{ "cells": [ { "cell_type": "markdown", "id": "fb9844d4-1d21-45a6-8383-61d18bdf96e4", "metadata": {}, "source": [ "# Segmentation Inference Example\n", "\n", "This notebooks aims to demo the inference on a custom data sample, instead of using pre-defined tfds dataset.\n", "\n", "To execute this notebook, please follow the [README](https://github.com/mathpluscode/ImgX-DiffSeg) to install the `imgx` package locally. The muscle ultrasound data set also needs to be built using `tfds build imgx/datasets/muscle_us`." ] }, { "cell_type": "code", "execution_count": 1, "id": "eac79551-2ff5-4bd0-b403-bfe45b794db2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/yunguanfu/miniforge3/envs/imgx/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "from pathlib import Path\n", "import numpy as np\n", "import jax\n", "from flax.training import common_utils\n", "from omegaconf import OmegaConf\n", "import matplotlib.pyplot as plt\n", "\n", "from imgx.task.segmentation.experiment import SegmentationExperiment\n", "from imgx.datasets.constant import IMAGE, LABEL, LABEL_PRED\n", "from imgx.datasets.save import load_2d_grayscale_image" ] }, { "cell_type": "markdown", "id": "39287037-c4c7-46cb-b678-2858a2af814e", "metadata": {}, "source": [ "## Load a model\n", "\n", "The model is a supervisedly trained four-layer U-net with channels [8, 16, 32, 64]." ] }, { "cell_type": "code", "execution_count": 2, "id": "b6115f20-beb6-43db-8ead-8e4d90bc69fc", "metadata": {}, "outputs": [], "source": [ "config_path = \"config.yaml\" # backup config stored\n", "ckpt_dir = \"files/ckpt\"\n", "step = 1900" ] }, { "cell_type": "code", "execution_count": 3, "id": "be7db5f1-fc70-4775-adc4-2abbb8e9b4a1", "metadata": {}, "outputs": [], "source": [ "config = OmegaConf.load(config_path)\n", "run = SegmentationExperiment(config=config)" ] }, { "cell_type": "markdown", "id": "62df3c6b-bef7-45e2-93e1-a3aa6c94bea6", "metadata": {}, "source": [ "## Evaluate on a custom image\n", "\n", "`BB_anon_348_1` is a training data from `muscle_us` data set." ] }, { "cell_type": "code", "execution_count": 4, "id": "22f79c47-1e54-48d2-b187-8dce98190066", "metadata": {}, "outputs": [], "source": [ "seed = 0\n", "out_dir = Path(\"outputs\")\n", "\n", "image = load_2d_grayscale_image(\"BB_anon_348_1.png\", dtype=np.float32)\n", "label = load_2d_grayscale_image(\"BB_anon_348_1_mask.png\")\n", "# image.shape = (1, 1, 480, 512, 1) label.shape = (1, 1, 480, 512)\n", "# the first axis is shard axis for pmap, the second axis is batch axis\n", "batch = {IMAGE:image[None, None,..., None], LABEL:label[None, None, ...]}\n", "uids = [\"BB_anon_348_1\"]" ] }, { "cell_type": "code", "execution_count": 5, "id": "a23030e3-77a9-420f-9599-ca69ea1d61f7", "metadata": {}, "outputs": [], "source": [ "device_cpu = jax.devices(\"cpu\")[0]\n", "key = jax.random.PRNGKey(seed)\n", "key = common_utils.shard_prng_key(key)\n", "\n", "train_state, _ = run.train_init(batch=batch, ckpt_dir=ckpt_dir, step=step) # still loads data from tfds\n", "uids, metrics, preds = run.eval_batch(\n", " train_state=train_state,\n", " key=key,\n", " batch=batch,\n", " uids=uids,\n", " device_cpu=device_cpu,\n", " )\n", "label_pred = preds[LABEL_PRED]" ] }, { "cell_type": "markdown", "id": "c443fddd-b876-4b7f-be23-88d0db08de40", "metadata": {}, "source": [ "## Visualize output" ] }, { "cell_type": "code", "execution_count": 6, "id": "7c148884-844d-4635-bf6c-4e978f8c2a70", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'binary_dice_score_class_0': 0.9713174104690552, 'binary_dice_score_class_1': 0.8994527459144592, 'centroid_dist_class_0': 3.0381574630737305, 'centroid_dist_class_1': 12.569477081298828, 'class_0_proportion_label': 0.7809703946113586, 'class_0_proportion_pred': 0.7751233577728271, 'class_0_volume_label': 191.9320068359375, 'class_0_volume_pred': 190.49501037597656, 'class_1_proportion_label': 0.21902620792388916, 'class_1_proportion_pred': 0.22487325966358185, 'class_1_volume_label': 53.8280029296875, 'class_1_volume_pred': 55.2650032043457, 'hausdorff_dist_class_0': 27.434404589355008, 'hausdorff_dist_class_1': 36.00693109995926, 'iou_class_0': 0.944234311580658, 'iou_class_1': 0.817277729511261, 'mean_binary_dice_score': 0.9353851079940796, 'mean_binary_dice_score_without_background': 0.8994527459144592, 'mean_centroid_dist': 7.803817272186279, 'mean_centroid_dist_without_background': 12.569477081298828, 'mean_hausdorff_dist': 31.720667844657136, 'mean_hausdorff_dist_without_background': 36.00693109995926, 'mean_iou': 0.8807560205459595, 'mean_iou_without_background': 0.817277729511261, 'mean_mean_surface_dist': 8.354319260467175, 'mean_mean_surface_dist_without_background': 12.598374857096667, 'mean_normalised_surface_dice': 0.41048414519710497, 'mean_normalised_surface_dice_without_background': 0.10594594594594595, 'mean_stability': 0.6953535079956055, 'mean_stability_without_background': 0.5474743247032166, 'mean_surface_dist_class_0': 4.110263663837684, 'mean_surface_dist_class_1': 12.598374857096667, 'normalised_surface_dice_class_0': 0.715022344448264, 'normalised_surface_dice_class_1': 0.10594594594594595, 'stability_class_0': 0.8432326316833496, 'stability_class_1': 0.5474743247032166}\n" ] } ], "source": [ "# get scalar values\n", "print(jax.tree_map(lambda x: x.item(), metrics))" ] }, { "cell_type": "code", "execution_count": 7, "id": "fe6aa939-1488-4ab4-b53f-da22b42d1838", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 3)\n", "axs[0].imshow(image, cmap='gray', interpolation='nearest')\n", "axs[1].imshow(label, cmap='gray', interpolation='nearest')\n", "axs[2].imshow(label_pred[0], cmap='gray', interpolation='nearest')\n", "for i in range(3):\n", " axs[i].xaxis.set_tick_params(labelbottom=False)\n", " axs[i].yaxis.set_tick_params(labelleft=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "c45aa552-042c-43a4-8219-f68d6a8f6259", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }