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Running on Zero
Running on Zero
File size: 1,585 Bytes
de2e2e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import torch
import torch.nn as nn
from gemmasight.config import DIM_FUSED
class MSIClassifier(nn.Module):
def __init__(self, input_dim=DIM_FUSED):
super().__init__()
# Dense(512) -> BatchNorm -> ReLU -> Dropout(0.4)
self.layer1 = nn.Sequential(
nn.Linear(input_dim, 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(0.4)
)
# Dense(256) -> BatchNorm -> ReLU -> Dropout(0.3)
self.layer2 = nn.Sequential(
nn.Linear(512, 256),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(0.3)
)
# Dense(128) -> BatchNorm -> ReLU -> Dropout(0.2)
self.layer3 = nn.Sequential(
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.Dropout(0.2)
)
# Dense(1) -> Sigmoid
self.out = nn.Sequential(
nn.Linear(128, 1),
nn.Sigmoid()
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Input: Tensor of shape (Batch, 1536)
Output: Tensor of shape (Batch, 1) representing MSI-High probability
"""
# Handle single batch case for input format compatibility
is_single = len(x.shape) == 1
if is_single:
x = x.unsqueeze(0)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
prob = self.out(x)
if is_single:
prob = prob.squeeze(0)
return prob
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