def test_similarity_transform_keep_ratio():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(keep_ratio=True)
similarity_other = ops.SimilarityTransform()
sim_t = similarity(0, (512, 288), (256, 256), rng)
sim_t_other = similarity_other(0, (512, 288), (256, 256), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
np.asarray(sim_t_other).reshape((3, 3)).dot((0, 0, 1)))
< 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((255, 255, 1)) -
np.asarray(sim_t_other).reshape((3, 3))
.dot((255, 255, 1)))
< 0.0001).all()
similarity = ops.SimilarityTransform(keep_ratio=True,
resize=True)
sim_t = similarity(0, (512, 288), (256, 256), rng)
o_top_left = np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1))
o_bot_right = np.asarray(sim_t).reshape((3, 3)).dot((255, 255, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Resize factors are 288/256
assert (o_width * 256/288) + 1 - 256 < 0.0001
assert (o_height * 256/288) + 1 - 256 < 0.0001
def test_similarity_transform_flip():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(flip_h=1.0)
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert sim_t == (-1, 0, 255,
0, 1, 0,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(255, 0, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) ==
(0, 143, 1)).all()
similarity = ops.SimilarityTransform(flip_h=1.0)
sim_t = similarity(0, (256, 144), (128, 72), rng)
assert sim_t == (-1, 0, 191,
0, 1, 36,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(191, 36, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((127, 71, 1)) ==
(64, 107, 1)).all()
similarity = ops.SimilarityTransform(flip_v=1.0)
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert sim_t == (1, 0, 0,
0, -1, 143,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(0, 143, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) ==
(255, 0, 1)).all()
similarity = ops.SimilarityTransform(flip_v=1.0)
sim_t = similarity(0, (256, 144), (128, 72), rng)
assert sim_t == (1, 0, 64,
0, -1, 107,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(64, 107, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((127, 71, 1)) ==
(191, 36, 1)).all()
similarity = ops.SimilarityTransform(flip_h=1.0, flip_v=1.0)
sim_t = similarity(0, (256, 144), (128, 72), rng)
assert sim_t == (-1, 0, 191,
0, -1, 107,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(191, 107, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((127, 71, 1)) ==
(64, 36, 1)).all()
def test_similarity_transform_degrees():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(degrees=(90, 90))
sim_t = similarity(0, (256, 144), (144, 256), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(255, 0, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((143, 255, 1)) -
(0, 143, 1)) < 0.0001).all()
sim_t = similarity(0, (256, 144), (72, 128), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(191, 36, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((71, 127, 1)) -
(64, 107, 1)) < 0.0001).all()
similarity = ops.SimilarityTransform(degrees=(-90, -90))
sim_t = similarity(0, (256, 144), (144, 256), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(0, 143, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((143, 255, 1)) -
(255, 0, 1)) < 0.0001).all()
sim_t = similarity(0, (256, 144), (72, 128), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(64, 107, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((71, 127, 1)) -
(191, 36, 1)) < 0.0001).all()
similarity = ops.SimilarityTransform(degrees=(180, 180))
sim_t = similarity(0, (256, 144), (144, 256), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(199, 199, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((143, 255, 1)) -
(56, -56, 1)) < 0.0001).all()
similarity = ops.SimilarityTransform(degrees=(180, 180))
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(255, 143, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) -
(0, 0, 1)) < 0.0001).all()
def test_similarity_transform_ratio():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(ratio=(16 / 9, 16 / 9))
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert sim_t == (1, 0, 0,
0, 1, 0,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(0, 0, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) ==
(255, 143, 1)).all()
similarity = ops.SimilarityTransform(ratio=(2, 2))
sim_t = similarity(0, (512, 144), (288, 144), rng)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(112, 0, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((287, 143, 1)) ==
(399, 143, 1)).all()
sim_t = similarity(0, (512, 144), (144, 288), rng)
# x: (288/144) * 0.5 - 0.5 = 0.5
# y: (144/288) * 0.5 - 0.5 = -0.25
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(112.5, -0.25, 1)) < 0.0001).all()
# x: -1 * (288/144) + 0.5 = -1.5
# y: -1 * (144/288) + -0.25 = -0.75
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((143, 287, 1)) -
(398.5, 143.25, 1)) < 0.0001).all()
similarity = ops.SimilarityTransform(ratio=(0.5, 0.5))
sim_t = similarity(0, (144, 512), (288, 144), rng)
# x: (144/288) * 0.5 - 0.5 = -0.25
# y: (288/144) * 0.5 - 0.5 = 0.5
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(-0.25, 112.5, 1)).all()
# x: -1 * (144/288) + -0.25 = -0.75
# y: -1 * (288/144) + 0.5 = -1.5
assert (np.asarray(sim_t).reshape((3, 3)).dot((287, 143, 1)) ==
(143.25, 398.5, 1)).all()
def test_similarity_transform_scale():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(scale=(0.25, 0.25))
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) -
(63.75, 35.75, 1)) < 0.0001).all()
assert (np.absolute(np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) -
(191.25, 107.25, 1)) < 0.0001).all()
sim_t = similarity(0, (256, 144), (224, 126), rng)
o_top_left = np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1))
o_bot_right = np.asarray(sim_t).reshape((3, 3)).dot((223, 125, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Crop scale is 0.5 and resize factors are 256/224 and 144/126 respectively
assert abs(o_width * 224/256) * 2 + 1 - 224 < 0.0001
assert abs(o_height * 126/144) * 2 + 1 - 126 < 0.0001
def test_center_resized_crop():
rng = np.random.RandomState(0)
center_resized_crop = ops.CenterResizedCrop(0.5)
similarity = ops.SimilarityTransform(scale=(0.25, 0.25), resize=True,
keep_ratio=True)
assert center_resized_crop.s != None
assert center_resized_crop.s != (1.0, 1.0)
assert center_resized_crop.keep_ratio
assert center_resized_crop.s == similarity.s
assert center_resized_crop.ar == similarity.ar
assert center_resized_crop.r == similarity.r
assert center_resized_crop.t == similarity.t
assert center_resized_crop.fh == similarity.fh
assert center_resized_crop.fv == similarity.fv
assert center_resized_crop.resize == similarity.resize
assert center_resized_crop.keep_ratio == similarity.keep_ratio
assert center_resized_crop.random_crop == similarity.random_crop
def test_random_resized_crop():
random_resized_crop = ops.RandomResizedCrop()
similarity = ops.SimilarityTransform(scale=(0.08, 1.0),
ratio=(3./4., 4./3.),
random_crop=True)
assert random_resized_crop.s != None
assert random_resized_crop.s != (1.0, 1.0)
assert random_resized_crop.ar != None
assert random_resized_crop.resize
assert random_resized_crop.random_crop
assert random_resized_crop.s == similarity.s
assert random_resized_crop.ar == similarity.ar
assert random_resized_crop.r == similarity.r
assert random_resized_crop.t == similarity.t
assert random_resized_crop.fh == similarity.fh
assert random_resized_crop.fv == similarity.fv
assert random_resized_crop.resize == similarity.resize
assert random_resized_crop.keep_ratio == similarity.keep_ratio
assert random_resized_crop.random_crop == similarity.random_crop
def test_similarity_transform_resize():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(resize=True)
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert sim_t == (1, 0, 0,
0, 1, 0,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(0, 0, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) ==
(255, 143, 1)).all()
sim_t = similarity(0, (256, 144), (144, 256), rng)
o_top_left = np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1))
o_bot_right = np.asarray(sim_t).reshape((3, 3)).dot((143, 255, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Resize factors are 256/144 and 144/256 respectively
assert abs(o_width * 144/256) + 1 - 144 < 0.0001
assert abs(o_height * 256/144) + 1 - 256 < 0.0001
def test_similarity_transform_vanilla():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform()
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert sim_t == (1, 0, 0,
0, 1, 0,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(0, 0, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) ==
(255, 143, 1)).all()
sim_t = similarity(0, (256, 144), (224, 126), rng)
assert sim_t == (1, 0, 16,
0, 1, 9,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(16, 9, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((223, 125, 1)) ==
(239, 134, 1)).all()
sim_t = similarity(0, (256, 144), (144, 256), rng)
assert sim_t == (1, 0, 56,
0, 1, -56,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(56, -56, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((143, 255, 1)) ==
(199, 199, 1)).all()
sim_t = similarity(0, (256, 144), (126, 224), rng)
assert sim_t == (1, 0, 65,
0, 1, -40,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(65, -40, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((125, 223, 1)) ==
(190, 183, 1)).all()
def test_similarity_transform():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform()
assert similarity(0, (256, 256), (256, 256),
rng) == (1, 0, 0, 0, 1, 0, 0, 0, 1)
assert similarity(0, (256, 256), (224, 224),
rng) == (1, 0, 16, 0, 1, 16, 0, 0, 1)
similarity = ops.SimilarityTransform(scale=(0.5, 0.5))
assert similarity(0, (256, 256), (128, 128),
rng) == (2, 0, 0.5, 0, 2, 0.5, 0, 0, 1)
assert similarity(0, (256, 128), (128, 64),
rng) == (2, 0, 0.5, 0, 2, 0.5, 0, 0, 1)
assert similarity(0, (256, 128), (64, 64),
rng) == (2, 0, 64.5, 0, 2, 0.5, 0, 0, 1)
similarity = ops.SimilarityTransform(translation_x=(64, 64))
assert similarity(0, (256, 256), (256, 256),
rng) == (1, 0, 64, 0, 1, 0, 0, 0, 1)
assert similarity(0, (256, 256), (64, 64),
rng) == (1, 0, 160, 0, 1, 96, 0, 0, 1)
similarity = ops.SimilarityTransform(translation_y=(64, 64))
assert similarity(0, (256, 256), (256, 256),
rng) == (1, 0, 0, 0, 1, 64, 0, 0, 1)
assert similarity(0, (256, 256), (64, 64),
rng) == (1, 0, 96, 0, 1, 160, 0, 0, 1)
similarity = ops.SimilarityTransform(translation_x=(-96, -96),
translation_y=(-96, -96))
assert similarity(0, (256, 256), (64, 64),
rng) == (1, 0, 0, 0, 1, 0, 0, 0, 1)
similarity = ops.SimilarityTransform(flip_h=1, flip_v=1)
assert similarity(0, (256, 256), (256, 256),
rng) == (-1, 0, 255, 0, -1, 255, 0, 0, 1)
similarity = ops.SimilarityTransform(autoscale=True)
assert similarity(0, (256, 256), (128, 128),
rng) == (2, 0, 0.5, 0, 2, 0.5, 0, 0, 1)
assert similarity(0, (256, 256), (64, 64),
rng) == (4, 0, 1.5, 0, 4, 1.5, 0, 0, 1)
assert similarity(0, (512, 256), (64, 64),
rng) == (4, 0, 129.5, 0, 4, 1.5, 0, 0, 1)
similarity = ops.SimilarityTransform(scale=(0.5, 0.5), autoscale=True)
assert similarity(0, (256, 256), (128, 128),
rng) == (4, 0, -126.5, 0, 4, -126.5, 0, 0, 1)
assert similarity(0, (512, 256), (64, 64),
rng) == (8, 0, 3.5, 0, 8, -124.5, 0, 0, 1)
similarity = ops.SimilarityTransform(random_crop=True)
rng = np.random.RandomState(0)
assert similarity(0, (256, 256), (256, 256),
rng) == (1, 0, 0, 0, 1, 0, 0, 0, 1)
assert similarity(0, (256, 256), (128, 128),
rng) == (1, 0, 9.092615449329529, 0, 1,
11.15255036179721, 0, 0, 1)
assert similarity(0, (256, 256), (128, 128),
rng) == (1, 0, 59.069358368375276, 0, 1,
99.9077345646663, 0, 0, 1)
assert similarity(0, (256, 256), (64, 256),
rng) == (1, 0, 50.79467752408837, 0, 1, 0, 0, 0, 1)
assert similarity(0, (256, 256), (256, 64),
rng) == (1, 0, 0, 0, 1, 130.9094974278688, 0, 0, 1)
def main_worker(gpu, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
### Benzina ###
train_dataset = bz.dataset.ImageNet(args.data, split="train")
bias = ops.ConstantBiasTransform(bias=(0.485 * 255, 0.456 * 255, 0.406 * 255))
std = ops.ConstantNormTransform(norm=(0.229 * 255, 0.224 * 255, 0.225 * 255))
train_loader = bz.DataLoader(
train_dataset, shape=(224, 224), batch_size=args.batch_size,
shuffle=True, seed=args.seed,
bias_transform=bias,
norm_transform=std,
warp_transform=ops.SimilarityTransform(
scale=(0.08, 1.0),
ratio=(3./4., 4./3.),
flip_h=0.5,
random_crop=True))
val_loader = bz.DataLoader(
bz.dataset.ImageNet(args.data, split="val"), shape=(224, 224),
batch_size=args.batch_size, shuffle=False, seed=args.seed,
bias_transform=bias,
norm_transform=std,
warp_transform=ops.CenterResizedCrop(224/256))
### Benzina - end ###
if args.evaluate:
validate(val_loader, model, criterion, args)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best)
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
### Benzina ###
# Dataset
dataset = bz.ImageNet(args.data)
indices = list(range(len(dataset)))
n_valid = 50000
n_test = 100000
n_train = len(dataset) - n_valid - n_test
train_sampler = torch.utils.data.SubsetRandomSampler(indices[:n_train])
valid_sampler = torch.utils.data.SubsetRandomSampler(
indices[n_train:-n_test])
# Dataloaders
bias = ops.ConstantBiasTransform(bias=(123.675, 116.28, 103.53))
std = ops.ConstantNormTransform(norm=(58.395, 57.12, 57.375))
train_loader = bz.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=train_sampler,
seed=args.seed,
shape=(224, 224),
bias_transform=bias,
norm_transform=std,
warp_transform=ops.SimilarityTransform(flip_h=0.5))
val_loader = bz.DataLoader(dataset,
batch_size=args.batch_size,
sampler=valid_sampler,
seed=args.seed,
shape=(224, 224),
bias_transform=bias,
norm_transform=std,
warp_transform=ops.SimilarityTransform())
### Benzina - end ###
if args.evaluate:
validate(val_loader, model, criterion, args)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, is_best)
def test_similarity_transform_mix():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(scale=(0.25, 0.25),
ratio=(2, 2),
degrees=(90, 90),
flip_h=1.0,
flip_v=1.0)
sim_t = similarity(0, (512, 144), (192, 384), rng)
o_top_left = np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1))
o_bot_right = np.asarray(sim_t).reshape((3, 3)).dot((191, 383, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Crop scale is 0.5 and resize factors are 384/384 and 192/192
assert abs(o_width) * 2 + 1 - 384 < 0.0001
assert abs(o_height) * 2 + 1 - 192 < 0.0001
similarity = ops.SimilarityTransform(scale=(0.0625, 0.0625),
keep_ratio=True)
sim_t = similarity(0, (768, 288), (256, 144), rng)
o_top_left = np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1))
o_bot_right = np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Crop scale is 0.25 and resize factors are 72/144
assert abs(o_width * 144/72) + 1 - 256 < 0.0001
assert abs(o_height * 144/72) + 1 - 144 < 0.0001
similarity = ops.SimilarityTransform(scale=(0.0625, 0.0625),
keep_ratio=True)
sim_t = similarity(0, (768, 288), (144, 256), rng)
o_top_left = np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1))
o_bot_right = np.asarray(sim_t).reshape((3, 3)).dot((143, 255, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Crop scale is 0.25 and resize factors are 72/256
assert abs(o_width * 256/72) + 1 - 144 < 0.0001
assert abs(o_height * 256/72) + 1 - 256 < 0.0001
sim_t = ops.compute_affine_matrix((256, 144),
(144, 256),
crop=(10, -10, 40, 20),
degrees=90)
assert (np.absolute(sim_t.dot((0, 0, 1)) - (265, -10, 1)) < 0.0001).all()
assert (np.absolute(sim_t.dot((143, 255, 1)) - (10, 133, 1))
< 0.0001).all()
sim_t = ops.compute_affine_matrix((256, 144),
(144, 256),
crop=(10, -10, 40, 20),
degrees=90,
resize=True)
o_top_left = sim_t.dot((0, 0, 1))
o_bot_right = sim_t.dot((255, 143, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Resize factors are 40/256 and 20/144 respectively
assert abs(o_width * 256/40) + 1 - 144 < 0.0001
assert abs(o_height * 144/20) + 1 - 256 < 0.0001
sim_t = ops.compute_affine_matrix((256, 144),
(256, 144),
crop=(10, -10, 40, 20),
translate=(-15, 15))
assert (sim_t == ((1, 0, -5),
(0, 1, 5),
(0, 0, 1))).all()
assert (sim_t.dot((0, 0, 1)) == (-5, 5, 1)).all()
assert (sim_t.dot((255, 143, 1)) == (250, 148, 1)).all()
sim_t = ops.compute_affine_matrix((256, 144),
(128, 72),
crop=(10, -10, 40, 20),
translate=(-15, 15))
assert (sim_t == ((1, 0, 59),
(0, 1, 41),
(0, 0, 1))).all()
assert (sim_t.dot((0, 0, 1)) == (59, 41, 1)).all()
assert (sim_t.dot((127, 71, 1)) == (186, 112, 1)).all()
sim_t = ops.compute_affine_matrix((256, 144),
(256, 144),
crop=(10, -10, 40, 20),
translate=(-15, 15),
resize=True)
sim_t_other = ops.compute_affine_matrix((256, 144),
(256, 144),
crop=(10, -10, 40, 20),
resize=True)
assert (np.absolute(sim_t.dot((15, -15, 1)) - sim_t_other.dot((0, 0, 1)))
< 0.0001).all()
assert (np.absolute(sim_t.dot((270, 128, 1)) -
sim_t_other.dot((255, 143, 1)))
< 0.0001).all()
sim_t = ops.compute_affine_matrix((256, 144),
(128, 72),
crop=(10, -10, 40, 20),
translate=(-15, 15),
resize=True)
sim_t_other = ops.compute_affine_matrix((256, 144),
(128, 72),
crop=(10, -10, 40, 20),
resize=True)
assert (np.absolute(sim_t.dot((15, -15, 1)) - sim_t_other.dot((0, 0, 1)))
< 0.0001).all()
assert (np.absolute(sim_t.dot((142, 56, 1)) -
sim_t_other.dot((127, 71, 1)))
< 0.0001).all()
sim_t = ops.compute_affine_matrix((256, 144),
(144, 256),
crop=(10, -10, 40, 20),
degrees=90,
translate=(-15, 15),
resize=True)
sim_t_other = ops.compute_affine_matrix((256, 144),
(144, 256),
crop=(10, -10, 40, 20),
degrees=90,
resize=True)
assert (np.absolute(sim_t.dot((15, -15, 1)) - sim_t_other.dot((0, 0, 1)))
< 0.0001).all()
assert (np.absolute(sim_t.dot((270, 128, 1)) -
sim_t_other.dot((255, 143, 1)))
< 0.0001).all()
def test_similarity_transform_crop():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(random_crop=True)
sim_t = similarity(0, (256, 144), (256, 144), rng)
assert sim_t == (1, 0, 0,
0, 1, 0,
0, 0, 1)
assert (np.asarray(sim_t).reshape((3, 3)).dot((0, 0, 1)) ==
(0, 0, 1)).all()
assert (np.asarray(sim_t).reshape((3, 3)).dot((255, 143, 1)) ==
(255, 143, 1)).all()
sim_t_samples = np.asarray([similarity(0, (256, 144), (128, 72), rng)
for _ in range(10000)])
samples_mean = sim_t_samples.mean(axis=0)
assert ((samples_mean == (1, 0, None,
0, 1, None,
0, 0, 1)) ==
(True, True, False,
True, True, False,
True, True, True)).all()
assert abs(samples_mean[2]) - 64 < 0.01 * 128
assert abs(samples_mean[5]) - 36 < 0.01 * 72
assert sim_t_samples[:, 2].max() < 128
assert sim_t_samples[:, 5].max() < 72
assert sim_t_samples[:, 2].min() >= 0
assert sim_t_samples[:, 5].min() >= 0
sim_t_samples = np.asarray([similarity(0, (256, 144), (144, 256), rng)
for _ in range(10000)])
samples_mean = sim_t_samples.mean(axis=0)
assert ((samples_mean == (1, 0, None,
0, 1, -56,
0, 0, 1)) ==
(True, True, False,
True, True, True,
True, True, True)).all()
assert abs(samples_mean[2]) - 56 < 0.01 * 112
assert sim_t_samples[:, 2].max() <= 112
assert sim_t_samples[:, 2].min() >= 0
sim_t = ops.compute_affine_matrix((256, 144),
(256, 144),
crop=(10, -10, 40, 20))
assert (sim_t == ((1, 0, 10),
(0, 1, -10),
(0, 0, 1))).all()
assert (sim_t.dot((0, 0, 1)) == (10, -10, 1)).all()
assert (sim_t.dot((255, 143, 1)) == (265, 133, 1)).all()
sim_t = ops.compute_affine_matrix((256, 144),
(128, 72),
crop=(10, -10, 40, 20))
assert (sim_t == ((1, 0, 74),
(0, 1, 26),
(0, 0, 1))).all()
assert (sim_t.dot((0, 0, 1)) == (74, 26, 1)).all()
assert (sim_t.dot((127, 71, 1)) == (201, 97, 1)).all()
sim_t = ops.compute_affine_matrix((256, 144),
(256, 144),
crop=(10, -10, 40, 20),
resize=True)
o_top_left = sim_t.dot((0, 0, 1))
o_bot_right = sim_t.dot((255, 143, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Resize factors are 40/256 and 20/144 respectively
assert abs(o_width * 256/40) + 1 - 256 < 0.0001
assert abs(o_height * 144/20) + 1 - 144 < 0.0001
sim_t = ops.compute_affine_matrix((256, 144),
(128, 72),
crop=(10, -10, 40, 20),
resize=True)
o_top_left = sim_t.dot((0, 0, 1))
o_bot_right = sim_t.dot((255, 143, 1))
o_width, o_height = (o_bot_right - o_top_left)[0:2]
# Resize factors are 40/128 and 20/72 respectively
assert abs(o_width * 128/40) + 1 - 256 < 0.0001
assert abs(o_height * 72/20) + 1 - 144 < 0.0001
def test_similarity_transform_translate():
rng = np.random.RandomState(0)
similarity = ops.SimilarityTransform(translate=(0.1, 0.1))
sim_t_samples = np.asarray([similarity(0, (256, 144), (256, 144), rng)
for _ in range(10000)])
samples_mean = sim_t_samples.mean(axis=0)
assert ((samples_mean == (1, 0, None,
0, 1, None,
0, 0, 1)) ==
(True, True, False,
True, True, False,
True, True, True)).all()
assert abs(samples_mean[2]) < 0.01 * 2 * 256 * similarity.t[0]
assert abs(samples_mean[5]) < 0.01 * 2 * 144 * similarity.t[0]
assert sim_t_samples[:, 2].max() < 256 * similarity.t[0]
assert sim_t_samples[:, 5].max() < 144 * similarity.t[0]
assert sim_t_samples[:, 2].min() > -256 * similarity.t[0]
assert sim_t_samples[:, 5].min() > -144 * similarity.t[0]
sim_t_samples = np.asarray([similarity(0, (256, 144), (128, 72), rng)
for _ in range(10000)])
samples_mean = sim_t_samples.mean(axis=0)
assert ((samples_mean == (1, 0, None,
0, 1, None,
0, 0, 1)) ==
(True, True, False,
True, True, False,
True, True, True)).all()
assert abs(samples_mean[2] - 64) < 0.01 * 2 * 128 * similarity.t[0]
assert abs(samples_mean[5] - 36) < 0.01 * 2 * 72 * similarity.t[0]
assert sim_t_samples[:, 2].max() - 64 < 128 * similarity.t[0]
assert sim_t_samples[:, 5].max() - 36 < 72 * similarity.t[0]
assert sim_t_samples[:, 2].min() + 64 > -128 * similarity.t[0]
assert sim_t_samples[:, 5].min() + 36 > -72 * similarity.t[0]
similarity = ops.SimilarityTransform(translate=(0.2, 0.2))
sim_t_samples = np.asarray([similarity(0, (256, 144), (256, 144), rng)
for _ in range(10000)])
samples_mean = sim_t_samples.mean(axis=0)
assert ((samples_mean == (1, 0, None,
0, 1, None,
0, 0, 1)) ==
(True, True, False,
True, True, False,
True, True, True)).all()
assert abs(samples_mean[2]) < 0.01 * 2 * 256 * similarity.t[0]
assert abs(samples_mean[5]) < 0.01 * 2 * 144 * similarity.t[0]
assert sim_t_samples[:, 2].max() < 256 * similarity.t[0]
assert sim_t_samples[:, 5].max() < 144 * similarity.t[0]
assert sim_t_samples[:, 2].min() > -256 * similarity.t[0]
assert sim_t_samples[:, 5].min() > -144 * similarity.t[0]
sim_t = ops.compute_affine_matrix((256, 144),
(256, 144),
translate=(10, -10))
assert (sim_t == ((1, 0, 10),
(0, 1, -10),
(0, 0, 1))).all()
assert (sim_t.dot((0, 0, 1)) == (10, -10, 1)).all()
assert (sim_t.dot((255, 143, 1)) == (265, 133, 1)).all()
sim_t = ops.compute_affine_matrix((256, 144),
(128, 72),
translate=(10, -10))
assert (sim_t == ((1, 0, 74),
(0, 1, 26),
(0, 0, 1))).all()
assert (sim_t.dot((0, 0, 1)) == (74, 26, 1)).all()
assert (sim_t.dot((127, 71, 1)) == (201, 97, 1)).all()