| import math |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from utils.math import truncated_normal_ |
|
|
|
|
| class Downsample2D(nn.Module): |
| def __init__(self, mode='nearest', scale=4): |
| super().__init__() |
| self.mode = mode |
| self.scale = scale |
|
|
| def forward(self, x): |
| n, c, h, w = x.size() |
| x = F.interpolate(x, |
| size=(h // self.scale + 1, w // self.scale + 1), |
| mode=self.mode) |
| return x |
|
|
|
|
| def generate_coord(x): |
| _, _, h, w = x.size() |
| device = x.device |
| col = torch.arange(0, h, device=device) |
| row = torch.arange(0, w, device=device) |
| grid_h, grid_w = torch.meshgrid(col, row) |
| return grid_h, grid_w |
|
|
|
|
| class PositionEmbeddingSine(nn.Module): |
| 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, x): |
| grid_y, grid_x = generate_coord(x) |
|
|
| y_embed = grid_y.unsqueeze(0).float() |
| x_embed = grid_x.unsqueeze(0).float() |
|
|
| 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) |
| return pos |
|
|
|
|
| class PositionEmbeddingLearned(nn.Module): |
| def __init__(self, num_pos_feats=64, H=30, W=30): |
| super().__init__() |
| self.H = H |
| self.W = W |
| self.pos_emb = nn.Parameter( |
| truncated_normal_(torch.zeros(1, num_pos_feats, H, W))) |
|
|
| def forward(self, x): |
| bs, _, h, w = x.size() |
| pos_emb = self.pos_emb |
| if h != self.H or w != self.W: |
| pos_emb = F.interpolate(pos_emb, size=(h, w), mode="bilinear") |
| return pos_emb |
|
|
