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import math
import numpy as np
from torch import nn
from scipy import stats
import torch.nn.functional as F
from einops import rearrange
from sklearn.metrics import roc_auc_score, r2_score
from torch.nn.modules import activation
from torch.nn.modules.dropout import Dropout
class Conv1d_block(nn.Module):
"""
the Convolution backbone define by a list of convolution block
"""
def __init__(self,channel_ls,kernel_size,stride, padding_ls=None,diliation_ls=None,pad_to=None, activation='ReLU'):
"""
Argument
channel_ls : list, [int] , channel for each conv layer
kernel_size : int
stride : list , [int]
padding_ls : list , [int]
diliation_ls : list , [int]
"""
super(Conv1d_block,self).__init__()
### property
self.activation = activation
self.channel_ls = channel_ls
self.kernel_size = kernel_size
self.stride = stride
if padding_ls is None:
self.padding_ls = [0] * (len(channel_ls) - 1)
else:
assert len(padding_ls) == len(channel_ls) - 1
self.padding_ls = padding_ls
if diliation_ls is None:
self.diliation_ls = [1] * (len(channel_ls) - 1)
else:
assert len(diliation_ls) == len(channel_ls) - 1
self.diliation_ls = diliation_ls
self.encoder = nn.ModuleList(
# in_C out_C padding diliation
[self.Conv_block(channel_ls[i],channel_ls[i+1],self.padding_ls[i],self.diliation_ls[i],self.stride[i]) for i in range(len(self.padding_ls))]
)
def Conv_block(self,in_Chan,out_Chan,padding,dilation,stride):
activation_layer = eval(f"nn.{self.activation}")
block = nn.Sequential(
nn.Conv1d(in_Chan,out_Chan,self.kernel_size,stride,padding,dilation),
nn.BatchNorm1d(out_Chan),
activation_layer())
return block
def forward(self,x):
if x.shape[2] == 4:
out = x.transpose(1,2)
else:
out = x
for block in self.encoder:
out = block(out)
return out
def forward_stage(self,x,stage):
"""
return the activation of each stage for exchanging information
"""
assert stage < len(self.encoder)
out = self.encoder[stage](x)
return out
def cal_out_shape(self,L_in=100,padding=0,diliation=1,stride=2):
"""
For convolution 1D encoding , compute the final length
"""
L_out = 1+ (L_in + 2*padding -diliation*(self.kernel_size-1) -1)/stride
return L_out
def last_out_len(self,L_in=100):
for i in range(len(self.padding_ls)):
padding = self.padding_ls[i]
diliation = self.diliation_ls[i]
stride = self.stride[i]
L_in = self.cal_out_shape(L_in,padding,diliation,stride)
# assert int(L_in) == L_in , "convolution out shape is not int"
return int(L_in) if L_in >=0 else 1
class ConvTranspose1d_block(Conv1d_block):
"""
the Convolution transpose backbone define by a list of convolution block
"""
def __init__(self,channel_ls,kernel_size,stride,padding_ls=None,diliation_ls=None,pad_to=None):
channel_ls = channel_ls[::-1]
stride = stride[::-1]
padding_ls = padding_ls[::-1] if padding_ls is not None else [0] * (len(channel_ls) - 1)
diliation_ls = diliation_ls[::-1] if diliation_ls is not None else [1] * (len(channel_ls) - 1)
super(ConvTranspose1d_block,self).__init__(channel_ls,kernel_size,stride,padding_ls,diliation_ls,pad_to)
def Conv_block(self,in_Chan,out_Chan,padding,dilation,stride):
"""
replace `Conv1d` with `ConvTranspose1d`
"""
block = nn.Sequential(
nn.ConvTranspose1d(in_Chan,out_Chan,self.kernel_size,stride,padding,dilation=dilation),
nn.BatchNorm1d(out_Chan),
nn.ReLU())
return block
def cal_out_shape(self,L_in,padding=0,diliation=1,stride=1,out_padding=0):
# L_in=100,padding=0,diliation=1,stride=2
"""
For convolution Transpose 1D decoding , compute the final length
"""
L_out = (L_in -1 )*stride + diliation*(self.kernel_size -1 )+1-2*padding + out_padding
return L_out
class linear_block(nn.Module):
def __init__(self,in_Chan,out_Chan,dropout_rate=0.2):
"""
building block func to define dose network
"""
super(linear_block,self).__init__()
self.block = nn.Sequential(
nn.Linear(in_Chan,out_Chan),
nn.Dropout(dropout_rate),
nn.BatchNorm1d(out_Chan),
nn.ReLU()
)
def forward(self,x):
return self.block(x)
class Self_Attention(nn.Module):
"""
self attention operator for Conv1d sequences output
"""
def __init__(self, in_dim:int, out_dim:int, qk_dim:int, n_head:int):
super().__init__()
self.n_head = n_head
self.total_qk_dim = qk_dim * n_head
self.transform = nn.ModuleDict({
k : nn.Linear(in_dim, self.total_qk_dim) for k in ['k', 'q', 'v']
})
self.fc_out = nn.Linear(self.total_qk_dim, out_dim)
def dim_rerrange(self, x):
# first break down total qk dimension
# then transpose length with heads
x1 = rearrange(x, "b l (n c) -> b n c l", n=self.n_head)
return x1
def _get_attention_map(self,X):
"""
break the forward function to access attention mat
"""
# assume we have a 3 dimension input X (b, len, in_dim)
# each out in qkv is also 3 dimension (b, len , qk_dim)
qkv = [self.transform[key](X) for key in ['k', 'q', 'v']]
q, k, v = map(self.dim_rerrange, qkv)
# here i and j is the channel
sim = torch.einsum("b n c i, b n c j -> b n i j", q, k)
sim = sim - sim.amax(dim=-1, keepdim=True).detach()
attn = sim.softmax(dim=-1)
return attn, v
def forward(self, X):
attn, v = self._get_attention_map(X)
out = torch.einsum("b n i j, b n c j -> b n i c", attn, v)
out = rearrange(out, "b n i c -> b i (n c)")
return self.fc_out(out)
class Self_Attention_for_GP(Self_Attention):
"""
self attention operator for Conv1d sequences Global Pooling output
The input has 2 dimension (no length dim),
"""
def __init__(self, in_dim:int, out_dim:int, qk_dim:int, n_head:int):
super().__init__(in_dim, out_dim, qk_dim, n_head)
def _get_attention_map(self,X):
# assume we have a 3 dimension input X (b, len, in_dim)
# each out in qkv is also 3 dimension (b, len , qk_dim)
qkv = [self.transform[key](X) for key in ['k', 'q', 'v']]
q, k, v = map(
lambda x : rearrange(x, "b (n c)-> b n c"), qkv
)
# here i and j is the channel
sim = torch.einsum("b n i, b n j -> b n i j", q, k).softmax(dim=-2, keepdim=True)
sim = sim - attn.amax(dim=-1, keepdim=True).detach()
attn = sim.softmax(dim=-1)
return attn, v
def forward(self, X):
#
attn, v = self._get_attention_map(X)
out = torch.einsum("b n i j, b n j -> b n i", attn, v)
out = rearrange(out, "b n i -> b (n i)")
return self.fc_out(out)
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, *args, **kwargs):
return self.fn(x, *args, **kwargs) + x
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = nn.GroupNorm(1, dim)
def forward(self, x):
x = self.norm(x.transpose(1,2))
return self.fn(x.transpose(1,2))
class SinusoidalPositionEmbeddings(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, time):
device = time.device
half_dim = self.dim // 2
embeddings = math.log(10000) / (half_dim -1) # why do we minus 1 ?
embeddings = torch.exp(torch.arange(half_dim, device=device)* -embeddings)
embeddings = time[:, None] * embeddings[None, :] # expand to 2 dimension
embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
return embeddings |