policy/DP/diffusion_policy/model/diffusion/conditional_unet1d.py

from typing import Union
import logging
import torch
import torch.nn as nn
import einops
from einops.layers.torch import Rearrange

from diffusion_policy.model.diffusion.conv1d_components import (
    Downsample1d,
    Upsample1d,
    Conv1dBlock,
)
from diffusion_policy.model.diffusion.positional_embedding import SinusoidalPosEmb

logger = logging.getLogger(__name__)


class ConditionalResidualBlock1D(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        cond_dim,
        kernel_size=3,
        n_groups=8,
        cond_predict_scale=False,
    ):
        super().__init__()

        self.blocks = nn.ModuleList([
            Conv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups),
            Conv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups),
        ])

        # FiLM modulation https://arxiv.org/abs/1709.07871
        # predicts per-channel scale and bias
        cond_channels = out_channels
        if cond_predict_scale:
            cond_channels = out_channels * 2
        self.cond_predict_scale = cond_predict_scale
        self.out_channels = out_channels
        self.cond_encoder = nn.Sequential(
            nn.Mish(),
            nn.Linear(cond_dim, cond_channels),
            Rearrange("batch t -> batch t 1"),
        )

        # make sure dimensions compatible
        self.residual_conv = (nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity())

    def forward(self, x, cond):
        """
        x : [ batch_size x in_channels x horizon ]
        cond : [ batch_size x cond_dim]

        returns:
        out : [ batch_size x out_channels x horizon ]
        """
        out = self.blocks[0](x)
        embed = self.cond_encoder(cond)
        if self.cond_predict_scale:
            embed = embed.reshape(embed.shape[0], 2, self.out_channels, 1)
            scale = embed[:, 0, ...]
            bias = embed[:, 1, ...]
            out = scale * out + bias
        else:
            out = out + embed
        out = self.blocks[1](out)
        out = out + self.residual_conv(x)
        return out


class ConditionalUnet1D(nn.Module):

    def __init__(
        self,
        input_dim,
        local_cond_dim=None,
        global_cond_dim=None,
        diffusion_step_embed_dim=256,
        down_dims=[256, 512, 1024],
        kernel_size=3,
        n_groups=8,
        cond_predict_scale=False,
    ):
        super().__init__()
        all_dims = [input_dim] + list(down_dims)
        start_dim = down_dims[0]

        dsed = diffusion_step_embed_dim
        diffusion_step_encoder = nn.Sequential(
            SinusoidalPosEmb(dsed),
            nn.Linear(dsed, dsed * 4),
            nn.Mish(),
            nn.Linear(dsed * 4, dsed),
        )
        cond_dim = dsed
        if global_cond_dim is not None:
            cond_dim += global_cond_dim

        in_out = list(zip(all_dims[:-1], all_dims[1:]))

        local_cond_encoder = None
        if local_cond_dim is not None:
            _, dim_out = in_out[0]
            dim_in = local_cond_dim
            local_cond_encoder = nn.ModuleList([
                # down encoder
                ConditionalResidualBlock1D(
                    dim_in,
                    dim_out,
                    cond_dim=cond_dim,
                    kernel_size=kernel_size,
                    n_groups=n_groups,
                    cond_predict_scale=cond_predict_scale,
                ),
                # up encoder
                ConditionalResidualBlock1D(
                    dim_in,
                    dim_out,
                    cond_dim=cond_dim,
                    kernel_size=kernel_size,
                    n_groups=n_groups,
                    cond_predict_scale=cond_predict_scale,
                ),
            ])

        mid_dim = all_dims[-1]
        self.mid_modules = nn.ModuleList([
            ConditionalResidualBlock1D(
                mid_dim,
                mid_dim,
                cond_dim=cond_dim,
                kernel_size=kernel_size,
                n_groups=n_groups,
                cond_predict_scale=cond_predict_scale,
            ),
            ConditionalResidualBlock1D(
                mid_dim,
                mid_dim,
                cond_dim=cond_dim,
                kernel_size=kernel_size,
                n_groups=n_groups,
                cond_predict_scale=cond_predict_scale,
            ),
        ])

        down_modules = nn.ModuleList([])
        for ind, (dim_in, dim_out) in enumerate(in_out):
            is_last = ind >= (len(in_out) - 1)
            down_modules.append(
                nn.ModuleList([
                    ConditionalResidualBlock1D(
                        dim_in,
                        dim_out,
                        cond_dim=cond_dim,
                        kernel_size=kernel_size,
                        n_groups=n_groups,
                        cond_predict_scale=cond_predict_scale,
                    ),
                    ConditionalResidualBlock1D(
                        dim_out,
                        dim_out,
                        cond_dim=cond_dim,
                        kernel_size=kernel_size,
                        n_groups=n_groups,
                        cond_predict_scale=cond_predict_scale,
                    ),
                    Downsample1d(dim_out) if not is_last else nn.Identity(),
                ]))

        up_modules = nn.ModuleList([])
        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
            is_last = ind >= (len(in_out) - 1)
            up_modules.append(
                nn.ModuleList([
                    ConditionalResidualBlock1D(
                        dim_out * 2,
                        dim_in,
                        cond_dim=cond_dim,
                        kernel_size=kernel_size,
                        n_groups=n_groups,
                        cond_predict_scale=cond_predict_scale,
                    ),
                    ConditionalResidualBlock1D(
                        dim_in,
                        dim_in,
                        cond_dim=cond_dim,
                        kernel_size=kernel_size,
                        n_groups=n_groups,
                        cond_predict_scale=cond_predict_scale,
                    ),
                    Upsample1d(dim_in) if not is_last else nn.Identity(),
                ]))

        final_conv = nn.Sequential(
            Conv1dBlock(start_dim, start_dim, kernel_size=kernel_size),
            nn.Conv1d(start_dim, input_dim, 1),
        )

        self.diffusion_step_encoder = diffusion_step_encoder
        self.local_cond_encoder = local_cond_encoder
        self.up_modules = up_modules
        self.down_modules = down_modules
        self.final_conv = final_conv

        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))

    def forward(self,
                sample: torch.Tensor,
                timestep: Union[torch.Tensor, float, int],
                local_cond=None,
                global_cond=None,
                **kwargs):
        """
        x: (B,T,input_dim)
        timestep: (B,) or int, diffusion step
        local_cond: (B,T,local_cond_dim)
        global_cond: (B,global_cond_dim)
        output: (B,T,input_dim)
        """
        sample = einops.rearrange(sample, "b h t -> b t h")

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)
        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        global_feature = self.diffusion_step_encoder(timesteps)

        if global_cond is not None:
            global_feature = torch.cat([global_feature, global_cond], axis=-1)

        # encode local features
        h_local = list()
        if local_cond is not None:
            local_cond = einops.rearrange(local_cond, "b h t -> b t h")
            resnet, resnet2 = self.local_cond_encoder
            x = resnet(local_cond, global_feature)
            h_local.append(x)
            x = resnet2(local_cond, global_feature)
            h_local.append(x)

        x = sample
        h = []
        for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules):
            x = resnet(x, global_feature)
            if idx == 0 and len(h_local) > 0:
                x = x + h_local[0]
            x = resnet2(x, global_feature)
            h.append(x)
            x = downsample(x)

        for mid_module in self.mid_modules:
            x = mid_module(x, global_feature)

        for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules):
            x = torch.cat((x, h.pop()), dim=1)
            x = resnet(x, global_feature)
            # The correct condition should be:
            # if idx == (len(self.up_modules)-1) and len(h_local) > 0:
            # However this change will break compatibility with published checkpoints.
            # Therefore it is left as a comment.
            if idx == len(self.up_modules) and len(h_local) > 0:
                x = x + h_local[1]
            x = resnet2(x, global_feature)
            x = upsample(x)

        x = self.final_conv(x)

        x = einops.rearrange(x, "b t h -> b h t")
        return x