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import json |
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import os |
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from typing import * |
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import numpy as np |
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import torch |
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import utils3d.torch |
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from .components import StandardDatasetBase, TextConditionedMixin, ImageConditionedMixin |
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from ..modules.sparse.basic import SparseTensor |
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from .. import models |
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from ..utils.render_utils import get_renderer |
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from ..utils.data_utils import load_balanced_group_indices |
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class SLatVisMixin: |
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def __init__( |
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self, |
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*args, |
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pretrained_slat_dec: str = 'microsoft/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16', |
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slat_dec_path: Optional[str] = None, |
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slat_dec_ckpt: Optional[str] = None, |
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**kwargs |
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): |
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super().__init__(*args, **kwargs) |
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self.slat_dec = None |
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self.pretrained_slat_dec = pretrained_slat_dec |
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self.slat_dec_path = slat_dec_path |
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self.slat_dec_ckpt = slat_dec_ckpt |
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def _loading_slat_dec(self): |
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if self.slat_dec is not None: |
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return |
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if self.slat_dec_path is not None: |
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cfg = json.load(open(os.path.join(self.slat_dec_path, 'config.json'), 'r')) |
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decoder = getattr(models, cfg['models']['decoder']['name'])(**cfg['models']['decoder']['args']) |
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ckpt_path = os.path.join(self.slat_dec_path, 'ckpts', f'decoder_{self.slat_dec_ckpt}.pt') |
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decoder.load_state_dict(torch.load(ckpt_path, map_location='cpu', weights_only=True)) |
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else: |
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decoder = models.from_pretrained(self.pretrained_slat_dec) |
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self.slat_dec = decoder.cuda().eval() |
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def _delete_slat_dec(self): |
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del self.slat_dec |
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self.slat_dec = None |
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@torch.no_grad() |
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def decode_latent(self, z, batch_size=4): |
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self._loading_slat_dec() |
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reps = [] |
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if self.normalization is not None: |
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z = z * self.std.to(z.device) + self.mean.to(z.device) |
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for i in range(0, z.shape[0], batch_size): |
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reps.append(self.slat_dec(z[i:i+batch_size])) |
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reps = sum(reps, []) |
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self._delete_slat_dec() |
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return reps |
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@torch.no_grad() |
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def visualize_sample(self, x_0: Union[SparseTensor, dict]): |
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x_0 = x_0 if isinstance(x_0, SparseTensor) else x_0['x_0'] |
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reps = self.decode_latent(x_0.cuda()) |
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yaws = [0, np.pi / 2, np.pi, 3 * np.pi / 2] |
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yaws_offset = np.random.uniform(-np.pi / 4, np.pi / 4) |
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yaws = [y + yaws_offset for y in yaws] |
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pitch = [np.random.uniform(-np.pi / 4, np.pi / 4) for _ in range(4)] |
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exts = [] |
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ints = [] |
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for yaw, pitch in zip(yaws, pitch): |
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orig = torch.tensor([ |
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np.sin(yaw) * np.cos(pitch), |
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np.cos(yaw) * np.cos(pitch), |
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np.sin(pitch), |
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]).float().cuda() * 2 |
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fov = torch.deg2rad(torch.tensor(40)).cuda() |
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extrinsics = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda()) |
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intrinsics = utils3d.torch.intrinsics_from_fov_xy(fov, fov) |
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exts.append(extrinsics) |
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ints.append(intrinsics) |
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renderer = get_renderer(reps[0]) |
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images = [] |
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for representation in reps: |
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image = torch.zeros(3, 1024, 1024).cuda() |
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tile = [2, 2] |
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for j, (ext, intr) in enumerate(zip(exts, ints)): |
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res = renderer.render(representation, ext, intr) |
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image[:, 512 * (j // tile[1]):512 * (j // tile[1] + 1), 512 * (j % tile[1]):512 * (j % tile[1] + 1)] = res['color'] |
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images.append(image) |
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images = torch.stack(images) |
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return images |
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class SLat(SLatVisMixin, StandardDatasetBase): |
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""" |
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structured latent dataset |
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Args: |
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roots (str): path to the dataset |
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latent_model (str): name of the latent model |
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min_aesthetic_score (float): minimum aesthetic score |
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max_num_voxels (int): maximum number of voxels |
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normalization (dict): normalization stats |
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pretrained_slat_dec (str): name of the pretrained slat decoder |
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slat_dec_path (str): path to the slat decoder, if given, will override the pretrained_slat_dec |
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slat_dec_ckpt (str): name of the slat decoder checkpoint |
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""" |
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def __init__(self, |
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roots: str, |
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*, |
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latent_model: str, |
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min_aesthetic_score: float = 5.0, |
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max_num_voxels: int = 32768, |
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normalization: Optional[dict] = None, |
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pretrained_slat_dec: str = 'microsoft/TRELLIS-image-large/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16', |
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slat_dec_path: Optional[str] = None, |
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slat_dec_ckpt: Optional[str] = None, |
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): |
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self.normalization = normalization |
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self.latent_model = latent_model |
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self.min_aesthetic_score = min_aesthetic_score |
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self.max_num_voxels = max_num_voxels |
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self.value_range = (0, 1) |
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super().__init__( |
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roots, |
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pretrained_slat_dec=pretrained_slat_dec, |
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slat_dec_path=slat_dec_path, |
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slat_dec_ckpt=slat_dec_ckpt, |
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) |
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self.loads = [self.metadata.loc[sha256, 'num_voxels'] for _, sha256 in self.instances] |
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if self.normalization is not None: |
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self.mean = torch.tensor(self.normalization['mean']).reshape(1, -1) |
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self.std = torch.tensor(self.normalization['std']).reshape(1, -1) |
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def filter_metadata(self, metadata): |
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stats = {} |
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metadata = metadata[metadata[f'latent_{self.latent_model}']] |
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stats['With latent'] = len(metadata) |
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metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score] |
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stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata) |
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metadata = metadata[metadata['num_voxels'] <= self.max_num_voxels] |
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stats[f'Num voxels <= {self.max_num_voxels}'] = len(metadata) |
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return metadata, stats |
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def get_instance(self, root, instance): |
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data = np.load(os.path.join(root, 'latents', self.latent_model, f'{instance}.npz')) |
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coords = torch.tensor(data['coords']).int() |
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feats = torch.tensor(data['feats']).float() |
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if self.normalization is not None: |
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feats = (feats - self.mean) / self.std |
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return { |
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'coords': coords, |
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'feats': feats, |
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} |
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@staticmethod |
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def collate_fn(batch, split_size=None): |
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if split_size is None: |
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group_idx = [list(range(len(batch)))] |
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else: |
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group_idx = load_balanced_group_indices([b['coords'].shape[0] for b in batch], split_size) |
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packs = [] |
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for group in group_idx: |
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sub_batch = [batch[i] for i in group] |
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pack = {} |
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coords = [] |
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feats = [] |
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layout = [] |
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start = 0 |
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for i, b in enumerate(sub_batch): |
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coords.append(torch.cat([torch.full((b['coords'].shape[0], 1), i, dtype=torch.int32), b['coords']], dim=-1)) |
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feats.append(b['feats']) |
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layout.append(slice(start, start + b['coords'].shape[0])) |
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start += b['coords'].shape[0] |
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coords = torch.cat(coords) |
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feats = torch.cat(feats) |
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pack['x_0'] = SparseTensor( |
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coords=coords, |
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feats=feats, |
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) |
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pack['x_0']._shape = torch.Size([len(group), *sub_batch[0]['feats'].shape[1:]]) |
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pack['x_0'].register_spatial_cache('layout', layout) |
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keys = [k for k in sub_batch[0].keys() if k not in ['coords', 'feats']] |
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for k in keys: |
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if isinstance(sub_batch[0][k], torch.Tensor): |
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pack[k] = torch.stack([b[k] for b in sub_batch]) |
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elif isinstance(sub_batch[0][k], list): |
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pack[k] = sum([b[k] for b in sub_batch], []) |
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else: |
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pack[k] = [b[k] for b in sub_batch] |
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packs.append(pack) |
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if split_size is None: |
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return packs[0] |
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return packs |
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class TextConditionedSLat(TextConditionedMixin, SLat): |
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""" |
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Text conditioned structured latent dataset |
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""" |
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pass |
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class ImageConditionedSLat(ImageConditionedMixin, SLat): |
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""" |
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Image conditioned structured latent dataset |
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""" |
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pass |
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