Add MobileNetV3 architecture for Detection (#3253)

* Minor refactoring of a private method to make it reusuable.

* Adding a FasterRCNN + MobileNetV3 with & w/o FPN models.

* Reducing Resolution to 320-640 and anchor sizes to 16-256.

* Increase anchor sizes.

* Adding rpn score threshold param on the train script.

* Adding trainable_backbone_layers param on the train script.

* Adding rpn_score_thresh param directly in fasterrcnn_mobilenet_v3_large_fpn.

* Remove fasterrcnn_mobilenet_v3_large prototype and update expected file.

* Update documentation and adding weights.

* Use buildin Identity.

* Fix spelling.

docs/source/models.rst

@@ -358,13 +358,14 @@ models return the predictions of the following classes:
 Here are the summary of the accuracies for the models trained on
 the instances set of COCO train2017 and evaluated on COCO val2017.
 
-================================  =======  ========  ===========
-Network                           box AP   mask AP   keypoint AP
-================================  =======  ========  ===========
-Faster R-CNN ResNet-50 FPN        37.0     -         -
-RetinaNet ResNet-50 FPN           36.4     -         -
-Mask R-CNN ResNet-50 FPN          37.9     34.6      -
-================================  =======  ========  ===========
+==================================  =======  ========  ===========
+Network                             box AP   mask AP   keypoint AP
+==================================  =======  ========  ===========
+Faster R-CNN ResNet-50 FPN          37.0     -         -
+Faster R-CNN MobileNetV3-Large FPN  23.0     -         -
+RetinaNet ResNet-50 FPN             36.4     -         -
+Mask R-CNN ResNet-50 FPN            37.9     34.6      -
+==================================  =======  ========  ===========
 
 For person keypoint detection, the accuracies for the pre-trained
 models are as follows
@@ -414,20 +415,22 @@ For test time, we report the time for the model evaluation and postprocessing
 (including mask pasting in image), but not the time for computing the
 precision-recall.
 
-==============================  ===================  ==================  ===========
-Network                         train time (s / it)  test time (s / it)  memory (GB)
-==============================  ===================  ==================  ===========
-Faster R-CNN ResNet-50 FPN      0.2288               0.0590              5.2
-RetinaNet ResNet-50 FPN         0.2514               0.0939              4.1
-Mask R-CNN ResNet-50 FPN        0.2728               0.0903              5.4
-Keypoint R-CNN ResNet-50 FPN    0.3789               0.1242              6.8
-==============================  ===================  ==================  ===========
+==================================  ===================  ==================  ===========
+Network                             train time (s / it)  test time (s / it)  memory (GB)
+==================================  ===================  ==================  ===========
+Faster R-CNN ResNet-50 FPN          0.2288               0.0590              5.2
+Faster R-CNN MobileNetV3-Large FPN  0.0978               0.0376              0.6
+RetinaNet ResNet-50 FPN             0.2514               0.0939              4.1
+Mask R-CNN ResNet-50 FPN            0.2728               0.0903              5.4
+Keypoint R-CNN ResNet-50 FPN        0.3789               0.1242              6.8
+==================================  ===================  ==================  ===========
 
 
 Faster R-CNN
 ------------
 
 .. autofunction:: torchvision.models.detection.fasterrcnn_resnet50_fpn
+.. autofunction:: torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn
 
 
 RetinaNet

references/detection/README.md

@@ -20,13 +20,20 @@ You must modify the following flags:
 
 Except otherwise noted, all models have been trained on 8x V100 GPUs. 
 
-### Faster R-CNN
+### Faster R-CNN ResNet-50 FPN
 ```
 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
     --dataset coco --model fasterrcnn_resnet50_fpn --epochs 26\
     --lr-steps 16 22 --aspect-ratio-group-factor 3
 ```
 
+### Faster R-CNN MobileNetV3-Large FPN
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
+    --dataset coco --model fasterrcnn_mobilenet_v3_large_fpn --epochs 26\
+    --lr-steps 16 22 --aspect-ratio-group-factor 3
+```
+
 ### RetinaNet
 ```
 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\

references/detection/train.py

@@ -92,9 +92,12 @@ def main(args):
         collate_fn=utils.collate_fn)
 
     print("Creating model")
-    kwargs = {}
+    kwargs = {
+        "trainable_backbone_layers": args.trainable_backbone_layers
+    }
     if "rcnn" in args.model:
-        kwargs["rpn_score_thresh"] = 0.0
+        if args.rpn_score_thresh is not None:
+            kwargs["rpn_score_thresh"] = args.rpn_score_thresh
     model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes, pretrained=args.pretrained,
                                                               **kwargs)
     model.to(device)
@@ -177,6 +180,9 @@ if __name__ == "__main__":
     parser.add_argument('--resume', default='', help='resume from checkpoint')
     parser.add_argument('--start_epoch', default=0, type=int, help='start epoch')
     parser.add_argument('--aspect-ratio-group-factor', default=3, type=int)
+    parser.add_argument('--rpn-score-thresh', default=None, type=float, help='rpn score threshold for faster-rcnn')
+    parser.add_argument('--trainable-backbone-layers', default=None, type=int,
+                        help='number of trainable layers of backbone')
     parser.add_argument(
         "--test-only",
         dest="test_only",

test/test_models.py

@@ -37,6 +37,7 @@ script_model_unwrapper = {
     'googlenet': lambda x: x.logits,
     'inception_v3': lambda x: x.logits,
     "fasterrcnn_resnet50_fpn": lambda x: x[1],
+    "fasterrcnn_mobilenet_v3_large_fpn": lambda x: x[1],
     "maskrcnn_resnet50_fpn": lambda x: x[1],
     "keypointrcnn_resnet50_fpn": lambda x: x[1],
     "retinanet_resnet50_fpn": lambda x: x[1],
@@ -105,6 +106,8 @@ class ModelTester(TestCase):
         if "retinanet" in name:
             # Reduce the default threshold to ensure the returned boxes are not empty.
             kwargs["score_thresh"] = 0.01
+        elif "fasterrcnn_mobilenet" in name:
+            kwargs["box_score_thresh"] = 0.02076
         model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False, **kwargs)
         model.eval().to(device=dev)
         input_shape = (3, 300, 300)

test/test_models_detection_negative_samples.py

@@ -97,14 +97,15 @@ class Tester(unittest.TestCase):
         self.assertEqual(labels[0].dtype, torch.int64)
 
     def test_forward_negative_sample_frcnn(self):
-        model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
-            num_classes=2, min_size=100, max_size=100)
+        for name in ["fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large_fpn"]:
+            model = torchvision.models.detection.__dict__[name](
+                num_classes=2, min_size=100, max_size=100)
 
-        images, targets = self._make_empty_sample()
-        loss_dict = model(images, targets)
+            images, targets = self._make_empty_sample()
+            loss_dict = model(images, targets)
 
-        self.assertEqual(loss_dict["loss_box_reg"], torch.tensor(0.))
-        self.assertEqual(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
+            self.assertEqual(loss_dict["loss_box_reg"], torch.tensor(0.))
+            self.assertEqual(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
 
     def test_forward_negative_sample_mrcnn(self):
         model = torchvision.models.detection.maskrcnn_resnet50_fpn(
@@ -130,7 +131,7 @@ class Tester(unittest.TestCase):
 
     def test_forward_negative_sample_retinanet(self):
         model = torchvision.models.detection.retinanet_resnet50_fpn(
-            num_classes=2, min_size=100, max_size=100)
+            num_classes=2, min_size=100, max_size=100, pretrained_backbone=False)
 
         images, targets = self._make_empty_sample()
         loss_dict = model(images, targets)

test/test_models_detection_utils.py

@@ -36,17 +36,17 @@ class Tester(unittest.TestCase):
 
     def test_validate_resnet_inputs_detection(self):
         # default number of backbone layers to train
-        ret = backbone_utils._validate_resnet_trainable_layers(
-            pretrained=True, trainable_backbone_layers=None)
+        ret = backbone_utils._validate_trainable_layers(
+            pretrained=True, trainable_backbone_layers=None, max_value=5, default_value=3)
         self.assertEqual(ret, 3)
         # can't go beyond 5
         with self.assertRaises(AssertionError):
-            ret = backbone_utils._validate_resnet_trainable_layers(
-                pretrained=True, trainable_backbone_layers=6)
+            ret = backbone_utils._validate_trainable_layers(
+                pretrained=True, trainable_backbone_layers=6, max_value=5, default_value=3)
         # if not pretrained, should use all trainable layers and warn
         with self.assertWarns(UserWarning):
-            ret = backbone_utils._validate_resnet_trainable_layers(
-                pretrained=False, trainable_backbone_layers=0)
+            ret = backbone_utils._validate_trainable_layers(
+                pretrained=False, trainable_backbone_layers=0, max_value=5, default_value=3)
         self.assertEqual(ret, 5)
 
     def test_transform_copy_targets(self):

torchvision/models/detection/backbone_utils.py

 import warnings
-from collections import OrderedDict
 from torch import nn
 from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
 
 from torchvision.ops import misc as misc_nn_ops
 from .._utils import IntermediateLayerGetter
+from .. import mobilenet
 from .. import resnet
 
 
@@ -108,17 +108,65 @@ def resnet_fpn_backbone(
     return BackboneWithFPN(backbone, return_layers, in_channels_list, out_channels, extra_blocks=extra_blocks)
 
 
-def _validate_resnet_trainable_layers(pretrained, trainable_backbone_layers):
+def _validate_trainable_layers(pretrained, trainable_backbone_layers, max_value, default_value):
     # dont freeze any layers if pretrained model or backbone is not used
     if not pretrained:
         if trainable_backbone_layers is not None:
             warnings.warn(
                 "Changing trainable_backbone_layers has not effect if "
                 "neither pretrained nor pretrained_backbone have been set to True, "
-                "falling back to trainable_backbone_layers=5 so that all layers are trainable")
-        trainable_backbone_layers = 5
-    # by default, freeze first 2 blocks following Faster R-CNN
+                "falling back to trainable_backbone_layers={} so that all layers are trainable".format(max_value))
+        trainable_backbone_layers = max_value
+
+    # by default freeze first blocks
     if trainable_backbone_layers is None:
-        trainable_backbone_layers = 3
-    assert trainable_backbone_layers <= 5 and trainable_backbone_layers >= 0
+        trainable_backbone_layers = default_value
+    assert 0 <= trainable_backbone_layers <= max_value
     return trainable_backbone_layers
+
+
+def mobilenet_backbone(
+    backbone_name,
+    pretrained,
+    fpn,
+    norm_layer=misc_nn_ops.FrozenBatchNorm2d,
+    trainable_layers=2,
+    returned_layers=None,
+    extra_blocks=None
+):
+    backbone = mobilenet.__dict__[backbone_name](pretrained=pretrained, norm_layer=norm_layer).features
+
+    # Gather the indeces 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_strided", 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 = num_stages if trainable_layers == 0 else stage_indices[num_stages - trainable_layers]
+
+    # freeze layers only if pretrained backbone is used
+    for b in backbone[:freeze_before]:
+        for parameter in b.parameters():
+            parameter.requires_grad_(False)
+
+    out_channels = 256
+    if fpn:
+        if extra_blocks is None:
+            extra_blocks = LastLevelMaxPool()
+
+        if returned_layers is None:
+            returned_layers = [num_stages - 2, num_stages - 1]
+        assert min(returned_layers) >= 0 and max(returned_layers) < num_stages
+        return_layers = {f'{stage_indices[k]}': str(v) for v, k in enumerate(returned_layers)}
+
+        in_channels_list = [backbone[stage_indices[i]].out_channels for i in returned_layers]
+        return BackboneWithFPN(backbone, return_layers, in_channels_list, out_channels, extra_blocks=extra_blocks)
+    else:
+        m = nn.Sequential(
+            backbone,
+            # depthwise linear combination of channels to reduce their size
+            nn.Conv2d(backbone[-1].out_channels, out_channels, 1),
+        )
+        m.out_channels = out_channels
+        return m

torchvision/models/detection/faster_rcnn.py

-from collections import OrderedDict
-
 import torch
 from torch import nn
 import torch.nn.functional as F
 
-from torchvision.ops import misc as misc_nn_ops
 from torchvision.ops import MultiScaleRoIAlign
 
 from ._utils import overwrite_eps
@@ -15,11 +12,11 @@ from .generalized_rcnn import GeneralizedRCNN
 from .rpn import RPNHead, RegionProposalNetwork
 from .roi_heads import RoIHeads
 from .transform import GeneralizedRCNNTransform
-from .backbone_utils import resnet_fpn_backbone, _validate_resnet_trainable_layers
+from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers, mobilenet_backbone
 
 
 __all__ = [
-    "FasterRCNN", "fasterrcnn_resnet50_fpn",
+    "FasterRCNN", "fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large_fpn"
 ]
 
 
@@ -291,6 +288,8 @@ class FastRCNNPredictor(nn.Module):
 model_urls = {
     'fasterrcnn_resnet50_fpn_coco':
         'https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth',
+    'fasterrcnn_mobilenet_v3_large_fpn_coco':
+        'https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-907ea3f9.pth',
 }
 
 
@@ -353,9 +352,8 @@ def fasterrcnn_resnet50_fpn(pretrained=False, progress=True,
         trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
     """
-    # check default parameters and by default set it to 3 if possible
-    trainable_backbone_layers = _validate_resnet_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers)
+    trainable_backbone_layers = _validate_trainable_layers(
+        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3)
 
     if pretrained:
         # no need to download the backbone if pretrained is set
@@ -368,3 +366,48 @@ def fasterrcnn_resnet50_fpn(pretrained=False, progress=True,
         model.load_state_dict(state_dict)
         overwrite_eps(model, 0.0)
     return model
+
+
+def fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True,
+                                      trainable_backbone_layers=None, min_size=320, max_size=640, rpn_score_thresh=0.05,
+                                      **kwargs):
+    """
+    Constructs a Faster R-CNN model with a MobileNetV3-Large FPN backbone. It works similarly
+    to Faster R-CNN with ResNet-50 FPN backbone. See `fasterrcnn_resnet50_fpn` for more details.
+
+    Example::
+
+        >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=True)
+        >>> model.eval()
+        >>> x = [torch.rand(3, 300, 400), 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.
+        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
+        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        rpn_score_thresh (float): during inference, only return proposals with a classification score
+            greater than rpn_score_thresh
+    """
+    trainable_backbone_layers = _validate_trainable_layers(
+        pretrained or pretrained_backbone, trainable_backbone_layers, 6, 3)
+
+    if pretrained:
+        pretrained_backbone = False
+    backbone = mobilenet_backbone("mobilenet_v3_large", pretrained_backbone, True,
+                                  trainable_layers=trainable_backbone_layers)
+
+    anchor_sizes = ((32, 64, 128, 256, 512, ), ) * 3
+    aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
+
+    model = FasterRCNN(backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios),
+                       min_size=min_size, max_size=max_size, rpn_score_thresh=rpn_score_thresh, **kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls['fasterrcnn_mobilenet_v3_large_fpn_coco'], progress=progress)
+        model.load_state_dict(state_dict)
+    return model

torchvision/models/detection/keypoint_rcnn.py

@@ -7,7 +7,7 @@ from ._utils import overwrite_eps
 from ..utils import load_state_dict_from_url
 
 from .faster_rcnn import FasterRCNN
-from .backbone_utils import resnet_fpn_backbone, _validate_resnet_trainable_layers
+from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers
 
 
 __all__ = [
@@ -322,9 +322,8 @@ def keypointrcnn_resnet50_fpn(pretrained=False, progress=True,
         trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
     """
-    # check default parameters and by default set it to 3 if possible
-    trainable_backbone_layers = _validate_resnet_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers)
+    trainable_backbone_layers = _validate_trainable_layers(
+        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3)
 
     if pretrained:
         # no need to download the backbone if pretrained is set

torchvision/models/detection/mask_rcnn.py

@@ -8,7 +8,7 @@ from ._utils import overwrite_eps
 from ..utils import load_state_dict_from_url
 
 from .faster_rcnn import FasterRCNN
-from .backbone_utils import resnet_fpn_backbone, _validate_resnet_trainable_layers
+from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers
 
 __all__ = [
     "MaskRCNN", "maskrcnn_resnet50_fpn",
@@ -317,9 +317,8 @@ def maskrcnn_resnet50_fpn(pretrained=False, progress=True,
         trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
     """
-    # check default parameters and by default set it to 3 if possible
-    trainable_backbone_layers = _validate_resnet_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers)
+    trainable_backbone_layers = _validate_trainable_layers(
+        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3)
 
     if pretrained:
         # no need to download the backbone if pretrained is set

torchvision/models/detection/retinanet.py

@@ -12,14 +12,14 @@ from ..utils import load_state_dict_from_url
 from . import _utils as det_utils
 from .anchor_utils import AnchorGenerator
 from .transform import GeneralizedRCNNTransform
-from .backbone_utils import resnet_fpn_backbone, _validate_resnet_trainable_layers
+from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers
 from ...ops.feature_pyramid_network import LastLevelP6P7
 from ...ops import sigmoid_focal_loss
 from ...ops import boxes as box_ops
 
 
 __all__ = [
-    "RetinaNet", "retinanet_resnet50_fpn",
+    "RetinaNet", "retinanet_resnet50_fpn"
 ]
 
 
@@ -605,9 +605,8 @@ def retinanet_resnet50_fpn(pretrained=False, progress=True,
         trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
     """
-    # check default parameters and by default set it to 3 if possible
-    trainable_backbone_layers = _validate_resnet_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers)
+    trainable_backbone_layers = _validate_trainable_layers(
+        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3)
 
     if pretrained:
         # no need to download the backbone if pretrained is set

torchvision/models/mobilenetv3.py

@@ -18,16 +18,6 @@ model_urls = {
 }
 
 
-class Identity(nn.Module):
-
-    def __init__(self, inplace: bool = False):
-        super().__init__()
-        self.inplace = inplace
-
-    def forward(self, input: Tensor) -> Tensor:
-        return input
-
-
 class SqueezeExcitation(nn.Module):
 
     def __init__(self, input_channels: int, squeeze_factor: int = 4):
@@ -88,7 +78,7 @@ class InvertedResidual(nn.Module):
 
         # project
         layers.append(ConvBNActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer,
-                                       activation_layer=Identity))
+                                       activation_layer=nn.Identity))
 
         self.block = nn.Sequential(*layers)
         self.out_channels = cnf.out_channels