Python tensorflow.keras.utils.Sequence() Examples
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code examples of tensorflow.keras.utils.Sequence().
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Example #1
| Source File: data.py From keras-YOLOv3-model-set with MIT License | 6 votes |
|
def __init__(self, annotation_lines, batch_size, input_shape, anchors, num_classes, enhance_augment=None, rescale_interval=-1, shuffle=True):
self.annotation_lines = annotation_lines
self.batch_size = batch_size
self.input_shape = input_shape
self.anchors = anchors
self.num_classes = num_classes
self.enhance_augment = enhance_augment
self.indexes = np.arange(len(self.annotation_lines))
self.shuffle = shuffle
# prepare multiscale config
# TODO: error happens when using Sequence data generator with
# multiscale input shape, disable multiscale first
if rescale_interval != -1:
raise ValueError("tf.keras.Sequence generator doesn't support multiscale input, pls remove related config")
#self.rescale_interval = rescale_interval
self.rescale_interval = -1
self.rescale_step = 0
self.input_shape_list = get_multiscale_list()
Example #2
| Source File: data.py From keras-YOLOv3-model-set with MIT License | 6 votes |
|
def __init__(self, annotation_lines, batch_size, input_shape, anchors, num_classes, enhance_augment=None, rescale_interval=-1, shuffle=True):
self.annotation_lines = annotation_lines
self.batch_size = batch_size
self.input_shape = input_shape
self.anchors = anchors
self.num_classes = num_classes
self.enhance_augment = enhance_augment
self.indexes = np.arange(len(self.annotation_lines))
self.shuffle = shuffle
# prepare multiscale config
# TODO: error happens when using Sequence data generator with
# multiscale input shape, disable multiscale first
if rescale_interval != -1:
raise ValueError("tf.keras.Sequence generator doesn't support multiscale input, pls remove related config")
#self.rescale_interval = rescale_interval
self.rescale_interval = -1
self.rescale_step = 0
self.input_shape_list = get_multiscale_list()
Example #3
| Source File: base_sequence.py From U-Time with MIT License | 5 votes |
|
def seed(self):
"""
If multiprocessing, the processes will inherit the RNG state of the
main process - here we reseed each process once so that the batches
are randomly generated across multi-processes calls to the Sequence
batch generator methods
If multi-threading this method will just re-seed the 'MainProcess'
process once
"""
pname = current_process().name
if pname not in self.is_seeded or not self.is_seeded[pname]:
# Re-seed this process
np.random.seed()
self.is_seeded[pname] = True
Example #4
| Source File: base_sequence.py From U-Time with MIT License | 5 votes |
|
def _assert_scaled(self, warn_mean=5, warn_std=5, n_batches=5):
"""
Samples n_batches random batches from the sub-class Sequencer object
and computes the mean and STD of the values across the batches. If
their absolute values are higher than 'warn_mean' and 'warn_std'
respectively, a warning is printed.
Note: Does not raise an Error or Warning
Args:
warn_mean: Maximum allowed abs(mean) before warning is invoked
warn_std: Maximum allowed std before warning is invoked
n_batches: Number of batches to sample for mean/std computation
"""
# Get a set of random batches
batches = []
for ind in np.random.randint(0, len(self), n_batches):
X, _ = self[ind] # Use __getitem__ of the given Sequence class
batches.append(X)
mean, std = np.abs(np.mean(batches)), np.std(batches)
self.logger("Mean assertion ({} batches): {:.3f}".format(n_batches,
mean))
self.logger("Scale assertion ({} batches): {:.3f}".format(n_batches,
std))
if mean > warn_mean or std > warn_std:
self.logger.warn("OBS: Found large abs(mean) and std values over 5"
" sampled batches ({:.3f} and {:.3f})."
" Make sure scaling is active at either the "
"global level (attribute 'scaler' has been set on"
" individual SleepStudy objects, typically via the"
" SleepStudyDataset set_scaler method), or "
"batch-wise via the batch_scaler attribute of the"
" Sequence object.".format(mean, std))
Example #5
| Source File: multi_task_sequence.py From MultiPlanarUNet with MIT License | 5 votes |
|
def __len__(self):
"""Number of batch in the Sequence.
Returns:
The number of batches in the Sequence.
"""
return sum([len(seq) for seq in self.sequencers])
Example #6
| Source File: test_models.py From megnet with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def setUpClass(cls):
cls.n_feature = 3
cls.n_bond_features = 10
cls.n_global_features = 2
class Generator(Sequence):
def __init__(self, x, y):
self.x = x
self.y = y
def __len__(self):
return 10
def __getitem__(self, index):
return self.x, self.y
x_crystal = [np.array([1, 2, 3, 4]).reshape((1, -1)),
np.random.normal(size=(1, 6, cls.n_bond_features)),
np.random.normal(size=(1, 2, cls.n_global_features)),
np.array([[0, 0, 1, 1, 2, 3]]),
np.array([[1, 1, 0, 0, 3, 2]]),
np.array([[0, 0, 1, 1]]),
np.array([[0, 0, 0, 0, 1, 1]]),
]
y = np.random.normal(size=(1, 2, 1))
cls.train_gen_crystal = Generator(x_crystal, y)
x_mol = [np.random.normal(size=(1, 4, cls.n_feature)),
np.random.normal(size=(1, 6, cls.n_bond_features)),
np.random.normal(size=(1, 2, cls.n_global_features)),
np.array([[0, 0, 1, 1, 2, 3]]),
np.array([[1, 1, 0, 0, 3, 2]]),
np.array([[0, 0, 1, 1]]),
np.array([[0, 0, 0, 0, 1, 1]]),
]
y = np.random.normal(size=(1, 2, 1))
cls.train_gen_mol = Generator(x_mol, y)
cls.model = MEGNetModel(10, 2, nblocks=1, lr=1e-2,
n1=4, n2=4, n3=4, npass=1, ntarget=1,
graph_converter=CrystalGraph(bond_converter=GaussianDistance(np.linspace(0, 5, 10), 0.5)),
)
cls.model2 = MEGNetModel(10, 2, nblocks=1, lr=1e-2,
n1=4, n2=4, n3=4, npass=1, ntarget=2,
graph_converter=CrystalGraph(bond_converter=GaussianDistance(np.linspace(0, 5, 10), 0.5)),
)
