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import torch
from torch import nn
import warnings
warnings.filterwarnings("ignore")
class Transpose(nn.Module):
def __init__(self):
super(Transpose, self).__init__()
def forward(self, x):
return torch.transpose(x, 2, 1)
"""
-------------------------------------------
RnaModel Class Construction
-------------------------------------------
"""
class Linear2D(nn.Module):
def __init__(self, channel_dim, input_dim, output_dim, inter_dim, only_A=False, flatten_dim=None, input_length=None, add_bias=False, reduction=None, dropout_rate=None):
super(Linear2D, self).__init__()
self.inter_dim = inter_dim
self.flatten_dim = flatten_dim
self.only_A = only_A
self.reduction = reduction
self.dropout_rate = dropout_rate
if flatten_dim is not None:
self.flatten_proj = nn.Parameter(torch.randn(channel_dim, input_dim, flatten_dim))
self.transA = nn.Parameter(torch.randn(channel_dim, flatten_dim * input_length, inter_dim))
self.transB = nn.Parameter(torch.randn(channel_dim, inter_dim, output_dim))
for param in [self.transA, self.transB, self.flatten_proj]:
nn.init.xavier_uniform_(param)
elif only_A:
self.transA = nn.Parameter(torch.randn(channel_dim, input_dim, output_dim))
nn.init.xavier_uniform_(self.transA)
else:
self.transA = nn.Parameter(torch.randn(channel_dim, input_dim, inter_dim))
self.transB = nn.Parameter(torch.randn(channel_dim, inter_dim, output_dim))
for param in [self.transA, self.transB]:
nn.init.xavier_uniform_(param)
self.add_bias = add_bias
if add_bias:
self.bias = nn.Parameter(torch.zeros(channel_dim, output_dim))
if dropout_rate is not None:
self.factor_drop = nn.Dropout1d(dropout_rate)
if reduction is None:
self.reduction_fun = lambda x: x # 恒等函数
elif reduction == 'mean':
self.reduction_fun = lambda x: torch.mean(x, 0) # 对第 0 维求均值
elif reduction == 'max':
self.reduction_fun = lambda x: torch.max(x, 0)[0] # 对第 0 维求均值
elif reduction == 'mlp':
self.channel_factor = nn.Parameter(torch.randn(output_dim, channel_dim, 1))
nn.init.xavier_uniform_(self.channel_factor)
def forward(self, A, is_dropout=True):
dimA = A.dim()
if is_dropout and self.dropout_rate is not None:
transA = self.factor_drop(self.transA)
if self.flatten_dim is not None:
flatten_proj = self.factor_drop(self.flatten_proj)
else:
transA = self.transA
if self.flatten_dim is not None:
flatten_proj = self.flatten_proj
if self.flatten_dim is not None:
output = torch.einsum('bld,cdr->cblr', A, flatten_proj)
output = torch.flatten(output, start_dim=2)
output = torch.einsum('cbd,cdr->cbr', output, transA)
output = torch.einsum('cbr,cro->cbo', output, self.transB)
elif self.only_A:
output = torch.einsum('bd,cdr->cbr', A, transA)
elif dimA == 3:
output = torch.einsum('bld,cdr->cblr', A, transA)
output = torch.einsum('cblr,cro->cblo', output, self.transB)
elif dimA == 2:
output = torch.einsum('bd,cdr->cbr', A, transA)
output = torch.einsum('cbr,cro->cbo', output, self.transB)
if self.add_bias:
bias = self.bias
if dimA == 2 or self.flatten_dim is not None or self.only_A:
bias = bias.unsqueeze(1)
else:
bias = bias.unsqueeze(1).unsqueeze(1)
output = output + bias
if self.reduction == 'mlp':
output = torch.einsum('cbd,dcr->bdr', output, self.channel_factor)
return output.squeeze(-1)
else:
return self.reduction_fun(output)
class CNN_Encoder_Model(nn.Module):
r"""
input shape must be: [batch_size, channel_size, sequence_length]
"""
def __init__(self, model_config, input_shapes):
super(CNN_Encoder_Model, self).__init__()
self.model_config = model_config
self.input_shapes = input_shapes
print("input shapes is {}".format(self.input_shapes))
hyper_use = model_config["training"]["hyper_use"]
p = model_config[hyper_use]
self.hyperParam = p
self.motif_depth = 4
self.bias = nn.Parameter(torch.zeros([1]))
self.moving_bias = 0
self.detectorDict = nn.ModuleDict()
for k in ["reg", "cds"]:
self.detectorDict[k] = nn.ModuleList()
for _ in range(self.motif_depth):
self.detectorDict[k].append(
nn.Sequential(
nn.Conv1d(
in_channels=input_shapes[k][0],
out_channels=int(p["filters1"]),
kernel_size=int(p["kernel_size1"]),
stride=int(p["filters_stride1"]),
dilation=int(p["dilation1"]),
),
nn.ReLU(),
nn.BatchNorm1d(int(p["filters1"])),
)
)
self.filterDict = nn.ModuleDict()
for k in ["reg", "cds"]:
self.filterDict[k] = nn.Sequential(
nn.BatchNorm1d(int(p["filters1"])),
nn.Dropout1d(float(p["dropout1"])),
nn.MaxPool1d(
kernel_size=int(p["pool_size1"]),
stride=int(p["stride1"]),
padding=0,
),
)
self.encoderDict = nn.ModuleDict()
for k in ["reg", "cds"]:
self.encoderDict[k] = nn.Sequential(
nn.Conv1d(
in_channels=int(p["filters1"]),
out_channels=int(p["filters3"]),
kernel_size=int(p["kernel_size3"]),
stride=int(p["filters_stride3"]),
dilation=int(p["dilation3"]),
),
nn.ReLU(),
nn.BatchNorm1d(int(p["filters3"])),
nn.MaxPool1d(
kernel_size=int(p["pool_size3"]),
stride=int(p["stride3"]),
padding=0,
),
Transpose(),
nn.Dropout1d(float(p["dropout3"])),
#nn.Flatten(),
)
utr_flatten_size = self.conv_shape("reg")
self.utr_mlp = nn.Sequential(
#nn.Linear(in_features=utr_flatten_size, out_features=int(p["dense4"])),
Linear2D(channel_dim=8, input_dim=utr_flatten_size[2],
input_length=utr_flatten_size[1], output_dim=int(p['dense4']),
inter_dim=4, dropout_rate=0.3, reduction='mlp',
flatten_dim=16),
nn.ReLU(),
nn.BatchNorm1d(int(p["dense4"])),
nn.Dropout(float(p["dropout4"])),
nn.Linear(in_features=int(p["dense4"]), out_features=int(p["dense6"])),
nn.ReLU(),
nn.BatchNorm1d(int(p["dense6"])),
)
cds_flatten_size = self.conv_shape("cds")
self.RPF_fc = nn.Sequential(
#nn.Linear(in_features=cds_flatten_size, out_features=int(p["dense5"])),
Linear2D(channel_dim=8, input_dim=cds_flatten_size[2],
input_length=cds_flatten_size[1], output_dim=int(p['dense5']),
inter_dim=4, dropout_rate=0.3, reduction='mlp',
flatten_dim=16),
nn.ReLU(),
nn.BatchNorm1d(int(p["dense5"])),
nn.Dropout(float(p["dropout5"])),
nn.Linear(in_features=int(p["dense5"]), out_features=1),
)
self.attention_utr = nn.Sequential(
nn.Linear(
in_features=int(p["dense6"]),
out_features=int(p["filters1"]) * self.motif_depth,
),
nn.Tanh(),
)
self.attention_cds = nn.Sequential(
nn.Linear(
in_features=int(p["dense6"]) + 1,
out_features=int(p["filters1"]) * self.motif_depth,
),
nn.Tanh(),
)
self.mRNA_layer = nn.Sequential(
nn.Linear(
in_features=self.input_shapes["trans"][0], out_features=int(p["dense4"])
),
nn.ReLU(),
nn.BatchNorm1d(int(p["dense4"])),
nn.Dropout(float(p["dropout4"])),
nn.Linear(in_features=int(p["dense4"]), out_features=int(p["dense6"])),
nn.ReLU(),
nn.BatchNorm1d(int(p["dense6"])),
)
def motif_detection(self, sequence_input, a, seq_type: str):
results = [
motif_detector(sequence_input)
for motif_detector in self.detectorDict[seq_type]
]
avg_filter = torch.exp(a)
features = torch.sum(torch.stack(results, 3) * avg_filter, 3) / torch.sum(
avg_filter, 3
)
motif_result = self.filterDict[seq_type](features)
return motif_result
def conv_shape(self, seqType: str):
x_input_1 = torch.zeros(1, *self.input_shapes[seqType])
x_output = self.motif_detection(
x_input_1, torch.zeros([1, 256, 1, self.motif_depth]), seqType
)
x_output = self.encoderDict[seqType](x_output)
flatten_shape = x_output.size()#int(np.prod(x_output.size()))
return flatten_shape
def forward(self, regSequence, cdsSequence, transArray, rnaCount):
mRNA_features = self.mRNA_layer(transArray)
attention_Reg = torch.reshape(
self.attention_utr(mRNA_features),
[-1, int(self.hyperParam["filters1"]), 1, self.motif_depth],
)
regOutput = self.motif_detection(regSequence, attention_Reg, "reg")
regOutput = self.encoderDict["reg"](regOutput)
regFeatures = self.utr_mlp(regOutput)
attention_cds = torch.reshape(
self.attention_cds(torch.concat([regFeatures, rnaCount], dim=1)),
[-1, self.hyperParam["filters1"], 1, self.motif_depth],
)
cds_output = self.motif_detection(cdsSequence, attention_cds, "cds")
cds_seq_features = self.encoderDict["cds"](cds_output)
#self.new_bias = 0.99 * self.moving_bias + 0.01 * self.bias
TE = self.RPF_fc(cds_seq_features)
RPF = TE * rnaCount + self.bias
#self.moving_bias = self.new_bias.detach()
return torch.concat([RPF, TE], 1)
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