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models.py
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models.py
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from torch import nn
import torch.nn.functional as F
class MLPEncoder(nn.Module):
def __init__(self, in_dim, config):
super().__init__()
if config['encoder_batchnorm']:
self.fc = nn.Sequential(nn.Linear(in_dim, config['layer_width']),
nn.BatchNorm1d(config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.BatchNorm1d(config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.BatchNorm1d(config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.BatchNorm1d(config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.BatchNorm1d(config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['d_model']))
else:
self.fc = nn.Sequential(nn.Linear(in_dim, config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['layer_width']),
nn.ReLU(),
nn.Linear(config['layer_width'], config['d_model']))
def forward(self, x, scalar=None):
if scalar != None:
return self.fc(x * scalar)
return self.fc(x)
class Predictor(nn.Module):
def __init__(self, in_dim, config):
super().__init__()
if config['pred_dropout']:
if config['pred_batchnorm']:
self.fc = nn.Sequential(nn.Linear(in_dim, 2048),
nn.ReLU(),
nn.Dropout(config['pred_dropout_p']),
nn.BatchNorm1d(2048),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Dropout(config['pred_dropout_p']),
nn.BatchNorm1d(2048),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Dropout(config['pred_dropout_p']),
nn.BatchNorm1d(2048),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Linear(1024, 1))
else:
self.fc = nn.Sequential(nn.Linear(in_dim, 2048),
nn.ReLU(),
nn.Dropout(config['pred_dropout_p']),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Dropout(config['pred_dropout_p']),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Dropout(config['pred_dropout_p']),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Linear(1024, 1))
else:
if config['pred_batchnorm']:
self.fc = nn.Sequential(nn.Linear(in_dim, 2048),
nn.ReLU(),
nn.BatchNorm1d(2048),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.BatchNorm1d(2048),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.BatchNorm1d(2048),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Linear(1024, 1))
else:
self.fc = nn.Sequential(nn.Linear(in_dim, 2048),
nn.ReLU(),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Linear(1024, 1))
def forward(self, x):
return self.fc(x)