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| import utils.binvox_rw as binvox_rw | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| from models.encoder import Encoder | |
| from models.decoder import Decoder | |
| from models.merger import Merger | |
| from models.refiner import Refiner | |
| from config import cfg | |
| import torch | |
| from datetime import datetime as dt | |
| import utils.data_transforms | |
| device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| print(device) | |
| # device='cpu' | |
| cfg.CONST.WEIGHTS='saved_model/Pix2Vox.pth' | |
| def read_binvox(file) -> np.ndarray: | |
| model = binvox_rw.read_as_3d_array(file) | |
| return model.data.astype(np.uint8) | |
| def voxel_to_plotly(voxels): | |
| x, y, z = voxels.nonzero() | |
| fig = go.Figure(data=[ | |
| go.Scatter3d( | |
| x=x, y=y, z=z, | |
| mode='markers', | |
| marker=dict(size=3, color=z, colorscale='Viridis', opacity=0.7) | |
| ) | |
| ]) | |
| fig.update_layout(scene=dict(aspectmode='data')) | |
| return fig | |
| IMG_SIZE = cfg.CONST.IMG_H, cfg.CONST.IMG_W | |
| CROP_SIZE = cfg.CONST.CROP_IMG_H, cfg.CONST.CROP_IMG_W | |
| test_transforms = utils.data_transforms.Compose([ | |
| utils.data_transforms.CenterCrop(IMG_SIZE, CROP_SIZE), | |
| utils.data_transforms.RandomBackground(cfg.TEST.RANDOM_BG_COLOR_RANGE), | |
| utils.data_transforms.Normalize(mean=cfg.DATASET.MEAN, std=cfg.DATASET.STD), | |
| utils.data_transforms.ToTensor(), | |
| ]) | |
| def predict_voxel_from_images(rendering_images): | |
| transformed_images = test_transforms(rendering_images) | |
| encoder = Encoder(cfg) | |
| decoder = Decoder(cfg) | |
| refiner = Refiner(cfg) | |
| merger = Merger(cfg) | |
| if torch.cuda.is_available(): | |
| encoder = torch.nn.DataParallel(encoder).cuda() | |
| decoder = torch.nn.DataParallel(decoder).cuda() | |
| refiner = torch.nn.DataParallel(refiner).cuda() | |
| merger = torch.nn.DataParallel(merger).cuda() | |
| print('[INFO] %s Loading weights from %s ...' % (dt.now(), cfg.CONST.WEIGHTS)) | |
| checkpoint = torch.load(cfg.CONST.WEIGHTS) | |
| epoch_idx = checkpoint['epoch_idx'] | |
| encoder.load_state_dict(checkpoint['encoder_state_dict']) | |
| decoder.load_state_dict(checkpoint['decoder_state_dict']) | |
| if cfg.NETWORK.USE_REFINER: | |
| refiner.load_state_dict(checkpoint['refiner_state_dict']) | |
| if cfg.NETWORK.USE_MERGER: | |
| merger.load_state_dict(checkpoint['merger_state_dict']) | |
| encoder.eval() | |
| decoder.eval() | |
| merger.eval() | |
| refiner.eval() | |
| with torch.no_grad(): | |
| transformed_images = transformed_images.unsqueeze(0) #adding the batch_dim | |
| transformed_images = transformed_images.to(device) | |
| # print(rendering_images.shape) | |
| image_features = encoder(transformed_images) | |
| print(image_features.shape) | |
| raw_features, generated_volume = decoder(image_features) | |
| print(generated_volume.shape) | |
| if cfg.NETWORK.USE_MERGER: | |
| generated_volume = merger(raw_features, generated_volume) | |
| else: | |
| generated_volume = torch.mean(generated_volume, dim=1) | |
| # encoder_loss = bce_loss(generated_volume, ground_truth_volume) * 10 | |
| if cfg.NETWORK.USE_REFINER: | |
| generated_volume = refiner(generated_volume) | |
| # refiner_loss = bce_loss(generated_volume, ground_truth_volume) * 10 | |
| else: | |
| # refiner_loss = encoder_loss | |
| pass | |
| generated_volume=generated_volume.squeeze(0) | |
| gv = generated_volume.cpu().numpy() | |
| gv = (gv >= 0.5).astype(np.uint8) | |
| torch.cuda.empty_cache() | |
| return gv | |