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import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig
class BallNetConfig(PretrainedConfig):
model_type = "ballnet"
def __init__(
self,
x_dim=[6],
y_dim=[6, 1800],
h1_dim=[100, 1000],
h2_dim=[100, 1000],
**kwargs,
):
super().__init__(**kwargs)
self.x_dim = x_dim
self.y_dim = y_dim
self.h1_dim = h1_dim
self.h2_dim = h2_dim
class BallNet(PreTrainedModel):
config_class = BallNetConfig
def __init__(self, config):
super().__init__(config)
self.x_dim = config.x_dim
self.y_dim = config.y_dim
self.h1_dim = config.h1_dim
self.h2_dim = config.h2_dim
# build sub-networks
self.branches = nn.ModuleList()
for i in range(len(self.y_dim)):
net = nn.Sequential(
nn.Linear(self.x_dim[0], self.h1_dim[i]),
nn.ReLU(),
nn.Linear(self.h1_dim[i], self.h2_dim[i]),
nn.ReLU(),
nn.Linear(self.h2_dim[i], self.y_dim[i]),
)
self.branches.append(net)
# initialize weights
self.post_init()
def forward(self, x):
if isinstance(x, (list, tuple)):
x = torch.tensor(x, dtype=torch.float32)
outputs = [branch(x) for branch in self.branches]
return tuple(outputs)
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