mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-09-16 18:15:28 +00:00
chore: move notebooks in parant folder
Former-commit-id: 56075c7a86305a7a9644dfaacd5bdcb62a863baf [formerly 35967dee0a3c159669b50e2823f36ff25e036f1e] Former-commit-id: b98a086c0ea8970562055cc3ad1a025a77b94b35
This commit is contained in:
Laurent Fainsin
parent
b1d7369188
commit
5fe7ceb306
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@ -7,7 +7,7 @@ repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: "v4.1.0"
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hooks:
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- id: check-added-large-files
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# - id: check-added-large-files
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- id: check-executables-have-shebangs
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- id: check-merge-conflict
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- id: check-symlinks
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@ -1 +0,0 @@
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ecf0b9ce39e210bc605fd3eab9db8b1215c35fda
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@ -1,99 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"\n",
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"import albumentations as A\n",
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"import torchvision.transforms as T\n",
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"\n",
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"import numpy as np\n",
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"from utils import RandomPaste\n",
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"from data.dataset import SyntheticDataset\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"from joblib import Parallel, delayed\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"transform = A.Compose(\n",
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" [\n",
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" A.Resize(512, 512),\n",
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" A.Flip(),\n",
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" A.ColorJitter(),\n",
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" RandomPaste(5, \"/media/disk1/lfainsin/SPHERES/\"),\n",
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" A.GaussianBlur(),\n",
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" A.ISONoise(),\n",
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" ],\n",
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")\n",
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"\n",
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"dataset = SyntheticDataset(image_dir=\"/media/disk1/lfainsin/BACKGROUND/\", transform=transform)\n",
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"transform = T.ToPILImage()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def render(i, image, mask):\n",
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"\n",
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" image = transform(image)\n",
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" mask = transform(mask)\n",
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"\n",
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" path = f\"/media/disk1/lfainsin/TRAIN_prerender/{i}/\"\n",
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" Path(path).mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" image.save(f\"{path}/image.jpg\")\n",
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" mask.save(f\"{path}/MASK.PNG\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"Parallel(n_jobs=-1)(delayed(render)(i, image, mask) for i, (image, mask) in enumerate(dataset))\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.0 ('.venv': poetry)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "dc80d2c03865715c8671359a6bf138f6c8ae4e26ae025f2543e0980b8db0ed7e"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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1
src/predict.ipynb.REMOVED.git-id
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1
src/predict.ipynb.REMOVED.git-id
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@ -0,0 +1 @@
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c1a69536ef50112fdf8384eed234407b31a1dfdb
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@ -1,80 +0,0 @@
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import argparse
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import logging
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import albumentations as A
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import numpy as np
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import onnx
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import onnxruntime
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from albumentations.pytorch import ToTensorV2
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from PIL import Image
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def get_args():
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parser = argparse.ArgumentParser(
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description="Predict masks from input images",
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)
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parser.add_argument(
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"--model",
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"-m",
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default="model.pth",
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metavar="FILE",
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help="Specify the file in which the model is stored",
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)
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parser.add_argument(
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"--input",
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"-i",
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metavar="INPUT",
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help="Filenames of input images",
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required=True,
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)
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parser.add_argument(
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"--output",
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"-o",
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metavar="OUTPUT",
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help="Filenames of output images",
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)
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return parser.parse_args()
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def sigmoid(x):
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return 1 / (1 + np.exp(-x))
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if __name__ == "__main__":
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args = get_args()
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logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
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onnx_model = onnx.load(args.model)
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onnx.checker.check_model(onnx_model)
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ort_session = onnxruntime.InferenceSession(args.model)
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def to_numpy(tensor):
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return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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img = Image.open(args.input).convert("RGB")
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logging.info(f"Preprocessing image {args.input}")
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transform = A.Compose(
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[
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A.ToFloat(max_value=255),
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ToTensorV2(),
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],
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)
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aug = transform(image=np.asarray(img))
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img = aug["image"]
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logging.info(f"Predicting image {args.input}")
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img = img.unsqueeze(0)
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# compute ONNX Runtime output prediction
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ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img)}
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ort_outs = ort_session.run(None, ort_inputs)
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img_out_y = ort_outs[0]
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img_out_y = Image.fromarray(np.uint8((img_out_y[0] * 255.0).clip(0, 255)[0]), mode="L")
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img_out_y.save(args.output)
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119
src/prerender.ipynb
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119
src/prerender.ipynb
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@ -0,0 +1,119 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"/home/laurent_fainsin/unet/.venv/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'SyntheticDataset' from 'data.dataset' (/home/laurent_fainsin/unet/src/data/dataset.py)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/home/laurent_fainsin/unet/src/prerender.ipynb Cell 1\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bdld2/home/laurent_fainsin/unet/src/prerender.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bdld2/home/laurent_fainsin/unet/src/prerender.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m RandomPaste\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bdld2/home/laurent_fainsin/unet/src/prerender.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdata\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdataset\u001b[39;00m \u001b[39mimport\u001b[39;00m SyntheticDataset\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bdld2/home/laurent_fainsin/unet/src/prerender.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpathlib\u001b[39;00m \u001b[39mimport\u001b[39;00m Path\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bdld2/home/laurent_fainsin/unet/src/prerender.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mjoblib\u001b[39;00m \u001b[39mimport\u001b[39;00m Parallel, delayed\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'SyntheticDataset' from 'data.dataset' (/home/laurent_fainsin/unet/src/data/dataset.py)"
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]
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}
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],
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"source": [
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"from PIL import Image\n",
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"\n",
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"import albumentations as A\n",
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"import torchvision.transforms as T\n",
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"\n",
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"import numpy as np\n",
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"from utils import RandomPaste\n",
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"from data.dataset import SyntheticDataset\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"from joblib import Parallel, delayed\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"transform = A.Compose(\n",
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" [\n",
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" A.Resize(512, 512),\n",
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" A.Flip(),\n",
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" A.ColorJitter(),\n",
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" RandomPaste(5, \"/media/disk1/lfainsin/SPHERES/\"),\n",
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" A.GaussianBlur(),\n",
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" A.ISONoise(),\n",
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" ],\n",
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")\n",
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"\n",
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"dataset = SyntheticDataset(image_dir=\"/media/disk1/lfainsin/BACKGROUND/\", transform=transform)\n",
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"transform = T.ToPILImage()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def render(i, image, mask):\n",
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"\n",
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" image = transform(image)\n",
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" mask = transform(mask)\n",
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"\n",
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" path = f\"/media/disk1/lfainsin/TRAIN_prerender/{i}/\"\n",
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" Path(path).mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" image.save(f\"{path}/image.jpg\")\n",
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" mask.save(f\"{path}/MASK.PNG\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"Parallel(n_jobs=-1)(delayed(render)(i, image, mask) for i, (image, mask) in enumerate(dataset))\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.10 ('.venv': poetry)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "177da549c0efb5403fabf2463095e744d5ed7be0fe3e2a9b077a1765a2012433"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -28,7 +28,7 @@ WORKERS:
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value: 16
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EPOCHS:
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value: 50
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value: 100
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TRAIN_BATCH_SIZE:
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value: 10
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VALID_BATCH_SIZE:
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