Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-09-15 14:13:49 +08:00
commit 7e4ef6f98b
7002 changed files with 1452249 additions and 0 deletions
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+
+import torch
+from torch.utils.tensorboard import SummaryWriter
+
+
+SUM_FREQ = 100
+
+class Logger:
+    def __init__(self, name, scheduler):
+        self.total_steps = 0
+        self.running_loss = {}
+        self.writer = None
+        self.name = name
+        self.scheduler = scheduler
+
+    def _print_training_status(self):
+        if self.writer is None:
+            self.writer = SummaryWriter('runs/%s' % self.name)
+            print([k for k in self.running_loss])
+
+        lr = self.scheduler.get_lr().pop()
+        metrics_data = [self.running_loss[k]/SUM_FREQ for k in self.running_loss.keys()]
+        training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, lr)
+        metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data)
+        
+        # print the training status
+        print(training_str + metrics_str)
+
+        for key in self.running_loss:
+            val = self.running_loss[key] / SUM_FREQ
+            self.writer.add_scalar(key, val, self.total_steps)
+            self.running_loss[key] = 0.0
+
+    def push(self, metrics):
+
+        for key in metrics:
+            if key not in self.running_loss:
+                self.running_loss[key] = 0.0
+
+            self.running_loss[key] += metrics[key]
+
+        if self.total_steps % SUM_FREQ == SUM_FREQ-1:
+            self._print_training_status()
+            self.running_loss = {}
+
+        self.total_steps += 1
+
+    def write_dict(self, results):
+        for key in results:
+            self.writer.add_scalar(key, results[key], self.total_steps)
+
+    def close(self):
+        self.writer.close()
+