File size: 1,831 Bytes
c1f2cce | 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 | from transformers import PretrainedConfig
import warnings
class AudioProtoNetConfig(PretrainedConfig):
_auto_class = "AutoConfig"
model_type = "AudioProtoNet"
def __init__(
self,
prototypes_per_class: int = 1,
channels: int = 1024,
height: int = 1,
width: int = 1,
num_classes: int = 9736,
topk_k: int = 1,
margin: float = None,
add_on_layers_type: str = "upsample",
incorrect_class_connection: float = None,
correct_class_connection: float = 1.0,
bias_last_layer: float = -2.0,
non_negative_last_layer: bool = True,
embedded_spectrogram_height: int = None,
**kwargs,
):
super().__init__(**kwargs)
self.prototypes_per_class = prototypes_per_class
self.num_prototypes_after_pruning = None
self.channels = channels
self.height = height
self.width = width
self.num_classes = num_classes
self.topk_k = topk_k
self.margin = margin
self.relu_on_cos = True
self.add_on_layers_type = add_on_layers_type
self.incorrect_class_connection = incorrect_class_connection
self.correct_class_connection = correct_class_connection
self.input_vector_length = 64
self.n_eps_channels = 2
self.epsilon_val = 1e-4
self.bias_last_layer = bias_last_layer
self.non_negative_last_layer = non_negative_last_layer
self.embedded_spectrogram_height = embedded_spectrogram_height
if self.bias_last_layer:
self.use_bias_last_layer = True
else:
self.use_bias_last_layer = False
self.prototype_class_identity = None |