Upload configuration_neuroclr.py

configuration_neuroclr.py

@@ -0,0 +1,59 @@
+# classification/configuration_neuroclr.py
+from transformers import PretrainedConfig
+
+class NeuroCLRConfig(PretrainedConfig):
+    model_type = "neuroclr"
+
+    def __init__(
+        self,
+        # Encoder / SSL
+        TSlength: int = 128,
+        nhead: int = 4,
+        nlayer: int = 4,
+        projector_out1: int = 256,
+        projector_out2: int = 128,
+        pooling: str = "flatten",      # input is [B,1,128]
+        normalize_input: bool = True,
+
+        # Classification
+        n_rois: int = 200,
+        num_labels: int = 2,
+
+        # ResNet1D head hyperparams
+        base_filters: int = 256,
+        kernel_size: int = 16,
+        stride: int = 2,
+        groups: int = 32,
+        n_block: int = 48,
+        downsample_gap: int = 6,
+        increasefilter_gap: int = 12,
+        use_bn: bool = True,
+        use_do: bool = True,
+
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        # Encoder
+        self.TSlength = TSlength
+        self.nhead = nhead
+        self.nlayer = nlayer
+        self.projector_out1 = projector_out1
+        self.projector_out2 = projector_out2
+        self.pooling = pooling
+        self.normalize_input = normalize_input
+
+        # Classification
+        self.n_rois = n_rois
+        self.num_labels = num_labels
+
+        # ResNet1D head
+        self.base_filters = base_filters
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.groups = groups
+        self.n_block = n_block
+        self.downsample_gap = downsample_gap
+        self.increasefilter_gap = increasefilter_gap
+        self.use_bn = use_bn
+        self.use_do = use_do