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inception_score.py
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inception_score.py
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"""This file is a modification of inception_score.py in
https://github.com/mattya/chainer-inception-score
"""
import math
import chainer
from chainer import Chain
from chainer import functions as F
from chainer import links as L
from chainer import Variable
from tqdm.auto import tqdm
def inception_score(model, ims, batch_size=100, splits=10, verbose=False):
"""Compute the inception score for given images.
Default batch_size is 100 and split size is 10. Please refer to the
official implementation. It is recommended to to use at least 50000
images to obtain a reliable score.
Reference:
https://github.com/openai/improved-gan/blob/master/inception_score/model.py
"""
n, c, w, h = ims.shape
n_batches = int(math.ceil(float(n) / float(batch_size)))
xp = model.xp
if verbose:
print('Batch size:', batch_size)
print('Total number of images:', n)
print('Total number of batches:', n_batches)
# Compute the softmax predicitions for for all images, split into batches
# in order to fit in memory
ys = xp.empty((n, 1008), dtype=xp.float32) # Softmax container
for i in tqdm(range(n_batches)):
# print('Running batch', i+1, '/', n_batches, '...')
batch_start = (i * batch_size)
batch_end = min((i + 1) * batch_size, n)
ims_batch = ims[batch_start:batch_end]
ims_batch = xp.asarray(ims_batch) # To GPU if using CuPy
ims_batch = Variable(ims_batch)
# Resize image to the shape expected by the inception module
if (w, h) != (299, 299):
ims_batch = F.resize_images(ims_batch, (299, 299)) # bilinear
# Feed images to the inception module to get the softmax predictions
with chainer.using_config('train', False), \
chainer.using_config('enable_backprop', False):
y = model(ims_batch)
ys[batch_start:batch_end] = y.data
# Compute the inception score based on the softmax predictions of the
# inception module.
scores = xp.empty((splits), dtype=xp.float32) # Split inception scores
for i in range(splits):
part = ys[(i * n // splits):((i + 1) * n // splits), :]
kl = part * (xp.log(part) -
xp.log(xp.expand_dims(xp.mean(part, 0), 0)))
kl = xp.mean(xp.sum(kl, 1))
scores[i] = xp.exp(kl)
return xp.mean(scores), xp.std(scores)
class Mixed(Chain):
def __init__(self, trunk):
super().__init__()
for name, link in trunk:
self.add_link(name, link)
self.trunk = trunk
def __call__(self, x):
hs = []
for name, _ in self.trunk:
h = getattr(self, name)(x)
hs.append(h)
return F.concat(hs)
class AveragePooling2D(chainer.Link):
def __init__(self, ksize, stride=None, pad=0):
super().__init__()
self.ksize = ksize
self.stride = stride
self.pad = pad
def forward(self, x):
return F.average_pooling_2d(x, self.ksize, self.stride, self.pad)
class MaxPooling2D(chainer.Link):
def __init__(self, ksize, stride=None, pad=0):
super().__init__()
self.ksize = ksize
self.stride = stride
self.pad = pad
def forward(self, x):
return F.max_pooling_2d(x, self.ksize, self.stride, self.pad)
class Tower(Chain):
def __init__(self, trunk):
super().__init__()
for name, link in trunk:
if not name.startswith('_'):
self.add_link(name, link)
self.trunk = trunk
def __call__(self, x):
h = x
for name, f in self.trunk:
if not name.startswith('_'): # Link
if 'bn' in name:
h = getattr(self, name)(h)
else:
h = getattr(self, name)(h)
else: # AveragePooling2D, MaxPooling2D or relu
h = f(h)
return h
class Inception(Chain):
def __init__(self):
super().__init__(
conv=L.Convolution2D(3, 32, 3, stride=2, pad=0),
conv_1=L.Convolution2D(32, 32, 3, stride=1, pad=0),
conv_2=L.Convolution2D(32, 64, 3, stride=1, pad=1),
conv_3=L.Convolution2D(64, 80, 1, stride=1, pad=0),
conv_4=L.Convolution2D(80, 192, 3, stride=1, pad=0),
bn_conv=L.BatchNormalization(32),
bn_conv_1=L.BatchNormalization(32),
bn_conv_2=L.BatchNormalization(64),
bn_conv_3=L.BatchNormalization(80),
bn_conv_4=L.BatchNormalization(192),
mixed=Mixed([
('conv', Tower([
('conv', L.Convolution2D(192, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(192, 48, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(48)),
('_relu', F.relu),
('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)),
('bn_conv_1', L.BatchNormalization(64)),
('_relu_1', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(192, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu),
('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),
('bn_conv_1', L.BatchNormalization(96)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)),
('bn_conv_2', L.BatchNormalization(96)),
('_relu_2', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(192, 32, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(32)),
('_relu', F.relu)
]))
]),
mixed_1=Mixed([
('conv', Tower([
('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(256, 48, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(48)),
('_relu', F.relu),
('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)),
('bn_conv_1', L.BatchNormalization(64)),
('_relu_1', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu),
('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),
('bn_conv_1', L.BatchNormalization(96)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)),
('bn_conv_2', L.BatchNormalization(96)),
('_relu_2', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu)
]))
]),
mixed_2=Mixed([
('conv', Tower([
('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(288, 48, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(48)),
('_relu', F.relu),
('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)),
('bn_conv_1', L.BatchNormalization(64)),
('_relu_1', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu),
('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),
('bn_conv_1', L.BatchNormalization(96)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)),
('bn_conv_2', L.BatchNormalization(96)),
('_relu_2', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu)
]))
]),
mixed_3=Mixed([
('conv', Tower([
('conv', L.Convolution2D(288, 384, 3, stride=2, pad=0)),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(64)),
('_relu', F.relu),
('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),
('bn_conv_1', L.BatchNormalization(96)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(96, 96, 3, stride=2, pad=0)),
('bn_conv_2', L.BatchNormalization(96)),
('_relu_2', F.relu)
])),
('pool', Tower([
('_pooling', MaxPooling2D(3, 2, pad=0))
]))
]),
mixed_4=Mixed([
('conv', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(768, 128, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(128)),
('_relu', F.relu),
('conv_1', L.Convolution2D(128, 128, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_1', L.BatchNormalization(128)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(128, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_2', L.BatchNormalization(192)),
('_relu_2', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(768, 128, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(128)),
('_relu', F.relu),
('conv_1', L.Convolution2D(128, 128, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_1', L.BatchNormalization(128)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(128, 128, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_2', L.BatchNormalization(128)),
('_relu_2', F.relu),
('conv_3', L.Convolution2D(128, 128, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_3', L.BatchNormalization(128)),
('_relu_3', F.relu),
('conv_4', L.Convolution2D(128, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_4', L.BatchNormalization(192)),
('_relu_4', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
]))
]),
mixed_5=Mixed([
('conv', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(160)),
('_relu', F.relu),
('conv_1', L.Convolution2D(160, 160, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_1', L.BatchNormalization(160)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(160, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_2', L.BatchNormalization(192)),
('_relu_2', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(160)),
('_relu', F.relu),
('conv_1', L.Convolution2D(160, 160, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_1', L.BatchNormalization(160)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(160, 160, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_2', L.BatchNormalization(160)),
('_relu_2', F.relu),
('conv_3', L.Convolution2D(160, 160, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_3', L.BatchNormalization(160)),
('_relu_3', F.relu),
('conv_4', L.Convolution2D(160, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_4', L.BatchNormalization(192)),
('_relu_4', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
]))
]),
mixed_6=Mixed([
('conv', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(160)),
('_relu', F.relu),
('conv_1', L.Convolution2D(160, 160, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_1', L.BatchNormalization(160)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(160, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_2', L.BatchNormalization(192)),
('_relu_2', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(160)),
('_relu', F.relu),
('conv_1', L.Convolution2D(160, 160, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_1', L.BatchNormalization(160)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(160, 160, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_2', L.BatchNormalization(160)),
('_relu_2', F.relu),
('conv_3', L.Convolution2D(160, 160, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_3', L.BatchNormalization(160)),
('_relu_3', F.relu),
('conv_4', L.Convolution2D(160, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_4', L.BatchNormalization(192)),
('_relu_4', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
]))
]),
mixed_7=Mixed([
('conv', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
])),
('tower', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu),
('conv_1', L.Convolution2D(192, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_1', L.BatchNormalization(192)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(192, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_2', L.BatchNormalization(192)),
('_relu_2', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu),
('conv_1', L.Convolution2D(192, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_1', L.BatchNormalization(192)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(192, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_2', L.BatchNormalization(192)),
('_relu_2', F.relu),
('conv_3', L.Convolution2D(192, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_3', L.BatchNormalization(192)),
('_relu_3', F.relu),
('conv_4', L.Convolution2D(192, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_4', L.BatchNormalization(192)),
('_relu_4', F.relu)
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
]))
]),
mixed_8=Mixed([
('tower', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu),
('conv_1', L.Convolution2D(192, 320, 3, stride=2, pad=0)),
('bn_conv_1', L.BatchNormalization(320)),
('_relu_1', F.relu)
])),
('tower_1', Tower([
('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu),
('conv_1', L.Convolution2D(192, 192, (1, 7), stride=1,
pad=(0, 3))),
('bn_conv_1', L.BatchNormalization(192)),
('_relu_1', F.relu),
('conv_2', L.Convolution2D(192, 192, (7, 1), stride=1,
pad=(3, 0))),
('bn_conv_2', L.BatchNormalization(192)),
('_relu_2', F.relu),
('conv_3', L.Convolution2D(192, 192, 3, stride=2, pad=0)),
('bn_conv_3', L.BatchNormalization(192)),
('_relu_3', F.relu)
])),
('pool', Tower([
('_pooling', MaxPooling2D(3, 2, pad=0))
]))
]),
mixed_9=Mixed([
('conv', Tower([
('conv', L.Convolution2D(1280, 320, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(320)),
('_relu', F.relu),
])),
('tower', Tower([
('conv', L.Convolution2D(1280, 384, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu),
('mixed', Mixed([
('conv', Tower([
('conv', L.Convolution2D(384, 384, (1, 3),
stride=1, pad=(0, 1))),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu),
])),
('conv_1', Tower([
('conv_1', L.Convolution2D(384, 384, (3, 1),
stride=1, pad=(1, 0))),
('bn_conv_1', L.BatchNormalization(384)),
('_relu_1', F.relu),
]))
]))
])),
('tower_1', Tower([
('conv', L.Convolution2D(1280, 448, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(448)),
('_relu', F.relu),
('conv_1', L.Convolution2D(448, 384, 3, stride=1, pad=1)),
('bn_conv_1', L.BatchNormalization(384)),
('_relu_1', F.relu),
('mixed', Mixed([
('conv', Tower([
('conv', L.Convolution2D(384, 384, (1, 3),
stride=1, pad=(0, 1))),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu),
])),
('conv_1', Tower([
('conv_1', L.Convolution2D(384, 384, (3, 1),
stride=1, pad=(1, 0))),
('bn_conv_1', L.BatchNormalization(384)),
('_relu_1', F.relu),
]))
]))
])),
('tower_2', Tower([
('_pooling', AveragePooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(1280, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
]))
]),
mixed_10=Mixed([
('conv', Tower([
('conv', L.Convolution2D(2048, 320, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(320)),
('_relu', F.relu),
])),
('tower', Tower([
('conv', L.Convolution2D(2048, 384, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu),
('mixed', Mixed([
('conv', Tower([
('conv', L.Convolution2D(384, 384, (1, 3),
stride=1, pad=(0, 1))),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu),
])),
('conv_1', Tower([
('conv_1', L.Convolution2D(384, 384, (3, 1),
stride=1, pad=(1, 0))),
('bn_conv_1', L.BatchNormalization(384)),
('_relu_1', F.relu),
]))
]))
])),
('tower_1', Tower([
('conv', L.Convolution2D(2048, 448, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(448)),
('_relu', F.relu),
('conv_1', L.Convolution2D(448, 384, 3, stride=1, pad=1)),
('bn_conv_1', L.BatchNormalization(384)),
('_relu_1', F.relu),
('mixed', Mixed([
('conv', Tower([
('conv', L.Convolution2D(384, 384, (1, 3),
stride=1, pad=(0, 1))),
('bn_conv', L.BatchNormalization(384)),
('_relu', F.relu)
])),
('conv_1', Tower([
('conv_1', L.Convolution2D(384, 384, (3, 1),
stride=1, pad=(1, 0))),
('bn_conv_1', L.BatchNormalization(384)),
('_relu_1', F.relu)
]))
]))
])),
('tower_2', Tower([
('_pooling', MaxPooling2D(3, 1, pad=1)),
('conv', L.Convolution2D(2048, 192, 1, stride=1, pad=0)),
('bn_conv', L.BatchNormalization(192)),
('_relu', F.relu)
]))
]),
logit=L.Linear(2048, 1008)
)
def __call__(self, x, get_feature=False):
"""Input dims are (batch_size, 3, 299, 299)."""
# assert x.shape[1:] == (3, 299, 299)
x -= 128.0
x *= 0.0078125
h = F.relu(self.bn_conv(self.conv(x)))
# assert h.shape[1:] == (32, 149, 149)
h = F.relu(self.bn_conv_1(self.conv_1(h)))
# assert h.shape[1:] == (32, 147, 147)
h = F.relu(self.bn_conv_2(self.conv_2(h)))
# assert h.shape[1:] == (64, 147, 147)
h = F.max_pooling_2d(h, 3, stride=2, pad=0)
# assert h.shape[1:] == (64, 73, 73)
h = F.relu(self.bn_conv_3(self.conv_3(h)))
# assert h.shape[1:] == (80, 73, 73)
h = F.relu(self.bn_conv_4(self.conv_4(h)))
# assert h.shape[1:] == (192, 71, 71)
h = F.max_pooling_2d(h, 3, stride=2, pad=0)
# assert h.shape[1:] == (192, 35, 35)
h = self.mixed(h)
# assert h.shape[1:] == (256, 35, 35)
h = self.mixed_1(h)
# assert h.shape[1:] == (288, 35, 35)
h = self.mixed_2(h)
# assert h.shape[1:] == (288, 35, 35)
h = self.mixed_3(h)
# assert h.shape[1:] == (768, 17, 17)
h = self.mixed_4(h)
# assert h.shape[1:] == (768, 17, 17)
h = self.mixed_5(h)
# assert h.shape[1:] == (768, 17, 17)
h = self.mixed_6(h)
# assert h.shape[1:] == (768, 17, 17)
h = self.mixed_7(h)
# assert h.shape[1:] == (768, 17, 17)
h = self.mixed_8(h)
# assert h.shape[1:] == (1280, 8, 8)
h = self.mixed_9(h)
# assert h.shape[1:] == (2048, 8, 8)
h = self.mixed_10(h)
# assert h.shape[1:] == (2048, 8, 8)
h = F.average_pooling_2d(h, 8, 1)
# assert h.shape[1:] == (2048, 1, 1)
h = F.reshape(h, (-1, 2048))
if get_feature:
return h
else:
h = self.logit(h)
h = F.softmax(h)
# assert h.shape[1:] == (1008,)
return h