Update for 1.0

README.md

@@ -31,11 +31,9 @@ If you use the MedNIST dataset, please acknowledge the source.
 Assuming the current directory is the bundle directory, and the dataset was extracted to the directory `./MedNIST`, the following command will train the network for 50 epochs:
 
 ```
-PYTHONPATH=./scripts python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf --bundle_root .
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf --bundle_root .
 ```
 
-Note that the training code relies on extra scripts in the `scripts` directory which are made accessible by changing the `PYTHONPATH` variable in this invocation. If your `PYTHONPATH` is already used for other things you will have to add `./scripts` to the variable rather than replace it.
-
 Not also the output from the training will be placed in the `models` directory but will not overwrite the `model.pt` file that may be there already. You will have to manually rename the most recent checkpoint file to `model.pt` to use the inference script mentioned below after checking the results are correct. This saved checkpoint contains a dictionary with the generator weights stored as `model` and omits the discriminator.
 
 Another feature in the training file is the addition of sigmoid activation to the network by modifying it's structure at runtime. This is done with a line in the `training` section calling `add_module` on a layer of the network. This works best for training although the definition of the model now doesn't strictly match what it is in the `generator` section.
@@ -66,6 +64,6 @@ The model can be loaded without MONAI code after this operation. For example, an
 ```python
 import torch
 net = torch.jit.load("mednist_gan.ts")
-latent = torch.rand(1,64)
+latent = torch.rand(1, 64)
 img = net(latent)  # (1,1,64,64)
 ```

configs/inference.json

@@ -52,6 +52,11 @@
                 "keys": "pred",
                 "sigmoid": true
             },
+            {
+                "_target_": "ToTensord",
+                "keys": "pred",
+                "track_meta": false
+            },
             {
                 "_target_": "SaveImaged",
                 "keys": "pred",

configs/metadata.json

@@ -1,11 +1,12 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_generator_20220718.json",
-    "version": "0.2.0",
+    "version": "0.3.0",
     "changelog": {
-        "0.2.0": "unify naming",
+        "0.3.0": "Update for 1.0",
+        "0.2.0": "Unify naming",
         "0.1.0": "Initial version"
     },
-    "monai_version": "0.9.0",
+    "monai_version": "1.0.0",
     "pytorch_version": "1.10.0",
     "numpy_version": "1.21.0",
     "optional_packages_version": {

configs/train.json

@@ -1,9 +1,8 @@
 {
     "imports": [
-        "$from functools import partial",
+        "$import functools",
         "$import glob",
-        "$from losses import discriminator_loss",
-        "$from losses import generator_loss"
+        "$import scripts"
     ],
     "bundle_root": ".",
     "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
@@ -66,7 +65,7 @@
                 "keys": "reals"
             },
             {
-                "_target_": "AddChanneld",
+                "_target_": "EnsureChannelFirstd",
                 "keys": "reals"
             },
             {
@@ -148,10 +147,10 @@
         "train_data_loader": "@real_dataloader",
         "g_network": "@gnetwork",
         "g_optimizer": "@goptimizer",
-        "g_loss_function": "$partial(generator_loss, disc_net=@dnetwork)",
+        "g_loss_function": "$functools.partial(scripts.losses.generator_loss, disc_net=@dnetwork)",
         "d_network": "@dnetwork",
         "d_optimizer": "@doptimizer",
-        "d_loss_function": "$partial(discriminator_loss, disc_net=@dnetwork)",
+        "d_loss_function": "$functools.partial(scripts.losses.discriminator_loss, disc_net=@dnetwork)",
         "d_train_steps": 5,
         "g_update_latents": true,
         "latent_shape": "@latent_size",

docs/README.md

@@ -24,11 +24,9 @@ If you use the MedNIST dataset, please acknowledge the source.
 Assuming the current directory is the bundle directory, and the dataset was extracted to the directory `./MedNIST`, the following command will train the network for 50 epochs:
 
 ```
-PYTHONPATH=./scripts python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf --bundle_root .
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf --bundle_root .
 ```
 
-Note that the training code relies on extra scripts in the `scripts` directory which are made accessible by changing the `PYTHONPATH` variable in this invocation. If your `PYTHONPATH` is already used for other things you will have to add `./scripts` to the variable rather than replace it.
-
 Not also the output from the training will be placed in the `models` directory but will not overwrite the `model.pt` file that may be there already. You will have to manually rename the most recent checkpoint file to `model.pt` to use the inference script mentioned below after checking the results are correct. This saved checkpoint contains a dictionary with the generator weights stored as `model` and omits the discriminator.
 
 Another feature in the training file is the addition of sigmoid activation to the network by modifying it's structure at runtime. This is done with a line in the `training` section calling `add_module` on a layer of the network. This works best for training although the definition of the model now doesn't strictly match what it is in the `generator` section.
@@ -59,6 +57,6 @@ The model can be loaded without MONAI code after this operation. For example, an
 ```python
 import torch
 net = torch.jit.load("mednist_gan.ts")
-latent = torch.rand(1,64)
+latent = torch.rand(1, 64)
 img = net(latent)  # (1,1,64,64)
 ```

models/model.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5ee4b4a812063936e95a5e39e628884b0160ab5be4897d99ede3dc7746c4308a
-size 1272514
+oid sha256:5302e6adf714d568bfc6d5238fc28c03cc99f4d623e3e1c9c5b1a6a56960d395
+size 1272066

scripts/__init__.py

@@ -0,0 +1 @@
+from . import losses