Source code for torchvision.models.detection.ssdlite

import torch
import warnings

from collections import OrderedDict
from functools import partial
from torch import nn, Tensor
from typing import Any, Callable, Dict, List, Optional, Tuple

from . import _utils as det_utils
from .ssd import SSD, SSDScoringHead
from .anchor_utils import DefaultBoxGenerator
from .backbone_utils import _validate_trainable_layers
from .. import mobilenet
from ..mobilenetv3 import ConvBNActivation
from ..utils import load_state_dict_from_url

__all__ = ['ssdlite320_mobilenet_v3_large']

model_urls = {

# Building blocks of SSDlite as described in section 6.2 of MobileNetV2 paper
def _prediction_block(in_channels: int, out_channels: int, kernel_size: int,
                      norm_layer: Callable[..., nn.Module]) -> nn.Sequential:
    return nn.Sequential(
        # 3x3 depthwise with stride 1 and padding 1
        ConvBNActivation(in_channels, in_channels, kernel_size=kernel_size, groups=in_channels,
                         norm_layer=norm_layer, activation_layer=nn.ReLU6),

        # 1x1 projetion to output channels
        nn.Conv2d(in_channels, out_channels, 1)

def _extra_block(in_channels: int, out_channels: int, norm_layer: Callable[..., nn.Module]) -> nn.Sequential:
    activation = nn.ReLU6
    intermediate_channels = out_channels // 2
    return nn.Sequential(
        # 1x1 projection to half output channels
        ConvBNActivation(in_channels, intermediate_channels, kernel_size=1,
                         norm_layer=norm_layer, activation_layer=activation),

        # 3x3 depthwise with stride 2 and padding 1
        ConvBNActivation(intermediate_channels, intermediate_channels, kernel_size=3, stride=2,
                         groups=intermediate_channels, norm_layer=norm_layer, activation_layer=activation),

        # 1x1 projetion to output channels
        ConvBNActivation(intermediate_channels, out_channels, kernel_size=1,
                         norm_layer=norm_layer, activation_layer=activation),

def _normal_init(conv: nn.Module):
    for layer in conv.modules():
        if isinstance(layer, nn.Conv2d):
            torch.nn.init.normal_(layer.weight, mean=0.0, std=0.03)
            if layer.bias is not None:
                torch.nn.init.constant_(layer.bias, 0.0)

class SSDLiteHead(nn.Module):
    def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int,
                 norm_layer: Callable[..., nn.Module]):
        self.classification_head = SSDLiteClassificationHead(in_channels, num_anchors, num_classes, norm_layer)
        self.regression_head = SSDLiteRegressionHead(in_channels, num_anchors, norm_layer)

    def forward(self, x: List[Tensor]) -> Dict[str, Tensor]:
        return {
            'bbox_regression': self.regression_head(x),
            'cls_logits': self.classification_head(x),

class SSDLiteClassificationHead(SSDScoringHead):
    def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int,
                 norm_layer: Callable[..., nn.Module]):
        cls_logits = nn.ModuleList()
        for channels, anchors in zip(in_channels, num_anchors):
            cls_logits.append(_prediction_block(channels, num_classes * anchors, 3, norm_layer))
        super().__init__(cls_logits, num_classes)

class SSDLiteRegressionHead(SSDScoringHead):
    def __init__(self, in_channels: List[int], num_anchors: List[int], norm_layer: Callable[..., nn.Module]):
        bbox_reg = nn.ModuleList()
        for channels, anchors in zip(in_channels, num_anchors):
            bbox_reg.append(_prediction_block(channels, 4 * anchors, 3, norm_layer))
        super().__init__(bbox_reg, 4)

class SSDLiteFeatureExtractorMobileNet(nn.Module):
    def __init__(self, backbone: nn.Module, c4_pos: int, norm_layer: Callable[..., nn.Module], width_mult: float = 1.0,
                 min_depth: int = 16, **kwargs: Any):

        assert not backbone[c4_pos].use_res_connect
        self.features = nn.Sequential(
            # As described in section 6.3 of MobileNetV3 paper
            nn.Sequential(*backbone[:c4_pos], backbone[c4_pos].block[0]),  # from start until C4 expansion layer
            nn.Sequential(backbone[c4_pos].block[1:], *backbone[c4_pos + 1:]),  # from C4 depthwise until end

        get_depth = lambda d: max(min_depth, int(d * width_mult))  # noqa: E731
        extra = nn.ModuleList([
            _extra_block(backbone[-1].out_channels, get_depth(512), norm_layer),
            _extra_block(get_depth(512), get_depth(256), norm_layer),
            _extra_block(get_depth(256), get_depth(256), norm_layer),
            _extra_block(get_depth(256), get_depth(128), norm_layer),

        self.extra = extra

    def forward(self, x: Tensor) -> Dict[str, Tensor]:
        # Get feature maps from backbone and extra. Can't be refactored due to JIT limitations.
        output = []
        for block in self.features:
            x = block(x)

        for block in self.extra:
            x = block(x)

        return OrderedDict([(str(i), v) for i, v in enumerate(output)])

def _mobilenet_extractor(backbone_name: str, progress: bool, pretrained: bool, trainable_layers: int,
                         norm_layer: Callable[..., nn.Module], **kwargs: Any):
    backbone = mobilenet.__dict__[backbone_name](pretrained=pretrained, progress=progress,
                                                 norm_layer=norm_layer, **kwargs).features
    if not pretrained:
        # Change the default initialization scheme if not pretrained

    # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
    # The first and last blocks are always included because they are the C0 (conv1) and Cn.
    stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1]
    num_stages = len(stage_indices)

    # find the index of the layer from which we wont freeze
    assert 0 <= trainable_layers <= num_stages
    freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers]

    for b in backbone[:freeze_before]:
        for parameter in b.parameters():

    return SSDLiteFeatureExtractorMobileNet(backbone, stage_indices[-2], norm_layer, **kwargs)

[docs]def ssdlite320_mobilenet_v3_large(pretrained: bool = False, progress: bool = True, num_classes: int = 91, pretrained_backbone: bool = False, trainable_backbone_layers: Optional[int] = None, norm_layer: Optional[Callable[..., nn.Module]] = None, **kwargs: Any): """Constructs an SSDlite model with input size 320x320 and a MobileNetV3 Large backbone, as described at `"Searching for MobileNetV3" <>`_ and `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <>`_. See :func:`~torchvision.models.detection.ssd300_vgg16` for more details. Example: >>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(pretrained=True) >>> model.eval() >>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)] >>> predictions = model(x) Args: pretrained (bool): If True, returns a model pre-trained on COCO train2017 progress (bool): If True, displays a progress bar of the download to stderr num_classes (int): number of output classes of the model (including the background) pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. norm_layer (callable, optional): Module specifying the normalization layer to use. """ if "size" in kwargs: warnings.warn("The size of the model is already fixed; ignoring the argument.") trainable_backbone_layers = _validate_trainable_layers( pretrained or pretrained_backbone, trainable_backbone_layers, 6, 6) if pretrained: pretrained_backbone = False # Enable reduced tail if no pretrained backbone is selected. See Table 6 of MobileNetV3 paper. reduce_tail = not pretrained_backbone if norm_layer is None: norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.03) backbone = _mobilenet_extractor("mobilenet_v3_large", progress, pretrained_backbone, trainable_backbone_layers, norm_layer, reduced_tail=reduce_tail, **kwargs) size = (320, 320) anchor_generator = DefaultBoxGenerator([[2, 3] for _ in range(6)], min_ratio=0.2, max_ratio=0.95) out_channels = det_utils.retrieve_out_channels(backbone, size) num_anchors = anchor_generator.num_anchors_per_location() assert len(out_channels) == len(anchor_generator.aspect_ratios) defaults = { "score_thresh": 0.001, "nms_thresh": 0.55, "detections_per_img": 300, "topk_candidates": 300, # Rescale the input in a way compatible to the backbone: # The following mean/std rescale the data from [0, 1] to [-1, -1] "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5], } kwargs = {**defaults, **kwargs} model = SSD(backbone, anchor_generator, size, num_classes, head=SSDLiteHead(out_channels, num_anchors, num_classes, norm_layer), **kwargs) if pretrained: weights_name = 'ssdlite320_mobilenet_v3_large_coco' if model_urls.get(weights_name, None) is None: raise ValueError("No checkpoint is available for model {}".format(weights_name)) state_dict = load_state_dict_from_url(model_urls[weights_name], progress=progress) model.load_state_dict(state_dict) return model


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