Python generate_anchors.generate_anchors方法代码示例

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def __init__(self, dim_in, spatial_scale):
        super().__init__()
        self.dim_in = dim_in
        self.dim_out = dim_in if cfg.RPN.OUT_DIM_AS_IN_DIM else cfg.RPN.OUT_DIM
        anchors = generate_anchors(
            stride=1. / spatial_scale,
            sizes=cfg.RPN.SIZES,
            aspect_ratios=cfg.RPN.ASPECT_RATIOS)
        num_anchors = anchors.shape[0]

        # RPN hidden representation
        self.RPN_conv = nn.Conv2d(self.dim_in, self.dim_out, 3, 1, 1)
        # Proposal classification scores
        self.n_score_out = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
            else num_anchors
        self.RPN_cls_score = nn.Conv2d(self.dim_out, self.n_score_out, 1, 1, 0)
        # Proposal bbox regression deltas
        self.RPN_bbox_pred = nn.Conv2d(self.dim_out, num_anchors * 4, 1, 1, 0)

        self.RPN_GenerateProposals = GenerateProposalsOp(anchors, spatial_scale)
        self.RPN_GenerateProposalLabels = GenerateProposalLabelsOp()

        self._init_weights() 

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def __init__(self, dim_in, spatial_scales):
        super().__init__()
        self.dim_in = dim_in
        self.spatial_scales = spatial_scales
        self.dim_out = self.dim_in
        num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)

        # Create conv ops shared by all FPN levels
        self.FPN_RPN_conv = nn.Conv2d(dim_in, self.dim_out, 3, 1, 1)
        dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
            else num_anchors
        self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0)
        self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1, 0)

        self.GenerateProposals_modules = nn.ModuleList()
        k_max = cfg.FPN.RPN_MAX_LEVEL  # coarsest level of pyramid
        k_min = cfg.FPN.RPN_MIN_LEVEL  # finest level of pyramid
        for lvl in range(k_min, k_max + 1):
            sc = self.spatial_scales[k_max - lvl]  # in reversed order
            lvl_anchors = generate_anchors(
                stride=2.**lvl,
                sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2.**(lvl - k_min), ),
                aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS
            )
            self.GenerateProposals_modules.append(GenerateProposalsOp(lvl_anchors, sc))

        self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp()

        self._init_weights() 

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def __init__(self, dim_in, spatial_scales):
        super().__init__()
        self.dim_in = dim_in
        self.spatial_scales = spatial_scales
        self.dim_out = self.dim_in
        num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)

        # Create conv ops shared by all FPN levels
        self.FPN_RPN_conv = nn.Conv2d(dim_in, self.dim_out, 3, 1, 1)
        dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
            else num_anchors
        self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0)
        self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1, 0)

        self.GenerateProposals_modules = nn.ModuleList()
        k_max = cfg.FPN.RPN_MAX_LEVEL  # coarsest level of pyramid
        k_min = cfg.FPN.RPN_MIN_LEVEL  # finest level of pyramid
        for lvl in range(k_min, k_max + 1):
            sc = self.spatial_scales[k_max - lvl]  # in reversed order
            lvl_anchors = generate_anchors(
                stride=2. ** lvl,
                sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2. ** (lvl - k_min),),
                aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS
            )
            self.GenerateProposals_modules.append(GenerateProposalsOp(lvl_anchors, sc))

        self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp()

        self._init_weights() 

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def _create_cell_anchors():
    """
    Generate all types of anchors for all fpn levels/scales/aspect ratios.
    This function is called only once at the beginning of inference.
    """
    k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
    scales_per_octave = cfg.RETINANET.SCALES_PER_OCTAVE
    aspect_ratios = cfg.RETINANET.ASPECT_RATIOS
    anchor_scale = cfg.RETINANET.ANCHOR_SCALE
    A = scales_per_octave * len(aspect_ratios)
    anchors = {}
    for lvl in range(k_min, k_max + 1):
        # create cell anchors array
        stride = 2. ** lvl
        cell_anchors = np.zeros((A, 4))
        a = 0
        for octave in range(scales_per_octave):
            octave_scale = 2 ** (octave / float(scales_per_octave))
            for aspect in aspect_ratios:
                anchor_sizes = (stride * octave_scale * anchor_scale, )
                anchor_aspect_ratios = (aspect, )
                cell_anchors[a, :] = generate_anchors(
                    stride=stride, sizes=anchor_sizes,
                    aspect_ratios=anchor_aspect_ratios)
                a += 1
        anchors[lvl] = cell_anchors
    return anchors 

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def get_anchors(spatial_scale):
    anchors = generate_anchors.generate_anchors(
        stride=1. / spatial_scale,
        sizes=cfg.RPN.SIZES,
        aspect_ratios=cfg.RPN.ASPECT_RATIOS).astype(np.float32)
    return anchors 

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def create_cell_anchors():
    """
    Generate all types of anchors for all fpn levels/scales/aspect ratios.
    This function is called only once at the beginning of inference.
    """
    k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
    scales_per_octave = cfg.RETINANET.SCALES_PER_OCTAVE
    aspect_ratios = cfg.RETINANET.ASPECT_RATIOS
    anchor_scale = cfg.RETINANET.ANCHOR_SCALE
    A = scales_per_octave * len(aspect_ratios)
    anchors = {}
    for lvl in range(k_min, k_max + 1):
        # create cell anchors array
        stride = 2. ** lvl
        cell_anchors = np.zeros((A, 4))
        a = 0
        for octave in range(scales_per_octave):
            octave_scale = 2 ** (octave / float(scales_per_octave))
            for aspect in aspect_ratios:
                anchor_sizes = (stride * octave_scale * anchor_scale, )
                anchor_aspect_ratios = (aspect, )
                cell_anchors[a, :] = generate_anchors(
                    stride=stride, sizes=anchor_sizes,
                    aspect_ratios=anchor_aspect_ratios)
                a += 1
        anchors[lvl] = cell_anchors
    return anchors 

# 需要导入模块: from modeling import generate_anchors [as 别名]
# 或者: from modeling.generate_anchors import generate_anchors [as 别名]
def get_field_of_anchors(
    stride, anchor_sizes, anchor_aspect_ratios, octave=None, aspect=None
):
    global _threadlocal_foa
    if not hasattr(_threadlocal_foa, 'cache'):
        _threadlocal_foa.cache = {}

    cache_key = str(stride) + str(anchor_sizes) + str(anchor_aspect_ratios)
    if cache_key in _threadlocal_foa.cache:
        return _threadlocal_foa.cache[cache_key]

    # Anchors at a single feature cell
    cell_anchors = generate_anchors(
        stride=stride, sizes=anchor_sizes, aspect_ratios=anchor_aspect_ratios
    )
    num_cell_anchors = cell_anchors.shape[0]

    # Generate canonical proposals from shifted anchors
    # Enumerate all shifted positions on the (H, W) grid
    fpn_max_size = cfg.FPN.COARSEST_STRIDE * np.ceil(
        cfg.TRAIN.MAX_SIZE / float(cfg.FPN.COARSEST_STRIDE)
    )
    field_size = int(np.ceil(fpn_max_size / float(stride)))
    shifts = np.arange(0, field_size) * stride
    shift_x, shift_y = np.meshgrid(shifts, shifts)
    shift_x = shift_x.ravel()
    shift_y = shift_y.ravel()
    shifts = np.vstack((shift_x, shift_y, shift_x, shift_y)).transpose()

    # Broacast anchors over shifts to enumerate all anchors at all positions
    # in the (H, W) grid:
    #   - add A cell anchors of shape (1, A, 4) to
    #   - K shifts of shape (K, 1, 4) to get
    #   - all shifted anchors of shape (K, A, 4)
    #   - reshape to (K*A, 4) shifted anchors
    A = num_cell_anchors
    K = shifts.shape[0]
    field_of_anchors = (
        cell_anchors.reshape((1, A, 4)) +
        shifts.reshape((1, K, 4)).transpose((1, 0, 2))
    )
    field_of_anchors = field_of_anchors.reshape((K * A, 4))
    foa = FieldOfAnchors(
        field_of_anchors=field_of_anchors.astype(np.float32),
        num_cell_anchors=num_cell_anchors,
        stride=stride,
        field_size=field_size,
        octave=octave,
        aspect=aspect
    )
    _threadlocal_foa.cache[cache_key] = foa
    return foa