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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import cache
import torch
from einops import reduce
from jaxtyping import Float
from lpips import LPIPS
from skimage.metrics import structural_similarity
from torch import Tensor
from torchvision.io import read_video
import torch.nn.functional as F
import matplotlib.pyplot as plt
from typing import Union, Optional
import os
def compute_std(val_sum, val_avg, count):
variance = (val_sum / count) - (val_avg ** 2)
std = torch.sqrt(torch.clamp(variance, min=0))
return std
def plot_average_metric_per_frame(
psnr_avg: Union[torch.Tensor, list],
out_path: str = "average_psnr_per_frame.png",
psnr_std: Optional[Union[torch.Tensor, list]] = None,
metric_name: str = "PSNR",
) -> None:
"""
Plots and saves a line plot of average PSNR values per frame, with optional std error bars.
Args:
psnr_avg (Tensor or list): 1D tensor or list of average PSNR values (length = num_frames).
out_path (str): Path to save the output PNG file.
psnr_std (Tensor or list, optional): 1D tensor or list of standard deviation values for each frame.
"""
out_dir = os.path.dirname(out_path)
if out_dir and not os.path.exists(out_dir):
os.makedirs(out_dir)
if isinstance(psnr_avg, list):
psnr_avg = torch.tensor(psnr_avg)
if psnr_std is not None and isinstance(psnr_std, list):
psnr_std = torch.tensor(psnr_std)
num_frames = len(psnr_avg)
x = list(range(num_frames))
plt.figure(figsize=(12, 4))
if psnr_std is not None:
plt.errorbar(
x, psnr_avg.tolist(), yerr=psnr_std.tolist(),
fmt='-o', color='steelblue', ecolor='lightgray', elinewidth=1, capsize=3, linewidth=2
)
else:
plt.plot(x, psnr_avg.tolist(), color='steelblue', linewidth=2)
plt.xlabel('Frame Index')
plt.ylabel(f'Average {metric_name}')
plt.title('Average {metric_name} per Frame across Dataset')
plt.grid(True, linestyle='--', alpha=0.5)
plt.xlim(0, num_frames - 1)
plt.ylim(bottom=0)
plt.tight_layout()
plt.savefig(out_path, dpi=300)
plt.close()
def read_mp4_to_tensor(path: str, device: torch.device) -> torch.Tensor:
"""
Reads an MP4 video file using torchvision and returns a tensor of shape (B, C, H, W),
with values in [0, 1] as torch.float32.
Args:
path (str): Path to the MP4 file.
Returns:
torch.Tensor: Tensor of shape (B, C, H, W) with dtype float32 and values in [0, 1].
"""
video, _, _ = read_video(path, pts_unit='sec') # shape: (B, H, W, C)
video = video.permute(0, 3, 1, 2) # (B, C, H, W)
video = video.float() / 255.0 # Normalize to [0, 1]
return video.to(device)
def resize_and_crop_video(
video: torch.Tensor,
target_height: int,
target_width: int,
direct_crop: bool = False
) -> torch.Tensor:
"""
Resize and center-crop a video tensor to the target resolution.
Args:
video (Tensor): Input tensor of shape (B, C, H, W), values in [0, 1].
target_height (int): Desired output height.
target_width (int): Desired output width.
direct_crop (bool): If True, skip resizing and only crop.
Returns:
Tensor: Resized and cropped tensor of shape (B, C, target_height, target_width).
"""
B, C, H, W = video.shape
if not direct_crop:
# Determine scale factor to preserve aspect ratio
scale_h = target_height / H
scale_w = target_width / W
# Scale based on the smaller factor (resize one side to target)
scale = max(scale_h, scale_w)
new_H = int(round(H * scale))
new_W = int(round(W * scale))
# Resize using bilinear interpolation
video = F.interpolate(video, size=(new_H, new_W), mode='bilinear', align_corners=False)
# Crop center
_, _, H_new, W_new = video.shape
top = (H_new - target_height) // 2
left = (W_new - target_width) // 2
cropped = video[:, :, top:top + target_height, left:left + target_width]
return cropped
@torch.no_grad()
def compute_psnr(
ground_truth: Float[Tensor, "batch channel height width"],
predicted: Float[Tensor, "batch channel height width"],
) -> Float[Tensor, " batch"]:
ground_truth = ground_truth.clip(min=0, max=1)
predicted = predicted.clip(min=0, max=1)
mse = reduce((ground_truth - predicted) ** 2, "b c h w -> b", "mean")
return -10 * mse.log10()
@cache
def get_lpips(device: torch.device) -> LPIPS:
return LPIPS(net="vgg").to(device)
@torch.no_grad()
def compute_lpips(
ground_truth: Float[Tensor, "batch channel height width"],
predicted: Float[Tensor, "batch channel height width"],
sub_batch_size: int = 32,
) -> Float[Tensor, " batch"]:
lpips_model = get_lpips(predicted.device)
B = ground_truth.shape[0]
scores = []
for i in range(0, B, sub_batch_size):
gt_chunk = ground_truth[i : i + sub_batch_size]
pred_chunk = predicted[i : i + sub_batch_size]
value = lpips_model(gt_chunk, pred_chunk, normalize=True)
scores.append(value[:, 0, 0, 0])
return torch.cat(scores, dim=0)
@torch.no_grad()
def compute_ssim(
ground_truth: Float[Tensor, "batch channel height width"],
predicted: Float[Tensor, "batch channel height width"],
) -> Float[Tensor, " batch"]:
ssim = [
structural_similarity(
gt.detach().cpu().numpy(),
hat.detach().cpu().numpy(),
win_size=11,
gaussian_weights=True,
channel_axis=0,
data_range=1.0,
)
for gt, hat in zip(ground_truth, predicted)
]
return torch.tensor(ssim, dtype=predicted.dtype, device=predicted.device) |