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import torch.nn as nn
import torch
import math
import torch.nn.functional as F
class single_conv(nn.Module):
def __init__(self, in_ch, out_ch):
super(single_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class double_conv(nn.Module):
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1), nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True), nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),
nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class double_conv_down(nn.Module):
def __init__(self, in_ch, out_ch):
super(double_conv_down, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, stride=2, padding=1), nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True), nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),
nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class double_conv_up(nn.Module):
def __init__(self, in_ch, out_ch):
super(double_conv_up, self).__init__()
self.conv = nn.Sequential(
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1), nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True), nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),
nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class PosEnSine(nn.Module):
"""
Code borrowed from DETR: models/positional_encoding.py
output size: b*(2.num_pos_feats)*h*w
"""
def __init__(self, num_pos_feats):
super(PosEnSine, self).__init__()
self.num_pos_feats = num_pos_feats
self.normalize = True
self.scale = 2 * math.pi
self.temperature = 10000
def forward(self, x, pt_coord=None):
b, c, h, w = x.shape
if pt_coord is not None:
z_embed = pt_coord[:, :, 2].unsqueeze(-1) + 1.
y_embed = pt_coord[:, :, 1].unsqueeze(-1) + 1.
x_embed = pt_coord[:, :, 0].unsqueeze(-1) + 1.
else:
not_mask = torch.ones(1, h, w, device=x.device)
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
z_embed = torch.ones_like(x_embed)
if self.normalize:
eps = 1e-6
z_embed = z_embed / (torch.max(z_embed) + eps) * self.scale
y_embed = y_embed / (torch.max(y_embed) + eps) * self.scale
x_embed = x_embed / (torch.max(x_embed) + 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=4).flatten(3)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos_z = torch.stack((pos_z[:, :, :, 0::2].sin(), pos_z[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos = torch.cat((pos_x, pos_y, pos_z), dim=3).permute(0, 3, 1, 2)
# if pt_coord is None:
pos = pos.repeat(b, 1, 1, 1)
return pos
def softmax_attention(q, k, v):
# b x n x d x h x w
h, w = q.shape[-2], q.shape[-1]
q = q.flatten(-2).transpose(-2, -1) # b x n x hw x d
k = k.flatten(-2) # b x n x d x hw
v = v.flatten(-2).transpose(-2, -1)
print('softmax', q.shape, k.shape, v.shape)
N = k.shape[-1] # ?????? maybe change to k.shape[-2]????
attn = torch.matmul(q / N**0.5, k)
attn = F.softmax(attn, dim=-1)
output = torch.matmul(attn, v)
output = output.transpose(-2, -1)
output = output.view(*output.shape[:-1], h, w)
return output, attn
def dotproduct_attention(q, k, v):
# b x n x d x h x w
h, w = q.shape[-2], q.shape[-1]
q = q.flatten(-2).transpose(-2, -1) # b x n x hw x d
k = k.flatten(-2) # b x n x d x hw
v = v.flatten(-2).transpose(-2, -1)
N = k.shape[-1]
attn = None
tmp = torch.matmul(k, v) / N
output = torch.matmul(q, tmp)
output = output.transpose(-2, -1)
output = output.view(*output.shape[:-1], h, w)
return output, attn
def long_range_attention(q, k, v, P_h, P_w): # fixed patch size
B, N, C, qH, qW = q.size()
_, _, _, kH, kW = k.size()
qQ_h, qQ_w = qH // P_h, qW // P_w
kQ_h, kQ_w = kH // P_h, kW // P_w
q = q.reshape(B, N, C, qQ_h, P_h, qQ_w, P_w)
k = k.reshape(B, N, C, kQ_h, P_h, kQ_w, P_w)
v = v.reshape(B, N, -1, kQ_h, P_h, kQ_w, P_w)
q = q.permute(0, 1, 4, 6, 2, 3, 5) # [b, n, Ph, Pw, d, Qh, Qw]
k = k.permute(0, 1, 4, 6, 2, 3, 5)
v = v.permute(0, 1, 4, 6, 2, 3, 5)
output, attn = softmax_attention(q, k, v) # attn: [b, n, Ph, Pw, qQh*qQw, kQ_h*kQ_w]
output = output.permute(0, 1, 4, 5, 2, 6, 3)
output = output.reshape(B, N, -1, qH, qW)
return output, attn
def short_range_attention(q, k, v, Q_h, Q_w): # fixed patch number
B, N, C, qH, qW = q.size()
_, _, _, kH, kW = k.size()
qP_h, qP_w = qH // Q_h, qW // Q_w
kP_h, kP_w = kH // Q_h, kW // Q_w
q = q.reshape(B, N, C, Q_h, qP_h, Q_w, qP_w)
k = k.reshape(B, N, C, Q_h, kP_h, Q_w, kP_w)
v = v.reshape(B, N, -1, Q_h, kP_h, Q_w, kP_w)
q = q.permute(0, 1, 3, 5, 2, 4, 6) # [b, n, Qh, Qw, d, Ph, Pw]
k = k.permute(0, 1, 3, 5, 2, 4, 6)
v = v.permute(0, 1, 3, 5, 2, 4, 6)
output, attn = softmax_attention(q, k, v) # attn: [b, n, Qh, Qw, qPh*qPw, kPh*kPw]
output = output.permute(0, 1, 4, 2, 5, 3, 6)
output = output.reshape(B, N, -1, qH, qW)
return output, attn
def space_to_depth(x, block_size):
x_shape = x.shape
c, h, w = x_shape[-3:]
if len(x.shape) >= 5:
x = x.view(-1, c, h, w)
unfolded_x = torch.nn.functional.unfold(x, block_size, stride=block_size)
return unfolded_x.view(*x_shape[0:-3], c * block_size**2, h // block_size, w // block_size)
def depth_to_space(x, block_size):
x_shape = x.shape
c, h, w = x_shape[-3:]
x = x.view(-1, c, h, w)
y = torch.nn.functional.pixel_shuffle(x, block_size)
return y.view(*x_shape[0:-3], -1, h * block_size, w * block_size)
def patch_attention(q, k, v, P):
# q: [b, nhead, c, h, w]
q_patch = space_to_depth(q, P) # [b, nhead, cP^2, h/P, w/P]
k_patch = space_to_depth(k, P)
v_patch = space_to_depth(v, P)
# output: [b, nhead, cP^2, h/P, w/P]
# attn: [b, nhead, h/P*w/P, h/P*w/P]
output, attn = softmax_attention(q_patch, k_patch, v_patch)
output = depth_to_space(output, P) # output: [b, nhead, c, h, w]
return output, attn
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