diff --git a/artifacts/deepsdf_2d.pth b/artifacts/deepsdf_2d.pth new file mode 100644 index 0000000..32d21d1 Binary files /dev/null and b/artifacts/deepsdf_2d.pth differ diff --git a/artifacts/deepsdf_2d_unit_init.pth b/artifacts/deepsdf_2d_unit_init.pth new file mode 100644 index 0000000..2f26eb9 Binary files /dev/null and b/artifacts/deepsdf_2d_unit_init.pth differ diff --git a/artifacts/experiment_20250717_115709/data_processing_results.txt b/artifacts/experiment_20250717_115709/data_processing_results.txt deleted file mode 100644 index 59da04e..0000000 --- a/artifacts/experiment_20250717_115709/data_processing_results.txt +++ /dev/null @@ -1,48 +0,0 @@ -Artifact: data_processing_results -Generated: 2025-07-17 11:57:10 -Experiment: 20250717_115709 -================================================== - -{ - "processed_files": [ - "bimba", - "bunny", - "torus" - ], - "train_files": [ - "bimba", - "bunny" - ], - "val_files": [ - "torus" - ], - "point_cloud_files": { - "train": [ - 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c80a183..0000000 --- a/artifacts/experiment_20250717_115709/model_architecture.txt +++ /dev/null @@ -1,10 +0,0 @@ -Artifact: model_architecture -Generated: 2025-07-17 11:57:11 -Experiment: 20250717_115709 -================================================== - -{ - "model_class": "SimpleMLP", - "total_parameters": 8489736, - "trainable_parameters": 8489736 -} \ No newline at end of file diff --git a/artifacts/experiment_20250717_115709/pipeline_summary.txt b/artifacts/experiment_20250717_115709/pipeline_summary.txt deleted file mode 100644 index 134f68b..0000000 --- a/artifacts/experiment_20250717_115709/pipeline_summary.txt +++ /dev/null @@ -1,20 +0,0 @@ -Artifact: pipeline_summary -Generated: 2025-07-17 11:57:14 -Experiment: 20250717_115709 -================================================== - -{ - "pipeline_completed": true, - "steps_completed": [ - "data_processing", - "model_building", - "training", - "skipped" - ], - "artifacts_generated": [ - "processing_report", - "model", - "trained_model", - "training_results" - ] -} \ No newline at end of file diff --git a/artifacts/experiment_20250717_115709/trained_model_20250717_115714_val0.003175.pt b/artifacts/experiment_20250717_115709/trained_model_20250717_115714_val0.003175.pt deleted file mode 100644 index dd2e8d8..0000000 Binary files a/artifacts/experiment_20250717_115709/trained_model_20250717_115714_val0.003175.pt and /dev/null differ diff --git a/artifacts/experiment_20250717_115709/trained_model_20250717_115714_val0.003175_metadata.txt b/artifacts/experiment_20250717_115709/trained_model_20250717_115714_val0.003175_metadata.txt deleted file mode 100644 index 759ee38..0000000 --- a/artifacts/experiment_20250717_115709/trained_model_20250717_115714_val0.003175_metadata.txt +++ /dev/null @@ -1,11 +0,0 @@ -Artifact: trained_model_20250717_115714_val0.003175_metadata -Generated: 2025-07-17 11:57:14 -Experiment: 20250717_115709 -================================================== - -{ - "best_val_loss": 0.003175197634845972, - "total_epochs": 20, - "final_train_loss": 0.0018756706267595291, - "final_val_loss": 0.003384882351383567 -} \ No newline at end of file diff --git a/artifacts/experiment_20250717_115709/training_results.txt b/artifacts/experiment_20250717_115709/training_results.txt deleted file mode 100644 index f90dd2d..0000000 --- a/artifacts/experiment_20250717_115709/training_results.txt +++ /dev/null @@ -1,138 +0,0 @@ -Artifact: training_results -Generated: 2025-07-17 11:57:14 -Experiment: 20250717_115709 -================================================== - -{ - "total_epochs": 20, - "best_val_loss": 0.003175197634845972, - "final_train_loss": 0.0018756706267595291, - "final_val_loss": 0.003384882351383567, - "total_training_time": 0.809342622756958, - "avg_epoch_time": 0.0404671311378479, - "training_history": [ - { - "epoch": 1, - "train_loss": 0.00845915824174881, - "val_loss": 0.006244876887649298, - "epoch_time": 0.19067645072937012 - }, - { - "epoch": 2, - 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index 1198607..3084715 100644 --- a/config/config_examples.yaml +++ b/config/config_examples.yaml @@ -1,4 +1,4 @@ -home: &home_path /Users/marina.levay/Documents/GitHub/topology-control # all other paths are relative to home +home: &home_path /homes/dnogina/code/topology-control # all other paths are relative to home artifacts_config: save_artifacts_to: artifacts @@ -9,7 +9,7 @@ processor_config: raw: &raw_data_path data/raw processed: &processed_data_path data/processed - train_val_split: 0.8 + train_val_split: .99 # Updated point cloud params to match your notebook point_cloud_params: @@ -17,7 +17,7 @@ processor_config: sigma: 0.02 # Match your notebook's sigma = radius mu: 0.0 n_gaussian: 5 # N = 10 from your notebook - n_uniform: 10000 # NUM_SAMPLES = 10000 from your notebook + n_uniform: 50000 # NUM_SAMPLES = 10000 from your notebook # Volume processor params for SDF generation volume_processor_params: @@ -26,28 +26,29 @@ processor_config: model_config: skip_building: false - model_name: deepsdf + model_name: deepsdf #lipschitz_deepsdf #deepsdf # DeepSDF specific parameters matching your notebook - z_dim: 16 # Z_DIM = 16 from your notebook - layer_size: 64 # HIDDEN_DIM = 64 from your notebook + z_dim: 1 #16 # Z_DIM = 16 from your notebook + layer_size: 256 # HIDDEN_DIM = 64 from your notebook coord_dim: 3 # 3D coordinates dropout_p: 0.2 # Match your notebook's dropout_p = 0.2 trainer_config: skip_training: false processed_data_path: *processed_data_path - dataset_type: sdf # Important: specify SDF dataset type + dataset_type: sdf #lipschitz_sdf #sdf + lipschitz_lambda: 0.00001 loss_function: deepsdf # Use custom DeepSDF loss optimizer: adam network_learning_rate: 0.001 # lr=1e-3 from your notebook - latent_learning_rate: 0.01 + latent_learning_rate: 0 #0.01 batch_size: 8 # BATCH_SIZE = 16 from your notebook num_epochs: 1000 # max_epochs=20 from your notebook - max_points: 15000 # Allow more points for better SDF learning + max_points: 300000 # Allow more points for better SDF learning # DeepSDF specific training parameters sdf_delta: 1.0 # DELTA = 1.0 from your notebook - latent_sd: 0.01 # LATENT_SD = 0.01 from your notebook + latent_sd: .01 latent_mean: 0.0 # LATENT_MEAN = 0 from your notebook # SDF Dataset configuration - ensure dataset_info is available @@ -61,10 +62,10 @@ trainer_config: # Model saving save_model: true save_best_only: true - save_frequency: 5 + save_frequency: 20 evaluator_config: - skip_evaluation: false + skip_evaluation: true # Evaluation specific configs for mesh extraction batch_size: 8 resolution: 50 # Match training resolution for mesh extraction diff --git a/data/_processed/train/bimba_sampled_points.npy b/data/_processed/train/bimba_sampled_points.npy new file mode 100644 index 0000000..a2882da Binary files /dev/null and b/data/_processed/train/bimba_sampled_points.npy differ diff --git a/data/_processed/train/bimba_signed_distances.npy b/data/_processed/train/bimba_signed_distances.npy new file mode 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10082 10100 10099 +f 10081 10103 10100 +f 8451 8474 10103 +f 8474 8479 10101 +f 8479 8478 10102 +f 8478 5099 10026 +f 10102 10026 10025 +f 10098 10025 10024 +f 10106 10107 10104 +f 10109 10106 10105 +f 10110 10111 10107 +f 10112 10110 10106 +f 10104 7830 1946 +f 10105 10104 1944 +f 10108 10105 1943 +f 1862 10108 1942 +f 1861 10109 10108 +f 1860 10112 10109 +f 1859 1903 10112 +f 1903 1902 10110 +f 1902 1900 10111 +f 1900 1899 7824 +f 10111 7824 7831 +f 10107 7831 7830 + diff --git a/deepSDF_interpolation.ipynb b/deepSDF_interpolation.ipynb new file mode 100644 index 0000000..334a8b5 --- /dev/null +++ b/deepSDF_interpolation.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "354adf35-3236-4d2f-824f-e639c81af77b", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "import numpy as np\n", + "import skimage\n", + "\n", + "os.chdir('/homes/dnogina/code/topology-control/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57e2043e-91bc-49fc-81d7-8c5fc039c96a", + "metadata": {}, + "outputs": [], + "source": [ + "from src.CPipelineOrchestrator import CPipelineOrchestrator\n", + "from src.CModelTrainer import SDFDataset\n", + "from src.CEvaluator import CEvaluator\n", + "from src.CGeometryUtils import VolumeProcessor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4384796f-4c03-48f3-8b7e-5878c1755304", + "metadata": {}, + "outputs": [], + "source": [ + "ckpt = torch.load('artifacts/first_working.pth')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf1c8178-38ef-4791-b3da-4e2054c12c44", + "metadata": {}, + "outputs": [], + "source": [ + "orc = CPipelineOrchestrator()\n", + "deepsdf_model = orc.architecture_manager.get_model()\n", + "_ = deepsdf_model.load_state_dict(ckpt['model_state_dict'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de754291-edc2-454b-b8b4-f2712b4c5dec", + "metadata": {}, + "outputs": [], + "source": [ + "processing_results = orc.data_processor.generate_sdf_dataset(\n", + " z_dim=16,\n", + " latent_mean=0,\n", + " latent_sd=0.02\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eec875f-ae91-4397-94e6-a932901bee11", + "metadata": {}, + "outputs": [], + "source": [ + "ds = SDFDataset(processing_results['dataset_info'])\n", + "ds.latent_vectors = ckpt['latent_vectors']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bce09a6-7618-4dff-861d-cc45d6701e29", + "metadata": {}, + "outputs": [], + "source": [ + "ds.latent_vectors.min(), ds.latent_vectors.max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d21148cb-853a-4d6a-8990-c6120c2e3952", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('/homes/dnogina/code/topology-control/volume/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab556de3-6c4e-48fc-b1d4-4e974ce5f916", + "metadata": {}, + "outputs": [], + "source": [ + "from visualize import visualize_latent_vs_genera, construct_from_latent\n", + "from config import COORDS_FIRST" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3143c40-1813-425f-ac98-7e4421a2b1fc", + "metadata": {}, + "outputs": [], + "source": [ + "visualize_latent_vs_genera(path='/homes/dnogina/code/topology-control/volume/data/2d_latents_volumes_deepsdf.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b36d0b09-0c00-4c4d-9cff-4cc93df844ce", + "metadata": {}, + "outputs": [], + "source": [ + "from compute_path_opt import compute_path\n", + "from model import Latent2Genera\n", + "from dataset import GeneraDataset\n", + "\n", + "dataset = GeneraDataset(\n", + " dataset_path=os.path.join( \"data\", \"2d_latents_volumes_deepsdf.npz\")\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a88902c4-883a-4d9b-8bec-e36be7077914", + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint = torch.load(\"checkpoints/latent2genera_deefsdf_best.pt\", map_location=\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69a67ded-5698-4729-affd-7e85634444a0", + "metadata": {}, + "outputs": [], + "source": [ + "model = Latent2Genera(16, num_classes=dataset.num_classes).to(\"cuda\")\n", + "\n", + "# Load the saved state_dict (weights)\n", + "model.load_state_dict(checkpoint[\"model_state_dict\"])\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24557724-1ca7-4b0c-b4a1-7103c779f760", + "metadata": {}, + "outputs": [], + "source": [ + "path = compute_path(ds.latent_vectors[-2].to(\"cuda\"), ds.latent_vectors[-1].to(\"cuda\"), model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f665bf68-2407-48ad-b383-8740ec53a7bb", + "metadata": {}, + "outputs": [], + "source": [ + "path.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46dccf41-6a0c-48c1-a003-a2944bea76ef", + "metadata": {}, + "outputs": [], + "source": [ + "import igl\n", + "from visualize import get_volume_coords #,generate_mesh\n", + "from compute_volume import predict_sdf\n", + "\n", + "def construct_from_latent(model, latent, positioning, index, type):\n", + " \"\"\"\n", + " Reconstruct the mesh from a latent vector using the model\n", + " \n", + " Parameters:\n", + " model: Trained DeepSDF model or SDF_interpolator object\n", + " latent: A latent vector\n", + " positioning: helix (circular) or row\n", + " index: the index used for angle or shift calculation\n", + " Returns:\n", + " vertices, faces\n", + " \"\"\"\n", + " coords, grid_size = get_volume_coords()\n", + "\n", + " # vertices, faces = generate_mesh(latent.to(\"cuda\"), coords[None].to(\"cuda\"), grid_size, model.to(\"cuda\"), type)\n", + "\n", + " if positioning == \"row\":\n", + " vertices[:, 0] += 2 * index\n", + " else:\n", + " angle = (index % 10) * 2 * np.pi / 10 \n", + " vertices[:, 0] += 3 * np.cos(angle)\n", + " vertices[:, 1] += 3 * np.sin(angle)\n", + " vertices[:, 2] += 0.2 * index\n", + " \n", + " return vertices, faces\n", + "\n", + "i = 49\n", + "latent = path[i]\n", + "V, F = construct_from_latent(deepsdf_model, latent, \"helix\", i, COORDS_FIRST)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaf39940-fd82-4532-b419-39f9d3bd2d0a", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import numpy as np\n", + "from trimesh import Trimesh\n", + "\n", + "import plotly.io as pio\n", + "pio.renderers.default='notebook'\n", + "\n", + "x, y, z = -np.array(V).T\n", + "i, j, k = np.array(F).T\n", + "\n", + "fig = go.Figure(data=[\n", + " go.Mesh3d(x=x, y=y, z=z, i=i, j=j, k=k, opacity=1),\n", + " # go.Scatter3d(x=coords[:,0], y=coords[:,1], z=coords[:,2], mode='markers', marker=dict(size=3, color=sdfs),marker_colorscale='Viridis')\n", + "])\n", + "fig.update_layout(template='plotly_white')\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e7c6ed3-64e5-44b3-b331-9b3e7f9f55d9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main-tc.py b/main-tc.py index 24e1f6b..dfde1d5 100644 --- a/main-tc.py +++ b/main-tc.py @@ -52,8 +52,9 @@ N = 10 noise = np.random.normal(mu, sigma, (N, surface_points.shape[0], 3)) - # add the noise to each of thes sampled points - for i in range(N): + # add the noise to each of thes sampled points + from tqdm import tqdm + for i in tqdm(range(N)): new_points = surface_points + noise[i] # concatenate these to the sampled points sampled_points = np.concatenate((sampled_points, new_points), axis=0) diff --git a/scripts/main-tc.py b/scripts/main-tc.py index 24e1f6b..dfde1d5 100644 --- a/scripts/main-tc.py +++ b/scripts/main-tc.py @@ -52,8 +52,9 @@ N = 10 noise = np.random.normal(mu, sigma, (N, surface_points.shape[0], 3)) - # add the noise to each of thes sampled points - for i in range(N): + # add the noise to each of thes sampled points + from tqdm import tqdm + for i in tqdm(range(N)): new_points = surface_points + noise[i] # concatenate these to the sampled points sampled_points = np.concatenate((sampled_points, new_points), axis=0) diff --git a/src/CArchitectureManager.py b/src/CArchitectureManager.py index dc07b04..dbeca92 100644 --- a/src/CArchitectureManager.py +++ b/src/CArchitectureManager.py @@ -1,9 +1,117 @@ import torch +import math # ============================================================================ # Models # ============================================================================ + +class LipschitzLinear(torch.nn.Module): + def __init__(self, in_features, out_features): + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), requires_grad=True)) + self.bias = torch.nn.Parameter(torch.empty((out_features), requires_grad=True)) + self.c = torch.nn.Parameter(torch.empty((1), requires_grad=True)) + self.softplus = torch.nn.Softplus() + self.initialize_parameters() + + def initialize_parameters(self): + stdv = 1. / math.sqrt(self.weight.size(1)) + self.weight.data.uniform_(-stdv, stdv) + if self.bias is not None: + self.bias.data.uniform_(-stdv, stdv) + + W = self.weight.data + W_abs_row_sum = torch.abs(W).sum(1) + self.c.data = W_abs_row_sum.max() + + def get_lipschitz_constant(self): + return self.softplus(self.c) + + def forward(self, input): + lipc = self.softplus(self.c) + scale = lipc / torch.abs(self.weight).sum(1) + scale = torch.clamp(scale, max=1.0) + return torch.nn.functional.linear(input, self.weight * scale.unsqueeze(1), self.bias) + +class LipschitzDeepSDF(torch.nn.Module): + + def __init__(self, config=None): + super(LipschitzDeepSDF, self).__init__() + + if config is None: + config = {} + + self.z_dim = config.get('z_dim', 128) + self.layer_size = config.get('layer_size', 256) + self.coord_dim = config.get('coord_dim', 3) + + self.input_dim = self.z_dim + self.coord_dim + + min_layer_size = max(self.layer_size, self.input_dim + 32) + self.layer_size = min_layer_size + + self.input_layer = self.create_layer_block(self.input_dim, self.layer_size) + self.layer2 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer3 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer4 = self.create_layer_block(self.layer_size, self.layer_size - self.input_dim) + self.layer5 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer6 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer7 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer8 = LipschitzLinear(self.layer_size, 1) # Final layer is also Lipschitz + + def create_layer_block(self, input_size, output_size): + return torch.nn.Sequential( + LipschitzLinear(input_size, output_size), + torch.nn.ReLU(), + ) + + def get_lipschitz_loss(self): + """ + Computes the product of the Lipschitz constants of all LipschitzLinear layers. + This serves as the regularization term to be added to the main loss. + """ + loss_lipc = 1.0 + for module in self.modules(): + if isinstance(module, LipschitzLinear): + loss_lipc = loss_lipc * module.get_lipschitz_constant() + return loss_lipc + + def forward(self, latent_vec, coords): + if latent_vec.dim() != 2: + raise ValueError(f"latent_vec must be 2D [batch_size, z_dim], got shape {latent_vec.shape}") + if coords.dim() != 3: + raise ValueError(f"coords must be 3D [batch_size, num_coords, 3], got shape {coords.shape}") + + batch_size, num_coords, _ = coords.shape + z_dim = latent_vec.shape[1] + + latent_expanded = latent_vec.unsqueeze(1).expand(batch_size, num_coords, z_dim) + x = torch.cat([latent_expanded, coords], dim=-1) + + skip_x = x + + original_shape = x.shape + x = x.view(-1, x.shape[-1]) + skip_x_flat = skip_x.view(-1, skip_x.shape[-1]) + + x = self.input_layer(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.layer5(torch.cat([x, skip_x_flat], dim=-1)) + x = self.layer6(x) + x = self.layer7(x) + x = self.layer8(x) + + x = x.view(original_shape[0], original_shape[1], -1) + x = x.squeeze(-1) + + return x.tanh() + class SimpleMLP(torch.nn.Module): def __init__(self, input_dim=3, hidden_dims=[128, 64, 32], output_dim=1, activation='relu', dropout=0.1): """ @@ -116,13 +224,17 @@ class DeepSDF(torch.nn.Module): def __init__(self, config=None): super(DeepSDF, self).__init__() + # Handle configuration if config is None: config = {} + # Extract parameters with defaults (no hardcoded constants) self.z_dim = config.get('z_dim', 128) # Latent vector dimension self.layer_size = config.get('layer_size', 256) # Hidden layer dimension + self.dropout_p = config.get('dropout_p', 0.2) self.coord_dim = config.get('coord_dim', 3) # 3D coordinates + # Store architecture info self.input_dim = self.z_dim + self.coord_dim self.output_dim = 1 self.task = "signed_distance_prediction" @@ -130,15 +242,18 @@ def __init__(self, config=None): # Build network layers input_dim = self.z_dim + self.coord_dim # latent + coordinates - # Ensure layer_size is large enough for introducing a connectino skip - min_layer_size = max(self.layer_size, input_dim + 32) + # Ensure layer_size is large enough for the skip connection architecture + min_layer_size = max(self.layer_size, input_dim + 32) # Ensure sufficient capacity self.layer_size = min_layer_size self.input_layer = self.create_layer_block(input_dim, self.layer_size) - self.layer2 = self.create_layer_block(self.layer_size, self.layer_size - input_dim) - # Skip connection happens on layer 3 (concatenate with original input) + self.layer2 = self.create_layer_block(self.layer_size, self.layer_size) self.layer3 = self.create_layer_block(self.layer_size, self.layer_size) - self.output_layer = torch.nn.Linear(self.layer_size, 1) + self.layer4 = self.create_layer_block(self.layer_size, self.layer_size - input_dim) + self.layer5 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer6 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer7 = self.create_layer_block(self.layer_size, self.layer_size) + self.layer8 = torch.nn.Linear(self.layer_size, 1) # Initialize weights self._initialize_weights() @@ -154,11 +269,12 @@ def create_layer_block(self, input_size, output_size): return torch.nn.Sequential( torch.nn.Linear(input_size, output_size), torch.nn.ReLU(), + # torch.nn.Dropout(self.dropout_p) ) def forward(self, latent_vec, coords): """ - Forward pass for DeepSDF - 4-layer architecture. + Forward pass for DeepSDF - simplified and robust. Args: latent_vec: Latent vector of shape [batch_size, z_dim] @@ -182,6 +298,7 @@ def forward(self, latent_vec, coords): # Concatenate latent vector and coordinates # [batch_size, z_dim + 3] + coords = coords * 100 x = torch.cat([latent_expanded, coords], dim=-1) # Store for skip connection @@ -193,13 +310,17 @@ def forward(self, latent_vec, coords): x = x.view(-1, x.shape[-1]) skip_x_flat = skip_x.view(-1, skip_x.shape[-1]) - # Forward pass through 4-layer network - x = self.input_layer(x) # [batch*coords, layer_size] - x = self.layer2(x) # [batch*coords, layer_size - input_dim] + # Forward pass through network + x = self.input_layer(x) # [batch*coords, layer_size] + x = self.layer2(x) # [batch*coords, layer_size] + x = self.layer3(x) # [batch*coords, layer_size] + x = self.layer4(x) # [batch*coords, layer_size - input_dim] - # Skip connection and concatenate with original input - x = self.layer3(torch.cat([x, skip_x_flat], dim=-1)) # [batch*coords, layer_size] - x = self.output_layer(x) # [batch*coords, 1] + # Skip connection: concatenate with original input + x = self.layer5(torch.cat([x, skip_x_flat], dim=-1)) # [batch*coords, layer_size] + x = self.layer6(x) # [batch*coords, layer_size] + x = self.layer7(x) # [batch*coords, layer_size] + x = self.layer8(x) # [batch*coords, 1] # Reshape back to original batch structure # [batch*coords, 1] -> [batch_size, num_coords, 1] -> [batch_size, num_coords] @@ -215,7 +336,9 @@ def get_architecture_info(self): trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad) layer_info = [] - for i, layer in enumerate([self.input_layer, self.layer2, self.layer3, self.output_layer]): + for i, layer in enumerate([self.input_layer, self.layer2, self.layer3, + self.layer4, self.layer5, self.layer6, + self.layer7, self.layer8]): if hasattr(layer, '__len__') and len(layer) > 0: # Sequential layer block linear_layer = layer[0] # First layer is Linear @@ -243,6 +366,7 @@ def get_architecture_info(self): 'z_dim': self.z_dim, 'coord_dim': self.coord_dim, 'layer_size': self.layer_size, + 'dropout_p': self.dropout_p, 'total_parameters': total_params, 'trainable_parameters': trainable_params, 'layers': layer_info, @@ -261,6 +385,7 @@ def __init__(self, config): self.architecture_registry = { 'mlp': SimpleMLP, 'deepsdf': DeepSDF, + 'lipschitz_deepsdf': LipschitzDeepSDF, } def get_model(self, device=None): @@ -292,6 +417,12 @@ def get_model(self, device=None): 'layer_size': self.config.get('layer_size', 32), 'coord_dim': self.config.get('coord_dim', 3) } + elif architecture_name == 'lipschitz_deepsdf': + architecture_config = { + 'z_dim': self.config.get('z_dim', 256), + 'layer_size': self.config.get('layer_size', 32), + 'coord_dim': self.config.get('coord_dim', 3) + } # Validate architecture exists if architecture_name not in self.architecture_registry: diff --git a/src/CDataProcessor.py b/src/CDataProcessor.py index 3039166..2a50233 100644 --- a/src/CDataProcessor.py +++ b/src/CDataProcessor.py @@ -217,10 +217,10 @@ def _split_files(self, mesh_paths): # If only one file, put it in training print("Warning: Only one file found, assigning to training set") return mesh_paths, [] - elif len(mesh_paths) == 2: - # If only two files, one in each set - print("Warning: Only two files found, one assigned to each set") - return [mesh_paths[0]], [mesh_paths[1]] + # elif len(mesh_paths) == 2: + # # If only two files, one in each set + # print("Warning: Only two files found, one assigned to each set") + # return [mesh_paths[0]], [mesh_paths[1]] # For multiple files, use the split ratio # Sort paths for reproducible splits @@ -230,11 +230,11 @@ def _split_files(self, mesh_paths): train_count = max(1, int(len(sorted_paths) * self.train_val_split)) # Ensure at least one file in validation if we have more than one file - if train_count >= len(sorted_paths): - train_count = len(sorted_paths) - 1 - - train_files = sorted_paths[:train_count] - val_files = sorted_paths[train_count:] + # if train_count >= len(sorted_paths): + # train_count = len(sorted_paths) - 1 + print("???>>>>>>>>>>>>>>>>>>>>>>>>>>.sorted_paths", sorted_paths) + train_files = sorted_paths #[:train_count] + val_files = [] #sorted_paths[train_count:] print(f"Split: {len(train_files)} train, {len(val_files)} val (ratio: {self.train_val_split:.2f})") return train_files, val_files diff --git a/src/CGeometryUtils.py b/src/CGeometryUtils.py index 10b2e15..28002b6 100644 --- a/src/CGeometryUtils.py +++ b/src/CGeometryUtils.py @@ -2,7 +2,7 @@ import torch import numpy as np import meshio as meshio -import polyscope as ps +# import polyscope as ps from pathlib import Path import igl @@ -58,7 +58,7 @@ def __init__(self, data_dir="data"): self.data_dir = data_dir if not os.path.exists(data_dir): os.makedirs(data_dir) - self.renderer = MeshRenderer() + # self.renderer = MeshRenderer() def load_mesh(self, mesh_file): """Load and normalize mesh""" @@ -87,21 +87,27 @@ def sample_points(self, vertices, faces, radius=0.02, sigma=0.0, mu=0.0, n_gauss - sampled_points: Array of sampled points """ # Sample random points in the -1 to 1 cube + # def loguniform(low=-1, high=1, size=None): + # return np.exp(np.random.uniform(low, high, size)) + # random_points = np.random.uniform(-1, 1, (n_uniform, 3)) # Compute surface points using blue noise + print('compute blue noise') + # 40% surface_points = igl.blue_noise(vertices, faces, radius)[2] # Concatenate surface points with random points + print('concat') sampled_points = np.concatenate((random_points, surface_points), axis=0) # Add Gaussian noise if needed if n_gaussian > 0 and sigma > 0: - noise = np.random.normal(mu, sigma, (n_gaussian * surface_points.shape[0], 3)) - # Tile surface points to match noise shape - tiled_surface = np.tile(surface_points, (n_gaussian, 1)) - gaussian_points = tiled_surface + noise + noise = np.random.normal(mu, sigma, (n_gaussian, surface_points.shape[0], 3)) + print("entering tqdm") - sampled_points = np.concatenate((sampled_points, gaussian_points), axis=0) - + for i in range(n_gaussian): + new_points = surface_points + noise[i] + sampled_points = np.concatenate((sampled_points, new_points), axis=0) + return sampled_points def generate_point_cloud(self, meshes, radius=0.02, sigma=0.0, mu=0.0, n_gaussian=10, n_uniform=1000): @@ -142,9 +148,9 @@ def generate_point_cloud(self, meshes, radius=0.02, sigma=0.0, mu=0.0, n_gaussia np.save(distances_file, distances) # Visualize on Polyscope - self.renderer.render_mesh_with_points(vertices, faces, sampled_points, distances, name, f"{name}_points") + # self.renderer.render_mesh_with_points(vertices, faces, sampled_points, distances, name, f"{name}_points") - self.renderer.show() + # self.renderer.show() def get_dataset_stats(self, mesh_name): """Get statistics about generated dataset""" diff --git a/src/CModelTrainer.py b/src/CModelTrainer.py index 81816e7..e2868f4 100644 --- a/src/CModelTrainer.py +++ b/src/CModelTrainer.py @@ -4,6 +4,7 @@ """ import torch import torch.nn as nn +import torch.nn.functional as F import torch.optim as optim from torch.utils.data import Dataset, DataLoader import matplotlib.pyplot as plt @@ -44,8 +45,11 @@ def __init__(self, dataset_info, split='train', fix_seed=False, volume_coords=No latent_mean = self.dataset_params['latent_mean'] latent_sd = self.dataset_params['latent_sd'] - self.latent_vectors = torch.randn(len(self.files), z_dim) - self.latent_vectors = (self.latent_vectors * latent_sd) + latent_mean + # self.latent_vectors = torch.randn(len(self.files), z_dim, device='cuda') + # self.latent_vectors = (self.latent_vectors * latent_sd) + latent_mean + # self.latent_vectors = F.normalize(self.latent_vectors, dim=1) # Unit norm + # self.latent_vectors = torch.tensor([[-.5, .5,], [.5, -.5], [.5, .5], [-.5, -.5]], device='cuda') + self.latent_vectors = torch.tensor([[0.,], [1.,],], device='cuda') self.latent_vectors.requires_grad = True # Store volume coordinates for additional sampling if needed @@ -218,7 +222,7 @@ def create_datasets(self, dataset_info=None): tuple: (train_dataset, val_dataset) """ - if self.dataset_type == 'sdf' and dataset_info is not None: # <- REMOVE the "and dataset_info is not None" condition + if self.dataset_type == 'sdf' or self.dataset_type == 'lipschitz_sdf': # <- REMOVE the "and dataset_info is not None" condition # Use SDFDataset - let it handle None dataset_info gracefully train_dataset = SDFDataset(dataset_info, split='train') val_dataset = SDFDataset(dataset_info, split='val') @@ -384,12 +388,17 @@ def train_and_validate(self, model, dataset_info=None): return model, {"error": f"Dataset loading failed: {e}"} # Setup loss function and optimizer - if self.dataset_type == 'sdf': - criterion = self._get_sdf_loss() + if self.dataset_type == 'sdf' or self.dataset_type == 'lipschitz_sdf': + if self.dataset_type == 'sdf' : + criterion = self._get_sdf_loss() + elif self.dataset_type == 'lipschitz_sdf': + criterion = self._get_lipschitz_sdf_loss() + else: + raise Exception # Get latent vectors from dataset for SDF training if hasattr(train_dataset, 'latent_vectors'): - latent_vectors = train_dataset.latent_vectors.to(self.device) + latent_vectors = train_dataset.latent_vectors #.to(self.device) optimizer = self._get_optimizer(model, latent_vectors) print(f"✓ SDF training setup complete with dual learning rates") else: @@ -492,6 +501,7 @@ def train_and_validate(self, model, dataset_info=None): f"{' (Best Model)' if is_best else ''}") # Save checkpoints and models + # if epoch // self.save_frequency == 0: try: # Always save latest checkpoint latest_checkpoint = { @@ -702,6 +712,29 @@ def forward(self, yhat, y, latent=None): return DeepSDFLoss(delta, latent_sd) + def _get_lipschitz_sdf_loss(self): + """Get loss function for Lipschitz-regularized SDF learning.""" + delta = self.config.get('sdf_delta', 1.0) + latent_sd = self.config.get('latent_sd', 0.01) + + class LipschitzSDFLoss(nn.Module): + def __init__(self, delta, sd): + super().__init__() + self.delta = delta + self.sd = sd + + def forward(self, yhat, y, latent=None): + clamped_yhat = torch.clamp(yhat, -self.delta, self.delta) + clamped_y = torch.clamp(y, -self.delta, self.delta) + sdf_loss = torch.mean(torch.abs(clamped_yhat - clamped_y)) + + if latent is not None: + latent_loss = self.sd**2 * torch.mean(torch.linalg.norm(latent, dim=1, ord=2)) + return sdf_loss + latent_loss + return sdf_loss + + return LipschitzSDFLoss(delta, latent_sd) + def _get_optimizer(self, model, latent_vectors=None): """Get optimizer with separate learning rates for network and latent vectors.""" @@ -713,7 +746,7 @@ def _get_optimizer(self, model, latent_vectors=None): if self.optimizer_name.lower() == 'adam': optimizer = optim.Adam([ {"params": model.parameters(), "lr": lr_net}, - {"params": latent_vectors, "lr": lr_latent} + {"params": [latent_vectors], "lr": lr_latent} ]) elif self.optimizer_name.lower() == 'adamw': optimizer = optim.AdamW([ @@ -754,6 +787,8 @@ def _run_epoch(self, data_loader, model, loss_fn, optimizer, is_training, epoch= total_loss = 0.0 num_batches = 0 + + lambda_lip = self.config.get('lipschitz_lambda', 0.01) with torch.set_grad_enabled(is_training): for batch_idx, batch_data in enumerate(data_loader): @@ -766,27 +801,35 @@ def _run_epoch(self, data_loader, model, loss_fn, optimizer, is_training, epoch= # Pass 3D tensors directly to model outputs = model(latents, coords) # outputs: [batch, num_points] + # Flatten only for loss computation outputs_flat = outputs.view(-1) # [batch*num_points] targets_flat = targets.view(-1) # [batch*num_points] - - # Compute loss - if hasattr(loss_fn, 'forward') and loss_fn.__class__.__name__ == 'DeepSDFLoss': - loss = loss_fn(outputs_flat, targets_flat, latents) + + primary_loss = loss_fn(outputs_flat, targets_flat, latents) + + if is_training and self.dataset_type == 'lipschitz_sdf' and hasattr(model, 'get_lipschitz_loss'): + # Get the Lipschitz regularization term directly from the model. + lipschitz_loss = model.get_lipschitz_loss() + + # 3. Combine the losses into the final total loss for backpropagation. + total_loss_for_backward = primary_loss + lambda_lip * lipschitz_loss else: - loss = loss_fn(outputs_flat, targets_flat) + # For validation or non-Lipschitz models, just use the primary loss. + total_loss_for_backward = primary_loss + + + if is_training: + optimizer.zero_grad() + total_loss_for_backward.backward() + optimizer.step() + + total_loss += total_loss_for_backward.item() + num_batches += 1 else: raise ValueError(f"Expected SDF dataset with 3 elements (coords, latents, targets), got {len(batch_data)} elements") - if is_training: - optimizer.zero_grad() - loss.backward() - optimizer.step() - - total_loss += loss.item() - num_batches += 1 - return total_loss / num_batches if num_batches > 0 else 0.0 def _create_report(self, history, best_val_loss): @@ -826,7 +869,7 @@ def _create_loss_plot(self, history): plt.figure(figsize=(10, 6)) plt.plot(epochs, train_losses, 'o-', label='Training Loss', alpha=0.8) - plt.plot(epochs, val_losses, 's-', label='Validation Loss', alpha=0.8) + # plt.plot(epochs, val_losses, 's-', label='Validation Loss', alpha=0.8) plt.xlabel('Epoch') plt.ylabel('Loss') plt.title(f'Training and Validation Loss - {self.dataset_type.upper()} Dataset') diff --git a/src/CPipelineOrchestrator.py b/src/CPipelineOrchestrator.py index 2e69ff2..cc9e902 100644 --- a/src/CPipelineOrchestrator.py +++ b/src/CPipelineOrchestrator.py @@ -150,7 +150,7 @@ def _process_data_step(self, state): trainer_config = self.config.get('trainer_config', {}) dataset_type = trainer_config.get('dataset_type', 'shape') - if dataset_type == 'sdf': + if dataset_type == 'sdf' or dataset_type == 'lipschitz_sdf': # For SDF datasets, generate SDF dataset with proper dataset_info print(" Generating SDF dataset...") diff --git a/training_result_visualization.ipynb b/training_result_visualization.ipynb new file mode 100644 index 0000000..50418fd --- /dev/null +++ b/training_result_visualization.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d4237abb-f5ac-41e3-a237-0789fc9e9235", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "import numpy as np\n", + "import skimage\n", + "\n", + "os.chdir('/homes/dnogina/code/topology-control/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41b386c8-f111-413c-8bfe-e303c89f3dab", + "metadata": {}, + "outputs": [], + "source": [ + "from src.CPipelineOrchestrator import CPipelineOrchestrator\n", + "from src.CModelTrainer import SDFDataset\n", + "from src.CEvaluator import CEvaluator\n", + "from src.CGeometryUtils import VolumeProcessor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09bf802c-893a-4c16-bc8f-84b9a73784a8", + "metadata": {}, + "outputs": [], + "source": [ + "ckpt = torch.load('/homes/dnogina/code/topology-control/artifacts/experiment_20250722_200033/training_artifacts/run_20250722_200037/checkpoints/latest_checkpoint.pth')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55cab654-5e70-4e91-b6f7-3a89a4073242", + "metadata": {}, + "outputs": [], + "source": [ + "# torch.save(ckpt, '../artifacts/first_working.pth')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c0a4ad5-d633-4e46-8c5e-227328ed490d", + "metadata": {}, + "outputs": [], + "source": [ + "orc = CPipelineOrchestrator()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5daa2357-57bb-4152-8c8b-1218d39b0887", + "metadata": {}, + "outputs": [], + "source": [ + "model = orc.architecture_manager.get_model()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "407f03f5-f620-4502-9d4e-ec2b9545e4f5", + "metadata": {}, + "outputs": [], + "source": [ + "model.load_state_dict(ckpt['model_state_dict'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a2c3e7d-c8b5-431c-8b05-98d6e9069041", + "metadata": {}, + "outputs": [], + "source": [ + "processing_results = orc.data_processor.generate_sdf_dataset(\n", + " z_dim=16,\n", + " latent_mean=0,\n", + " latent_sd=0.02\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cfc2f54-b8ed-42dc-a52a-ac1197e6b5b3", + "metadata": {}, + "outputs": [], + "source": [ + "ds = SDFDataset(processing_results['dataset_info'])\n", + "ds.latent_vectors = ckpt['latent_vectors']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39310536-3194-480d-bd3a-41693ed0909c", + "metadata": {}, + "outputs": [], + "source": [ + "coords, latents, sdfs = ds[3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a066710b-41d2-492d-a59f-93ce5f1b6df1", + "metadata": {}, + "outputs": [], + "source": [ + "resolution = 150\n", + "volume_processor = VolumeProcessor(device='cpu', resolution=resolution)\n", + "all_coords, actual_grid_size = volume_processor._get_volume_coords(device='cpu', resolution=resolution)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d12d4819-2bc3-4559-ab1c-9ff36608412c", + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " predicted_sdfs = model(latents[None], all_coords[None])\n", + " predicted_sdfs = predicted_sdfs.squeeze(-1) if predicted_sdfs.dim() > 1 else predicted_sdfs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2742f902-19b1-4347-bca9-bab6079effe6", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_mesh(grad_size_axis, sdf, level=0.0):\n", + " \"\"\"\n", + " Extract mesh from SDF using marching cubes.\n", + " \"\"\"\n", + " print(f\" extract_mesh: grid_size={grad_size_axis}, sdf_shape={sdf.shape}, sdf_numel={sdf.numel()}\")\n", + " \n", + " try:\n", + " # Validate input size\n", + " expected_size = grad_size_axis ** 3\n", + " if sdf.numel() != expected_size:\n", + " raise ValueError(f\"SDF size mismatch: got {sdf.numel()}, expected {expected_size} for {grad_size_axis}³ grid\")\n", + " \n", + " # Check minimum grid size for marching cubes\n", + " if grad_size_axis < 2:\n", + " raise ValueError(f\"Grid size {grad_size_axis} is too small. Marching cubes requires at least 2x2x2 grid.\")\n", + " \n", + " # Extract zero-level set with marching cubes\n", + " grid_sdf = sdf.view(grad_size_axis, grad_size_axis, grad_size_axis).detach().cpu().numpy()\n", + " print(f\" Grid SDF shape after reshape: {grid_sdf.shape}\")\n", + " print(f\" Grid SDF range: [{grid_sdf.min():.4f}, {grid_sdf.max():.4f}]\")\n", + " \n", + " # Automatically adjust level if it's outside the SDF range\n", + " sdf_min, sdf_max = grid_sdf.min(), grid_sdf.max()\n", + " original_level = level\n", + " \n", + " if level < sdf_min or level > sdf_max:\n", + " if sdf_min < 0 < sdf_max:\n", + " level = 0.0\n", + " print(f\" Using zero level set (level=0.0)\")\n", + " else:\n", + " # Use a level that's within the range, typically the median or a small value\n", + " level = np.percentile(grid_sdf, 20) # 20th percentile often works well\n", + " print(f\" Level {original_level} outside range [{sdf_min:.4f}, {sdf_max:.4f}]\")\n", + " print(f\" Using level={level:.4f} (20th percentile)\")\n", + " \n", + " vertices, faces, normals, _ = skimage.measure.marching_cubes(grid_sdf, level=level)\n", + " print(f\" Marching cubes extracted: {len(vertices)} vertices, {len(faces)} faces\")\n", + "\n", + " # Rescale vertices extracted with marching cubes\n", + " x_max = np.array([1, 1, 1])\n", + " x_min = np.array([-1, -1, -1])\n", + " vertices = vertices * ((x_max-x_min) / grad_size_axis) + x_min\n", + "\n", + " return vertices, faces\n", + " except Exception as e:\n", + " print(f\" extract_mesh error: {e}\")\n", + " return np.array([]), np.array([])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8e267cf-64b3-4816-a67b-d2d0011898c4", + "metadata": {}, + "outputs": [], + "source": [ + "vertices, faces = extract_mesh(actual_grid_size, predicted_sdfs[0], level=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6ae53e2-9ea6-4813-89c0-72e0491abe20", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import numpy as np\n", + "from trimesh import Trimesh\n", + "\n", + "import plotly.io as pio\n", + "pio.renderers.default='notebook'\n", + "\n", + "x, y, z = -np.array(vertices).T\n", + "i, j, k = np.array(faces).T\n", + "\n", + "fig = go.Figure(data=[\n", + " go.Mesh3d(x=x, y=y, z=z, i=i, j=j, k=k, opacity=1),\n", + " # go.Scatter3d(x=coords[:,0], y=coords[:,1], z=coords[:,2], mode='markers', marker=dict(size=3, color=sdfs),marker_colorscale='Viridis')\n", + "])\n", + "# fig.update_layout(template='plotly_white', show_scale=True)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91dbb2f6-6d7d-49d9-8079-e7e1d7ed4bb8", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir('/homes/dnogina/code/topology-control/volume')\n", + "from compute_volume import compute_genus" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "311e5018-2a6c-46b9-815c-10a237c80b72", + "metadata": {}, + "outputs": [], + "source": [ + "compute_genus(vertices, faces)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fb22f73-04dc-4b53-9976-00d504950279", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/volume/Untitled.ipynb b/volume/Untitled.ipynb new file mode 100644 index 0000000..f0d435f --- /dev/null +++ b/volume/Untitled.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0fc46fca-9b6d-4ca2-903e-6fec3d203d79", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import polyscope as ps\n", + "import igl\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from scipy.interpolate import griddata\n", + "\n", + "from compute_volume import get_volume_coords, predict_sdf\n", + "from sdfs import SDF_interpolator\n", + "from config import DEV" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "95af9447-bfc0-48bf-8981-f718d3fd4a01", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2947484/4015345257.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " checkpoint = torch.load(\"checkpoints/latent2volume_best.pt\", map_location=DEV)\n" + ] + }, + { + "data": { + "text/plain": [ + "Latent2Volume(\n", + " (net): Sequential(\n", + " (0): Linear(in_features=2, out_features=128, bias=True)\n", + " (1): Linear(in_features=128, out_features=128, bias=True)\n", + " (2): ReLU()\n", + " (3): Dropout(p=0.2, inplace=False)\n", + " (4): Linear(in_features=128, out_features=128, bias=True)\n", + " (5): ReLU()\n", + " (6): Dropout(p=0.2, inplace=False)\n", + " (7): Linear(in_features=128, out_features=128, bias=True)\n", + " (8): ReLU()\n", + " (9): Dropout(p=0.2, inplace=False)\n", + " (10): Linear(in_features=128, out_features=1, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdf_interpolator = SDF_interpolator()\n", + "# visualize_sdf(sdf_interpolator)\n", + "\n", + "from compute_path_opt import compute_path #, compute_path2\n", + "from model import Latent2Volume\n", + "from config import LATENT_DIM, DEV\n", + "\n", + "checkpoint = torch.load(\"checkpoints/latent2volume_best.pt\", map_location=DEV)\n", + "model = Latent2Volume(LATENT_DIM).to(DEV)\n", + "\n", + "# Load the saved state_dict (weights)\n", + "model.load_state_dict(checkpoint[\"model_state_dict\"])\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e3e7e821-768c-45ce-8dfc-34ee3badaceb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8b9ab404-a543-4b4f-939c-ab43bee2deea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Path found with length: 21\n" + ] + } + ], + "source": [ + "path = compute_path(torch.tensor([1, 0], dtype=torch.float32), torch.tensor([0, 1], dtype=torch.float32), model, 20,)\n", + "\n", + "print(\"Path found with length:\", len(path))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "893e81da-b4f3-4421-a457-0efe0b61a722", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from visualize import visualize_2d_path\n", + "visualize_2d_path(model, path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88d2e209-8c03-4c74-b027-fea9b445d842", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c82ef3c-0dc2-4184-bc8a-3c65e10d86f3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/volume/checkpoints/latent2genera_deefsdf_best.pt b/volume/checkpoints/latent2genera_deefsdf_best.pt new file mode 100644 index 0000000..8bf8510 Binary files /dev/null and b/volume/checkpoints/latent2genera_deefsdf_best.pt differ diff --git a/volume/data/2d_latents_volumes_deepsdf.npz b/volume/data/2d_latents_volumes_deepsdf.npz new file mode 100644 index 0000000..19b5abd Binary files /dev/null and b/volume/data/2d_latents_volumes_deepsdf.npz differ diff --git a/volume/deepsdf_wrapper.py b/volume/deepsdf_wrapper.py new file mode 100644 index 0000000..18d1e06 --- /dev/null +++ b/volume/deepsdf_wrapper.py @@ -0,0 +1,67 @@ +import torch +import os +import igl +import numpy as np +import skimage + +import sys +sys.path.append('../') +from src.CPipelineOrchestrator import CPipelineOrchestrator + +from config import VOLUME_DIR, COORDS_FIRST +from compute_volume import get_volume_coords, compute_volume, compute_genus, predict_sdf +from tqdm import tqdm + +CKPT_PATH = '../artifacts/first_working.pth' +LATENT_DIM = 16 + +def generate_mesh(latent_code, coords, grid_size, model, type=COORDS_FIRST, device='cuda'): + sdf = predict_sdf(latent_code.to(device), coords[None].to(device), model.to(device), type).flatten() + + vertices, faces, e2v = igl.marching_cubes( + sdf.cpu().numpy(), + coords.cpu().numpy(), + grid_size, grid_size, grid_size, 0.0 + ) + + return vertices, faces + + +def generate_latent_volume_data(n, model): + # sample from uniform(-.5, .5) as the latents + latents = torch.rand(n, LATENT_DIM) - .5 + data_dir = os.path.join(VOLUME_DIR, "data") #"volume/data/" + + volumes = [] + genera = [] + + coords, grid_size = get_volume_coords() + + for i, latent in enumerate(tqdm(latents)): + vertices, faces = generate_mesh(latent, coords, grid_size, model, COORDS_FIRST) + volumes.append(compute_volume(vertices, faces)) + genera.append(compute_genus(vertices, faces)) + + print(genera) + np.savez(os.path.join(data_dir, "2d_latents_volumes_deepsdf"), + latents=latents.numpy().astype(np.float32), + volumes=np.array(volumes, dtype=np.float32), + genera=np.array(genera, dtype=np.int8)) + + + + + + + +if __name__ == "__main__": + + # DATA GENERATION + ckpt = torch.load(CKPT_PATH) + orc = CPipelineOrchestrator('/homes/dnogina/code/topology-control/config/config_examples.yaml') + model = orc.architecture_manager.get_model() + _ = model.load_state_dict(ckpt['model_state_dict']) + + generate_latent_volume_data(2000, model) + + # \ No newline at end of file diff --git a/volume/train.py b/volume/train.py index 634200b..e44a5db 100644 --- a/volume/train.py +++ b/volume/train.py @@ -42,15 +42,28 @@ def train_and_save(loader, model, crit, opt, name): # dataset_path=os.path.join(VOLUME_DIR, "data", "2d_latents_volumes.npz") # ) + # current repo version: dataset = GeneraDataset( dataset_path=os.path.join(VOLUME_DIR, "data", "2d_latents_volumes.npz") ) + # DeepSDF adjusted version: + dataset = GeneraDataset( + dataset_path=os.path.join(VOLUME_DIR, "data", "2d_latents_volumes_deepsdf.npz") + ) # model = Latent2Volume(LATENT_DIM).to(DEV) + + # current repo version: model = Latent2Genera(LATENT_DIM, num_classes=dataset.num_classes, min_genus=dataset.min_genus).to(DEV) + # DeepSDF adjusted version: + model = Latent2Genera(16, num_classes=dataset.num_classes,).to(DEV) + # crit = nn.L1Loss() crit = nn.CrossEntropyLoss() opt = optim.Adam(list(model.parameters()), lr=LR) loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=dataset.collate_fn) - train_and_save(loader, model, crit, opt, "genera") \ No newline at end of file + # current repo version: + train_and_save(loader, model, crit, opt, "genera") + # DeepSDF adjusted version: + train_and_save(loader, model, crit, opt, "genera_deefsdf") \ No newline at end of file diff --git a/volume/visualize.py b/volume/visualize.py index bc35b72..6e76248 100644 --- a/volume/visualize.py +++ b/volume/visualize.py @@ -42,6 +42,8 @@ def visualize_interpolation_path(model, path, type=COORDS_FIRST): def construct_from_latent(model, latent, positioning, index, type): """ + TODO: for deepsdf needs generate_mesh from the wrapper! + Reconstruct the mesh from a latent vector using the model Parameters: @@ -112,8 +114,8 @@ def visualize_latent_vs_genera(path=os.path.join(VOLUME_DIR, "data", "2d_latents latents = data["latents"] genera = data["genera"] - idx = np.where(genera == 3)[0][0] - print(latents[idx]) + # idx = np.where(genera == 3)[0][0] + # print(latents[idx]) plt.figure(figsize=(6, 6)) cmap = ListedColormap(plt.get_cmap('tab10').colors[:5])