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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dd07a8e6-5809-4bb7-ba3a-bd6c15b22ff2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/user/conda/envs/senv/lib/python3.9/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 random\n",
"from statistics import mean\n",
"from datetime import datetime\n",
"from typing import List, Tuple\n",
"import copy\n",
"\n",
"import torch as th\n",
"import pytorch_lightning as pl\n",
"from pytorch_lightning.callbacks import ModelCheckpoint\n",
"from jaxtyping import Float, Float16, Int\n",
"\n",
"import trimesh as tm\n",
"import numpy as np\n",
"import numba\n",
"\n",
"from torch_geometric.nn.conv import GATv2Conv\n",
"\n",
"import h5py\n",
"\n",
"# Clone SAP from original repo https://github.com/autonomousvision/shape_as_points.git\n",
"from SAP.dpsr import DPSR\n",
"from SAP.model import PSR2Mesh"
]
},
{
"cell_type": "markdown",
"id": "59c87491-5650-4c59-8d33-5153d29fb1a9",
"metadata": {
"tags": []
},
"source": [
"# Constants"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "26d62fb9-dae9-406b-ba30-3fec1a43a29a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"th.manual_seed(0)\n",
"np.random.seed(0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ab9502f-e822-4475-9c90-019ff28f12d0",
"metadata": {},
"outputs": [],
"source": [
"IS_DEBUG = True"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7095231b-e8ed-4c4d-997f-8f58664e9877",
"metadata": {},
"outputs": [],
"source": [
"BATCH_SIZE = 1 # BS\n",
"LR = 0.001\n",
"\n",
"IN_DIM = 1 \n",
"OUT_DIM = 1\n",
"LATENT_DIM = 32\n",
"\n",
"DROPOUT_PROB = 0.1\n",
"\n",
"PADDING = 1.2 # Scaling\n",
"\n",
"GRID_SIZE = 128\n",
"SIGMA = 5.0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "27b7a406-cbb0-4a36-be1e-a8d8aa82c702",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"DATASET = \"Synthetic\"\n",
"LOG_IDX = 14\n",
"LOG_VISUALS = not IS_DEBUG\n",
"\n",
"CHECKPOINTS_PATH = \"./checkpoints/\"\n",
"\n",
"FIELDS_H5_PATH = f\"./Standart_fields/{DATASET}_fields_32_512.h5\"\n",
"PATH_ORIG_H5 = f\"./Standart_h5/{DATASET}.h5\"\n",
"PATH_NOISY_H5 = f\"./Standart_h5/{DATASET}_noisy.h5\"\n",
"MIN_V_NUMBER = 1_000\n",
"MAX_V_NUMBER = 100_000"
]
},
{
"cell_type": "markdown",
"id": "1690b667-0af4-465a-8e3c-4a29622e9e66",
"metadata": {
"tags": []
},
"source": [
"# Data Preparation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2e774809-1293-4f80-8350-59ae7fc86cbb",
"metadata": {},
"outputs": [],
"source": [
"@numba.njit\n",
"def generate_grid_edge_list(gs: int = 128):\n",
" grid_edge_list = []\n",
"\n",
" for k in range(gs):\n",
" for j in range(gs):\n",
" for i in range(gs):\n",
" current_idx = i + gs*j + k*gs*gs\n",
" if (i - 1) >= 0:\n",
" grid_edge_list.append([current_idx, i-1 + gs*j + k*gs*gs])\n",
" if (i + 1) < gs:\n",
" grid_edge_list.append([current_idx, i+1 + gs*j + k*gs*gs])\n",
" if (j - 1) >= 0:\n",
" grid_edge_list.append([current_idx, i + gs*(j-1) + k*gs*gs])\n",
" if (j + 1) < gs:\n",
" grid_edge_list.append([current_idx, i + gs*(j+1) + k*gs*gs])\n",
" if (k - 1) >= 0:\n",
" grid_edge_list.append([current_idx, i + gs*j + (k-1)*gs*gs])\n",
" if (k + 1) < gs:\n",
" grid_edge_list.append([current_idx, i + gs*j + (k+1)*gs*gs])\n",
" return grid_edge_list\n",
"\n",
"GRID_EDGE_LIST = None"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4486968b-3416-41c5-9ecd-429f7cf193de",
"metadata": {},
"outputs": [],
"source": [
"class StandartH5DataSet(th.utils.data.Dataset):\n",
" \n",
" def _load_data(self, key: str):\n",
" key_orig = key.replace(\"_n1\", \"\")\n",
" key_orig = key_orig.replace(\"_n2\", \"\")\n",
" key_orig = key_orig.replace(\"_n3\", \"\")\n",
" key_orig = key_orig.replace(\"_noisy\", \"\")\n",
"\n",
" vertices = th.tensor(self._noisy_meshes_h5[key][\"vertices\"][:], dtype=th.float)\n",
" vertices_normals = th.tensor(self._noisy_meshes_h5[key][\"vertices_normals\"][:], dtype=th.float)\n",
" vertices_gt = th.tensor(self._orig_meshes_h5[key_orig][\"vertices\"][:], dtype=th.float)\n",
" vertices_normals_gt = th.tensor(self._orig_meshes_h5[key_orig][\"vertices_normals\"][:], dtype=th.float)\n",
" field_gt = self.dpsr(vertices_gt.unsqueeze(0), vertices_normals_gt.unsqueeze(0)).squeeze(0)\n",
"\n",
" adj = np.array(self._noisy_meshes_h5[key][\"edge_index\"][:], dtype=np.int64)\n",
" adj = th.tensor(adj, dtype=th.int64)\n",
" \n",
" return vertices, vertices_normals, vertices_gt, vertices_normals_gt, field_gt, adj\n",
" \n",
" def __init__(self, \n",
" orig_meshes_h5: h5py.Group,\n",
" noisy_meshes_h5: h5py.Group,\n",
" fields_grid_size: int,\n",
" min_verts: int,\n",
" max_verts: int) -> None:\n",
" super().__init__()\n",
" \n",
" self.dpsr = DPSR([GRID_SIZE, GRID_SIZE, GRID_SIZE], sig=SIGMA)\n",
" \n",
" self._orig_meshes_h5 = orig_meshes_h5\n",
" self._noisy_meshes_h5 = noisy_meshes_h5\n",
" \n",
" self._fields_grid_size = str(fields_grid_size)\n",
" self._min_verts = min_verts\n",
" self._max_verts = max_verts\n",
" \n",
" self._data = {}\n",
" self._keys = []\n",
" \n",
" # filter keys to load only meshes with requested amount of vertices\n",
" for key in self._noisy_meshes_h5.keys():\n",
" v_number = self._noisy_meshes_h5[key][\"vertices\"].shape[0]\n",
" if (v_number >= self._min_verts) and (v_number <= self._max_verts):\n",
" self._keys.append(key)\n",
" self._keys = np.array(self._keys, dtype=str)\n",
" self._loaded = np.full(shape=self._keys.shape, fill_value=False, dtype=bool)\n",
" \n",
" def __len__(self) -> int:\n",
" return self._keys.shape[0]\n",
" \n",
" def __getitem__(self, index: int) -> Tuple[Float[th.Tensor, \"N 3\"],\n",
" Float[th.Tensor, \"N 3\"],\n",
" Float[th.Tensor, \"N 3\"],\n",
" Float[th.Tensor, \"N 3\"],\n",
" Float[th.Tensor, \"GR GR GR\"],\n",
" Float[th.Tensor, \"2 E\"]]:\n",
" if self._loaded[index] == False:\n",
" data = self._load_data(self._keys[index])\n",
" self._data[index] = data\n",
" self._loaded[index] = True\n",
" return copy.deepcopy(self._data[index])\n",
" \n",
" @property\n",
" def fields_grid_size(self):\n",
" return int(self._fields_grid_size)\n",
" \n",
" def renew_grid_size(self, new_grid_size: int):\n",
" self._fields_grid_size = str(new_grid_size)\n",
" self._loaded = np.full(shape=self._keys.shape, fill_value=False, dtype=bool)"
]
},
{
"cell_type": "markdown",
"id": "13c69a49-5107-4d3e-9b14-1d456768f128",
"metadata": {
"tags": []
},
"source": [
"# Model"
]
},
{
"cell_type": "markdown",
"id": "1d9a9aac-d229-489a-844d-a1d1cbd34c56",
"metadata": {
"tags": []
},
"source": [
"### Form Optimizer "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "940babdc-3e4f-4310-8bfd-48b23d0758dc",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"class FormOptimizer(th.nn.Module):\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
" \n",
" layers = []\n",
" \n",
" self.gconv1 = GATv2Conv(in_channels=IN_DIM, out_channels=LATENT_DIM, heads=1, dropout=DROPOUT_PROB)\n",
" self.gconv2 = GATv2Conv(in_channels=LATENT_DIM, out_channels=LATENT_DIM, heads=1, dropout=DROPOUT_PROB)\n",
" \n",
" self.actv = th.nn.Sigmoid()\n",
" self.head = th.nn.Linear(in_features=LATENT_DIM, out_features=OUT_DIM)\n",
"\n",
" def forward(self, \n",
" field: Float[th.Tensor, \"GS GS GS\"]) -> Float[th.Tensor, \"GS GS GS\"]:\n",
" \"\"\"\n",
" Args:\n",
" field (Tensor [GS, GS, GS]): vertices and normals tensor.\n",
" \"\"\"\n",
" vertex_features = field.clone()\n",
" vertex_features = vertex_features.reshape(GRID_SIZE*GRID_SIZE*GRID_SIZE, IN_DIM)\n",
" \n",
" vertex_features = self.gconv1(x=vertex_features, edge_index=GRID_EDGE_LIST) \n",
" vertex_features = self.gconv2(x=vertex_features, edge_index=GRID_EDGE_LIST) \n",
" field_delta = self.head(self.actv(vertex_features))\n",
" \n",
" field_delta = field_delta.reshape(BATCH_SIZE, GRID_SIZE, GRID_SIZE, GRID_SIZE)\n",
" field_delta += field \n",
" field_delta = th.clamp(field_delta, min=-0.5, max=0.5)\n",
" \n",
" return field_delta"
]
},
{
"cell_type": "markdown",
"id": "67b40c5b-ff1b-416d-b892-c544386eaa95",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"### Full"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bce3aa63-9bd7-4ac8-939d-395d63dd3cad",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"class Model(pl.LightningModule):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.form_optimizer = FormOptimizer()\n",
" \n",
" self.dpsr = DPSR([GRID_SIZE, GRID_SIZE, GRID_SIZE], sig=SIGMA)\n",
" self.field2mesh = PSR2Mesh().apply\n",
"\n",
" self.metric = th.nn.MSELoss()\n",
"\n",
" #video logging databases\n",
" dateTimeObj = datetime.now()\n",
" start_time = dateTimeObj.strftime(\"%d-%b-%Y_%H-%M-%S\")\n",
" \n",
" if LOG_VISUALS:\n",
" self.h5_frame = 0\n",
" self.log_points_file = h5py.File(f\"./logs/points_{start_time}\", \"w\")\n",
" self.log_normals_file = h5py.File(f\"./logs/normals_{start_time}\", \"w\")\n",
" \n",
" self.val_losses = []\n",
" self.train_losses = []\n",
"\n",
" def log_h5(self, points, normals):\n",
" dset = self.log_points_file.create_dataset(\n",
" name=str(self.h5_frame),\n",
" shape=points.shape,\n",
" dtype=np.float16, \n",
" compression=\"gzip\")\n",
" dset[:] = points\n",
" dset = self.log_normals_file.create_dataset(\n",
" name=str(self.h5_frame),\n",
" shape=normals.shape,\n",
" dtype=np.float16, \n",
" compression=\"gzip\")\n",
" dset[:] = normals\n",
" self.h5_frame += 1\n",
" \n",
" def forward(self, \n",
" v: Float[th.Tensor, \"BS N 3\"],\n",
" n: Float[th.Tensor, \"BS N 3\"]) -> Tuple[Float[th.Tensor, \"BS N 3\"], # v - vertices\n",
" Int[th.Tensor, \"2 E\"], # f - faces\n",
" Float[th.Tensor, \"BS N 3\"], # n - vertices normals\n",
" Float[th.Tensor, \"BS GR GR GR\"]]: # field: \n",
" field = self.dpsr(v, n)\n",
" field = self.form_optimizer(field)\n",
" v, f, n = self.field2mesh(field)\n",
" return v, f, n, field\n",
"\n",
" def training_step(self, batch, batch_idx) -> Float[th.Tensor, \"1\"]:\n",
" vertices, vertices_normals, vertices_gt, vertices_normals_gt, field_gt, adj = batch\n",
" \n",
" mask = th.rand((vertices.shape[1], ), device=th.device(\"cuda\")) < (random.random() / 2.0 + 0.5)\n",
" vertices = vertices[:, mask]\n",
" vertices_normals = vertices_normals[:, mask]\n",
" \n",
" vr, fr, nr, field_r = model(vertices, vertices_normals)\n",
" \n",
" loss = self.metric(field_r, field_gt)\n",
" if LOG_VISUALS and (LOG_IDX == batch_idx):\n",
" self.log_h5(vr.squeeze(0).detach().cpu().numpy(), nr.squeeze(0).detach().cpu().numpy())\n",
" train_per_step_loss = loss.item()\n",
" self.train_losses.append(train_per_step_loss)\n",
" \n",
" return loss\n",
" \n",
" def on_train_epoch_end(self):\n",
" mean_train_per_epoch_loss = mean(self.train_losses)\n",
" self.log(\"mean_train_per_epoch_loss\", mean_train_per_epoch_loss, on_step=False, on_epoch=True)\n",
" self.train_losses = []\n",
" \n",
" def validation_step(self, batch, batch_idx):\n",
" vertices, vertices_normals, vertices_gt, vertices_normals_gt, field_gt, adj = batch\n",
" \n",
" vr, fr, nr, field_r = model(vertices, vertices_normals)\n",
" \n",
" loss = self.metric(field_r, field_gt)\n",
" val_per_step_loss = loss.item()\n",
" self.val_losses.append(val_per_step_loss)\n",
" return loss\n",
" \n",
" def on_validation_epoch_end(self):\n",
" mean_val_per_epoch_loss = mean(self.val_losses)\n",
" self.log(\"mean_val_per_epoch_loss\", mean_val_per_epoch_loss, on_step=False, on_epoch=True)\n",
" self.val_losses = []\n",
"\n",
" def configure_optimizers(self):\n",
" optimizer = th.optim.Adam(self.parameters(), lr=LR)\n",
" scheduler = th.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5)\n",
" \n",
" return {\n",
" \"optimizer\": optimizer,\n",
" \"lr_scheduler\": {\n",
" \"scheduler\": scheduler, \n",
" \"monitor\": \"mean_val_per_epoch_loss\",\n",
" \"interval\": \"epoch\",\n",
" \"frequency\": 1,\n",
" \"strict\": True,\n",
" \"name\": None,\n",
" }\n",
" }\n"
]
},
{
"cell_type": "markdown",
"id": "1fb2c5a5-43ee-4a4e-be08-0dcfcb6816de",
"metadata": {
"tags": []
},
"source": [
"# Loop"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c94c6a68-3986-48af-9da5-cab8c02a8b7b",
"metadata": {},
"outputs": [],
"source": [
"checkpoint_callback = ModelCheckpoint(\n",
" monitor='mean_val_per_epoch_loss', # monitor the validation loss\n",
" mode='min', # mode 'min' to save the lowest monitored value\n",
" save_top_k=1, # save only the best checkpoint (top 1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "03cdddbc-223e-4d40-9fb0-e663beddefda",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/user/conda/envs/senv/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/TensorShape.cpp:3190.)\n",
" return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n",
"/home/user/conda/envs/senv/lib/python3.9/site-packages/lightning_fabric/connector.py:554: UserWarning: 16 is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n",
" rank_zero_warn(\n",
"Using 16bit Automatic Mixed Precision (AMP)\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"Running in `fast_dev_run` mode: will run the requested loop using 300 batch(es). Logging and checkpointing is suppressed.\n",
"You are using a CUDA device ('A100-PCIE-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
"/home/user/conda/envs/senv/lib/python3.9/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:617: UserWarning: Checkpoint directory /home/jovyan/Mashurov/GINSAP/checkpoints exists and is not empty.\n",
" rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"-------------------------------------------------\n",
"0 | form_optimizer | FormOptimizer | 2.4 K \n",
"1 | dpsr | DPSR | 0 \n",
"2 | metric | MSELoss | 0 \n",
"-------------------------------------------------\n",
"2.4 K Trainable params\n",
"0 Non-trainable params\n",
"2.4 K Total params\n",
"0.010 Total estimated model params size (MB)\n",
"/home/user/conda/envs/senv/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" rank_zero_warn(\n",
"/home/user/conda/envs/senv/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" rank_zero_warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0: 100%|██████████| 60/60 [00:17<00:00, 3.52it/s]\n",
"Validation: 0it [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/84 [00:00<?, ?it/s]\u001b[A\n",
"Validation DataLoader 0: 0%| | 0/84 [00:00<?, ?it/s]\u001b[A\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=1` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0: 100%|██████████| 60/60 [00:30<00:00, 1.94it/s]\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" \n",
" GRID_EDGE_LIST = generate_grid_edge_list(GRID_SIZE)\n",
" GRID_EDGE_LIST = th.tensor(GRID_EDGE_LIST, dtype=th.int)\n",
" GRID_EDGE_LIST = GRID_EDGE_LIST.T\n",
" GRID_EDGE_LIST = GRID_EDGE_LIST.to(th.device(\"cuda\"))\n",
" \n",
" noisy_meshes_h5 = h5py.File(\"./Standart_h5/Synthetic_noisy.h5\", \"r\")\n",
" orig_meshes_h5 = h5py.File(\"./Standart_h5/Synthetic.h5\", \"r\")\n",
" \n",
" train_dataset = StandartH5DataSet(orig_meshes_h5=orig_meshes_h5['train'],\n",
" noisy_meshes_h5=noisy_meshes_h5['train'],\n",
" fields_grid_size=GRID_SIZE,\n",
" min_verts=MIN_V_NUMBER,\n",
" max_verts=MAX_V_NUMBER)\n",
" test_dataset = StandartH5DataSet(orig_meshes_h5=orig_meshes_h5['test'],\n",
" noisy_meshes_h5=noisy_meshes_h5['test'],\n",
" fields_grid_size=GRID_SIZE,\n",
" min_verts=MIN_V_NUMBER,\n",
" max_verts=MAX_V_NUMBER)\n",
"\n",
" train_dataloader = th.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
" test_dataloader = th.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
"\n",
" trainer = pl.Trainer(accelerator=\"gpu\", \n",
" callbacks=[checkpoint_callback],\n",
" log_every_n_steps=len(train_dataset)+len(test_dataset),\n",
" fast_dev_run=(300 if IS_DEBUG else False),\n",
" max_epochs=200,\n",
" precision=16)\n",
" \n",
" model = Model()\n",
" trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=test_dataloader)\n",
" if LOG_VISUALS:\n",
" model.log_points_file.close()\n",
" model.log_normals_file.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bda6c1bf-7674-4e59-8cc7-dfcba9d689d9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "senv",
"language": "python",
"name": "senv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|