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from transformers import PretrainedConfig
class EfficientViTConfig(PretrainedConfig):
r"""
```"""
model_type = "efficientvit"
def __init__(
self,
num_classes=2,
num_channels=3,
widths=(32, 64, 128, 256, 512),
head_dim=32,
num_stages=4,
depths=(1, 1, 1, 6, 6),
strides=(2, 2, 2, 2, 2),
hidden_sizes=(32, 64, 160, 256),
patch_size=(7, 7),
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
classifier_dropout_prob=0.0,
layer_norm_eps=1e-6,
decoder_layer_hidden_size=128,
decoder_hidden_size=512,
semantic_loss_ignore_index=255,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.num_classes = num_classes
self.widths = widths
self.head_dim = head_dim
self.num_channels = num_channels
self.num_stages = num_stages
self.depths = depths
self.strides = strides
self.hidden_sizes = hidden_sizes
self.patch_size = patch_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.classifier_dropout_prob = classifier_dropout_prob
self.layer_norm_eps = layer_norm_eps
self.decoder_hidden_size = decoder_hidden_size
self.decoder_layer_hidden_size = decoder_layer_hidden_size
self.semantic_loss_ignore_index = semantic_loss_ignore_index
self.initializer_range = initializer_range |