File size: 10,972 Bytes
6e601ed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
"""Define ExtraAdam and schedulers
"""
import math

import torch
from torch.optim import Adam, Optimizer, RMSprop, lr_scheduler
from torch_optimizer import NovoGrad, RAdam


def get_scheduler(optimizer, hyperparameters, iterations=-1):
    """Get an optimizer's learning rate scheduler based on opts

    Args:
        optimizer (torch.Optimizer): optimizer for which to schedule the learning rate
        hyperparameters (addict.Dict): configuration options
        iterations (int, optional): The index of last epoch. Defaults to -1.
            When last_epoch=-1, sets initial lr as lr.

    Returns:
        [type]: [description]
    """

    policy = hyperparameters.get("lr_policy")
    lr_step_size = hyperparameters.get("lr_step_size")
    lr_gamma = hyperparameters.get("lr_gamma")
    milestones = hyperparameters.get("lr_milestones")

    if policy is None or policy == "constant":
        scheduler = None  # constant scheduler
    elif policy == "step":
        scheduler = lr_scheduler.StepLR(
            optimizer, step_size=lr_step_size, gamma=lr_gamma, last_epoch=iterations,
        )
    elif policy == "multi_step":
        if isinstance(milestones, (list, tuple)):
            milestones = milestones
        elif isinstance(milestones, int):
            assert "lr_step_size" in hyperparameters
            if iterations == -1:
                last_milestone = 1000
            else:
                last_milestone = iterations
            milestones = list(range(milestones, last_milestone, lr_step_size))
        scheduler = lr_scheduler.MultiStepLR(
            optimizer, milestones=milestones, gamma=lr_gamma, last_epoch=iterations,
        )
    else:
        return NotImplementedError(
            "learning rate policy [%s] is not implemented", hyperparameters["lr_policy"]
        )
    return scheduler


def get_optimizer(net, opt_conf, tasks=None, is_disc=False, iterations=-1):
    """Returns a tuple (optimizer, scheduler) according to opt_conf which
    should come from the trainer's opts as: trainer.opts.<model>.opt

    Args:
        net (nn.Module): Network to update
        opt_conf (addict.Dict): optimizer and scheduler options
        tasks: list of tasks
        iterations (int, optional): Last epoch number. Defaults to -1, meaning
            start with base lr.

    Returns:
        Tuple: (torch.Optimizer, torch._LRScheduler)
    """
    opt = scheduler = None
    lr_names = []
    if tasks is None:
        lr_default = opt_conf.lr
        params = net.parameters()
        lr_names.append("full")
    elif isinstance(opt_conf.lr, float):  # Use default for all tasks
        lr_default = opt_conf.lr
        params = net.parameters()
        lr_names.append("full")
    elif len(opt_conf.lr) == 1:  # Use default for all tasks
        lr_default = opt_conf.lr.default
        params = net.parameters()
        lr_names.append("full")
    else:
        lr_default = opt_conf.lr.default
        params = list()
        for task in tasks:
            lr = opt_conf.lr.get(task, lr_default)
            parameters = None
            # Parameters for encoder
            if not is_disc:
                if task == "m":
                    parameters = net.encoder.parameters()
                    params.append({"params": parameters, "lr": lr})
                    lr_names.append("encoder")
                # Parameters for decoders
                if task == "p":
                    if hasattr(net, "painter"):
                        parameters = net.painter.parameters()
                        lr_names.append("painter")
                else:
                    parameters = net.decoders[task].parameters()
                    lr_names.append(f"decoder_{task}")
            else:
                if task in net:
                    parameters = net[task].parameters()
                    lr_names.append(f"disc_{task}")

            if parameters is not None:
                params.append({"params": parameters, "lr": lr})

    if opt_conf.optimizer.lower() == "extraadam":
        opt = ExtraAdam(params, lr=lr_default, betas=(opt_conf.beta1, 0.999))
    elif opt_conf.optimizer.lower() == "novograd":
        opt = NovoGrad(
            params, lr=lr_default, betas=(opt_conf.beta1, 0)
        )  # default for beta2 is 0
    elif opt_conf.optimizer.lower() == "radam":
        opt = RAdam(params, lr=lr_default, betas=(opt_conf.beta1, 0.999))
    elif opt_conf.optimizer.lower() == "rmsprop":
        opt = RMSprop(params, lr=lr_default)
    else:
        opt = Adam(params, lr=lr_default, betas=(opt_conf.beta1, 0.999))
    scheduler = get_scheduler(opt, opt_conf, iterations)
    return opt, scheduler, lr_names


"""
Extragradient Optimizer

Mostly copied from the extragrad paper repo.

MIT License
Copyright (c) Facebook, Inc. and its affiliates.
written by Hugo Berard (berard.hugo@gmail.com) while at Facebook.
"""


class Extragradient(Optimizer):
    """Base class for optimizers with extrapolation step.
        Arguments:
        params (iterable): an iterable of :class:`torch.Tensor` s or
            :class:`dict` s. Specifies what Tensors should be optimized.
        defaults: (dict): a dict containing default values of optimization
            options (used when a parameter group doesn't specify them).
    """

    def __init__(self, params, defaults):
        super(Extragradient, self).__init__(params, defaults)
        self.params_copy = []

    def update(self, p, group):
        raise NotImplementedError

    def extrapolation(self):
        """Performs the extrapolation step and save a copy of the current
        parameters for the update step.
        """
        # Check if a copy of the parameters was already made.
        is_empty = len(self.params_copy) == 0
        for group in self.param_groups:
            for p in group["params"]:
                u = self.update(p, group)
                if is_empty:
                    # Save the current parameters for the update step.
                    # Several extrapolation step can be made before each update but
                    # only the parametersbefore the first extrapolation step are saved.
                    self.params_copy.append(p.data.clone())
                if u is None:
                    continue
                # Update the current parameters
                p.data.add_(u)

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        if len(self.params_copy) == 0:
            raise RuntimeError("Need to call extrapolation before calling step.")

        loss = None
        if closure is not None:
            loss = closure()

        i = -1
        for group in self.param_groups:
            for p in group["params"]:
                i += 1
                u = self.update(p, group)
                if u is None:
                    continue
                # Update the parameters saved during the extrapolation step
                p.data = self.params_copy[i].add_(u)

        # Free the old parameters
        self.params_copy = []
        return loss


class ExtraAdam(Extragradient):
    """Implements the Adam algorithm with extrapolation step.
    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
    """

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(
            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
        )
        super(ExtraAdam, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(ExtraAdam, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault("amsgrad", False)

    def update(self, p, group):
        if p.grad is None:
            return None
        grad = p.grad.data
        if grad.is_sparse:
            raise RuntimeError(
                "Adam does not support sparse gradients,"
                + " please consider SparseAdam instead"
            )
        amsgrad = group["amsgrad"]

        state = self.state[p]

        # State initialization
        if len(state) == 0:
            state["step"] = 0
            # Exponential moving average of gradient values
            state["exp_avg"] = torch.zeros_like(p.data)
            # Exponential moving average of squared gradient values
            state["exp_avg_sq"] = torch.zeros_like(p.data)
            if amsgrad:
                # Maintains max of all exp. moving avg. of sq. grad. values
                state["max_exp_avg_sq"] = torch.zeros_like(p.data)

        exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
        if amsgrad:
            max_exp_avg_sq = state["max_exp_avg_sq"]
        beta1, beta2 = group["betas"]

        state["step"] += 1

        if group["weight_decay"] != 0:
            grad = grad.add(group["weight_decay"], p.data)

        # Decay the first and second moment running average coefficient
        exp_avg.mul_(beta1).add_(1 - beta1, grad)
        exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
        if amsgrad:
            # Maintains the maximum of all 2nd moment running avg. till now
            torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)  # type: ignore
            # Use the max. for normalizing running avg. of gradient
            denom = max_exp_avg_sq.sqrt().add_(group["eps"])  # type: ignore
        else:
            denom = exp_avg_sq.sqrt().add_(group["eps"])

        bias_correction1 = 1 - beta1 ** state["step"]
        bias_correction2 = 1 - beta2 ** state["step"]
        step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1

        return -step_size * exp_avg / denom