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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 |