| import json | |
| import numpy as np | |
| from transformers import AutoTokenizer, SiglipTextModel, SiglipTextConfig | |
| import pdb | |
| import pickle | |
| import torch | |
| from matplotlib import pyplot as plt | |
| import uuid | |
| from scipy.special import softmax | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| import uuid | |
| from dotenv import load_dotenv | |
| import traceback | |
| # from sentence_transformers import SentenceTransformer | |
| import os | |
| import copy | |
| class AssetRetrievalModule(nn.Module): | |
| def __init__(self, lambd, sigma, temp, top_p, top_k, asset_size_threshold, rand_seed=None, accelerator=None, dvc=None, do_print=False, is_sft_training=False): | |
| # torch.use_deterministic_algorithms(False) | |
| super().__init__() | |
| self.accelerator = accelerator | |
| self.dvc = dvc | |
| self.all_assets_metadata = json.load(open(os.getenv("PTH_ASSETS_METADATA"))) | |
| self.all_assets_metadata_scaled = json.load(open(os.getenv("PTH_ASSETS_METADATA_SCALED"))) | |
| # config = SiglipTextConfig.from_pretrained("google/siglip-so400m-patch14-384") | |
| # config.max_position_embeddings = 64 | |
| # self.siglip_model = SiglipTextModel.from_pretrained("google/siglip-so400m-patch14-384", config=config) | |
| self.siglip_model = SiglipTextModel.from_pretrained("google/siglip-so400m-patch14-384") | |
| self.siglip_tokenizer = AutoTokenizer.from_pretrained("google/siglip-so400m-patch14-384") | |
| with open(os.getenv("PTH_ASSETS_EMBED"), 'rb') as fp: | |
| model_info_martin_embeds = pickle.load(fp) | |
| all_embeds = np.array(model_info_martin_embeds.get("embeds")) | |
| all_sizes = np.array(model_info_martin_embeds.get("sizes")) | |
| # sort by product and ignore all objects that have size > 100 for their product across x, y, z | |
| all_sizes_prod = np.prod(model_info_martin_embeds.get("sizes"), axis=1) | |
| size_prod_threshold = 150.0 | |
| all_embeds = all_embeds[all_sizes_prod < size_prod_threshold, :] | |
| all_sizes = all_sizes[all_sizes_prod < size_prod_threshold] | |
| all_jids = np.array(model_info_martin_embeds.get("jids"))[all_sizes_prod < size_prod_threshold].tolist() | |
| print(f"removed {all_sizes_prod[all_sizes_prod >= size_prod_threshold].shape[0]} assets with size > 100") | |
| print("shapes: ", all_embeds.shape, all_sizes.shape, len(all_jids)) | |
| # cast to tensor for later | |
| all_embeds = torch.tensor(all_embeds) | |
| all_sizes = torch.tensor(all_sizes) | |
| # print all objects with size and desc that contain "L-shaped sofa" in their "desc". | |
| # we can get the "desc" via "jid" that has the same index as size | |
| # for i in range(all_embeds.shape[0]): | |
| # jid = all_jids[i] | |
| # asset = self.all_assets_metadata.get(jid) | |
| # if asset is not None: | |
| # desc = asset.get("summary") | |
| # else: | |
| # asset = self.all_assets_metadata_scaled.get(jid) | |
| # orig_jid = asset.get("jid") | |
| # orig_asset = self.all_assets_metadata.get(orig_jid) | |
| # desc = orig_asset.get("summary") | |
| # if "l-shaped sofa" in desc.lower(): | |
| # print(f"idx: {i} — jid: {jid}") | |
| # print(f"\t desc: {desc}") | |
| # print(f"\t size: {all_sizes[i]}") | |
| # print(f"\t all_embeds[i]: {all_embeds[i]}") | |
| # print(f"\t all_sizes[i]: {all_sizes[i]}") | |
| # print("") | |
| # exit() | |
| if self.accelerator: | |
| # self.siglip_model = self.accelerator.prepare() | |
| self.siglip_model = self.siglip_model.to(accelerator.device) | |
| all_embeds = all_embeds.to(self.accelerator.device) | |
| # all_sizes = all_sizes.to(torch.float32).to(self.accelerator.device) | |
| all_sizes = all_sizes.to(self.accelerator.device) | |
| else: | |
| self.siglip_model = self.siglip_model.to(dvc) | |
| all_embeds = all_embeds.to(dvc) | |
| all_sizes = all_sizes.to(dvc) | |
| self.all_embeds_catalog = torch.nn.functional.normalize(all_embeds, p=2, dim=1) | |
| self.all_sizes_catalog = all_sizes | |
| self.all_jids_catalog = all_jids | |
| # Learnable parameters | |
| self.lambd = torch.tensor(lambd) | |
| self.sigma = torch.tensor(sigma) | |
| self.temp = torch.tensor(temp) | |
| # Fixed hyperparameters | |
| self.top_p = top_p | |
| self.top_k = top_k | |
| self.asset_size_threshold = asset_size_threshold | |
| self.do_print = do_print | |
| self.is_sft_training = is_sft_training | |
| # self.sbert_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') | |
| def get_text_embeddings(self, txts): | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — before siglip tokenizer") | |
| # print(txts) | |
| try: | |
| inputs = self.siglip_tokenizer(txts, truncation=True, padding="max_length", return_tensors="pt", return_attention_mask=True) | |
| if self.accelerator: | |
| inputs = {k: v.to(self.accelerator.device) for k, v in inputs.items()} | |
| else: | |
| inputs = {k: v.to(self.dvc) for k, v in inputs.items()} | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — before siglip model") | |
| # print(inputs) | |
| # print(f"Model device: {next(self.siglip_model.parameters()).device}") | |
| # print(f"Input device: {inputs['input_ids'].device}") | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — GPU Memory before forward pass:") | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — Allocated: {torch.cuda.memory_allocated(self.accelerator.device) / 1e9:.2f} GB") | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — Reserved: {torch.cuda.memory_reserved(self.accelerator.device) / 1e9:.2f} GB") | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — Max allocated: {torch.cuda.max_memory_allocated(self.accelerator.device) / 1e9:.2f} GB") | |
| # forward pass | |
| with torch.inference_mode(): | |
| if self.accelerator: | |
| with self.accelerator.no_sync(self.siglip_model): | |
| outputs = self.siglip_model(**inputs) | |
| else: | |
| outputs = self.siglip_model(**inputs) | |
| embed = outputs.pooler_output | |
| return embed | |
| # print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — after siglip pooled output") | |
| except Exception as exc: | |
| print(f"idx [{self.accelerator.process_index if self.accelerator else None}] — could not compute text embeddings") | |
| print(exc) | |
| return None | |
| def compute_text_similarity(self, text1, text2): | |
| embeddings = self.get_text_embeddings([text1, text2]) | |
| similarities = torch.cosine_similarity(embeddings[0], embeddings[1], dim=0).item() | |
| return similarities | |
| def compute_semantic_similarities(self, embeds): | |
| embeds_norm = torch.nn.functional.normalize(embeds, p=2, dim=1) | |
| torch.use_deterministic_algorithms(False) | |
| torch.backends.cudnn.deterministic = False | |
| similarities = torch.matmul(self.all_embeds_catalog, embeds_norm.T) | |
| # similarities = similarities / 0.000000001 | |
| # similarities = torch.pow(similarities, 0.5) / 0.1 | |
| return similarities | |
| def compute_size_similarities(self, query_sizes): | |
| size_diffs = torch.sum(torch.square(self.all_sizes_catalog.unsqueeze(1) - query_sizes.unsqueeze(0)), dim=-1) | |
| similarities = torch.exp(-size_diffs / (2 * (self.sigma ** 2))) | |
| return similarities | |
| def compute_final_probabilities(self, sims_batch): | |
| # sims_batch: A matrix where each column corresponds to one query's similarities across all assets | |
| all_probs_batch = [] | |
| # loop through each batch (each query) and apply top-k and top-p filtering | |
| for sims in sims_batch.T: # Iterate over columns (each query) | |
| # temp scaling | |
| scaled_sims = sims / self.temp | |
| # scaled_sims = sims | |
| # probs_sorted = np.sort(scaled_sims.detach().numpy())[::-1][:50] | |
| # print(probs_sorted) | |
| # plt.bar(np.arange(probs_sorted.shape[0]), probs_sorted) | |
| # plt.show() | |
| # exit() | |
| # apply top-k filtering | |
| top_k = min(self.top_k, len(scaled_sims)) | |
| top_k_sims, top_k_indices = torch.topk(scaled_sims, k=top_k) | |
| # apply softmax to top-k probs | |
| top_k_probs = torch.softmax(top_k_sims, dim=0) | |
| # Scatter top-k probabilities back into the full similarity tensor | |
| all_probs = torch.zeros_like(scaled_sims) | |
| all_probs.scatter_(0, top_k_indices, top_k_probs) | |
| # Normalize after scattering | |
| all_probs = all_probs / all_probs.sum() | |
| # Apply top-p (nucleus sampling) | |
| sorted_probs, sorted_indices = torch.sort(all_probs, descending=True) | |
| if self.is_sft_training: | |
| torch.use_deterministic_algorithms(False) | |
| cumulative_probs = torch.cumsum(sorted_probs, dim=0) | |
| torch.use_deterministic_algorithms(True) | |
| else: | |
| cumulative_probs = torch.cumsum(sorted_probs, dim=0) | |
| # Find which indices to remove based on top-p | |
| sorted_indices_to_remove = cumulative_probs > self.top_p | |
| sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].clone() # Shift by one | |
| sorted_indices_to_remove[0] = False # Ensure we don't remove the first element | |
| indices_to_remove = sorted_indices[sorted_indices_to_remove] | |
| # Set probabilities at these indices to zero | |
| all_probs[indices_to_remove] = 0 | |
| # Normalize again after top-p filtering | |
| all_probs = all_probs / all_probs.sum() | |
| # probs_sorted = np.sort(all_probs[all_probs > 0.0].detach().numpy())[::-1] | |
| # print(probs_sorted) | |
| # plt.bar(np.arange(probs_sorted.shape[0]), probs_sorted) | |
| # plt.show() | |
| # exit() | |
| # Store the final probabilities for this query | |
| all_probs_batch.append(all_probs) | |
| # Stack all probability vectors into a matrix | |
| return torch.stack(all_probs_batch, dim=1).T | |
| def forward_batch(self, query_texts, query_sizes): | |
| # print("before text embeddings") | |
| query_embeds = self.get_text_embeddings(query_texts) | |
| # print("after text embeds") | |
| semantic_sims = self.compute_semantic_similarities(query_embeds) | |
| # print("after semantic sims") | |
| query_sizes = torch.tensor(query_sizes) | |
| if self.accelerator: | |
| query_sizes = query_sizes.to(self.accelerator.device) | |
| else: | |
| query_sizes = query_sizes.to(self.dvc) | |
| size_sims = self.compute_size_similarities(query_sizes) | |
| # print("after size sims") | |
| weighted_sims = self.lambd * semantic_sims + (1 - self.lambd) * size_sims | |
| probs = self.compute_final_probabilities(weighted_sims) | |
| # print("after final probs") | |
| return probs | |
| def create_sampled_obj(self, obj, probs, is_greedy_sampling): | |
| if self.do_print: | |
| print(f"sampling obj with desc: {obj.get('desc')} and size {obj.get('size')}") | |
| n_top = min(5, self.top_k) | |
| idxs_top = torch.argsort(probs, descending=True)[:n_top] | |
| print("top probs:", torch.sort(probs, descending=True)[0].detach().cpu().numpy()[:n_top].tolist()) | |
| jids = [ self.all_jids_catalog[idx.item()] for idx in idxs_top ] | |
| for idx, jid in zip(idxs_top, jids): | |
| asset = self.all_assets_metadata.get(jid) | |
| if asset == None: | |
| asset = self.all_assets_metadata_scaled.get(jid) | |
| print(jid, asset) | |
| orig_jid = asset.get("jid") | |
| orig_asset = self.all_assets_metadata.get(orig_jid) | |
| desc = orig_asset.get("summary") | |
| else: | |
| desc = asset.get("summary") | |
| print(f"") | |
| print(f"\t idx: [{idx}] — jid: {jid}") | |
| print(f"\t desc: {desc}") | |
| print(f"\t size: {asset.get('size')}") | |
| print("") | |
| # get jid for sampled object but skip if already set (for GT assets) | |
| if obj.get("jid") == None: | |
| if is_greedy_sampling: | |
| _, idx_sampled = torch.max(probs, dim=0) | |
| else: | |
| idx_sampled = torch.multinomial(probs, num_samples=1) | |
| if self.do_print: | |
| print("idx_sampled:", idx_sampled) | |
| jid_sampled_obj = self.all_jids_catalog[idx_sampled] | |
| else: | |
| jid_sampled_obj = obj.get("jid") | |
| asset = self.all_assets_metadata.get(jid_sampled_obj) | |
| if asset == None: | |
| asset = self.all_assets_metadata_scaled.get(jid_sampled_obj) | |
| size_sampled_obj = asset.get("size") | |
| orig_jid = asset.get("jid") | |
| orig_asset = self.all_assets_metadata.get(orig_jid) | |
| desc_sampled_obj = orig_asset.get("summary") | |
| else: | |
| desc_sampled_obj = asset.get("summary") | |
| size_sampled_obj = asset.get("size") | |
| new_obj = copy.deepcopy(obj) | |
| new_obj.update({ | |
| "sampled_asset_jid": jid_sampled_obj, | |
| "sampled_asset_desc": desc_sampled_obj, | |
| "sampled_asset_size": size_sampled_obj, | |
| "uuid": str(uuid.uuid4()) | |
| }) | |
| if self.do_print: | |
| print(obj) | |
| print("\n") | |
| return new_obj | |
| def sample_all_assets(self, scene, batch_size=64, is_greedy_sampling=True): | |
| if self.do_print: | |
| print(f"sampling full scene... (# of objects: {len(scene.get('objects', []))})") | |
| sampled_scene = copy.deepcopy(scene) | |
| sampled_scene["objects"] = [] | |
| desc_size_map = {} | |
| descriptions = [obj.get("desc") for obj in scene.get("objects", [])] | |
| sizes = [obj.get("size", []) for obj in scene.get("objects", [])] | |
| for batch_start in range(0, len(descriptions), batch_size): | |
| batch_end = min(batch_start + batch_size, len(descriptions)) | |
| batch_descriptions = descriptions[batch_start:batch_end] | |
| batch_sizes = sizes[batch_start:batch_end] | |
| batch_probs = self.forward_batch(batch_descriptions, batch_sizes) | |
| for i, obj in enumerate(scene.get("objects", [])[batch_start:batch_end]): | |
| desc = obj.get("desc") | |
| size = obj.get("size", []) | |
| if desc in desc_size_map: | |
| # Try to find existing sampled assets with the same description and size within the threshold | |
| matching_obj = None | |
| for sampled_obj in desc_size_map[desc]: | |
| if self.calculate_size_difference(size, sampled_obj["size"]) <= self.asset_size_threshold: | |
| matching_obj = sampled_obj | |
| break | |
| if matching_obj: | |
| new_obj = copy.deepcopy(obj) | |
| new_obj.update({ | |
| "sampled_asset_jid": matching_obj["sampled_asset_jid"], | |
| "sampled_asset_desc": matching_obj["sampled_asset_desc"], | |
| "sampled_asset_size": matching_obj["sampled_asset_size"], | |
| "uuid": str(uuid.uuid4()) | |
| }) | |
| else: | |
| new_obj = self.create_sampled_obj(obj, batch_probs[i], is_greedy_sampling) | |
| desc_size_map[desc].append(new_obj) | |
| else: | |
| new_obj = self.create_sampled_obj(obj, batch_probs[i], is_greedy_sampling) | |
| desc_size_map[desc] = [new_obj] | |
| sampled_scene["objects"].append(new_obj) | |
| return sampled_scene | |
| # sample only last object in the list | |
| def sample_last_asset(self, scene, is_greedy_sampling=True): | |
| if self.do_print: | |
| print(f"sampling last object in scene...") | |
| sampled_scene = copy.deepcopy(scene) | |
| sampled_scene["objects"] = scene.get("objects", [])[:-1] | |
| if len(scene.get("objects", [])) > 0: | |
| last_obj = scene.get("objects")[-1] | |
| desc = last_obj.get("desc") | |
| size = last_obj.get("size", []) | |
| probs = self.forward_batch([desc], [size]) | |
| new_obj = self.create_sampled_obj(last_obj, probs[0], is_greedy_sampling) | |
| sampled_scene["objects"].append(new_obj) | |
| return sampled_scene | |
| def calculate_size_difference(size1, size2): | |
| return np.linalg.norm(np.array(size1) - np.array(size2)) | |
| # ********************************************************************************************************** | |
| # def train(model, optimizer, queries, sizes, num_epochs, batch_size): | |
| # for epoch in range(num_epochs): | |
| # for i in range(0, len(queries), batch_size): | |
| # batch_queries = queries[i:i+batch_size] | |
| # batch_sizes = sizes[i:i+batch_size] | |
| # optimizer.zero_grad() | |
| # total_loss = 0 | |
| # for query, size in zip(batch_queries, batch_sizes): | |
| # probs = model(query, size) | |
| # samples = model.sample(probs) | |
| # # Here, you would use GPT-4V to rate the samples | |
| # ratings = rate_samples_with_gpt4v(query, size, samples) | |
| # pos_samples = samples[ratings > 8] | |
| # neg_samples = samples[ratings < 3] | |
| # if len(pos_samples) > 0 and len(neg_samples) > 0: | |
| # pos_probs = probs[pos_samples] | |
| # neg_probs = probs[neg_samples] | |
| # # Contrastive loss | |
| # loss = -torch.log(pos_probs.sum()) - torch.log(1 - neg_probs.sum()) | |
| # total_loss += loss | |
| # if total_loss > 0: | |
| # total_loss.backward() | |
| # optimizer.step() | |
| # print(f"Epoch {epoch+1}, Loss: {total_loss.item()}, " | |
| # f"Top-k: {model.top_k_param.item()}, Top-p: {model.top_p_param.item()}") | |
| # ********************************************************************************************************** | |
| # train | |
| # Initialize the model | |
| # model = AssetRetrievalModule(siglip_model, siglip_tokenizer, all_embeds_catalog, all_sizes_catalog, all_jids_catalog) | |
| # Create an optimizer | |
| # optimizer = torch.optim.Adam(model.parameters(), lr=0.001) | |
| # Train the model | |
| # train(model, optimizer, queries, sizes, num_epochs=10, batch_size=32) | |
| # ********************************************************************************************************** | |
| # test | |
| if __name__ == "__main__": | |
| load_dotenv(".env.local") | |
| sampling_engine = AssetRetrievalModule(lambd=0.5, sigma=0.05, temp=0.2, top_p=0.95, top_k=20, asset_size_threshold=0.5, rand_seed=1234, do_print=True) | |
| # pth_src = "/Volumes/apollo11/data/3D-FRONT-martin-rooms-train-v1/946dac58-6180-434b-a23e-ecebc7704411-a43d4a4d-4c6a-4158-a26c-f6d2f019e1e7.json" | |
| # scene = json.load(open(pth_src)) | |
| # scene = json.loads('{"objects":[{"desc":"Modern minimalist white desk with metal frame, wooden rectangular top, and open leg design.","size":[1.2,0.75,0.6],"pos":[0.17,0.0,0.71],"rot":[0,0.70711,0,0.70711]},{"desc":"Modern ergonomic chair with a mesh backrest, padded seat, foldable arms, and metal frame in black, gray, and white colors.","size":[0.64,1.02,0.68],"pos":[0.78,0.0,0.97],"rot":[0,-0.70711,0,0.70711]},{"desc":"Modern minimalist lounge chair with black fabric cushions, natural wood armrests, and a sleek metallic frame.","size":[0.92,0.93,0.92],"pos":[-0.78,0.0,-0.31],"rot":[0,0.38268,0,0.92388]},{"desc":"This contemporary-traditional rectangular bookcase in white wood features symmetrical open shelves, bottom cabinets with metal handles, making it a versatile and elegant storage solution.","size":[2.0,2.51,0.4],"pos":[0.24,0.0,-0.97],"rot":[0,0,0,1]}]}') | |
| # scene = json.loads('{"bounds_top": [[-1.9, 2.7, 1.9], [1.9, 2.7, 1.9], [1.9, 2.7, -1.9], [-1.9, 2.7, -1.9]], "bounds_bottom": [[-1.9, 0.0, 1.9], [1.9, 0.0, 1.9], [1.9, 0.0, -1.9], [-1.9, 0.0, -1.9]], "room_type": "bedroom", "objects": [{"desc": "Minimalist modern brown wooden open back bookcase with clean lines, six shelves per unit, and a paired unit structure.", "size": [2.09, 2.0, 0.3], "pos": [-1.72, 0.0, -0.82], "rot": [0, 0.70711, 0, 0.70711]}]}') | |
| # scene = json.loads('{"bounds_top": [[2.69, 2.7, 0.01], [0.01, 2.7, -2.69], [-2.69, 2.7, -0.01], [-0.01, 2.7, 2.69]], "bounds_bottom": [[2.69, 0.0, 0.01], [0.01, 0.0, -2.69], [-2.69, 0.0, -0.01], [-0.01, 0.0, 2.69]], "room_type": "bedroom", "objects": [{"desc": "Minimalist modern brown wooden open back bookcase with clean lines, six shelves per unit, and a paired unit structure.", "size": [2.09, 2.0, 0.3], "pos": [0.63, 0.0, 1.8], "rot": [0.0, 0.924431453527687, 0.0, -0.3813482499352634]}]}') | |
| # scene = json.loads('{"bounds_top": [[-1.95, 2.6, 3.1], [1.95, 2.6, 3.1], [1.95, 2.6, -1.1], [-0.65, 2.6, -1.1], [-0.65, 2.6, -3.1], [-1.95, 2.6, -3.1]], "bounds_bottom": [[-1.95, 0.0, 3.1], [1.95, 0.0, 3.1], [1.95, 0.0, -1.1], [-0.65, 0.0, -1.1], [-0.65, 0.0, -3.1], [-1.95, 0.0, -3.1]], "room_type": "bedroom", "objects": [{"desc": "Modern minimalist nightstand with black wood frame, white drawers, and sleek metal legs.", "size": [0.63, 0.51, 0.39], "pos": [1.76, 0.0, -0.01], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "Modern minimalist nightstand with black wood frame, white drawers, and sleek metal legs.", "size": [0.63, 0.51, 0.39], "pos": [1.84, 0.0, 2.7], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "Modern minimalist king-size bed with dark brown fabric upholstery, low-profile wooden frame, and sleek design.", "size": [2.3, 0.78, 2.42], "pos": [0.74, 0.0, 1.28], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "A modern minimalist dark gray wardrobe with sliding mirror doors, shelves, and a hanging rod.", "size": [2.54, 2.43, 0.78], "pos": [0.67, 0.0, -0.72], "rot": [0, 0, 0, 1]}]}') | |
| # scene = json.loads('{"bounds_top": [[2.6869945511849864, 2.7, 0.007764142076983778], [0.007764142076983852, 2.7, -2.6869945511849864], [-2.6869945511849864, 2.7, -0.007764142076983778], [-0.007764142076983852, 2.7, 2.6869945511849864]], "bounds_bottom": [[2.6869945511849864, 0.0, 0.007764142076983778], [0.007764142076983852, 0.0, -2.6869945511849864], [-2.6869945511849864, 0.0, -0.007764142076983778], [-0.007764142076983852, 0.0, 2.6869945511849864]], "room_type": "bedroom", "objects": [{"desc": "Minimalist modern brown wooden open back bookcase with clean lines, six shelves per unit, and a paired unit structure.", "size": [2.09, 2.0, 0.3], "pos": [0.6312037303134077, 0.0, 1.7978826020734608], "rot": [0, 0.70711, 0, 0.70711]}]}') | |
| # scene = json.loads('{"bounds_top": [[1.96, 2.6, 0.98], [1.61, 2.6, -1.49], [-1.96, 2.6, -0.98], [-1.61, 2.6, 1.49]], "bounds_bottom": [[1.96, 0.0, 0.98], [1.61, 0.0, -1.49], [-1.96, 0.0, -0.98], [-1.61, 0.0, 1.49]], "room_type": "bedroom", "objects": [{"desc": "Contemporary minimalist desk featuring brown and white wood composite materials with open cubbies, closed shelves, double-door cabinets, and sleek handles.", "size": [1.4, 1.86, 0.61], "pos": [-0.84, 0.0, 1.11], "rot": [0.0, 0.9974822068295638, 0.0, -0.07091718450716644]}, {"desc": "A modern, avant-garde pendant lamp with a sculptural gold frame, featuring candle-like glass tubes creating a contemporary, linear aesthetic.", "size": [1.68, 0.69, 0.32], "pos": [-0.13, 1.96, -0.14], "rot": [0.0, 0.6551804104944319, 0.0, -0.7554724546297819]}, {"desc": "A playful animal-shaped desk made of vibrant colors and whimsical patterns, adding a fun touch to the room.", "size": [1.2, 1.2, 0.5], "pos": [0.0, 0.0, 0.0], "rot": [0.0, 0.0, 0.0, 1.0]}]}') | |
| # scene = json.loads('{"bounds_top": [[-1.7, 2.6, 2.1], [1.7, 2.6, 2.1], [1.7, 2.6, -2.1], [-1.7, 2.6, -2.1]], "bounds_bottom": [[-1.7, 0.0, 2.1], [1.7, 0.0, 2.1], [1.7, 0.0, -2.1], [-1.7, 0.0, -2.1]], "room_type": "bedroom", "objects": [{"desc": "A minimalist modern ceramic vase with sleek black branches, perfect for contemporary aesthetics.", "size": [0.13, 0.49, 0.31], "pos": [1.41, 0.0, 1.28], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "A modern minimalist gray wooden nightstand with three drawers and angled metal legs, boasting sleek and clean lines.", "size": [0.51, 0.56, 0.5], "pos": [-1.44, 0.0, 1.05], "rot": [0, 0.70711, 0, 0.70711]}, {"desc": "Modern bed frame with dark wood and padded dark gray leather headboard, blending simplicity and mid-century elements.", "size": [2.04, 1.08, 2.37], "pos": [-0.51, 0.0, -0.28], "rot": [0, 0.70711, 0, 0.70711]}, {"desc": "A sleek, black, contemporary wardrobe with clean minimalist lines, four doors, and subtle metal handles.", "size": [2.0, 2.36, 0.66], "pos": [-0.69, 0.0, 1.72], "rot": [0, 1, 0, 0]}, {"desc": "A modern minimalist gray wooden nightstand with three drawers and angled metal legs, boasting sleek and clean lines.", "size": [0.51, 0.56, 0.5], "pos": [-1.46, 0.0, -1.62], "rot": [0, 0.70711, 0, 0.70711]}]}') | |
| # scene = json.loads('{"bounds_top": [[-1.25, 2.8, 1.85], [1.25, 2.8, 1.85], [1.25, 2.8, -1.85], [-1.25, 2.8, -1.85]], "bounds_bottom": [[-1.25, 0.0, 1.85], [1.25, 0.0, 1.85], [1.25, 0.0, -1.85], [-1.25, 0.0, -1.85]], "room_type": "bedroom", "objects": [{"desc": "A contemporary minimalist pendant lamp featuring a linear metal design with gray and gold finish, characterized by suspended circular accents.", "size": [1.09, 0.89, 0.05], "pos": [-0.03, 1.98, -0.28], "rot": [0, 0, 0, 1]}, {"desc": "An antique-inspired light gray wooden nightstand with two drawers, an open shelf, ornate accents, and brass knobs.", "size": [0.3, 0.44, 0.24], "pos": [0.99, 0.0, -1.37], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "Classic grey wooden wardrobe with 4 louvered doors, crown molding, and base plinth.", "size": [2.04, 2.25, 0.65], "pos": [0.22, 0.0, 1.52], "rot": [0, -1, 0, 0]}]}') | |
| # scene = json.loads('{"bounds_top": [[-1.85, 2.8, -1.25], [-1.85, 2.8, 1.25], [1.85, 2.8, 1.25], [1.85, 2.8, -1.25]], "bounds_bottom": [[-1.85, 0.0, -1.25], [-1.85, 0.0, 1.25], [1.85, 0.0, 1.25], [1.85, 0.0, -1.25]], "room_type": "bedroom", "objects": [{"desc": "A contemporary minimalist pendant lamp featuring a linear metal design with gray and gold finish, characterized by suspended circular accents.", "size": [1.09, 0.89, 0.05], "pos": [0.28, 1.98, -0.03], "rot": [0.0, 0.70711, 0.0, -0.70711]}, {"desc": "An antique-inspired light gray wooden nightstand with two drawers, an open shelf, ornate accents, and brass knobs.", "size": [0.3, 0.44, 0.24], "pos": [1.37, 0.0, 0.99], "rot": [0.0, 1.0, 0.0, 0.0]}, {"desc": "Classic grey wooden wardrobe with 4 louvered doors, crown molding, and base plinth.", "size": [2.04, 2.25, 0.65], "pos": [-1.52, 0.0, 0.22], "rot": [0.0, 0.70711, 0.0, 0.70711]}]}') | |
| # scene = json.loads('{"bounds_top": [[1.85, 2.8, -1.25], [1.85, 2.8, 1.25], [-1.85, 2.8, 1.25], [-1.85, 2.8, -1.25]], "bounds_bottom": [[1.85, 0.0, -1.25], [1.85, 0.0, 1.25], [-1.85, 0.0, 1.25], [-1.85, 0.0, -1.25]], "room_type": "bedroom", "objects": [{"desc": "A contemporary minimalist pendant lamp featuring a linear metal design with gray and gold finish, characterized by suspended circular accents.", "size": [1.09, 0.89, 0.05], "pos": [-0.28, 1.98, -0.03], "rot": [0.0, 0.70711, 0.0, -0.70711]}, {"desc": "An antique-inspired light gray wooden nightstand with two drawers, an open shelf, ornate accents, and brass knobs.", "size": [0.3, 0.44, 0.24], "pos": [-1.37, 0.0, 0.99], "rot": [0.0, 1.0, 0.0, 0.0]}, {"desc": "Classic grey wooden wardrobe with 4 louvered doors, crown molding, and base plinth.", "size": [2.04, 2.25, 0.65], "pos": [1.52, 0.0, 0.22], "rot": [0.0, 0.70711, 0.0, 0.70711]}]}') | |
| # scene = json.loads('{"bounds_top": [[1.55, 2.6, 2.5], [1.55, 2.6, -2.5], [-1.55, 2.6, -2.5], [-1.55, 2.6, 2.5]], "bounds_bottom": [[1.55, 0.0, 2.5], [1.55, 0.0, -2.5], [-1.55, 0.0, -2.5], [-1.55, 0.0, 2.5]], "room_type": "bedroom", "objects": [{"desc": "Modern king-size bed with a quilted high-back headboard, side panels, and plush cushions accented by geometric pillows.", "size": [2.0, 0.97, 2.08], "pos": [-0.49, 0.0, -0.84], "rot": [0.0, -0.70711, 0.0, -0.70711]}, {"desc": "Modern minimalist nightstand with gray upholstery, single drawer, and distinctive curved wood legs.", "size": [0.44, 0.44, 0.35], "pos": [-1.32, 0.0, -2.04], "rot": [0.0, 0.70711, 0.0, 0.70711]}, {"desc": "Modern minimalist nightstand with gray upholstery, single drawer, and distinctive curved wood legs.", "size": [0.44, 0.44, 0.35], "pos": [-1.33, 0.0, 0.35], "rot": [0.0, 0.70711, 0.0, 0.70711]}]}') | |
| # scene = json.loads('{"bounds_top": [[1.55, 2.6, -2.5], [1.55, 2.6, 2.5], [-1.55, 2.6, 2.5], [-1.55, 2.6, -2.5]], "bounds_bottom": [[1.55, 0.0, -2.5], [1.55, 0.0, 2.5], [-1.55, 0.0, 2.5], [-1.55, 0.0, -2.5]], "room_type": "bedroom", "objects": [{"desc": "Modern king-size bed with a quilted high-back headboard, side panels, and plush cushions accented by geometric pillows.", "size": [2.0, 0.97, 2.08], "pos": [-0.49, 0.0, 0.84], "rot": [0.0, 0.70711, 0.0, -0.70711]}, {"desc": "Modern minimalist nightstand with gray upholstery, single drawer, and distinctive curved wood legs.", "size": [0.44, 0.44, 0.35], "pos": [-1.32, 0.0, 2.04], "rot": [0.0, 0.70711, 0.0, -0.70711]}, {"desc": "Modern minimalist nightstand with gray upholstery, single drawer, and distinctive curved wood legs.", "size": [0.44, 0.44, 0.35], "pos": [-1.33, 0.0, -0.35], "rot": [0.0, 0.70711, 0.0, -0.70711]}]}') | |
| # test_local_rotations() | |
| # exit() | |
| scene = json.loads('{"bounds_top": [[-1.35, 2.6, 1.45], [0.15, 2.6, 1.45], [0.15, 2.6, 2.15], [1.35, 2.6, 2.15], [1.35, 2.6, -2.15], [-1.35, 2.6, -2.15]], "bounds_bottom": [[-1.35, 0.0, 1.45], [0.15, 0.0, 1.45], [0.15, 0.0, 2.15], [1.35, 0.0, 2.15], [1.35, 0.0, -2.15], [-1.35, 0.0, -2.15]], "room_type": "bedroom", "objects": [{"desc": "Mid-Century Modern nightstand with a wood construction, featuring a rectangular shape, single drawer, tapered legs, and a distinctive cut-out handle.", "size": [0.55, 0.45, 0.43], "pos": [1.1, 0.0, 0.95], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "Mid-Century Modern nightstand with a wood construction, featuring a rectangular shape, single drawer, tapered legs, and a distinctive cut-out handle.", "size": [0.55, 0.45, 0.43], "pos": [1.23, 0.0, -1.42], "rot": [0, -0.70711, 0, 0.70711]}]}') | |
| # scene = do_random_augm_on_sgs(scene) | |
| print(scene) | |
| # sampled_scene = scene | |
| # sampled_scene = scene | |
| # scene = json.loads('{"bounds_top": [[-1.7, 2.6, 2.1], [1.7, 2.6, 2.1], [1.7, 2.6, -2.1], [-1.7, 2.6, -2.1]], "bounds_bottom": [[-1.7, 0.0, 2.1], [1.7, 0.0, 2.1], [1.7, 0.0, -2.1], [-1.7, 0.0, -2.1]], "room_type": "bedroom", "objects": [{"desc": "A minimalist modern ceramic vase with sleek black branches, perfect for contemporary aesthetics.", "size": [0.13, 0.49, 0.31], "pos": [1.41, 0.0, 1.28], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "A modern minimalist gray wooden nightstand with three drawers and angled metal legs, boasting sleek and clean lines.", "size": [0.51, 0.56, 0.5], "pos": [-1.44, 0.0, 1.05], "rot": [0, 0.70711, 0, 0.70711]}, {"desc": "Modern bed frame with dark wood and padded dark gray leather headboard, blending simplicity and mid-century elements.", "size": [2.04, 1.08, 2.37], "pos": [-0.51, 0.0, -0.28], "rot": [0, 0.70711, 0, 0.70711]}, {"desc": "A sleek, black, contemporary wardrobe with clean minimalist lines, four doors, and subtle metal handles.", "size": [2.0, 2.36, 0.66], "pos": [-0.69, 0.0, 1.72], "rot": [0, 1, 0, 0]}, {"desc": "A modern minimalist gray wooden nightstand with three drawers and angled metal legs, boasting sleek and clean lines.", "size": [0.51, 0.56, 0.5], "pos": [-1.46, 0.0, -1.62], "rot": [0, 0.70711, 0, 0.70711]}]}') | |
| sampled_scene = sampling_engine.sample_all_assets(scene, is_greedy_sampling=True) | |
| print(sampled_scene) | |
| # scene = json.loads('{"bounds_top": [[1.55, 2.6, -2.5], [1.55, 2.6, 2.5], [-1.55, 2.6, 2.5], [-1.55, 2.6, -2.5]], "bounds_bottom": [[1.55, 0.0, -2.5], [1.55, 0.0, 2.5], [-1.55, 0.0, 2.5], [-1.55, 0.0, -2.5]], "room_type": "bedroom", "objects": [{"desc": "Modern king-size bed with a quilted high-back headboard, side panels, and plush cushions accented by geometric pillows.", "size": [2.0, 0.97, 2.08], "pos": [-0.49, 0.0, 0.84], "rot": [0.0, -0.70711, 0.0, -0.70711]}, {"desc": "Modern minimalist nightstand with gray upholstery, single drawer, and distinctive curved wood legs.", "size": [0.44, 0.44, 0.35], "pos": [-1.32, 0.0, 2.04], "rot": [0.0, 0.70711, 0.0, 0.70711]}, {"desc": "Modern minimalist nightstand with gray upholstery, single drawer, and distinctive curved wood legs.", "size": [0.44, 0.44, 0.35], "pos": [-1.33, 0.0, -0.35], "rot": [0.0, 0.70711, 0.0, 0.70711]}]}') | |
| # output = "{'bounds_top': [[1.96, 2.6, 0.98], [1.61, 2.6, -1.49], [-1.96, 2.6, -0.98], [-1.61, 2.6, 1.49]], 'bounds_bottom': [[1.96, 0.0, 0.98], [1.61, 0.0, -1.49], [-1.96, 0.0, -0.98], [-1.61, 0.0, 1.49]], 'room_type': 'bedroom', 'objects': [{'desc': 'Contemporary minimalist desk featuring brown and white wood composite materials with open cubbies, closed shelves, double-door cabinets, and sleek handles.', 'size': [1.4, 1.86, 0.61], 'pos': [-0.84, 0.0, 1.11], 'rot': [0.0, 0.9974822068295638, 0.0, -0.07091718450716644], 'sampled_asset_jid': 'fec2a457-251b-40ad-94b8-ee4760e78153', 'sampled_asset_desc': 'Contemporary minimalist desk featuring brown and white wood composite materials with open cubbies, closed shelves, double-door cabinets, and sleek handles.', 'sampled_asset_size': [1.399999976158142, 1.8640500122123846, 0.6095100939273834], 'uuid': '2e4fd652-5110-43b0-82aa-4d68c2b4cc4f'}, {'desc': 'A modern, avant-garde pendant lamp with a sculptural gold frame, featuring candle-like glass tubes creating a contemporary, linear aesthetic.', 'size': [1.68, 0.69, 0.32], 'pos': [-0.13, 1.96, -0.14], 'rot': [0.0, 0.6551804104944319, 0.0, -0.7554724546297819], 'sampled_asset_jid': 'da2f4cae-4c21-4671-aaaf-2eb1f5424f16', 'sampled_asset_desc': 'A modern, avant-garde pendant lamp with a sculptural gold frame, featuring candle-like glass tubes creating a contemporary, linear aesthetic.', 'sampled_asset_size': [1.6789209842681885, 0.692858770955354, 0.318820983171463], 'uuid': 'c3dc3408-ef23-484a-bd03-29cf2fadf44b'}, {'desc': 'A playful animal-shaped desk made of vibrant colors and whimsical patterns, adding a fun touch to the room.', 'size': [1.2, 1.2, 0.5], 'pos': [0.0, 0.0, 0.0], 'rot': [0.0, 0.0, 0.0, 1.0], 'sampled_asset_jid': 'c56b454e-5c55-4dcb-bccd-3c67357bc9ec-(1.2)-(1.0)-(1.09)', 'sampled_asset_desc': 'Minimalist beige modern dressing table with hidden storage, rectangular mirror, and unique curved leg design.', 'sampled_asset_size': [1.2, 1.22, 0.51], 'uuid': '48104a85-0162-45c9-972b-1055747dee5b'}]}" | |
| # 0.48 | |
| # 0.4609 | |
| # score = sampling_engine.compute_text_similarity("modern king-size bed", "A modern minimalist king-size bed with a padded headboard, solid wood frame, and decorative blue, yellow, and white pillows") | |
| # score = sampling_engine.compute_text_similarity("Modern minimalist black wood nightstand with drawers and metal legs", "Modern minimalist nightstand with black wood frame, white drawers, and sleek metal legs.") | |
| # print(score) | |
| # score = sampling_engine.compute_text_similarity("blue metal nightstand", "Modern minimalist nightstand with black wood frame, white drawers, and sleek metal legs.") | |
| # print(score) | |
| # print("==") | |
| # score = sampling_engine.compute_text_similarity("modern dark gray wardrobe", "A modern minimalist dark gray wardrobe with sliding mirror doors, shelves, and a hanging rod.") | |
| # print(score) | |
| # score = sampling_engine.compute_text_similarity("blue wardrobe", "A modern minimalist dark gray wardrobe with sliding mirror doors, shelves, and a hanging rod.") | |
| # print(score) | |
| # exit() | |
| #sampled_scene = sampling_engine.sample_all_assets(scene, is_greedy_sampling=False) | |
| #print(sampled_scene) | |
| # Save the sampled scene | |
| pth_tgt = "/Volumes/apollo11/data/sampled/example7.json" | |
| with open(pth_tgt, "w") as fp: | |
| json.dump(sampled_scene, fp, indent=4) | |
| # # temp = 0.0000000001 | |
| # # temp = 0.6 | |
| # sigma = 0.1 | |
| # lambd = 0.3 | |
| # temp = 10.0 | |
| # size_threshold = 0.5 | |
| # # scene = json.loads('{"objects": [{"desc": "Modern minimalist white desk with metal frame, wooden rectangular top, and open leg design.", "size": [1.2, 0.75, 0.6], "pos": [0.17, 0.0, 0.71], "rot": [0, 0.70711, 0, 0.70711]}, {"desc": "Modern ergonomic chair with a mesh backrest, padded seat, foldable arms, and metal frame in black, gray, and white colors.", "size": [0.64, 1.02, 0.68], "pos": [0.78, 0.0, 0.97], "rot": [0, -0.70711, 0, 0.70711]}, {"desc": "Modern minimalist lounge chair with black fabric cushions, natural wood armrests, and a sleek metallic frame.", "size": [0.92, 0.93, 0.92], "pos": [-0.78, 0.0, -0.31], "rot": [0, 0.38268, 0, 0.92388]}, {"desc": "Contemporary-traditional rectangular bookcase with a wooden frame, multiple shelves, and a mix of glass and wood materials.", "size": [1.38, 0.86, 1.22], "pos": [0.0, 0.0, 0.0], "rot": [0, 0, 0, 1]}]}') | |
| # # scene = json.loads('{"objects":[{"desc":"Modern minimalist white desk with metal frame, wooden rectangular top, and open leg design.","size":[1.2,0.75,0.6],"pos":[0.17,0.0,0.71],"rot":[0,0.70711,0,0.70711]},{"desc":"Modern ergonomic chair with a mesh backrest, padded seat, foldable arms, and metal frame in black, gray, and white colors.","size":[0.64,1.02,0.68],"pos":[0.78,0.0,0.97],"rot":[0,-0.70711,0,0.70711]},{"desc":"Modern minimalist lounge chair with black fabric cushions, natural wood armrests, and a sleek metallic frame.","size":[0.92,0.93,0.92],"pos":[-0.78,0.0,-0.31],"rot":[0,0.38268,0,0.92388]},{"desc":"Traditional wooden bookcase with four shelves, made of solid white wood, and a simple design.","size":[1.35,2.33,0.75],"pos":[0.78,0.0,0.47],"rot":[0,0,0,1]}]}') | |
| # # scene = json.loads('{"objects":[{"desc":"Modern minimalist white desk with metal frame, wooden rectangular top, and open leg design.","size":[1.2,0.75,0.6],"pos":[0.17,0.0,0.71],"rot":[0,0.70711,0,0.70711]},{"desc":"Modern ergonomic chair with a mesh backrest, padded seat, foldable arms, and metal frame in black, gray, and white colors.","size":[0.64,1.02,0.68],"pos":[0.78,0.0,0.97],"rot":[0,-0.70711,0,0.70711]},{"desc":"Modern minimalist lounge chair with black fabric cushions, natural wood armrests, and a sleek metallic frame.","size":[0.92,0.93,0.92],"pos":[-0.78,0.0,-0.31],"rot":[0,0.38268,0,0.92388]},{"desc":"This modern minimalist white wood bookcase has rectangular open shelves, metal frame, and wooden vertical panels.","size":[2.0,2.51,0.4],"pos":[0.24,0.0,-0.97],"rot":[0,0,0,1]}]}') | |
| # descs_idxs = {} | |
| # idx = 0 | |
| # sampled_scene = process_scene(scene, lambd, sigma, temp, size_threshold) | |
| # with open(pth_tgt, "w") as write_file: | |
| # json.dump(sampled_scene, write_file, indent=4) |