Eric Rollei
d6dd6470cf
Features: - 40+ image processing algorithms - Traditional denoising: Wavelet, NLM, BM3D (GPU-accelerated) - AI models: DnCNN, SwinIR, SCUNet, NAFNet, Noise-DA - DeepInv diffusion denoising integration - Advanced film grain detection and processing - Frequency domain processing - Richardson-Lucy deconvolution - Wiener filter - External DeepInv service with FastAPI - Comprehensive documentation and guides
216 lines
7.2 KiB
Python
216 lines
7.2 KiB
Python
"""
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Simplified SCUNet Implementation for Memory Safety
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A streamlined version that avoids complex dimension mismatches
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from typing import Optional
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class SimplifiedSCUNet(nn.Module):
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"""Simplified SCUNet for memory-safe processing"""
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def __init__(self, in_chans=3, out_chans=3, base_dim=32, num_layers=3):
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super().__init__()
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self.in_chans = in_chans
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self.out_chans = out_chans
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self.base_dim = base_dim
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self.num_layers = num_layers
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# Initial convolution
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self.conv_in = nn.Conv2d(in_chans, base_dim, 3, 1, 1)
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# Encoder blocks
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self.encoder = nn.ModuleList()
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self.downsample = nn.ModuleList()
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for i in range(num_layers):
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in_dim = base_dim * (2 ** i)
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out_dim = base_dim * (2 ** (i + 1)) if i < num_layers - 1 else in_dim
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# Encoder block
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encoder_block = nn.Sequential(
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nn.Conv2d(in_dim, in_dim, 3, 1, 1),
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nn.BatchNorm2d(in_dim),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_dim, in_dim, 3, 1, 1),
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nn.BatchNorm2d(in_dim),
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nn.ReLU(inplace=True)
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)
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self.encoder.append(encoder_block)
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# Downsampling (except for last layer)
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if i < num_layers - 1:
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downsample = nn.Conv2d(in_dim, out_dim, 3, 2, 1)
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self.downsample.append(downsample)
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# Decoder blocks
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self.decoder = nn.ModuleList()
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self.upsample = nn.ModuleList()
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for i in range(num_layers - 1):
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layer_idx = num_layers - 2 - i
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in_dim = base_dim * (2 ** (layer_idx + 1))
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out_dim = base_dim * (2 ** layer_idx)
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# Upsampling
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upsample = nn.Sequential(
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nn.ConvTranspose2d(in_dim, out_dim, 4, 2, 1),
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nn.BatchNorm2d(out_dim),
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nn.ReLU(inplace=True)
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)
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self.upsample.append(upsample)
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# Decoder block (with skip connection)
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decoder_block = nn.Sequential(
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nn.Conv2d(out_dim * 2, out_dim, 3, 1, 1), # *2 for skip connection
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nn.BatchNorm2d(out_dim),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_dim, out_dim, 3, 1, 1),
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nn.BatchNorm2d(out_dim),
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nn.ReLU(inplace=True)
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)
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self.decoder.append(decoder_block)
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# Final convolution
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self.conv_out = nn.Conv2d(base_dim, out_chans, 3, 1, 1)
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def forward(self, x):
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# Store input for residual connection
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input_x = x
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# Initial conv
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x = self.conv_in(x)
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# Store skip connections
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skip_connections = []
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# Encoder path
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for i in range(self.num_layers):
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x = self.encoder[i](x)
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if i < self.num_layers - 1:
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skip_connections.append(x)
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x = self.downsample[i](x)
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# Decoder path
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for i in range(self.num_layers - 1):
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x = self.upsample[i](x)
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# Skip connection
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skip = skip_connections[-(i + 1)]
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# Ensure dimensions match
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if x.shape[-2:] != skip.shape[-2:]:
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x = F.interpolate(x, size=skip.shape[-2:], mode='bilinear', align_corners=False)
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x = torch.cat([x, skip], dim=1)
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x = self.decoder[i](x)
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# Final conv
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x = self.conv_out(x)
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# Add residual connection to maintain input structure
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# This helps with randomly initialized weights
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x = x + input_x
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return x
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class SimplifiedSCUNetProcessor:
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"""Memory-safe simplified SCUNet processor"""
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def __init__(self, device: str = 'auto', lightweight: bool = True):
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# Simple device selection
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if device == 'auto':
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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else:
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self.device = device if torch.cuda.is_available() else 'cpu'
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self.model = None
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self.lightweight = lightweight
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print(f"Simplified SCUNet initializing on {self.device}")
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self._load_model()
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def _load_model(self):
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"""Load simplified model"""
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try:
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if self.lightweight:
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# Very lightweight configuration
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self.model = SimplifiedSCUNet(
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in_chans=3, out_chans=3, base_dim=16, num_layers=2
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)
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else:
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# Standard configuration
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self.model = SimplifiedSCUNet(
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in_chans=3, out_chans=3, base_dim=32, num_layers=3
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)
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self.model.to(self.device)
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self.model.eval()
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print(f"Simplified SCUNet loaded successfully ({self.device})")
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except Exception as e:
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print(f"Error loading simplified SCUNet: {e}")
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# Fallback to minimal model
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self.model = SimplifiedSCUNet(in_chans=3, out_chans=3, base_dim=8, num_layers=2)
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self.model.to(self.device)
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self.model.eval()
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def process_image(self, image: np.ndarray) -> np.ndarray:
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"""Process image with simplified SCUNet"""
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if self.model is None:
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return image
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try:
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# Preprocess
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if image.dtype == np.uint8:
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image_normalized = image.astype(np.float32) / 255.0
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else:
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image_normalized = image.astype(np.float32)
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# Convert to tensor
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input_tensor = torch.from_numpy(image_normalized.transpose(2, 0, 1)).unsqueeze(0)
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input_tensor = input_tensor.to(self.device)
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# Process
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with torch.no_grad():
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output_tensor = self.model(input_tensor)
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# Convert back
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output = output_tensor.squeeze(0).cpu().numpy().transpose(1, 2, 0)
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output = np.clip(output * 255, 0, 255).astype(np.uint8)
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return output
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except Exception as e:
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print(f"Simplified SCUNet processing error: {e}")
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return image
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# Test function
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def test_simplified_scunet():
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"""Test simplified SCUNet"""
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print("Testing Simplified SCUNet...")
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# Create test image
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test_image = np.random.rand(256, 256, 3).astype(np.float32)
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test_image = (test_image * 255).astype(np.uint8)
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# Test processor
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processor = SimplifiedSCUNetProcessor(device='cpu', lightweight=True)
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result = processor.process_image(test_image)
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print(f"Input shape: {test_image.shape}")
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print(f"Output shape: {result.shape}")
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print(f"Input range: [{test_image.min()}, {test_image.max()}]")
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print(f"Output range: [{result.min()}, {result.max()}]")
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print("Simplified SCUNet test completed successfully!")
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return result
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if __name__ == "__main__":
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test_simplified_scunet()
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