迁移学习（六）：多任务学习

  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
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import torchvision
import numpy as np
from typing import List, Dict, Optional


# ===== 网络架构 =====

class SharedEncoder(nn.Module):
    """共享主干：ResNet-18 的前 3 个 block。"""
    def __init__(self):
        super().__init__()
        resnet = torchvision.models.resnet18(pretrained=False)
        self.stem = nn.Sequential(
            resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool)
        self.layer1 = resnet.layer1
        self.layer2 = resnet.layer2
        self.layer3 = resnet.layer3

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        return self.layer3(x)


class TaskHead(nn.Module):
    """任务专属的分类或回归头。"""
    def __init__(self, in_channels, num_outputs, task_type='classification'):
        super().__init__()
        self.task_type = task_type
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(in_channels, 256), nn.ReLU(), nn.Dropout(0.5),
            nn.Linear(256, num_outputs))

    def forward(self, x):
        return self.fc(self.pool(x).flatten(1))


class MultiTaskNet(nn.Module):
    """硬参数共享：共享编码器 + 任务专属头部。"""
    def __init__(self, task_configs):
        super().__init__()
        self.encoder = SharedEncoder()
        self.heads = nn.ModuleDict({
            cfg['name']: TaskHead(256, cfg['num_classes'], cfg['type'])
            for cfg in task_configs
        })

    def forward(self, x):
        features = self.encoder(x)
        return {name: head(features) for name, head in self.heads.items()}


# ===== PCGrad =====

class PCGrad:
    """投影冲突梯度（Yu et al., NeurIPS 2020）。"""
    def __init__(self, optimizer, task_names):
        self.optimizer = optimizer
        self.task_names = task_names

    @staticmethod
    def _project(g_i, g_j):
        dot = torch.dot(g_i, g_j)
        if dot < 0:
            g_i = g_i - (dot / (g_j.norm() ** 2 + 1e-8)) * g_j
        return g_i

    def step(self, losses):
        # 1. 各任务在共享参数上的扁平化梯度。
        grads = {}
        for name in self.task_names:
            self.optimizer.zero_grad()
            losses[name].backward(retain_graph=True)
            grads[name] = torch.cat([
                p.grad.flatten() for p in self.optimizer.param_groups[0]['params']
                if p.grad is not None
            ]).clone()

        # 2. 两两投影掉冲突分量。
        modified = {}
        for i, ni in enumerate(self.task_names):
            g = grads[ni].clone()
            for j, nj in enumerate(self.task_names):
                if i != j:
                    g = self._project(g, grads[nj])
            modified[ni] = g

        # 3. 用修改后梯度的均值进行优化器步骤。
        avg_grad = sum(modified.values()) / len(modified)
        self.optimizer.zero_grad()
        idx = 0
        for p in self.optimizer.param_groups[0]['params']:
            if p.grad is not None:
                n = p.numel()
                p.grad = avg_grad[idx:idx + n].view_as(p)
                idx += n
        self.optimizer.step()


# ===== GradNorm =====

class GradNorm:
    """梯度归一化以自适应平衡损失（Chen et al., ICML 2018）。"""
    def __init__(self, model, task_names, alpha=1.5, lr=0.025):
        self.model = model
        self.task_names = task_names
        self.alpha = alpha
        self.weights = nn.Parameter(torch.ones(len(task_names)))
        self.weight_optim = optim.Adam([self.weights], lr=lr)
        self.initial_losses = None

    def step(self, losses):
        if self.initial_losses is None:
            self.initial_losses = {n: l.item() for n, l in losses.items()}

        weighted = [self.weights[i] * losses[n]
                    for i, n in enumerate(self.task_names)]
        total = sum(weighted)

        # 只在共享编码器上计算各任务的梯度范数。
        shared_params = list(self.model.encoder.parameters())
        grad_norms = []
        for wl in weighted:
            grads = torch.autograd.grad(
                wl, shared_params, retain_graph=True, create_graph=True)
            grad_norms.append(
                torch.norm(torch.cat([g.flatten() for g in grads])))

        avg_norm = sum(grad_norms) / len(grad_norms)
        avg_ratio = sum(
            losses[n].item() / (self.initial_losses[n] + 1e-8)
            for n in self.task_names) / len(self.task_names)

        # GradNorm 损失：让 ||w_t * grad_t|| 趋向 avg_norm * r_t^alpha。
        gn_loss = sum(
            torch.abs(grad_norms[i] - avg_norm * (
                (losses[n].item() / (self.initial_losses[n] + 1e-8))
                / (avg_ratio + 1e-8)) ** self.alpha)
            for i, n in enumerate(self.task_names))

        self.weight_optim.zero_grad()
        gn_loss.backward()
        self.weight_optim.step()
        # 重新归一化让权重之和等于任务数。
        with torch.no_grad():
            self.weights.data *= len(self.task_names) / self.weights.sum()

        return total, {n: self.weights[i].item()
                       for i, n in enumerate(self.task_names)}


# ===== 训练器 =====

class MTLTrainer:
    """多任务训练器，支持 uniform / PCGrad / GradNorm。"""
    def __init__(self, model, task_configs, device='cpu', method='uniform'):
        self.model = model.to(device)
        self.device = device
        self.task_configs = {c['name']: c for c in task_configs}
        self.task_names = [c['name'] for c in task_configs]
        self.method = method
        self.optimizer = optim.Adam(model.parameters(), lr=1e-3)

        if method == 'pcgrad':
            self.pcgrad = PCGrad(self.optimizer, self.task_names)
        elif method == 'gradnorm':
            self.gradnorm = GradNorm(model, self.task_names)

    def _losses(self, outputs, targets):
        losses = {}
        for n in self.task_names:
            if self.task_configs[n]['type'] == 'classification':
                losses[n] = F.cross_entropy(outputs[n], targets[n])
            else:
                losses[n] = F.mse_loss(outputs[n], targets[n])
        return losses

    def train_epoch(self, loader, epoch):
        self.model.train()
        stats = {n: 0.0 for n in self.task_names + ['total']}
        for batch in loader:
            inputs = batch['input'].to(self.device)
            targets = {n: batch[n].to(self.device) for n in self.task_names}
            outputs = self.model(inputs)
            losses = self._losses(outputs, targets)

            if self.method == 'uniform':
                total = sum(losses.values())
                self.optimizer.zero_grad()
                total.backward()
                self.optimizer.step()
            elif self.method == 'pcgrad':
                self.pcgrad.step(losses)
                total = sum(l.item() for l in losses.values())
            elif self.method == 'gradnorm':
                total, _ = self.gradnorm.step(losses)
                self.optimizer.zero_grad()
                total.backward()
                self.optimizer.step()

            for n in self.task_names:
                stats[n] += (losses[n].item()
                             if isinstance(losses[n], torch.Tensor)
                             else losses[n])
            stats['total'] += (total.item()
                               if isinstance(total, torch.Tensor) else total)
        nb = len(loader)
        return {k: v / nb for k, v in stats.items()}

    @torch.no_grad()
    def evaluate(self, loader):
        self.model.eval()
        correct = {n: 0 for n in self.task_names}
        total = 0
        for batch in loader:
            inputs = batch['input'].to(self.device)
            targets = {n: batch[n].to(self.device) for n in self.task_names}
            outputs = self.model(inputs)
            total += inputs.size(0)
            for n in self.task_names:
                if self.task_configs[n]['type'] == 'classification':
                    correct[n] += (outputs[n].argmax(1) == targets[n]).sum().item()
        return {n: 100.0 * correct[n] / total for n in self.task_names}


# ===== 演示 =====

class DummyMTLDataset(Dataset):
    def __init__(self, n=1000):
        self.n = n
    def __len__(self):
        return self.n
    def __getitem__(self, i):
        return {
            'input': torch.randn(3, 32, 32),
            'task1': torch.randint(0, 10, ()).item(),
            'task2': torch.randint(0, 5, ()).item(),
        }


def main():
    configs = [
        {'name': 'task1', 'num_classes': 10, 'type': 'classification'},
        {'name': 'task2', 'num_classes': 5,  'type': 'classification'},
    ]
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    loader = DataLoader(DummyMTLDataset(1000), batch_size=32, shuffle=True)
    test_loader = DataLoader(DummyMTLDataset(200), batch_size=32)

    for method in ['uniform', 'pcgrad', 'gradnorm']:
        print(f"\n{'=' * 50}\nMethod: {method}\n{'=' * 50}")
        model = MultiTaskNet(configs)
        trainer = MTLTrainer(model, configs, device, method=method)
        for epoch in range(10):
            stats = trainer.train_epoch(loader, epoch)
            metrics = trainer.evaluate(test_loader)
            print(f"Epoch {epoch+1}: "
                  + " ".join(f"{k}={v:.4f}" for k, v in stats.items())
                  + " | "
                  + " ".join(f"{k}={v:.1f}%" for k, v in metrics.items()))


if __name__ == '__main__':
    main()