| | |
| | |
| | |
| | |
| | import torch.nn as nn |
| | import qat_core.layers as layers |
| |
|
| | class mnv2_SSDlite(nn.Module): |
| | def __init__(self, in_channels=3, n_classes=1): |
| | super(mnv2_SSDlite, self).__init__() |
| |
|
| | self.conv1 = layers.conv(C_in_channels=in_channels, D_out_channels=32, K_kernel_dimension=3, stride=2, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.epw_conv2 = layers.conv(C_in_channels=32, D_out_channels=32, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv2 = layers.conv(C_in_channels=32, D_out_channels=32, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=32, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv2 = layers.conv(C_in_channels=32, D_out_channels=16, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.epw_conv3 = layers.conv(C_in_channels=16, D_out_channels=96, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv3 = layers.conv(C_in_channels=96, D_out_channels=96, K_kernel_dimension=3, stride=2, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=96, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv3 = layers.conv(C_in_channels=96, D_out_channels=24, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv4 = layers.conv(C_in_channels=24, D_out_channels=144, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv4 = layers.conv(C_in_channels=144, D_out_channels=144, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=144, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv4 = layers.conv(C_in_channels=144, D_out_channels=24, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.epw_conv5 = layers.conv(C_in_channels=24, D_out_channels=144, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv5 = layers.conv(C_in_channels=144, D_out_channels=144, K_kernel_dimension=3, stride=2, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=144, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv5 = layers.conv(C_in_channels=144, D_out_channels=32, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv6 = layers.conv(C_in_channels=32, D_out_channels=192, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv6 = layers.conv(C_in_channels=192, D_out_channels=192, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=192, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv6 = layers.conv(C_in_channels=192, D_out_channels=32, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv7 = layers.conv(C_in_channels=32, D_out_channels=192, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv7 = layers.conv(C_in_channels=192, D_out_channels=192, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=192, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv7 = layers.conv(C_in_channels=192, D_out_channels=32, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.epw_conv8 = layers.conv(C_in_channels=32, D_out_channels=192, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv8 = layers.conv(C_in_channels=192, D_out_channels=192, K_kernel_dimension=3, stride=2, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=192, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv8 = layers.conv(C_in_channels=192, D_out_channels=64, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv9 = layers.conv(C_in_channels=64, D_out_channels=384, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv9 = layers.conv(C_in_channels=384, D_out_channels=384, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=384, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv9 = layers.conv(C_in_channels=384, D_out_channels=64, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv10 = layers.conv(C_in_channels=64, D_out_channels=384, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv10 = layers.conv(C_in_channels=384, D_out_channels=384, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=384, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv10 = layers.conv(C_in_channels=384, D_out_channels=64, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv11 = layers.conv(C_in_channels=64, D_out_channels=384, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv11 = layers.conv(C_in_channels=384, D_out_channels=384, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=384, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv11 = layers.conv(C_in_channels=384, D_out_channels=64, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | |
| | self.epw_conv12 = layers.conv(C_in_channels=64, D_out_channels=384, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv12 = layers.conv(C_in_channels=384, D_out_channels=384, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=384, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv12 = layers.conv(C_in_channels=384, D_out_channels=96, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv13 = layers.conv(C_in_channels=96, D_out_channels=576, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv13 = layers.conv(C_in_channels=576, D_out_channels=576, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=576, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv13 = layers.conv(C_in_channels=576, D_out_channels=96, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv14 = layers.conv(C_in_channels=96, D_out_channels=576, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv14 = layers.conv(C_in_channels=576, D_out_channels=576, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=576, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv14 = layers.conv(C_in_channels=576, D_out_channels=96, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.epw_conv15 = layers.conv(C_in_channels=96, D_out_channels=576, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv15 = layers.conv(C_in_channels=576, D_out_channels=576, K_kernel_dimension=3, stride=2, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=576, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv15 = layers.conv(C_in_channels=576, D_out_channels=160, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv16 = layers.conv(C_in_channels=160, D_out_channels=960, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv16 = layers.conv(C_in_channels=960, D_out_channels=960, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=960, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv16 = layers.conv(C_in_channels=960, D_out_channels=160, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| | self.epw_conv17 = layers.conv(C_in_channels=160, D_out_channels=960, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv17 = layers.conv(C_in_channels=960, D_out_channels=960, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=960, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv17 = layers.conv(C_in_channels=960, D_out_channels=160, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.epw_conv18 = layers.conv(C_in_channels=160, D_out_channels=960, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', quantization_mode = 'fpt_unconstrained') |
| | self.dw_conv18 = layers.conv(C_in_channels=960, D_out_channels=960, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, bias=False, activation='relu', num_groups=960, quantization_mode = 'fpt_unconstrained') |
| | self.ppw_conv18 = layers.conv(C_in_channels=960, D_out_channels=320, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), batchnorm=True, batchnorm_affine=True, bias=False, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.head1_dw_classification = layers.conv(C_in_channels=96, D_out_channels=96, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, activation='relu', num_groups=96, quantization_mode = 'fpt_unconstrained') |
| | self.head1_pw_classification = layers.conv(C_in_channels=96, D_out_channels=8, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), output_width_30b = True, quantization_mode = 'fpt_unconstrained') |
| | self.head1_dw_regression = layers.conv(C_in_channels=96, D_out_channels=96, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, activation='relu', num_groups=96, quantization_mode = 'fpt_unconstrained') |
| | self.head1_pw_regression = layers.conv(C_in_channels=96, D_out_channels=16, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), output_width_30b = True, quantization_mode = 'fpt_unconstrained') |
| |
|
| | self.head2_dw_classification = layers.conv(C_in_channels=320, D_out_channels=320, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, activation='relu', num_groups=320, quantization_mode = 'fpt_unconstrained') |
| | self.head2_pw_classification = layers.conv(C_in_channels=320, D_out_channels=10, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), output_width_30b = True, quantization_mode = 'fpt_unconstrained') |
| | self.head2_dw_regression = layers.conv(C_in_channels=320, D_out_channels=320, K_kernel_dimension=3, stride=1, padding=(1,1,1,1), batchnorm=True, batchnorm_affine=True, activation='relu', num_groups=320, quantization_mode = 'fpt_unconstrained') |
| | self.head2_pw_regression = layers.conv(C_in_channels=320, D_out_channels=20, K_kernel_dimension=1, stride=1, padding=(0,0,0,0), output_width_30b = True, quantization_mode = 'fpt_unconstrained') |
| | |
| | self.add_residual = layers.add_residual(quantization_mode = 'fpt_unconstrained') |
| |
|
| |
|
| | def forward(self, x): |
| | x = self.conv1(x) |
| |
|
| | x = self.epw_conv2(x) |
| | x = self.dw_conv2(x) |
| | x = self.ppw_conv2(x) |
| |
|
| | x = self.epw_conv3(x) |
| | x = self.dw_conv3(x) |
| | x = self.ppw_conv3(x) |
| | res4 = x |
| | x = self.epw_conv4(x) |
| | x = self.dw_conv4(x) |
| | x = self.ppw_conv4(x) |
| | x = self.add_residual(x,res4) |
| |
|
| | x = self.epw_conv5(x) |
| | x = self.dw_conv5(x) |
| | x = self.ppw_conv5(x) |
| | res6 = x |
| | x = self.epw_conv6(x) |
| | x = self.dw_conv6(x) |
| | x = self.ppw_conv6(x) |
| | x = self.add_residual(x,res6) |
| | res7 = x |
| | x = self.epw_conv7(x) |
| | x = self.dw_conv7(x) |
| | x = self.ppw_conv7(x) |
| | x = self.add_residual(x,res7) |
| |
|
| | x = self.epw_conv8(x) |
| | x = self.dw_conv8(x) |
| | x = self.ppw_conv8(x) |
| | res9 = x |
| | x = self.epw_conv9(x) |
| | x = self.dw_conv9(x) |
| | x = self.ppw_conv9(x) |
| | x = self.add_residual(x,res9) |
| | res10 = x |
| | x = self.epw_conv10(x) |
| | x = self.dw_conv10(x) |
| | x = self.ppw_conv10(x) |
| | x = self.add_residual(x,res10) |
| | res11 = x |
| | x = self.epw_conv11(x) |
| | x = self.dw_conv11(x) |
| | x = self.ppw_conv11(x) |
| | x = self.add_residual(x,res11) |
| |
|
| | x = self.epw_conv12(x) |
| | x = self.dw_conv12(x) |
| | x = self.ppw_conv12(x) |
| | res13 = x |
| | x = self.epw_conv13(x) |
| | x = self.dw_conv13(x) |
| | x = self.ppw_conv13(x) |
| | x = self.add_residual(x,res13) |
| | res14 = x |
| | x = self.epw_conv14(x) |
| | x = self.dw_conv14(x) |
| | x = self.ppw_conv14(x) |
| | x = self.add_residual(x,res14) |
| | output1 = x |
| |
|
| | x = self.epw_conv15(x) |
| | x = self.dw_conv15(x) |
| | x = self.ppw_conv15(x) |
| | res16 = x |
| | x = self.epw_conv16(x) |
| | x = self.dw_conv16(x) |
| | x = self.ppw_conv16(x) |
| | x = self.add_residual(x,res16) |
| | res17 = x |
| | x = self.epw_conv17(x) |
| | x = self.dw_conv17(x) |
| | x = self.ppw_conv17(x) |
| | x = self.add_residual(x,res17) |
| |
|
| | x = self.epw_conv18(x) |
| | x = self.dw_conv18(x) |
| | x = self.ppw_conv18(x) |
| | output2 = x |
| |
|
| | output1_class = self.head1_dw_classification(output1) |
| | output1_class = self.head1_pw_classification(output1_class) |
| | output1_reg = self.head1_dw_regression(output1) |
| | output1_reg = self.head1_pw_regression(output1_reg) |
| |
|
| | output2_class = self.head2_dw_classification(output2) |
| | output2_class = self.head2_pw_classification(output2_class) |
| | output2_reg = self.head2_dw_regression(output2) |
| | output2_reg = self.head2_pw_regression(output2_reg) |
| |
|
| | |
| | return (output1_reg, output1_class, output2_reg, output2_class) |
