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import os
import sys
import torch
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
# Add the hf_model_files directory to the path
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'hf_model_files'))
from model import UNet, marginal_prob_std, diffusion_coeff, Euler_Maruyama_sampler
class CompatibleUNet(UNet):
"""A UNet model that's compatible with the saved weights."""
def __init__(self, marginal_prob_std, channels=[32, 64, 128, 256, 512], embed_dim=256,
embed_dim_mask=256, input_dim_mask=1*256*256): # Changed to 1*256*256
# Override the parent's __init__ to set the correct input channels
super().__init__(marginal_prob_std, channels, embed_dim, embed_dim_mask, input_dim_mask)
# Replace the first conv layer to accept 1 input channel instead of 4
self.conv1 = torch.nn.Conv2d(1, channels[0], 3, stride=2, bias=False, padding=1)
# Also need to fix the output layer if it exists
if hasattr(self, 'tconv0'):
self.tconv0 = torch.nn.ConvTranspose2d(channels[0], 1, 3, stride=1, padding=1, output_padding=0)
class HFDiffusionService:
"""Service class for the Hugging Face conditional diffusion model."""
def __init__(self):
# Check if CUDA is available and print status
cuda_available = torch.cuda.is_available()
print(f"CUDA available for HF diffusion: {cuda_available}")
if not cuda_available:
print("Warning: CUDA is not available for HF diffusion. Using CPU instead. This might be slower.")
self.device = torch.device('cuda:0' if cuda_available else 'cpu')
self.Lambda = 25.0
# Initialize the model functions
self.marginal_prob_std_fn = lambda t: marginal_prob_std(t, Lambda=self.Lambda, device=self.device)
self.diffusion_coeff_fn = lambda t: diffusion_coeff(t, Lambda=self.Lambda, device=self.device)
# Model path for the downloaded Hugging Face model
self.model_path = os.path.join("hf_model_files", "pytorch_model.bin")
try:
# Load the state dict first to understand the architecture
state_dict = torch.load(self.model_path, map_location=self.device)
# Analyze the state dict to determine the correct architecture
conv1_weight = state_dict.get('conv1.weight', None)
cond_embed_weight = state_dict.get('cond_embed.1.weight', None)
if conv1_weight is not None:
actual_input_channels = conv1_weight.shape[1]
print(f"Detected input channels from state dict: {actual_input_channels}")
if cond_embed_weight is not None:
actual_input_dim_mask = cond_embed_weight.shape[1]
print(f"Detected input_dim_mask from state dict: {actual_input_dim_mask}")
# Create a compatible model
if actual_input_channels == 1 and actual_input_dim_mask == 65536:
# The saved model expects 1 input channel and 65536 flattened input
# This suggests it was trained with 1*256*256 = 65536
self.score_model = CompatibleUNet(
marginal_prob_std=self.marginal_prob_std_fn,
input_dim_mask=65536
)
self.input_channels = 1
self.input_dim_mask = 65536
else:
# Use the original architecture
self.score_model = UNet(marginal_prob_std=self.marginal_prob_std_fn)
self.input_channels = 4
self.input_dim_mask = 262144
else:
# Fallback to original
self.score_model = UNet(marginal_prob_std=self.marginal_prob_std_fn)
self.input_channels = 4
self.input_dim_mask = 262144
else:
# Fallback to original
self.score_model = UNet(marginal_prob_std=self.marginal_prob_std_fn)
self.input_channels = 4
self.input_dim_mask = 262144
# Load the weights
self.score_model.load_state_dict(state_dict)
self.score_model.to(self.device)
self.score_model.eval()
print(f"HF Diffusion model loaded successfully from {self.model_path}")
print(f"Model configured for {self.input_channels} input channels and {self.input_dim_mask} mask dimensions")
except Exception as e:
print(f"Error loading HF diffusion model: {e}")
raise e
def generate_image(self, mask):
"""
Generate a medical image based on a conditioning mask.
Args:
mask: Conditioning mask tensor of shape (1, 4, 256, 256) or PIL Image
Returns:
Generated image as PIL Image
"""
try:
# Process the mask input
processed_mask = self.process_mask(mask)
# Generate the image
generated_tensor = self.generate_from_mask(processed_mask)
# Convert tensor to PIL Image
return self.tensor_to_image(generated_tensor)
except Exception as e:
print(f"Error generating HF diffusion image: {e}")
return None
def process_mask(self, mask):
"""
Process the input mask to the correct format for the model.
Args:
mask: Input mask (PIL Image, numpy array, or tensor)
Returns:
Processed mask tensor of shape (1, 1, 256, 256) for 1-channel model
"""
try:
# If mask is a PIL Image, convert to tensor
if isinstance(mask, Image.Image):
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.Resize((256, 256), antialias=True),
transforms.ToTensor()
])
tensor = transform(mask).unsqueeze(0) # Add batch dimension
elif isinstance(mask, np.ndarray):
# Convert numpy array to tensor
if mask.ndim == 2:
mask = mask[np.newaxis, :, :] # Add channel dimension
tensor = torch.from_numpy(mask).float()
if tensor.dim() == 3:
tensor = tensor.unsqueeze(0) # Add batch dimension
elif isinstance(mask, torch.Tensor):
tensor = mask
if tensor.dim() == 3:
tensor = tensor.unsqueeze(0) # Add batch dimension
else:
raise ValueError(f"Unsupported mask type: {type(mask)}")
# Ensure the tensor has the correct shape based on model input
if self.input_channels == 1:
# Model expects 1 channel
if tensor.shape[1] != 1:
# Take the first channel or average if multiple channels
if tensor.shape[1] > 1:
tensor = tensor.mean(dim=1, keepdim=True)
else:
tensor = tensor[:, :1, :, :]
else:
# Model expects 4 channels
if tensor.shape[1] == 1:
# If single channel, repeat to 4 channels
tensor = tensor.repeat(1, 4, 1, 1)
elif tensor.shape[1] != 4:
raise ValueError(f"Expected 1 or 4 channels, got {tensor.shape[1]}")
# Ensure correct size
if tensor.shape[2] != 256 or tensor.shape[3] != 256:
tensor = torch.nn.functional.interpolate(tensor, size=(256, 256), mode='bilinear', align_corners=False)
print(f"Processed mask shape: {tensor.shape}")
return tensor.to(self.device)
except Exception as e:
print(f"Error processing mask: {e}")
raise e
def generate_from_mask(self, conditioning_mask, num_steps=250, eps=1e-3):
"""
Generate image from conditioning mask using the diffusion model.
Args:
conditioning_mask: Conditioning mask tensor
num_steps: Number of sampling steps
eps: Smallest time step for numerical stability
Returns:
Generated image tensor
"""
try:
# Determine the output shape based on the model
if self.input_channels == 1:
x_shape = (1, 256, 256)
else:
x_shape = (4, 256, 256)
with torch.no_grad():
samples = Euler_Maruyama_sampler(
self.score_model,
self.marginal_prob_std_fn,
self.diffusion_coeff_fn,
batch_size=1,
x_shape=x_shape,
num_steps=num_steps,
device=self.device,
eps=eps,
y=conditioning_mask
)
# Clamp values to [0, 1] range
return samples.clamp(0, 1)
except Exception as e:
print(f"Error in generate_from_mask: {e}")
raise e
def tensor_to_image(self, tensor):
"""
Convert tensor to PIL Image.
Args:
tensor: Generated tensor
Returns:
PIL Image
"""
try:
# Take the first channel for visualization (or average all channels)
if tensor.shape[1] > 1:
# Average the channels
image_tensor = tensor.squeeze(0).mean(dim=0)
else:
image_tensor = tensor.squeeze(0).squeeze(0)
# Convert to numpy and scale to 0-255
image_array = (image_tensor.cpu().numpy() * 255).astype(np.uint8)
# Create PIL Image
image = Image.fromarray(image_array, mode='L')
return image
except Exception as e:
print(f"Error converting tensor to image: {e}")
raise e
def generate_batch(self, masks, num_steps=250, eps=1e-3):
"""
Generate multiple images from a batch of masks.
Args:
masks: List of masks or batch tensor
num_steps: Number of sampling steps
eps: Smallest time step for numerical stability
Returns:
List of generated PIL Images
"""
try:
if isinstance(masks, list):
# Process each mask individually
results = []
for mask in masks:
result = self.generate_image(mask)
results.append(result)
return results
else:
# Process as batch
processed_masks = self.process_mask(masks)
batch_size = processed_masks.shape[0]
# Determine the output shape based on the model
if self.input_channels == 1:
x_shape = (1, 256, 256)
else:
x_shape = (4, 256, 256)
with torch.no_grad():
samples = Euler_Maruyama_sampler(
self.score_model,
self.marginal_prob_std_fn,
self.diffusion_coeff_fn,
batch_size=batch_size,
x_shape=x_shape,
num_steps=num_steps,
device=self.device,
eps=eps,
y=processed_masks
)
# Convert each sample to image
results = []
for i in range(batch_size):
sample = samples[i:i+1]
image = self.tensor_to_image(sample)
results.append(image)
return results
except Exception as e:
print(f"Error in generate_batch: {e}")
raise e |