Deep Learning, Sora, Computer Vision, Transformers, Attention.
centers+(patch_size/2) fractional = centers/max(w, h) pe_out = self.pe(fractional) pe_out = 2* torch.pi * pe_out pe_out = torch.cat([torch.sin(pe_out), torch.cos(pe_out)], dim=-1) return pe_out Combining embedding and positional encoding class PatchEmbed(nn.Module): def __init__(self, in_chns,