| from collections import OrderedDict |
| from typing import Any, List, Mapping, Optional |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.onnx import OnnxConfigWithPast, PatchingSpec |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class MLEConfig(PretrainedConfig): |
| model_type = "mle" |
|
|
| def __init__( |
| self, |
| in_channels=1, |
| num_encoder_layers=[2, 3, 5, 7, 12], |
| num_decoder_layers=[7, 5, 3, 2, 2], |
| last_hidden_channels=16, |
| block_stride_size=4, |
| block_kernel_size=3, |
| block_patch_size=24, |
| upsample_ratio=2, |
| batch_norm_eps=1e-3, |
| hidden_act="leaky_relu", |
| negative_slope=0.2, |
| **kwargs, |
| ): |
| self.in_channels = in_channels |
| self.num_encoder_layers = num_encoder_layers |
| self.num_decoder_layers = num_decoder_layers |
| self.last_hidden_channels = last_hidden_channels |
|
|
| self.block_stride_size = block_stride_size |
| |
| |
| self.block_kernel_size = block_kernel_size |
| self.block_patch_size = block_patch_size |
|
|
| self.upsample_ratio = upsample_ratio |
| self.batch_norm_eps = batch_norm_eps |
| self.hidden_act = hidden_act |
| self.negative_slope = negative_slope |
|
|
| super().__init__(**kwargs) |
|
|
