Spaces:
Runtime error
Runtime error
File size: 13,498 Bytes
ba58eb0 029bab4 d8baef5 029bab4 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 27d82ef 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d3607a8 3bb59c3 d8baef5 3bb59c3 d8baef5 3bb59c3 d8baef5 3bb59c3 d8baef5 3bb59c3 d8baef5 3bb59c3 d8baef5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef 608ebe5 27d82ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 |
# import gradio as gr
# import torch
# from PIL import Image
# from model import CRM
# from inference import generate3d
# import numpy as np
# # Load model
# crm_path = "CRM.pth" # Make sure the model is uploaded to the Space
# model = CRM(torch.load(crm_path, map_location="cpu"))
# model = model.to("cuda:0" if torch.cuda.is_available() else "cpu")
# def generate_3d(image_path, seed=1234, scale=5.5, step=30):
# image = Image.open(image_path).convert("RGB")
# np_img = np.array(image)
# glb_path = generate3d(model, np_img, np_img, "cuda:0" if torch.cuda.is_available() else "cpu")
# return glb_path
# iface = gr.Interface(
# fn=generate_3d,
# inputs=gr.Image(type="filepath"),
# outputs=gr.Model3D(),
# title="Convolutional Reconstruction Model (CRM)",
# description="Upload an image to generate a 3D model."
# )
# iface.launch()
#############2nd################3
# import os
# import torch
# import gradio as gr
# from huggingface_hub import hf_hub_download
# from model import CRM # Make sure this matches your model file structure
# # Define model details
# REPO_ID = "Mariam-Elz/CRM" # Hugging Face model repo
# MODEL_FILES = {
# "ccm-diffusion": "ccm-diffusion.pth",
# "pixel-diffusion": "pixel-diffusion.pth",
# "CRM": "CRM.pth"
# }
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# # Download models from Hugging Face if not already present
# MODEL_DIR = "./models"
# os.makedirs(MODEL_DIR, exist_ok=True)
# for name, filename in MODEL_FILES.items():
# model_path = os.path.join(MODEL_DIR, filename)
# if not os.path.exists(model_path):
# print(f"Downloading {filename}...")
# hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=MODEL_DIR)
# # Load the model
# print("Loading CRM Model...")
# model = CRM()
# model.load_state_dict(torch.load(os.path.join(MODEL_DIR, MODEL_FILES["CRM"]), map_location=DEVICE))
# model.to(DEVICE)
# model.eval()
# print("✅ Model Loaded Successfully!")
# # Define Gradio Interface
# def predict(input_image):
# with torch.no_grad():
# output = model(input_image.to(DEVICE)) # Modify based on model input format
# return output.cpu()
# demo = gr.Interface(
# fn=predict,
# inputs=gr.Image(type="pil"),
# outputs=gr.Image(type="pil"),
# title="Convolutional Reconstruction Model (CRM)",
# description="Upload an image to generate a reconstructed output."
# )
# if __name__ == "__main__":
# demo.launch()
########################3rd-MAIN######################3
# import torch
# import gradio as gr
# import requests
# import os
# # Download model weights from Hugging Face model repo (if not already present)
# model_repo = "Mariam-Elz/CRM" # Your Hugging Face model repo
# model_files = {
# "ccm-diffusion.pth": "ccm-diffusion.pth",
# "pixel-diffusion.pth": "pixel-diffusion.pth",
# "CRM.pth": "CRM.pth",
# }
# os.makedirs("models", exist_ok=True)
# for filename, output_path in model_files.items():
# file_path = f"models/{output_path}"
# if not os.path.exists(file_path):
# url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
# print(f"Downloading {filename}...")
# response = requests.get(url)
# with open(file_path, "wb") as f:
# f.write(response.content)
# # Load model (This part depends on how the model is defined)
# device = "cuda" if torch.cuda.is_available() else "cpu"
# def load_model():
# model_path = "models/CRM.pth"
# model = torch.load(model_path, map_location=device)
# model.eval()
# return model
# model = load_model()
# # Define inference function
# def infer(image):
# """Process input image and return a reconstructed image."""
# with torch.no_grad():
# # Assuming model expects a tensor input
# image_tensor = torch.tensor(image).to(device)
# output = model(image_tensor)
# return output.cpu().numpy()
# # Create Gradio UI
# demo = gr.Interface(
# fn=infer,
# inputs=gr.Image(type="numpy"),
# outputs=gr.Image(type="numpy"),
# title="Convolutional Reconstruction Model",
# description="Upload an image to get the reconstructed output."
# )
# if __name__ == "__main__":
# demo.launch()
#################4th##################
# import torch
# import gradio as gr
# import requests
# import os
# # Define model repo
# model_repo = "Mariam-Elz/CRM"
# # Define model files and download paths
# model_files = {
# "CRM.pth": "models/CRM.pth"
# }
# os.makedirs("models", exist_ok=True)
# # Download model files only if they don't exist
# for filename, output_path in model_files.items():
# if not os.path.exists(output_path):
# url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
# print(f"Downloading {filename}...")
# response = requests.get(url)
# with open(output_path, "wb") as f:
# f.write(response.content)
# # Load model with low memory usage
# def load_model():
# model_path = "models/CRM.pth"
# model = torch.load(model_path, map_location="cpu") # Load on CPU to reduce memory usage
# model.eval()
# return model
# model = load_model()
# # Define inference function
# def infer(image):
# """Process input image and return a reconstructed image."""
# with torch.no_grad():
# image_tensor = torch.tensor(image).unsqueeze(0) # Add batch dimension
# image_tensor = image_tensor.to("cpu") # Keep on CPU to save memory
# output = model(image_tensor)
# return output.squeeze(0).numpy()
# # Create Gradio UI
# demo = gr.Interface(
# fn=infer,
# inputs=gr.Image(type="numpy"),
# outputs=gr.Image(type="numpy"),
# title="Convolutional Reconstruction Model",
# description="Upload an image to get the reconstructed output."
# )
# if __name__ == "__main__":
# demo.launch()
# ##############5TH#################
# import torch
# import torch.nn as nn
# import gradio as gr
# import requests
# import os
# # Define model repo
# model_repo = "Mariam-Elz/CRM"
# # Define model files and download paths
# model_files = {
# "CRM.pth": "models/CRM.pth"
# }
# os.makedirs("models", exist_ok=True)
# # Download model files only if they don't exist
# for filename, output_path in model_files.items():
# if not os.path.exists(output_path):
# url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
# print(f"Downloading {filename}...")
# response = requests.get(url)
# with open(output_path, "wb") as f:
# f.write(response.content)
# # Define the model architecture (you MUST replace this with your actual model)
# class CRM_Model(nn.Module):
# def __init__(self):
# super(CRM_Model, self).__init__()
# self.layer1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
# self.relu = nn.ReLU()
# self.layer2 = nn.Conv2d(64, 3, kernel_size=3, padding=1)
# def forward(self, x):
# x = self.layer1(x)
# x = self.relu(x)
# x = self.layer2(x)
# return x
# # Load model with proper architecture
# def load_model():
# model = CRM_Model() # Instantiate the model architecture
# model_path = "models/CRM.pth"
# model.load_state_dict(torch.load(model_path, map_location="cpu")) # Load weights
# model.eval() # Set to evaluation mode
# return model
# model = load_model()
# # Define inference function
# def infer(image):
# """Process input image and return a reconstructed image."""
# with torch.no_grad():
# image_tensor = torch.tensor(image).unsqueeze(0).permute(0, 3, 1, 2).float() / 255.0 # Convert to tensor
# output = model(image_tensor) # Run through model
# output = output.squeeze(0).permute(1, 2, 0).numpy() * 255.0 # Convert back to image
# return output.astype("uint8")
# # Create Gradio UI
# demo = gr.Interface(
# fn=infer,
# inputs=gr.Image(type="numpy"),
# outputs=gr.Image(type="numpy"),
# title="Convolutional Reconstruction Model",
# description="Upload an image to get the reconstructed output."
# )
# if __name__ == "__main__":
# demo.launch()
#############6th-worked-proc##################
# import torch
# import gradio as gr
# import requests
# import os
# import numpy as np
# # Hugging Face Model Repository
# model_repo = "Mariam-Elz/CRM"
# # Download Model Weights (Only CRM.pth to Save Memory)
# model_path = "models/CRM.pth"
# os.makedirs("models", exist_ok=True)
# if not os.path.exists(model_path):
# url = f"https://huggingface.co/{model_repo}/resolve/main/CRM.pth"
# print(f"Downloading CRM.pth...")
# response = requests.get(url)
# with open(model_path, "wb") as f:
# f.write(response.content)
# # Set Device (Use CPU to Reduce RAM Usage)
# device = "cpu"
# # Load Model Efficiently
# def load_model():
# model = torch.load(model_path, map_location=device)
# if isinstance(model, torch.nn.Module):
# model.eval() # Ensure model is in inference mode
# return model
# # Load model only when needed (saves memory)
# model = load_model()
# # Define Inference Function with Memory Optimizations
# def infer(image):
# """Process input image and return a reconstructed image."""
# with torch.no_grad():
# # Convert image to torch tensor & normalize (float16 to save RAM)
# image_tensor = torch.tensor(image, dtype=torch.float16).unsqueeze(0).permute(0, 3, 1, 2) / 255.0
# image_tensor = image_tensor.to(device)
# # Model Inference
# output = model(image_tensor)
# # Convert back to numpy image format
# output_image = output.squeeze(0).permute(1, 2, 0).cpu().numpy() * 255.0
# output_image = np.clip(output_image, 0, 255).astype(np.uint8)
# # Free Memory
# del image_tensor, output
# torch.cuda.empty_cache()
# return output_image
# # Create Gradio UI
# demo = gr.Interface(
# fn=infer,
# inputs=gr.Image(type="numpy"),
# outputs=gr.Image(type="numpy"),
# title="Optimized Convolutional Reconstruction Model",
# description="Upload an image to get the reconstructed output with reduced memory usage."
# )
# if __name__ == "__main__":
# demo.launch()
#############7tth################
import torch
import torch.nn as nn
import gradio as gr
import requests
import os
import torchvision.transforms as transforms
import numpy as np
from PIL import Image
# Hugging Face Model Repository
model_repo = "Mariam-Elz/CRM"
# Model File Path
model_path = "models/CRM.pth"
os.makedirs("models", exist_ok=True)
# Download model weights if not present
if not os.path.exists(model_path):
url = f"https://huggingface.co/{model_repo}/resolve/main/CRM.pth"
print(f"Downloading CRM.pth...")
response = requests.get(url)
with open(model_path, "wb") as f:
f.write(response.content)
# Set Device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Define Model Architecture (Replace with your actual model)
class CRMModel(nn.Module):
def __init__(self):
super(CRMModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.conv1(x))
x = self.relu(self.conv2(x))
return x
# Load Model
def load_model():
print("Loading model...")
model = CRMModel() # Use the correct architecture here
state_dict = torch.load(model_path, map_location=device)
if isinstance(state_dict, dict): # Ensure it's a valid state_dict
model.load_state_dict(state_dict)
else:
raise ValueError("Error: The loaded state_dict is not in the correct format.")
model.to(device)
model.eval()
print("Model loaded successfully!")
return model
# Load the model
model = load_model()
# Define Inference Function
def infer(image):
"""Process input image and return a reconstructed 3D output."""
try:
print("Preprocessing image...")
# Convert image to PyTorch tensor & normalize
transform = transforms.Compose([
transforms.Resize((256, 256)), # Resize to fit model input
transforms.ToTensor(), # Converts to tensor (C, H, W)
transforms.Normalize(mean=[0.5], std=[0.5]), # Normalize
])
image_tensor = transform(image).unsqueeze(0).to(device) # Add batch dimension
print("Running inference...")
with torch.no_grad():
output = model(image_tensor) # Forward pass
# Ensure output is a valid tensor
if isinstance(output, torch.Tensor):
output_image = output.squeeze(0).permute(1, 2, 0).cpu().numpy()
output_image = np.clip(output_image * 255.0, 0, 255).astype(np.uint8)
print("Inference complete! Returning output.")
return output_image
else:
print("Error: Model output is not a tensor.")
return None
except Exception as e:
print(f"Error during inference: {e}")
return None
# Create Gradio UI
demo = gr.Interface(
fn=infer,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="numpy"),
title="Convolutional Reconstruction Model",
description="Upload an image to get the reconstructed output."
)
if __name__ == "__main__":
demo.launch()
|