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import numpy as np |
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import torch |
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import torch.nn as nn |
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import math |
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y_table = np.array( |
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[[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, |
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55], [14, 13, 16, 24, 40, 57, 69, 56], |
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[14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, |
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77], [24, 35, 55, 64, 81, 104, 113, 92], |
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[49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], |
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dtype=np.float32).T |
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y_table = nn.Parameter(torch.from_numpy(y_table)) |
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c_table = np.empty((8, 8), dtype=np.float32) |
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c_table.fill(99) |
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c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], |
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[24, 26, 56, 99], [47, 66, 99, 99]]).T |
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c_table = nn.Parameter(torch.from_numpy(c_table)) |
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def diff_round_back(x): |
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""" Differentiable rounding function |
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Input: |
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x(tensor) |
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Output: |
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x(tensor) |
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""" |
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return torch.round(x) + (x - torch.round(x))**3 |
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def diff_round(input_tensor): |
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test = 0 |
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for n in range(1, 10): |
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test += math.pow(-1, n+1) / n * torch.sin(2 * math.pi * n * input_tensor) |
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final_tensor = input_tensor - 1 / math.pi * test |
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return final_tensor |
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class Quant(torch.autograd.Function): |
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@staticmethod |
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def forward(ctx, input): |
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input = torch.clamp(input, 0, 1) |
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output = (input * 255.).round() / 255. |
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return output |
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@staticmethod |
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def backward(ctx, grad_output): |
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return grad_output |
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class Quantization(nn.Module): |
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def __init__(self): |
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super(Quantization, self).__init__() |
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def forward(self, input): |
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return Quant.apply(input) |
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def quality_to_factor(quality): |
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""" Calculate factor corresponding to quality |
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Input: |
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quality(float): Quality for jpeg compression |
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Output: |
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factor(float): Compression factor |
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""" |
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if quality < 50: |
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quality = 5000. / quality |
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else: |
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quality = 200. - quality*2 |
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return quality / 100. |
