def __init__(self, dim_in):
super().__init__()
self.dim_in = dim_in
n_classes = cfg.MODEL.NUM_CLASSES if cfg.MRCNN.CLS_SPECIFIC_MASK else 1
if cfg.MRCNN.USE_FC_OUTPUT:
# Predict masks with a fully connected layer
self.classify = nn.Linear(dim_in, n_classes * cfg.MRCNN.RESOLUTION**2)
else:
# Predict mask using Conv
self.classify = nn.Conv2d(dim_in, n_classes, 1, 1, 0)
if cfg.MRCNN.UPSAMPLE_RATIO > 1:
self.upsample = mynn.BilinearInterpolation2d(
n_classes, n_classes, cfg.MRCNN.UPSAMPLE_RATIO)
self._init_weights()
def __init__(self, dim_in):
super(keypoint_outputs, self).__init__()
self.upsample_heatmap = (cfg.KRCNN.UP_SCALE > 1)
if cfg.KRCNN.USE_DECONV:
# Apply ConvTranspose to the feature representation; results in 2x # upsampling
self.deconv = nn.ConvTranspose2d(
dim_in,
cfg.KRCNN.DECONV_DIM,
cfg.KRCNN.DECONV_KERNEL,
2,
padding=int(cfg.KRCNN.DECONV_KERNEL / 2) - 1)
dim_in = cfg.KRCNN.DECONV_DIM
if cfg.KRCNN.USE_DECONV_OUTPUT:
# Use ConvTranspose to predict heatmaps; results in 2x upsampling
self.classify = nn.ConvTranspose2d(
dim_in,
cfg.KRCNN.NUM_KEYPOINTS,
cfg.KRCNN.DECONV_KERNEL,
2,
padding=int(cfg.KRCNN.DECONV_KERNEL / 2 - 1))
else:
# Use Conv to predict heatmaps; does no upsampling
self.classify = nn.Conv2d(dim_in,
cfg.KRCNN.NUM_KEYPOINTS,
1,
1,
padding=0)
if self.upsample_heatmap:
# self.upsample = nn.UpsamplingBilinear2d(scale_factor=cfg.KRCNN.UP_SCALE)
self.upsample = mynn.BilinearInterpolation2d(
cfg.KRCNN.NUM_KEYPOINTS, cfg.KRCNN.NUM_KEYPOINTS,
cfg.KRCNN.UP_SCALE)
self._init_weights()
