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* only run heads that dont have existing outputs * make gan subclass of multitask model * update saving code * remove conv_proj_layer * make superres head class gan and resblockshead * remove metric calculation * update configs * update readme * remove conv_proj_layer; * precommit --------- Co-authored-by: Benjamin Morris <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,205 @@ | ||
| _target_: cyto_dl.datamodules.dataframe.DataframeDatamodule | ||
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| path: | ||
| cache_dir: | ||
|
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| num_workers: 0 | ||
| batch_size: 1 | ||
| pin_memory: True | ||
| split_column: | ||
| columns: | ||
| - ${source_col} | ||
| - ${target_col} | ||
|
|
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| transforms: | ||
| train: | ||
| _target_: monai.transforms.Compose | ||
| transforms: | ||
| # channels are [blank, membrane,blank, structure, blank, nuclear dye, brightfield ] | ||
| # target is the nuclear dyeimage | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${target_col} | ||
| reader: | ||
| - _target_: | ||
| cyto_dl.image.io.MonaiBioReader | ||
| # NOTE: eval is used so only the experiment file is required to change for beginning users. This is not recommended when creating your own configs. | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 5 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| # channels are [nucseg, cellseg, nuclear boundary seg, cell boundary seg] | ||
| # source image is the segmentation | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${source_col} | ||
| reader: | ||
| - _target_: cyto_dl.image.io.MonaiBioReader | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 0 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| - _target_: monai.transforms.EnsureChannelFirstd | ||
| channel_dim: "no_channel" | ||
| keys: ${data.columns} | ||
| - _target_: monai.transforms.Zoomd | ||
| keys: ${source_col} | ||
| zoom: 0.25 | ||
| keep_size: False | ||
| - _target_: monai.transforms.ToTensord | ||
| keys: ${data.columns} | ||
| # GANs use Tanh as final activation, target has to be in range [-1,1] | ||
| - _target_: monai.transforms.ScaleIntensityRangePercentilesd | ||
| keys: ${target_col} | ||
| lower: 0.01 | ||
| upper: 99.99 | ||
| b_min: -1 | ||
| b_max: 1 | ||
| clip: True | ||
| # input to synthetic image generation model is a semantic segmentation | ||
| - _target_: monai.transforms.ThresholdIntensityd | ||
| keys: ${source_col} | ||
| threshold: 0.1 | ||
| above: False | ||
| cval: 1 | ||
| - _target_: cyto_dl.image.transforms.RandomMultiScaleCropd | ||
| keys: ${data.columns} | ||
| patch_shape: ${data._aux.patch_shape} | ||
| patch_per_image: 1 | ||
| scales_dict: ${kv_to_dict:${data._aux._scales_dict}} | ||
|
|
||
| test: | ||
| _target_: monai.transforms.Compose | ||
| transforms: | ||
| # channels are [blank, membrane,blank, structure, blank, nuclear dye, brightfield ] | ||
| # target is the nuclear dyeimage | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${target_col} | ||
| reader: | ||
| - _target_: cyto_dl.image.io.MonaiBioReader | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 5 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| # channels are [nucseg, cellseg, nuclear boundary seg, cell boundary seg] | ||
| # source image is the segmentation | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${source_col} | ||
| reader: | ||
| - _target_: cyto_dl.image.io.MonaiBioReader | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 0 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| - _target_: monai.transforms.EnsureChannelFirstd | ||
| channel_dim: "no_channel" | ||
| keys: ${data.columns} | ||
| #crop image so that it is divisible by 4 for downsampling | ||
| - _target_: monai.transforms.CenterSpatialCropd | ||
| keys: ${data.columns} | ||
| roi_size: [64, -1, -1] | ||
| - _target_: monai.transforms.Zoomd | ||
| keys: ${source_col} | ||
| zoom: 0.25 | ||
| keep_size: False | ||
| - _target_: monai.transforms.ToTensord | ||
| keys: ${data.columns} | ||
| # GANs use Tanh as final activation, target has to be in range [-1,1] | ||
| - _target_: monai.transforms.ScaleIntensityRangePercentilesd | ||
| keys: ${target_col} | ||
| lower: 0.01 | ||
| upper: 99.99 | ||
| b_min: -1 | ||
| b_max: 1 | ||
| clip: True | ||
| # input to synthetic image generation model is a semantic segmentation | ||
| - _target_: monai.transforms.ThresholdIntensityd | ||
| keys: ${source_col} | ||
| threshold: 0.1 | ||
| above: False | ||
| cval: 1 | ||
|
|
||
| predict: | ||
| _target_: monai.transforms.Compose | ||
| transforms: | ||
| # channels are [nucseg, cellseg, nuclear boundary seg, cell boundary seg] | ||
| # source image is the segmentation | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${source_col} | ||
| reader: | ||
| - _target_: cyto_dl.image.io.MonaiBioReader | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 0 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| - _target_: monai.transforms.EnsureChannelFirstd | ||
| channel_dim: "no_channel" | ||
| keys: ${source_col} | ||
| #crop image so that it is divisible by 4 for downsampling | ||
| - _target_: monai.transforms.CenterSpatialCropd | ||
| keys: ${source_col} | ||
| roi_size: [64, -1, -1] | ||
| - _target_: monai.transforms.Zoomd | ||
| keys: ${source_col} | ||
| zoom: 0.25 | ||
| keep_size: False | ||
|
|
||
| - _target_: monai.transforms.ToTensord | ||
| keys: ${source_col} | ||
| # input to synthetic image generation model is a semantic segmentation | ||
| - _target_: monai.transforms.ThresholdIntensityd | ||
| keys: ${source_col} | ||
| threshold: 0.1 | ||
| above: False | ||
| cval: 1 | ||
|
|
||
| valid: | ||
| _target_: monai.transforms.Compose | ||
| transforms: | ||
| # channels are [blank, membrane,blank, structure, blank, nuclear dye, brightfield ] | ||
| # target is the nuclear dyeimage | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${target_col} | ||
| reader: | ||
| - _target_: cyto_dl.image.io.MonaiBioReader | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 5 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| # channels are [nucseg, cellseg, nuclear boundary seg, cell boundary seg] | ||
| # source image is the segmentation | ||
| - _target_: monai.transforms.LoadImaged | ||
| keys: ${source_col} | ||
| reader: | ||
| - _target_: cyto_dl.image.io.MonaiBioReader | ||
| dimension_order_out: ${eval:'"ZYX" if ${spatial_dims}==3 else "YX"'} | ||
| C: 0 | ||
| Z: ${eval:'None if ${spatial_dims}==3 else 38'} | ||
| - _target_: monai.transforms.EnsureChannelFirstd | ||
| channel_dim: "no_channel" | ||
| keys: ${data.columns} | ||
| - _target_: monai.transforms.Zoomd | ||
| keys: ${source_col} | ||
| zoom: 0.25 | ||
| keep_size: False | ||
|
|
||
| - _target_: monai.transforms.ToTensord | ||
| keys: ${data.columns} | ||
| # GANs use Tanh as final activation, target has to be in range [-1,1] | ||
| - _target_: monai.transforms.ScaleIntensityRangePercentilesd | ||
| keys: ${target_col} | ||
| lower: 0.01 | ||
| upper: 99.99 | ||
| b_min: -1 | ||
| b_max: 1 | ||
| clip: True | ||
| # input to synthetic image generation model is a semantic segmentation | ||
| - _target_: monai.transforms.ThresholdIntensityd | ||
| keys: ${source_col} | ||
| threshold: 0.1 | ||
| above: False | ||
| cval: 1 | ||
| - _target_: cyto_dl.image.transforms.RandomMultiScaleCropd | ||
| keys: ${data.columns} | ||
| patch_shape: ${data._aux.patch_shape} | ||
| patch_per_image: 1 | ||
| scales_dict: ${kv_to_dict:${data._aux._scales_dict}} | ||
|
|
||
| _aux: | ||
| _scales_dict: | ||
| - - ${target_col} | ||
| - [4] | ||
| - - ${source_col} | ||
| - [1] |
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