn.Silu() |
| self.conv2d44 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu44 = paddle.nn.Silu() |
| self.conv2d45 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu45 = paddle.nn.Silu() |
| self.conv2d46 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256) |
| self.silu46 = paddle.nn.Silu() |
| self.conv2d47 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=128) |
| self.silu47 = paddle.nn.Silu() |
| self.conv2d48 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu48 = paddle.nn.Silu() |
| self.conv2d49 = paddle.nn.Conv2D(out_channels=128, kernel_size=(1, 1), in_channels=256) |
| self.silu49 = paddle.nn.Silu() |
| self.conv2d50 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256) |
| self.silu50 = paddle.nn.Silu() |
| self.conv2d51 = paddle.nn.Conv2D(stride=2, padding=1, out_channels=256, kernel_size=(3, 3), in_channels=256) |
| self.silu51 = paddle.nn.Silu() |
| self.conv2d52 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512) |
| self.silu52 = paddle.nn.Silu() |
| self.conv2d53 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=256) |
| self.silu53 = paddle.nn.Silu() |
| self.conv2d54 = paddle.nn.Conv2D(padding=1, out_channels=256, kernel_size=(3, 3), in_channels=256) |
| self.silu54 = paddle.nn.Silu() |
| self.conv2d55 = paddle.nn.Conv2D(out_channels=256, kernel_size=(1, 1), in_channels=512) |
| self.silu55 = paddle.nn.Silu() |
| self.conv2d56 = paddle.nn.Conv2D(out_channels=512, kernel_size=(1, 1), in_channels=512) |
| self.silu56 = paddle.nn.Silu() |
| self.x731 = self.create_parameter(dtype='float32', shape=(1, 8400), default_initializer=paddle.nn.initializer.Constant(value=0.0)) |
| self.conv2d57 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=128) |
| self.silu57 = paddle.nn.Silu() |
| self.conv2d58 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64) |
| self.silu58 = paddle.nn.Silu() |
| self.conv2d59 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.conv2d60 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=256) |
| self.silu59 = paddle.nn.Silu() |
| self.conv2d61 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64) |
| self.silu60 = paddle.nn.Silu() |
| self.conv2d62 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.conv2d63 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=512) |
| self.silu61 = paddle.nn.Silu() |
| self.conv2d64 = paddle.nn.Conv2D(padding=1, out_channels=64, kernel_size=(3, 3), in_channels=64) |
| self.silu62 = paddle.nn.Silu() |
| self.conv2d65 = paddle.nn.Conv2D(out_channels=64, kernel_size=(1, 1), in_channels=64) |
| self.conv2d66 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu63 = paddle.nn.Silu() |
| self.conv2d67 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu64 = paddle.nn.Silu() |
| self.conv2d68 = paddle.nn.Conv2D(out_channels=80, kernel_size=(1, 1), in_channels=128) |
| self.conv2d69 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=256) |
| self.silu65 = paddle.nn.Silu() |
| self.conv2d70 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu66 = paddle.nn.Silu() |
| self.conv2d71 = paddle.nn.Conv2D(out_channels=80, kernel_size=(1, 1), in_channels=128) |
| self.conv2d72 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=512) |
| self.silu67 = paddle.nn.Silu() |
| self.conv2d73 = paddle.nn.Conv2D(padding=1, out_channels=128, kernel_size=(3, 3), in_channels=128) |
| self.silu68 = paddle.nn.Silu() |
| self.conv2d74 = paddle.nn.Conv2D(out_channels=80, kernel_size=(1, 1), in_channels=128) |
| self.softmax0 = paddle.nn.Softmax(axis=1) |
| self.conv2d75 = paddle.nn.Conv2D(out_channels=1, kernel_size=(1, 1), bias_attr=False, in_channels=16) |
| self.x949 = self.create_parameter(dtype='float32', shape=(1, 2, 8400), default_initializer=paddle.nn.initializer.Constant(value=0.0)) |
| self.sigmoid0 = paddle.nn.Sigmoid() |
|
|
| def forward(self, x0): |
| x49 = self.conv2d0(x0) |
| x50 = self.silu0(x49) |
| x61 = self.conv2d1(x50) |
| x62 = self.silu1(x61) |
| x77 = self.conv2d2(x62) |
| x78 = self.silu2(x77) |
| x89 = self.conv2d3(x78) |
| x90 = self.silu3(x89) |
| x98 = self.conv2d4(x90) |
| x99 = self.silu4(x98) |
| x100 = x78 + x99 |
| x108 = self.conv2d5(x62) |
| x109 = self.silu5(x108) |
| x110 = [x100, x109] |
| x111 = paddle.concat(x=x110, axis=1) |
| x119 = self.conv2d6(x111) |
| x120 = self.silu6(x119) |
| x131 = self.conv2d7(x120) |
| x132 = self.silu7(x131) |
| x147 = self.conv2d8(x132) |
| x148 = self.silu8(x147) |
| x160 = self.conv2d9(x148) |
| x161 = self.silu9(x160) |
| x169 = self.conv2d10(x161) |
| x170 = self.silu10(x169) |
| x171 = x148 + x170 |
| x181 = self.conv2d11(x171) |
| x182 = self.silu11(x181) |
| x190 = self.conv2d12(x182) |
| x191 = self.silu12(x190) |
| x192 = x171 + x191 |
| x200 = self.conv2d13(x132) |
| x201 = self.silu13(x200) |
| x202 = [x192, x201] |
| x203 = paddle.concat(x=x202, axis=1) |
| x211 = self.conv2d14(x203) |
| x212 = self.silu14(x211) |
| x223 = self.conv2d15(x212) |
| x224 = self.silu15(x223) |
| x239 = self.conv2d16(x224) |
| x240 = self.silu16(x239) |
| x253 = self.conv2d17(x240) |
| x254 = self.silu17(x253) |
| x262 = self.conv2d18(x254) |
| x263 = self.silu18(x262) |
| x264 = x240 + x263 |
| x274 = self.conv2d19(x264) |
| x275 = self.silu19(x274) |
| x283 = self.conv2d20(x275) |
| x284 = self.silu20(x283) |
| x285 = x264 + x284 |
| x295 = self.conv2d21(x285) |
| x296 = self.silu21(x295) |
| x304 = self.conv2d22(x296) |
| x305 = self.silu22(x304) |
| x306 = x285 + x305 |
| x314 = self.conv2d23(x224) |
| x315 = self.silu23(x314) |
| x316 = [x306, x315] |
| x317 = paddle.concat(x=x316, axis=1) |
| x325 = self.conv2d24(x317) |
| x326 = self.silu24(x325) |
| x337 = self.conv2d25(x326) |
| x338 = self.silu25(x337) |
| x353 = self.conv2d26(x338) |
| x354 = self.silu26(x353) |
| x365 = self.conv2d27(x354) |
| x366 = self.silu27(x365) |
| x374 = self.conv2d28(x366) |
| x375 = self.silu28(x374) |
| x376 = x354 + x375 |
| x384 = self.conv2d29(x338) |
| x385 = self.silu29(x384) |
| x386 = [x376, x385] |
| x387 = paddle.concat(x=x386, axis=1) |
| x395 = self.conv2d30(x387) |
| x396 = self.silu30(x395) |
| x409 = self.conv2d31(x396) |
| x410 = self.silu31(x409) |
| x415 = self.pool2d0(x410) |
| x420 = self.pool2d1(x415) |
| x425 = self.pool2d2(x420) |
| x426 = [x410, x415, x420, x425] |
| x427 = paddle.concat(x=x426, axis=1) |
| x435 = self.conv2d32(x427) |
| x436 = self.silu32(x435) |
| x447 = self.conv2d33(x436) |
| x448 = self.silu33(x447) |
| x450 = [2.0, 2.0] |
| x451 = paddle.nn.functional.interpolate(x=x448, scale_factor=x450, mode='nearest') |
| x453 = [x451, x326] |
| x454 = paddle.concat(x=x453, axis=1) |
| x469 = self.conv2d34(x454) |
| x470 = self.silu34(x469) |
| x481 = self.conv2d35(x470) |
| x482 = self.silu35(x481) |
| x490 = self.conv2d36(x482) |
| x491 = self.silu36(x490) |
| x499 = self.conv2d37(x454) |
| x500 = self.silu37(x499) |
| x501 = [x491, x500] |
| x502 = paddle.concat(x=x501, axis=1) |
| x510 = self.conv2d38(x502) |
| x511 = self.silu38(x510) |
| x522 = self.conv2d39(x511) |
| x523 = self.silu39(x522) |
| x525 = [2.0, 2.0] |
| x526 = paddle.nn.functional.interpolate(x=x523, scale_factor=x525, mode='nearest') |
| x528 = [x526, x212] |
| x529 = paddle.concat(x=x528, axis=1) |
| x544 = self.conv2d40(x529) |
| x545 = self.silu40(x544) |
| x556 = self.conv2d41(x545) |
| x557 = self.silu41(x556) |
| x565 = self.conv2d42(x557) |
| x566 = self.silu42(x565) |
| x574 = self.conv2d43(x529) |
| x575 = self.silu43(x574) |
| x576 = [x566, x575] |
| x577 = paddle.concat(x=x576, axis=1) |
| x585 = self.conv2d44(x577) |
| x586 = self.silu44(x585) |
| x597 = self.conv2d45(x586) |
| x598 = self.silu45(x597) |
| x600 = [x598, x523] |
| x601 = paddle.concat(x=x600, axis=1) |
| x616 = self.conv2d46(x601) |
| x617 = self.silu46(x616) |
| x628 = self.conv2d47(x617) |
| x629 = self.silu47(x628) |
| x637 = self.conv2d48(x629) |
| x638 = self.silu48(x637) |
| x646 = self.conv2d49(x601) |
| x647 = self.silu49(x646) |
| x648 = [x638, x647] |
| x649 = paddle.concat(x=x648, axis=1) |
| x657 = self.conv2d50(x649) |
| x658 = self.silu50(x657) |
| x669 = self.conv2d51(x658) |
| x670 = self.silu51(x669) |
| x672 = [x670, x448] |
| x673 = paddle.concat(x=x672, axis=1) |
| x688 = self.conv2d52(x673) |
| x689 = self.silu52(x688) |
| x700 = self.conv2d53(x689) |
| x701 = self.silu53(x700) |
| x709 = self.conv2d54(x701) |
| x710 = self.silu54(x709) |
| x718 = self.conv2d55(x673) |
| x719 = self.silu55(x718) |
| x720 = [x710, x719] |
| x721 = paddle.concat(x=x720, axis=1) |
| x729 = self.conv2d56(x721) |
| x730 = self.silu56(x729) |
| x731 = self.x731 |
| x732 = 2 |
| x733 = 2 |
| x736 = 1 |
| x762 = self.conv2d57(x586) |
| x763 = self.silu57(x762) |
| x771 = self.conv2d58(x763) |
| x772 = self.silu58(x771) |
| x779 = self.conv2d59(x772) |
| x781 = paddle.reshape(x=x779, shape=[1, 64, -1]) |
| x792 = self.conv2d60(x658) |
| x793 = self.silu59(x792) |
| x801 = self.conv2d61(x793) |
| x802 = self.silu60(x801) |
| x809 = self.conv2d62(x802) |
| x811 = paddle.reshape(x=x809, shape=[1, 64, -1]) |
| x822 = self.conv2d63(x730) |
| x823 = self.silu61(x822) |
| x831 = self.conv2d64(x823) |
| x832 = self.silu62(x831) |
| x839 = self.conv2d65(x832) |
| x841 = paddle.reshape(x=x839, shape=[1, 64, -1]) |
| x842 = [x781, x811, x841] |
| x843 = paddle.concat(x=x842, axis=-1) |
| x854 = self.conv2d66(x586) |
| x855 = self.silu63(x854) |
| x863 = self.conv2d67(x855) |
| x864 = self.silu64(x863) |
| x871 = self.conv2d68(x864) |
| x873 = paddle.reshape(x=x871, shape=[1, 80, -1]) |
| x884 = self.conv2d69(x658) |
| x885 = self.silu65(x884) |
| x893 = self.conv2d70(x885) |
| x894 = self.silu66(x893) |
| x901 = self.conv2d71(x894) |
| x903 = paddle.reshape(x=x901, shape=[1, 80, -1]) |
| x914 = self.conv2d72(x730) |
| x915 = self.silu67(x914) |
| x923 = self.conv2d73(x915) |
| x924 = self.silu68(x923) |
| x931 = self.conv2d74(x924) |
| x933 = paddle.reshape(x=x931, shape=[1, 80, -1]) |
| x934 = [x873, x903, x933] |
| x935 = paddle.concat(x=x934, axis=-1) |
| x938 = paddle.reshape(x=x843, shape=[1, 4, 16, 8400]) |
| x939_shape = x938.shape |
| x939_len = len(x939_shape) |
| x939_list = [] |
| for i in range(x939_len): |
| x939_list.append(i) |
| if x733 < 0: |
| x733_new = x733 + x939_len |
| else: |
| x733_new = x733 |
| if x736 < 0: |
| x736_new = x736 + x939_len |
| else: |
| x736_new = x736 |
| x939_list[x733_new] = x736_new |
| x939_list[x736_new] = x733_new |
| x939 = paddle.transpose(x=x938, perm=x939_list) |
| x940 = self.softmax0(x939) |
| x946 = self.conv2d75(x940) |
| x948 = paddle.reshape(x=x946, shape=[1, 4, 8400]) |
| x949 = self.x949 |
| x950 = paddle.split(x=x948, num_or_sections=2, axis=1) |
| x951, x952 = x950 |
| x953 = x949 - x951 |
| x954 = x949 + x952 |
| x955 = x953 + x954 |
| x956 = x955 / x732 |
| x957 = x954 - x953 |
| x958 = [x956, x957] |
| x959 = paddle.concat(x=x958, axis=1) |
| x960 = x959 * x731 |
| x961 = self.sigmoid0(x935) |
| x962 = [x960, x961] |
| x963 = paddle.concat(x=x962, axis=1) |
| return x963 |
|
|
| def main(x0): |
| |
| |
| paddle.disable_static() |
| params = paddle.load(r'/work/models/yolo/yolov5su_paddle_model/model.pdparams') |
| model = DetectionModel() |
| model.set_dict(params, use_structured_name=True) |
| model.eval() |
| out = model(x0) |
| return out |
|
|
