Detection modules

MobileNetV1Backbone

Bases: MultiOutputBackbone

MobileNetV1 backbone with an option to return output of any layer

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class MobileNetV1Backbone(MultiOutputBackbone):
    """MobileNetV1 backbone with an option to return output of any layer"""

    def __init__(self, in_channels: int, out_layers: List):
        backbone = MobileNet(backbone_mode=True, num_classes=None, in_channels=in_channels)
        super().__init__(in_channels, backbone, out_layers)

MobileNetV2Backbone

Bases: MultiOutputBackbone

MobileNetV2 backbone with an option to return output of any layer

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class MobileNetV2Backbone(MultiOutputBackbone):
    """MobileNetV2 backbone with an option to return output of any layer"""

    def __init__(self, in_channels: int, out_layers: List, width_mult: float = 1.0, structure: List[List] = None, grouped_conv_size: int = 1):
        backbone = MobileNetV2(
            backbone_mode=True,
            num_classes=None,
            dropout=0.0,
            width_mult=width_mult,
            structure=structure,
            grouped_conv_size=grouped_conv_size,
            in_channels=in_channels,
        )
        super().__init__(in_channels, backbone, out_layers)

MultiOutputBackbone

Bases: BaseDetectionModule

Defines a backbone using MultiOutputModule with the interface of BaseDetectionModule

Source code in V3_2/src/super_gradients/modules/detection_modules.py

class MultiOutputBackbone(BaseDetectionModule):
    """
    Defines a backbone using MultiOutputModule with the interface of BaseDetectionModule
    """

    def __init__(self, in_channels: int, backbone: nn.Module, out_layers: List):
        super().__init__(in_channels)
        self.multi_output_backbone = MultiOutputModule(backbone, out_layers)
        self._out_channels = [x.shape[1] for x in self.forward(torch.empty((1, in_channels, 64, 64)))]

    @property
    def out_channels(self) -> Union[List[int], int]:
        return self._out_channels

    def forward(self, x):
        return self.multi_output_backbone(x)

NHeads

Bases: BaseDetectionModule

Apply N heads in parallel and combine predictions into the shape expected by SG detection losses

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class NHeads(BaseDetectionModule):
    """
    Apply N heads in parallel and combine predictions into the shape expected by SG detection losses
    """

    def __init__(self, in_channels: List[int], num_classes: int, heads_list: Union[str, HpmStruct, DictConfig]):
        import super_gradients.common.factories.detection_modules_factory as det_factory

        super().__init__(in_channels)
        factory = det_factory.DetectionModulesFactory()
        heads_list = self._pass_num_classes(heads_list, factory, num_classes)

        self.num_heads = len(heads_list)
        for i in range(self.num_heads):
            new_head = factory.get(factory.insert_module_param(heads_list[i], "in_channels", in_channels[i]))
            setattr(self, f"head{i + 1}", new_head)

    @staticmethod
    def _pass_num_classes(heads_list, factory, num_classes):
        for i in range(len(heads_list)):
            heads_list[i] = factory.insert_module_param(heads_list[i], "num_classes", num_classes)
        return heads_list

    @property
    def out_channels(self):
        return None

    def forward(self, inputs):
        outputs = []
        for i in range(self.num_heads):
            outputs.append(getattr(self, f"head{i + 1}")(inputs[i]))

        return self.combine_preds(outputs)

    def combine_preds(self, preds):
        outputs = []
        outputs_logits = []
        for output, output_logits in preds:
            outputs.append(output)
            outputs_logits.append(output_logits)

        return outputs if self.training else (torch.cat(outputs, 1), outputs_logits)

NStageBackbone

Bases: BaseDetectionModule

A backbone with a stem -> N stages -> context module Returns outputs of the layers listed in out_layers

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class NStageBackbone(BaseDetectionModule):
    """
    A backbone with a stem -> N stages -> context module
    Returns outputs of the layers listed in out_layers
    """

    def __init__(
        self,
        in_channels: int,
        out_layers: List[str],
        stem: Union[str, HpmStruct, DictConfig],
        stages: Union[str, HpmStruct, DictConfig],
        context_module: Union[str, HpmStruct, DictConfig],
    ):
        import super_gradients.common.factories.detection_modules_factory as det_factory

        """
        :param out_layers: names of layers to output from the following options: 'stem', 'stageN', 'context_module'
        """
        super().__init__(in_channels)
        factory = det_factory.DetectionModulesFactory()

        self.num_stages = len(stages)
        self.stem = factory.get(factory.insert_module_param(stem, "in_channels", in_channels))
        prev_channels = self.stem.out_channels
        for i in range(self.num_stages):
            new_stage = factory.get(factory.insert_module_param(stages[i], "in_channels", prev_channels))
            setattr(self, f"stage{i + 1}", new_stage)
            prev_channels = new_stage.out_channels
        self.context_module = factory.get(factory.get(factory.insert_module_param(context_module, "in_channels", prev_channels)))

        self.out_layers = out_layers
        self._out_channels = self._define_out_channels()

    def _define_out_channels(self):
        out_channels = []
        for layer in self.out_layers:
            out_channels.append(getattr(self, layer).out_channels)
        return out_channels

    @property
    def out_channels(self):
        return self._out_channels

    def forward(self, x):

        outputs = []
        all_layers = ["stem"] + [f"stage{i}" for i in range(1, self.num_stages + 1)] + ["context_module"]
        for layer in all_layers:
            x = getattr(self, layer)(x)
            if layer in self.out_layers:
                outputs.append(x)

        return outputs

PANNeck

Bases: BaseDetectionModule

A PAN (path aggregation network) neck with 4 stages (2 up-sampling and 2 down-sampling stages) Returns outputs of neck stage 2, stage 3, stage 4

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class PANNeck(BaseDetectionModule):
    """
    A PAN (path aggregation network) neck with 4 stages (2 up-sampling and 2 down-sampling stages)
    Returns outputs of neck stage 2, stage 3, stage 4
    """

    def __init__(
        self,
        in_channels: List[int],
        neck1: Union[str, HpmStruct, DictConfig],
        neck2: Union[str, HpmStruct, DictConfig],
        neck3: Union[str, HpmStruct, DictConfig],
        neck4: Union[str, HpmStruct, DictConfig],
    ):
        import super_gradients.common.factories.detection_modules_factory as det_factory

        super().__init__(in_channels)
        c3_out_channels, c4_out_channels, c5_out_channels = in_channels

        factory = det_factory.DetectionModulesFactory()
        self.neck1 = factory.get(factory.insert_module_param(neck1, "in_channels", [c5_out_channels, c4_out_channels]))
        self.neck2 = factory.get(factory.insert_module_param(neck2, "in_channels", [self.neck1.out_channels[1], c3_out_channels]))
        self.neck3 = factory.get(factory.insert_module_param(neck3, "in_channels", [self.neck2.out_channels[1], self.neck2.out_channels[0]]))
        self.neck4 = factory.get(factory.insert_module_param(neck4, "in_channels", [self.neck3.out_channels, self.neck1.out_channels[0]]))

        self._out_channels = [
            self.neck2.out_channels[1],
            self.neck3.out_channels,
            self.neck4.out_channels,
        ]

    @property
    def out_channels(self):
        return self._out_channels

    def forward(self, inputs):
        c3, c4, c5 = inputs

        x_n1_inter, x = self.neck1([c5, c4])
        x_n2_inter, p3 = self.neck2([x, c3])
        p4 = self.neck3([p3, x_n2_inter])
        p5 = self.neck4([p4, x_n1_inter])

        return p3, p4, p5

SSDBottleneckNeck

Bases: SSDNeck

Consecutive bottleneck blocks

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class SSDBottleneckNeck(SSDNeck):
    """
    Consecutive bottleneck blocks
    """

    def create_blocks(self, prev_channels: int, blocks_out_channels: List[int], bottleneck_channels: List[int], kernel_sizes: List[int], strides: List[int]):
        neck_blocks = []
        for i in range(len(blocks_out_channels)):
            mid_channels = bottleneck_channels[i]
            out_channels = blocks_out_channels[i]
            kernel_size = kernel_sizes[i]
            stride = strides[i]
            padding = 1 if stride == 2 else 0
            neck_blocks.append(
                nn.Sequential(
                    nn.Conv2d(prev_channels, mid_channels, kernel_size=1, bias=False),
                    nn.BatchNorm2d(mid_channels),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(mid_channels, out_channels, kernel_size=kernel_size, padding=padding, stride=stride, bias=False),
                    nn.BatchNorm2d(out_channels),
                    nn.ReLU(inplace=True),
                )
            )
            prev_channels = out_channels
        return neck_blocks

SSDHead

Bases: BaseDetectionModule

A one-layer conv head attached to each input feature map. A conv is implemented as two branches: localization and classification

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class SSDHead(BaseDetectionModule):
    """
    A one-layer conv head attached to each input feature map.
    A conv is implemented as two branches: localization and classification
    """

    def __init__(self, in_channels: Union[int, List[int]], num_classes, anchors, lite):
        in_channels = in_channels if isinstance(in_channels, (list, ListConfig)) else [in_channels]
        super().__init__(in_channels)

        self.num_classes = num_classes
        self.dboxes_xy = nn.Parameter(anchors("xywh")[:, :2], requires_grad=False)
        self.dboxes_wh = nn.Parameter(anchors("xywh")[:, 2:], requires_grad=False)
        scale_xy = anchors.scale_xy
        scale_wh = anchors.scale_wh
        scales = torch.tensor([scale_xy, scale_xy, scale_wh, scale_wh])
        self.scales = nn.Parameter(scales, requires_grad=False)
        self.img_size = nn.Parameter(torch.tensor([anchors.fig_size]), requires_grad=False)
        self.num_anchors = anchors.num_anchors

        loc_blocks = []
        conf_blocks = []

        for i, (num_anch, in_c) in enumerate(zip(self.num_anchors, in_channels)):
            conv = SeperableConv2d if lite and i < len(self.num_anchors) - 1 else nn.Conv2d
            loc_blocks.append(conv(in_c, num_anch * 4, kernel_size=3, padding=1))
            conf_blocks.append(conv(in_c, num_anch * (self.num_classes + 1), kernel_size=3, padding=1))

        self.loc = nn.ModuleList(loc_blocks)
        self.conf = nn.ModuleList(conf_blocks)

    @property
    def out_channels(self) -> Union[List[int], int]:
        return None

    def forward(self, inputs):
        inputs = inputs if isinstance(inputs, list) else [inputs]

        preds = []
        for i in range(len(inputs)):
            boxes_preds = self.loc[i](inputs[i])
            class_preds = self.conf[i](inputs[i])
            preds.append([boxes_preds, class_preds])

        return self.combine_preds(preds)

    def combine_preds(self, preds):
        batch_size = preds[0][0].shape[0]

        for i in range(len(preds)):
            box_pred_map, conf_pred_map = preds[i]
            preds[i][0] = box_pred_map.view(batch_size, 4, -1)
            preds[i][1] = conf_pred_map.view(batch_size, self.num_classes + 1, -1)

        locs, confs = list(zip(*preds))
        locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()

        if self.training:
            # FOR 300X300 INPUT - RETURN N_BATCH X 8732 X {N_LABELS, N_LOCS} RESULTS
            return locs, confs
        else:
            bboxes_in = locs.permute(0, 2, 1)
            scores_in = confs.permute(0, 2, 1)

            bboxes_in *= self.scales

            # CONVERT RELATIVE LOCATIONS INTO ABSOLUTE LOCATION (OUTPUT LOCATIONS ARE RELATIVE TO THE DBOXES)
            xy = (bboxes_in[:, :, :2] * self.dboxes_wh + self.dboxes_xy) * self.img_size
            wh = (bboxes_in[:, :, 2:].exp() * self.dboxes_wh) * self.img_size

            # REPLACE THE CONFIDENCE OF CLASS NONE WITH OBJECT CONFIDENCE
            # SSD DOES NOT OUTPUT OBJECT CONFIDENCE, REQUIRED FOR THE NMS
            scores_in = torch.softmax(scores_in, dim=-1)
            classes_conf = scores_in[:, :, 1:]
            obj_conf = torch.max(classes_conf, dim=2)[0].unsqueeze(dim=-1)

            return torch.cat((xy, wh, obj_conf, classes_conf), dim=2), (locs, confs)

SSDInvertedResidualNeck

Bases: SSDNeck

Consecutive InvertedResidual blocks each starting with stride 2

Source code in V3_2/src/super_gradients/modules/detection_modules.py

@register_detection_module()
class SSDInvertedResidualNeck(SSDNeck):
    """
    Consecutive InvertedResidual blocks each starting with stride 2
    """

    def create_blocks(self, prev_channels: int, blocks_out_channels: List[int], expand_ratios: List[float], grouped_conv_size: int):
        neck_blocks = []
        for i in range(len(blocks_out_channels)):
            out_channels = blocks_out_channels[i]
            neck_blocks.append(InvertedResidual(prev_channels, out_channels, stride=2, expand_ratio=expand_ratios[i], grouped_conv_size=grouped_conv_size))
            prev_channels = out_channels
        return neck_blocks

SSDNeck

Bases: BaseDetectionModule, ABC

SSD neck which returns: * outputs of the backbone, unchanged * outputs of a custom number of additional blocks added after the last backbone stage (returns output of each block) Has no skips to the backbone

Source code in V3_2/src/super_gradients/modules/detection_modules.py

class SSDNeck(BaseDetectionModule, ABC):
    """
    SSD neck which returns:
     * outputs of the backbone, unchanged
     * outputs of a custom number of additional blocks added after the last backbone stage (returns output of each block)
    Has no skips to the backbone
    """

    def __init__(self, in_channels: Union[int, List[int]], blocks_out_channels: List[int], **kwargs):
        in_channels = in_channels if isinstance(in_channels, (list, ListConfig)) else [in_channels]
        super().__init__(in_channels)
        self.neck_blocks = nn.ModuleList(self.create_blocks(in_channels[-1], blocks_out_channels, **kwargs))
        self._out_channels = in_channels + list(blocks_out_channels)

    @property
    def out_channels(self) -> Union[List[int], int]:
        return self._out_channels

    @abstractmethod
    def create_blocks(self, in_channels: int, blocks_out_channels, **kwargs):
        raise NotImplementedError()

    def forward(self, inputs):
        outputs = inputs if isinstance(inputs, list) else [inputs]

        x = outputs[-1]
        for block in self.neck_blocks:
            x = block(x)
            outputs.append(x)

        return outputs

SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)

Depthwise Conv2d and Pointwise Conv2d.

Source code in V3_2/src/super_gradients/modules/detection_modules.py

def SeperableConv2d(in_channels: int, out_channels: int, kernel_size: int = 1, stride: int = 1, padding: int = 0, bias: bool = True):
    """Depthwise Conv2d and Pointwise Conv2d."""
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, groups=in_channels, stride=stride, padding=padding, bias=bias),
        nn.BatchNorm2d(in_channels),
        nn.ReLU(),
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),
    )