import numpy as np
from skimage import measure
from scipy import linalg
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.utils import to_tensors
def calculate_epe(flow1, flow2):
"""Calculate End point errors."""
epe = torch.sum((flow1 - flow2)**2, dim=1).sqrt()
epe = epe.view(-1)
return epe.mean().item()
def calculate_psnr(img1, img2):
"""Calculate PSNR (Peak Signal-to-Noise Ratio).
Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Args:
img1 (ndarray): Images with range [0, 255].
img2 (ndarray): Images with range [0, 255].
Returns:
float: psnr result.
"""
assert img1.shape == img2.shape, \
(f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
mse = np.mean((img1 - img2)**2)
if mse == 0:
return float('inf')
return 20. * np.log10(255. / np.sqrt(mse))
def calc_psnr_and_ssim(img1, img2):
"""Calculate PSNR and SSIM for images.
img1: ndarray, range [0, 255]
img2: ndarray, range [0, 255]
"""
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
psnr = calculate_psnr(img1, img2)
ssim = measure.compare_ssim(img1,
img2,
data_range=255,
multichannel=True,
win_size=65)
return psnr, ssim
###########################
# I3D models
###########################
def init_i3d_model(i3d_model_path):
print(f"[Loading I3D model from {i3d_model_path} for FID score ..]")
i3d_model = InceptionI3d(400, in_channels=3, final_endpoint='Logits')
i3d_model.load_state_dict(torch.load(i3d_model_path))
i3d_model.to(torch.device('cuda:0'))
return i3d_model
def calculate_i3d_activations(video1, video2, i3d_model, device):
"""Calculate VFID metric.
video1: list[PIL.Image]
video2: list[PIL.Image]
"""
video1 = to_tensors()(video1).unsqueeze(0).to(device)
video2 = to_tensors()(video2).unsqueeze(0).to(device)
video1_activations = get_i3d_activations(
video1, i3d_model).cpu().numpy().flatten()
video2_activations = get_i3d_activations(
video2, i3d_model).cpu().numpy().flatten()
return video1_activations, video2_activations
def calculate_vfid(real_activations, fake_activations):
"""
Given two distribution of features, compute the FID score between them
Params:
real_activations: list[ndarray]
fake_activations: list[ndarray]
"""
m1 = np.mean(real_activations, axis=0)
m2 = np.mean(fake_activations, axis=0)
s1 = np.cov(real_activations, rowvar=False)
s2 = np.cov(fake_activations, rowvar=False)
return calculate_frechet_distance(m1, s1, m2, s2)
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
"""Numpy implementation of the Frechet Distance.
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Stable version by Dougal J. Sutherland.
Params:
-- mu1 : Numpy array containing the activations of a layer of the
inception net (like returned by the function 'get_predictions')
for generated samples.
-- mu2 : The sample mean over activations, precalculated on an
representive data set.
-- sigma1: The covariance matrix over activations for generated samples.
-- sigma2: The covariance matrix over activations, precalculated on an
representive data set.
Returns:
-- : The Frechet Distance.
"""
mu1 = np.atleast_1d(mu1)
mu2 = np.atleast_1d(mu2)
sigma1 = np.atleast_2d(sigma1)
sigma2 = np.atleast_2d(sigma2)
assert mu1.shape == mu2.shape, \
'Training and test mean vectors have different lengths'
assert sigma1.shape == sigma2.shape, \
'Training and test covariances have different dimensions'
diff = mu1 - mu2
# Product might be almost singular
covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
if not np.isfinite(covmean).all():
msg = ('fid calculation produces singular product; '
'adding %s to diagonal of cov estimates') % eps
print(msg)
offset = np.eye(sigma1.shape[0]) * eps
covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
# Numerical error might give slight imaginary component
if np.iscomplexobj(covmean):
if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
m = np.max(np.abs(covmean.imag))
raise ValueError('Imaginary component {}'.format(m))
covmean = covmean.real
tr_covmean = np.trace(covmean)
return (diff.dot(diff) + np.trace(sigma1) + # NOQA
np.trace(sigma2) - 2 * tr_covmean)
def get_i3d_activations(batched_video,
i3d_model,
target_endpoint='Logits',
flatten=True,
grad_enabled=False):
"""
Get features from i3d model and flatten them to 1d feature,
valid target endpoints are defined in InceptionI3d.VALID_ENDPOINTS
VALID_ENDPOINTS = (
'Conv3d_1a_7x7',
'MaxPool3d_2a_3x3',
'Conv3d_2b_1x1',
'Conv3d_2c_3x3',
'MaxPool3d_3a_3x3',
'Mixed_3b',
'Mixed_3c',
'MaxPool3d_4a_3x3',
'Mixed_4b',
'Mixed_4c',
'Mixed_4d',
'Mixed_4e',
'Mixed_4f',
'MaxPool3d_5a_2x2',
'Mixed_5b',
'Mixed_5c',
'Logits',
'Predictions',
)
"""
with torch.set_grad_enabled(grad_enabled):
feat = i3d_model.extract_features(batched_video.transpose(1, 2),
target_endpoint)
if flatten:
feat = feat.view(feat.size(0), -1)
return feat
# This code is from https://github.com/piergiaj/pytorch-i3d/blob/master/pytorch_i3d.py
# I only fix flake8 errors and do some cleaning here
class MaxPool3dSamePadding(nn.MaxPool3d):
def compute_pad(self, dim, s):
if s % self.stride[dim] == 0:
return max(self.kernel_size[dim] - self.stride[dim], 0)
else:
return max(self.kernel_size[dim] - (s % self.stride[dim]), 0)
def forward(self, x):
# compute 'same' padding
(batch, channel, t, h, w) = x.size()
pad_t = self.compute_pad(0, t)
pad_h = self.compute_pad(1, h)
pad_w = self.compute_pad(2, w)
pad_t_f = pad_t // 2
pad_t_b = pad_t - pad_t_f
pad_h_f = pad_h // 2
pad_h_b = pad_h - pad_h_f
pad_w_f = pad_w // 2
pad_w_b = pad_w - pad_w_f
pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
x = F.pad(x, pad)
return super(MaxPool3dSamePadding, self).forward(x)
class Unit3D(nn.Module):
def __init__(self,
in_channels,
output_channels,
kernel_shape=(1, 1, 1),
stride=(1, 1, 1),
padding=0,
activation_fn=F.relu,
use_batch_norm=True,
use_bias=False,
name='unit_3d'):
"""Initializes Unit3D module."""
super(Unit3D, self).__init__()
self._output_channels = output_channels
self._kernel_shape = kernel_shape
self._stride = stride
self._use_batch_norm = use_batch_norm
self._activation_fn = activation_fn
self._use_bias = use_bias
self.name = name
self.padding = padding
self.conv3d = nn.Conv3d(
in_channels=in_channels,
out_channels=self._output_channels,
kernel_size=self._kernel_shape,
stride=self._stride,
padding=0, # we always want padding to be 0 here. We will
# dynamically pad based on input size in forward function
bias=self._use_bias)
if self._use_batch_norm:
self.bn = nn.BatchNorm3d(self._output_channels,
eps=0.001,
momentum=0.01)
def compute_pad(self, dim, s):
if s % self._stride[dim] == 0:
return max(self._kernel_shape[dim] - self._stride[dim], 0)
else:
return max(self._kernel_shape[dim] - (s % self._stride[dim]), 0)
def forward(self, x):
# compute 'same' padding
(batch, channel, t, h, w) = x.size()
pad_t = self.compute_pad(0, t)
pad_h = self.compute_pad(1, h)
pad_w = self.compute_pad(2, w)
pad_t_f = pad_t // 2
pad_t_b = pad_t - pad_t_f
pad_h_f = pad_h // 2
pad_h_b = pad_h - pad_h_f
pad_w_f = pad_w // 2
pad_w_b = pad_w - pad_w_f
pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
x = F.pad(x, pad)
x = self.conv3d(x)
if self._use_batch_norm:
x = self.bn(x)
if self._activation_fn is not None:
x = self._activation_fn(x)
return x
class InceptionModule(nn.Module):
def __init__(self, in_channels, out_channels, name):
super(InceptionModule, self).__init__()
self.b0 = Unit3D(in_channels=in_channels,
output_channels=out_channels[0],
kernel_shape=[1, 1, 1],
padding=0,
name=name + '/Branch_0/Conv3d_0a_1x1')
self.b1a = Unit3D(in_channels=in_channels,
output_channels=out_channels[1],
kernel_shape=[1, 1, 1],
padding=0,
name=name + '/Branch_1/Conv3d_0a_1x1')
self.b1b = Unit3D(in_channels=out_channels[1],
output_channels=out_channels[2],
kernel_shape=[3, 3, 3],
name=name + '/Branch_1/Conv3d_0b_3x3')
self.b2a = Unit3D(in_channels=in_channels,
output_channels=out_channels[3],
kernel_shape=[1, 1, 1],
padding=0,
name=name + '/Branch_2/Conv3d_0a_1x1')
self.b2b = Unit3D(in_channels=out_channels[3],
output_channels=out_channels[4],
kernel_shape=[3, 3, 3],
name=name + '/Branch_2/Conv3d_0b_3x3')
self.b3a = MaxPool3dSamePadding(kernel_size=[3, 3, 3],
stride=(1, 1, 1),
padding=0)
self.b3b = Unit3D(in_channels=in_channels,
output_channels=out_channels[5],
kernel_shape=[1, 1, 1],
padding=0,
name=name + '/Branch_3/Conv3d_0b_1x1')
self.name = name
def forward(self, x):
b0 = self.b0(x)
b1 = self.b1b(self.b1a(x))
b2 = self.b2b(self.b2a(x))
b3 = self.b3b(self.b3a(x))
return torch.cat([b0, b1, b2, b3], dim=1)
class InceptionI3d(nn.Module):
"""Inception-v1 I3D architecture.
The model is introduced in:
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Joao Carreira, Andrew Zisserman
https://arxiv.org/pdf/1705.07750v1.pdf.
See also the Inception architecture, introduced in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
"""
# Endpoints of the model in order. During construction, all the endpoints up
# to a designated `final_endpoint` are returned in a dictionary as the
# second return value.
VALID_ENDPOINTS = (
'Conv3d_1a_7x7',
'MaxPool3d_2a_3x3',
'Conv3d_2b_1x1',
'Conv3d_2c_3x3',
'MaxPool3d_3a_3x3',
'Mixed_3b',
'Mixed_3c',
'MaxPool3d_4a_3x3',
'Mixed_4b',
'Mixed_4c',
'Mixed_4d',
'Mixed_4e',
'Mixed_4f',
'MaxPool3d_5a_2x2',
'Mixed_5b',
'Mixed_5c',
'Logits',
'Predictions',
)
def __init__(self,
num_classes=400,
spatial_squeeze=True,
final_endpoint='Logits',
name='inception_i3d',
in_channels=3,
dropout_keep_prob=0.5):
"""Initializes I3D model instance.
Args:
num_classes: The number of outputs in the logit layer (default 400, which
matches the Kinetics dataset).
spatial_squeeze: Whether to squeeze the spatial dimensions for the logits
before returning (default True).
final_endpoint: The model contains many possible endpoints.
`final_endpoint` specifies the last endpoint for the model to be built
up to. In addition to the output at `final_endpoint`, all the outputs
at endpoints up to `final_endpoint` will also be returned, in a
dictionary. `final_endpoint` must be one of
InceptionI3d.VALID_ENDPOINTS (default 'Logits').
name: A string (optional). The name of this module.
Raises:
ValueError: if `final_endpoint` is not recognized.
"""
if final_endpoint not in self.VALID_ENDPOINTS:
raise ValueError('Unknown final endpoint %s' % final_endpoint)
super(InceptionI3d, self).__init__()
self._num_classes = num_classes
self._spatial_squeeze = spatial_squeeze
self._final_endpoint = final_endpoint
self.logits = None
if self._final_endpoint not in self.VALID_ENDPOINTS:
raise ValueError('Unknown final endpoint %s' %
self._final_endpoint)
self.end_points = {}
end_point = 'Conv3d_1a_7x7'
self.end_points[end_point] = Unit3D(in_channels=in_channels,
output_channels=64,
kernel_shape=[7, 7, 7],
stride=(2, 2, 2),
padding=(3, 3, 3),
name=name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'MaxPool3d_2a_3x3'
self.end_points[end_point] = MaxPool3dSamePadding(
kernel_size=[1, 3, 3], stride=(1, 2, 2), padding=0)
if self._final_endpoint == end_point:
return
end_point = 'Conv3d_2b_1x1'
self.end_points[end_point] = Unit3D(in_channels=64,
output_channels=64,
kernel_shape=[1, 1, 1],
padding=0,
name=name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Conv3d_2c_3x3'
self.end_points[end_point] = Unit3D(in_channels=64,
output_channels=192,
kernel_shape=[3, 3, 3],
padding=1,
name=name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'MaxPool3d_3a_3x3'
self.end_points[end_point] = MaxPool3dSamePadding(
kernel_size=[1, 3, 3], stride=(1, 2, 2), padding=0)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_3b'
self.end_points[end_point] = InceptionModule(192,
[64, 96, 128, 16, 32, 32],
name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_3c'
self.end_points[end_point] = InceptionModule(
256, [128, 128, 192, 32, 96, 64], name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'MaxPool3d_4a_3x3'
self.end_points[end_point] = MaxPool3dSamePadding(
kernel_size=[3, 3, 3], stride=(2, 2, 2), padding=0)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_4b'
self.end_points[end_point] = InceptionModule(
128 + 192 + 96 + 64, [192, 96, 208, 16, 48, 64], name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_4c'
self.end_points[end_point] = InceptionModule(
192 + 208 + 48 + 64, [160, 112, 224, 24, 64, 64], name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_4d'
self.end_points[end_point] = InceptionModule(
160 + 224 + 64 + 64, [128, 128, 256, 24, 64, 64], name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_4e'
self.end_points[end_point] = InceptionModule(
128 + 256 + 64 + 64, [112, 144, 288, 32, 64, 64], name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_4f'
self.end_points[end_point] = InceptionModule(
112 + 288 + 64 + 64, [256, 160, 320, 32, 128, 128],
name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'MaxPool3d_5a_2x2'
self.end_points[end_point] = MaxPool3dSamePadding(
kernel_size=[2, 2, 2], stride=(2, 2, 2), padding=0)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_5b'
self.end_points[end_point] = InceptionModule(
256 + 320 + 128 + 128, [256, 160, 320, 32, 128, 128],
name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Mixed_5c'
self.end_points[end_point] = InceptionModule(
256 + 320 + 128 + 128, [384, 192, 384, 48, 128, 128],
name + end_point)
if self._final_endpoint == end_point:
return
end_point = 'Logits'
self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7], stride=(1, 1, 1))
self.dropout = nn.Dropout(dropout_keep_prob)
self.logits = Unit3D(in_channels=384 + 384 + 128 + 128,
output_channels=self._num_classes,
kernel_shape=[1, 1, 1],
padding=0,
activation_fn=None,
use_batch_norm=False,
use_bias=True,
name='logits')
self.build()
def replace_logits(self, num_classes):
self._num_classes = num_classes
self.logits = Unit3D(in_channels=384 + 384 + 128 + 128,
output_channels=self._num_classes,
kernel_shape=[1, 1, 1],
padding=0,
activation_fn=None,
use_batch_norm=False,
use_bias=True,
name='logits')
def build(self):
for k in self.end_points.keys():
self.add_module(k, self.end_points[k])
def forward(self, x):
for end_point in self.VALID_ENDPOINTS:
if end_point in self.end_points:
x = self._modules[end_point](
x) # use _modules to work with dataparallel
x = self.logits(self.dropout(self.avg_pool(x)))
if self._spatial_squeeze:
logits = x.squeeze(3).squeeze(3)
# logits is batch X time X classes, which is what we want to work with
return logits
def extract_features(self, x, target_endpoint='Logits'):
for end_point in self.VALID_ENDPOINTS:
if end_point in self.end_points:
x = self._modules[end_point](x)
if end_point == target_endpoint:
break
if target_endpoint == 'Logits':
return x.mean(4).mean(3).mean(2)
else:
return x