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Source code for mmdet.core.anchor.point_generator

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from torch.nn.modules.utils import _pair

from .builder import PRIOR_GENERATORS


@PRIOR_GENERATORS.register_module()
class PointGenerator:

    def _meshgrid(self, x, y, row_major=True):
        xx = x.repeat(len(y))
        yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
        if row_major:
            return xx, yy
        else:
            return yy, xx

    def grid_points(self, featmap_size, stride=16, device='cuda'):
        feat_h, feat_w = featmap_size
        shift_x = torch.arange(0., feat_w, device=device) * stride
        shift_y = torch.arange(0., feat_h, device=device) * stride
        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
        stride = shift_x.new_full((shift_xx.shape[0], ), stride)
        shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1)
        all_points = shifts.to(device)
        return all_points

    def valid_flags(self, featmap_size, valid_size, device='cuda'):
        feat_h, feat_w = featmap_size
        valid_h, valid_w = valid_size
        assert valid_h <= feat_h and valid_w <= feat_w
        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
        valid_x[:valid_w] = 1
        valid_y[:valid_h] = 1
        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
        valid = valid_xx & valid_yy
        return valid


[docs]@PRIOR_GENERATORS.register_module() class MlvlPointGenerator: """Standard points generator for multi-level (Mlvl) feature maps in 2D points-based detectors. Args: strides (list[int] | list[tuple[int, int]]): Strides of anchors in multiple feature levels in order (w, h). offset (float): The offset of points, the value is normalized with corresponding stride. Defaults to 0.5. """ def __init__(self, strides, offset=0.5): self.strides = [_pair(stride) for stride in strides] self.offset = offset @property def num_levels(self): """int: number of feature levels that the generator will be applied""" return len(self.strides) @property def num_base_priors(self): """list[int]: The number of priors (points) at a point on the feature grid""" return [1 for _ in range(len(self.strides))] def _meshgrid(self, x, y, row_major=True): yy, xx = torch.meshgrid(y, x) if row_major: # warning .flatten() would cause error in ONNX exporting # have to use reshape here return xx.reshape(-1), yy.reshape(-1) else: return yy.reshape(-1), xx.reshape(-1)
[docs] def grid_priors(self, featmap_sizes, dtype=torch.float32, device='cuda', with_stride=False): """Generate grid points of multiple feature levels. Args: featmap_sizes (list[tuple]): List of feature map sizes in multiple feature levels, each size arrange as as (h, w). dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. device (str): The device where the anchors will be put on. with_stride (bool): Whether to concatenate the stride to the last dimension of points. Return: list[torch.Tensor]: Points of multiple feature levels. The sizes of each tensor should be (N, 2) when with stride is ``False``, where N = width * height, width and height are the sizes of the corresponding feature level, and the last dimension 2 represent (coord_x, coord_y), otherwise the shape should be (N, 4), and the last dimension 4 represent (coord_x, coord_y, stride_w, stride_h). """ assert self.num_levels == len(featmap_sizes) multi_level_priors = [] for i in range(self.num_levels): priors = self.single_level_grid_priors( featmap_sizes[i], level_idx=i, dtype=dtype, device=device, with_stride=with_stride) multi_level_priors.append(priors) return multi_level_priors
[docs] def single_level_grid_priors(self, featmap_size, level_idx, dtype=torch.float32, device='cuda', with_stride=False): """Generate grid Points of a single level. Note: This function is usually called by method ``self.grid_priors``. Args: featmap_size (tuple[int]): Size of the feature maps, arrange as (h, w). level_idx (int): The index of corresponding feature map level. dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. device (str, optional): The device the tensor will be put on. Defaults to 'cuda'. with_stride (bool): Concatenate the stride to the last dimension of points. Return: Tensor: Points of single feature levels. The shape of tensor should be (N, 2) when with stride is ``False``, where N = width * height, width and height are the sizes of the corresponding feature level, and the last dimension 2 represent (coord_x, coord_y), otherwise the shape should be (N, 4), and the last dimension 4 represent (coord_x, coord_y, stride_w, stride_h). """ feat_h, feat_w = featmap_size stride_w, stride_h = self.strides[level_idx] shift_x = (torch.arange(0, feat_w, device=device) + self.offset) * stride_w # keep featmap_size as Tensor instead of int, so that we # can convert to ONNX correctly shift_x = shift_x.to(dtype) shift_y = (torch.arange(0, feat_h, device=device) + self.offset) * stride_h # keep featmap_size as Tensor instead of int, so that we # can convert to ONNX correctly shift_y = shift_y.to(dtype) shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) if not with_stride: shifts = torch.stack([shift_xx, shift_yy], dim=-1) else: # use `shape[0]` instead of `len(shift_xx)` for ONNX export stride_w = shift_xx.new_full((shift_xx.shape[0], ), stride_w).to(dtype) stride_h = shift_xx.new_full((shift_yy.shape[0], ), stride_h).to(dtype) shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h], dim=-1) all_points = shifts.to(device) return all_points
[docs] def valid_flags(self, featmap_sizes, pad_shape, device='cuda'): """Generate valid flags of points of multiple feature levels. Args: featmap_sizes (list(tuple)): List of feature map sizes in multiple feature levels, each size arrange as as (h, w). pad_shape (tuple(int)): The padded shape of the image, arrange as (h, w). device (str): The device where the anchors will be put on. Return: list(torch.Tensor): Valid flags of points of multiple levels. """ assert self.num_levels == len(featmap_sizes) multi_level_flags = [] for i in range(self.num_levels): point_stride = self.strides[i] feat_h, feat_w = featmap_sizes[i] h, w = pad_shape[:2] valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h) valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w) flags = self.single_level_valid_flags((feat_h, feat_w), (valid_feat_h, valid_feat_w), device=device) multi_level_flags.append(flags) return multi_level_flags
[docs] def single_level_valid_flags(self, featmap_size, valid_size, device='cuda'): """Generate the valid flags of points of a single feature map. Args: featmap_size (tuple[int]): The size of feature maps, arrange as as (h, w). valid_size (tuple[int]): The valid size of the feature maps. The size arrange as as (h, w). device (str, optional): The device where the flags will be put on. Defaults to 'cuda'. Returns: torch.Tensor: The valid flags of each points in a single level \ feature map. """ feat_h, feat_w = featmap_size valid_h, valid_w = valid_size assert valid_h <= feat_h and valid_w <= feat_w valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device) valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device) valid_x[:valid_w] = 1 valid_y[:valid_h] = 1 valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) valid = valid_xx & valid_yy return valid
[docs] def sparse_priors(self, prior_idxs, featmap_size, level_idx, dtype=torch.float32, device='cuda'): """Generate sparse points according to the ``prior_idxs``. Args: prior_idxs (Tensor): The index of corresponding anchors in the feature map. featmap_size (tuple[int]): feature map size arrange as (w, h). level_idx (int): The level index of corresponding feature map. dtype (obj:`torch.dtype`): Date type of points. Defaults to ``torch.float32``. device (obj:`torch.device`): The device where the points is located. Returns: Tensor: Anchor with shape (N, 2), N should be equal to the length of ``prior_idxs``. And last dimension 2 represent (coord_x, coord_y). """ height, width = featmap_size x = (prior_idxs % width + self.offset) * self.strides[level_idx][0] y = ((prior_idxs // width) % height + self.offset) * self.strides[level_idx][1] prioris = torch.stack([x, y], 1).to(dtype) prioris = prioris.to(device) return prioris
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