Refactor AutoencoderKlMaisi

monai/apps/generation/maisi/networks/autoencoderkl_maisi.py

@@ -13,25 +13,17 @@
 
 import gc
 import logging
-from typing import TYPE_CHECKING, Sequence, cast
+from typing import Sequence
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from monai.networks.blocks import Convolution
-from monai.utils import optional_import
+from monai.networks.blocks.spatialattention import SpatialAttentionBlock
+from monai.networks.nets.autoencoderkl import AEKLResBlock, AutoencoderKL
 from monai.utils.type_conversion import convert_to_tensor
 
-AttentionBlock, has_attentionblock = optional_import("generative.networks.nets.autoencoderkl", name="AttentionBlock")
-AutoencoderKL, has_autoencoderkl = optional_import("generative.networks.nets.autoencoderkl", name="AutoencoderKL")
-ResBlock, has_resblock = optional_import("generative.networks.nets.autoencoderkl", name="ResBlock")
-
-if TYPE_CHECKING:
-    from generative.networks.nets.autoencoderkl import AutoencoderKL as AutoencoderKLType
-else:
-    AutoencoderKLType = cast(type, AutoencoderKL)
-
 # Set up logging configuration
 logger = logging.getLogger(__name__)
 
@@ -518,11 +510,13 @@ class MaisiEncoder(nn.Module):
         in_channels: Number of input channels.
         num_channels: Sequence of block output channels.
         out_channels: Number of channels in the bottom layer (latent space) of the autoencoder.
-        num_res_blocks: Number of residual blocks (see ResBlock) per level.
+        num_res_blocks: Number of residual blocks (see AEKLResBlock) per level.
         norm_num_groups: Number of groups for the group norm layers.
         norm_eps: Epsilon for the normalization.
         attention_levels: Indicate which level from num_channels contain an attention block.
         with_nonlocal_attn: If True, use non-local attention block.
+        include_fc: whether to include the final linear layer in the attention block. Default to False.
+        use_combined_linear: whether to use a single linear layer for qkv projection in the attention block, default to False.
         use_flash_attention: If True, use flash attention for a memory efficient attention mechanism.
         num_splits: Number of splits for the input tensor.
         dim_split: Dimension of splitting for the input tensor.
@@ -547,6 +541,8 @@ def __init__(
         print_info: bool = False,
         save_mem: bool = True,
         with_nonlocal_attn: bool = True,
+        include_fc: bool = False,
+        use_combined_linear: bool = False,
         use_flash_attention: bool = False,
     ) -> None:
         super().__init__()
@@ -603,11 +599,13 @@ def __init__(
                 input_channel = output_channel
                 if attention_levels[i]:
                     blocks.append(
-                        AttentionBlock(
+                        SpatialAttentionBlock(
                             spatial_dims=spatial_dims,
                             num_channels=input_channel,
                             norm_num_groups=norm_num_groups,
                             norm_eps=norm_eps,
+                            include_fc=include_fc,
+                            use_combined_linear=use_combined_linear,
                             use_flash_attention=use_flash_attention,
                         )
                     )
@@ -626,7 +624,7 @@ def __init__(
 
         if with_nonlocal_attn:
             blocks.append(
-                ResBlock(
+                AEKLResBlock(
                     spatial_dims=spatial_dims,
                     in_channels=num_channels[-1],
                     norm_num_groups=norm_num_groups,
@@ -636,16 +634,18 @@ def __init__(
             )
 
             blocks.append(
-                AttentionBlock(
+                SpatialAttentionBlock(
                     spatial_dims=spatial_dims,
                     num_channels=num_channels[-1],
                     norm_num_groups=norm_num_groups,
                     norm_eps=norm_eps,
+                    include_fc=include_fc,
+                    use_combined_linear=use_combined_linear,
                     use_flash_attention=use_flash_attention,
                 )
             )
             blocks.append(
-                ResBlock(
+                AEKLResBlock(
                     spatial_dims=spatial_dims,
                     in_channels=num_channels[-1],
                     norm_num_groups=norm_num_groups,
@@ -699,11 +699,13 @@ class MaisiDecoder(nn.Module):
         num_channels: Sequence of block output channels.
         in_channels: Number of channels in the bottom layer (latent space) of the autoencoder.
         out_channels: Number of output channels.
-        num_res_blocks: Number of residual blocks (see ResBlock) per level.
+        num_res_blocks: Number of residual blocks (see AEKLResBlock) per level.
         norm_num_groups: Number of groups for the group norm layers.
         norm_eps: Epsilon for the normalization.
         attention_levels: Indicate which level from num_channels contain an attention block.
         with_nonlocal_attn: If True, use non-local attention block.
+        include_fc: whether to include the final linear layer in the attention block. Default to False.
+        use_combined_linear: whether to use a single linear layer for qkv projection in the attention block, default to False.
         use_flash_attention: If True, use flash attention for a memory efficient attention mechanism.
         use_convtranspose: If True, use ConvTranspose to upsample feature maps in decoder.
         num_splits: Number of splits for the input tensor.
@@ -729,6 +731,8 @@ def __init__(
         print_info: bool = False,
         save_mem: bool = True,
         with_nonlocal_attn: bool = True,
+        include_fc: bool = False,
+        use_combined_linear: bool = False,
         use_flash_attention: bool = False,
         use_convtranspose: bool = False,
     ) -> None:
@@ -758,7 +762,7 @@ def __init__(
 
         if with_nonlocal_attn:
             blocks.append(
-                ResBlock(
+                AEKLResBlock(
                     spatial_dims=spatial_dims,
                     in_channels=reversed_block_out_channels[0],
                     norm_num_groups=norm_num_groups,
@@ -767,16 +771,18 @@ def __init__(
                 )
             )
             blocks.append(
-                AttentionBlock(
+                SpatialAttentionBlock(
                     spatial_dims=spatial_dims,
                     num_channels=reversed_block_out_channels[0],
                     norm_num_groups=norm_num_groups,
                     norm_eps=norm_eps,
+                    include_fc=include_fc,
+                    use_combined_linear=use_combined_linear,
                     use_flash_attention=use_flash_attention,
                 )
             )
             blocks.append(
-                ResBlock(
+                AEKLResBlock(
                     spatial_dims=spatial_dims,
                     in_channels=reversed_block_out_channels[0],
                     norm_num_groups=norm_num_groups,
@@ -812,11 +818,13 @@ def __init__(
 
                 if reversed_attention_levels[i]:
                     blocks.append(
-                        AttentionBlock(
+                        SpatialAttentionBlock(
                             spatial_dims=spatial_dims,
                             num_channels=block_in_ch,
                             norm_num_groups=norm_num_groups,
                             norm_eps=norm_eps,
+                            include_fc=include_fc,
+                            use_combined_linear=use_combined_linear,
                             use_flash_attention=use_flash_attention,
                         )
                     )
@@ -870,7 +878,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
-class AutoencoderKlMaisi(AutoencoderKLType):
+class AutoencoderKlMaisi(AutoencoderKL):
     """
     AutoencoderKL with custom MaisiEncoder and MaisiDecoder.
 
@@ -886,6 +894,8 @@ class AutoencoderKlMaisi(AutoencoderKLType):
         norm_eps: Epsilon for the normalization.
         with_encoder_nonlocal_attn: If True, use non-local attention block in the encoder.
         with_decoder_nonlocal_attn: If True, use non-local attention block in the decoder.
+        include_fc: whether to include the final linear layer. Default to False.
+        use_combined_linear: whether to use a single linear layer for qkv projection, default to False.
         use_flash_attention: If True, use flash attention for a memory efficient attention mechanism.
         use_checkpointing: If True, use activation checkpointing.
         use_convtranspose: If True, use ConvTranspose to upsample feature maps in decoder.
@@ -909,6 +919,8 @@ def __init__(
         norm_eps: float = 1e-6,
         with_encoder_nonlocal_attn: bool = False,
         with_decoder_nonlocal_attn: bool = False,
+        include_fc: bool = False,
+        use_combined_linear: bool = False,
         use_flash_attention: bool = False,
         use_checkpointing: bool = False,
         use_convtranspose: bool = False,
@@ -930,12 +942,14 @@ def __init__(
             norm_eps,
             with_encoder_nonlocal_attn,
             with_decoder_nonlocal_attn,
-            use_flash_attention,
             use_checkpointing,
             use_convtranspose,
+            include_fc,
+            use_combined_linear,
+            use_flash_attention,
         )
 
-        self.encoder = MaisiEncoder(
+        self.encoder: nn.Module = MaisiEncoder(
             spatial_dims=spatial_dims,
             in_channels=in_channels,
             num_channels=num_channels,
@@ -945,6 +959,8 @@ def __init__(
             norm_eps=norm_eps,
             attention_levels=attention_levels,
             with_nonlocal_attn=with_encoder_nonlocal_attn,
+            include_fc=include_fc,
+            use_combined_linear=use_combined_linear,
             use_flash_attention=use_flash_attention,
             num_splits=num_splits,
             dim_split=dim_split,
@@ -953,7 +969,7 @@ def __init__(
             save_mem=save_mem,
         )
 
-        self.decoder = MaisiDecoder(
+        self.decoder: nn.Module = MaisiDecoder(
             spatial_dims=spatial_dims,
             num_channels=num_channels,
             in_channels=latent_channels,
@@ -963,6 +979,8 @@ def __init__(
             norm_eps=norm_eps,
             attention_levels=attention_levels,
             with_nonlocal_attn=with_decoder_nonlocal_attn,
+            include_fc=include_fc,
+            use_combined_linear=use_combined_linear,
             use_flash_attention=use_flash_attention,
             use_convtranspose=use_convtranspose,
             num_splits=num_splits,

monai/networks/nets/autoencoderkl.py

@@ -532,7 +532,7 @@ def __init__(
                 "`num_channels`."
             )
 
-        self.encoder = Encoder(
+        self.encoder: nn.Module = Encoder(
             spatial_dims=spatial_dims,
             in_channels=in_channels,
             channels=channels,
@@ -546,7 +546,7 @@ def __init__(
             use_combined_linear=use_combined_linear,
             use_flash_attention=use_flash_attention,
         )
-        self.decoder = Decoder(
+        self.decoder: nn.Module = Decoder(
             spatial_dims=spatial_dims,
             channels=channels,
             in_channels=latent_channels,

tests/test_autoencoderkl_maisi.py

@@ -16,16 +16,13 @@
 import torch
 from parameterized import parameterized
 
+from monai.apps.generation.maisi.networks.autoencoderkl_maisi import AutoencoderKlMaisi
 from monai.networks import eval_mode
 from monai.utils import optional_import
 from tests.utils import SkipIfBeforePyTorchVersion
 
 tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
 _, has_einops = optional_import("einops")
-_, has_generative = optional_import("generative")
-
-if has_generative:
-    from monai.apps.generation.maisi.networks.autoencoderkl_maisi import AutoencoderKlMaisi
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
@@ -79,7 +76,6 @@
     CASES = CASES_NO_ATTENTION
 
 
-@unittest.skipUnless(has_generative, "monai-generative required")
 class TestAutoencoderKlMaisi(unittest.TestCase):
 
     @parameterized.expand(CASES)