chore: various files from 3 weeks ago

Former-commit-id: 502f9821bddd45574a46cdfe017524a6fac9f102 [formerly 5e2f3679b65b8ae554e1cdd3e1c13ae190e07bd0]
Former-commit-id: 6337e42ef9b051064a9cb2716ef37b272b09673e

.devcontainer.tmp/.env

@@ -0,0 +1,3 @@
+WANDB_VOLUME_PATH=/media/disk2/lfainsin/wandb-local/
+WANDB_BASE_URL=http://wandb:8080
+WANDB_API_KEY=XXXX

.devcontainer.tmp/Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3
+
+WORKDIR /workspace
+
+RUN pip install --upgrade pip --no-input
+RUN curl -sSL https://install.python-poetry.org | python3 -
+
+RUN echo 'export PATH="$PATH:/root/.local/bin"' >> /root/.bashrc
+RUN echo '/root/.local/bin/poetry install --no-interaction' >> /root/.bashrc
+RUN echo '/root/.local/bin/poetry shell' >> /root/.bashrc
+
+SHELL ["/bin/bash", "-ec"]

.devcontainer.tmp/devcontainer.json

@@ -0,0 +1,6 @@
+{
+  "name": "devcontainer-example",
+  "dockerComposeFile": "docker-compose.yml",
+  "service": "development",
+  "workspaceFolder": "/workspace"
+}

.devcontainer.tmp/docker-compose.yml

@@ -0,0 +1,24 @@
+version: "3"
+
+services:
+
+  # development container
+  development:
+    container_name: dev
+    hostname: dev
+    build:
+      context: .
+      dockerfile: Dockerfile
+    volumes:
+      - ..:/workspace
+    stdin_open: true
+
+  # wandb dashboard
+  wandb:
+    image: wandb/local
+    container_name: wandb-local
+    hostname: wandb-local
+    volumes:
+      - /media/disk2/lfainsin/wandb-local/:/vol
+    ports:
+      - 8080:8080

.vscode/settings.json

@@ -5,6 +5,7 @@
   "python.linting.lintOnSave": true,
   // "python.linting.flake8Enabled": true,
   // "python.linting.banditEnabled": true,
+  "python.defaultInterpreterPath": ".venv/bin/python",
   "[python]": {
     "editor.codeActionsOnSave": {
       "source.organizeImports": true

pyproject.toml

@@ -14,11 +14,10 @@ python = ">=3.8,<3.11"
 pytorch-lightning = "^1.6.4"
 rich = "^12.4.4"
 scipy = "^1.8.1"
-torch = "^1.11.0"
+torch = "1.11.0"
 torchvision = "^0.12.0"
 tqdm = "^4.64.0"
 wandb = "^0.12.19"
-Pillow-SIMD = "^9.0.0"
 
 [tool.poetry.dev-dependencies]
 black = "^22.3.0"

src/notebooks/convert.ipynb

@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/laurent_fainsin/Documents/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"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "from unet.model import UNet\n"
@@ -12,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -34,54 +43,41 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "net.load_state_dict(\n",
-    "    torch.load(\"../../checkpoints/best.pth\")\n",
+    "    torch.load(\"../../checkpoints/best.pth\", map_location=torch.device('cpu'))\n",
     ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n"
-     ]
-    }
-   ],
    "source": [
-    "dummy_input = torch.randn(1, 3, 1024, 1024, requires_grad=True)\n",
+    "dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True)\n",
     "torch.onnx.export(\n",
     "    net,\n",
     "    dummy_input,\n",
-    "    \"../../checkpoints/best.onnx\",\n",
+    "    \"../../checkpoints/best-fixed.onnx\",\n",
-    "    opset_version=14,\n",
+    "    opset_version=10,\n",
-    "    input_names=[\"input\"],\n",
+    "    # input_names=[\"input\"],\n",
-    "    output_names=[\"output\"],\n",
+    "    # output_names=[\"output\"],\n",
-    "    dynamic_axes={\n",
+    "    # dynamic_axes={\n",
-    "        \"input\": {\n",
+    "    #     \"input\": {\n",
-    "            2: \"height\",\n",
+    "    #         2: \"height\",\n",
-    "            3: \"width\",\n",
+    "    #         3: \"width\",\n",
-    "        },\n",
+    "    #     },\n",
-    "        \"output\": {\n",
+    "    #     \"output\": {\n",
-    "            2: \"height\",\n",
+    "    #         2: \"height\",\n",
-    "            3: \"width\",\n",
+    "    #         3: \"width\",\n",
-    "        },\n",
+    "    #     },\n",
-    "    },\n",
+    "    # },\n",
     ")\n"
    ]
   }

src/notebooks/predict.ipynb.REMOVED.git-id

@@ -1 +1 @@
-dde3ef9f5b63d6bac80a9cf7e4409a17c831d771
+559ebbcd9a27a78eac0984fea6717b20b5084ae0

src/notebooks/prerender.ipynb

@@ -2,35 +2,27 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/laurent_fainsin/Documents/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",
-      "/home/laurent_fainsin/Documents/unet/.venv/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /home/laurent_fainsin/Documents/unet/.venv/lib/python3.8/site-packages/torchvision/image.so: undefined symbol: _ZNK3c1010TensorImpl36is_contiguous_nondefault_policy_implENS_12MemoryFormatE\n",
-      "  warn(f\"Failed to load image Python extension: {e}\")\n"
-     ]
-    }
-   ],
    "source": [
     "from PIL import Image\n",
     "\n",
-    "import numpy as np\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"
+    "from pathlib import Path\n",
+    "\n",
+    "from joblib import Parallel, delayed\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,39 +31,41 @@
     "        A.Resize(512, 512),\n",
     "        A.Flip(),\n",
     "        A.ColorJitter(),\n",
-    "        RandomPaste(5, \"/home/lilian/data_disk/lfainsin/spheres/\"),\n",
+    "        RandomPaste(5, \"/media/disk1/lfainsin/SPHERES/\"),\n",
     "        A.GaussianBlur(),\n",
     "        A.ISONoise(),\n",
     "    ],\n",
     ")\n",
     "\n",
-    "dataset = SyntheticDataset(image_dir=\"/home/lilian/data_disk/lfainsin/train/\", transform=transform)\n"
+    "dataset = SyntheticDataset(image_dir=\"/media/disk1/lfainsin/BACKGROUND/\", transform=transform)\n",
+    "transform = T.ToPILImage()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10001\r"
-     ]
-    }
-   ],
    "source": [
-    "for i, (image, mask) in enumerate(dataset):\n",
+    "def render(i, image, mask):\n",
-    "    path = f\"/home/lilian/data_disk/lfainsin/prerender/{i}/\"\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",
-    "    Image.fromarray(image).save(f\"{path}/image.jpg\")\n",
-    "    Image.fromarray(mask*255).save(f\"{path}/MASK.PNG\")\n",
     "    \n",
-    "    print(i, end=\"\\r\")\n",
+    "    image.save(f\"{path}/image.jpg\")\n",
-    "\n",
+    "    mask.save(f\"{path}/MASK.PNG\")\n"
-    "    if i > 10000:\n",
+   ]
-    "        break"
+  },
+  {
+   "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"
    ]
   }
  ],