def initialize(self, ctx): super(ObjectDetector, self).initialize(ctx) version = torchvision_version.split(".") if int(version[0]) == 0 and int(version[1]) < 6: self.initialized = False self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.model.to(self.device) self.model.eval() self.initialized = True
def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True): """ Args: output_size (tuple): h, w spatial_scale (float): scale the input boxes by this number sampling_ratio (int): number of inputs samples to take for each output sample. 0 to take samples densely. aligned (bool): if False, use the legacy implementation in Detectron. If True, align the results more perfectly. Note: The meaning of aligned=True: Given a continuous coordinate c, its two neighboring pixel indices (in our pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled from the underlying signal at continuous coordinates 0.5 and 1.5). But the original roi_align (aligned=False) does not subtract the 0.5 when computing neighboring pixel indices and therefore it uses pixels with a slightly incorrect alignment (relative to our pixel model) when performing bilinear interpolation. With `aligned=True`, we first appropriately scale the ROI and then shift it by -0.5 prior to calling roi_align. This produces the correct neighbors; see detectron2/tests/test_roi_align.py for verification. The difference does not make a difference to the model's performance if ROIAlign is used together with conv layers. """ super().__init__() self.output_size = output_size self.spatial_scale = spatial_scale self.sampling_ratio = sampling_ratio self.aligned = aligned from torchvision import __version__ version = tuple(int(x) for x in __version__.split(".")[:2])
import torch from torch import nn from torchvision.ops import roi_align as tv_roi_align try: from torchvision import __version__ version = tuple(int(x) for x in __version__.split(".")[:2]) USE_TORCHVISION = version >= (0, 7) # https://github.com/pytorch/vision/pull/2438 except ImportError: # only open source torchvision has __version__ USE_TORCHVISION = True if USE_TORCHVISION: roi_align = tv_roi_align else: from torch.nn.modules.utils import _pair from torch.autograd import Function from torch.autograd.function import once_differentiable from detectron2 import _C class _ROIAlign(Function): @staticmethod def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio, aligned): ctx.save_for_backward(roi) ctx.output_size = _pair(output_size) ctx.spatial_scale = spatial_scale ctx.sampling_ratio = sampling_ratio ctx.input_shape = input.size() ctx.aligned = aligned output = _C.roi_align_forward(