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import os
import numpy as np
import json
def read_ldr_file(file_path):
with open(file_path, 'r') as f:
lines = f.readlines()
return lines
def parse_ldr_lines(lines):
parts = []
for line in lines:
if line.startswith('1'): # LDR文件中的零件数据行通常以"1"开头
parts.append(line.strip()) # 处理零件信息
elif line.startswith('0'): # "0"行通常是注释或其他控制信息
pass
else:
pass
return parts
class SingLegoDataset:
def __init__(self, args, split_set="train"):
super().__init__()
self.split_set = split_set
data = np.load(os.path.join(args.data_dir, "Car Arcade_wrdhot" + ".npy"), allow_pickle=True)
self.data = [data]#[data[name] for name in data.files]
#self.prompts = json.load(open(os.path.join(args.data_dir, "text.json"), 'r'))['minecraft']
print(f"{split_set} dataset total data samples: {len(self.data)}")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
data = self.data[idx]
prompt = self.prompts[idx]
#import ipdb; ipdb.set_trace()
data_dict = {}
data_dict['prompt'] = prompt
data_dict['latent'] = data
return data_dict
class LegosDataset:
def __init__(self, args, split_set="train"):
super().__init__()
self.max_num_tokens = 410
self.perm_num = -1
self.split_set = split_set
#data = np.load(os.path.join(args.data_dir, "all_ldr_data_lr30_train_sort.npz"), allow_pickle=True)['data']
data = np.load(os.path.join(args.data_dir, "train_1k.npz"), allow_pickle=True)['data']
#self.data = [self.padding(data[i], self.max_num_tokens) for i in range(len(data))]
#self.data = [data[i] for i in range(len(data))]
prompts = json.load(open(os.path.join(args.data_dir, "dense_captions", "dense_captions_rmthan300.json"), 'r'))['Car']
#latent = np.load(os.path.join(args.data_dir, "latents_train.npy"), allow_pickle=True)
bboxs = np.load(os.path.join(args.data_dir, "all_coordinates_train.npy"), allow_pickle=True)
self.data, self.prompts, self.bboxs = self.process_data(data, prompts, bboxs)
# self.latent = self.padding_latent(latent, self.max_num_tokens).astype(np.int64)
# self.data = [self.data[0]]
# self.prompts = [self.prompts[0]]
# self.bboxs = [self.bboxs[0]]
print(f"{split_set} dataset total data samples: {len(self.data)}")
def padding_latent(self, data, max_len=300):
# if data.shape[0] > max_len:
# print(data.shape[0])
pad_data = np.pad(data, ((0, max_len - data.shape[0]), (0, 0)), 'constant', constant_values=16386)
# pad_data[data.shape[0]-max_len:,-1] = 1 #flag label
# pad_data[data.shape[0]-max_len:,-2] = 0
return pad_data
def padding(self, data, max_len=300):
# if data.shape[0] > max_len:
# print(data.shape[0])
pad_data = np.pad(data, ((0, max_len - data.shape[0]), (0, 0)), 'constant', constant_values=-1)
pad_data[data.shape[0]-max_len:,-1] = 1 #flag label
pad_data[data.shape[0]-max_len:,-2] = 0
return pad_data
def permute(self, data, n_permutations=3):
return [data] + [data[np.random.permutation(len(data))] for _ in range(n_permutations-1)]
def process_data(self, data, prompts, bboxs):
processed_data, processed_prompts, processed_bboxs = [], [], []
for i in range(len(data)):
if self.perm_num > 0:
permuted_samples = self.permute(data[i], self.perm_num)
processed_data.extend([self.padding(p, self.max_num_tokens) for p in permuted_samples])
processed_prompts.extend([prompts[i]] * self.perm_num)
processed_bboxs.extend([bboxs[i]] * self.perm_num)
else:
processed_data.append(self.padding(data[i], self.max_num_tokens))
processed_prompts.append(prompts[i])
processed_bboxs.append(bboxs[i])
return processed_data, processed_prompts, np.array(processed_bboxs)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
data = self.data[idx]
prompt = self.prompts[idx]
bbox = self.bboxs[idx]
#latent = self.latent[idx]
#import ipdb; ipdb.set_trace()
data_dict = {}
data_dict['prompt'] = prompt
data_dict['target'] = data
data_dict['bbox'] = bbox
#data_dict['latent'] = latent
return data_dict
class LegosTestDataset:
def __init__(self, args, split_set="test"):
super().__init__()
self.max_num_tokens = 410
self.perm_num = -1
self.split_set = split_set
data = np.load(os.path.join(args.data_dir, "test_1k.npz"), allow_pickle=True)['data']
#self.data = [self.padding(data[i], self.max_num_tokens) for i in range(len(data))]
#self.data = [data[i] for i in range(len(data))]
prompts = json.load(open(os.path.join(args.data_dir, "dense_captions", "dense_captions_rmthan300.json"), 'r'))['Car']
bboxs = np.load(os.path.join(args.data_dir, "all_coordinates_test.npy"), allow_pickle=True)
self.data, self.prompts, self.bboxs = self.process_data(data, prompts, bboxs)
# latent = np.load(os.path.join(args.data_dir, "latents_test.npy"), allow_pickle=True)
# self.latent = self.padding_latent(latent, self.max_num_tokens).astype(np.int64)
#import ipdb; ipdb.set_trace()
# self.data = [self.data[1]]
# self.prompts = [self.prompts[0]]
# self.bboxs = [self.bboxs[1]]
print(f"{split_set} dataset total data samples: {len(self.data)}")
def padding_latent(self, data, max_len=300):
# if data.shape[0] > max_len:
# print(data.shape[0])
pad_data = np.pad(data, ((0, max_len - data.shape[0]), (0, 0)), 'constant', constant_values=16386)
# pad_data[data.shape[0]-max_len:,-1] = 1 #flag label
# pad_data[data.shape[0]-max_len:,-2] = 0
return pad_data
def padding(self, data, max_len=300):
# if data.shape[0] > max_len:
# print(data.shape[0])
pad_data = np.pad(data, ((0, max_len - data.shape[0]), (0, 0)), 'constant', constant_values=-1)
pad_data[data.shape[0]-max_len:,-1] = 1 #flag label
pad_data[data.shape[0]-max_len:,-2] = 0
return pad_data
def permute(self, data, n_permutations=3):
return [data] + [data[np.random.permutation(len(data))] for _ in range(n_permutations-1)]
def process_data(self, data, prompts, bboxs):
processed_data, processed_prompts, processed_bboxs = [], [], []
for i in range(len(data)):
if self.perm_num > 0:
permuted_samples = self.permute(data[i], self.perm_num)
processed_data.extend([self.padding(p, self.max_num_tokens) for p in permuted_samples])
processed_prompts.extend([prompts[i]] * self.perm_num)
processed_bboxs.extend([bboxs[i]] * self.perm_num)
else:
processed_data.append(self.padding(data[i], self.max_num_tokens))
processed_prompts.append(prompts[i])
processed_bboxs.append(bboxs[i])
return processed_data, processed_prompts, np.array(processed_bboxs)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
data = self.data[idx]
prompt = self.prompts[idx]
bbox = self.bboxs[idx]
#latent = self.latent[idx]
#import ipdb; ipdb.set_trace()
data_dict = {}
data_dict['prompt'] = prompt
data_dict['target'] = data
#data_dict['latent'] = latent
data_dict['bbox'] = bbox
return data_dict
class CubeDataset:
def __init__(self, args, split_set="train"):
super().__init__()
# self.num_tokens = args.n_discrete_size
# self.no_aug = args.no_aug
self.split_set = split_set
# if split_set == "test":
# self.no_aug = True
data = np.load(os.path.join(args.data_dir, split_set + ".npz"), allow_pickle=True)
self.data = [data[name] for name in data.files]
# if cur_data['faces_num'] <= self.max_triangles
# and cur_data['faces_num'] >= self.min_triangles]
self.prompts = json.load(open(os.path.join(args.data_dir, "text.json"), 'r'))['minecraft']
print(f"{split_set} dataset total data samples: {len(self.data)}")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
data = self.data[idx]
#prompt = self.prompts[idx]
#import ipdb; ipdb.set_trace()
data_dict = {}
#data_dict['prompt'] = prompt
data_dict['latent'] = data
return data_dict
if __name__ == "__main__":
file_path = '/public/home/wangshuo/gap/assembly/data/blue classic car/blue classic car.ldr'
ldr_lines = read_ldr_file(file_path)
parsed_parts = parse_ldr_lines(ldr_lines)
# import ipdb; ipdb.set_trace()
for part in parsed_parts:
print(part)
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