first commit

ssd/solver/build.py

@@ -0,0 +1,16 @@
+import torch
+
+from .lr_scheduler import WarmupMultiStepLR
+
+
+def make_optimizer(cfg, model, lr=None):
+    lr = cfg.SOLVER.BASE_LR if lr is None else lr
+    return torch.optim.SGD(model.parameters(), lr=lr, momentum=cfg.SOLVER.MOMENTUM, weight_decay=cfg.SOLVER.WEIGHT_DECAY)
+
+
+def make_lr_scheduler(cfg, optimizer, milestones=None):
+    return WarmupMultiStepLR(optimizer=optimizer,
+                             milestones=cfg.SOLVER.LR_STEPS if milestones is None else milestones,
+                             gamma=cfg.SOLVER.GAMMA,
+                             warmup_factor=cfg.SOLVER.WARMUP_FACTOR,
+                             warmup_iters=cfg.SOLVER.WARMUP_ITERS)

ssd/solver/lr_scheduler.py

@@ -0,0 +1,31 @@
+from bisect import bisect_right
+
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class WarmupMultiStepLR(_LRScheduler):
+    def __init__(self, optimizer, milestones, gamma=0.1, warmup_factor=1.0 / 3,
+                 warmup_iters=500, last_epoch=-1):
+        if not list(milestones) == sorted(milestones):
+            raise ValueError(
+                "Milestones should be a list of" " increasing integers. Got {}",
+                milestones,
+            )
+
+        self.milestones = milestones
+        self.gamma = gamma
+        self.warmup_factor = warmup_factor
+        self.warmup_iters = warmup_iters
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        warmup_factor = 1
+        if self.last_epoch < self.warmup_iters:
+            alpha = float(self.last_epoch) / self.warmup_iters
+            warmup_factor = self.warmup_factor * (1 - alpha) + alpha
+        return [
+            base_lr
+            * warmup_factor
+            * self.gamma ** bisect_right(self.milestones, self.last_epoch)
+            for base_lr in self.base_lrs
+        ]