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import torch
from torch import nn
import numpy as np
class PPEG(nn.Module):
def __init__(self, dim=512,k=7,conv_1d=False,bias=True):
super(PPEG, self).__init__()
self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim,bias=bias) if not conv_1d else nn.Conv2d(dim, dim, (k,1), 1, (k//2,0), groups=dim,bias=bias)
self.proj1 = nn.Conv2d(dim, dim, 5, 1, 5//2, groups=dim,bias=bias) if not conv_1d else nn.Conv2d(dim, dim, (5,1), 1, (5//2,0), groups=dim,bias=bias)
self.proj2 = nn.Conv2d(dim, dim, 3, 1, 3//2, groups=dim,bias=bias) if not conv_1d else nn.Conv2d(dim, dim, (3,1), 1, (3//2,0), groups=dim,bias=bias)
def forward(self, x):
B, N, C = x.shape
# padding
H, W = int(np.ceil(np.sqrt(N))), int(np.ceil(np.sqrt(N)))
add_length = H * W - N
# if add_length >0:
x = torch.cat([x, x[:,:add_length,:]],dim = 1)
if H < 7:
H,W = 7,7
zero_pad = H * W - (N+add_length)
x = torch.cat([x, torch.zeros((B,zero_pad,C),device=x.device)],dim = 1)
add_length += zero_pad
# H, W = int(N**0.5),int(N**0.5)
# cls_token, feat_token = x[:, 0], x[:, 1:]
# feat_token = x
cnn_feat = x.transpose(1, 2).view(B, C, H, W)
x = self.proj(cnn_feat)+cnn_feat+self.proj1(cnn_feat)+self.proj2(cnn_feat)
x = x.flatten(2).transpose(1, 2)
# print(add_length)
if add_length >0:
x = x[:,:-add_length]
# x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
return x
class PEG(nn.Module):
def __init__(self, dim=512,k=7,bias=True,conv_1d=False):
super(PEG, self).__init__()
self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim,bias=bias) if not conv_1d else nn.Conv2d(dim, dim, (k,1), 1, (k//2,0), groups=dim,bias=bias)
def forward(self, x):
B, N, C = x.shape
# padding
H, W = int(np.ceil(np.sqrt(N))), int(np.ceil(np.sqrt(N)))
add_length = H * W - N
x = torch.cat([x, x[:,:add_length,:]],dim = 1)
feat_token = x
cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
x = self.proj(cnn_feat)+cnn_feat
x = x.flatten(2).transpose(1, 2)
if add_length >0:
x = x[:,:-add_length]
# x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
return x
class SINCOS(nn.Module):
def __init__(self,embed_dim=512):
super(SINCOS, self).__init__()
self.embed_dim = embed_dim
self.pos_embed = self.get_2d_sincos_pos_embed(embed_dim, 8)
def get_1d_sincos_pos_embed_from_grid(self,embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def get_2d_sincos_pos_embed_from_grid(self,embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = self.get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = self.get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_2d_sincos_pos_embed(self,embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = self.get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
def forward(self, x):
#B, N, C = x.shape
B,H,W,C = x.shape
# # padding
# H, W = int(np.ceil(np.sqrt(N))), int(np.ceil(np.sqrt(N)))
# add_length = H * W - N
# x = torch.cat([x, x[:,:add_length,:]],dim = 1)
# pos_embed = torch.zeros(1, H * W + 1, self.embed_dim)
# pos_embed = self.get_2d_sincos_pos_embed(pos_embed.shape[-1], int(H), cls_token=True)
#pos_embed = torch.from_numpy(self.pos_embed).float().unsqueeze(0).to(x.device)
pos_embed = torch.from_numpy(self.pos_embed).float().to(x.device)
# print(pos_embed.size())
# print(x.size())
x = x + pos_embed.unsqueeze(1).unsqueeze(1).repeat(1,H,W,1)
#x = x + pos_embed[:, 1:, :]
# if add_length >0:
# x = x[:,:-add_length]
return x |