adding efficientnet spectrogram models

	new file:   efficientnet/__init__.py
	new file:   efficientnet/efficientnet2d.py

torchsig/models/spectrogram_models/efficientnet/__init__.py

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+from .efficientnet2d import EfficientNet2d

torchsig/models/spectrogram_models/efficientnet/efficientnet2d.py

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+import timm
+from torch.nn import Linear
+
+from torchsig.models.model_utils.model_utils_1d.conversions_to_1d import convert_2d_model_to_1d
+
+__all__ = ["EfficientNet1d"]
+
+def EfficientNet2d(
+    input_channels: int,
+    n_features: int,
+    efficientnet_version: str = "b0",
+    drop_path_rate: float = 0.2,
+    drop_rate: float = 0.3,
+):
+    """Constructs and returns a 1d version of the EfficientNet model described in
+    `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" <https://arxiv.org/abs/1905.11946>`_.
+
+    Args:
+
+        input_channels (int):
+            Number of 1d input channels; e.g., common practice is to split complex number time-series data into 2 channels, representing the real and imaginary parts respectively
+        
+        n_features (int):
+            Number of output features; should be the number of classes when used directly for classification
+
+        efficientnet_version (str):
+            Specifies the version of efficientnet to use. See the timm efficientnet documentation for details. Examples are 'b0', 'b1', and 'b4'
+
+        drop_path_rate (float):
+            Drop path rate for training
+
+        drop_rate (float):
+            Dropout rate for training
+
+    """
+    #mdl = #convert_2d_model_to_1d(
+    mdl = timm.create_model(
+        "efficientnet_" + efficientnet_version,
+        in_chans=input_channels,
+        drop_path_rate=drop_path_rate,
+        drop_rate=drop_rate,
+    )
+    #)
+    mdl.classifier = Linear(mdl.classifier.in_features, n_features)
+    return mdl