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October 11, 2019 11:40
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class KeypointRCNN(FasterRCNN): | |
""" | |
Implements Keypoint R-CNN. | |
The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each | |
image, and should be in 0-1 range. Different images can have different sizes. | |
The behavior of the model changes depending if it is in training or evaluation mode. | |
During training, the model expects both the input tensors, as well as a targets (list of dictionary), | |
containing: | |
- boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values | |
between 0 and H and 0 and W | |
- labels (Int64Tensor[N]): the class label for each ground-truth box | |
- keypoints (FloatTensor[N, K, 3]): the K keypoints location for each of the N instances, in the | |
format [x, y, visibility], where visibility=0 means that the keypoint is not visible. | |
The model returns a Dict[Tensor] during training, containing the classification and regression | |
losses for both the RPN and the R-CNN, and the keypoint loss. | |
During inference, the model requires only the input tensors, and returns the post-processed | |
predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as | |
follows: | |
- boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between | |
0 and H and 0 and W | |
- labels (Int64Tensor[N]): the predicted labels for each image | |
- scores (Tensor[N]): the scores or each prediction | |
- keypoints (FloatTensor[N, K, 3]): the locations of the predicted keypoints, in [x, y, v] format. | |
Arguments: | |
backbone (nn.Module): the network used to compute the features for the model. | |
It should contain a out_channels attribute, which indicates the number of output | |
channels that each feature map has (and it should be the same for all feature maps). | |
The backbone should return a single Tensor or and OrderedDict[Tensor]. | |
num_classes (int): number of output classes of the model (including the background). | |
If box_predictor is specified, num_classes should be None. | |
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 | |
image_mean (Tuple[float, float, float]): mean values used for input normalization. | |
They are generally the mean values of the dataset on which the backbone has been trained | |
on | |
image_std (Tuple[float, float, float]): std values used for input normalization. | |
They are generally the std values of the dataset on which the backbone has been trained on | |
rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature | |
maps. | |
rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN | |
rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training | |
rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing | |
rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training | |
rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing | |
rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals | |
rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be | |
considered as positive during training of the RPN. | |
rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be | |
considered as negative during training of the RPN. | |
rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN | |
for computing the loss | |
rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training | |
of the RPN | |
box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in | |
the locations indicated by the bounding boxes | |
box_head (nn.Module): module that takes the cropped feature maps as input | |
box_predictor (nn.Module): module that takes the output of box_head and returns the | |
classification logits and box regression deltas. | |
box_score_thresh (float): during inference, only return proposals with a classification score | |
greater than box_score_thresh | |
box_nms_thresh (float): NMS threshold for the prediction head. Used during inference | |
box_detections_per_img (int): maximum number of detections per image, for all classes. | |
box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be | |
considered as positive during training of the classification head | |
box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be | |
considered as negative during training of the classification head | |
box_batch_size_per_image (int): number of proposals that are sampled during training of the | |
classification head | |
box_positive_fraction (float): proportion of positive proposals in a mini-batch during training | |
of the classification head | |
bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the | |
bounding boxes | |
keypoint_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in | |
the locations indicated by the bounding boxes, which will be used for the keypoint head. | |
keypoint_head (nn.Module): module that takes the cropped feature maps as input | |
keypoint_predictor (nn.Module): module that takes the output of the keypoint_head and returns the | |
heatmap logits | |
Example:: | |
>>> import torchvision | |
>>> from torchvision.models.detection import KeypointRCNN | |
>>> from torchvision.models.detection.rpn import AnchorGenerator | |
>>> | |
>>> # load a pre-trained model for classification and return | |
>>> # only the features | |
>>> backbone = torchvision.models.mobilenet_v2(pretrained=True).features | |
>>> # KeypointRCNN needs to know the number of | |
>>> # output channels in a backbone. For mobilenet_v2, it's 1280 | |
>>> # so we need to add it here | |
>>> backbone.out_channels = 1280 | |
>>> | |
>>> # let's make the RPN generate 5 x 3 anchors per spatial | |
>>> # location, with 5 different sizes and 3 different aspect | |
>>> # ratios. We have a Tuple[Tuple[int]] because each feature | |
>>> # map could potentially have different sizes and | |
>>> # aspect ratios | |
>>> anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), | |
>>> aspect_ratios=((0.5, 1.0, 2.0),)) | |
>>> | |
>>> # let's define what are the feature maps that we will | |
>>> # use to perform the region of interest cropping, as well as | |
>>> # the size of the crop after rescaling. | |
>>> # if your backbone returns a Tensor, featmap_names is expected to | |
>>> # be [0]. More generally, the backbone should return an | |
>>> # OrderedDict[Tensor], and in featmap_names you can choose which | |
>>> # feature maps to use. | |
>>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0], | |
>>> output_size=7, | |
>>> sampling_ratio=2) | |
>>> | |
>>> keypoint_roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0], | |
>>> output_size=14, | |
>>> sampling_ratio=2) | |
>>> # put the pieces together inside a FasterRCNN model | |
>>> model = KeypointRCNN(backbone, | |
>>> num_classes=2, | |
>>> rpn_anchor_generator=anchor_generator, | |
>>> box_roi_pool=roi_pooler, | |
>>> keypoint_roi_pool=keypoint_roi_pooler) | |
>>> model.eval() | |
>>> model.eval() | |
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] | |
>>> predictions = model(x) | |
""" | |
def __init__(self, backbone, num_classes=None, | |
# transform parameters | |
min_size=None, max_size=1333, | |
image_mean=None, image_std=None, | |
# RPN parameters | |
rpn_anchor_generator=None, rpn_head=None, | |
rpn_pre_nms_top_n_train=2000, rpn_pre_nms_top_n_test=1000, | |
rpn_post_nms_top_n_train=2000, rpn_post_nms_top_n_test=1000, | |
rpn_nms_thresh=0.7, | |
rpn_fg_iou_thresh=0.7, rpn_bg_iou_thresh=0.3, | |
rpn_batch_size_per_image=256, rpn_positive_fraction=0.5, | |
# Box parameters | |
box_roi_pool=None, box_head=None, box_predictor=None, | |
box_score_thresh=0.05, box_nms_thresh=0.5, box_detections_per_img=100, | |
box_fg_iou_thresh=0.5, box_bg_iou_thresh=0.5, | |
box_batch_size_per_image=512, box_positive_fraction=0.25, | |
bbox_reg_weights=None, | |
# keypoint parameters | |
keypoint_roi_pool=None, keypoint_head=None, keypoint_predictor=None, | |
num_keypoints=17): | |
assert isinstance(keypoint_roi_pool, (MultiScaleRoIAlign, type(None))) | |
if min_size is None: | |
min_size = (640, 672, 704, 736, 768, 800) | |
if num_classes is not None: | |
if keypoint_predictor is not None: | |
raise ValueError("num_classes should be None when keypoint_predictor is specified") | |
out_channels = backbone.out_channels | |
if keypoint_roi_pool is None: | |
keypoint_roi_pool = MultiScaleRoIAlign( | |
featmap_names=[0, 1, 2, 3], | |
output_size=14, | |
sampling_ratio=2) | |
if keypoint_head is None: | |
keypoint_head = KeypointRCNNHeads(out_channels) | |
keypoint_head.init_weights() | |
if keypoint_predictor is None: | |
keypoint_dim_reduced = 384 | |
keypoint_predictor = KeypointRCNNPredictor(keypoint_dim_reduced, num_keypoints) | |
keypoint_predictor.init_weights() | |
super(KeypointRCNN, self).__init__( | |
backbone, num_classes, | |
# transform parameters | |
min_size, max_size, | |
image_mean, image_std, | |
# RPN-specific parameters | |
rpn_anchor_generator, rpn_head, | |
rpn_pre_nms_top_n_train, rpn_pre_nms_top_n_test, | |
rpn_post_nms_top_n_train, rpn_post_nms_top_n_test, | |
rpn_nms_thresh, | |
rpn_fg_iou_thresh, rpn_bg_iou_thresh, | |
rpn_batch_size_per_image, rpn_positive_fraction, | |
# Box parameters | |
box_roi_pool, box_head, box_predictor, | |
box_score_thresh, box_nms_thresh, box_detections_per_img, | |
box_fg_iou_thresh, box_bg_iou_thresh, | |
box_batch_size_per_image, box_positive_fraction, | |
bbox_reg_weights) | |
self.roi_heads.keypoint_roi_pool = keypoint_roi_pool | |
self.roi_heads.keypoint_head = keypoint_head | |
self.roi_heads.keypoint_predictor = keypoint_predictor | |
class KeypointRCNNHeads(nn.Sequential): | |
def __init__(self, in_channels): | |
super(KeypointRCNNHeads, self).__init__() | |
self.inplanes = in_channels | |
self.outplanes = 384 | |
self.deconv_layers = self._make_deconv_layer(3) | |
def forward(self, input: torch.Tensor): | |
x = self.deconv_layers(input) | |
return x | |
def _make_deconv_layer(self, num_layers): | |
layers = [] | |
for i in range(num_layers): | |
layers.append( | |
nn.ConvTranspose2d( | |
in_channels=self.inplanes, | |
out_channels=self.outplanes, | |
kernel_size=4, | |
stride=2, | |
padding=1, | |
output_padding=0, | |
bias=False)) | |
layers.append(nn.BatchNorm2d(self.outplanes)) | |
layers.append(nn.ReLU(inplace=True)) | |
self.inplanes = self.outplanes | |
return nn.Sequential(*layers) | |
def init_weights(self): | |
for name, m in self.deconv_layers.named_modules(): | |
if isinstance(m, nn.ConvTranspose2d): | |
nn.init.normal_(m.weight, std=0.001) | |
elif isinstance(m, nn.BatchNorm2d): | |
nn.init.constant_(m.weight, 1) | |
nn.init.constant_(m.bias, 0) | |
class KeypointRCNNPredictor(nn.Module): | |
def __init__(self, in_channels, num_keypoints): | |
super(KeypointRCNNPredictor, self).__init__() | |
self.final_layer = nn.Conv2d( | |
in_channels=in_channels, | |
out_channels=num_keypoints, | |
kernel_size=1, | |
stride=1, | |
padding=0 | |
) | |
self.out_channels = num_keypoints | |
def forward(self, x): | |
x = self.final_layer(x) | |
print(x.size(), "this is the ouput size ") | |
return x | |
def init_weights(self): | |
for m in self.final_layer.modules(): | |
if isinstance(m, nn.Conv2d): | |
nn.init.normal_(m.weight, std=0.001) | |
nn.init.constant_(m.bias, 0) | |
model_urls = { | |
'keypointrcnn_resnet50_fpn_coco': | |
'https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth', | |
} | |
def keypointrcnn_resnet50_fpn(pretrained=False, progress=True, | |
num_classes=2, num_keypoints=17, | |
pretrained_backbone=True, **kwargs): | |
""" | |
Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone. | |
The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each | |
image, and should be in ``0-1`` range. Different images can have different sizes. | |
The behavior of the model changes depending if it is in training or evaluation mode. | |
During training, the model expects both the input tensors, as well as a targets (list of dictionary), | |
containing: | |
- boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with values | |
between ``0`` and ``H`` and ``0`` and ``W`` | |
- labels (``Int64Tensor[N]``): the class label for each ground-truth box | |
- keypoints (``FloatTensor[N, K, 3]``): the ``K`` keypoints location for each of the ``N`` instances, in the | |
format ``[x, y, visibility]``, where ``visibility=0`` means that the keypoint is not visible. | |
The model returns a ``Dict[Tensor]`` during training, containing the classification and regression | |
losses for both the RPN and the R-CNN, and the keypoint loss. | |
During inference, the model requires only the input tensors, and returns the post-processed | |
predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as | |
follows: | |
- boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with values between | |
``0`` and ``H`` and ``0`` and ``W`` | |
- labels (``Int64Tensor[N]``): the predicted labels for each image | |
- scores (``Tensor[N]``): the scores or each prediction | |
- keypoints (``FloatTensor[N, K, 3]``): the locations of the predicted keypoints, in ``[x, y, v]`` format. | |
Example:: | |
>>> model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=True) | |
>>> model.eval() | |
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] | |
>>> predictions = model(x) | |
Arguments: | |
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 | |
""" | |
if pretrained: | |
# no need to download the backbone if pretrained is set | |
pretrained_backbone = False | |
backbone = resnet_fpn_backbone('resnet50', pretrained_backbone) | |
model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs) | |
if pretrained: | |
state_dict = load_state_dict_from_url(model_urls['keypointrcnn_resnet50_fpn_coco'], | |
progress=progress) | |
model.load_state_dict(state_dict) | |
return model |
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