Adds 3D vSHARP model (#273)

* Adding 3D UNet & config
* Add 3d vsharp & config
* Minor fixes in typing and software package versions

Makefile

@@ -26,7 +26,7 @@ BROWSER := python -c "$$BROWSER_PYSCRIPT"
 help:
 	@python -c "$$PRINT_HELP_PYSCRIPT" < $(MAKEFILE_LIST)
 
-clean: clean-build clean-pyc clean-cpy clean-test clean-docs ## remove all build, test, coverage, docs and Python and cython artifacts
+clean: clean-build clean-pyc clean-cpy clean-ipynb clean-test clean-docs ## remove all build, test, coverage, docs and Python and cython artifacts
 
 clean-build: ## remove build artifacts
 	rm -fr build/
@@ -46,6 +46,9 @@ clean-cpy: ## remove cython file artifacts
 	find . -name '*.cpp' -exec rm -f {} +
 	find . -name '*.so' -exec rm -f {} +
 
+clean-ipynb: ## remove ipynb artifacts
+	find . -name '.ipynb_checkpoints' -exec rm -rf {} +
+
 clean-test: ## remove test and coverage artifacts
 	rm -fr .tox/
 	rm -f .coverage

direct/nn/unet/config.py

@@ -1,8 +1,9 @@
-# coding=utf-8
 # Copyright (c) DIRECT Contributors
+
 from dataclasses import dataclass
 
 from direct.config.defaults import ModelConfig
+from direct.nn.types import InitType
 
 
 @dataclass
@@ -30,4 +31,13 @@ class Unet2dConfig(ModelConfig):
     dropout_probability: float = 0.0
     skip_connection: bool = False
     normalized: bool = False
-    image_initialization: str = "zero_filled"
+    image_initialization: InitType = InitType.ZERO_FILLED
+
+
+@dataclass
+class UnetModel3dConfig(ModelConfig):
+    in_channels: int = 2
+    out_channels: int = 2
+    num_filters: int = 16
+    num_pool_layers: int = 4
+    dropout_probability: float = 0.0

direct/nn/unet/unet_2d.py

@@ -10,6 +10,7 @@
 from torch.nn import functional as F
 
 from direct.data import transforms as T
+from direct.nn.types import InitType
 
 
 class ConvBlock(nn.Module):
@@ -334,7 +335,7 @@ def __init__(
         dropout_probability: float,
         skip_connection: bool = False,
         normalized: bool = False,
-        image_initialization: str = "zero_filled",
+        image_initialization: InitType = InitType.ZERO_FILLED,
         **kwargs,
     ):
         """Inits :class:`Unet2d`.
@@ -355,8 +356,8 @@ def __init__(
             If True, skip connection is used for the output. Default: False.
         normalized: bool
             If True, Normalized Unet is used. Default: False.
-        image_initialization: str
-            Type of image initialization. Default: "zero-filled".
+        image_initialization: InitType
+            Type of image initialization. Default: InitType.ZERO_FILLED.
         kwargs: dict
         """
         super().__init__()
@@ -437,18 +438,18 @@ def forward(
         output: torch.Tensor
             Output image of shape (N, height, width, complex=2).
         """
-        if self.image_initialization == "sense":
+        if self.image_initialization == InitType.SENSE:
             if sensitivity_map is None:
-                raise ValueError("Expected sensitivity_map not to be None with 'sense' image_initialization.")
+                raise ValueError("Expected sensitivity_map not to be None with InitType.SENSE image_initialization.")
             input_image = self.compute_sense_init(
                 kspace=masked_kspace,
                 sensitivity_map=sensitivity_map,
             )
-        elif self.image_initialization == "zero_filled":
+        elif self.image_initialization == InitType.ZERO_FILLED:
             input_image = self.backward_operator(masked_kspace, dim=self._spatial_dims).sum(self._coil_dim)
         else:
             raise ValueError(
-                f"Unknown image_initialization. Expected `sense` or `zero_filled`. "
+                f"Unknown image_initialization. Expected InitType.ZERO_FILLED or InitType.SENSE. "
                 f"Got {self.image_initialization}."
             )