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chore: move notebooks in parant folder

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Former-commit-id: 56075c7a86305a7a9644dfaacd5bdcb62a863baf [formerly 35967dee0a3c159669b50e2823f36ff25e036f1e]
Former-commit-id: b98a086c0ea8970562055cc3ad1a025a77b94b35
This commit is contained in:
Laurent Fainsin 2022-09-07 14:34:32 +02:00
parent b1d7369188
commit 5fe7ceb306
10 changed files with 122 additions and 182 deletions
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2
.pre-commit-config.yaml
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@ -7,7 +7,7 @@ repos:
- repo: https://github.com/pre-commit/pre-commit-hooks - repo: https://github.com/pre-commit/pre-commit-hooks
rev: "v4.1.0" rev: "v4.1.0"
hooks: hooks:
- id: check-added-large-files # - id: check-added-large-files
- id: check-executables-have-shebangs - id: check-executables-have-shebangs
- id: check-merge-conflict - id: check-merge-conflict
- id: check-symlinks - id: check-symlinks

0
src/notebooks/comp.ipynb.REMOVED.git-id → src/comp.ipynb.REMOVED.git-id
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0
src/notebooks/convert.ipynb.REMOVED.git-id → src/convert.ipynb.REMOVED.git-id
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0
src/notebooks/extract.ipynb → src/extract.ipynb
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1
src/notebooks/predict.ipynb.REMOVED.git-id
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@ -1 +0,0 @@
ecf0b9ce39e210bc605fd3eab9db8b1215c35fda

99
src/notebooks/prerender.ipynb
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@ -1,99 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"\n",
"import albumentations as A\n",
"import torchvision.transforms as T\n",
"\n",
"import numpy as np\n",
"from utils import RandomPaste\n",
"from data.dataset import SyntheticDataset\n",
"\n",
"from pathlib import Path\n",
"\n",
"from joblib import Parallel, delayed\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"transform = A.Compose(\n",
" [\n",
" A.Resize(512, 512),\n",
" A.Flip(),\n",
" A.ColorJitter(),\n",
" RandomPaste(5, \"/media/disk1/lfainsin/SPHERES/\"),\n",
" A.GaussianBlur(),\n",
" A.ISONoise(),\n",
" ],\n",
")\n",
"\n",
"dataset = SyntheticDataset(image_dir=\"/media/disk1/lfainsin/BACKGROUND/\", transform=transform)\n",
"transform = T.ToPILImage()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def render(i, image, mask):\n",
"\n",
" image = transform(image)\n",
" mask = transform(mask)\n",
"\n",
" path = f\"/media/disk1/lfainsin/TRAIN_prerender/{i}/\"\n",
" Path(path).mkdir(parents=True, exist_ok=True)\n",
" \n",
" image.save(f\"{path}/image.jpg\")\n",
" mask.save(f\"{path}/MASK.PNG\")\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"Parallel(n_jobs=-1)(delayed(render)(i, image, mask) for i, (image, mask) in enumerate(dataset))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.0 ('.venv': poetry)",
"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": "3.8.0"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "dc80d2c03865715c8671359a6bf138f6c8ae4e26ae025f2543e0980b8db0ed7e"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

1
src/predict.ipynb.REMOVED.git-id Normal file
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@ -0,0 +1 @@
c1a69536ef50112fdf8384eed234407b31a1dfdb

80
src/predict.py
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@ -1,80 +0,0 @@
import argparse
import logging
import albumentations as A
import numpy as np
import onnx
import onnxruntime
from albumentations.pytorch import ToTensorV2
from PIL import Image
def get_args():
parser = argparse.ArgumentParser(
description="Predict masks from input images",
)
parser.add_argument(
"--model",
"-m",
default="model.pth",
metavar="FILE",
help="Specify the file in which the model is stored",
)
parser.add_argument(
"--input",
"-i",
metavar="INPUT",
help="Filenames of input images",
required=True,
)
parser.add_argument(
"--output",
"-o",
metavar="OUTPUT",
help="Filenames of output images",
)
return parser.parse_args()
def sigmoid(x):
return 1 / (1 + np.exp(-x))
if __name__ == "__main__":
args = get_args()
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
onnx_model = onnx.load(args.model)
onnx.checker.check_model(onnx_model)
ort_session = onnxruntime.InferenceSession(args.model)
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
img = Image.open(args.input).convert("RGB")
logging.info(f"Preprocessing image {args.input}")
transform = A.Compose(
[
A.ToFloat(max_value=255),
ToTensorV2(),
],
)
aug = transform(image=np.asarray(img))
img = aug["image"]
logging.info(f"Predicting image {args.input}")
img = img.unsqueeze(0)
# compute ONNX Runtime output prediction
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img)}
ort_outs = ort_session.run(None, ort_inputs)
img_out_y = ort_outs[0]
img_out_y = Image.fromarray(np.uint8((img_out_y[0] * 255.0).clip(0, 255)[0]), mode="L")
img_out_y.save(args.output)

119
src/prerender.ipynb Normal file
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@ -0,0 +1,119 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"ename": "ImportError",
"evalue": "cannot import name 'SyntheticDataset' from 'data.dataset' (/home/laurent_fainsin/unet/src/data/dataset.py)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\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",
"\u001b[0;31mImportError\u001b[0m: cannot import name 'SyntheticDataset' from 'data.dataset' (/home/laurent_fainsin/unet/src/data/dataset.py)"
]
}
],
"source": [
"from PIL import Image\n",
"\n",
"import albumentations as A\n",
"import torchvision.transforms as T\n",
"\n",
"import numpy as np\n",
"from utils import RandomPaste\n",
"from data.dataset import SyntheticDataset\n",
"\n",
"from pathlib import Path\n",
"\n",
"from joblib import Parallel, delayed\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"transform = A.Compose(\n",
" [\n",
" A.Resize(512, 512),\n",
" A.Flip(),\n",
" A.ColorJitter(),\n",
" RandomPaste(5, \"/media/disk1/lfainsin/SPHERES/\"),\n",
" A.GaussianBlur(),\n",
" A.ISONoise(),\n",
" ],\n",
")\n",
"\n",
"dataset = SyntheticDataset(image_dir=\"/media/disk1/lfainsin/BACKGROUND/\", transform=transform)\n",
"transform = T.ToPILImage()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def render(i, image, mask):\n",
"\n",
" image = transform(image)\n",
" mask = transform(mask)\n",
"\n",
" path = f\"/media/disk1/lfainsin/TRAIN_prerender/{i}/\"\n",
" Path(path).mkdir(parents=True, exist_ok=True)\n",
" \n",
" image.save(f\"{path}/image.jpg\")\n",
" mask.save(f\"{path}/MASK.PNG\")\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"Parallel(n_jobs=-1)(delayed(render)(i, image, mask) for i, (image, mask) in enumerate(dataset))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.10 ('.venv': poetry)",
"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"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "177da549c0efb5403fabf2463095e744d5ed7be0fe3e2a9b077a1765a2012433"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

2
wandb.yaml
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@ -28,7 +28,7 @@ WORKERS:
value: 16 value: 16
EPOCHS: EPOCHS:
value: 50 value: 100
TRAIN_BATCH_SIZE: TRAIN_BATCH_SIZE:
value: 10 value: 10
VALID_BATCH_SIZE: VALID_BATCH_SIZE: