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docs: minor typo fixes in example notebooks
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‎examples/pannuke_nuclei_segmentation_cellpose.ipynb‎

Lines changed: 13 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -41,10 +41,12 @@
4141
}
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],
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"source": [
44-
"import torch\n",
44+
"from platform import python_version\n",
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"\n",
4546
"import accelerate\n",
47+
"import torch\n",
48+
"\n",
4649
"import cellseg_models_pytorch\n",
47-
"from platform import python_version\n",
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"\n",
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"print(\"torch version:\", torch.__version__)\n",
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"print(\"accelerate version:\", accelerate.__version__)\n",
@@ -86,6 +88,7 @@
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],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"from cellseg_models_pytorch.datamodules import PannukeDataModule\n",
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"\n",
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"fold_split = {\"fold1\": \"train\", \"fold2\": \"train\", \"fold3\": \"valid\"}\n",
@@ -146,13 +149,14 @@
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}
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],
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"source": [
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"import numpy as np\n",
150152
"import matplotlib.pyplot as plt\n",
153+
"import numpy as np\n",
151154
"from skimage.color import label2rgb\n",
152155
"\n",
156+
"from cellseg_models_pytorch.transforms.functional import gen_flow_maps\n",
157+
"\n",
153158
"# filehandler contains methods to read and write images and masks\n",
154159
"from cellseg_models_pytorch.utils import FileHandler\n",
155-
"from cellseg_models_pytorch.transforms.functional import gen_flow_maps\n",
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"\n",
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"img_dir = save_dir / \"train\" / \"images\"\n",
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"mask_dir = save_dir / \"train\" / \"labels\"\n",
@@ -216,7 +220,7 @@
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"First, we will define the CellPose nuclei segmentation model with a `imagenet` pre-trained encoder. Specifically, we will use the `convnext_small` backbone for this demonstration. Many more encoders can be used, since these are imported from the `timm` library. There are also support for some transformer based encoders, but these are shown in another notebooks.\n",
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"\n",
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"**Branch losses**.\n",
219-
"For each output of the model, we define a joint-loss function. These losses are summed together during backprop to form a multi-task loss. For the `\"cellpose\"`branch output we set a joint-loss composed of SSIM-loss (Structural Similarity Index) and MSE-loss (Mean Squared Error) and for the `\"type\"` (cell type predictions) outputs we will set a joint-loss composed of CE-loss (Cross Entropy) and DICE-loss for both. For the CE-losses, we will also be using [spectral decoupling](https://arxiv.org/abs/2011.09468) to regularize the model. \n",
223+
"For each output of the model, we define a joint-loss function. These losses are summed together during backprop to form a multi-task loss. For the `\"cellpose\"`branch output we set the MSE-loss (Mean Squared Error) and for the `\"type\"` (cell type predictions) outputs we will set a joint-loss composed of CE-loss (Cross Entropy) and DICE-loss for both. For the CE-losses, we will also be using [spectral decoupling](https://arxiv.org/abs/2011.09468) to regularize the model. \n",
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"\n",
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"\n",
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"**Logging metrics.**\n",
@@ -236,16 +240,15 @@
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"outputs": [],
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"source": [
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"import accelerate\n",
239-
"from accelerate.utils import set_seed\n",
240-
"\n",
241243
"import torch.nn as nn\n",
242244
"import torch.nn.functional as F\n",
245+
"from accelerate.utils import set_seed\n",
243246
"from torch.optim.lr_scheduler import OneCycleLR\n",
244247
"from torchmetrics import JaccardIndex, MeanSquaredError\n",
245248
"from tqdm import tqdm\n",
246249
"\n",
250+
"from cellseg_models_pytorch.losses import CELoss, DiceLoss, JointLoss, MultiTaskLoss\n",
247251
"from cellseg_models_pytorch.models import cellpose_base\n",
248-
"from cellseg_models_pytorch.losses import DiceLoss, CELoss, JointLoss, MultiTaskLoss\n",
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"\n",
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"\n",
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"# Quick wrapper for MSE loss to make it fit the JointLoss API\n",
@@ -821,7 +824,6 @@
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"source": [
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"from accelerate import load_checkpoint_and_dispatch\n",
823826
"\n",
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"\n",
825827
"# The model state dict was saved in the project_dir\n",
826828
"model = cellpose_base(\n",
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" enc_name=\"convnext_small\",\n",
@@ -967,10 +969,10 @@
967969
}
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],
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"source": [
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"import numpy as np\n",
971-
"from cellseg_models_pytorch.utils import draw_thing_contours\n",
972972
"import matplotlib.patches as mpatches\n",
973+
"import numpy as np\n",
973974
"\n",
975+
"from cellseg_models_pytorch.utils import draw_thing_contours\n",
974976
"\n",
975977
"fig, ax = plt.subplots(5, 2, figsize=(10, 17))\n",
976978
"ax = ax.flatten()\n",

‎examples/pannuke_nuclei_segmentation_cellpose_dinov2.ipynb‎

Lines changed: 17 additions & 186 deletions
Original file line numberDiff line numberDiff line change
@@ -41,10 +41,12 @@
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}
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],
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"source": [
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"import torch\n",
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"from platform import python_version\n",
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"\n",
4546
"import lightning\n",
47+
"import torch\n",
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"\n",
4649
"import cellseg_models_pytorch\n",
47-
"from platform import python_version\n",
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"\n",
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"print(\"torch version:\", torch.__version__)\n",
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"print(\"lightning version:\", lightning.__version__)\n",
@@ -88,6 +90,7 @@
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],
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"source": [
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"from pathlib import Path\n",
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"\n",
9194
"from cellseg_models_pytorch.datamodules import PannukeDataModule\n",
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"\n",
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"fold_split = {\"fold1\": \"train\", \"fold2\": \"train\", \"fold3\": \"valid\"}\n",
@@ -150,13 +153,14 @@
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}
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],
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"source": [
153-
"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
157+
"import numpy as np\n",
155158
"from skimage.color import label2rgb\n",
156159
"\n",
160+
"from cellseg_models_pytorch.transforms.functional import gen_flow_maps\n",
161+
"\n",
157162
"# filehandler contains methods to read and write images and masks\n",
158163
"from cellseg_models_pytorch.utils import FileHandler\n",
159-
"from cellseg_models_pytorch.transforms.functional import gen_flow_maps\n",
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"\n",
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"img_dir = save_dir / \"train\" / \"images\"\n",
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"mask_dir = save_dir / \"train\" / \"labels\"\n",
@@ -219,11 +223,12 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Dict, List, Tuple\n",
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"\n",
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"import lightning.pytorch as pl\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
225-
"import lightning.pytorch as pl\n",
226-
"from typing import List, Tuple, Dict\n",
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"\n",
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"from cellseg_models_pytorch.losses import MultiTaskLoss\n",
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"\n",
@@ -388,7 +393,7 @@
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}
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],
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"source": [
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"from cellseg_models_pytorch.losses import JointLoss, CELoss, DiceLoss, SSIM, MSE\n",
396+
"from cellseg_models_pytorch.losses import MSE, SSIM, CELoss, DiceLoss, JointLoss\n",
392397
"from cellseg_models_pytorch.models import cellpose_base\n",
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"\n",
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"# initialize hovernet\n",
@@ -464,183 +469,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"You are using a CUDA device ('NVIDIA GeForce RTX 3080 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
475-
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Found all folds. Skip downloading.\n",
483-
"Found processed pannuke data. If in need of a re-download, please empty the `save_dir` folder.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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" | Name | Type | Params | Mode \n",
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"----------------------------------------------------\n",
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"0 | model | CellPoseUnet | 92.4 M | train\n",
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"1 | criterion | MultiTaskLoss | 0 | train\n",
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"----------------------------------------------------\n",
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"3.8 M Trainable params\n",
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"88.6 M Non-trainable params\n",
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"92.4 M Total params\n",
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"369.433 Total estimated model params size (MB)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6d39790609e94710a1c2db412e95e672",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Sanity Checking: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9e149243fda244edad091b1ebdb4bbb5",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Training: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6915db3487c54dba98ad2bc147b1d75f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 0, global step 648: 'val_loss' reached 1.21952 (best 1.21952), saving model to '/home/leos/pannuke/dino_cellpose/epoch=0-step=648.ckpt' as top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "93cfe15ce7b444b8b5ccc4e509f86e7b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 1, global step 1296: 'val_loss' reached 1.19438 (best 1.19438), saving model to '/home/leos/pannuke/dino_cellpose/epoch=1-step=1296.ckpt' as top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "36558f8ef059416d9b6d5db628bf34fb",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 2, global step 1944: 'val_loss' reached 1.07785 (best 1.07785), saving model to '/home/leos/pannuke/dino_cellpose/epoch=2-step=1944.ckpt' as top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f33dbb9ed6794453950d33f3069861fe",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 3, global step 2592: 'val_loss' reached 1.04576 (best 1.04576), saving model to '/home/leos/pannuke/dino_cellpose/epoch=3-step=2592.ckpt' as top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "45c76d38563b471ea2996d14d6bdbd8e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: | | 0/? [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 4, global step 3240: 'val_loss' reached 0.94926 (best 0.94926), saving model to '/home/leos/pannuke/dino_cellpose/epoch=4-step=3240.ckpt' as top 1\n",
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"`Trainer.fit` stopped: `max_epochs=5` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"gg\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Train the model\n",
646477
"trainer.fit(model=experiment, datamodule=pannuke_module)"
@@ -688,8 +519,8 @@
688519
],
689520
"source": [
690521
"import torch.nn.functional as F\n",
691-
"from cellseg_models_pytorch.utils import percentile_normalize_torch\n",
692522
"\n",
523+
"from cellseg_models_pytorch.utils import percentile_normalize_torch\n",
693524
"\n",
694525
"img_dir = save_dir / \"valid\" / \"images\"\n",
695526
"mask_dir = save_dir / \"valid\" / \"labels\"\n",
@@ -816,10 +647,10 @@
816647
}
817648
],
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"source": [
819-
"import numpy as np\n",
820-
"from cellseg_models_pytorch.utils import draw_thing_contours\n",
821650
"import matplotlib.patches as mpatches\n",
651+
"import numpy as np\n",
822652
"\n",
653+
"from cellseg_models_pytorch.utils import draw_thing_contours\n",
823654
"\n",
824655
"fig, ax = plt.subplots(5, 2, figsize=(10, 17))\n",
825656
"ax = ax.flatten()\n",

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