diff --git a/reconstruction/MRI_reconstruction/README.md b/reconstruction/MRI_reconstruction/unet_demo/README.md
similarity index 58%
rename from reconstruction/MRI_reconstruction/README.md
rename to reconstruction/MRI_reconstruction/unet_demo/README.md
index 6988047d76..82563f0bba 100644
--- a/reconstruction/MRI_reconstruction/README.md
+++ b/reconstruction/MRI_reconstruction/unet_demo/README.md
@@ -30,24 +30,17 @@ This folder contains code to train and validate a U-Net for accelerated MRI reco
# Dataset
The experiments are performed on the [fastMRI](https://fastmri.org/dataset) dataset. Users should request access to the dataset
-from the [owner's website](https://fastmri.org/dataset).
+from the [owner's website](https://fastmri.org/dataset). Remember to use the `$PATH` where you downloaded the data in `train.py`
+or `inference.ipynb` accordingly.
-**Note.** Since the ground truth is not released with the test set of the fastMRI dataset, it is a common practice in the literature
-to perform inference on the validation set of the fastMRI dataset. This could be in the form of testing on the whole validation
-set (for example this work [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767765/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767765/)).
-
-Another approach is to split the validation set into validation and test sets and keep the test portion for inference (for exmple this work [https://arxiv.org/pdf/2111.02549.pdf](https://arxiv.org/pdf/2111.02549.pdf)). Note that both approaches are conceptually similar
-in that splitting the validation set does not change the fact that the splits belong to the same distribution.
-
-Other workarounds to this problem include (1) skipping validation during training and saving the model checkpoint of the last epoch for inference on the validation set, and (2) submitting model results to the [fastMRI public leaderboard](https://fastmri.org/leaderboards/).
+For our experiments we created a subset of the fastMRI dataset which contains a `500/179/133` split for `train/val/test`. Please download [fastmri_data_split.json](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/fastmri_data_split.json) and put it here under `./data`.
# Model checkpoint
We have already provided a model checkpoint [unet_mri_reconstruction.pt](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/unet_mri_reconstruction.pt) for a U-Net with `7,782,849` parameters. To obtain this checkpoint, we trained
-a U-Net with the default hyper-parameters in `train.py` on the T2 subset of the brain dataset (`500` training and `180` validation volumes). The user can train their model on an arbitrary portion of the dataset.
+a U-Net with the default hyper-parameters in `train.py` on the T2 subset of the brain dataset. The user can train their model on an arbitrary portion of the dataset.
-Our checkpoint achieves `0.9496` SSIM on the fastMRI T2 validation subset which is comparabale to the original result reported on the
-[fastMRI public leaderboard](https://fastmri.org/leaderboards/) (which is `0.9474` SSIM). The training dynamics for our checkpoint is depicted in the figure below.
+The training dynamics for our checkpoint is depicted in the figure below.
@@ -71,5 +64,9 @@ Running `train.py` trains a U-Net. The default setup automatically detects a GPU
# Inference
-The notebook `inference.ipynb` contains an example to perform validation. Average SSIM score over the validation set is computed and then
+The notebook `inference.ipynb` contains an example to perform inference. Average SSIM score over the test subset is computed and then
one sample is picked for visualization.
+
+Our checkpoint achieves `0.9436` SSIM on our test subset which is comparable to the original result reported on the
+[fastMRI public leaderboard](https://fastmri.org/leaderboards/) (which is `0.9474` SSIM). Note that the results reported
+on the leaderboard are for the unreleased test set. Moreover, the leaderboard model is trained on the validation set.
diff --git a/reconstruction/MRI_reconstruction/fastmri_ssim.py b/reconstruction/MRI_reconstruction/unet_demo/fastmri_ssim.py
similarity index 100%
rename from reconstruction/MRI_reconstruction/fastmri_ssim.py
rename to reconstruction/MRI_reconstruction/unet_demo/fastmri_ssim.py
diff --git a/reconstruction/MRI_reconstruction/figures/dynamics.PNG b/reconstruction/MRI_reconstruction/unet_demo/figures/dynamics.PNG
similarity index 100%
rename from reconstruction/MRI_reconstruction/figures/dynamics.PNG
rename to reconstruction/MRI_reconstruction/unet_demo/figures/dynamics.PNG
diff --git a/reconstruction/MRI_reconstruction/figures/workflow.PNG b/reconstruction/MRI_reconstruction/unet_demo/figures/workflow.PNG
similarity index 100%
rename from reconstruction/MRI_reconstruction/figures/workflow.PNG
rename to reconstruction/MRI_reconstruction/unet_demo/figures/workflow.PNG
diff --git a/reconstruction/MRI_reconstruction/inference.ipynb b/reconstruction/MRI_reconstruction/unet_demo/inference.ipynb
similarity index 99%
rename from reconstruction/MRI_reconstruction/inference.ipynb
rename to reconstruction/MRI_reconstruction/unet_demo/inference.ipynb
index 87c035af0f..9a755c1963 100644
--- a/reconstruction/MRI_reconstruction/inference.ipynb
+++ b/reconstruction/MRI_reconstruction/unet_demo/inference.ipynb
@@ -29,6 +29,7 @@
"import torch\n",
"import warnings\n",
"import random\n",
+ "import json\n",
"from fastmri_ssim import skimage_ssim\n",
"import matplotlib.pyplot as plt\n",
"\n",
@@ -76,7 +77,7 @@
" self.batch_size = 1 # can be set to >1 when input sizes are not different\n",
" self.num_workers = 0\n",
" self.cache_rate = 0.0 # what fraction of the data to be cached for faster loading\n",
- " self.data_path_val = '/data/fastmri/fastMRI/multicoil_val_t2/' # path to the validation set\n",
+ " self.data_path_val = '/data/fastmri/multicoil_val/' # path to the validation set\n",
" self.sample_rate = 0.9 # select 0.9 of the validation set for inference\n",
" self.accelerations = [4] # acceleration factors used for valdiation.\n",
" self.center_fractions = [0.08] # center_fractions used for valdiation.\n",
@@ -104,16 +105,29 @@
"# Create validation data loader"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(\"./data/fastmri_data_split.json\", \"r\") as fn:\n",
+ " data = json.load(fn)\n",
+ "test_files = data['test_files']\n",
+ "fastmri_val_set = list(Path(args.data_path_val).iterdir())\n",
+ "test_files = [f for f in fastmri_val_set if str(f).split('/')[-1] in test_files]"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "val_files = list(Path(args.data_path_val).iterdir())\n",
- "random.shuffle(val_files)\n",
- "val_files = val_files[:int(args.sample_rate*len(val_files))] # select a subset of the data according to sample_rate\n",
- "val_files = [dict([(\"kspace\", val_files[i])]) for i in range(len(val_files))]\n",
+ "random.shuffle(test_files)\n",
+ "test_files = test_files[:int(args.sample_rate*len(test_files))] # select a subset of the data according to sample_rate\n",
+ "test_files = [dict([(\"kspace\", test_files[i])]) for i in range(len(test_files))]\n",
+ "print(f'#test files: {len(test_files)}')\n",
"\n",
"# define mask transform type (e.g., whether it is equispaced or random)\n",
"if args.mask_type == 'random':\n",
@@ -129,7 +143,7 @@
" spatial_dims=2,\n",
" is_complex=True)\n",
"\n",
- "val_transforms = Compose(\n",
+ "test_transforms = Compose(\n",
" [\n",
" LoadImaged(keys=[\"kspace\"], reader=FastMRIReader, dtype=np.complex64),\n",
" # user can also add other random transforms\n",
@@ -145,10 +159,10 @@
" ]\n",
")\n",
"\n",
- "val_ds = CacheDataset(\n",
- " data=val_files, transform=val_transforms,\n",
+ "test_ds = CacheDataset(\n",
+ " data=test_files, transform=test_transforms,\n",
" cache_rate=args.cache_rate, num_workers=args.num_workers)\n",
- "val_loader = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)"
+ "test_loader = DataLoader(test_ds, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)"
]
},
{
@@ -203,30 +217,22 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "161 volume out of 161 done. \r"
- ]
- }
- ],
+ "outputs": [],
"source": [
"outputs = defaultdict(list)\n",
"targets = defaultdict(list)\n",
"with torch.no_grad():\n",
" val_ssim = list()\n",
" step = 1\n",
- " for val_data in val_loader:\n",
+ " for test_data in test_loader:\n",
" input, target, mean, std, fname = (\n",
- " val_data[\"kspace_masked_ifft\"],\n",
- " val_data[\"reconstruction_rss\"],\n",
- " val_data[\"mean\"],\n",
- " val_data[\"std\"],\n",
- " val_data[\"kspace_meta_dict\"][\"filename\"]\n",
+ " test_data[\"kspace_masked_ifft\"],\n",
+ " test_data[\"reconstruction_rss\"],\n",
+ " test_data[\"mean\"],\n",
+ " test_data[\"std\"],\n",
+ " test_data[\"kspace_meta_dict\"][\"filename\"]\n",
" )\n",
"\n",
" # iterate through all slices:\n",
@@ -247,7 +253,7 @@
" # save volume slices according to volume name given by fname\n",
" outputs[fname[0]].append(output.data.cpu().numpy()[0][0]*_std+_mean)\n",
" targets[fname[0]].append(tar.numpy()[0][0]*_std+_mean)\n",
- " print(step, ' volume out of', len(val_files), 'done.', '\\r', end='')\n",
+ " print(step, ' volume out of', len(test_files), 'done.', '\\r', end='')\n",
" step += 1\n",
"\n",
" # compute validation ssims values for all validation samples\n",
@@ -261,14 +267,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "average SSIM score over the validation set: 0.9496\n"
+ "average SSIM score over the validation set: 0.9436\n"
]
}
],
diff --git a/reconstruction/MRI_reconstruction/train.py b/reconstruction/MRI_reconstruction/unet_demo/train.py
similarity index 100%
rename from reconstruction/MRI_reconstruction/train.py
rename to reconstruction/MRI_reconstruction/unet_demo/train.py
diff --git a/reconstruction/MRI_reconstruction/varnet_demo/README.md b/reconstruction/MRI_reconstruction/varnet_demo/README.md
new file mode 100644
index 0000000000..fd86deab37
--- /dev/null
+++ b/reconstruction/MRI_reconstruction/varnet_demo/README.md
@@ -0,0 +1,69 @@
+# Accelerated MRI reconstruction with the end-to-end variational network (e2e-VarNet)
+
+
+
+
+This folder contains code to train and validate an e2e-VarNet ([https://arxiv.org/pdf/2004.06688.pdf](https://arxiv.org/pdf/2004.06688.pdf)) for accelerated MRI reconstruction. Accelerated MRI reconstruction is a compressed sensing task where the goal is to recover a ground-truth image from an under-sampled measurement. The under-sampled measurement is based in the frequency domain and is often called the $k$-space.
+
+***
+
+### List of contents
+
+* [Questions and bugs](#Questions-and-bugs)
+
+* [Dataset](#Dataset)
+
+* [Model checkpoint](#Model-checkpoint)
+
+* [Training](#Training)
+
+* [Inference](#Inference)
+
+***
+
+# Questions and bugs
+
+- For questions relating to the use of MONAI, please us our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI.
+- For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues).
+- For bugs relating to the running of a tutorial, please create an issue in [this repository](https://github.com/Project-MONAI/Tutorials/issues).
+
+# Dataset
+
+Please see [dataset description](../unet_demo/README.md#dataset) for our dataset preparation.
+
+
+# Model checkpoint
+
+We have already provided a model checkpoint [varnet_mri_reconstruction.pt](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/varnet_mri_reconstruction.pt) for a VarNet with `30,069,558` parameters. To obtain this checkpoint, we trained
+a VarNet with the default hyper-parameters in `train.py` on our T2 subset of the brain dataset. The user can train their model on an arbitrary portion of the dataset.
+
+The training dynamics for our checkpoint is depicted in the figure below.
+
+
+
+# Training
+
+Running `train.py` trains a VarNet. The default setup automatically detects a GPU for training; if not available, CPU will be used.
+
+ # Run this to get a full list of training arguments
+ python ./train.py -h
+
+ # This is an example of calling train.py
+ python ./train.py
+ --data_path_train train_dir \
+ --data_path_val val_dir \
+ --exp varnet_mri_recon \
+ --exp_dir ./ \
+ --mask_type equispaced \
+ --num_epochs 50 \
+ --num_workers 0 \
+ --lr 0.00001
+
+# Inference
+
+The notebook `inference.ipynb` contains an example to perform inference. Average SSIM score over the test subset is computed and then
+one sample is picked for visualization.
+
+Our checkpoint achieves `0.9650` SSIM on our test subset which is comparable to the original result reported on the
+[fastMRI public leaderboard](https://fastmri.org/leaderboards/) (which is `0.9606` SSIM). Note that the results reported
+on the leaderboard are for the unreleased test set. Moreover, the leaderboard model is trained on the validation set.
diff --git a/reconstruction/MRI_reconstruction/varnet_demo/figures/dynamics.PNG b/reconstruction/MRI_reconstruction/varnet_demo/figures/dynamics.PNG
new file mode 100644
index 0000000000..b4955c0d66
Binary files /dev/null and b/reconstruction/MRI_reconstruction/varnet_demo/figures/dynamics.PNG differ
diff --git a/reconstruction/MRI_reconstruction/varnet_demo/figures/workflow.PNG b/reconstruction/MRI_reconstruction/varnet_demo/figures/workflow.PNG
new file mode 100644
index 0000000000..1ddc1cc5fe
Binary files /dev/null and b/reconstruction/MRI_reconstruction/varnet_demo/figures/workflow.PNG differ
diff --git a/reconstruction/MRI_reconstruction/varnet_demo/inference.ipynb b/reconstruction/MRI_reconstruction/varnet_demo/inference.ipynb
new file mode 100644
index 0000000000..9d86557dc2
--- /dev/null
+++ b/reconstruction/MRI_reconstruction/varnet_demo/inference.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Accelerated MRI reconstruction with the end-to-end variational network (e2e-VarNet)\n",
+ "Accelerated MRI reconstruction is a compressed sensing task where the goal is to recover a ground-truth image from an under-sampled measurement. The under-sampled measurement is based in the frequency domain and is often called the $k$-space.\n",
+ "\n",
+ "VarNet based accelerated MRI reconstruction works as follows. First the under-sampled measurement is passed to a U-Net for estimating coil sensitivity maps. Then, the under-sampled measurement passes through several cascades. Each cascade applies data consistency and refinement. Data consistency is analytical whereas the refinement utilizes a U-Net with learnable parameters. VarNet is trained supervised to learn a mapping from the under-sampled measurement domain to the ground-truth image.\n",
+ "\n",
+ "Suppose the input of cascade $i$ is denoted by $k^i$. Then the output is:\n",
+ "\\begin{align}\n",
+ "k^{i+1} = k^i - \\eta^i M (k - k^i) + G(k^i)\n",
+ "\\end{align}\n",
+ "Here, $M$ is the under-sampling mask, $k$ is the under-sampled measurement, and $G$ is the refinement model. Please see the original paper for further details: \n",
+ "[https://arxiv.org/pdf/2004.06688.pdf](https://arxiv.org/pdf/2004.06688.pdf)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Import packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append(\"../unet_demo\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "import random\n",
+ "import json\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from monai.transforms import (\n",
+ " Compose,\n",
+ " SpatialCrop,\n",
+ " LoadImaged,\n",
+ " EnsureTyped,\n",
+ ")\n",
+ "\n",
+ "from monai.apps.reconstruction.transforms.dictionary import (\n",
+ " ExtractDataKeyFromMetaKeyd,\n",
+ " RandomKspaceMaskd,\n",
+ " EquispacedKspaceMaskd,\n",
+ ")\n",
+ "\n",
+ "from fastmri_ssim import skimage_ssim\n",
+ "from monai.apps.reconstruction.fastmri_reader import FastMRIReader\n",
+ "from monai.apps.reconstruction.networks.nets.varnet import VariationalNetworkModel\n",
+ "from monai.apps.reconstruction.networks.nets.complex_unet import ComplexUnet\n",
+ "from monai.apps.reconstruction.networks.nets.coil_sensitivity_model import CoilSensitivityModel\n",
+ "\n",
+ "\n",
+ "from pathlib import Path\n",
+ "from monai.data import CacheDataset, DataLoader\n",
+ "\n",
+ "from collections import defaultdict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Setup validation hyper-parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Args():\n",
+ " def __init__(self):\n",
+ " self.batch_size = 1 # can be set to >1 when input sizes are not different\n",
+ " self.num_workers = 0\n",
+ " self.cache_rate = 0.0 # what fraction of the data to be cached for faster loading\n",
+ " self.data_path_val = '/data/fastmri/multicoil_val/' # path to the validation set\n",
+ " self.sample_rate = 0.9 # select 0.9 of the test subset for inference\n",
+ " self.accelerations = [4] # acceleration factors used for valdiation.\n",
+ " self.center_fractions = [0.08] # center_fractions used for valdiation.\n",
+ "\n",
+ " self.mask_type = 'equispaced' # mask type used for validation, current options: ['equispaced', 'random']\n",
+ "\n",
+ " self.drop_prob = 0.0 # inference-time dropout rate\n",
+ " self.features = [18, 36, 72, 144, 288, 18] # default feature sizes based on our model checkpoint\n",
+ " self.sensitivity_model_features = [8, 16, 32, 64, 128, 8] # default sensitivity map feature sizes\n",
+ " self.num_cascades = 12 # number of model cascades\n",
+ "\n",
+ "\n",
+ "args = Args()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "if multiple values are given for accelerations or center_fractions, one will be uniformly chosen for each sample."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Create validation data loader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(\"./data/fastmri_data_split.json\", \"r\") as fn:\n",
+ " data = json.load(fn)\n",
+ "test_files = data['test_files']\n",
+ "fastmri_val_set = list(Path(args.data_path_val).iterdir())\n",
+ "test_files = [f for f in fastmri_val_set if str(f).split('/')[-1] in test_files]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "#test files: 119\n"
+ ]
+ }
+ ],
+ "source": [
+ "random.shuffle(test_files)\n",
+ "test_files = test_files[:int(args.sample_rate*len(test_files))] # select a subset of the data according to sample_rate\n",
+ "test_files = [dict([(\"kspace\", test_files[i])]) for i in range(len(test_files))]\n",
+ "print(f'#test files: {len(test_files)}')\n",
+ "\n",
+ "# define mask transform type (e.g., whether it is equispaced or random)\n",
+ "if args.mask_type == 'random':\n",
+ " MaskTransform = RandomKspaceMaskd(keys=[\"kspace\"],\n",
+ " center_fractions=args.center_fractions,\n",
+ " accelerations=args.accelerations,\n",
+ " spatial_dims=2,\n",
+ " is_complex=True)\n",
+ "elif args.mask_type == 'equispaced':\n",
+ " MaskTransform = EquispacedKspaceMaskd(keys=[\"kspace\"],\n",
+ " center_fractions=args.center_fractions,\n",
+ " accelerations=args.accelerations,\n",
+ " spatial_dims=2,\n",
+ " is_complex=True)\n",
+ "\n",
+ "test_transforms = Compose(\n",
+ " [\n",
+ " LoadImaged(keys=[\"kspace\"], reader=FastMRIReader, dtype=np.complex64),\n",
+ " # user can also add other random transforms\n",
+ " ExtractDataKeyFromMetaKeyd(keys=[\"reconstruction_rss\", \"mask\"], meta_key=\"kspace_meta_dict\"),\n",
+ " MaskTransform,\n",
+ " EnsureTyped(keys=[\"kspace\", \"kspace_masked_ifft\", \"reconstruction_rss\"]),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "test_ds = CacheDataset(\n",
+ " data=test_files, transform=test_transforms,\n",
+ " cache_rate=args.cache_rate, num_workers=args.num_workers)\n",
+ "test_loader = DataLoader(test_ds, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load model checkpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BasicUNet features: (8, 16, 32, 64, 128, 8).\n",
+ "BasicUNet features: (18, 36, 72, 144, 288, 18).\n",
+ "#model_params: 30069558\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "coil_sens_model = CoilSensitivityModel(spatial_dims=2, features=args.sensitivity_model_features)\n",
+ "refinement_model = ComplexUnet(spatial_dims=2, features=args.features)\n",
+ "\n",
+ "model = VariationalNetworkModel(coil_sens_model, refinement_model, num_cascades=args.num_cascades).to(device)\n",
+ "print('#model_params:', np.sum([len(p.flatten()) for p in model.parameters()]))\n",
+ "\n",
+ "checkpoint = torch.load('./varnet_mri_reconstruction.pt', map_location=device)\n",
+ "\n",
+ "# comment out the following line if you're using your own checkpoint\n",
+ "# this line is because our checkpoint is obtained from DDP training\n",
+ "checkpoint = {key[7:]: checkpoint[key] for key in checkpoint}\n",
+ "\n",
+ "model.load_state_dict(checkpoint)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Perform inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "119 volume out of 119 done. \r"
+ ]
+ }
+ ],
+ "source": [
+ "outputs = defaultdict(list)\n",
+ "targets = defaultdict(list)\n",
+ "with torch.no_grad():\n",
+ " test_ssim = list()\n",
+ " step = 1\n",
+ " for test_data in test_loader:\n",
+ " input, mask, target, fname = (\n",
+ " test_data[\"kspace_masked\"].to(device),\n",
+ " test_data[\"mask\"][0].to(device),\n",
+ " test_data[\"reconstruction_rss\"].to(device),\n",
+ " test_data[\"kspace_meta_dict\"][\"filename\"]\n",
+ " )\n",
+ "\n",
+ " final_shape = target.shape[-2:]\n",
+ "\n",
+ " # iterate through all slices:\n",
+ " slice_dim = 1 # change this if another dimension is your slice dimension\n",
+ " num_slices = input.shape[slice_dim]\n",
+ " outputs_ = []\n",
+ " targets_ = []\n",
+ " for i in range(num_slices):\n",
+ " inp = input[:, i, ...].unsqueeze(slice_dim)\n",
+ " tar = target[:, i, ...].unsqueeze(slice_dim)\n",
+ "\n",
+ " # forward pass\n",
+ " output = model(inp[0], mask.bool())\n",
+ "\n",
+ " # crop output to match target size\n",
+ " roi_center = tuple(i // 2 for i in output.shape[-2:])\n",
+ " cropper = SpatialCrop(roi_center=roi_center, roi_size=final_shape)\n",
+ " output_crp = cropper(output).unsqueeze(0)\n",
+ "\n",
+ " outputs_.append(output_crp.data.cpu().numpy()[0][0])\n",
+ " targets_.append(tar.data.cpu().numpy()[0][0])\n",
+ "\n",
+ " outputs_ = np.stack(outputs_)\n",
+ " targets_ = np.stack(targets_)\n",
+ " test_ssim.append(skimage_ssim(targets_, outputs_))\n",
+ "\n",
+ " outputs[fname[0]] = outputs_\n",
+ " targets[fname[0]] = targets_\n",
+ "\n",
+ " print(step, ' volume out of', len(test_files), 'done.', '\\r', end='')\n",
+ " step += 1\n",
+ "\n",
+ " metric = np.mean(test_ssim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "average SSIM score over the validation set: 0.9650\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f'average SSIM score over the validation set: {metric:.4f}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A sample vizualization\n",
+ "We next randomly select a validation sample and visualize its middle-slice (both the ground truth and the reconstruction)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "file = random.choice(list(outputs))\n",
+ "output = outputs[file]\n",
+ "target = targets[file]\n",
+ "slice = output.shape[0]//2\n",
+ "\n",
+ "# compute skimage-format ssim score\n",
+ "score = skimage_ssim(np.array([target[slice]]), np.array([output[slice]]))\n",
+ "\n",
+ "# visualize\n",
+ "fig = plt.figure(figsize=(14, 7))\n",
+ "ax = fig.add_subplot(121)\n",
+ "ax.imshow(target[slice], 'gray')\n",
+ "ax.set_title('ground truth')\n",
+ "ax.axis('off')\n",
+ "\n",
+ "ax = fig.add_subplot(122)\n",
+ "ax.imshow(output[slice], 'gray')\n",
+ "ax.set_title('reconstruction {0:.4f}'.format(score))\n",
+ "ax.axis('off')\n",
+ "\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/reconstruction/MRI_reconstruction/varnet_demo/train.py b/reconstruction/MRI_reconstruction/varnet_demo/train.py
new file mode 100644
index 0000000000..7674b61ca8
--- /dev/null
+++ b/reconstruction/MRI_reconstruction/varnet_demo/train.py
@@ -0,0 +1,379 @@
+import numpy as np
+import torch
+import warnings
+from fastmri_ssim import skimage_ssim
+
+from monai.transforms import (
+ Compose,
+ SpatialCrop,
+ LoadImaged,
+ EnsureTyped,
+)
+
+from monai.apps.reconstruction.transforms.dictionary import (
+ ExtractDataKeyFromMetaKeyd,
+ RandomKspaceMaskd,
+ EquispacedKspaceMaskd,
+)
+
+from monai.apps.reconstruction.fastmri_reader import FastMRIReader
+from monai.apps.reconstruction.networks.nets.varnet import VariationalNetworkModel
+from monai.apps.reconstruction.networks.nets.complex_unet import ComplexUnet
+from monai.apps.reconstruction.networks.nets.coil_sensitivity_model import CoilSensitivityModel
+from monai.losses.ssim_loss import SSIMLoss
+
+from pathlib import Path
+import argparse
+from monai.data import CacheDataset, DataLoader, decollate_batch
+from torch.utils.tensorboard import SummaryWriter
+
+import logging
+import os
+import sys
+from datetime import datetime
+import time
+from collections import defaultdict
+import random
+
+seed = 123
+torch.manual_seed(seed)
+torch.cuda.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+np.random.seed(seed)
+random.seed(seed)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+torch.backends.cudnn.enabled = False
+
+warnings.filterwarnings('ignore')
+
+
+def trainer(args):
+ logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+ outpath = os.path.join(args.exp_dir,args.exp)
+ Path(outpath).mkdir(parents=True, exist_ok=True) # create output directory to store model checkpoints
+ now = datetime.now()
+ date = now.strftime('%m-%d-%y_%H-%M')
+ writer = SummaryWriter(outpath+'/'+date) # create a date directory within the output directory for storing training logs
+
+ # create training-validation data loaders
+ train_files = list(Path(args.data_path_train).iterdir())
+ random.shuffle(train_files)
+ train_files = train_files[:int(args.sample_rate*len(train_files))] # select a subset of the data according to sample_rate
+ train_files = [dict([("kspace",train_files[i])]) for i in range(len(train_files))]
+ print(f'#train files: {len(train_files)}')
+
+ val_files = list(Path(args.data_path_val).iterdir())
+ random.shuffle(val_files)
+ val_files = val_files[:int(args.sample_rate*len(val_files))] # select a subset of the data according to sample_rate
+ val_files = [dict([("kspace",val_files[i])]) for i in range(len(val_files))]
+ print(f'#validation files: {len(val_files)}')
+
+ # define mask transform type (e.g., whether it is equispaced or random)
+ if args.mask_type == 'random':
+ MaskTransform = RandomKspaceMaskd(keys=["kspace"],center_fractions=args.center_fractions, accelerations=args.accelerations, spatial_dims=2, is_complex=True)
+ elif args.mask_type == 'equispaced':
+ MaskTransform = EquispacedKspaceMaskd(keys=["kspace"],center_fractions=args.center_fractions, accelerations=args.accelerations, spatial_dims=2, is_complex=True)
+
+ train_transforms = Compose(
+ [
+ LoadImaged(keys=["kspace"], reader=FastMRIReader, dtype=np.complex64),
+ # user can also add other random transforms but remember to disable randomness for val_transforms
+ ExtractDataKeyFromMetaKeyd(keys=["reconstruction_rss", "mask"], meta_key="kspace_meta_dict"),
+ MaskTransform,
+ EnsureTyped(keys=["kspace", "kspace_masked_ifft", "reconstruction_rss"]),
+ ]
+ )
+
+ train_ds = CacheDataset(
+ data=train_files, transform=train_transforms,
+ cache_rate=args.cache_rate, num_workers=args.num_workers)
+ train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
+
+ # since there's no randomness in train_transforms, we use it for val_transforms as well
+ val_ds = CacheDataset(
+ data=val_files, transform=train_transforms,
+ cache_rate=args.cache_rate, num_workers=args.num_workers)
+ val_loader = DataLoader(val_ds, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
+
+ # create the model
+ coil_sens_model = CoilSensitivityModel(spatial_dims=2, features=args.sensitivity_model_features)
+ refinement_model = ComplexUnet(spatial_dims=2, features=args.features)
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model = VariationalNetworkModel(coil_sens_model, refinement_model, num_cascades=args.num_cascades).to(device)
+ print('#model_params:',np.sum([len(p.flatten()) for p in model.parameters()]))
+
+ if args.resume_checkpoint:
+ model.load_state_dict(torch.load(args.checkpoint_dir))
+ print('resume training from a given checkpoint...')
+
+ # create the loss function
+ loss_function = SSIMLoss(spatial_dims=2).to(device)
+
+ # create the optimizer and the learning rate scheduler
+ optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,weight_decay=args.weight_decay)
+ scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_step_size, args.lr_gamma)
+
+ # start a typical PyTorch training loop
+ val_interval = 2 # doing validation every 2 epochs
+ best_metric = -1
+ best_metric_epoch = -1
+ tic = time.time()
+ for epoch in range(args.num_epochs):
+ print("-" * 10)
+ print(f"epoch {epoch + 1}/{args.num_epochs}")
+ model.train()
+ epoch_loss = 0
+ step = 0
+ for batch_data in train_loader:
+ input, mask, target, max_value = batch_data["kspace_masked"].to(device), batch_data["mask"][0].to(device), batch_data["reconstruction_rss"].to(device), batch_data["kspace_meta_dict"]["max"]
+
+ final_shape = target.shape[-2:]
+ max_value = torch.tensor(max_value).unsqueeze(0).to(device)
+
+ # iterate through all slices
+ slice_dim = 1 # change this if another dimension is your slice dimension
+ num_slices = input.shape[slice_dim]
+ for i in range(num_slices):
+ step += 1
+ optimizer.zero_grad()
+
+ # forward pass
+ inp = input[:,i,...].unsqueeze(slice_dim)
+ tar = target[:,i,...].unsqueeze(slice_dim)
+ output = model(inp[0], mask.bool())
+
+ # crop output to match target size
+ roi_center = tuple(i // 2 for i in output.shape[-2:])
+ cropper = SpatialCrop(roi_center=roi_center, roi_size=final_shape)
+ output_crp = cropper(output).unsqueeze(0)
+
+ loss = loss_function(output_crp, tar, max_value)
+
+ loss.backward()
+ optimizer.step()
+ epoch_loss += loss.item()
+ print(f"{step}, train_loss: {epoch_loss/step:.4f}",'\r',end='')
+ scheduler.step()
+ epoch_loss /= step
+ writer.add_scalar("train_loss", epoch_loss, epoch+1)
+ print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f} time elapsed: {(time.time()-tic)/60:.2f} mins")
+
+ # validation
+ if (epoch + 1) % val_interval == 0:
+ model.eval()
+ with torch.no_grad():
+ val_ssim = list()
+ for val_data in val_loader:
+ input, mask, target, fname = val_data["kspace_masked"].to(device), val_data["mask"][0].to(device), val_data["reconstruction_rss"].to(device), val_data["kspace_meta_dict"]["filename"]
+
+ final_shape = target.shape[-2:]
+
+ # iterate through all slices:
+ slice_dim = 1 # change this if another dimension is your slice dimension
+ num_slices = input.shape[slice_dim]
+ outputs = []
+ targets = []
+ for i in range(num_slices):
+ inp = input[:,i,...].unsqueeze(slice_dim)
+ tar = target[:,i,...].unsqueeze(slice_dim)
+
+ # forward pass
+ output = model(inp[0], mask.bool())
+
+ # crop output to match target size
+ roi_center = tuple(i // 2 for i in output.shape[-2:])
+ cropper = SpatialCrop(roi_center=roi_center, roi_size=final_shape)
+ output_crp = cropper(output).unsqueeze(0)
+
+ outputs.append(output_crp.data.cpu().numpy()[0][0])
+ targets.append(tar.data.cpu().numpy()[0][0])
+
+ outputs = np.stack(outputs)
+ targets = np.stack(targets)
+ val_ssim.append(skimage_ssim(targets,outputs))
+
+ metric = np.mean(val_ssim)
+
+ # save the best checkpoint so far
+ if metric > best_metric:
+ best_metric = metric
+ best_metric_epoch = epoch + 1
+ torch.save(model.state_dict(), os.path.join(outpath,"varnet_mri_reconstruction.pt"))
+ print("saved new best metric model")
+ print(
+ "current epoch: {} current mean ssim: {:.4f} best mean ssim: {:.4f} at epoch {}".format(
+ epoch + 1, metric, best_metric, best_metric_epoch
+ )
+ )
+ writer.add_scalar("val_mean_ssim", metric, epoch + 1)
+
+ print(f"training completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")
+ writer.close()
+
+def __main__():
+ parser = argparse.ArgumentParser()
+
+ # data loader arguments
+ parser.add_argument(
+ "--batch_size",
+ default=1,
+ type=int,
+ help="Data loader batch size (batch_size>1 is suitable for varying input size"
+ )
+
+ parser.add_argument(
+ "--num_workers",
+ default=4,
+ type=int,
+ help="Number of workers to use in data loader",
+ )
+
+ parser.add_argument(
+ "--cache_rate",
+ default=0.0,
+ type=float,
+ help="The fraction of the data to be cached when being loaded",
+ )
+
+ parser.add_argument(
+ "--data_path_train",
+ default=None,
+ type=Path,
+ help="Path to the fastMRI training set",
+ )
+
+ parser.add_argument(
+ "--data_path_val",
+ default=None,
+ type=Path,
+ help="Path to the fastMRI validation set",
+ )
+
+ parser.add_argument(
+ "--sample_rate",
+ default=1.0,
+ type=float,
+ help="what fraction of the dataset to use for training (also, what fraction of validation set to use)"
+ )
+
+ # Mask parameters
+ parser.add_argument(
+ "--accelerations",
+ default=[4],
+ type=list,
+ help="acceleration factors used during training"
+ )
+
+ parser.add_argument(
+ "--center_fractions",
+ default=[0.08],
+ type=list,
+ help="center fractions used during training (center fraction denotes the center region to exclude from masking)"
+ )
+
+ # training params
+ parser.add_argument(
+ "--num_epochs",
+ default=50,
+ type=int,
+ help="number of training epochs"
+ )
+
+ parser.add_argument(
+ "--exp_dir",
+ default='./',
+ type=Path,
+ help="output directory to save training logs"
+ )
+
+ parser.add_argument(
+ "--exp",
+ default='varnet_mri_recon',
+ type=str,
+ help="experiment name (a folder will be created with this name to store the results)"
+ )
+
+ parser.add_argument(
+ "--lr",
+ default=5e-5,
+ type=float,
+ help="learning rate"
+ )
+
+ parser.add_argument(
+ "--lr_step_size",
+ default=40,
+ type=int,
+ help="decay learning rate every lr_step_size epochs"
+ )
+
+ parser.add_argument(
+ "--lr_gamma",
+ default=0.1,
+ type=float,
+ help="every lr_step_size epochs, decay learning rate by a factor of lr_gamma"
+ )
+
+ parser.add_argument(
+ "--weight_decay",
+ default=0.0,
+ type=float,
+ help="ridge regularization factor"
+ )
+
+ parser.add_argument(
+ "--mask_type",
+ default='random',
+ type=str,
+ help="under-sampling mask type: ['random','equispaced']"
+ )
+
+ # model specific args
+ parser.add_argument(
+ "--drop_prob",
+ default=0.0,
+ type=float,
+ help="dropout probability for U-Net"
+ )
+
+ parser.add_argument(
+ "--features",
+ default=[18,36,72,144,288,18],
+ type=list,
+ help="six integers as numbers of features (see monai.networks.nets.basic_unet)"
+ )
+
+ parser.add_argument(
+ "--sensitivity_model_features",
+ default= [8,16,32,64,128,8],
+ type=list,
+ help="six integers as numbers of sensitivity model features (see monai.networks.nets.basic_unet)"
+ )
+
+ parser.add_argument(
+ "--num_cascades",
+ default=12,
+ type=int,
+ help="number of cascades"
+ )
+
+ parser.add_argument(
+ "--resume_checkpoint",
+ default=False,
+ type=bool,
+ help="if True, training statrts from a model checkpoint"
+ )
+
+ parser.add_argument(
+ "--checkpoint_dir",
+ default=None,
+ type=Path,
+ help="model checkpoint path to resume training from"
+ )
+
+ args = parser.parse_args()
+ trainer(args)
+
+__main__()