def __init__(self,
in_channels,
num_anchors,
num_classes,
num_layers,
onnx_export=False):
super(Classifier, self).__init__()
self.num_anchors = num_anchors
self.num_classes = num_classes
self.num_layers = num_layers
self.conv_list = nn.ModuleList([
SeparableConvBlock(in_channels,
in_channels,
norm=False,
activation=False) for i in range(num_layers)
])
self.bn_list = nn.ModuleList([
nn.ModuleList([
nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3)
for i in range(num_layers)
]) for j in range(5)
])
self.header = SeparableConvBlock(in_channels,
num_anchors * num_classes,
norm=False,
activation=False)
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
def __init__(self,
in_channels,
num_anchors,
num_layers,
onnx_export=False):
"""
num_layers 的选值 [3, 3, 3, 4, 4, 4, 5, 5]
num_anchors = len(self.aspect_ratios) * self.num_scales
aspect_ratios = kwargs.get('ratios', [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)])
num_scales = len(kwargs.get('scales', [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]))
"""
super(Regressor, self).__init__()
self.num_layers = num_layers
self.num_layers = num_layers
self.conv_list = nn.ModuleList([
SeparableConvBlock(in_channels,
in_channels,
norm=False,
activation=False) for i in range(num_layers)
])
self.bn_list = nn.ModuleList([
nn.ModuleList([
nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3)
for i in range(num_layers)
]) for j in range(5)
])
self.header = SeparableConvBlock(in_channels,
num_anchors * 4,
norm=False,
activation=False)
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
def __init__(self, in_channels, out_channels=None, norm=True, activation=False, onnx_export=False):
super(SeparableConvBlock, self).__init__()
if out_channels is None:
out_channels = in_channels
# Q: whether separate conv
# share bias between depthwise_conv and pointwise_conv
# or just pointwise_conv apply bias.
# A: Confirmed, just pointwise_conv applies bias, depthwise_conv has no bias.
self.depthwise_conv = Conv2dStaticSamePadding(in_channels, in_channels,
kernel_size=3, stride=1, groups=in_channels, bias=False)
self.pointwise_conv = Conv2dStaticSamePadding(in_channels, out_channels, kernel_size=1, stride=1)
self.norm = norm
if self.norm:
# Warning: pytorch momentum is different from tensorflow's, momentum_pytorch = 1 - momentum_tensorflow
self.bn = nn.BatchNorm2d(num_features=out_channels, momentum=0.01, eps=1e-3)
self.activation = activation
if self.activation:
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
def __init__(self,
num_channels,
conv_channels,
first_time=False,
epsilon=1e-4,
onnx_export=False,
attention=True):
"""
Args:
num_channels:
conv_channels:
first_time: whether the input comes directly from the efficientnet,
if True, downchannel it first, and downsample P5 to generate P6 then P7
epsilon: epsilon of fast weighted attention sum of BiFPN, not the BN's epsilon
onnx_export: if True, use Swish instead of MemoryEfficientSwish
"""
super(BiFPN, self).__init__()
self.epsilon = epsilon
# Conv layers
self.conv6_up = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv5_up = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv4_up = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv3_up = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv4_down = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv5_down = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv6_down = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
self.conv7_down = SeparableConvBlock(num_channels,
onnx_export=onnx_export)
# Feature scaling layers
self.p6_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p5_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p4_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p3_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p4_downsample = MaxPool2dStaticSamePadding(3, 2)
self.p5_downsample = MaxPool2dStaticSamePadding(3, 2)
self.p6_downsample = MaxPool2dStaticSamePadding(3, 2)
self.p7_downsample = MaxPool2dStaticSamePadding(3, 2)
self.swish = MemoryEfficientSwish() if not onnx_export else Swish()
self.first_time = first_time
if self.first_time:
self.p5_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p4_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[1], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p3_down_channel = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[0], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p5_to_p6 = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
MaxPool2dStaticSamePadding(3, 2))
self.p6_to_p7 = nn.Sequential(MaxPool2dStaticSamePadding(3, 2))
self.p4_down_channel_2 = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[1], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
self.p5_down_channel_2 = nn.Sequential(
Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
)
# Weight
self.p6_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32),
requires_grad=True)
self.p6_w1_relu = nn.ReLU()
self.p5_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32),
requires_grad=True)
self.p5_w1_relu = nn.ReLU()
self.p4_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32),
requires_grad=True)
self.p4_w1_relu = nn.ReLU()
self.p3_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32),
requires_grad=True)
self.p3_w1_relu = nn.ReLU()
self.p4_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32),
requires_grad=True)
self.p4_w2_relu = nn.ReLU()
self.p5_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32),
requires_grad=True)
self.p5_w2_relu = nn.ReLU()
self.p6_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32),
requires_grad=True)
self.p6_w2_relu = nn.ReLU()
self.p7_w2 = nn.Parameter(torch.ones(2, dtype=torch.float32),
requires_grad=True)
self.p7_w2_relu = nn.ReLU()
if not attention:
self.p6_w1.requires_grad = False
self.p5_w1.requires_grad = False
self.p4_w1.requires_grad = False
self.p3_w1.requires_grad = False
self.p4_w2.requires_grad = False
self.p5_w2.requires_grad = False
self.p6_w2.requires_grad = False
self.p7_w2.requires_grad = False
