File size: 3,343 Bytes
32da3e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import math
import sys
import numpy as np
import torch
from tqdm import trange
from torchmetrics.functional.image.ssim import structural_similarity_index_measure
from stage1.disc.lpips import LPIPS
from .utils import to_torch_tensor
@torch.no_grad()
def calculate_psnr(arr1, arr2, bs, device="cuda", disable_bar: bool = True) -> float:
"""
Computes PSNR between two single images or batches of images.
PSNR is averaged over the batch if given as (B, C, H, W).
"""
B = arr1.shape[0]
n_batches = math.ceil(B / bs)
psnr = torch.zeros(B, device=device)
for i in trange(n_batches, desc="Calculating PSNR", file=sys.stdout, disable=disable_bar):
start_idx = i * bs
end_idx = min((i + 1) * bs, B)
# PSNR expects input in [0, 1], (B, C, H, W)
batch_arr1 = to_torch_tensor(arr1[start_idx:end_idx]).to(device)
batch_arr2 = to_torch_tensor(arr2[start_idx:end_idx]).to(device)
batch_mse = torch.mean((batch_arr1 - batch_arr2) ** 2, dim=[1,2,3]) # shape [bs]
batch_mse = torch.clamp(batch_mse, min=1e-10)
batch_psnr = 20.0 * torch.log10(1 / torch.sqrt(batch_mse)) # shape [bs]
psnr[start_idx:end_idx] = batch_psnr
return psnr.mean().item()
@torch.no_grad()
def calculate_lpips(arr1, arr2, bs, device="cuda", disable_bar: bool = True) -> float:
"""
Computes LPIPS between two single images or batches of images.
LPIPS is averaged over the batch if given as (B, C, H, W).
"""
B = arr1.shape[0]
n_batches = math.ceil(B / bs)
loss_fn = LPIPS().eval().to(device)
lpips = torch.zeros(B, device=device)
for i in trange(n_batches, desc="Calculating LPIPS", file=sys.stdout, disable=disable_bar):
start_idx = i * bs
end_idx = min((i + 1) * bs, B)
# LPIPS expects input in [-1, 1], (B, C, H, W)
batch_arr1 = (to_torch_tensor(arr1[start_idx:end_idx]).to(device) - 0.5) * 2.
batch_arr2 = (to_torch_tensor(arr2[start_idx:end_idx]).to(device) - 0.5) * 2.
batch_lpips = loss_fn(batch_arr1, batch_arr2).squeeze() # shape [bs]
lpips[start_idx:end_idx] = batch_lpips
return lpips.mean().item()
######################################################
# 4. SSIM Calculation
######################################################
@torch.no_grad()
def calculate_ssim(arr1, arr2, bs, device="cuda", disable_bar: bool = True) -> float:
"""
Computes SSIM between two single images or batches of images.
SSIM is averaged over the batch if given as (B, C, H, W).
"""
B = arr1.shape[0]
n_batches = math.ceil(B / bs)
ssim_val = torch.zeros(B, device=device)
for i in trange(n_batches, desc="Calculating SSIM", file=sys.stdout, disable=disable_bar):
start_idx = i * bs
end_idx = min((i + 1) * bs, B)
# SSIM expects input in [0, 1], (B, C, H, W)
batch_arr1 = to_torch_tensor(arr1[start_idx:end_idx]).to(device)
batch_arr2 = to_torch_tensor(arr2[start_idx:end_idx]).to(device)
# SSIM expects input in [0, 1], (B, C, H, W)
batch_ssim = structural_similarity_index_measure(
target=batch_arr1,
preds=batch_arr2,
data_range=1.0,
reduction="none"
)
ssim_val[start_idx:end_idx] = batch_ssim
return ssim_val.mean().item()
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