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Merge branch 'changes_2024' of github.com:dlmbl/image_restoration int…
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Cateek committed Aug 22, 2024
2 parents 3945ebe + 7e7b76d commit e8948fb
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32 changes: 9 additions & 23 deletions 03_COSDD/exercise.ipynb
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Expand Up @@ -30,7 +30,6 @@
"outputs": [],
"source": [
"import os\n",
"import logging\n",
"\n",
"import torch\n",
"import tifffile\n",
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"metadata": {},
"outputs": [],
"source": [
"real_batch_size = 16\n",
"n_grad_batches = 1\n",
"real_batch_size = 4\n",
"n_grad_batches = 4\n",
"print(f\"Effective batch size: {real_batch_size * n_grad_batches}\")\n",
"crop_size = (256, 256)\n",
"train_split = 0.9\n",
Expand Down Expand Up @@ -408,9 +407,9 @@
"outputs": [],
"source": [
"dimensions = ... ### Insert a value here\n",
"s_code_channels = 16\n",
"s_code_channels = 32\n",
"\n",
"n_layers = 4\n",
"n_layers = 6\n",
"z_dims = [s_code_channels // 2] * n_layers\n",
"downsampling = [1] * n_layers\n",
"lvae = LadderVAE(\n",
Expand All @@ -428,8 +427,8 @@
" s_code_channels=s_code_channels,\n",
" kernel_size=5,\n",
" noise_direction=... ### Insert a value here\n",
" n_filters=16,\n",
" n_layers=3,\n",
" n_filters=32,\n",
" n_layers=4,\n",
" n_gaussians=4,\n",
" dimensions=dimensions,\n",
")\n",
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" data_mean=low_snr.mean(),\n",
" data_std=low_snr.std(),\n",
" n_grad_batches=n_grad_batches,\n",
" checkpointed=False,\n",
" checkpointed=True,\n",
")"
]
},
Expand Down Expand Up @@ -579,7 +578,6 @@
" max_time=max_time, # Remove this time limit to train the model fully\n",
" log_every_n_steps=len(train_set) // (n_grad_batches * real_batch_size),\n",
" callbacks=[EarlyStopping(patience=patience, monitor=\"val/elbo\")],\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
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"# Exercise 2. Inference with COSDD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"logger = logging.getLogger('pytorch_lightning')\n",
"logger.setLevel(logging.WARNING)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1. Load test data\n",
"The images that we want to denoise are loaded here. These are the same that we used for training, but we'll only load 2 to speed up inference."
"The images that we want to denoise are loaded here. These are the same that we used for training, but we'll only load 10 to speed up inference."
]
},
{
Expand All @@ -638,7 +626,7 @@
"outputs": [],
"source": [
"lowsnr_path = \"./../data/mito-confocal-lowsnr.tif\"\n",
"n_test_images = 2\n",
"n_test_images = 10\n",
"# load the data\n",
"test_set = tifffile.imread(lowsnr_path)\n",
"test_set = test_set[:n_test_images, np.newaxis]\n",
Expand Down Expand Up @@ -724,7 +712,6 @@
" enable_progress_bar=False,\n",
" enable_checkpointing=False,\n",
" logger=False,\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
Expand All @@ -747,7 +734,6 @@
" enable_progress_bar=False,\n",
" enable_checkpointing=False,\n",
" logger=False,\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
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95 changes: 25 additions & 70 deletions 03_COSDD/solution.ipynb
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Expand Up @@ -25,12 +25,11 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import logging\n",
"\n",
"import torch\n",
"import tifffile\n",
Expand All @@ -53,7 +52,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -120,7 +119,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {
"tags": [
"solution"
Expand Down Expand Up @@ -359,20 +358,12 @@
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Effective batch size: 16\n"
]
}
],
"source": [
"real_batch_size = 16\n",
"n_grad_batches = 1\n",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"real_batch_size = 4\n",
"n_grad_batches = 4\n",
"print(f\"Effective batch size: {real_batch_size * n_grad_batches}\")\n",
"crop_size = (256, 256)\n",
"train_split = 0.9\n",
Expand Down Expand Up @@ -472,9 +463,9 @@
"outputs": [],
"source": [
"dimensions = ... ### Insert a value here\n",
"s_code_channels = 16\n",
"s_code_channels = 32\n",
"\n",
"n_layers = 4\n",
"n_layers = 6\n",
"z_dims = [s_code_channels // 2] * n_layers\n",
"downsampling = [1] * n_layers\n",
"lvae = LadderVAE(\n",
Expand All @@ -492,8 +483,8 @@
" s_code_channels=s_code_channels,\n",
" kernel_size=5,\n",
" noise_direction=... ### Insert a value here\n",
" n_filters=16,\n",
" n_layers=3,\n",
" n_filters=32,\n",
" n_layers=4,\n",
" n_gaussians=4,\n",
" dimensions=dimensions,\n",
")\n",
Expand Down Expand Up @@ -526,35 +517,24 @@
" data_mean=low_snr.mean(),\n",
" data_std=low_snr.std(),\n",
" n_grad_batches=n_grad_batches,\n",
" checkpointed=False,\n",
" checkpointed=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {
"tags": [
"solution"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/TA-bs/miniforge3/envs/05_image_restoration/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:208: Attribute 'vae' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['vae'])`.\n",
"/home/TA-bs/miniforge3/envs/05_image_restoration/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:208: Attribute 'ar_decoder' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['ar_decoder'])`.\n",
"/home/TA-bs/miniforge3/envs/05_image_restoration/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:208: Attribute 's_decoder' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['s_decoder'])`.\n",
"/home/TA-bs/miniforge3/envs/05_image_restoration/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:208: Attribute 'direct_denoiser' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['direct_denoiser'])`.\n"
]
}
],
"outputs": [],
"source": [
"dimensions = 2 ### Insert a value here\n",
"s_code_channels = 16\n",
"s_code_channels = 32\n",
"\n",
"n_layers = 4\n",
"n_layers = 6\n",
"z_dims = [s_code_channels // 2] * n_layers\n",
"downsampling = [1] * n_layers\n",
"lvae = LadderVAE(\n",
Expand All @@ -572,8 +552,8 @@
" s_code_channels=s_code_channels,\n",
" kernel_size=5,\n",
" noise_direction=\"x\", ### Insert a value here\n",
" n_filters=16,\n",
" n_layers=3,\n",
" n_filters=32,\n",
" n_layers=4,\n",
" n_gaussians=4,\n",
" dimensions=dimensions,\n",
")\n",
Expand Down Expand Up @@ -606,7 +586,7 @@
" data_mean=low_snr.mean(),\n",
" data_std=low_snr.std(),\n",
" n_grad_batches=n_grad_batches,\n",
" checkpointed=False,\n",
" checkpointed=True,\n",
")"
]
},
Expand Down Expand Up @@ -723,30 +703,18 @@
" max_time=max_time, # Remove this time limit to train the model fully\n",
" log_every_n_steps=len(train_set) // (n_grad_batches * real_batch_size),\n",
" callbacks=[EarlyStopping(patience=patience, monitor=\"val/elbo\")],\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {
"tags": [
"solution"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using bfloat16 Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"HPU available: False, using: 0 HPUs\n"
]
}
],
"outputs": [],
"source": [
"model_name = \"mito-confocal\" ### Insert a value here\n",
"checkpoint_path = os.path.join(\"checkpoints\", model_name)\n",
Expand All @@ -764,7 +732,6 @@
" max_time=max_time, # Remove this time limit to train the model fully\n",
" log_every_n_steps=len(train_set) // (n_grad_batches * real_batch_size),\n",
" callbacks=[EarlyStopping(patience=patience, monitor=\"val/elbo\")],\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
Expand Down Expand Up @@ -798,22 +765,12 @@
"# Exercise 2. Inference with COSDD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"logger = logging.getLogger('pytorch_lightning')\n",
"logger.setLevel(logging.WARNING)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1. Load test data\n",
"The images that we want to denoise are loaded here. These are the same that we used for training, but we'll only load 2 to speed up inference."
"The images that we want to denoise are loaded here. These are the same that we used for training, but we'll only load 10 to speed up inference."
]
},
{
Expand All @@ -823,7 +780,7 @@
"outputs": [],
"source": [
"lowsnr_path = \"./../data/mito-confocal-lowsnr.tif\"\n",
"n_test_images = 2\n",
"n_test_images = 10\n",
"# load the data\n",
"test_set = tifffile.imread(lowsnr_path)\n",
"test_set = test_set[:n_test_images, np.newaxis]\n",
Expand Down Expand Up @@ -909,7 +866,6 @@
" enable_progress_bar=False,\n",
" enable_checkpointing=False,\n",
" logger=False,\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
Expand All @@ -932,7 +888,6 @@
" enable_progress_bar=False,\n",
" enable_checkpointing=False,\n",
" logger=False,\n",
" precision=\"bf16-mixed\",\n",
")"
]
},
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2 changes: 1 addition & 1 deletion 04_DenoiSplit/exercise.ipynb
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Expand Up @@ -796,7 +796,7 @@
},
{
"cell_type": "markdown",
"id": "ccb2694d",
"id": "337b207c",
"metadata": {},
"source": [
"<hr style=\"height:2px;\"><div class=\"alert alert-block alert-success\"><h1>End of the exercise</h1>\n",
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