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How to use render_chunk method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

region.py

Source:region.py

...18        # which causes issues due to dictionaries resizing19        self._chunk_temp: queue.Queue = queue.Queue()20        self._chunk_temp_set = set()21        self._rebuild_regions = []22    def add_render_chunk(self, render_chunk: RenderChunk):23        """Add a RenderChunk to the database.24        A call to _merge_chunk_temp from the main thread will be needed for them to be drawn.25        This is done after the next draw call."""26        self._chunk_temp.put(render_chunk)27        chunk_coords = (render_chunk.cx, render_chunk.cz)28        self._chunk_temp_set.add(chunk_coords)29    def render_chunk_needs_rebuild(self, chunk_coords: Tuple[int, int]) -> bool:30        return (31            chunk_coords not in self._chunk_temp_set32            and self.render_chunk_in_main_database(chunk_coords)33            and self.get_render_chunk(chunk_coords).needs_rebuild()34        )35    def get_render_chunk(self, chunk_coords: Tuple[int, int]) -> RenderChunk:36        """Get a RenderChunk from the database.37        Might throw a key error if it has not been added to the real database yet."""38        return self._regions[self.region_coords(*chunk_coords)].get_render_chunk(39            chunk_coords40        )41    def _merge_chunk_temp(self):42        for _ in range(self._chunk_temp.qsize()):43            render_chunk = self._chunk_temp.get()44            region_coords = self.region_coords(render_chunk.cx, render_chunk.cz)45            if region_coords not in self._regions:46                self._regions[region_coords] = RenderRegion(47                    region_coords[0],48                    region_coords[1],49                    self.region_size,50                    self.context_identifier,51                    self._resource_pack,52                )53            self._regions[region_coords].add_render_chunk(render_chunk)54        self._chunk_temp_set.clear()55    def __contains__(self, chunk_coords: Tuple[int, int]):56        return (57            chunk_coords in self._chunk_temp_set58            or self.render_chunk_in_main_database(chunk_coords)59        )60    def render_chunk_in_main_database(self, chunk_coords: Tuple[int, int]) -> bool:61        region_coords = self.region_coords(*chunk_coords)62        return (63            region_coords in self._regions64            and chunk_coords in self._regions[region_coords]65        )66    def region_coords(self, cx, cz):67        return cx // self.region_size, cz // self.region_size68    def draw(self, camera_matrix, camera):69        cam_rx, cam_rz = numpy.floor(70            numpy.array(camera)[[0, 2]] / (16 * self.region_size)71        )72        cam_cx, cam_cz = numpy.floor(numpy.array(camera)[[0, 2]] / 16)73        for region in sorted(74            self._regions.values(),75            key=lambda x: abs(x.rx - cam_rx) + abs(x.rz - cam_rz),76            reverse=True,77        ):78            region.draw(camera_matrix, cam_cx, cam_cz)79        self._merge_chunk_temp()80    def unload(self, safe_area: Tuple[int, int, int, int] = None):81        if safe_area is None:82            for _ in range(self._chunk_temp.qsize()):83                self._chunk_temp.get()84            self._chunk_temp_set.clear()85            for region in self._regions.values():86                region.unload()87            self._regions.clear()88        else:89            min_rx, min_rz = self.region_coords(*safe_area[:2])90            max_rx, max_rz = self.region_coords(*safe_area[2:])91            delete_regions = []92            for region in self._regions.values():93                if not (94                    min_rx <= region.rx <= max_rx and min_rz <= region.rz <= max_rz95                ):96                    region.unload()97                    delete_regions.append((region.rx, region.rz))98            for region in delete_regions:99                del self._regions[region]100    def rebuild(self):101        """Rebuild a single region which was last rebuild the longest ago.102        Put this on a semi-fast clock to rebuild all regions."""103        if not self._rebuild_regions:104            self._rebuild_regions = list(self._regions.keys())105        if self._rebuild_regions:106            region = self._rebuild_regions.pop(0)107            if region in self._regions:108                self._regions[region].rebuild()109class RenderRegion(TriMesh):110    def __init__(111        self,112        rx: int,113        rz: int,114        region_size: int,115        context_identifier: str,116        resource_pack: OpenGLResourcePack,117    ):118        """A group of RenderChunks to minimise the number of draw calls"""119        super().__init__(120            context_identifier, resource_pack.get_atlas_id(context_identifier)121        )122        self.rx = rx123        self.rz = rz124        self._chunks: Dict[Tuple[int, int], RenderChunk] = {}125        self._merged_chunk_locations: Dict[126            Tuple[int, int], Tuple[int, int, int, int]127        ] = {}128        self._manual_chunks: Dict[Tuple[int, int], RenderChunk] = {}129        self.region_transform = numpy.eye(4, dtype=numpy.float64)130        self.region_transform[3, [0, 2]] = numpy.array([rx, rz]) * region_size * 16131    @property132    def vertex_usage(self):133        return GL_DYNAMIC_DRAW134    def __repr__(self):135        return f"RenderRegion({self.rx}, {self.rz})"136    def __contains__(self, item):137        return item in self._chunks138    def add_render_chunk(self, render_chunk: RenderChunk):139        """Add a chunk to the region"""140        chunk_coords = (render_chunk.cx, render_chunk.cz)141        if chunk_coords in self._chunks:142            self._chunks[chunk_coords].unload()143        self._disable_merged_chunk(chunk_coords)144        self._chunks[chunk_coords] = render_chunk145        self._manual_chunks[chunk_coords] = render_chunk146    def get_render_chunk(self, chunk_coords: Tuple[int, int]):147        return self._chunks[chunk_coords]148    def _disable_merged_chunk(self, chunk_coords: Tuple[int, int]):149        """Zero out the region of memory in the merged chunks related to a given chunk"""150        if chunk_coords in self._merged_chunk_locations:151            (152                offset,153                size,154                translucent_offset,155                translucent_size,156            ) = self._merged_chunk_locations.pop(chunk_coords)157            glBindVertexArray(self._vao)158            glBindBuffer(GL_ARRAY_BUFFER, self._vbo)159            glBufferSubData(160                GL_ARRAY_BUFFER,...

train.py

Source:train.py

1import os.path2import numpy as np3import imageio4import torch5from options.options import get_parser6from data import creat_dataset7from model import creat_nerf8from tqdm import tqdm, trange9from model.networks import *10import datetime11import sys121314if __name__ == "__main__":15    begin = datetime.datetime.now()16    torch.set_default_tensor_type('torch.cuda.FloatTensor')  # ææå®ä¹çtensorå¨é¨éå°cudaä¸17    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')18    opt = get_parser()19    dataset = creat_dataset(opt)20    global_step, optimizer, train_kwargs, test_kwargs, result_dir = creat_nerf(opt)21    bds_dict = {'near': dataset['near'], 'far': dataset['far']}22    train_kwargs.update(bds_dict)23    test_kwargs.update(bds_dict)24    print('begin')25    print('View for train', dataset['i_train'], '\nView for validation', dataset['i_val'])26    # to tensor27    val_poses = torch.from_numpy(dataset['val_poses']).to(device)28    test_poses = torch.from_numpy(dataset['test_poses']).to(device)29    rays = torch.from_numpy(dataset['rays_train']).to(device)3031    if opt.render_only:32        with torch.no_grad():33            rend_dir = os.path.join(result_dir, 'render_only_{:06d}'.format(global_step))34            os.makedirs(rend_dir, exist_ok=True)35            rgbs, disps = render_path(test_poses[:1], dataset['hwf'], opt.render_chunk, rend_dir, **test_kwargs)36            rgb_dir = os.path.join(rend_dir, 'rgb.mp4')37            disp_dir = os.path.join(rend_dir, 'disp.mp4')38            imageio.mimwrite(rgb_dir, tobyte(rgbs), fps=30, quality=8)39            imageio.mimwrite(disp_dir, tobyte(disps / np.max(disps)), fps=30, quality=8)40            sys.exit()41    i_batch = 042    for i in trange(global_step + 1, opt.N_iters + 1):43        num = int(rays.shape[0])44        if i_batch >= num:45            i_batch = 046            idx = torch.randperm(num)47            rays = rays[idx]48            print('shuffle data after an epoch')49        batch = rays[i_batch: i_batch + opt.N_batch_rays]50        batch_rays, target = batch[:, :2], batch[:, -1]51        i_batch += opt.N_batch_rays52        out = render(batch_rays, dataset['hwf'], opt.render_chunk, **train_kwargs)53        rgb = out['rgb']54        rgb0 = out['rgb0']55        optimizer.zero_grad()56        loss = img2mse(rgb, target)57        psnr = mse2psnr(loss)58        loss += img2mse(target, rgb0)59        loss.backward()60        optimizer.step()61        # update learning rate62        decay_rate = 0.163        lr_decay = opt.lr_decay * 100064        new_lr = opt.lr * (decay_rate ** (i / lr_decay))65        for param_group in optimizer.param_groups:66            param_group['lr'] = new_lr6768        if i % opt.i_print == 0:69            tqdm.write(f"[Train] Iter: {i} Loss: {loss.item()} PSNR: {psnr.item()}")70        if i % opt.i_weights == 0:71            dir_w = os.path.join(result_dir, '{:06d}.tar'.format(i))72            torch.save({73                'global_step': i,74                'optimizer_dict': optimizer.state_dict(),75                'model_dict': train_kwargs['model'].state_dict(),76                'model_fn_dict': train_kwargs['model_fn'].state_dict()77            }, dir_w)78            print(f'checkpoints saved at {result_dir}')79        if i % opt.i_video == 0:80            test_dir = os.path.join(result_dir, 'spiral_{:06d}'.format(i))81            os.makedirs(test_dir, exist_ok=True)82            with torch.no_grad():83                rgbs, disps = render_path(test_poses, dataset['hwf'], opt.render_chunk, test_dir, **test_kwargs)84            save_dir_rgb = os.path.join(test_dir, 'rgb.mp4')85            save_dir_disp = os.path.join(test_dir, 'disp.mp4')86            imageio.mimwrite(save_dir_rgb, tobyte(rgbs), fps=30, quality=8)87            imageio.mimwrite(save_dir_disp, tobyte(disps/np.max(disps)), fps=30, quality=8)88        if i % opt.i_test == 0:89            val_dir = os.path.join(result_dir, 'val_{:06d}'.format(i))90            os.makedirs(val_dir, exist_ok=True)91            with torch.no_grad():92                render_path(val_poses, dataset['hwf'], opt.render_chunk, val_dir, **test_kwargs)93        # torch.cuda.empty_cache()
...

MainPage.py

Source:MainPage.py

...34		self.render('templates/index.html')35		36	def get_articles(self):37		return model.Article.all().filter('draft', False).filter('deletion_date', None).order('-creation_date').fetch(5)38		# return self.render_chunk('templates/index-articles.html', {'articles': articles})39	def get_communities(self):40		return model.Community.all().order('-members').fetch(5)41		# return self.render_chunk('templates/index-communities.html', {'communities': communities})42	def get_threads(self):43		return model.Thread.all().filter('parent_thread', None).order('-last_response_date').fetch(5)...

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