Merge pull request 'added aiunn inference' (#2) from add_use_class into develop
Reviewed-on: #2
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from .finetune.trainer import aiuNNTrainer
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from .upsampler.aiunn import aiuNN
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from .upsampler.config import aiuNNConfig
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from .inference.inference import aiuNNInference
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__version__ = "0.1.1"
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from .inference import aiuNNInference
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__all__ = ["aiuNNInference"]
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import os
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import torch
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from albumentations import Compose, Normalize
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from albumentations.pytorch import ToTensorV2
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from PIL import Image
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import numpy as np
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from PIL import Image
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import io
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from torch import nn
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from aiia import AIIABase
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from typing import Union, Optional, Tuple, List
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from ..upsampler.aiunn import aiuNN
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class Upscaler(nn.Module):
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class aiuNNInference:
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"""
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Transforms the base model's final feature map using a transposed convolution.
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The base model produces a feature map of size 512x512.
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This layer upsamples by a factor of 2 (yielding 1024x1024) and maps the hidden features
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to the output channels using a single ConvTranspose2d layer.
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Inference class for aiuNN upsampling model.
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Handles model loading, image upscaling, and output processing.
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"""
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def __init__(self, base_model: AIIABase):
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super(Upscaler, self).__init__()
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self.base_model = base_model
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# Instead of adding separate upsampling and convolutional layers, we use a ConvTranspose2d layer.
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self.last_transform = nn.ConvTranspose2d(
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in_channels=base_model.config.hidden_size,
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out_channels=base_model.config.num_channels,
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kernel_size=base_model.config.kernel_size,
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stride=2,
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padding=1,
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output_padding=1
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)
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def __init__(self, model_path: str, precision: Optional[str] = None, device: Optional[str] = None):
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"""
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Initialize the inference class by loading the aiuNN model.
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def forward(self, x):
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features = self.base_model(x)
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return self.last_transform(features)
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Args:
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model_path: Path to the saved model directory
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precision: Optional precision setting ('fp16', 'bf16', or None for default)
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device: Optional device specification ('cuda', 'cpu', or None for auto-detection)
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"""
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# Set device
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if device is None:
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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
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# Load the model with specified precision
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self.model = aiuNN.load(model_path, precision=precision)
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self.model.to(self.device)
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self.model.eval()
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# Store configuration for reference
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self.config = self.model.config
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def preprocess_image(self, image: Union[str, Image.Image, np.ndarray, torch.Tensor]) -> torch.Tensor:
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"""
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Preprocess the input image to match model requirements.
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Args:
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image: Input image as file path, PIL Image, numpy array, or torch tensor
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Returns:
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Preprocessed tensor ready for model input
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"""
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# Handle different input types
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if isinstance(image, str):
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# Load from file path
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image = Image.open(image).convert('RGB')
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if isinstance(image, Image.Image):
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# Convert PIL Image to tensor
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image = np.array(image)
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image = image.transpose(2, 0, 1) # HWC to CHW
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image = torch.from_numpy(image).float()
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if isinstance(image, np.ndarray):
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# Convert numpy array to tensor
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if image.shape[0] == 3:
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# Already in CHW format
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pass
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elif image.shape[-1] == 3:
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# HWC to CHW format
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image = image.transpose(2, 0, 1)
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image = torch.from_numpy(image).float()
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# Normalize to [0, 1] range if needed
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if image.max() > 1.0:
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image = image / 255.0
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# Add batch dimension if not present
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if len(image.shape) == 3:
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image = image.unsqueeze(0)
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# Move to device
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image = image.to(self.device)
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return image
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class ImageUpscaler:
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def __init__(self, model_path: str, device: str = 'cuda' if torch.cuda.is_available() else 'cpu'):
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self.device = torch.device(device)
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self.model = self.load_model(model_path)
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self.model.eval() # Set to evaluation mode
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def postprocess_tensor(self, tensor: torch.Tensor) -> Image.Image:
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"""
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Convert output tensor to PIL Image.
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# Define preprocessing transformations
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self.preprocess = Compose([
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Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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ToTensorV2()
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])
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Args:
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tensor: Output tensor from model
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Returns:
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Processed PIL Image
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"""
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# Move to CPU and convert to numpy
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output = tensor.detach().cpu().squeeze(0).numpy()
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# Ensure proper range [0, 255]
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output = np.clip(output * 255, 0, 255).astype(np.uint8)
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# Convert from CHW to HWC for PIL
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output = output.transpose(1, 2, 0)
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# Create PIL Image
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return Image.fromarray(output)
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def load_model(self, model_path: str):
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@torch.no_grad()
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def upscale(self, image: Union[str, Image.Image, np.ndarray, torch.Tensor]) -> Image.Image:
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"""
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Load the trained model from the specified path.
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Upscale an image using the aiuNN model.
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Args:
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image: Input image to upscale
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Returns:
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Upscaled image as PIL Image
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"""
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base_model = AIIABase.load(model_path) # Load base model
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model = Upscaler(base_model) # Wrap with Upscaler
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return model.to(self.device)
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# Preprocess input
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input_tensor = self.preprocess_image(image)
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# Run inference
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output_tensor = self.model(input_tensor)
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# Postprocess output
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upscaled_image = self.postprocess_tensor(output_tensor)
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return upscaled_image
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def preprocess_image(self, image: Image.Image):
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def save(self, image: Image.Image, output_path: str, format: Optional[str] = None) -> None:
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"""
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Preprocess input image for inference.
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Save the upscaled image to a file.
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Args:
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image: PIL Image to save
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output_path: Path where the image should be saved
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format: Optional format override (e.g., 'PNG', 'JPEG')
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"""
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if not isinstance(image, Image.Image):
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raise ValueError("Input must be a PIL.Image.Image object")
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# Create directory if it doesn't exist
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os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
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# Convert to numpy array and apply preprocessing
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image_array = np.array(image)
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augmented = self.preprocess(image=image_array)
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# Save the image
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image.save(output_path, format=format)
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# Add batch dimension and move to device
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return augmented['image'].unsqueeze(0).to(self.device)
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def convert_to_binary(self, image: Image.Image, format: str = 'PNG') -> bytes:
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"""
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Convert the image to binary data.
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Args:
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image: PIL Image to convert
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format: Image format to use for binary conversion
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Returns:
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Binary representation of the image
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"""
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# Use BytesIO to convert to binary
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binary_output = io.BytesIO()
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image.save(binary_output, format=format)
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# Get the binary data
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binary_data = binary_output.getvalue()
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return binary_data
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def postprocess_image(self, output_tensor: torch.Tensor):
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def process_batch(self,
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images: List[Union[str, Image.Image]],
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output_dir: Optional[str] = None,
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save_format: str = 'PNG',
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return_binary: bool = False) -> Union[List[Image.Image], List[bytes], None]:
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"""
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Convert output tensor back to an image.
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"""
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output_tensor = output_tensor.squeeze(0).cpu() # Remove batch dimension
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output_array = (output_tensor * 0.5 + 0.5).clamp(0, 1).numpy() * 255
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output_array = output_array.transpose(1, 2, 0).astype(np.uint8) # CHW -> HWC
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return Image.fromarray(output_array)
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def upscale_image(self, input_image_path: str):
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"""
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Perform upscaling on an input image.
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"""
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input_image = Image.open(input_image_path).convert('RGB') # Ensure RGB format
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preprocessed_image = self.preprocess_image(input_image)
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Process multiple images in batch.
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with torch.no_grad():
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with torch.amp.autocast(device_type="cuda"):
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output_tensor = self.model(preprocessed_image)
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Args:
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images: List of input images (paths or PIL Images)
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output_dir: Optional directory to save results
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save_format: Format to use when saving images
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return_binary: Whether to return binary data instead of PIL Images
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Returns:
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List of processed images or binary data, or None if only saving
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"""
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results = []
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return self.postprocess_image(output_tensor)
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for i, img in enumerate(images):
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# Upscale the image
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upscaled = self.upscale(img)
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# Save if output directory is provided
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if output_dir:
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# Extract filename if input is a path
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if isinstance(img, str):
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filename = os.path.basename(img)
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base, _ = os.path.splitext(filename)
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else:
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base = f"upscaled_{i}"
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output_path = os.path.join(output_dir, f"{base}.{save_format.lower()}")
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self.save(upscaled, output_path, format=save_format)
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# Add to results based on return type
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if return_binary:
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results.append(self.convert_to_binary(upscaled, format=save_format))
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else:
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results.append(upscaled)
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return results if (not output_dir or return_binary or not save_format) else None
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# Example usage:
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upscaler = ImageUpscaler(model_path="/root/vision/aiuNN/best_model")
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upscaled_image = upscaler.upscale_image("/root/vision/aiuNN/input.jpg")
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upscaled_image.save("upscaled_image.jpg")
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# Example usage (can be removed)
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if __name__ == "__main__":
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# Initialize inference with a model path
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inferencer = aiuNNInference("path/to/model", precision="bf16")
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# Upscale a single image
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upscaled_image = inferencer.upscale("input_image.jpg")
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# Save the result
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inferencer.save(upscaled_image, "output_image.png")
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# Convert to binary
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binary_data = inferencer.convert_to_binary(upscaled_image)
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# Process a batch of images
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inferencer.process_batch(
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["image1.jpg", "image2.jpg"],
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output_dir="output_folder",
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save_format="PNG"
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)
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