Add cellpose[gui] as a main dependency

pyproject.toml

@@ -15,12 +15,13 @@ license = { text = "BSD 3-Clause License" }
 authors = [
     { email = "[email protected]", name = "Jeff Rhoades" },
 ]
-version = "0.0.0"
+version = "0.0.1"
 dependencies = [
   'torch',
   'torchvision',
   'numpy',
-  "tqdm",
+  'tqdm',
+  'cellpose'
 ]
 
 [project.optional-dependencies]
@@ -32,8 +33,9 @@ dev = [
   'mypy',
   'pdoc',
   'pre-commit',
-  'cellpose[gui]'
 ]
+pretrained = ['cellpose[gui]']
+all = ["cellmap-models[dev,pretrained]"]
 
 [project.urls]
 homepage = "https://janelia-cellmap.github.io/cellmap-models"

src/cellmap_models/pytorch/cosem/README.md

@@ -20,12 +20,12 @@ Available models can be seen with the following code:
 cosem_models.models_list
 ```
 
-Each model has a separate unet backbone and single layer prediction head. The `unet` and `head` objects are both PyTorch modules and can be used as such. You can access the separate components of the model using the following code:
+Each model has a separate backbone and single layer prediction head. The `backbone` and `head` objects are both PyTorch modules and can be used as such. You can access the separate components of the model using the following code:
 
 ```python
 import cellmap_models.cosem as cosem_models
 model = cosem_models.load_model('setup04/1820500')
-unet = model.unet
+backnone = model.backbone
 head = model.prediction_head
 ```
 

src/cellmap_models/pytorch/cosem/load_model.py

@@ -127,7 +127,7 @@ def __init__(self, model_params):
             kernel_size=(1,) * 3,
         )
         model = torch.nn.Sequential(*layers)
-        self.unet = model
+        self.backbone = model
         self.prediction_head = prediction_head
         for k, v in model_params.__dict__.items():
             setattr(self, k, v)
@@ -136,7 +136,7 @@ def __init__(self, model_params):
         self.compute_minimal_shapes()
 
     def forward(self, x):
-        return self.prediction_head(self.unet(x))
+        return self.prediction_head(self.backbone(x))
 
     def compute_minimal_shapes(self):
         """