Feature/hiera

configs/data/im2im/ijepa.yaml

@@ -39,6 +39,7 @@ transforms:
       - _target_: cyto_dl.image.transforms.generate_jepa_masks.JEPAMaskGenerator
         mask_size: 4
         num_patches: ${model._aux.num_patches}
+        spatial_dims: ${spatial_dims}
 
   test:
     _target_: monai.transforms.Compose
@@ -69,6 +70,7 @@ transforms:
       - _target_: cyto_dl.image.transforms.generate_jepa_masks.JEPAMaskGenerator
         mask_size: 4
         num_patches: ${model._aux.num_patches}
+        spatial_dims: ${spatial_dims}
 
   predict:
     _target_: monai.transforms.Compose
@@ -127,6 +129,7 @@ transforms:
       - _target_: cyto_dl.image.transforms.generate_jepa_masks.JEPAMaskGenerator
         mask_size: 4
         num_patches: ${model._aux.num_patches}
+        spatial_dims: ${spatial_dims}
 
 _aux:
   _scales_dict:

configs/data/im2im/iwm.yaml

@@ -58,6 +58,7 @@ transforms:
       - _target_: cyto_dl.image.transforms.generate_jepa_masks.JEPAMaskGenerator
         mask_size: 4
         num_patches: ${model._aux.num_patches}
+        spatial_dims: ${spatial_dims}
 
   test:
     _target_: monai.transforms.Compose
@@ -176,5 +177,6 @@ transforms:
       - _target_: cyto_dl.image.transforms.generate_jepa_masks.JEPAMaskGenerator
         mask_size: 4
         num_patches: ${model._aux.num_patches}
+        spatial_dims: ${spatial_dims}
 
 _aux:

configs/data/im2im/mae.yaml

@@ -32,12 +32,12 @@ transforms:
       - _target_: monai.transforms.NormalizeIntensityd
         keys: ${source_col}
         channel_wise: True
-      - _target_: cyto_dl.image.transforms.RandomMultiScaleCropd
+      - _target_: monai.transforms.RandSpatialCropSamplesd
         keys:
           - ${source_col}
-        patch_shape: ${data._aux.patch_shape}
-        patch_per_image: 1
-        scales_dict: ${kv_to_dict:${data._aux._scales_dict}}
+        roi_size: ${data._aux.patch_shape}
+        num_samples: 1
+        random_size: False
 
   test:
     _target_: monai.transforms.Compose
@@ -104,14 +104,11 @@ transforms:
       - _target_: monai.transforms.NormalizeIntensityd
         keys: ${source_col}
         channel_wise: True
-      - _target_: cyto_dl.image.transforms.RandomMultiScaleCropd
+      - _target_: monai.transforms.RandSpatialCropSamplesd
         keys:
           - ${source_col}
-        patch_shape: ${data._aux.patch_shape}
-        patch_per_image: 1
-        scales_dict: ${kv_to_dict:${data._aux._scales_dict}}
+        roi_size: ${data._aux.patch_shape}
+        num_samples: 1
+        random_size: False
 
 _aux:
-  _scales_dict:
-    - - ${source_col}
-      - [1]

configs/experiment/im2im/hiera.yaml

@@ -0,0 +1,52 @@
+# @package _global_
+# to execute this experiment run:
+# python train.py experiment=example
+defaults:
+  - override /data: im2im/mae.yaml
+  - override /model: im2im/hiera.yaml
+  - override /callbacks: default.yaml
+  - override /trainer: gpu.yaml
+  - override /logger: csv.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["dev"]
+seed: 12345
+
+experiment_name: YOUR_EXP_NAME
+run_name: YOUR_RUN_NAME
+
+# only source_col is needed for masked autoencoder
+source_col: raw
+spatial_dims: 3
+raw_im_channels: 1
+
+trainer:
+  max_epochs: 100
+  gradient_clip_val: 10
+
+data:
+  path: ${paths.data_dir}/example_experiment_data/segmentation
+  cache_dir: ${paths.data_dir}/example_experiment_data/cache
+  batch_size: 1
+  _aux:
+    # 2D
+    # patch_shape: [16, 16]
+    # 3D
+    patch_shape: [16, 16, 16]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]

configs/experiment/im2im/ijepa.yaml

@@ -37,10 +37,3 @@ data:
     # patch_shape: [16, 16]
     # 3D
     patch_shape: [16, 16, 16]
-
-model:
-  _aux:
-    # 3D
-    num_patches: [8, 8, 8]
-    # 2d
-    # num_patches: [8, 8]

configs/experiment/im2im/iwm.yaml

@@ -38,13 +38,6 @@ data:
     # 3D
     patch_shape: [16, 16, 16]
 
-model:
-  _aux:
-    # 3D
-    num_patches: [8, 8, 8]
-    # 2d
-    # num_patches: [8, 8]
-
 callbacks:
   prediction_saver:
     _target_: cyto_dl.callbacks.csv_saver.CSVSaver

configs/model/im2im/hiera.yaml

@@ -0,0 +1,70 @@
+_target_: cyto_dl.models.im2im.MultiTaskIm2Im
+
+save_images_every_n_epochs: 1
+save_dir: ${paths.output_dir}
+
+x_key: ${source_col}
+
+backbone:
+  _target_: cyto_dl.nn.vits.mae.HieraMAE
+  spatial_dims: ${spatial_dims}
+  patch_size: 2 # patch_size * num_patches should be your image shape (data._aux.patch_shape)
+  num_patches: 8 # patch_size * num_patches = img_shape
+  num_mask_units: 4 #Mask units are used for local attention. img_shape / num_mask_units = size of each mask unit in pixels, num_patches/num_mask_units = number of patches permask unit
+  emb_dim: 4
+  # NOTE: this is a very small model for testing - for best performance, the downsampling ratios, embedding dimension, number of layers and number of heads should be adjusted to your data
+  architecture:
+    # mask_unit_attention blocks - attention is only done within a mask unit and not across mask units
+    # the total amount of q_stride across the architecture must be less than the number of patches per mask unit
+    - repeat: 1 # number of times to repeat this block
+      q_stride: 2 # size of downsampling within a mask unit
+      num_heads: 1
+    - repeat: 1
+      q_stride: 1
+      num_heads: 2
+    # self attention transformer - attention is done across all patches, irrespective of which mask unit they're in
+    - repeat: 2
+      num_heads: 4
+      self_attention: True
+  decoder_layer: 1
+  decoder_dim: 16
+  mask_ratio: 0.66666666666
+  context_pixels: 3
+  use_crossmae: True
+
+task_heads: ${kv_to_dict:${model._aux._tasks}}
+
+optimizer:
+  generator:
+    _partial_: True
+    _target_: torch.optim.AdamW
+    weight_decay: 0.05
+
+lr_scheduler:
+  generator:
+    _partial_: True
+    _target_: torch.optim.lr_scheduler.OneCycleLR
+    max_lr: 0.0001
+    epochs: ${trainer.max_epochs}
+    steps_per_epoch: 1
+    pct_start: 0.1
+
+inference_args:
+  sw_batch_size: 1
+  roi_size: ${data._aux.patch_shape}
+  overlap: 0
+  progress: True
+  mode: "gaussian"
+
+_aux:
+  _tasks:
+    - - ${source_col}
+      - _target_: cyto_dl.nn.head.mae_head.MAEHead
+        loss:
+        postprocess:
+          input:
+            _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel
+            rescale_dtype: numpy.uint8
+          prediction:
+            _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel
+            rescale_dtype: numpy.uint8

configs/model/im2im/ijepa.yaml

@@ -5,15 +5,16 @@ max_epochs: ${trainer.max_epochs}
 save_dir: ${paths.output_dir}
 
 encoder:
-  _target_: cyto_dl.nn.vits.jepa.JEPAEncoder
-  patch_size: 2
+  _target_: cyto_dl.nn.vits.encoder.JEPAEncoder
+  patch_size: 2 # patch_size * num_patches should equl data._aux.patch_shape
   num_patches: ${model._aux.num_patches}
   emb_dim: 16
   num_layer: 2
   num_head: 1
+  spatial_dims: ${spatial_dims}
 
 predictor:
-  _target_: cyto_dl.nn.vits.jepa.JEPAPredictor
+  _target_: cyto_dl.nn.vits.predictor.JEPAPredictor
   num_patches: ${model._aux.num_patches}
   input_dim: ${model.encoder.emb_dim}
   emb_dim: 8
@@ -34,4 +35,4 @@ lr_scheduler:
   pct_start: 0.1
 
 _aux:
-  num_patches:
+  num_patches: 8

configs/model/im2im/iwm.yaml

@@ -8,15 +8,16 @@ max_epochs: ${trainer.max_epochs}
 save_dir: ${paths.output_dir}
 
 encoder:
-  _target_: cyto_dl.nn.vits.jepa.JEPAEncoder
-  patch_size: 2
+  _target_: cyto_dl.nn.vits.encoder.JEPAEncoder
+  patch_size: 2 # patch_size * num_patches should be the same as data._aux.patch_shape
   num_patches: ${model._aux.num_patches}
   emb_dim: 16
   num_layer: 1
   num_head: 1
+  spatial_dims: ${spatial_dims}
 
 predictor:
-  _target_: cyto_dl.nn.vits.jepa.IWMPredictor
+  _target_: cyto_dl.nn.vits.predictor.IWMPredictor
   domains: [SEC61B]
   num_patches: ${model._aux.num_patches}
   input_dim: ${model.encoder.emb_dim}
@@ -38,4 +39,4 @@ lr_scheduler:
   pct_start: 0.1
 
 _aux:
-  num_patches:
+  num_patches: 8

configs/model/im2im/mae.yaml

@@ -6,10 +6,10 @@ save_dir: ${paths.output_dir}
 x_key: ${source_col}
 
 backbone:
-  _target_: cyto_dl.nn.vits.MAE_ViT
+  _target_: cyto_dl.nn.vits.MAE
   spatial_dims: ${spatial_dims}
-  # base_patch_size* num_patches should be your patch shape
-  base_patch_size: 2
+  # patch_size* num_patches should be your patch shape
+  patch_size: 2
   num_patches: 8
   emb_dim: 16
   encoder_layer: 2

configs/model/im2im/vit_segmentation_decoder.yaml

@@ -8,8 +8,8 @@ x_key: ${source_col}
 backbone:
   _target_: cyto_dl.nn.vits.Seg_ViT
   spatial_dims: ${spatial_dims}
-  # base_patch_size* num_patches should be your patch shape
-  base_patch_size: 2
+  # patch_size* num_patches should be your patch shape
+  patch_size: 2
   num_patches: 8
   emb_dim: 16
   encoder_layer: 2

cyto_dl/image/transforms/generate_jepa_masks.py

@@ -5,6 +5,8 @@
 from monai.transforms import RandomizableTransform
 from skimage.segmentation import find_boundaries
 
+from cyto_dl.nn.vits.utils import match_tuple_dimensions
+
 
 class JEPAMaskGenerator(RandomizableTransform):
     """Transform for generating Block-contiguous masks for JEPA training.
@@ -15,6 +17,7 @@ class JEPAMaskGenerator(RandomizableTransform):
 
     def __init__(
         self,
+        spatial_dims: int,
         mask_size: int = 12,
         block_aspect_ratio: Tuple[float] = (0.5, 1.5),
         num_patches: Tuple[float] = (6, 24, 24),
@@ -23,6 +26,8 @@ def __init__(
         """
         Parameters
         ----------
+        spatial_dims : int
+            The number of spatial dimensions of the image (2 or 3)
         mask_size : int, optional
             The size of the blocks used to generate mask. Block dimensions are determined by the mask size and an aspect ratio sampled from the range  `block_aspect_ratio`
         block_aspect_ratio : Tuple[float], optional
@@ -32,7 +37,9 @@ def __init__(
         mask_ratio : float, optional
             The proportion of the image to be masked
         """
-        assert mask_ratio < 1, "mask_ratio must be less than 1"
+        assert 0 < mask_ratio < 1, "mask_ratio must be between 0 and 1"
+
+        num_patches = match_tuple_dimensions(spatial_dims, [num_patches])[0]
         assert mask_size * max(block_aspect_ratio) < min(
             num_patches[-2:]
         ), "mask_size * max mask aspect ratio must be less than the smallest dimension of num_patches"
@@ -46,7 +53,7 @@ def __init__(
         self.mask = np.zeros(num_patches)
         self.edge_mask = np.ones(num_patches)
 
-        self.spatial_dims = len(num_patches)
+        self.spatial_dims = spatial_dims
         # create a mask that identified pixels on the edge of the image
         if self.spatial_dims == 3:
             self.edge_mask[1:-1, 1:-1, 1:-1] = 0

cyto_dl/nn/vits/__init__.py

@@ -1,4 +1,5 @@
-from .cross_mae import CrossMAE_Decoder
-from .mae import MAE_Decoder, MAE_Encoder, MAE_ViT
+from .decoder import CrossMAE_Decoder, MAE_Decoder
+from .encoder import HieraEncoder, MAE_Encoder
+from .mae import MAE, HieraMAE
 from .seg import Seg_ViT, SuperresDecoder
 from .utils import take_indexes