Args: cfg (CfgNode):

Args: model: a torch Module. Takes a data from data_loader and returns a dict of losses. data_loader:

Args: tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).

Arguments: tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1 image_sizes (list[tuple[int,

Arguments: keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint. The shape shou

Args: tensor (Tensor[float]): a Nx5 matrix. Each row is (x_center, y_center, width, height, angle),

Args: image_size (height, width): the spatial size of the image. kwargs: fields to add to this `Instances`.

Args: tensor: bool Tensor of N,H,W, representing N instances in the image.

Arguments: polygons (list[list[np.ndarray]]): The first level of the list correspond to individual instances,

Args: output_size (tuple): h, w spatial_scale (float): scale the input boxes by this number sampling_rati

Modulated deformable convolution. Arguments are similar to :class:`Conv2D`. Extra arguments: Args: deformable_g

Args: output_size (tuple): h, w spatial_scale (float): scale the input boxes by this number sampling_rati

Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: Args: norm (nn.Module, optional): a normali

Args: dataset_name (str): name of the dataset to be evaluated. It must have either the following corresponding me

Args: dataset_name (str): the name of the dataset. It must have the following metadata associated with it:

Args: dataset_name (str): name of the dataset, e.g., "voc_2007_test"

Initialize CocoEval using coco APIs for gt and dt :param cocoGt: coco object with ground truth annotations :param cocoDt: coc

Args: dataset_name (str): name of the dataset to be evaluated. It must have either the following corresponding me

Args: dataset_name (str): name of the dataset to be evaluated. It must have the following corresponding metadata:

Args: dataset_name (str): name of the dataset to be evaluated. distributed (True): if True, will collect results from

Args: dataset_name (str): name of the dataset output_dir (str): output directory to save results for evaluation

Args: predict_net, init_net (core.Net): caffe2 nets convert_outptus (callable): a function that converts caffe2

Yield a box as a Tensor of shape (4,) at a time.

Yield a box as a Tensor of shape (5,) at a time.

Yields: list[ndarray]: the polygons for one instance. Each Tensor is a float64 vector representing a polygon.

Produce something like: "MyTransformGen(field1={self.field1}, field2={self.field2})"

Evaluate predictions on the given tasks. Fill self._results with the metrics of the tasks.

Forward logic of the box prediction branch. Args: features (list[Tensor]): #level input features for box prediction

Returns: gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the total number of anchor