<reponame>nnmhuy/flownet2-tf
"""
Add dataset configurations here. Each dataset must have the following structure:

NAME = {
    IMAGE_HEIGHT: int,
    IMAGE_WIDTH: int,
    ITEMS_TO_DESCRIPTIONS: {
        'image_a': 'A 3-channel image.',
        'image_b': 'A 3-channel image.',
        'flow': 'A 2-channel optical flow field',
    },
    SIZES: {
        'train': int,
        'validate': int,    (optional)
        ...
    },
    BATCH_SIZE: int,
    PATHS: {
        'train': '',
        'validate': '', (optional)
        ...
    }
}
"""

"""
note that one step = one batch of data processed, ~not~ an entire epoch
'coeff_schedule_param': {
    'half_life': 50000,         after this many steps, the value will be i + (f - i)/2
    'initial_coeff': 0.5,       initial value
    'final_coeff': 1,           final value
},
"""

FLYING_CHAIRS_DATASET_CONFIG = {
    'IMAGE_HEIGHT': 384,
    'IMAGE_WIDTH': 512,
    'ITEMS_TO_DESCRIPTIONS': {
        'image_a': 'A 3-channel image.',
        'image_b': 'A 3-channel image.',
        'flow': 'A 2-channel optical flow field',
    },
    'SIZES': {
        'train': 22232,
        'validate': 640,
        'sample': 8,
    },
    'BATCH_SIZE': 8,
    'PATHS': {
        'train': './data/tfrecords/fc_train.tfrecords',
        'validate': './data/tfrecords/fc_val.tfrecords',
        'sample': './data/tfrecords/fc_sample.tfrecords',
    },
    'PREPROCESS': {
        'scale': False,
        'crop_height': 320,
        'crop_width': 448,
        'image_a': {
            'translate': {
                'rand_type': "uniform_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.4,
                'prob': 1.0,
            },
            'rotate': {
                'rand_type': "uniform_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.4,
                'prob': 1.0,
            },
            'zoom': {
                'rand_type': "uniform_bernoulli",
                'exp': True,
                'mean': 0.2,
                'spread': 0.4,
                'prob': 1.0,
            },
            'squeeze': {
                'rand_type': "uniform_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.3,
                'prob': 1.0,
            },
            'noise': {
                'rand_type': "uniform_bernoulli",
                'exp': False,
                'mean': 0.03,
                'spread': 0.03,
                'prob': 1.0,
            },
        },
        # All preprocessing to image A will be applied to image B in addition to the following.
        'image_b': {
            'translate': {
                'rand_type': "gaussian_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.03,
                'prob': 1.0,
            },
            'rotate': {
                'rand_type': "gaussian_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.03,
                'prob': 1.0,
            },
            'zoom': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.03,
                'prob': 1.0,
            },
            'gamma': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'brightness': {
                'rand_type': "gaussian_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'contrast': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'color': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'coeff_schedule_param': {
                'half_life': 50000,
                'initial_coeff': 0.5,
                'final_coeff': 1,
            },
        }
    },
}

"""
Add dataset configurations here. Each dataset must have the following structure:

NAME = {
    IMAGE_HEIGHT: int,
    IMAGE_WIDTH: int,
    ITEMS_TO_DESCRIPTIONS: {
        'image_a': 'A 3-channel image.',
        'image_b': 'A 3-channel image.',
        'flow': 'A 2-channel optical flow field',
    },
    SIZES: {
        'train': int,
        'validate': int,    (optional)
        ...
    },
    BATCH_SIZE: int,
    PATHS: {
        'train': '',
        'validate': '', (optional)
        ...
    }
}
"""

"""
note that one step = one batch of data processed, ~not~ an entire epoch
'coeff_schedule_param': {
    'half_life': 50000,         after this many steps, the value will be i + (f - i)/2
    'initial_coeff': 0.5,       initial value
    'final_coeff': 1,           final value
},
"""

FLYING_CHAIRS_DATASET_CONFIG = {
    'IMAGE_HEIGHT': 384,
    'IMAGE_WIDTH': 512,
    'ITEMS_TO_DESCRIPTIONS': {
        'image_a': 'A 3-channel image.',
        'image_b': 'A 3-channel image.',
        'flow': 'A 2-channel optical flow field',
    },
    'SIZES': {
        'train': 22232,
        'validate': 640,
        'sample': 8,
    },
    'BATCH_SIZE': 8,
    'PATHS': {
        'train': './data/tfrecords/fc_train.tfrecords',
        'validate': './data/tfrecords/fc_val.tfrecords',
        'sample': './data/tfrecords/fc_sample.tfrecords',
    },
    'PREPROCESS': {
        'scale': False,
        'crop_height': 320,
        'crop_width': 448,
        'image_a': {
            'translate': {
                'rand_type': "uniform_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.4,
                'prob': 1.0,
            },
            'rotate': {
                'rand_type': "uniform_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.4,
                'prob': 1.0,
            },
            'zoom': {
                'rand_type': "uniform_bernoulli",
                'exp': True,
                'mean': 0.2,
                'spread': 0.4,
                'prob': 1.0,
            },
            'squeeze': {
                'rand_type': "uniform_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.3,
                'prob': 1.0,
            },
            'noise': {
                'rand_type': "uniform_bernoulli",
                'exp': False,
                'mean': 0.03,
                'spread': 0.03,
                'prob': 1.0,
            },
        },
        # All preprocessing to image A will be applied to image B in addition to the following.
        'image_b': {
            'translate': {
                'rand_type': "gaussian_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.03,
                'prob': 1.0,
            },
            'rotate': {
                'rand_type': "gaussian_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.03,
                'prob': 1.0,
            },
            'zoom': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.03,
                'prob': 1.0,
            },
            'gamma': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'brightness': {
                'rand_type': "gaussian_bernoulli",
                'exp': False,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'contrast': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'color': {
                'rand_type': "gaussian_bernoulli",
                'exp': True,
                'mean': 0,
                'spread': 0.02,
                'prob': 1.0,
            },
            'coeff_schedule_param': {
                'half_life': 50000,
                'initial_coeff': 0.5,
                'final_coeff': 1,
            },
        }
    },
}
Add dataset configurations here. Each dataset must have the following structure:  NAME = {     IMAGE_HEIGHT: int,     IMAGE_WIDTH: int,     ITEMS_TO_DESCRIPTIONS: {         'image_a': 'A 3-channel image.',         'image_b': 'A 3-channel image.',         'flow': 'A 2-channel optical flow field',     },     SIZES: {         'train': int,         'validate': int,    (optional)         ...     },     BATCH_SIZE: int,     PATHS: {         'train': '',         'validate': '', (optional)         ...     } } note that one step = one batch of data processed, ~not~ an entire epoch 'coeff_schedule_param': {     'half_life': 50000,         after this many steps, the value will be i + (f - i)/2     'initial_coeff': 0.5,       initial value     'final_coeff': 1,           final value }, # All preprocessing to image A will be applied to image B in addition to the following.