diff --git a/.gitignore b/.gitignore index 48f3003..ec2b24d 100644 --- a/.gitignore +++ b/.gitignore @@ -28,3 +28,4 @@ schematics_data output datasets htmlcov +output_2 diff --git a/minecraft_copilot_ml/__main__.py b/minecraft_copilot_ml/__main__.py index c5b3239..9ed9cc9 100644 --- a/minecraft_copilot_ml/__main__.py +++ b/minecraft_copilot_ml/__main__.py @@ -5,35 +5,26 @@ from typing import List, Optional, Set, Tuple import boto3 +import lightning as pl import numpy as np -import pytorch_lightning as pl import torch +from improved_diffusion.unet import UNetModel # type: ignore[import-untyped] +from lightning.pytorch.callbacks import ModelCheckpoint +from lightning.pytorch.loggers import CSVLogger from loguru import logger -from pytorch_lightning.callbacks import ModelCheckpoint -from pytorch_lightning.loggers import CSVLogger -from sklearn.model_selection import train_test_split # type: ignore from torch.utils.data import DataLoader -from tqdm import tqdm from minecraft_copilot_ml.data_loader import ( MinecraftSchematicsDataset, MinecraftSchematicsDatasetItemType, - get_working_files_and_unique_blocks_and_counts, + get_working_files_and_unique_blocks, list_schematic_files_in_folder, ) -from minecraft_copilot_ml.model import UNet3d - - -def export_to_onnx(model: UNet3d, path_to_output: str) -> None: - torch.onnx.export( - model, - torch.randn(1, 1, 16, 16, 16).to("cuda" if torch.cuda.is_available() else "cpu"), - path_to_output, - input_names=["input"], - output_names=["output"], - # https://onnxruntime.ai/docs/reference/compatibility.html - opset_version=17, - ) +from minecraft_copilot_ml.model import MinecraftCopilotTrainer + +device_name = torch.cuda.get_device_name() +if device_name is not None and device_name == "GeForce RTX 3090": + torch.set_float32_matmul_precision("medium") def main(argparser: argparse.ArgumentParser) -> None: @@ -58,74 +49,60 @@ def main(argparser: argparse.ArgumentParser) -> None: schematics_list_files = schematics_list_files[start:end] # Set the dictionary size to the number of unique blocks in the dataset. # And also select the right files to load. - unique_blocks_dict, unique_counts_coefficients, loaded_schematic_files = ( - get_working_files_and_unique_blocks_and_counts(schematics_list_files) - ) + unique_blocks_dict, loaded_schematic_files = get_working_files_and_unique_blocks(schematics_list_files) logger.info(f"Unique blocks: {unique_blocks_dict}") logger.info(f"Number of unique blocks: {len(unique_blocks_dict)}") logger.info(f"Number of loaded schematics files: {len(loaded_schematic_files)}") - logger.info(f"Unique counts coefficients: {unique_counts_coefficients}") - train_schematics_list_files, test_schematics_list_files = train_test_split( - loaded_schematic_files, test_size=0.2, random_state=42 - ) - train_schematics_dataset = MinecraftSchematicsDataset(train_schematics_list_files) - val_schematics_dataset = MinecraftSchematicsDataset(test_schematics_list_files) + schematics_dataset = MinecraftSchematicsDataset(loaded_schematic_files) def collate_fn(batch: List[MinecraftSchematicsDatasetItemType]) -> MinecraftSchematicsDatasetItemType: - block_map, noisy_block_map, mask, loss_mask = zip(*batch) - return np.stack(block_map), np.stack(noisy_block_map), np.stack(mask), np.stack(loss_mask) - - train_schematics_dataloader = DataLoader( - train_schematics_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn + block_map, block_map_mask = zip(*batch) + return np.stack(block_map), np.stack(block_map_mask) + + num_workers = os.cpu_count() + if num_workers is None: + num_workers = 0 + + schematics_dataloader = DataLoader( + schematics_dataset, + batch_size=batch_size, + shuffle=True, + collate_fn=collate_fn, + num_workers=num_workers, ) - val_schematics_dataloader = DataLoader(val_schematics_dataset, batch_size=batch_size, collate_fn=collate_fn) - model = UNet3d(unique_blocks_dict, unique_counts_coefficients=unique_counts_coefficients) + unet_model = UNetModel( + in_channels=len(unique_blocks_dict), + model_channels=32, + out_channels=len(unique_blocks_dict), + num_res_blocks=2, + num_heads=2, + attention_resolutions=[1], + dropout=0.1, + channel_mult=(1, 2, 4, 8), + dims=3, + ) + model = MinecraftCopilotTrainer(unet_model, unique_blocks_dict, save_dir=path_to_output) csv_logger = CSVLogger(save_dir=path_to_output) - model_checkpoint = ModelCheckpoint(path_to_output, monitor="val_loss", save_top_k=1, save_last=True, mode="min") - trainer = pl.Trainer(logger=csv_logger, callbacks=model_checkpoint, max_epochs=epochs, log_every_n_steps=1) - trainer.fit(model, train_schematics_dataloader, val_schematics_dataloader) + model_checkpoint = ModelCheckpoint(path_to_output, save_last=True, mode="min") + trainer = pl.Trainer( + logger=csv_logger, callbacks=model_checkpoint, max_epochs=epochs, log_every_n_steps=1, accelerator="gpu" + ) + trainer.fit(model, schematics_dataloader) # Save the best and last model locally - logger.info(f"Best val_loss is: {model_checkpoint.best_model_score}") - best_model = UNet3d.load_from_checkpoint( - model_checkpoint.best_model_path, - unique_blocks_dict=unique_blocks_dict, - unique_counts_coefficients=unique_counts_coefficients, - ) - torch.save(best_model, os.path.join(path_to_output, "best_model.pth")) - last_model = UNet3d.load_from_checkpoint( + last_model = MinecraftCopilotTrainer.load_from_checkpoint( model_checkpoint.last_model_path, + unet_model=unet_model, unique_blocks_dict=unique_blocks_dict, - unique_counts_coefficients=unique_counts_coefficients, + save_dir=path_to_output, ) torch.save(last_model, os.path.join(path_to_output, "last_model.pth")) - export_to_onnx(best_model, os.path.join(path_to_output, "best_model.onnx")) - export_to_onnx(last_model, os.path.join(path_to_output, "last_model.onnx")) with open(os.path.join(path_to_output, "unique_blocks_dict.json"), "w") as f: json.dump(unique_blocks_dict, f) - # Save the best and last model to S3 - s3_client = boto3.client( - "s3", - region_name="eu-west-3", - aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"], - aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"], - ) - s3_client.upload_file(os.path.join(path_to_output, "best_model.pth"), "minecraft-copilot-models", "best_model.pth") - s3_client.upload_file(os.path.join(path_to_output, "last_model.pth"), "minecraft-copilot-models", "last_model.pth") - s3_client.upload_file( - os.path.join(path_to_output, "best_model.onnx"), "minecraft-copilot-models", "best_model.onnx" - ) - s3_client.upload_file( - os.path.join(path_to_output, "last_model.onnx"), "minecraft-copilot-models", "last_model.onnx" - ) - s3_client.upload_file( - os.path.join(path_to_output, "unique_blocks_dict.json"), "minecraft-copilot-models", "unique_blocks_dict.json" - ) - if __name__ == "__main__": argparser = argparse.ArgumentParser() diff --git a/minecraft_copilot_ml/data_loader.py b/minecraft_copilot_ml/data_loader.py index 68d2996..20af3fd 100644 --- a/minecraft_copilot_ml/data_loader.py +++ b/minecraft_copilot_ml/data_loader.py @@ -1,5 +1,4 @@ # flake8: noqa: E203 -import gc import os import re from pathlib import Path @@ -43,6 +42,10 @@ "4766.schematic", "10380.schematic", "12695.schematic", + "8675.schematic", + "10220.schematic", + "5096.schematic", + "14191.schematic" ] @@ -73,6 +76,9 @@ def litematic_to_numpy_minecraft_map( for z, k in zip(reg.zrange(), range(len(reg.zrange()))): b = reg.getblock(x, y, z) numpy_map[i, j, k] = b.blockid + numpy_map[numpy_map == "None"] = "minecraft:air" + numpy_map[numpy_map == None] = "minecraft:air" + del nbt_loaded return numpy_map @@ -94,13 +100,15 @@ def schematic_to_numpy_minecraft_map( raise Exception(f"Could not find Blocks or BlockData in {nbt_file}. Known keys: {res.keys()}") block_map = np.asarray(block_data).reshape(res["Height"], res["Length"], res["Width"]) block_map = np.vectorize(palette.get)(block_map) + block_map[block_map == "None"] = "minecraft:air" + block_map[block_map == None] = "minecraft:air" + del res return block_map def nbt_to_numpy_minecraft_map( nbt_file: str, ) -> np.ndarray: - gc.collect() if any([Path(nbt_file).parts[-1] == x for x in list_of_forbidden_files]): raise Exception( f"File {nbt_file} is forbidden. Skipping. If this file is here it is because it generates a SIGKILL." @@ -171,7 +179,7 @@ def get_random_block_map_and_mask_coordinates( ) -MinecraftSchematicsDatasetItemType = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray] +MinecraftSchematicsDatasetItemType = Tuple[np.ndarray, np.ndarray] class MinecraftSchematicsDataset(Dataset): @@ -195,27 +203,13 @@ def __getitem__(self, idx: int) -> MinecraftSchematicsDatasetItemType: minimum_height, minimum_depth, ) = get_random_block_map_and_mask_coordinates(numpy_minecraft_map, 16, 16, 16) - focused_block_map = block_map[ - random_roll_x_value : random_roll_x_value + minimum_width, - random_y_height_value : random_y_height_value + minimum_height, - random_roll_z_value : random_roll_z_value + minimum_depth, - ] - focused_noisy_block_map, unraveled_indices_of_noise = create_noisy_block_map(focused_block_map) - noisy_block_map = block_map.copy() - noisy_block_map[ - random_roll_x_value : random_roll_x_value + minimum_width, - random_y_height_value : random_y_height_value + minimum_height, - random_roll_z_value : random_roll_z_value + minimum_depth, - ] = focused_noisy_block_map block_map_mask = np.zeros((16, 16, 16), dtype=bool) block_map_mask[ random_roll_x_value : random_roll_x_value + minimum_width, random_y_height_value : random_y_height_value + minimum_height, random_roll_z_value : random_roll_z_value + minimum_depth, ] = True - loss_mask = np.zeros((16, 16, 16), dtype=bool) - loss_mask[unraveled_indices_of_noise] = True - return block_map, noisy_block_map, block_map_mask, loss_mask + return block_map, block_map_mask def list_schematic_files_in_folder(path_to_schematics: str) -> list[str]: @@ -229,22 +223,17 @@ def list_schematic_files_in_folder(path_to_schematics: str) -> list[str]: return schematics_list_files -def get_working_files_and_unique_blocks_and_counts( +def get_working_files_and_unique_blocks( schematics_list_files: list[str], -) -> Tuple[Dict[str, int], np.ndarray, list[str]]: +) -> Tuple[Dict[str, int], list[str]]: unique_blocks: Set[str] = set() - unique_counts: Dict[str, int] = {} loaded_schematic_files: List[str] = [] tqdm_list_files = tqdm(schematics_list_files, smoothing=0) for nbt_file in tqdm_list_files: tqdm_list_files.set_description(f"Processing {nbt_file}") try: numpy_minecraft_map = nbt_to_numpy_minecraft_map(nbt_file) - unique_blocks_in_map, unique_counts_in_map = np.unique(numpy_minecraft_map, return_counts=True) - for block, count in zip(unique_blocks_in_map, unique_counts_in_map): - if block not in unique_counts: - unique_counts[block] = 0 - unique_counts[block] += count + unique_blocks_in_map = set(numpy_minecraft_map.flatten()) for block in unique_blocks_in_map: if block not in unique_blocks: logger.info(f"Found new block: {block}") @@ -255,6 +244,4 @@ def get_working_files_and_unique_blocks_and_counts( logger.exception(e) continue unique_blocks_dict = {block: idx for idx, block in enumerate(unique_blocks)} - unique_counts_coefficients = np.array([unique_counts[block] for block in unique_blocks_dict]) - unique_counts_coefficients = unique_counts_coefficients.max() / unique_counts_coefficients - return unique_blocks_dict, unique_counts_coefficients, loaded_schematic_files + return unique_blocks_dict, loaded_schematic_files diff --git a/minecraft_copilot_ml/metrics_graph.ipynb b/minecraft_copilot_ml/metrics_graph.ipynb index f0601c6..f90870f 100644 --- a/minecraft_copilot_ml/metrics_graph.ipynb +++ b/minecraft_copilot_ml/metrics_graph.ipynb @@ -344,7 +344,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/minecraft_copilot_ml/model.py b/minecraft_copilot_ml/model.py index ba2c105..e875d85 100644 --- a/minecraft_copilot_ml/model.py +++ b/minecraft_copilot_ml/model.py @@ -1,103 +1,77 @@ # flake8: noqa: E203 +import os from typing import Any, Dict, Optional, Tuple +from loguru import logger import numpy as np -import pytorch_lightning as pl +import lightning as pl import torch import torch.nn as nn import torch.nn.functional as F +from improved_diffusion.unet import UNetModel # type: ignore[import-untyped] +from torchcfm import ExactOptimalTransportConditionalFlowMatcher # type: ignore[import-untyped] +import copy +from torchdyn.core import NeuralODE # type: ignore[import-untyped] from minecraft_copilot_ml.data_loader import MinecraftSchematicsDatasetItemType -class ConvBlock3d(nn.Module): - def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, padding: int = 1): - super(ConvBlock3d, self).__init__() - self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, padding=padding) - self.bn = nn.BatchNorm3d(out_channels) - self.relu = nn.LeakyReLU() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - result: torch.Tensor = self.relu(self.bn(self.conv(x))) - return result - - -class UNet3d(pl.LightningModule): - def __init__( +class MinecraftCopilotTrainer(pl.LightningModule): + def __init__( # type: ignore[no-any-unimported] self, + unet_model: UNetModel, unique_blocks_dict: Dict[str, int], - unique_counts_coefficients: Optional[np.ndarray] = None, - latent_dim: int = 64, + save_dir: str = "output", ): - super(UNet3d, self).__init__() + super(MinecraftCopilotTrainer, self).__init__() self.unique_blocks_dict = unique_blocks_dict self.reverse_unique_blocks_dict = {v: k for k, v in unique_blocks_dict.items()} - self.latent_dim = latent_dim - if unique_counts_coefficients is None: - unique_counts_coefficients = np.ones(len(unique_blocks_dict)) - self.unique_counts_coefficients = ( - torch.from_numpy(unique_counts_coefficients).float().to("cuda" if torch.cuda.is_available() else "cpu") - ) - self.conv_input = ConvBlock3d(1, 32) - self.conv1 = ConvBlock3d(32, 64) - self.conv2 = ConvBlock3d(64, 128) - self.conv3 = ConvBlock3d(128, 256) - self.conv4 = ConvBlock3d(256, 512) - - self.conv6 = ConvBlock3d(512, 256) - self.conv7 = ConvBlock3d(256, 128) - self.conv8 = ConvBlock3d(128, 64) - self.conv9 = ConvBlock3d(64, 32) - self.conv_output = nn.Conv3d(32, len(unique_blocks_dict), kernel_size=3, padding=1) - - def ml_core(self, x: torch.Tensor) -> torch.Tensor: - # Encode input - out_conv_input = self.conv_input(x) - out_conv_1 = self.conv1(out_conv_input) - out_conv_2 = self.conv2(out_conv_1) - out_conv_3 = self.conv3(out_conv_2) - out_conv_4 = self.conv4(out_conv_3) - - # Decode input - out_conv_6 = self.conv6(out_conv_4) + out_conv_3 - out_conv_7 = self.conv7(out_conv_6) + out_conv_2 - out_conv_8 = self.conv8(out_conv_7) + out_conv_1 - out_conv_9 = self.conv9(out_conv_8) + out_conv_input - out_conv_output: torch.Tensor = self.conv_output(out_conv_9) - return out_conv_output - - def forward(self, x: torch.Tensor) -> torch.Tensor: - reconstruction = self.ml_core(x) - reconstruction = F.softmax(reconstruction, dim=1) - return reconstruction - - def step(self, batch: MinecraftSchematicsDatasetItemType, batch_idx: int, mode: str) -> torch.Tensor: - block_maps, noisy_block_maps, block_map_masks, loss_masks = batch + self.unet_model = unet_model + self.flow_matcher = ExactOptimalTransportConditionalFlowMatcher(sigma=0.0) + self.step_number = 0 + self.save_dir = save_dir + if not os.path.exists(save_dir): + os.makedirs(save_dir) + self.automatic_optimization = False + + def forward(self, xt: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + return self.unet_model(xt, t) # type: ignore[no-any-return] + + def step(self, batch: MinecraftSchematicsDatasetItemType, batch_idx: int, mode: str) -> None: + optimizer = self.trainer.optimizers[0] + optimizer.zero_grad() + for param in self.parameters(): + # Taken from https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer.add_param_group + if param.grad is not None: + param.grad.detach_() + param.grad.zero_() + for param in self.unet_model.parameters(): + if param.grad is not None: + param.grad.detach_() + param.grad.zero_() + + block_maps, block_map_masks = batch pre_processed_block_maps = self.pre_process(block_maps) - pre_processed_noisy_block_maps = self.pre_process(noisy_block_maps).float().unsqueeze(1) - tensor_block_map_masks = torch.from_numpy(block_map_masks).float().to("cuda" if torch.cuda.is_available() else "cpu").long() - tensor_loss_masks = ( - torch.from_numpy(loss_masks).float().to("cuda" if torch.cuda.is_available() else "cpu").long() - ) - reconstruction = self.ml_core(pre_processed_noisy_block_maps) - - # Compute accuracy - accuracy_truth_map = (reconstruction.argmax(dim=1) == pre_processed_block_maps).float() - accuracy_on_block_map = accuracy_truth_map[tensor_block_map_masks.bool()].mean() - accuracy_on_loss_map = accuracy_truth_map[tensor_loss_masks.bool()].mean() - - # Compute reconstruction loss using categorical cross-entropy - reconstruction_loss = F.cross_entropy(reconstruction, pre_processed_block_maps, reduction="none") - reconstruction_loss = reconstruction_loss * tensor_block_map_masks - reconstruction_loss = reconstruction_loss * torch.where(tensor_loss_masks == 1, reconstruction_loss, 1) - reconstruction_loss = reconstruction_loss * self.unique_counts_coefficients[pre_processed_block_maps] - loss = reconstruction_loss.mean() + tensor_block_map_masks = torch.from_numpy(block_map_masks).float().to(self.device).long() + tensor_block_map_masks_for_one_hot = torch.zeros( + (len(self.unique_blocks_dict), block_maps.shape[0], 16, 16, 16) + ).to(self.device) + tensor_block_map_masks_for_one_hot[:, tensor_block_map_masks == 1] = 1 + tensor_block_map_masks_for_one_hot = tensor_block_map_masks_for_one_hot.permute(1, 0, 2, 3, 4) + x1 = pre_processed_block_maps + x0 = torch.randn_like(x1) + t, xt, ut = self.flow_matcher.sample_location_and_conditional_flow(x0, x1) + vt = self(xt, t) + loss = (ut - vt) ** 2 + loss = loss * tensor_block_map_masks_for_one_hot # Mask out the loss for the blocks outside the schematic + loss = loss.mean() + loss.backward() + torch.nn.utils.clip_grad_norm_(self.parameters(), 1.0) + optimizer.step() # Total loss loss_dict = { "loss": loss, - "accuracy_on_block_map": accuracy_on_block_map, - "accuracy_on_loss_map": accuracy_on_loss_map, "learning_rate": self.trainer.optimizers[0].param_groups[0]["lr"], } for name, value in loss_dict.items(): @@ -110,7 +84,21 @@ def step(self, batch: MinecraftSchematicsDatasetItemType, batch_idx: int, mode: logger=True, batch_size=block_maps.shape[0], ) - return loss + torch.cuda.empty_cache() + for tensor in [ + pre_processed_block_maps, + tensor_block_map_masks, + tensor_block_map_masks_for_one_hot, + x1, + x0, + t, + xt, + ut, + vt, + loss, + ]: + tensor.detach() + del tensor def pre_process(self, x: np.ndarray) -> torch.Tensor: vectorized_x = np.vectorize(lambda x: self.unique_blocks_dict.get(x, self.unique_blocks_dict["minecraft:air"]))( @@ -119,20 +107,55 @@ def pre_process(self, x: np.ndarray) -> torch.Tensor: vectorized_x = vectorized_x.astype(np.int64) x_tensor = torch.from_numpy(vectorized_x) x_tensor = x_tensor.to("cuda" if torch.cuda.is_available() else "cpu") + x_tensor = F.one_hot(x_tensor, num_classes=len(self.unique_blocks_dict)).permute(0, 4, 1, 2, 3).float() return x_tensor def post_process(self, x: torch.Tensor) -> np.ndarray: - predicted_block_maps: np.ndarray = np.vectorize(self.reverse_unique_blocks_dict.get)(x.argmax(dim=1).numpy()) + predicted_block_maps: np.ndarray = np.vectorize(self.reverse_unique_blocks_dict.get)( + x.argmax(dim=1).cpu().numpy() + ) return predicted_block_maps - def training_step(self, batch: MinecraftSchematicsDatasetItemType, batch_idx: int) -> torch.Tensor: - return self.step(batch, batch_idx, "train") - - def validation_step(self, batch: MinecraftSchematicsDatasetItemType, batch_idx: int) -> torch.Tensor: - return self.step(batch, batch_idx, "val") + def generate_samples(self, model: UNetModel, model_name: str) -> None: # type: ignore[no-any-unimported] + memory_train = model.training + + model.eval() + from scipy.integrate import solve_ivp + + def vector_field(t: float, x: np.ndarray): + reshaped_x = x.reshape(1, len(self.unique_blocks_dict), 16, 16, 16) + x_tensor = torch.from_numpy(reshaped_x).float().to(self.device) + res = model(x_tensor, torch.tensor([t], device=self.device).float()) + return res.detach().cpu().numpy().reshape(-1) + traj = solve_ivp(fun=vector_field, + t_span=(0, 1), + y0=np.random.standard_normal((1, len(self.unique_blocks_dict), 16, 16, 16)).reshape(-1), + t_eval=np.linspace(0, 1, 10) + ) + sol = traj["y"].transpose(1, 0) + for time_step in range(sol.shape[0]): + post_processed = self.post_process( + torch.from_numpy(sol[time_step].reshape(1, len(self.unique_blocks_dict), 16, 16, 16)) + ) + np.save(f"{self.save_dir}/sample_{model_name}_{time_step}.npy", post_processed, allow_pickle=True) + self.train(memory_train) + + def training_step(self, batch: MinecraftSchematicsDatasetItemType, batch_idx: int) -> None: # type: ignore[override] + self.step(batch, batch_idx, "train") + self.log("step", self.step_number, on_step=True, on_epoch=False, prog_bar=True, logger=True) + if self.step_number % 20_000 == 0: + logger.info(f"Generating samples at step {self.step_number}...") + self.generate_samples(self.unet_model, f"unet_{self.step_number}") + logger.info(f"Saving model at step {self.step_number}...") + torch.save( + { + "net_model": self.unet_model, + "step": self.step_number, + }, + self.save_dir + f"/model_{self.step_number}.pth", + ) + self.step_number += 1 def configure_optimizers(self) -> Any: - return torch.optim.Adam(self.parameters(), lr=1e-3) - - def on_train_start(self) -> None: - print(self) + optimizer = torch.optim.Adam(self.parameters(), lr=2e-4) + return optimizer diff --git a/minecraft_copilot_ml/otcfm_visualizer.ipynb b/minecraft_copilot_ml/otcfm_visualizer.ipynb new file mode 100644 index 0000000..b7f4720 --- /dev/null +++ b/minecraft_copilot_ml/otcfm_visualizer.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2143/1617969541.py:6: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n", + " colors_palette = cm.get_cmap(\"tab10\").colors\n" + ] + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "import matplotlib.cm as cm\n", + "from typing import Any\n", + "import numpy as np\n", + "\n", + "colors_palette = cm.get_cmap(\"tab10\").colors\n", + "\n", + "\n", + "def display_voxels(voxel: np.ndarray, zero_block: Any = 0):\n", + " ax = plt.figure().add_subplot(projection=\"3d\")\n", + " unique_values = set(np.unique(voxel))\n", + "\n", + " colors = np.empty(voxel.shape, dtype=object)\n", + " for i, value in enumerate(unique_values):\n", + " colors[voxel == value] = {colors_palette[i % len(colors_palette)]}\n", + " ax.voxels(voxel != zero_block, facecolors=colors)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m range_linspace:\n\u001b[1;32m 4\u001b[0m block_map \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/home/mehdi/minecraft-copilot-ml/output_2/sample_unet_400_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m----> 5\u001b[0m \u001b[43mdisplay_voxels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzero_block\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mminecraft:air\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m block_map \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/home/mehdi/minecraft-copilot-ml/output_2/sample_unet_400_99.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 7\u001b[0m display_voxels(block_map, zero_block\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mminecraft:air\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[1], line 17\u001b[0m, in \u001b[0;36mdisplay_voxels\u001b[0;34m(voxel, zero_block)\u001b[0m\n\u001b[1;32m 15\u001b[0m colors[voxel \u001b[38;5;241m==\u001b[39m value] \u001b[38;5;241m=\u001b[39m {colors_palette[i \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mlen\u001b[39m(colors_palette)]}\n\u001b[1;32m 16\u001b[0m ax\u001b[38;5;241m.\u001b[39mvoxels(voxel \u001b[38;5;241m!=\u001b[39m zero_block, facecolors\u001b[38;5;241m=\u001b[39mcolors)\n\u001b[0;32m---> 17\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/pyplot.py:527\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 484\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m 485\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 526\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[43m 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**kwargs)\u001b[0m\n\u001b[1;32m 296\u001b[0m publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m 300\u001b[0m \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/IPython/core/formatters.py:182\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m 180\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate..fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m 231\u001b[0m args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/IPython/core/formatters.py:226\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 226\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 168\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/backend_bases.py:2164\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2161\u001b[0m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m 2162\u001b[0m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m 2163\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2164\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m 2166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3151\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3155\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m 3158\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py:472\u001b[0m, in \u001b[0;36mAxes3D.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 468\u001b[0m zorder_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(axis\u001b[38;5;241m.\u001b[39mget_zorder()\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 470\u001b[0m collection_zorder \u001b[38;5;241m=\u001b[39m patch_zorder \u001b[38;5;241m=\u001b[39m zorder_offset\n\u001b[0;32m--> 472\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m 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\u001b[0;32m~/minecraft-copilot-ml/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py:473\u001b[0m, in \u001b[0;36mAxes3D.draw..\u001b[0;34m(artist)\u001b[0m\n\u001b[1;32m 468\u001b[0m zorder_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(axis\u001b[38;5;241m.\u001b[39mget_zorder()\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 470\u001b[0m collection_zorder \u001b[38;5;241m=\u001b[39m patch_zorder \u001b[38;5;241m=\u001b[39m zorder_offset\n\u001b[1;32m 472\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(collections_and_patches,\n\u001b[0;32m--> 473\u001b[0m key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m artist: 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1030\u001b[0m \u001b[38;5;66;03m# sort by depth (furthest drawn first)\u001b[39;00m\n\u001b[0;32m-> 1031\u001b[0m z_segments_2d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_zsortfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mzs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumn_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mxs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mys\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mec\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1034\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mxyzlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcface\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcedge\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1035\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1037\u001b[0m _, segments_2d, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_facecolors2d, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_edgecolors2d, idxs \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mz_segments_2d)\n\u001b[1;32m 1039\u001b[0m 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keepdims)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 520\u001b[0m avg \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mmean(axis, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkeepdims_kw)\n\u001b[0;32m--> 521\u001b[0m avg_as_array \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masanyarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mavg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 522\u001b[0m scl \u001b[38;5;241m=\u001b[39m avg_as_array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype(a\u001b[38;5;241m.\u001b[39msize\u001b[38;5;241m/\u001b[39mavg_as_array\u001b[38;5;241m.\u001b[39msize)\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import numpy as np\n", + "range_linspace = np.linspace(0, 99, 10).astype(int)\n", + "for i in range_linspace:\n", + " block_map = np.load(f\"/home/mehdi/minecraft-copilot-ml/output_2/sample_unet_400_{i}.npy\")[0]\n", + " display_voxels(block_map, zero_block=\"minecraft:air\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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"sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"}, + {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"}, ] [package.dependencies] @@ -4249,14 +4908,13 @@ filelock = ">=3.12.2,<4" platformdirs = ">=3.9.1,<5" [package.extras] -docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] [[package]] name = "watchdog" version = "4.0.0" description = "Filesystem events monitoring" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -4298,7 +4956,6 @@ watchmedo = ["PyYAML (>=3.10)"] name = "wcwidth" version = "0.2.13" description = "Measures the displayed width of unicode strings in a terminal" -category = "dev" optional = false python-versions = "*" files = [ @@ -4310,7 +4967,6 @@ files = [ name = "widgetsnbextension" version = "4.0.10" description = "Jupyter interactive widgets for Jupyter Notebook" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4322,7 +4978,6 @@ files = [ name = "win32-setctime" version = "1.1.0" description = "A small Python utility to set file creation time on Windows" -category = "main" optional = false python-versions = ">=3.5" files = [ @@ -4337,7 +4992,6 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] name = "wsproto" version = "1.2.0" description = "WebSockets state-machine based protocol implementation" -category = "main" optional = false python-versions = ">=3.7.0" files = [ @@ -4352,7 +5006,6 @@ h11 = ">=0.9.0,<1" name = "yarl" version = "1.9.4" description = "Yet another URL library" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4455,4 +5108,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "11648e0aea885d5de02653ae28ebd2a0dededd14dd7acaca0e14267199532ae1" +content-hash = "1a725c31e79fe3c386fb7a6851f1ba72fdb630ace4b73a91797a60f072921fbd" diff --git a/pyproject.toml b/pyproject.toml index d4ef82a..c84b208 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,17 +18,15 @@ boto3-stubs = { extras = ["essential"], version = "^1.34.35" } types-requests = "^2.31.0.1" loguru = "^0.7.0" types-tqdm = "^4.65.0.1" -types-xmltodict = "^0.13.0.2" pydantic = "2.5.3" pydantic-settings = "^2.1.0" -beautifulsoup4 = "^4.12.3" seaborn = "^0.13.2" # https://onnxruntime.ai/docs/reference/compatibility.html -onnx = "~1.12.0" -onnxruntime = "~1.13.0" litemapy = "^0.8.1b0" lightning = "^2.2.0.post0" nbtlib = "^2.0.4" +torchcfm = { git = "ssh://git@github.com/DemyCode/conditional-flow-matching" } +improved-diffusion = { git = "ssh://git@github.com/DemyCode/improved-diffusion" } [tool.poetry.group.dev.dependencies] ipympl = "^0.9.3"