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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
""" | |
Various positional encodings for the transformer. | |
""" | |
import math | |
import torch | |
from torch import nn | |
import pdb | |
from .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. | |
""" | |
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): | |
super().__init__() | |
self.num_pos_feats = num_pos_feats | |
self.temperature = temperature | |
self.normalize = normalize | |
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 | |
assert mask is not None | |
not_mask = ~mask | |
y_embed = not_mask.cumsum(1, dtype=torch.float32) | |
x_embed = not_mask.cumsum(2, dtype=torch.float32) | |
if self.normalize: | |
eps = 1e-6 | |
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_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) | |
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) | |
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) | |
#pdb.set_trace() | |
return pos | |
class PositionEmbeddingLearned(nn.Module): | |
""" | |
Absolute pos embedding, learned. | |
""" | |
def __init__(self, num_pos_feats=256): | |
super().__init__() | |
self.row_embed = nn.Embedding(50, num_pos_feats) | |
self.col_embed = nn.Embedding(50, num_pos_feats) | |
self.reset_parameters() | |
def reset_parameters(self): | |
nn.init.uniform_(self.row_embed.weight) | |
nn.init.uniform_(self.col_embed.weight) | |
def forward(self, tensor_list: NestedTensor): | |
x = tensor_list.tensors | |
h, w = x.shape[-2:] | |
i = torch.arange(w, device=x.device) | |
j = torch.arange(h, device=x.device) | |
x_emb = self.col_embed(i) | |
y_emb = self.row_embed(j) | |
pos = torch.cat([ | |
x_emb.unsqueeze(0).repeat(h, 1, 1), | |
y_emb.unsqueeze(1).repeat(1, w, 1), | |
], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) | |
return pos | |
def build_position_encoding(args): | |
N_steps = args.hidden_dim // 2 | |
print("biuld1") | |
if args.position_embedding in ('v2', 'sine'): | |
print("biuld2") | |
# TODO find a better way of exposing other arguments | |
position_embedding = PositionEmbeddingSine(N_steps, normalize=True) | |
elif args.position_embedding in ('v3', 'learned'): | |
position_embedding = PositionEmbeddingLearned(N_steps) | |
else: | |
raise ValueError(f"not supported {args.position_embedding}") | |
return position_embedding | |