def create_mb_tiny_fd(num_classes, is_test=False, device="cuda", onnx_export=False):
base_net = Mb_Tiny(2)
base_net_model = base_net.model # disable dropout layer
source_layer_indexes = [
8,
11,
13
]
extras = ModuleList([
Sequential(
Conv2d(in_channels=base_net.base_channel * 16, out_channels=base_net.base_channel * 4, kernel_size=1),
ReLU(),
SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=base_net.base_channel * 16, kernel_size=3, stride=2, padding=1),
ReLU()
)
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * 4, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * 4, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * 4, kernel_size=3, padding=1),
Conv2d(in_channels=base_net.base_channel * 16, out_channels=3 * 4, kernel_size=3, padding=1)
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=3 * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=2 * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=2 * num_classes, kernel_size=3, padding=1),
Conv2d(in_channels=base_net.base_channel * 16, out_channels=3 * num_classes, kernel_size=3, padding=1)
])
return SSD(num_classes, base_net_model, source_layer_indexes,
extras, classification_headers, regression_headers, is_test=is_test, config=config, device=device, onnx_export=onnx_export)
def create_Mb_Tiny_RFB_fd(num_classes, is_test=False, device="cuda"):
base_net = Mb_Tiny_RFB(num_classes)
base_net_model = base_net.model # disable dropout layer
prior_nums = [len(item) for item in config.min_boxes_w]
print('prior_nums in create_mb_tiny_fd: ',prior_nums)
source_layer_indexes = [
8,
11,
13
]
extras = ModuleList([
Sequential(
Conv2d(in_channels=base_net.base_channel * 16, out_channels=base_net.base_channel * 4, kernel_size=1),
ReLU(),
SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=base_net.base_channel * 16, kernel_size=3, stride=2, padding=1),
ReLU()
)
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=prior_nums[0] * 4, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=prior_nums[1] * 4, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=prior_nums[2] * 4, kernel_size=3, padding=1),
Conv2d(in_channels=base_net.base_channel * 16, out_channels=prior_nums[3] * 4, kernel_size=3, padding=1)
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=base_net.base_channel * 4, out_channels=prior_nums[0] * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 8, out_channels=prior_nums[1] * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 16, out_channels=prior_nums[2] * num_classes, kernel_size=3, padding=1),
Conv2d(in_channels=base_net.base_channel * 16, out_channels=prior_nums[3] * num_classes, kernel_size=3, padding=1)
])
return SSD(num_classes, base_net_model, source_layer_indexes,
extras, classification_headers, regression_headers, is_test=is_test, config=config, device=device)
def create_mobilenetv2_ssd_lite(num_classes, width_mult=1.0, use_batch_norm=True, onnx_compatible=False, is_test=False):
base_net = MobileNetV2(width_mult=width_mult, use_batch_norm=use_batch_norm,
onnx_compatible=onnx_compatible).features
source_layer_indexes = [
GraphPath(14, 'conv', 3),
19,
]
extras = ModuleList([
InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
InvertedResidual(256, 64, stride=2, expand_ratio=0.25)
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=round(576 * width_mult), out_channels=6 * 4,
kernel_size=3, padding=1, onnx_compatible=False),
SeperableConv2d(in_channels=1280, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=round(576 * width_mult), out_channels=6 * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=1280, out_channels=6 * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
])
return SSD(num_classes, base_net, source_layer_indexes,
extras, classification_headers, regression_headers, is_test=is_test, config=config)