1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
|
"""
Example: Creating a Custom Model
This example shows how to add a new model following the project architecture.
IMPORTANT: Models use a config-driven pattern where __init__ only accepts cfg.
Key Requirements:
- Use @register_model('ModelName') decorator
- __init__ accepts ONLY cfg parameter
- All hyperparameters come from cfg (cfg.model.*, cfg.dataset.*, etc.)
- forward() returns dict: {"loss": loss, "labels": labels, "logits": logits}
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Optional
# Import the register_model decorator
# Location may vary: src.model_module.brain_decoder or src.model_module.model
from src.model_module.brain_decoder import register_model
@register_model('SimpleMLP')
class SimpleMLP(nn.Module):
"""
Simple Multi-Layer Perceptron for classification tasks.
Config structure ( Hydra YAML ):
model:
input_dim: 100
hidden_dim: 256
output_dim: 10
num_layers: 3
dropout: 0.1
dataset:
task: classification # Used to get target_size
target_size:
classification: 10
"""
def __init__(self, cfg):
super().__init__()
# Store config
self.cfg = cfg
# Get task info from config
self.task = cfg.dataset.task
# Build model - ALL parameters from cfg
self.input_dim = cfg.model.input_dim
self.hidden_dim = cfg.model.get('hidden_dim', 256)
self.output_dim = cfg.dataset.target_size[cfg.dataset.task]
self.num_layers = cfg.model.get('num_layers', 3)
self.dropout = cfg.model.get('dropout', 0.1)
# Build layers
layers = []
in_dim = self.input_dim
for i in range(self.num_layers):
layers.extend([
nn.Linear(in_dim, self.hidden_dim),
nn.ReLU(),
nn.Dropout(self.dropout)
])
in_dim = self.hidden_dim
# Output layer
layers.append(nn.Linear(self.hidden_dim, self.output_dim))
self.network = nn.Sequential(*layers)
# Loss function
self.loss_fn = nn.CrossEntropyLoss()
def forward(
self,
x: torch.Tensor,
labels: Optional[torch.Tensor] = None,
**kwargs
) -> Dict[str, Optional[torch.Tensor]]:
"""
Forward pass.
Args:
x: Input tensor of shape (batch_size, input_dim)
labels: Ground truth labels (optional, for training)
Returns:
Dictionary with:
- loss: Computed loss (None if labels not provided)
- labels: Ground truth labels
- logits: Model predictions
"""
logits = self.network(x)
loss = None
if labels is not None:
# Convert labels to long type if needed
if labels.dtype != torch.long:
labels = labels.long()
loss = self.loss_fn(logits, labels)
return {
"loss": loss,
"labels": labels,
"logits": logits
}
# ============================================
# Example with Training/Inference Modes
# ============================================
@register_model('SimpleMLPWithModes')
class SimpleMLPWithModes(nn.Module):
"""
MLP with separate training and inference logic.
Shows how to handle different modes using self.training.
"""
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.task = cfg.dataset.task
self.input_dim = cfg.model.input_dim
self.hidden_dim = cfg.model.get('hidden_dim', 256)
self.output_dim = cfg.dataset.target_size[cfg.dataset.task]
self.fc_in = nn.Linear(self.input_dim, self.hidden_dim)
self.ln = nn.LayerNorm(self.hidden_dim)
self.fc_out = nn.Linear(self.hidden_dim, self.output_dim)
self.loss_fn = nn.CrossEntropyLoss()
# Test-time augmentation config
self.tta_times = cfg.model.get('tta_times', 1)
def forward(
self,
x: torch.Tensor,
labels: Optional[torch.Tensor] = None,
**kwargs
) -> Dict[str, Optional[torch.Tensor]]:
"""
Forward pass with training/inference modes.
"""
if self.training:
# Training mode
x = x.float()
x = self.fc_in(x)
x = self.ln(x)
x = F.relu(x)
logits = self.fc_out(x)
loss = None
if labels is not None:
if labels.dtype != torch.long:
labels = labels.long()
loss = self.loss_fn(logits, labels)
return {
"loss": loss,
"labels": labels,
"logits": logits
}
else:
# Inference mode with TTA
all_logits = []
with torch.no_grad():
x = x.float()
for _ in range(self.tta_times):
x_aug = x.clone()
# Apply TTA transformations here if needed
x_aug = self.fc_in(x_aug)
x_aug = self.ln(x_aug)
x_aug = F.relu(x_aug)
logits = self.fc_out(x_aug)
all_logits.append(logits)
# Average predictions
avg_logits = torch.mean(torch.stack(all_logits), dim=0)
loss = None
if labels is not None:
if labels.dtype != torch.long:
labels = labels.long()
loss = self.loss_fn(avg_logits, labels)
return {
"loss": loss,
"labels": labels,
"logits": avg_logits
}
# ============================================
# Config Example (Hydra YAML)
# ============================================
"""
# run/conf/model/simple_mlp.yaml
model:
name: SimpleMLP
input_dim: 100
hidden_dim: 256
output_dim: 10
num_layers: 3
dropout: 0.1
tta_times: 1
# Then in training pipeline:
# from src.model_module.brain_decoder import ModelFactory
# model = ModelFactory(cfg.model.name)(cfg)
"""
|
