diff --git a/comp.ipynb.REMOVED.git-id b/comp.ipynb.REMOVED.git-id
index b439b71..3c6779c 100644
--- a/comp.ipynb.REMOVED.git-id
+++ b/comp.ipynb.REMOVED.git-id
@@ -1 +1 @@
-9cbd3cff7e664a80a5a1fa1404898b7bba3cae0d
\ No newline at end of file
+3c9a34f197340a6051eb34d11695c7d6b72164f0
\ No newline at end of file
diff --git a/extract.ipynb b/extract.ipynb
new file mode 100644
index 0000000..f5c6750
--- /dev/null
+++ b/extract.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "import albumentations as A\n",
+ "\n",
+ "%config InlineBackend.figure_formats = ['svg']\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": "\n\n\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "img = Image.open(\"/tmp/extract/photo.jpg\").convert(\"RGBA\")\n",
+ "mask = Image.open(\"/tmp/extract/MASK.PNG\").convert(\"LA\")\n",
+ "\n",
+ "plt.figure(figsize=(18, 10))\n",
+ "\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(img)\n",
+ "\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(mask)\n",
+ "\n",
+ "ax = plt.gca()\n",
+ "ax.set_facecolor('xkcd:salmon')\n",
+ "\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": "\n\n\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(434, 425)\n"
+ ]
+ }
+ ],
+ "source": [
+ "box = mask.getbbox()\n",
+ "\n",
+ "crop_img = img.crop(box)\n",
+ "crop_mask = mask.crop(box)\n",
+ "\n",
+ "plt.figure(figsize=(18, 10))\n",
+ "\n",
+ "plt.subplot(2, 2, 1)\n",
+ "plt.imshow(crop_img)\n",
+ "\n",
+ "plt.subplot(2, 2, 2)\n",
+ "plt.imshow(crop_mask)\n",
+ "\n",
+ "ax = plt.gca()\n",
+ "ax.set_facecolor('xkcd:salmon')\n",
+ "\n",
+ "empty = Image.fromarray(np.zeros(crop_img.size), \"RGBA\")\n",
+ "empty.paste(crop_img, crop_mask)\n",
+ "\n",
+ "plt.subplot(2, 2, 3)\n",
+ "plt.imshow(empty.resize((100, 100)))\n",
+ "\n",
+ "plt.subplot(2, 2, 4)\n",
+ "plt.imshow(crop_mask.resize((100, 100)))\n",
+ "\n",
+ "ax = plt.gca()\n",
+ "ax.set_facecolor('xkcd:salmon')\n",
+ "\n",
+ "plt.show()\n",
+ "\n",
+ "print(crop_img.size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "spheres_to_extract_dir = \"/home/lilian/data_disk/lfainsin/test/\"\n",
+ "\n",
+ "spheres = list(Path(spheres_to_extract_dir).glob(\"**/*.jpg\"))\n",
+ "\n",
+ "parents = [path.parent for path in spheres]\n",
+ "parents = set(parents)\n",
+ "\n",
+ "for parent in parents:\n",
+ " mask_path = parent.joinpath(\"MASK.PNG\")\n",
+ " mask = Image.open(mask_path).convert(\"LA\")\n",
+ " box = mask.getbbox()\n",
+ " crop_mask = mask.crop(box)\n",
+ "\n",
+ " filename = Path(\"/tmp/saves/\" + str(mask_path).strip(spheres_to_extract_dir))\n",
+ " filename.parent.mkdir(parents=True, exist_ok=True)\n",
+ " crop_mask.save(filename)\n",
+ "\n",
+ " spheres = list(parent.glob(\"*.jpg\"))\n",
+ " for sphere in spheres:\n",
+ " img = Image.open(sphere).convert(\"RGB\")\n",
+ " crop_img = img.crop(box)\n",
+ "\n",
+ " filename = Path(\"/tmp/saves/\" + str(sphere).strip(spheres_to_extract_dir))\n",
+ " filename.parent.mkdir(parents=True, exist_ok=True)\n",
+ " crop_img.save(filename)\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
+}
diff --git a/src/train.py b/src/train.py
index d97802d..eb1b115 100644
--- a/src/train.py
+++ b/src/train.py
@@ -15,16 +15,22 @@ CONFIG = {
"DIR_TEST_IMG": "/home/lilian/data_disk/lfainsin/test/",
"DIR_SPHERE_IMG": "/home/lilian/data_disk/lfainsin/spheres/Images/",
"DIR_SPHERE_MASK": "/home/lilian/data_disk/lfainsin/spheres/Masks/",
- "FEATURES": [16, 32, 64, 128],
+ # "FEATURES": [1, 2, 4, 8],
+ # "FEATURES": [4, 8, 16, 32],
+ # "FEATURES": [8, 16, 32, 64],
+ # "FEATURES": [4, 8, 16, 32, 64],
+ "FEATURES": [8, 16, 32, 64, 128],
+ # "FEATURES": [16, 32, 64, 128],
+ # "FEATURES": [64, 128, 256, 512],
"N_CHANNELS": 3,
"N_CLASSES": 1,
"AMP": True,
"PIN_MEMORY": True,
"BENCHMARK": True,
"DEVICE": "gpu",
- "WORKERS": 8,
- "EPOCHS": 10,
- "BATCH_SIZE": 16,
+ "WORKERS": 10,
+ "EPOCHS": 1,
+ "BATCH_SIZE": 32,
"LEARNING_RATE": 1e-4,
"WEIGHT_DECAY": 1e-8,
"MOMENTUM": 0.9,
diff --git a/src/unet/model.py b/src/unet/model.py
index 4e87827..4508b27 100644
--- a/src/unet/model.py
+++ b/src/unet/model.py
@@ -82,7 +82,7 @@ class UNet(pl.LightningModule):
)
ds_train = SphereDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=tf_train)
- ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 5000)))
+ # ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 10000)))
return DataLoader(
ds_train,
@@ -178,6 +178,8 @@ class UNet(pl.LightningModule):
},
},
),
+ dice,
+ dice_bin,
]
)
@@ -199,7 +201,7 @@ class UNet(pl.LightningModule):
mae = torch.stack([d["mae"] for d in validation_outputs]).mean()
# table unpacking
- columns = ["ID", "image", "ground truth", "prediction"]
+ columns = ["ID", "image", "ground truth", "prediction", "dice", "dice_bin"]
rowss = [d["table_rows"] for d in validation_outputs]
rows = list(itertools.chain.from_iterable(rowss))
diff --git a/src/utils/paste.py b/src/utils/paste.py
index a1e24e4..a25289b 100644
--- a/src/utils/paste.py
+++ b/src/utils/paste.py
@@ -1,5 +1,6 @@
import os
import random as rd
+from pathlib import Path
import albumentations as A
import numpy as np
@@ -22,15 +23,15 @@ class RandomPaste(A.DualTransform):
def __init__(
self,
nb,
- path_paste_img_dir,
- path_paste_mask_dir,
+ image_dir,
scale_range=(0.1, 0.2),
always_apply=True,
p=1.0,
):
super().__init__(always_apply, p)
- self.path_paste_img_dir = path_paste_img_dir
- self.path_paste_mask_dir = path_paste_mask_dir
+ self.images = []
+ self.images.extend(list(Path(image_dir).glob("**/*.jpg")))
+ self.images.extend(list(Path(image_dir).glob("**/*.png")))
self.scale_range = scale_range
self.nb = nb
@@ -69,14 +70,15 @@ class RandomPaste(A.DualTransform):
return False
def get_params_dependent_on_targets(self, params):
- # choose a random image inside the image folder
- filename = rd.choice(os.listdir(self.path_paste_img_dir))
+ # choose a random image and its corresponding mask
+ img_path = rd.choice(self.images)
+ mask_path = img_path.parent.joinpath("MASK.PNG")
# load the "paste" image
paste_img = Image.open(
os.path.join(
self.path_paste_img_dir,
- filename,
+ img_path,
)
).convert("RGBA")
@@ -84,25 +86,23 @@ class RandomPaste(A.DualTransform):
paste_mask = Image.open(
os.path.join(
self.path_paste_mask_dir,
- filename,
+ mask_path,
)
).convert("LA")
# load the target image
target_img = params["image"]
+
+ # compute shapes, for easier computations
target_shape = np.array(target_img.shape[:2], dtype=np.uint)
paste_shape = np.array(paste_img.size, dtype=np.uint)
- # change paste_img's brightness randomly
- filter = ImageEnhance.Brightness(paste_img)
- paste_img = filter.enhance(rd.uniform(0.5, 1.5))
-
# change paste_img's contrast randomly
filter = ImageEnhance.Contrast(paste_img)
paste_img = filter.enhance(rd.uniform(0.5, 1.5))
- # change paste_img's sharpness randomly
- filter = ImageEnhance.Sharpness(paste_img)
+ # change paste_img's brightness randomly
+ filter = ImageEnhance.Brightness(paste_img)
paste_img = filter.enhance(rd.uniform(0.5, 1.5))
# compute the minimum scaling to fit inside target image