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import torch
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from PIL import Image
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import torchvision.transforms as T
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import os
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from typing import Union, List, Tuple
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import numpy as np
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from .utils.benchmark import benchmark
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"Using device: {device}")
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if device.type == 'cuda':
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print(f"CUDA Device Name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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def cityscape_benchmark(
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model: torch.nn.Module,
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image_path: str,
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batch_size: Union[int, List[int]] = 1,
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image_size: Union[Tuple[int], List[Tuple[int]]] = (3, 1024, 1024),
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save_output: bool = True,
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):
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"""
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Calculate the FPS of a given model using an actual Cityscapes image.
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Args:
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model: instance of a model (e.g. SegFormer)
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image_path: the path to the Cityscapes image
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batch_size: the batch size(s) at which to calculate the FPS (e.g. 1 or [1, 2, 4])
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image_size: the size of the images to use (e.g. (3, 1024, 1024))
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save_output: whether to save the output prediction (default True)
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Returns:
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the FPS values calculated for all image sizes and batch sizes in the form of a dictionary
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"""
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if isinstance(batch_size, int):
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batch_size = [batch_size]
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if isinstance(image_size, tuple):
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image_size = [image_size]
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values = {}
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throughput_values = []
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model = model.to(device)
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model.eval()
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assert os.path.exists(image_path), f"Image not found: {image_path}"
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image = Image.open(image_path).convert("RGB")
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img_tensor = T.ToTensor()(image)
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mean = img_tensor.mean(dim=(1, 2))
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std = img_tensor.std(dim=(1, 2))
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print(f"Calculated Mean: {mean}")
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print(f"Calculated Std: {std}")
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transform = T.Compose([
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T.Resize((image_size[0][1], image_size[0][2])),
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T.ToTensor(),
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T.Normalize(mean=mean.tolist(),
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std=std.tolist())
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])
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img_tensor = transform(image).unsqueeze(0).to(device)
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for i in image_size:
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fps = []
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for b in batch_size:
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for _ in range(4):
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if i[1] >= 1024:
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r = 16
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else:
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r = 32
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baseline_throughput = benchmark(
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model.to(device),
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device=device,
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verbose=True,
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runs=r,
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batch_size=b,
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input_size=i
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)
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throughput_values.append(baseline_throughput)
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throughput_values = np.asarray(throughput_values)
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throughput = np.around(np.mean(throughput_values), decimals=2)
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print('Im_size:', i, 'Batch_size:', b, 'Mean:', throughput, 'Std:',
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np.around(np.std(throughput_values), decimals=2))
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throughput_values = []
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fps.append({b: throughput})
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values[i] = fps
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if save_output:
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with torch.no_grad():
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output = model(img_tensor)
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pred = torch.argmax(output, dim=1).squeeze(0).cpu().numpy()
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cwd = os.getcwd()
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output_path = os.path.join(cwd, 'segformer_plusplus', 'cityscapes_prediction_output.txt')
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np.savetxt(output_path, pred, fmt="%d")
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print("Prediction saved as cityscapes_prediction_output.txt")
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return values
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