medical_diffusion/external/diffusers/unet.py

from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.utils.checkpoint


from .embeddings import TimeEmbbeding

from .unet_blocks import (
    CrossAttnDownBlock2D,
    CrossAttnUpBlock2D,
    DownBlock2D,
    UNetMidBlock2DCrossAttn,
    UpBlock2D,
    get_down_block,
    get_up_block,
)

class TimestepEmbedding(nn.Module):
    def __init__(self, channel, time_embed_dim, act_fn="silu"):
        super().__init__()

        self.linear_1 = nn.Linear(channel, time_embed_dim)
        self.act = None
        if act_fn == "silu":
            self.act = nn.SiLU()
        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)

    def forward(self, sample):
        sample = self.linear_1(sample)

        if self.act is not None:
            sample = self.act(sample)

        sample = self.linear_2(sample)
        return sample


class UNet2DConditionModel(nn.Module):
    r"""
    UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
    and returns sample shaped output.


    Parameters:
        sample_size (`int`, *optional*): The size of the input sample.
        in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
            The tuple of upsample blocks to use.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
        cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
    """

    _supports_gradient_checkpointing = True


    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        layers_per_block: int = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        act_fn: str = "silu",
        norm_num_groups: int = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: int = 768,
        attention_head_dim: int = 8,
    ):
        super().__init__()

        self.sample_size = sample_size
        time_embed_dim = block_out_channels[0] * 4

        self.emb = nn.Embedding(2, cross_attention_dim)

        # input
        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))

        # time
        self.time_embedding = TimeEmbbeding(block_out_channels[0], time_embed_dim)

        self.down_blocks = nn.ModuleList([])
        self.mid_block = None
        self.up_blocks = nn.ModuleList([])

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                attn_num_head_channels=attention_head_dim,
                downsample_padding=downsample_padding,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = UNetMidBlock2DCrossAttn(
            in_channels=block_out_channels[-1],
            temb_channels=time_embed_dim,
            resnet_eps=norm_eps,
            resnet_act_fn=act_fn,
            output_scale_factor=mid_block_scale_factor,
            resnet_time_scale_shift="default",
            cross_attention_dim=cross_attention_dim,
            attn_num_head_channels=attention_head_dim,
            resnet_groups=norm_num_groups,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block + 1,
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=time_embed_dim,
                add_upsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                attn_num_head_channels=attention_head_dim,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
        self.conv_act = nn.SiLU()
        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)



    def forward(
        self,
        sample: torch.FloatTensor,
        t: torch.Tensor,
        encoder_hidden_states: torch.Tensor = None,
        self_cond: torch.Tensor = None
    ):
        encoder_hidden_states = self.emb(encoder_hidden_states)
        # encoder_hidden_states = None # ------------------------ WARNING Disabled ---------------------
        """r
        Args:
            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
            encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states
   
        Returns:
            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is the sample tensor.
        """
        # 0. center input if necessary
        # if self.config.center_input_sample:
        #     sample = 2 * sample - 1.0

        # 1. time
        t_emb = self.time_embedding(t)

        # 2. pre-process
        sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=t_emb,
                    encoder_hidden_states=encoder_hidden_states,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=t_emb)

            down_block_res_samples += res_samples

        # 4. mid
        sample = self.mid_block(sample, t_emb, encoder_hidden_states=encoder_hidden_states)

        # 5. up
        for upsample_block in self.up_blocks:
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=t_emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                )
            else:
                sample = upsample_block(hidden_states=sample, temb=t_emb, res_hidden_states_tuple=res_samples)

        # 6. post-process
        # make sure hidden states is in float32
        # when running in half-precision
        sample = self.conv_norm_out(sample.float()).type(sample.dtype)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)


        return sample, []