e0842e71dc
* v0.5 (#9) * update idql configs * update awr configs * update dipo configs * update qsm configs * update dqm configs * update project version to 0.5.0
326 lines
11 KiB
Python
326 lines
11 KiB
Python
"""
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UNet implementation. Minorly modified from Diffusion Policy: https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/model/diffusion/conv1d_components.py
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Set `smaller_encoder` to False for using larger observation encoder in ResidualBlock1D
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"""
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import torch
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import torch.nn as nn
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import einops
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from einops.layers.torch import Rearrange
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import logging
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log = logging.getLogger(__name__)
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from model.diffusion.modules import (
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SinusoidalPosEmb,
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Downsample1d,
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Upsample1d,
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Conv1dBlock,
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)
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from model.common.mlp import ResidualMLP
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class ResidualBlock1D(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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cond_dim,
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kernel_size=5,
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n_groups=None,
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cond_predict_scale=False,
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larger_encoder=False,
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activation_type="Mish",
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groupnorm_eps=1e-5,
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):
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super().__init__()
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self.blocks = nn.ModuleList(
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[
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Conv1dBlock(
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in_channels,
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out_channels,
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kernel_size,
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n_groups=n_groups,
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activation_type=activation_type,
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eps=groupnorm_eps,
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),
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Conv1dBlock(
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out_channels,
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out_channels,
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kernel_size,
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n_groups=n_groups,
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activation_type=activation_type,
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eps=groupnorm_eps,
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),
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]
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)
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if activation_type == "Mish":
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act = nn.Mish()
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elif activation_type == "ReLU":
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act = nn.ReLU()
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else:
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raise "Unknown activation type for ConditionalResidualBlock1D"
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# FiLM modulation https://arxiv.org/abs/1709.07871
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# predicts per-channel scale and bias
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cond_channels = out_channels
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if cond_predict_scale:
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cond_channels = out_channels * 2
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self.cond_predict_scale = cond_predict_scale
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self.out_channels = out_channels
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if larger_encoder:
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self.cond_encoder = nn.Sequential(
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nn.Linear(cond_dim, cond_channels),
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act,
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nn.Linear(cond_channels, cond_channels),
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act,
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nn.Linear(cond_channels, cond_channels),
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Rearrange("batch t -> batch t 1"),
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)
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else:
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self.cond_encoder = nn.Sequential(
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act,
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nn.Linear(cond_dim, cond_channels),
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Rearrange("batch t -> batch t 1"),
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)
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# make sure dimensions compatible
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self.residual_conv = (
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nn.Conv1d(in_channels, out_channels, 1)
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if in_channels != out_channels
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else nn.Identity()
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)
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def forward(self, x, cond):
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"""
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x : [ batch_size x in_channels x horizon_steps ]
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cond : [ batch_size x cond_dim]
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returns:
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out : [ batch_size x out_channels x horizon_steps ]
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"""
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out = self.blocks[0](x)
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embed = self.cond_encoder(cond)
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if self.cond_predict_scale:
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embed = embed.reshape(embed.shape[0], 2, self.out_channels, 1)
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scale = embed[:, 0, ...]
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bias = embed[:, 1, ...]
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out = scale * out + bias
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else:
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out = out + embed
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out = self.blocks[1](out)
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return out + self.residual_conv(x)
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class Unet1D(nn.Module):
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def __init__(
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self,
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action_dim,
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cond_dim=None,
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diffusion_step_embed_dim=32,
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dim=32,
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dim_mults=(1, 2, 4, 8),
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smaller_encoder=False,
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cond_mlp_dims=None,
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kernel_size=5,
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n_groups=None,
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activation_type="Mish",
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cond_predict_scale=False,
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groupnorm_eps=1e-5,
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):
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super().__init__()
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dims = [action_dim, *map(lambda m: dim * m, dim_mults)]
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in_out = list(zip(dims[:-1], dims[1:]))
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log.info(f"Channel dimensions: {in_out}")
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dsed = diffusion_step_embed_dim
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self.time_mlp = nn.Sequential(
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SinusoidalPosEmb(dsed),
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nn.Linear(dsed, dsed * 4),
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nn.Mish(),
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nn.Linear(dsed * 4, dsed),
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)
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if cond_mlp_dims is not None:
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self.cond_mlp = ResidualMLP(
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dim_list=[cond_dim] + cond_mlp_dims,
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activation_type=activation_type,
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out_activation_type="Identity",
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)
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cond_block_dim = dsed + cond_mlp_dims[-1]
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else:
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cond_block_dim = dsed + cond_dim
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use_large_encoder_in_block = cond_mlp_dims is None and not smaller_encoder
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mid_dim = dims[-1]
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self.mid_modules = nn.ModuleList(
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[
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ResidualBlock1D(
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mid_dim,
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mid_dim,
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cond_dim=cond_block_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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larger_encoder=use_large_encoder_in_block,
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activation_type=activation_type,
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groupnorm_eps=groupnorm_eps,
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),
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ResidualBlock1D(
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mid_dim,
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mid_dim,
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cond_dim=cond_block_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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larger_encoder=use_large_encoder_in_block,
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activation_type=activation_type,
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groupnorm_eps=groupnorm_eps,
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),
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]
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)
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self.down_modules = nn.ModuleList([])
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for ind, (dim_in, dim_out) in enumerate(in_out):
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is_last = ind >= (len(in_out) - 1)
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self.down_modules.append(
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nn.ModuleList(
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[
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ResidualBlock1D(
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dim_in,
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dim_out,
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cond_dim=cond_block_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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larger_encoder=use_large_encoder_in_block,
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activation_type=activation_type,
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groupnorm_eps=groupnorm_eps,
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),
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ResidualBlock1D(
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dim_out,
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dim_out,
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cond_dim=cond_block_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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larger_encoder=use_large_encoder_in_block,
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activation_type=activation_type,
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groupnorm_eps=groupnorm_eps,
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),
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Downsample1d(dim_out) if not is_last else nn.Identity(),
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]
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)
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)
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self.up_modules = nn.ModuleList([])
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for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
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is_last = ind >= (len(in_out) - 1)
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self.up_modules.append(
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nn.ModuleList(
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[
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ResidualBlock1D(
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dim_out * 2,
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dim_in,
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cond_dim=cond_block_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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larger_encoder=use_large_encoder_in_block,
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activation_type=activation_type,
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groupnorm_eps=groupnorm_eps,
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),
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ResidualBlock1D(
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dim_in,
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dim_in,
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cond_dim=cond_block_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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larger_encoder=use_large_encoder_in_block,
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activation_type=activation_type,
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groupnorm_eps=groupnorm_eps,
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),
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Upsample1d(dim_in) if not is_last else nn.Identity(),
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]
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)
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)
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self.final_conv = nn.Sequential(
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Conv1dBlock(
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dim,
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dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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activation_type=activation_type,
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eps=groupnorm_eps,
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),
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nn.Conv1d(dim, action_dim, 1),
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)
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def forward(
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self,
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x,
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time,
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cond,
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**kwargs,
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):
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"""
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x: (B, Ta, act_dim)
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time: (B,) or int, diffusion step
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cond: dict with key state/rgb; more recent obs at the end
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state: (B, To, obs_dim)
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"""
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B = len(x)
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# move chunk dim to the end
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x = einops.rearrange(x, "b h t -> b t h")
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# flatten history
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state = cond["state"].view(B, -1)
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# obs encoder
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if hasattr(self, "cond_mlp"):
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state = self.cond_mlp(state)
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# 1. time
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if not torch.is_tensor(time):
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time = torch.tensor([time], dtype=torch.long, device=x.device)
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elif torch.is_tensor(time) and len(time.shape) == 0:
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time = time[None].to(x.device)
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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time = time.expand(x.shape[0])
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global_feature = self.time_mlp(time)
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global_feature = torch.cat([global_feature, state], axis=-1)
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# encode local features
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h_local = list()
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h = []
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for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules):
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x = resnet(x, global_feature)
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if idx == 0 and len(h_local) > 0:
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x = x + h_local[0]
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x = resnet2(x, global_feature)
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h.append(x)
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x = downsample(x)
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for mid_module in self.mid_modules:
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x = mid_module(x, global_feature)
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for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules):
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x = torch.cat((x, h.pop()), dim=1)
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x = resnet(x, global_feature)
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if idx == len(self.up_modules) and len(h_local) > 0:
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x = x + h_local[1]
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x = resnet2(x, global_feature)
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x = upsample(x)
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x = self.final_conv(x)
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x = einops.rearrange(x, "b t h -> b h t")
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return x
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