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main.py
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import os
import shutil
from functools import partial
import neptune
import numpy as np
import pandas as pd
from attrdict import AttrDict
from steppy.adapter import Adapter, E
from steppy.base import IdentityOperation, Step
from common_blocks import augmentation as aug
from common_blocks import metrics
from common_blocks import models
from common_blocks import pipelines
from common_blocks import postprocessing
from common_blocks.utils import io, misc
CTX = neptune.Context()
LOGGER = misc.init_logger()
# ______ ______ .__ __. _______ __ _______ _______.
# / | / __ \ | \ | | | ____|| | / _____| / |
# | ,----'| | | | | \| | | |__ | | | | __ | (----`
# | | | | | | | . ` | | __| | | | | |_ | \ \
# | `----.| `--' | | |\ | | | | | | |__| | .----) |
# \______| \______/ |__| \__| |__| |__| \______| |_______/
#
EXPERIMENT_DIR = '/output/experiment'
CLONE_EXPERIMENT_DIR_FROM = '' # When running eval in the cloud specify this as for example /input/SHIP-14/output/experiment
OVERWRITE_EXPERIMENT_DIR = False
DEV_MODE = False
USE_TTA = True
INFERENCE_WITH_SHIP_NO_SHIP = True
if OVERWRITE_EXPERIMENT_DIR and os.path.isdir(EXPERIMENT_DIR):
shutil.rmtree(EXPERIMENT_DIR)
if CLONE_EXPERIMENT_DIR_FROM != '':
if os.path.exists(EXPERIMENT_DIR):
shutil.rmtree(EXPERIMENT_DIR)
shutil.copytree(CLONE_EXPERIMENT_DIR_FROM, EXPERIMENT_DIR)
if CTX.params.__class__.__name__ == 'OfflineContextParams':
PARAMS = misc.read_yaml().parameters
else:
PARAMS = CTX.params
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
CHUNK_SIZE_SHIP_NO_SHIP = 2500
CHUNK_SIZE_SEGMENTATION = 2500
SEED = 1234
ID_COLUMN = 'id'
ID_BIG_IMAGE = 'BigImageId'
IS_NOT_EMPTY_COLUMN = 'is_not_empty'
X_COLUMN = 'file_path_image'
Y_COLUMN = 'file_path_mask'
CONFIG = AttrDict({
'execution': {'experiment_dir': EXPERIMENT_DIR,
'num_workers': PARAMS.num_workers,
'num_threads': PARAMS.num_threads
},
'general': {'img_H-W': (PARAMS.image_h, PARAMS.image_w),
'loader_mode': PARAMS.loader_mode,
'num_classes': 2,
'original_size': (768, 768),
},
'meta_reader': {
'segmentation_network': {'x_columns': [X_COLUMN],
'y_columns': [Y_COLUMN, IS_NOT_EMPTY_COLUMN],
},
'ship_no_ship_network': {'x_columns': [X_COLUMN],
'y_columns': [IS_NOT_EMPTY_COLUMN],
},
},
'loaders': {'resize': {'dataset_params': {'h': PARAMS.image_h,
'w': PARAMS.image_w,
'sns_h': PARAMS.sns_image_h,
'sns_w': PARAMS.sns_image_w,
'image_source': PARAMS.image_source,
'target_format': PARAMS.target_format,
'empty_fraction': PARAMS.training_sampler_empty_fraction,
'sample_size': PARAMS.training_sampler_size,
'sns_empty_fraction': PARAMS.sns_training_sampler_empty_fracion,
'MEAN': MEAN,
'STD': STD
},
'loader_params': {'training': {'batch_size': PARAMS.batch_size_train,
'shuffle': False,
'num_workers': PARAMS.num_workers,
'pin_memory': PARAMS.pin_memory
},
'inference': {'batch_size': PARAMS.batch_size_inference,
'shuffle': False,
'num_workers': PARAMS.num_workers,
'pin_memory': PARAMS.pin_memory
},
},
'augmentation_params': {'image_augment_train': aug.intensity_seq,
'image_augment_with_target_train': aug.resize_seq(
resize_target_size=PARAMS.resize_target_size),
'image_augment_inference': aug.resize_to_fit_net(
resize_target_size=PARAMS.resize_target_size),
'image_augment_with_target_inference': aug.resize_to_fit_net(
resize_target_size=PARAMS.resize_target_size)
},
},
'resize_tta': {'dataset_params': {'h': PARAMS.image_h,
'w': PARAMS.image_w,
'image_source': PARAMS.image_source,
'target_format': PARAMS.target_format,
'empty_fraction': PARAMS.training_sampler_empty_fraction,
'sample_size': PARAMS.training_sampler_size,
'MEAN': MEAN,
'STD': STD
},
'loader_params': {'training': {'batch_size': PARAMS.batch_size_train,
'shuffle': False,
'num_workers': PARAMS.num_workers,
'pin_memory': PARAMS.pin_memory
},
'inference': {'batch_size': PARAMS.batch_size_inference,
'shuffle': False,
'num_workers': PARAMS.num_workers,
'pin_memory': PARAMS.pin_memory
},
},
'augmentation_params': {
'image_augment_inference': aug.resize_to_fit_net(
resize_target_size=PARAMS.resize_target_size),
'image_augment_with_target_inference': aug.resize_to_fit_net(
resize_target_size=PARAMS.resize_target_size),
'tta_transform': aug.test_time_augmentation_transform
},
},
},
'model': {
'segmentation_network': {
'architecture_config': {'model_params': {'in_channels': PARAMS.image_channels,
'out_channels': PARAMS.network_output_channels,
'architecture': PARAMS.architecture,
'encoder': PARAMS.encoder,
'activation': PARAMS.network_activation,
},
'optimizer_params': {'lr': PARAMS.lr,
},
'regularizer_params': {'regularize': True,
'weight_decay_conv2d': PARAMS.l2_reg_conv,
},
'weights_init': {'function': 'xavier',
},
},
'training_config': {'epochs': PARAMS.epochs_nr,
'shuffle': True,
'batch_size': PARAMS.batch_size_train,
'fine_tuning': PARAMS.fine_tuning,
},
'callbacks_config': {'model_checkpoint': {
'filepath': os.path.join(EXPERIMENT_DIR, 'checkpoints', 'segmentation_network', 'best.torch'),
'epoch_every': 1,
'metric_name': PARAMS.validation_metric_name,
'minimize': PARAMS.minimize_validation_metric},
"one_cycle_scheduler": {
"enabled": PARAMS.use_one_cycle,
"number_of_batches_per_full_cycle": PARAMS.one_cycle_number_of_batches_per_full_cycle,
"max_lr": PARAMS.one_cycle_max_lr,
"momentum_range": (0.95, 0.8),
"prcnt_annihilate": 10,
"div": 10
},
'exponential_lr_scheduler': {'gamma': PARAMS.gamma,
'epoch_every': 1},
'reduce_lr_on_plateau_scheduler': {'metric_name': PARAMS.validation_metric_name,
'minimize': PARAMS.minimize_validation_metric,
'reduce_factor': PARAMS.reduce_factor,
'reduce_patience': PARAMS.reduce_patience,
'min_lr': PARAMS.min_lr},
'training_monitor': {'batch_every': 1,
'epoch_every': 1},
'experiment_timing': {'batch_every': 10,
'epoch_every': 1},
'validation_monitor': {'epoch_every': 1,
'data_dir': PARAMS.train_images_dir,
'loader_mode': PARAMS.loader_mode},
'neptune_monitor': {'model_name': 'network',
'image_nr': 16,
'image_resize': 1.0,
'image_every': 1},
'early_stopping': {'patience': PARAMS.patience,
'metric_name': PARAMS.validation_metric_name,
'minimize': PARAMS.minimize_validation_metric},
}
},
'ship_no_ship_network': {
'architecture_config': {'model_params': {'architecture': PARAMS.sns_architecture,
'activation': 'sigmoid'},
'optimizer_params': {'lr': PARAMS.sns_lr,
},
'regularizer_params': {'regularize': True,
'weight_decay_conv2d': PARAMS.sns_l2_reg_conv,
},
'weights_init': {'function': 'xavier',
},
},
'training_config': {'epochs': PARAMS.sns_epochs_nr,
'shuffle': True,
'batch_size': PARAMS.sns_batch_size_train,
'fine_tuning': PARAMS.fine_tuning,
},
'callbacks_config': {'model_checkpoint': {
'filepath': os.path.join(EXPERIMENT_DIR, 'checkpoints', 'ship_no_ship_network', 'best.torch'),
'epoch_every': 1,
'metric_name': PARAMS.sns_validation_metric_name,
'minimize': PARAMS.sns_minimize_validation_metric
},
"one_cycle_scheduler": {
"enabled": PARAMS.sns_use_one_cycle,
"number_of_batches_per_full_cycle": PARAMS.sns_one_cycle_number_of_batches_per_full_cycle,
"max_lr": PARAMS.sns_one_cycle_max_lr,
"momentum_range": (0.95, 0.7),
"prcnt_annihilate": 10,
"div": 10
},
'exponential_lr_scheduler': {'gamma': PARAMS.gamma,
'epoch_every': 1},
'reduce_lr_on_plateau_scheduler': {'metric_name': PARAMS.sns_validation_metric_name,
'minimize': PARAMS.sns_minimize_validation_metric,
'reduce_factor': PARAMS.reduce_factor,
'reduce_patience': PARAMS.reduce_patience,
'min_lr': PARAMS.min_lr},
'training_monitor': {'batch_every': 10,
'epoch_every': 1},
'experiment_timing': {'batch_every': 10,
'epoch_every': 1},
'validation_monitor': {'epoch_every': 1,
'data_dir': PARAMS.train_images_dir,
'loader_mode': PARAMS.loader_mode},
'neptune_monitor': {'model_name': 'network',
'image_nr': 16,
'image_resize': 1.0,
'image_every': 1},
'early_stopping': {'patience': PARAMS.patience,
'metric_name': PARAMS.validation_metric_name,
'minimize': PARAMS.minimize_validation_metric},
}
},
},
'tta_generator': {'flip_ud': True,
'flip_lr': True,
'rotation': True,
'color_shift_runs': False},
'tta_aggregator': {'tta_inverse_transform': aug.test_time_augmentation_inverse_transform,
'method': PARAMS.tta_aggregation_method,
'nthreads': PARAMS.num_threads
},
'thresholder': {'threshold_masks': PARAMS.threshold_masks,
'threshold_ship_no_ship': PARAMS.sns_threshold,
},
})
# .______ __ .______ _______ __ __ .__ __. _______ _______.
# | _ \ | | | _ \ | ____|| | | | | \ | | | ____| / |
# | |_) | | | | |_) | | |__ | | | | | \| | | |__ | (----`
# | ___/ | | | ___/ | __| | | | | | . ` | | __| \ \
# | | | | | | | |____ | `----.| | | |\ | | |____.----) |
# | _| |__| | _| |_______||_______||__| |__| \__| |_______|_______/
#
def ship_no_ship_pipeline(config, suffix='_ship_no_ship', train_mode=True):
if train_mode:
preprocessing = pipelines.preprocessing_binary_train(config, model_name='ship_no_ship_network', suffix=suffix)
else:
preprocessing = pipelines.preprocessing_binary_inference(config, model_name='ship_no_ship_network',
suffix=suffix)
preprocessing.set_parameters_upstream({'is_fittable': False})
sns_network = misc.FineTuneStep(name='ship_no_ship_network',
transformer=models.BinaryModel(**config.model['ship_no_ship_network']),
input_steps=[preprocessing],
)
sns_network.set_mode_train()
sns_network.set_parameters_upstream({'experiment_directory': config.execution.experiment_dir,
})
sns_network.force_fitting = False
sns_network.fine_tuning = config.model.segmentation_network.training_config.fine_tuning
if train_mode:
return sns_network
else:
class_prediction = Step(name='class_prediction',
transformer=misc.make_apply_transformer(
partial(postprocessing.get_class,
threshold=config.thresholder.threshold_ship_no_ship),
output_name='classes',
apply_on=['predictions']),
input_steps=[sns_network],
adapter=Adapter({'predictions': E(sns_network.name, 'ship_no_ship_prediction'),
}),
is_fittable=False
)
return class_prediction
def train_segmentation_pipeline(config):
preprocessing = pipelines.preprocessing_train(config, model_name='segmentation_network')
segmentation_network = misc.FineTuneStep(name='segmentation_network',
transformer=models.SegmentationModel(
**config.model['segmentation_network']),
input_data=['callback_input'],
input_steps=[preprocessing],
adapter=Adapter({'datagen': E(preprocessing.name, 'datagen'),
'validation_datagen': E(preprocessing.name,
'validation_datagen'),
'meta_valid': E('callback_input', 'meta_valid'),
}))
segmentation_network.set_mode_train()
segmentation_network.set_parameters_upstream({'experiment_directory': config.execution.experiment_dir,
})
segmentation_network.force_fitting = False
segmentation_network.fine_tuning = config.model.segmentation_network.training_config.fine_tuning
return segmentation_network
def inference_segmentation_pipeline(config):
if config.general.loader_mode == 'resize_and_pad':
size_adjustment_function = partial(postprocessing.crop_image, target_size=config.general.original_size)
elif config.general.loader_mode == 'resize' or config.general.loader_mode == 'stacking':
size_adjustment_function = partial(postprocessing.resize_image, target_size=config.general.original_size)
else:
raise NotImplementedError
if USE_TTA:
preprocessing, tta_generator = pipelines.preprocessing_inference_tta(config, model_name='segmentation_network')
segmentation_network = Step(name='segmentation_network',
transformer=models.SegmentationModel(**config.model['segmentation_network']),
input_steps=[preprocessing])
tta_aggregator = pipelines.aggregator('tta_aggregator', segmentation_network,
tta_generator=tta_generator,
config=config.tta_aggregator)
prediction_renamed = Step(name='prediction_renamed',
transformer=IdentityOperation(),
input_steps=[tta_aggregator],
adapter=Adapter({'mask_prediction': E(tta_aggregator.name, 'aggregated_prediction')
}))
mask_resize = Step(name='mask_resize',
transformer=misc.make_apply_transformer(size_adjustment_function,
output_name='resized_images',
apply_on=['images'],
n_threads=config.execution.num_threads,
),
input_steps=[prediction_renamed],
adapter=Adapter({'images': E(prediction_renamed.name, 'mask_prediction'),
}))
else:
preprocessing = pipelines.preprocessing_inference(config, model_name='segmentation_network')
segmentation_network = misc.FineTuneStep(name='segmentation_network',
transformer=models.SegmentationModel(
**config.model['segmentation_network']),
input_steps=[preprocessing],
)
mask_resize = Step(name='mask_resize',
transformer=misc.make_apply_transformer(size_adjustment_function,
output_name='resized_images',
apply_on=['images'],
n_threads=config.execution.num_threads,
),
input_steps=[segmentation_network],
adapter=Adapter({'images': E(segmentation_network.name, 'mask_prediction'),
}),
)
binarizer = Step(name='binarizer',
transformer=misc.make_apply_transformer(
partial(postprocessing.binarize, threshold=config.thresholder.threshold_masks),
output_name='binarized_images',
apply_on=['images'],
n_threads=config.execution.num_threads
),
input_steps=[mask_resize],
adapter=Adapter({'images': E(mask_resize.name, 'resized_images'),
}))
labeler = Step(name='labeler',
transformer=misc.make_apply_transformer(postprocessing.label,
output_name='labeled_images',
apply_on=['images'],
n_threads=config.execution.num_threads,
),
input_steps=[binarizer],
adapter=Adapter({'images': E(binarizer.name, 'binarized_images'),
}))
mask_postprocessing = Step(name='mask_postprocessing',
transformer=misc.make_apply_transformer(postprocessing.mask_postprocessing,
output_name='labeled_images',
apply_on=['images'],
n_threads=config.execution.num_threads,
),
input_steps=[labeler],
adapter=Adapter({'images': E(labeler.name, 'labeled_images'),
}))
mask_postprocessing.set_mode_inference()
mask_postprocessing.set_parameters_upstream({'experiment_directory': config.execution.experiment_dir,
'is_fittable': False
})
segmentation_network.is_fittable = True
return mask_postprocessing
# __________ ___ _______ ______ __ __ .___________. __ ______ .__ __.
# | ____\ \ / / | ____| / || | | | | || | / __ \ | \ | |
# | |__ \ V / | |__ | ,----'| | | | `---| |----`| | | | | | | \| |
# | __| > < | __| | | | | | | | | | | | | | | | . ` |
# | |____ / . \ | |____ | `----.| `--' | | | | | | `--' | | |\ |
# |_______/__/ \__\ |_______| \______| \______/ |__| |__| \______/ |__| \__|
#
def train_ship_no_ship():
meta = pd.read_csv(PARAMS.metadata_filepath)
meta_train = meta[meta['is_train'] == 1]
meta_train = add_big_image_id(meta_train)
meta_train_split, meta_valid_split = misc.train_test_split_with_empty_fraction_with_groups(meta_train,
groups=meta_train[
ID_BIG_IMAGE],
empty_fraction=PARAMS.evaluation_empty_fraction,
test_size=PARAMS.evaluation_size,
shuffle=True,
random_state=SEED)
meta_train_split = meta_train_split.sample(frac=1, random_state=SEED)
meta_valid_split = meta_valid_split.sample(frac=1, random_state=SEED)
if DEV_MODE:
meta_train_split = meta_train_split.sample(PARAMS.dev_mode_size, random_state=SEED)
meta_valid_split = meta_valid_split.sample(int(PARAMS.dev_mode_size / 2), random_state=SEED)
data = {'input': {'meta': meta_train_split
},
'callback_input': {'meta_valid': meta_valid_split
}
}
sns_pipe = ship_no_ship_pipeline(config=CONFIG, train_mode=True)
sns_pipe.fit_transform(data)
def train():
meta = pd.read_csv(PARAMS.metadata_filepath)
meta_train = meta[meta['is_train'] == 1]
meta_train = add_big_image_id(meta_train)
meta_train_split, meta_valid_split = misc.train_test_split_with_empty_fraction_with_groups(meta_train,
groups=meta_train[
ID_BIG_IMAGE],
empty_fraction=PARAMS.evaluation_empty_fraction,
test_size=PARAMS.evaluation_size,
shuffle=True,
random_state=SEED)
meta_valid_split = meta_valid_split[meta_valid_split[IS_NOT_EMPTY_COLUMN] == 1].sample(
PARAMS.in_train_evaluation_size, random_state=SEED)
if DEV_MODE:
meta_train_split = meta_train_split.sample(PARAMS.dev_mode_size, random_state=SEED)
meta_valid_split = meta_valid_split.sample(int(PARAMS.dev_mode_size / 2), random_state=SEED)
data = {'input': {'meta': meta_train_split
},
'callback_input': {'meta_valid': meta_valid_split
}
}
pipeline = train_segmentation_pipeline(config=CONFIG)
pipeline.fit_transform(data)
def evaluate():
meta = pd.read_csv(PARAMS.metadata_filepath)
meta_train = meta[meta['is_train'] == 1]
meta_train = add_big_image_id(meta_train)
_, meta_valid_split = misc.train_test_split_with_empty_fraction_with_groups(meta_train,
groups=meta_train[ID_BIG_IMAGE],
empty_fraction=PARAMS.evaluation_empty_fraction,
test_size=PARAMS.evaluation_size,
shuffle=True, random_state=SEED)
if DEV_MODE:
_, meta_valid_split = misc.train_test_split_with_empty_fraction_with_groups(meta_valid_split,
groups=meta_valid_split[
ID_BIG_IMAGE],
empty_fraction=PARAMS.evaluation_empty_fraction,
test_size=PARAMS.evaluation_size,
shuffle=True, random_state=SEED)
segm_pipe = inference_segmentation_pipeline(config=CONFIG)
valid_ids = meta_valid_split[ID_COLUMN] + '.jpg'
if INFERENCE_WITH_SHIP_NO_SHIP:
sns_pipe = ship_no_ship_pipeline(config=CONFIG, train_mode=False)
ids_ship, ids_no_ship = predict_ship_no_ship(meta_valid_split, sns_pipe, CHUNK_SIZE_SHIP_NO_SHIP)
meta_valid_ship = meta_valid_split[valid_ids.isin(ids_ship)]
prediction_ship = generate_submission(meta_valid_ship, segm_pipe, CHUNK_SIZE_SEGMENTATION)
prediction = misc.combine_two_stage_predictions(ids_no_ship, prediction_ship, valid_ids)
else:
prediction = generate_submission(meta_valid_split, segm_pipe, CHUNK_SIZE_SEGMENTATION)
gt = io.read_gt_subset(PARAMS.annotation_file, valid_ids)
f2_per_image, image_ids = metrics.f_beta_metric(gt, prediction, beta=2, apply_mean=False)
f2 = np.mean(f2_per_image)
LOGGER.info('f2 {}'.format(f2))
CTX.channel_send('f2', 0, f2)
LOGGER.info('preparing results')
results = misc.prepare_results(gt, prediction, meta_valid_split, f2_per_image, image_ids)
results_filepath = os.path.join(EXPERIMENT_DIR, 'validation_results.csv')
results.to_csv(results_filepath, index=None)
def predict():
meta = pd.read_csv(PARAMS.metadata_filepath)
meta_test = meta[meta['is_train'] == 0]
if DEV_MODE:
meta_test = meta_test.sample(PARAMS.dev_mode_size, random_state=SEED)
segm_pipe = inference_segmentation_pipeline(config=CONFIG)
test_ids = meta_test[ID_COLUMN] + '.jpg'
if INFERENCE_WITH_SHIP_NO_SHIP:
sns_pipe = ship_no_ship_pipeline(config=CONFIG, train_mode=False)
ids_ship, ids_no_ship = predict_ship_no_ship(meta_test, sns_pipe, CHUNK_SIZE_SHIP_NO_SHIP)
meta_test_ship = meta_test[test_ids.isin(ids_ship)]
prediction_ship = generate_submission(meta_test_ship, segm_pipe, CHUNK_SIZE_SEGMENTATION)
submission = misc.combine_two_stage_predictions(ids_no_ship, prediction_ship, test_ids)
else:
submission = generate_submission(meta_test, segm_pipe, CHUNK_SIZE_SEGMENTATION)
submission_filepath = os.path.join(EXPERIMENT_DIR, 'submission.csv')
submission.to_csv(submission_filepath, index=None, encoding='utf-8')
LOGGER.info('submission saved to {}'.format(submission_filepath))
LOGGER.info('submission head \n\n{}'.format(submission.head()))
# __ __ .___________. __ __ _______.
# | | | | | || | | | / |
# | | | | `---| |----`| | | | | (----`
# | | | | | | | | | | \ \
# | `--' | | | | | | `----.----) |
# \______/ |__| |__| |_______|_______/
#
def add_big_image_id(meta):
big_image_ids = pd.read_csv('big-images-ids_v2.csv')
meta['ImageId'] = meta[ID_COLUMN] + '.jpg'
meta_joined = pd.merge(meta, big_image_ids, on='ImageId')
return meta_joined
def generate_submission(meta_data, pipeline, chunk_size):
if chunk_size is not None:
return _generate_submission_in_chunks(meta_data, pipeline, chunk_size)
else:
return _generate_submission(meta_data, pipeline)
def _generate_submission(meta_data, pipeline):
prediction = _generate_prediction(meta_data, pipeline)
submission = misc.create_submission(meta_data[ID_COLUMN] + '.jpg', prediction)
return submission
def _generate_submission_in_chunks(meta_data, pipeline, chunk_size):
submissions = []
for meta_chunk in misc.generate_data_frame_chunks(meta_data, chunk_size):
prediction_chunk = _generate_prediction(meta_chunk, pipeline)
submission_chunk = misc.create_submission(meta_chunk[ID_COLUMN] + '.jpg', prediction_chunk)
submissions.append(submission_chunk)
submission = pd.concat(submissions)
return submission
def _generate_prediction(meta_data, pipeline):
data = {'input': {'meta': meta_data,
},
'callback_input': {'meta_valid': None
}
}
output = pipeline.transform(data)
y_pred = output['labeled_images']
return y_pred
def predict_ship_no_ship(meta_data, pipeline, chunk_size):
if chunk_size is not None:
return _predict_ship_no_ship_in_chunks(meta_data, pipeline, chunk_size)
else:
return _predict_ship_no_ship(meta_data, pipeline)
def _predict_ship_no_ship(meta_data, pipeline):
prediction = _generate_prediction_ship_no_ship(meta_data, pipeline)
ids_ship, ids_no_ship = misc.get_ship_no_ship_ids(meta_data[ID_COLUMN] + '.jpg', prediction)
return ids_ship, ids_no_ship
def _predict_ship_no_ship_in_chunks(meta_data, pipeline, chunk_size):
ids_ship, ids_no_ship = [], []
for meta_chunk in misc.generate_data_frame_chunks(meta_data, chunk_size):
prediction_chunk = _generate_prediction_ship_no_ship(meta_chunk, pipeline)
ids_ship_chunk, ids_no_ship_chunk = misc.get_ship_no_ship_ids(meta_chunk[ID_COLUMN] + '.jpg', prediction_chunk)
ids_ship.extend(ids_ship_chunk)
ids_no_ship.extend(ids_no_ship_chunk)
return ids_ship, ids_no_ship
def _generate_prediction_ship_no_ship(meta_data, pipeline):
data = {'input': {'meta': meta_data,
},
'callback_input': {'meta_valid': None
}
}
output = pipeline.transform(data)
y_pred = output['classes']
return y_pred
# .___ ___. ___ __ .__ __.
# | \/ | / \ | | | \ | |
# | \ / | / ^ \ | | | \| |
# | |\/| | / /_\ \ | | | . ` |
# | | | | / _____ \ | | | |\ |
# |__| |__| /__/ \__\ |__| |__| \__|
#
if __name__ == '__main__':
train_ship_no_ship()
train()
evaluate()
predict()