Add Aloha env and ACT policy

WIP Aloha env tests pass

Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1])

Update README

Copy past from act repo

Remove download.py add a WIP for Simxarm

Remove download.py add a WIP for Simxarm

Add act yaml (TODO: try train.py)

Training can runs (TODO: eval)

Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model)

Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm)

poetry lock

fix bug in compute_stats for action normalization

fix more bugs in normalization

fix training

fix import

PushtEnv inheriates AbstractEnv, Improve factory Normalization

Add _make_env to EnvAbstract

Add call_rendering_hooks to pusht env

SimxarmEnv inherites from AbstractEnv (NOT TESTED)

Add aloha tests artifacts + update pusht stats

fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255]

Small fix on simxarm

Add next to obs

Add top camera to Aloha env (TODO: make it compatible with set of cameras)

Add top camera to Aloha env (TODO: make it compatible with set of cameras)

lerobot/common/policies/act/backbone.py

@@ -0,0 +1,115 @@
+from typing import List
+
+import torch
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+
+from .position_encoding import build_position_encoding
+from .utils import NestedTensor, is_main_process
+
+
+class FrozenBatchNorm2d(torch.nn.Module):
+    """
+    BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
+    without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101]
+    produce nans.
+    """
+
+    def __init__(self, n):
+        super().__init__()
+        self.register_buffer("weight", torch.ones(n))
+        self.register_buffer("bias", torch.zeros(n))
+        self.register_buffer("running_mean", torch.zeros(n))
+        self.register_buffer("running_var", torch.ones(n))
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        num_batches_tracked_key = prefix + "num_batches_tracked"
+        if num_batches_tracked_key in state_dict:
+            del state_dict[num_batches_tracked_key]
+
+        super()._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        )
+
+    def forward(self, x):
+        # move reshapes to the beginning
+        # to make it fuser-friendly
+        w = self.weight.reshape(1, -1, 1, 1)
+        b = self.bias.reshape(1, -1, 1, 1)
+        rv = self.running_var.reshape(1, -1, 1, 1)
+        rm = self.running_mean.reshape(1, -1, 1, 1)
+        eps = 1e-5
+        scale = w * (rv + eps).rsqrt()
+        bias = b - rm * scale
+        return x * scale + bias
+
+
+class BackboneBase(nn.Module):
+    def __init__(
+        self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool
+    ):
+        super().__init__()
+        # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this?
+        #     if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
+        #         parameter.requires_grad_(False)
+        if return_interm_layers:
+            return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
+        else:
+            return_layers = {"layer4": "0"}
+        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
+        self.num_channels = num_channels
+
+    def forward(self, tensor):
+        xs = self.body(tensor)
+        return xs
+        # out: Dict[str, NestedTensor] = {}
+        # for name, x in xs.items():
+        #     m = tensor_list.mask
+        #     assert m is not None
+        #     mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+        #     out[name] = NestedTensor(x, mask)
+        # return out
+
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+
+    def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool):
+        backbone = getattr(torchvision.models, name)(
+            replace_stride_with_dilation=[False, False, dilation],
+            pretrained=is_main_process(),
+            norm_layer=FrozenBatchNorm2d,
+        )  # pretrained # TODO do we want frozen batch_norm??
+        num_channels = 512 if name in ("resnet18", "resnet34") else 2048
+        super().__init__(backbone, train_backbone, num_channels, return_interm_layers)
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self[0](tensor_list)
+        out: List[NestedTensor] = []
+        pos = []
+        for _, x in xs.items():
+            out.append(x)
+            # position encoding
+            pos.append(self[1](x).to(x.dtype))
+
+        return out, pos
+
+
+def build_backbone(args):
+    position_embedding = build_position_encoding(args)
+    train_backbone = args.lr_backbone > 0
+    return_interm_layers = args.masks
+    backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
+    model = Joiner(backbone, position_embedding)
+    model.num_channels = backbone.num_channels
+    return model