Uploaded AntiCheatPT_256 model

AntiCheatPT_256.py

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+import torch
+import torch.nn as nn
+from .PositionalEncoding import PositionalEncoding
+
+class AntiCheatPT_256(nn.Module):
+    def __init__(
+        self,
+        feature_dim=44,         # nr of features per tick
+        seq_len=256,           # nr of ticks
+        nhead=1,                # nr of attention heads
+        num_layers=4,           # nr of transformer encoder layers
+        dim_feedforward=176,    # hidden size of feedforward network (MLP)
+        dropout=0.1             # dropout rate
+    ):
+        super(AntiCheatPT_256, self).__init__()
+
+        self.positional_encoding = PositionalEncoding(d_model=feature_dim, max_len=seq_len + 1)  # +1 for CLS token
+
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=feature_dim,
+            nhead=nhead,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            batch_first=True
+        )   # input shape is (batch, seq_len, d_model)
+        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, feature_dim))  # add classification token
+
+        self.fc_out = nn.Sequential(
+            nn.Linear(feature_dim, 128),
+            nn.ReLU(),
+            nn.Linear(128, 1)
+        )
+
+    def forward(self, x):
+        # x shape: (batch_size, seq_len, feature_dim) = (batch_size, 256, d_model)
+        B = x.size(0)
+
+        # add classification token
+        cls_tokens = self.cls_token.expand(B, -1, -1).to(x.device)  # (batch_size, 1, d_model)
+        x = torch.cat((cls_tokens, x), dim=1)  # -> (batch_size, 257, d_model)
+
+        x = self.positional_encoding(x) # add positional encoding
+
+        x = self.transformer_encoder(x)  # -> (batch_size, 257, d_model)
+
+        cls_output = x[:, 0]  # get output for classification token
+        out = self.fc_out(cls_output)  # -> (batch_size, 1)
+
+        return out.squeeze(1)  # -> (batch_size,)

AntiCheatPT_256_model_state_dict.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d72263e7c94031d43a4df70e4e55fd16275c7a743b8e51b4467fe5b29abe8a0
+size 471582

PositionalEncoding.py

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+import torch
+import torch.nn as nn
+import math
+
+pe_scaling = 0.1 # Hyperparameter, 0.1 value was used from training
+    
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len):
+        super().__init__()
+        self.register_buffer("pe", self._generate_pe(max_len, d_model))
+
+    def _generate_pe(self, max_len, d_model):
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = (pe + 1) / 2 
+        pe = pe_scaling * pe
+        return pe.unsqueeze(0)
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1), :].to(x.device)
+        return x