9d002032d1
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)
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
from typing import List
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import torch
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import torchvision
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from torch import nn
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from torchvision.models._utils import IntermediateLayerGetter
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from .position_encoding import build_position_encoding
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from .utils import NestedTensor, is_main_process
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class FrozenBatchNorm2d(torch.nn.Module):
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"""
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BatchNorm2d where the batch statistics and the affine parameters are fixed.
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Copy-paste from torchvision.misc.ops with added eps before rqsrt,
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without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101]
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produce nans.
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"""
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def __init__(self, n):
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super().__init__()
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self.register_buffer("weight", torch.ones(n))
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self.register_buffer("bias", torch.zeros(n))
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self.register_buffer("running_mean", torch.zeros(n))
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self.register_buffer("running_var", torch.ones(n))
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def _load_from_state_dict(
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self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
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):
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num_batches_tracked_key = prefix + "num_batches_tracked"
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if num_batches_tracked_key in state_dict:
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del state_dict[num_batches_tracked_key]
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super()._load_from_state_dict(
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state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
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)
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def forward(self, x):
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# move reshapes to the beginning
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# to make it fuser-friendly
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w = self.weight.reshape(1, -1, 1, 1)
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b = self.bias.reshape(1, -1, 1, 1)
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rv = self.running_var.reshape(1, -1, 1, 1)
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rm = self.running_mean.reshape(1, -1, 1, 1)
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eps = 1e-5
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scale = w * (rv + eps).rsqrt()
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bias = b - rm * scale
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return x * scale + bias
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class BackboneBase(nn.Module):
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def __init__(
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self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool
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):
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super().__init__()
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# for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this?
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# if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
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# parameter.requires_grad_(False)
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if return_interm_layers:
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return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
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else:
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return_layers = {"layer4": "0"}
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self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
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self.num_channels = num_channels
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def forward(self, tensor):
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xs = self.body(tensor)
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return xs
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# out: Dict[str, NestedTensor] = {}
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# for name, x in xs.items():
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# m = tensor_list.mask
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# assert m is not None
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# mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
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# out[name] = NestedTensor(x, mask)
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# return out
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class Backbone(BackboneBase):
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"""ResNet backbone with frozen BatchNorm."""
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def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool):
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backbone = getattr(torchvision.models, name)(
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replace_stride_with_dilation=[False, False, dilation],
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pretrained=is_main_process(),
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norm_layer=FrozenBatchNorm2d,
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) # pretrained # TODO do we want frozen batch_norm??
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num_channels = 512 if name in ("resnet18", "resnet34") else 2048
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super().__init__(backbone, train_backbone, num_channels, return_interm_layers)
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class Joiner(nn.Sequential):
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def __init__(self, backbone, position_embedding):
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super().__init__(backbone, position_embedding)
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def forward(self, tensor_list: NestedTensor):
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xs = self[0](tensor_list)
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out: List[NestedTensor] = []
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pos = []
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for _, x in xs.items():
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out.append(x)
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# position encoding
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pos.append(self[1](x).to(x.dtype))
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return out, pos
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def build_backbone(args):
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position_embedding = build_position_encoding(args)
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train_backbone = args.lr_backbone > 0
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return_interm_layers = args.masks
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backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
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model = Joiner(backbone, position_embedding)
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model.num_channels = backbone.num_channels
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return model
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