Integrate pusht env from diffusion

lerobot/common/policies/diffusion/model/ema_model.py

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+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+class EMAModel:
+    """
+    Exponential Moving Average of models weights
+    """
+
+    def __init__(
+        self, model, update_after_step=0, inv_gamma=1.0, power=2 / 3, min_value=0.0, max_value=0.9999
+    ):
+        """
+        @crowsonkb's notes on EMA Warmup:
+            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
+            to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
+            gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
+            at 215.4k steps).
+        Args:
+            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
+            power (float): Exponential factor of EMA warmup. Default: 2/3.
+            min_value (float): The minimum EMA decay rate. Default: 0.
+        """
+
+        self.averaged_model = model
+        self.averaged_model.eval()
+        self.averaged_model.requires_grad_(False)
+
+        self.update_after_step = update_after_step
+        self.inv_gamma = inv_gamma
+        self.power = power
+        self.min_value = min_value
+        self.max_value = max_value
+
+        self.decay = 0.0
+        self.optimization_step = 0
+
+    def get_decay(self, optimization_step):
+        """
+        Compute the decay factor for the exponential moving average.
+        """
+        step = max(0, optimization_step - self.update_after_step - 1)
+        value = 1 - (1 + step / self.inv_gamma) ** -self.power
+
+        if step <= 0:
+            return 0.0
+
+        return max(self.min_value, min(value, self.max_value))
+
+    @torch.no_grad()
+    def step(self, new_model):
+        self.decay = self.get_decay(self.optimization_step)
+
+        # old_all_dataptrs = set()
+        # for param in new_model.parameters():
+        #     data_ptr = param.data_ptr()
+        #     if data_ptr != 0:
+        #         old_all_dataptrs.add(data_ptr)
+
+        # all_dataptrs = set()
+        for module, ema_module in zip(new_model.modules(), self.averaged_model.modules(), strict=False):
+            for param, ema_param in zip(
+                module.parameters(recurse=False), ema_module.parameters(recurse=False), strict=False
+            ):
+                # iterative over immediate parameters only.
+                if isinstance(param, dict):
+                    raise RuntimeError("Dict parameter not supported")
+
+                # data_ptr = param.data_ptr()
+                # if data_ptr != 0:
+                #     all_dataptrs.add(data_ptr)
+
+                if isinstance(module, _BatchNorm):
+                    # skip batchnorms
+                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
+                elif not param.requires_grad:
+                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
+                else:
+                    ema_param.mul_(self.decay)
+                    ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
+
+        # verify that iterating over module and then parameters is identical to parameters recursively.
+        # assert old_all_dataptrs == all_dataptrs
+        self.optimization_step += 1