Python realtime_augmentation.realtime_fixed_augmented_data_gen函数代码示例

def create_train_gen():
    """
    this generates the training data in order, for postprocessing. Do not use this for actual training.
    """
    data_gen_train = ra.realtime_fixed_augmented_data_gen(train_indices, 'train',
        ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes)
    return load_data.buffered_gen_mp(data_gen_train, buffer_size=GEN_BUFFER_SIZE)

def create_test_gen():
    data_gen_test = ra.realtime_fixed_augmented_data_gen(
        test_indices,
        "test",
        ds_transforms=ds_transforms,
        chunk_size=CHUNK_SIZE,
        target_sizes=input_sizes,
        processor_class=ra.LoadAndProcessFixedPysexGen1CenteringRescaling,
    )
    return load_data.buffered_gen_mp(data_gen_test, buffer_size=GEN_BUFFER_SIZE)

def create_train_gen():
    """
    this generates the training data in order, for postprocessing. Do not use this for actual training.
    """
    data_gen_train = ra.realtime_fixed_augmented_data_gen(
        train_indices,
        "train",
        ds_transforms=ds_transforms,
        chunk_size=CHUNK_SIZE,
        target_sizes=input_sizes,
        processor_class=ra.LoadAndProcessFixedPysexGen1CenteringRescaling,
    )
    return load_data.buffered_gen_mp(data_gen_train, buffer_size=GEN_BUFFER_SIZE)

print "Load model parameters"
layers.set_param_values(l6, analysis['param_values'])

print "Create generators"
# set here which transforms to use to make predictions
augmentation_transforms = []
for zoom in [1 / 1.2, 1.0, 1.2]:
    for angle in np.linspace(0, 360, 10, endpoint=False):
        augmentation_transforms.append(ra.build_augmentation_transform(rotation=angle, zoom=zoom))
        augmentation_transforms.append(ra.build_augmentation_transform(rotation=(angle + 180), zoom=zoom, shear=180)) # flipped

print "  %d augmentation transforms." % len(augmentation_transforms)


augmented_data_gen_valid = ra.realtime_fixed_augmented_data_gen(valid_indices, 'train', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms)
valid_gen = load_data.buffered_gen_mp(augmented_data_gen_valid, buffer_size=1)


augmented_data_gen_test = ra.realtime_fixed_augmented_data_gen(test_indices, 'test', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms)
test_gen = load_data.buffered_gen_mp(augmented_data_gen_test, buffer_size=1)


approx_num_chunks_valid = int(np.ceil(num_valid * len(augmentation_transforms) / float(CHUNK_SIZE)))
approx_num_chunks_test = int(np.ceil(num_test * len(augmentation_transforms) / float(CHUNK_SIZE)))

print "Approximately %d chunks for the validation set" % approx_num_chunks_valid
print "Approximately %d chunks for the test set" % approx_num_chunks_test


if DO_VALID:

def create_test_gen():
    data_gen_test = ra.realtime_fixed_augmented_data_gen(test_indices, 'test',
        ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes)
    return load_data.buffered_gen_mp(data_gen_test, buffer_size=GEN_BUFFER_SIZE)

def create_valid_gen():
    data_gen_valid = ra.realtime_fixed_augmented_data_gen(
        valid_indices, "train", ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes
    )
    return load_data.buffered_gen_mp(data_gen_valid, buffer_size=GEN_BUFFER_SIZE)

def create_valid_gen():
    data_gen_valid = ra.realtime_fixed_augmented_data_gen(valid_indices, 'train',
        ds_transforms=ds_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes,
        processor_class=ra.LoadAndProcessFixedPysexCenteringRescaling)
    return load_data.buffered_gen_mp(data_gen_valid, buffer_size=GEN_BUFFER_SIZE)

print "Load model parameters"
layers.set_param_values(l6, analysis['param_values'])

print "Create generators"
# set here which transforms to use to make predictions
augmentation_transforms = []
for zoom in [1 / 1.2, 1.0, 1.2]:
    for angle in np.linspace(0, 360, 10, endpoint=False):
        augmentation_transforms.append(ra.build_augmentation_transform(rotation=angle, zoom=zoom))
        augmentation_transforms.append(ra.build_augmentation_transform(rotation=(angle + 180), zoom=zoom, shear=180)) # flipped

print "  %d augmentation transforms." % len(augmentation_transforms)


augmented_data_gen_valid = ra.realtime_fixed_augmented_data_gen(valid_indices, 'train', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms, processor_class=ra.LoadAndProcessFixedPysexGen1CenteringRescaling)
valid_gen = load_data.buffered_gen_mp(augmented_data_gen_valid, buffer_size=1)


augmented_data_gen_test = ra.realtime_fixed_augmented_data_gen(test_indices, 'test', augmentation_transforms=augmentation_transforms, chunk_size=CHUNK_SIZE, target_sizes=input_sizes, ds_transforms=ds_transforms, processor_class=ra.LoadAndProcessFixedPysexGen1CenteringRescaling)
test_gen = load_data.buffered_gen_mp(augmented_data_gen_test, buffer_size=1)


approx_num_chunks_valid = int(np.ceil(num_valid * len(augmentation_transforms) / float(CHUNK_SIZE)))
approx_num_chunks_test = int(np.ceil(num_test * len(augmentation_transforms) / float(CHUNK_SIZE)))

print "Approximately %d chunks for the validation set" % approx_num_chunks_valid
print "Approximately %d chunks for the test set" % approx_num_chunks_test


if DO_VALID: