Первый коммит: код классификатора наручных знаков (Naruto)

src/train/lr_scheduler_v2/transformer_lr_scheduler.py

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+# MIT License
+#
+# Copyright (c) 2021 Soohwan Kim
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import math
+import torch
+from typing import Optional
+from torch.optim import Optimizer
+
+from src.train.lr_scheduler_v2.lr_scheduler import LearningRateScheduler
+
+
+class TransformerLRScheduler(LearningRateScheduler):
+    r"""
+    Transformer Learning Rate Scheduler proposed in "Attention Is All You Need"
+
+    Args:
+        optimizer (Optimizer): Optimizer.
+        init_lr (float): Initial learning rate.
+        peak_lr (float): Maximum learning rate.
+        final_lr (float): Final learning rate.
+        final_lr_scale (float): Final learning rate scale
+        warmup_steps (int): Warmup the learning rate linearly for the first N updates
+        decay_steps (int): Steps in decay stages
+    """
+    def __init__(
+            self,
+            optimizer: Optimizer,
+            init_lr: float,
+            peak_lr: float,
+            final_lr: float,
+            final_lr_scale: float,
+            warmup_steps: int,
+            decay_steps: int,
+    ) -> None:
+        assert isinstance(warmup_steps, int), "warmup_steps should be integer type"
+        assert isinstance(decay_steps, int), "total_steps should be integer type"
+
+        super(TransformerLRScheduler, self).__init__(optimizer, init_lr)
+        self.final_lr = final_lr
+        self.peak_lr = peak_lr
+        self.warmup_steps = warmup_steps
+        self.decay_steps = decay_steps
+
+        self.warmup_rate = self.peak_lr / self.warmup_steps
+        self.decay_factor = -math.log(final_lr_scale) / self.decay_steps
+
+        self.init_lr = init_lr
+        self.update_steps = 0
+
+    def _decide_stage(self):
+        if self.update_steps < self.warmup_steps:
+            return 0, self.update_steps
+
+        if self.warmup_steps <= self.update_steps < self.warmup_steps + self.decay_steps:
+            return 1, self.update_steps - self.warmup_steps
+
+        return 2, None
+
+    def step(self, val_loss: Optional[torch.FloatTensor] = None):
+        self.update_steps += 1
+        stage, steps_in_stage = self._decide_stage()
+
+        if stage == 0:
+            self.lr = self.update_steps * self.warmup_rate
+        elif stage == 1:
+            self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage)
+        elif stage == 2:
+            self.lr = self.final_lr
+        else:
+            raise ValueError("Undefined stage")
+
+        self.set_lr(self.optimizer, self.lr)
+
+        return self.lr