def _define_layers(self):
if self.back_model:
self.backbone = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=24,
kernel_size=5,
stride=2,
padding=0,
bias=True),
nn.ReLU(inplace=True),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24, stride=2),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 24),
BlazeBlock(24, 48, stride=2),
BlazeBlock(48, 48),
BlazeBlock(48, 48),
BlazeBlock(48, 48),
BlazeBlock(48, 48),
BlazeBlock(48, 48),
BlazeBlock(48, 48),
BlazeBlock(48, 48),
BlazeBlock(48, 96, stride=2),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
)
self.final = FinalBlazeBlock(96)
self.classifier_8 = nn.Conv2d(96, 2, 1, bias=True)
self.classifier_16 = nn.Conv2d(96, 6, 1, bias=True)
self.regressor_8 = nn.Conv2d(96, 32, 1, bias=True)
self.regressor_16 = nn.Conv2d(96, 96, 1, bias=True)
else:
self.backbone1 = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=24,
kernel_size=5,
stride=2,
padding=0,
bias=True),
nn.ReLU(inplace=True),
BlazeBlock(24, 24),
BlazeBlock(24, 28),
BlazeBlock(28, 32, stride=2),
BlazeBlock(32, 36),
BlazeBlock(36, 42),
BlazeBlock(42, 48, stride=2),
BlazeBlock(48, 56),
BlazeBlock(56, 64),
BlazeBlock(64, 72),
BlazeBlock(72, 80),
BlazeBlock(80, 88),
)
self.backbone2 = nn.Sequential(
BlazeBlock(88, 96, stride=2),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
BlazeBlock(96, 96),
)
self.classifier_8 = nn.Conv2d(88, 2, 1, bias=True)
self.classifier_16 = nn.Conv2d(96, 6, 1, bias=True)
self.regressor_8 = nn.Conv2d(88, 32, 1, bias=True)
self.regressor_16 = nn.Conv2d(96, 96, 1, bias=True)
def _define_layers(self):
self.backbone1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=2, padding=0, bias=True),
nn.ReLU(inplace=True),
BlazeBlock(32, 32),
BlazeBlock(32, 32),
BlazeBlock(32, 32),
BlazeBlock(32, 32),
BlazeBlock(32, 32),
BlazeBlock(32, 32),
BlazeBlock(32, 32),
BlazeBlock(32, 64, stride=2),
BlazeBlock(64, 64),
BlazeBlock(64, 64),
BlazeBlock(64, 64),
BlazeBlock(64, 64),
BlazeBlock(64, 64),
BlazeBlock(64, 64),
BlazeBlock(64, 64),
BlazeBlock(64, 128, stride=2),
BlazeBlock(128, 128),
BlazeBlock(128, 128),
BlazeBlock(128, 128),
BlazeBlock(128, 128),
BlazeBlock(128, 128),
BlazeBlock(128, 128),
BlazeBlock(128, 128),
)
self.backbone2 = nn.Sequential(
BlazeBlock(128, 256, stride=2),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
)
self.backbone3 = nn.Sequential(
BlazeBlock(256, 256, stride=2),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
BlazeBlock(256, 256),
)
self.conv_transpose1 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=2, stride=2, padding=0, bias=True)
self.blaze1 = BlazeBlock(256, 256)
self.conv_transpose2 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=2, stride=2, padding=0, bias=True)
self.blaze2 = BlazeBlock(128, 128)
self.classifier_32 = nn.Conv2d(128, 2, 1, bias=True)
self.classifier_16 = nn.Conv2d(256, 2, 1, bias=True)
self.classifier_8 = nn.Conv2d(256, 6, 1, bias=True)
self.regressor_32 = nn.Conv2d(128, 36, 1, bias=True)
self.regressor_16 = nn.Conv2d(256, 36, 1, bias=True)
self.regressor_8 = nn.Conv2d(256, 108, 1, bias=True)
def _define_layers(self):
self.backbone1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=48, kernel_size=5, stride=1, padding=2, bias=True),
nn.ReLU(inplace=True),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 64, 5, 2, skip_proj=True),
BlazeBlock(64, 64, 5),
BlazeBlock(64, 64, 5),
BlazeBlock(64, 64, 5),
BlazeBlock(64, 96, 5, 2, skip_proj=True),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 128, 5, 2, skip_proj=True),
BlazeBlock(128, 128, 5),
BlazeBlock(128, 128, 5),
BlazeBlock(128, 128, 5),
BlazeBlock(128, 128, 5),
BlazeBlock(128, 128, 5),
BlazeBlock(128, 128, 5),
BlazeBlock(128, 128, 5),
)
self.backbone2 = nn.Sequential(
BlazeBlock(128, 256, 5, 2, skip_proj=True),
BlazeBlock(256, 256, 5),
BlazeBlock(256, 256, 5),
BlazeBlock(256, 256, 5),
BlazeBlock(256, 256, 5),
BlazeBlock(256, 256, 5),
BlazeBlock(256, 256, 5),
)
self.classifier_8 = nn.Conv2d(128, 2, 1, bias=True)
self.classifier_16 = nn.Conv2d(256, 6, 1, bias=True)
self.regressor_8 = nn.Conv2d(128, 24, 1, bias=True)
self.regressor_16 = nn.Conv2d(256, 72, 1, bias=True)
def _define_layers(self):
self.backbone1 = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=24,
kernel_size=3,
stride=2,
padding=0,
bias=True),
nn.ReLU(inplace=True),
BlazeBlock(24, 24, 5),
BlazeBlock(24, 24, 5),
BlazeBlock(24, 48, 5, 2),
)
self.backbone2 = nn.Sequential(
BlazeBlock(48, 48, 5),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 96, 5, 2),
)
self.backbone3 = nn.Sequential(
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5, 2),
)
self.backbone4 = nn.Sequential(
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5, 2),
)
self.blaze5 = BlazeBlock(96, 96, 5)
self.blaze6 = BlazeBlock(96, 96, 5)
self.conv7 = nn.Conv2d(96, 48, 1, bias=True)
self.backbone8 = nn.Sequential(
BlazeBlock(48, 48, 5),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 48, 5),
BlazeBlock(48, 96, 5, 2),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 96, 5),
BlazeBlock(96, 288, 5, 2),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5, 2),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5, 2),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5, 2),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
BlazeBlock(288, 288, 5),
)
self.hand_flag = nn.Conv2d(288, 1, 2, bias=True)
self.handed = nn.Conv2d(288, 1, 2, bias=True)
self.landmarks = nn.Conv2d(288, 63, 2, bias=True)
def _define_layers(self):
self.backbone1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=0, bias=True),
nn.PReLU(16),
BlazeBlock(16, 16, 3, act='prelu'),
BlazeBlock(16, 16, 3, act='prelu'),
BlazeBlock(16, 32, 3, 2, act='prelu'),
BlazeBlock(32, 32, 3, act='prelu'),
BlazeBlock(32, 32, 3, act='prelu'),
BlazeBlock(32, 64, 3, 2, act='prelu'),
BlazeBlock(64, 64, 3, act='prelu'),
BlazeBlock(64, 64, 3, act='prelu'),
BlazeBlock(64, 128, 3, 2, act='prelu'),
BlazeBlock(128, 128, 3, act='prelu'),
BlazeBlock(128, 128, 3, act='prelu'),
BlazeBlock(128, 128, 3, 2, act='prelu'),
BlazeBlock(128, 128, 3, act='prelu'),
BlazeBlock(128, 128, 3, act='prelu'),
)
self.backbone2a = nn.Sequential(
BlazeBlock(128, 128, 3, 2, act='prelu'),
BlazeBlock(128, 128, 3, act='prelu'),
BlazeBlock(128, 128, 3, act='prelu'),
nn.Conv2d(128, 32, 1, padding=0, bias=True),
nn.PReLU(32),
BlazeBlock(32, 32, 3, act='prelu'),
nn.Conv2d(32, 1404, 3, padding=0, bias=True)
)
self.backbone2b = nn.Sequential(
BlazeBlock(128, 128, 3, 2, act='prelu'),
nn.Conv2d(128, 32, 1, padding=0, bias=True),
nn.PReLU(32),
BlazeBlock(32, 32, 3, act='prelu'),
nn.Conv2d(32, 1, 3, padding=0, bias=True)
)
def _define_layers(self):
self.backbone1 = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=24,
kernel_size=3,
stride=2,
padding=0,
bias=True),
nn.ReLU(inplace=True),
BlazeBlock(24, 24, 3),
BlazeBlock(24, 24, 3),
)
self.backbone2 = nn.Sequential(
BlazeBlock(24, 48, 3, 2),
BlazeBlock(48, 48, 3),
BlazeBlock(48, 48, 3),
BlazeBlock(48, 48, 3),
)
self.backbone3 = nn.Sequential(
BlazeBlock(48, 96, 3, 2),
BlazeBlock(96, 96, 3),
BlazeBlock(96, 96, 3),
BlazeBlock(96, 96, 3),
BlazeBlock(96, 96, 3),
)
self.backbone4 = nn.Sequential(
BlazeBlock(96, 192, 3, 2),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
)
self.backbone5 = nn.Sequential(
BlazeBlock(192, 288, 3, 2),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
)
self.up1 = nn.Sequential(
nn.Conv2d(288, 288, 3, 1, 1, groups=288, bias=True),
nn.Conv2d(288, 48, 1, bias=True),
nn.ReLU(True),
)
self.up2 = nn.Sequential(
nn.Conv2d(192, 192, 3, 1, 1, groups=192, bias=True),
nn.Conv2d(192, 48, 1, bias=True),
nn.ReLU(True),
)
self.up3 = nn.Sequential(
nn.Conv2d(96, 96, 3, 1, 1, groups=96, bias=True),
nn.Conv2d(96, 48, 1, bias=True),
nn.ReLU(True),
)
self.up4 = nn.Sequential(
nn.Conv2d(48, 48, 3, 1, 1, groups=48, bias=True),
nn.Conv2d(48, 48, 1, bias=True),
nn.ReLU(True),
)
self.block1 = nn.Sequential(
BlazeBlock(48, 96, 3, 2),
BlazeBlock(96, 96, 3),
BlazeBlock(96, 96, 3),
BlazeBlock(96, 96, 3),
BlazeBlock(96, 96, 3),
)
self.up5 = nn.Sequential(
nn.Conv2d(96, 96, 3, 1, 1, groups=96, bias=True),
nn.Conv2d(96, 96, 1, bias=True),
nn.ReLU(True),
)
self.block2 = nn.Sequential(
BlazeBlock(96, 192, 3, 2),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
BlazeBlock(192, 192, 3),
)
self.up6 = nn.Sequential(
nn.Conv2d(192, 192, 3, 1, 1, groups=192, bias=True),
nn.Conv2d(192, 192, 1, bias=True),
nn.ReLU(True),
)
self.block3 = nn.Sequential(
BlazeBlock(192, 288, 3, 2),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
)
self.up7 = nn.Sequential(
nn.Conv2d(288, 288, 3, 1, 1, groups=288, bias=True),
nn.Conv2d(288, 288, 1, bias=True),
nn.ReLU(True),
)
self.block4 = nn.Sequential(
BlazeBlock(288, 288, 3, 2),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3, 2),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
BlazeBlock(288, 288, 3),
)
self.up8 = nn.Sequential(
nn.Conv2d(48, 48, 3, 1, 1, groups=48, bias=True),
nn.Conv2d(48, 8, 1, bias=True),
nn.ReLU(True),
)
self.up9 = nn.Sequential(
nn.Conv2d(24, 24, 3, 1, 1, groups=24, bias=True),
nn.Conv2d(24, 8, 1, bias=True),
nn.ReLU(True),
)
self.block5 = BlazeBlock(288, 288, 3)
self.block6 = nn.Sequential(
nn.Conv2d(8, 8, 3, 1, 1, groups=8, bias=True),
nn.Conv2d(8, 8, 1, bias=True),
nn.ReLU(True),
)
self.flag = nn.Conv2d(288, 1, 2, bias=True)
self.segmentation = nn.Conv2d(8, 1, 3, padding=1, bias=True)
self.landmarks = nn.Conv2d(288, 124, 2, bias=True)