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Running on Zero
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Various positional encodings for the transformer. | |
| """ | |
| import math | |
| import torch | |
| from torch import nn | |
| from util.misc import NestedTensor | |
| class PositionEmbeddingSine(nn.Module): | |
| """ | |
| This is a more standard version of the position embedding, very similar to the one | |
| used by the Attention is all you need paper, generalized to work on images. | |
| Modified for 3dims (+Temporal) based on VisTR implementations (https://github.com/YuqingWang1029/VisTR/blob/master/models/position_encoding.py). | |
| """ | |
| def __init__(self, num_pos_feats=64, num_frames = 200, temperature=10000, normalize=False, scale=None): | |
| super().__init__() | |
| self.num_pos_feats = num_pos_feats | |
| self.temperature = temperature | |
| self.normalize = normalize | |
| self.frames = num_frames | |
| if scale is not None and normalize is False: | |
| raise ValueError("normalize should be True if scale is passed") | |
| if scale is None: | |
| scale = 2 * math.pi | |
| self.scale = scale | |
| def forward(self, tensor_list: NestedTensor): | |
| x = tensor_list.tensors | |
| mask = tensor_list.mask | |
| n,h,w = mask.shape | |
| mask = mask.reshape(n//self.frames, self.frames,h,w) | |
| assert mask is not None | |
| not_mask = ~mask | |
| z_embed = not_mask.cumsum(1, dtype=torch.float32) | |
| y_embed = not_mask.cumsum(2, dtype=torch.float32) | |
| x_embed = not_mask.cumsum(3, dtype=torch.float32) | |
| if self.normalize: | |
| eps = 1e-6 | |
| z_embed = z_embed / (z_embed[:, -1:, :, :] + eps) * self.scale | |
| y_embed = y_embed / (y_embed[:, :, -1:, :] + eps) * self.scale | |
| x_embed = x_embed / (x_embed[:, :, :, -1:] + eps) * self.scale | |
| dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) | |
| dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) | |
| pos_x = x_embed[:, :, :, :, None] / dim_t | |
| pos_y = y_embed[:, :, :, :, None] / dim_t | |
| pos_z = z_embed[:, :, :, :, None] / dim_t | |
| pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4) | |
| pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4) | |
| pos_z = torch.stack((pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()), dim=5).flatten(4) | |
| pos = torch.cat((pos_z, pos_y, pos_x), dim=4).permute(0, 1, 4, 2, 3) | |
| return pos | |
| def build_position_encoding(args): | |
| # Modify from 2 dimensions to 3 dimensions (+ Temporal) | |
| N_steps = args.hidden_dim // 3 | |
| if args.position_embedding in ('v2', 'sine'): | |
| # TODO find a better way of exposing other arguments | |
| position_embedding = PositionEmbeddingSine(N_steps, num_frames = args.max_process_window_length, normalize=True) | |
| else: | |
| raise ValueError(f"not supported {args.position_embedding}") | |
| return position_embedding | |