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import os
import shutil
import uuid
import torch
import gradio as gr
import numpy as np
import ray
import time
from pathlib import Path
from diffusion.utils import export_edges
from construct_brep import construct_brep_from_datanpz
from app.DataProcessor import DataProcessor
from app.ModelDirector import ModelDirector
_EDGE_FILE = 0
_SOLID_FILE = 1
_STEP_FILE = 2
class ConditionedGeneratingMethod():
def __init__(
self,
model_building_director: ModelDirector,
dataprocessor: DataProcessor,
model_num_to_return: int,
model_seed: int = 0,
output_main_dir: Path | str = Path('./outputs')
):
self.director = model_building_director
self.dataprocessor = dataprocessor
self.model_num_to_return = model_num_to_return
self.model_seed = model_seed
self.output_main_dir = output_main_dir
def generate(self):
def generating_method(browser_state: dict, *inputs):
try:
# Some checks
assert len(inputs) > 0
self._user_state_check(browser_state)
self._empty_input_check(inputs)
# Inference device(also shouldn't appear here)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Process user input data
tensor_data = self.dataprocessor.process(inputs)
# Basic configuration of a model
self.director.config_setup()
model_builder = self.director.buider
# Should be refactored in the future since picking an output folder is not the responsibility of a model
diffusion_output_dir = self._get_diffusion_output_dir(browser_state, self.director.get_generating_condition())
postprocess_output_dir = self._get_postprocess_output_dir(browser_state, self.director.get_generating_condition())
model_builder.setup_output_dir(diffusion_output_dir)
model_builder.setup_seed(self.model_seed)
model_builder.make_model(device)
model = model_builder.model
#############
# Inference #
#############
gr.Info("Start diffusing", title="Runtime Info")
with torch.no_grad():
pred_results = model.inference(self.dataprocessor.NUM_PROPOSALS, device, v_data=tensor_data, v_log=True)
# Save intermediate files for post-processing
for i, result in enumerate(pred_results):
diffusion_output_subdir = diffusion_output_dir / f"00_{i:02d}"
diffusion_output_subdir.mkdir(parents=True, exist_ok=True)
export_edges(result["pred_edge"], (diffusion_output_subdir / "edge.obj").as_posix())
np.savez_compressed(
file = (diffusion_output_subdir / "data.npz").as_posix(),
pred_face_adj_prob = result["pred_face_adj_prob"],
pred_face_adj = result["pred_face_adj"].cpu().numpy(),
pred_face = result["pred_face"],
pred_edge = result["pred_edge"],
pred_edge_face_connectivity = result["pred_edge_face_connectivity"],
)
gr.Info("Finished diffusing", title="Runtime Info")
###################
# Post-Processing #
###################
# Multi-thread preparation
gr.Info("Start post-processing!", title="Runtime Info")
if not ray.is_initialized():
ray.init(
num_cpus=2,
)
construct_brep_from_datanpz_ray = ray.remote(num_cpus=1, max_retries=0)(construct_brep_from_datanpz)
diffusion_results = sorted(os.listdir(diffusion_output_dir))
tasks = [
construct_brep_from_datanpz_ray.remote(
data_root=diffusion_output_dir,
out_root=postprocess_output_dir,
folder_name=model_number,
v_drop_num=1,
use_cuda=False,
from_scratch=True,
is_log=False,
is_ray=True,
is_optimize_geom=True,
isdebug=False,
is_save_data=True
)
for model_number in diffusion_results
]
results = []
success_count = 0
while tasks and success_count < self.model_num_to_return:
done_ids, tasks = ray.wait(tasks, num_returns=1, timeout=30)
for done_id in done_ids:
try:
result = ray.get(done_id)
results.append(result)
# Delay just a bit to ensure file handles are released
time.sleep(0.2)
# Check for 'success.txt' in output folders
for done_folder in postprocess_output_dir.iterdir():
output_files = os.listdir(done_folder)
if 'success.txt' in output_files:
success_count += 1
except Exception as e:
print(f"Task failed or timed out: {e}")
results.append(None)
if success_count >= self.model_num_to_return:
# Make sure the files are written successfully
time.sleep(5.0)
break
time.sleep(5.0)
gr.Info("Finished post-processing!", title="Runtime Info")
# Get valid model serial numbers
valid_models = self._get_valid_models(postprocess_output_dir)
#####################
# Update User State #
#####################
browser_state = self._update_user_state(browser_state, postprocess_output_dir, valid_models)
# Check if there's no valid output
self._postprocess_output_check(valid_models)
# Multi-thread processing may return valid models more than 4
gr.Info(f"{len(valid_models) if len(valid_models) < 4 else 4} valid models generated!", title="Finish generating")
condition = self.director.get_generating_condition()
# Return the first model as the default demonstration
edge_file = browser_state[condition][0][_EDGE_FILE]
solid_file = browser_state[condition][0][_SOLID_FILE]
step_file = browser_state[condition][0][_STEP_FILE]
return browser_state, edge_file, solid_file, step_file, browser_state[condition][0]
except EmptyInputException as input_e:
gr.Warning(str(input_e), title="Empty Input")
except GeneraingException as generating_e:
gr.Warning(str(generating_e), title="No Valid Generation")
except UnicodeEncodeError as uni_error:
gr.Warning("We sincerely apologize, but we currently only support English.", title="English Support Only")
except FileNotFoundError as file_e:
gr.Warning("The operation is too frequent!", title="Frequent Operation")
except Exception as e:
print(e)
gr.Warning("Something bad happened. Please try some other models", title="Unknown Error")
return browser_state, gr.update(), gr.update(), gr.update(), gr.update()
return generating_method
def _update_user_state(self, browser_state, postprocess_output_dir, valid_model):
# Unstable. May be refactored in the future
condition = self.director.get_generating_condition()
browser_state[condition] = list()
for i, model_number in enumerate(valid_model):
if (postprocess_output_dir / model_number / 'debug_face_loop' / 'optimized_edge.obj').exists():
edge = (postprocess_output_dir / model_number / 'debug_face_loop' / 'optimized_edge.obj').as_posix()
else:
edge = (postprocess_output_dir / model_number / 'debug_face_loop' / 'edge.obj').as_posix() # Hard coding is not good.
solid = (postprocess_output_dir / model_number / 'recon_brep.stl').as_posix()
step = (postprocess_output_dir / model_number / 'recon_brep.step').as_posix()
browser_state[condition].append([edge, solid, step])
return browser_state
def _postprocess_output_check(self, valid_model):
if len(valid_model) <= 0:
raise GeneraingException("No Valid Model Generated!")
def _empty_input_check(self, inputs):
for input_component in inputs:
if input_component is None:
raise EmptyInputException("Empty input exists!")
def _user_state_check(self, state_dict):
if state_dict['user_id'] is None:
state_dict['user_id'] = uuid.uuid4()
if state_dict['user_output_dir'] is None:
state_dict['user_output_dir'] = Path(self.output_main_dir) / f"user_{state_dict['user_id']}"
os.makedirs(state_dict['user_output_dir'], exist_ok=True)
def _get_valid_models(self, postprocess_output: Path):
# Get valid **model number** after post-processing
output_folders = [model_folder for model_folder in os.listdir(postprocess_output) if 'success.txt' in os.listdir(postprocess_output / model_folder)]
return output_folders
def _get_diffusion_output_dir(self, state_dict, condition):
# Create and clean the diffusion output directory
diffusion_output_dir = Path(state_dict['user_output_dir']) / condition
os.makedirs(diffusion_output_dir, exist_ok=True)
if len(os.listdir(diffusion_output_dir)) > 0:
shutil.rmtree(diffusion_output_dir)
return diffusion_output_dir
def _get_postprocess_output_dir(self, state_dict, condition):
# Create and clean the post-process output directory
postprocess_output_dir = Path(state_dict['user_output_dir']) / f'{condition}_post'
os.makedirs(postprocess_output_dir, exist_ok=True)
if len(os.listdir(postprocess_output_dir)) > 0:
shutil.rmtree(postprocess_output_dir)
return postprocess_output_dir
class GeneraingException(Exception):
"""Custom exception if generating failed."""
pass
class EmptyInputException(Exception):
"""Custom exception if the input is empty."""
pass
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