forked from platypush/platypush
1148 lines
54 KiB
Python
1148 lines
54 KiB
Python
import json
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import os
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import pathlib
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import random
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import shutil
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import threading
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from contextlib import contextmanager
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from datetime import datetime
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from typing import List, Dict, Any, Union, Optional, Tuple, Iterable
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import numpy as np
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from tensorflow.keras import Model
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from tensorflow.keras.layers import Layer
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras import utils
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from platypush.config import Config
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from platypush.context import get_bus
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from platypush.message.event.tensorflow import TensorflowEpochStartedEvent, TensorflowEpochEndedEvent, \
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TensorflowBatchStartedEvent, TensorflowBatchEndedEvent, TensorflowTrainStartedEvent, TensorflowTrainEndedEvent
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from platypush.message.response.tensorflow import TensorflowTrainResponse, TensorflowPredictResponse
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from platypush.plugins import Plugin, action
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class TensorflowPlugin(Plugin):
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"""
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This plugin can be used to create, train, load and make predictions with TensorFlow-compatible machine learning
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models.
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Triggers:
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- :class:`platypush.message.event.tensorflow.TensorflowEpochStartedEvent`
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when a Tensorflow model training/evaluation epoch begins.
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- :class:`platypush.message.event.tensorflow.TensorflowEpochEndedEvent`
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when a Tensorflow model training/evaluation epoch ends.
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- :class:`platypush.message.event.tensorflow.TensorflowBatchStartedEvent`
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when a Tensorflow model training/evaluation batch starts being processed.
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- :class:`platypush.message.event.tensorflow.TensorflowBatchEndedEvent`
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when a the processing of a Tensorflow model training/evaluation batch ends.
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- :class:`platypush.message.event.tensorflow.TensorflowTrainStartedEvent`
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when a Tensorflow model starts being trained.
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- :class:`platypush.message.event.tensorflow.TensorflowTrainEndedEvent`
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when the training phase of a Tensorflow model ends.
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Requires:
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* **numpy** (``pip install numpy``)
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* **pandas** (``pip install pandas``) (optional, for CSV parsing)
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* **tensorflow** (``pip install 'tensorflow>=2.0'``)
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* **keras** (``pip install keras``)
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"""
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_image_extensions = ['jpg', 'jpeg', 'bmp', 'tiff', 'tif', 'png', 'gif']
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_numpy_extensions = ['npy', 'npz']
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_csv_extensions = ['csv', 'tsv']
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_supported_data_file_extensions = [*_csv_extensions, *_numpy_extensions, *_image_extensions]
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def __init__(self, workdir: Optional[str] = None, **kwargs):
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"""
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:param workdir: Working directory for TensorFlow, where models will be stored and looked up by default
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(default: PLATYPUSH_WORKDIR/tensorflow).
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"""
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super().__init__(**kwargs)
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self.models: Dict[str, Model] = {}
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self._models_lock = threading.RLock()
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self._model_locks: Dict[str, threading.RLock()] = {}
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self._work_dir = os.path.abspath(os.path.expanduser(workdir)) if workdir else \
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os.path.join(Config.get('workdir'), 'tensorflow')
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self._models_dir = os.path.join(self._work_dir, 'models')
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pathlib.Path(self._models_dir).mkdir(mode=0o755, exist_ok=True, parents=True)
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@contextmanager
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def _lock_model(self, model_name: str):
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with self._models_lock:
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if model_name not in self._model_locks:
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self._model_locks[model_name] = threading.RLock()
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try:
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success = self._model_locks[model_name].acquire(blocking=True, timeout=30.)
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assert success, 'Unable to acquire the model lock'
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yield
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finally:
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# noinspection PyBroadException
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try:
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self._model_locks[model_name].release()
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except:
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pass
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def _load_model(self, model_name: str, reload: bool = False) -> Model:
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if model_name in self.models and not reload:
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return self.models[model_name]
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model = None
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model_dir = None
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if os.path.isdir(os.path.join(self._models_dir, model_name)):
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model_dir = os.path.join(self._models_dir, model_name)
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model = load_model(model_dir)
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else:
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model_name = os.path.abspath(os.path.expanduser(model_name))
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if model_name in self.models and not reload:
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return self.models[model_name]
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if os.path.isfile(model_name):
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model_dir = str(pathlib.Path(model_name).parent)
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model = load_model(model_name)
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elif os.path.isdir(model_name):
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model_dir = model_name
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model = load_model(model_dir)
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assert model, 'Could not find model: {}'.format(model_name)
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model.input_labels = []
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model.output_labels = []
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labels_file = os.path.join(model_dir, 'labels.json')
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if os.path.isfile(labels_file):
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with open(labels_file, 'r') as f:
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labels = json.load(f)
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if isinstance(labels, dict):
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if 'input' in labels:
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model.input_labels = labels['input']
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if 'output' in labels:
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model.output_labels = labels['output']
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elif hasattr(labels, '__iter__'):
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model.output_labels = labels
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with self._lock_model(model_name):
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self.models[model_name] = model
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return model
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def _generate_callbacks(self, model: str):
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from tensorflow.keras.callbacks import LambdaCallback
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return [LambdaCallback(
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on_epoch_begin=self.on_epoch_begin(model),
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on_epoch_end=self.on_epoch_end(model),
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on_batch_begin=self.on_batch_begin(model),
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on_batch_end=self.on_batch_end(model),
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on_train_begin=self.on_train_begin(model),
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on_train_end=self.on_train_end(model),
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)]
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@staticmethod
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def on_epoch_begin(model: str):
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def callback(epoch: int, logs: Optional[dict] = None):
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get_bus().post(TensorflowEpochStartedEvent(model=model, epoch=epoch, logs=logs))
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return callback
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@staticmethod
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def on_epoch_end(model: str):
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def callback(epoch: int, logs: Optional[dict] = None):
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get_bus().post(TensorflowEpochEndedEvent(model=model, epoch=epoch, logs=logs))
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return callback
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@staticmethod
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def on_batch_begin(model: str):
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def callback(batch: int, logs: Optional[dict] = None):
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get_bus().post(TensorflowBatchStartedEvent(model=model, batch=batch, logs=logs))
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return callback
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@staticmethod
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def on_batch_end(model: str):
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def callback(batch, logs: Optional[dict] = None):
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get_bus().post(TensorflowBatchEndedEvent(model=model, batch=batch, logs=logs))
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return callback
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@staticmethod
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def on_train_begin(model: str):
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def callback(logs: Optional[dict] = None):
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get_bus().post(TensorflowTrainStartedEvent(model=model, logs=logs))
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return callback
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@staticmethod
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def on_train_end(model: str):
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def callback(logs: Optional[dict] = None):
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get_bus().post(TensorflowTrainEndedEvent(model=model, logs=logs))
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return callback
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@action
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def load(self, model: str, reload: bool = False) -> Dict[str, Any]:
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"""
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(Re)-load a model from the file system.
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:param model: Name of the model. It can be a folder name stored under ``<workdir>/models``, or an absolute path
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to a model directory or file (Tensorflow directories, Protobuf models and HDF5 files are supported).
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:param reload: If ``True``, the model will be reloaded from the filesystem even if it's been already
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loaded, otherwise the model currently in memory will be kept (default: ``False``).
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:return: The model configuration.
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"""
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model = self._load_model(model, reload=reload)
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return model.get_config()
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@action
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def unload(self, model: str) -> None:
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"""
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Remove a loaded model from memory.
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:param model: Name of the model.
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"""
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with self._lock_model(model):
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assert model in self.models, 'The model {} is not loaded'.format(model)
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del self.models[model]
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@action
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def remove(self, model: str) -> None:
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"""
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Unload a module and, if stored on the filesystem, remove its resource files as well.
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WARNING: This operation is not reversible.
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:param model: Name of the model.
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"""
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with self._lock_model(model):
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if model in self.models:
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del self.models[model]
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model_dir = os.path.join(self._models_dir, model)
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if os.path.isdir(model_dir):
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shutil.rmtree(model_dir)
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@action
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def create_network(self,
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name: str,
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layers: List[Union[Layer, Dict[str, Any]]],
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input_names: Optional[List[str]] = None,
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output_names: Optional[List[str]] = None,
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optimizer: Optional[str] = 'rmsprop',
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loss: Optional[Union[str, List[str], Dict[str, str]]] = None,
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metrics: Optional[
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Union[str, List[Union[str, List[str]]], Dict[str, Union[str, List[str]]]]] = None,
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loss_weights: Optional[Union[List[float], Dict[str, float]]] = None,
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sample_weight_mode: Optional[Union[str, List[str], Dict[str, str]]] = None,
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weighted_metrics: Optional[List[str]] = None,
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target_tensors=None,
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**kwargs) -> Dict[str, Any]:
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"""
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Create a neural network TensorFlow Keras model.
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:param name: Name of the model.
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:param layers: List of layers. Example:
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.. code-block:: javascript
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[
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// Input flatten layer with 10 units
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{
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"type": "Flatten",
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"input_shape": [10, 10]
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},
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// Dense hidden layer with 500 units
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{
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"type": "Dense",
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"units": 500,
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"activation": "relu"
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},
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// Dense hidden layer with 100 units
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{
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"type": "Dense",
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"units": 100,
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"activation": "relu"
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},
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// Dense output layer with 2 units (labels) and ``softmax`` activation function
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{
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"type": "Dense",
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"units": 2,
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"activation": "softmax"
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}
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]
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:param input_names: List of names for the input units (default: TensorFlow name auto-assign logic).
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:param output_names: List of labels for the output units (default: TensorFlow name auto-assign logic).
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:param optimizer: Optimizer, see <https://keras.io/optimizers/> (default: ``rmsprop``).
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:param loss: Loss function, see <https://keras.io/losses/>. An objective function is any callable with
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the signature ``scalar_loss = fn(y_true, y_pred)``. If the model has multiple outputs, you can use a
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different loss on each output by passing a dictionary or a list of losses. The loss value that will be
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minimized by the model will then be the sum of all individual losses (default: None).
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:param metrics: List of metrics to be evaluated by the model during training and testing. Typically you will
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use ``metrics=['accuracy']``. To specify different metrics for different outputs of a multi-output model,
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you could also pass a dictionary, such as
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``metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}``. You can also pass a list
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``(len = len(outputs))`` of lists of metrics such as ``metrics=[['accuracy'], ['accuracy', 'mse']]`` or
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``metrics=['accuracy', ['accuracy', 'mse']]``. Default: ``['accuracy']``.
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:param loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the
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loss contributions of different model outputs. The loss value that will be minimized by the model
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will then be the *weighted sum* of all individual losses, weighted by the `loss_weights` coefficients.
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If a list, it is expected to have a 1:1 mapping to the model's outputs. If a tensor, it is expected to map
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output names (strings) to scalar coefficients.
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:param sample_weight_mode: If you need to do time-step-wise sample weighting (2D weights), set this to
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``"temporal"``. ``None`` defaults to sample-wise weights (1D). If the model has multiple outputs,
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you can use a different ``sample_weight_mode`` on each output by passing a dictionary or a list of modes.
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:param weighted_metrics: List of metrics to be evaluated and weighted by ``sample_weight`` or ``class_weight``
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during training and testing.
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:param target_tensors: By default, Keras will create placeholders for the model's target, which will be fed
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with the target data during training. If instead you would like to use your own target tensors (in turn,
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Keras will not expect external numpy data for these targets at training time), you can specify them via the
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``target_tensors`` argument. It can be a single tensor (for a single-output model), a list of tensors,
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or a dict mapping output names to target tensors.
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:param kwargs: Extra arguments to pass to ``Model.compile()``.
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:return: The model configuration, as a dict. Example:
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.. code-block:: json
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{
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"name": "test_model",
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"layers": [
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{
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"class_name": "Flatten",
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"config": {
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"name": "flatten",
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"trainable": true,
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"batch_input_shape": [
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null,
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10
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],
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"dtype": "float32",
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"data_format": "channels_last"
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}
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},
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{
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"class_name": "Dense",
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"config": {
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"name": "dense",
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"trainable": true,
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"dtype": "float32",
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"units": 100,
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"activation": "relu",
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"use_bias": true,
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"kernel_initializer": {
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"class_name": "GlorotUniform",
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"config": {
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"seed": null
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}
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},
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"bias_initializer": {
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"class_name": "Zeros",
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"config": {}
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},
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"kernel_regularizer": null,
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"bias_regularizer": null,
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"activity_regularizer": null,
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"kernel_constraint": null,
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"bias_constraint": null
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}
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},
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{
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"class_name": "Dense",
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"config": {
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"name": "dense_1",
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"trainable": true,
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"dtype": "float32",
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"units": 50,
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"activation": "relu",
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"use_bias": true,
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"kernel_initializer": {
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"class_name": "GlorotUniform",
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"config": {
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"seed": null
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}
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},
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"bias_initializer": {
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"class_name": "Zeros",
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"config": {}
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},
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"kernel_regularizer": null,
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"bias_regularizer": null,
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"activity_regularizer": null,
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"kernel_constraint": null,
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"bias_constraint": null
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}
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},
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{
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"class_name": "Dense",
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"config": {
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"name": "dense_2",
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"trainable": true,
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"dtype": "float32",
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"units": 2,
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"activation": "softmax",
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"use_bias": true,
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"kernel_initializer": {
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"class_name": "GlorotUniform",
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"config": {
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"seed": null
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}
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},
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"bias_initializer": {
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"class_name": "Zeros",
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"config": {}
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},
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"kernel_regularizer": null,
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"bias_regularizer": null,
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"activity_regularizer": null,
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"kernel_constraint": null,
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"bias_constraint": null
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}
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}
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]
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}
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"""
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from tensorflow.keras import Sequential
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model = Sequential(name=name)
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for layer in layers:
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if not isinstance(layer, Layer):
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layer = self._layer_from_dict(layer.pop('type'), **layer)
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model.add(layer)
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if not metrics:
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metrics = ['accuracy']
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model.compile(
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optimizer=optimizer,
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loss=loss,
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metrics=metrics,
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loss_weights=loss_weights,
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sample_weight_mode=sample_weight_mode,
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weighted_metrics=weighted_metrics,
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target_tensors=target_tensors,
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**kwargs
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)
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model.input_labels = input_names or []
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model.output_labels = output_names or []
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with self._lock_model(name):
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self.models[name] = model
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return model.get_config()
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@action
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def create_regression(self,
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name: str,
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units: int = 1,
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input_names: Optional[List[str]] = None,
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output_names: Optional[List[str]] = None,
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activation: str = 'linear',
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use_bias: bool = True,
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kernel_initializer: str = 'glorot_uniform',
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bias_initializer: str = 'zeros',
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kernel_regularizer: Optional[str] = None,
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bias_regularizer: Optional[str] = None,
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optimizer: Optional[str] = 'rmsprop',
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loss: Optional[Union[str, List[str], Dict[str, str]]] = 'mse',
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metrics: Optional[
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Union[str, List[Union[str, List[str]]], Dict[str, Union[str, List[str]]]]] = None,
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loss_weights: Optional[Union[List[float], Dict[str, float]]] = None,
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sample_weight_mode: Optional[Union[str, List[str], Dict[str, str]]] = None,
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weighted_metrics: Optional[List[str]] = None,
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target_tensors=None,
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**kwargs) -> Dict[str, Any]:
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"""
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|
Create a linear/logistic regression model.
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|
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:param name: Name of the model.
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|
:param units: Output dimension (default: 1).
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:param input_names: List of names for the input units (default: TensorFlow name auto-assign logic).
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|
:param output_names: List of labels for the output units (default: TensorFlow name auto-assign logic).
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|
:param activation: Activation function to be used (default: None).
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|
:param use_bias: Whether to calculate the bias/intercept for this model. If set
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|
to False, no bias/intercept will be used in calculations, e.g., the data
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is already centered (default: True).
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|
:param kernel_initializer: Initializer for the ``kernel`` weights matrices (default: ``glorot_uniform``).
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|
:param bias_initializer: Initializer for the bias vector (default: ``zeros``).
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:param kernel_regularizer: Regularizer for kernel vectors (default: None).
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:param bias_regularizer: Regularizer for bias vectors (default: None).
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:param optimizer: Optimizer, see <https://keras.io/optimizers/> (default: ``rmsprop``).
|
|
:param loss: Loss function, see <https://keras.io/losses/>. An objective function is any callable with
|
|
the signature ``scalar_loss = fn(y_true, y_pred)``. If the model has multiple outputs, you can use a
|
|
different loss on each output by passing a dictionary or a list of losses. The loss value that will be
|
|
minimized by the model will then be the sum of all individual losses (default: ``mse``, mean squared error).
|
|
|
|
:param metrics: List of metrics to be evaluated by the model during training and testing. Typically you will
|
|
use ``metrics=['accuracy']``. To specify different metrics for different outputs of a multi-output model,
|
|
you could also pass a dictionary, such as
|
|
``metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}``. You can also pass a list
|
|
``(len = len(outputs))`` of lists of metrics such as ``metrics=[['accuracy'], ['accuracy', 'mse']]`` or
|
|
``metrics=['accuracy', ['accuracy', 'mse']]``. Default: ``['mae', 'mse']``.
|
|
|
|
:param loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the
|
|
loss contributions of different model outputs. The loss value that will be minimized by the model
|
|
will then be the *weighted sum* of all individual losses, weighted by the `loss_weights` coefficients.
|
|
If a list, it is expected to have a 1:1 mapping to the model's outputs. If a tensor, it is expected to map
|
|
output names (strings) to scalar coefficients.
|
|
|
|
:param sample_weight_mode: If you need to do time-step-wise sample weighting (2D weights), set this to
|
|
``"temporal"``. ``None`` defaults to sample-wise weights (1D). If the model has multiple outputs,
|
|
you can use a different ``sample_weight_mode`` on each output by passing a dictionary or a list of modes.
|
|
|
|
:param weighted_metrics: List of metrics to be evaluated and weighted by ``sample_weight`` or ``class_weight``
|
|
during training and testing.
|
|
|
|
:param target_tensors: By default, Keras will create placeholders for the model's target, which will be fed
|
|
with the target data during training. If instead you would like to use your own target tensors (in turn,
|
|
Keras will not expect external numpy data for these targets at training time), you can specify them via the
|
|
``target_tensors`` argument. It can be a single tensor (for a single-output model), a list of tensors,
|
|
or a dict mapping output names to target tensors.
|
|
|
|
:param kwargs: Extra arguments to pass to ``Model.compile()``.
|
|
|
|
:return: Configuration of the model, as a dict. Example:
|
|
|
|
.. code-block:: json
|
|
|
|
{
|
|
"name": "test_regression_model",
|
|
"trainable": true,
|
|
"dtype": "float32",
|
|
"units": 1,
|
|
"activation": "linear",
|
|
"use_bias": true,
|
|
"kernel_initializer": {
|
|
"class_name": "GlorotUniform",
|
|
"config": {
|
|
"seed": null
|
|
}
|
|
},
|
|
"bias_initializer": {
|
|
"class_name": "Zeros",
|
|
"config": {}
|
|
},
|
|
"kernel_regularizer": null,
|
|
"bias_regularizer": null
|
|
}
|
|
|
|
"""
|
|
from tensorflow.keras.experimental import LinearModel
|
|
model = LinearModel(
|
|
units=units,
|
|
activation=activation,
|
|
use_bias=use_bias,
|
|
kernel_initializer=kernel_initializer,
|
|
bias_initializer=bias_initializer,
|
|
kernel_regularizer=kernel_regularizer,
|
|
bias_regularizer=bias_regularizer,
|
|
name=name)
|
|
|
|
model.input_names = input_names or []
|
|
|
|
if output_names:
|
|
assert units == len(output_names)
|
|
model.output_labels = output_names
|
|
else:
|
|
model.output_labels = []
|
|
|
|
if not metrics:
|
|
metrics = ['mae', 'mse']
|
|
|
|
model.compile(
|
|
optimizer=optimizer,
|
|
loss=loss,
|
|
metrics=metrics,
|
|
loss_weights=loss_weights,
|
|
sample_weight_mode=sample_weight_mode,
|
|
weighted_metrics=weighted_metrics,
|
|
target_tensors=target_tensors,
|
|
**kwargs
|
|
)
|
|
|
|
with self._lock_model(name):
|
|
self.models[name] = model
|
|
return model.get_config()
|
|
|
|
@staticmethod
|
|
def _layer_from_dict(layer_type: str, *args, **kwargs) -> Layer:
|
|
from tensorflow.keras import layers
|
|
cls = getattr(layers, layer_type)
|
|
assert issubclass(cls, Layer)
|
|
return cls(*args, **kwargs)
|
|
|
|
@staticmethod
|
|
def _get_csv_data(data_file: str) -> np.ndarray:
|
|
import pandas as pd
|
|
return pd.read_csv(data_file).to_numpy()
|
|
|
|
@staticmethod
|
|
def _get_numpy_data(data_file: str) -> np.ndarray:
|
|
return np.load(data_file)
|
|
|
|
@staticmethod
|
|
def _get_numpy_compressed_data(data_file: str) -> np.ndarray:
|
|
return list(np.load(data_file).values()).pop()
|
|
|
|
@classmethod
|
|
def _get_image(cls, image_file: str, model: Model) -> np.ndarray:
|
|
input_shape = model.inputs[0].shape
|
|
size = input_shape[1:3].as_list()
|
|
assert len(size) == 2, 'The model {} does not have enough dimensions to process an image (shape: {})'.format(
|
|
model.name, size)
|
|
|
|
colors = input_shape[3:]
|
|
if len(colors) == 0 or colors[0] == 1:
|
|
color_mode = 'grayscale'
|
|
elif colors[0] == 3:
|
|
color_mode = 'rgb'
|
|
elif colors[0] == 4:
|
|
color_mode = 'rgba'
|
|
else:
|
|
raise AssertionError('The input tensor should have either 1 (grayscale), 3 (rgb) or 4 (rgba) units. ' +
|
|
'Found: {}'.format(colors[0]))
|
|
|
|
img = image.load_img(image_file, target_size=size, color_mode=color_mode)
|
|
data = image.img_to_array(img)
|
|
if data.shape[-1] == 1:
|
|
# Squeeze extra color channels
|
|
data = np.squeeze(data)
|
|
return data
|
|
|
|
@classmethod
|
|
def _get_dir(cls, directory: str, model: Model) -> Dict[str, Iterable]:
|
|
labels = [f for f in os.listdir(directory) if os.path.isdir(os.path.join(directory, f))]
|
|
assert set(model.output_labels) == set(labels),\
|
|
'The directory {dir} should contain exactly {n} subfolders named {names}'.format(
|
|
dir=directory, n=len(model.output_labels), names=model.output.labels)
|
|
|
|
ret = {}
|
|
for label in labels:
|
|
subdir = os.path.join(directory, label)
|
|
ret[label] = [
|
|
cls._get_data(os.path.join(subdir, f), model)
|
|
for f in os.listdir(subdir)
|
|
if f.split('.')[-1] in cls._supported_data_file_extensions
|
|
]
|
|
|
|
return ret
|
|
|
|
@classmethod
|
|
def _get_outputs(cls, data: Union[str, np.ndarray, Iterable], model: Model) -> np.ndarray:
|
|
if isinstance(data, str):
|
|
if model.output_labels:
|
|
label_index = model.output_labels.index(data)
|
|
if label_index >= 0:
|
|
return np.array([1 if i == label_index else 0 for i in range(len(model.output_labels))])
|
|
|
|
return np.array([data])
|
|
|
|
if len(data) > 0 and isinstance(data[0], str):
|
|
return np.array([cls._get_outputs(item, model) for item in data])
|
|
|
|
return data
|
|
|
|
@classmethod
|
|
def _get_data(cls, data: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]], model: Model) \
|
|
-> Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]:
|
|
if isinstance(data, List) or isinstance(data, Tuple):
|
|
if len(data) and isinstance(data[0], str):
|
|
return np.array([cls._get_data(item, model) for item in data])
|
|
|
|
if not isinstance(data, str):
|
|
return data
|
|
|
|
if data.startswith('http://') or data.startswith('https://'):
|
|
filename = '{timestamp}_{filename}'.format(
|
|
timestamp=datetime.now().timestamp(), filename=data.split('/')[-1])
|
|
data_file = utils.get_file(filename, data)
|
|
else:
|
|
data_file = os.path.abspath(os.path.expanduser(data))
|
|
|
|
extensions = [ext for ext in cls._supported_data_file_extensions if data_file.endswith('.' + ext)]
|
|
|
|
if os.path.isfile(data_file):
|
|
assert extensions, 'Unsupported type for file {}. Supported extensions: {}'.format(
|
|
data_file, cls._supported_data_file_extensions
|
|
)
|
|
|
|
extension = extensions.pop()
|
|
if extension in cls._csv_extensions:
|
|
return cls._get_csv_data(data_file)
|
|
if extension == 'npy':
|
|
return cls._get_numpy_data(data_file)
|
|
if extension == 'npz':
|
|
return cls._get_numpy_compressed_data(data_file)
|
|
if extension in cls._image_extensions:
|
|
return cls._get_image(data_file, model)
|
|
|
|
raise AssertionError('Unsupported file type: {}'.format(data_file))
|
|
elif os.path.isdir(data_file):
|
|
return cls._get_dir(data_file, model)
|
|
|
|
return data
|
|
|
|
@classmethod
|
|
def _get_dataset(cls,
|
|
inputs: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]],
|
|
outputs: Optional[Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]],
|
|
model: Model) \
|
|
-> Tuple[Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]],
|
|
Optional[Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]]]:
|
|
inputs = cls._get_data(inputs, model)
|
|
if outputs:
|
|
outputs = cls._get_outputs(outputs, model)
|
|
elif isinstance(inputs, dict) and model.output_labels:
|
|
pairs = []
|
|
for i, label in enumerate(model.output_labels):
|
|
data = inputs.get(label, [])
|
|
pairs.extend([(d, tuple(1 if i == j else 0 for j, _ in enumerate(model.output_labels)))
|
|
for d in data])
|
|
|
|
random.shuffle(pairs)
|
|
inputs = np.asarray([p[0] for p in pairs])
|
|
outputs = np.asarray([p[1] for p in pairs])
|
|
|
|
return inputs, outputs
|
|
|
|
@action
|
|
def train(self,
|
|
model: str,
|
|
inputs: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]],
|
|
outputs: Optional[Union[str, np.ndarray, Iterable]] = None,
|
|
batch_size: Optional[int] = None,
|
|
epochs: int = 1,
|
|
verbose: int = 1,
|
|
validation_split: float = 0.,
|
|
validation_data: Optional[Tuple[Union[np.ndarray, Iterable]]] = None,
|
|
shuffle: Union[bool, str] = True,
|
|
class_weight: Optional[Dict[int, float]] = None,
|
|
sample_weight: Optional[Union[np.ndarray, Iterable]] = None,
|
|
initial_epoch: int = 0,
|
|
steps_per_epoch: Optional[int] = None,
|
|
validation_steps: int = None,
|
|
validation_freq: int = 1,
|
|
max_queue_size: int = 10,
|
|
workers: int = 1,
|
|
use_multiprocessing: bool = False) -> TensorflowTrainResponse:
|
|
"""
|
|
Trains a model on a dataset for a fixed number of epochs.
|
|
|
|
:param model: Name of the model. It can be a folder name stored under ``<workdir>/models``, or an absolute path
|
|
to a model directory or file (Tensorflow directories, Protobuf models and HDF5 files are supported).
|
|
:param inputs: Input data. It can be:
|
|
|
|
- A numpy array (or array-like), or a list of arrays in case the model has multiple inputs.
|
|
- A TensorFlow tensor, or a list of tensors in case the model has multiple inputs.
|
|
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
|
|
- A ``tf.data`` dataset. Should return a tuple of either ``(inputs, targets)`` or
|
|
``(inputs, targets, sample_weights)``.
|
|
- A generator or ``keras.utils.Sequence`` returning ``(inputs, targets)`` or
|
|
``(inputs, targets, sample weights)``.
|
|
- A string that points to a file. Supported formats:
|
|
|
|
- CSV with header (``.csv`` extension``)
|
|
- Numpy raw or compressed files (``.npy`` or ``.npz`` extension)
|
|
- Image files
|
|
- An HTTP URL pointing to one of the file types listed above
|
|
- Directories with images. If ``inputs`` points to a directory of images then the following
|
|
conventions are followed:
|
|
|
|
- The folder must contain exactly as many subfolders as the output units of your model. If
|
|
the model has ``output_labels`` then those subfolders should be named as the output labels.
|
|
Each subfolder will contain training examples that match the associated label (e.g.
|
|
``positive`` will contain all the positive images and ``negative`` all the negative images).
|
|
- ``outputs`` doesn't have to be specified.
|
|
|
|
:param outputs: Target data. Like the input data `x`, it can be a numpy array (or array-like) or TensorFlow
|
|
tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely).
|
|
If `x` is a dataset, generator, or `keras.utils.Sequence` instance, `y` should not be specified
|
|
(since targets will be obtained from `x`).
|
|
|
|
:param batch_size: Number of samples per gradient update. If unspecified, ``batch_size`` will default to 32.
|
|
Do not specify the ``batch_size`` if your data is in the form of symbolic tensors, datasets,
|
|
generators, or ``keras.utils.Sequence`` instances (since they generate batches).
|
|
|
|
:param epochs: Number of epochs to train the model. An epoch is an iteration over the entire ``x`` and ``y``
|
|
data provided. Note that in conjunction with ``initial_epoch``, ``epochs`` is to be understood as
|
|
"final epoch". The model is not trained for a number of iterations given by ``epochs``, but merely until
|
|
the epoch of index ``epochs`` is reached.
|
|
|
|
:param verbose: Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
|
|
Note that the progress bar is not particularly useful when
|
|
logged to a file, so verbose=2 is recommended when not running
|
|
interactively (eg, in a production environment).
|
|
|
|
:param validation_split: Float between 0 and 1.
|
|
Fraction of the training data to be used as validation data. The model will set apart this fraction
|
|
of the training data, will not train on it, and will evaluate the loss and any model metrics on this data
|
|
at the end of each epoch. The validation data is selected from the last samples in the ``x`` and ``y``
|
|
data provided, before shuffling. Not supported when ``x`` is a dataset, generator or ``keras.utils.Sequence`` instance.
|
|
|
|
:param validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch.
|
|
The model will not be trained on this data. ``validation_data`` will override ``validation_split``.
|
|
``validation_data`` could be:
|
|
|
|
- tuple ``(x_val, y_val)`` of arrays/numpy arrays/tensors
|
|
- tuple ``(x_val, y_val, val_sample_weights)`` of Numpy arrays
|
|
- dataset
|
|
|
|
For the first two cases, ``batch_size`` must be provided. For the last case, ``validation_steps`` could be
|
|
provided.
|
|
|
|
:param shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch').
|
|
'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized
|
|
chunks. Has no effect when ``steps_per_epoch`` is not ``None``.
|
|
|
|
:param class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used
|
|
for weighting the loss function (during training only). This can be useful to tell the model to
|
|
"pay more attention" to samples from an under-represented class.
|
|
|
|
:param sample_weight: Optional iterable/numpy array of weights for the training samples, used for weighting
|
|
the loss function (during training only). You can either pass a flat (1D) numpy array/iterable with the
|
|
same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data,
|
|
you can pass a 2D array with shape ``(samples, sequence_length)``, to apply a different weight to every
|
|
time step of every sample. In this case you should make sure to specify ``sample_weight_mode="temporal"``
|
|
in ``compile()``. This argument is not supported when ``x`` is a dataset, generator, or
|
|
``keras.utils.Sequence`` instance, instead provide the sample_weights as the third element of ``x``.
|
|
|
|
:param initial_epoch: Epoch at which to start training (useful for resuming a previous training run).
|
|
|
|
:param steps_per_epoch: Total number of steps (batches of samples) before declaring one epoch finished and
|
|
starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default
|
|
``None`` is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot
|
|
be determined. If x is a ``tf.data`` dataset, and 'steps_per_epoch' is None, the epoch will run until the
|
|
input dataset is exhausted. This argument is not supported with array inputs.
|
|
|
|
:param validation_steps: Only relevant if ``validation_data`` is provided and is a ``tf.data`` dataset. Total
|
|
number of steps (batches of samples) to draw before stopping when performing validation at the end of
|
|
every epoch. If 'validation_steps' is None, validation will run until the ``validation_data`` dataset is
|
|
exhausted. In the case of a infinite dataset, it will run into a infinite loop. If 'validation_steps' is
|
|
specified and only part of the dataset will be consumed, the evaluation will start from the beginning of
|
|
the dataset at each epoch. This ensures that the same validation samples are used every time.
|
|
|
|
:param validation_freq: Only relevant if validation data is provided. Integer or ``collections_abc.Container``
|
|
instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a
|
|
new validation run is performed, e.g. ``validation_freq=2`` runs validation every 2 epochs. If a
|
|
Container, specifies the epochs on which to run validation, e.g. ``validation_freq=[1, 2, 10]`` runs
|
|
validation at the end of the 1st, 2nd, and 10th epochs.
|
|
|
|
:param max_queue_size: Used for generator or ``keras.utils.Sequence`` input only. Maximum size for
|
|
the generator queue. If unspecified, ``max_queue_size`` will default to 10.
|
|
|
|
:param workers: Used for generator or ``keras.utils.Sequence`` input only. Maximum number of processes
|
|
to spin up when using process-based threading. If unspecified, ``workers`` will default to 1. If 0, will
|
|
execute the generator on the main thread.
|
|
|
|
:param use_multiprocessing: Used for generator or ``keras.utils.Sequence`` input only. If ``True``,
|
|
use process-based threading. If unspecified, ``use_multiprocessing`` will default to ``False``.
|
|
Note that because this implementation relies on multiprocessing, you should not pass non-picklable
|
|
arguments to the generator as they can't be passed easily to children processes.
|
|
|
|
:return: :class:`platypush.message.response.tensorflow.TensorflowTrainResponse`
|
|
"""
|
|
name = model
|
|
model = self._load_model(model)
|
|
inputs, outputs = self._get_dataset(inputs, outputs, model)
|
|
|
|
ret = model.fit(
|
|
x=inputs,
|
|
y=outputs,
|
|
batch_size=batch_size,
|
|
epochs=epochs,
|
|
verbose=verbose,
|
|
callbacks=self._generate_callbacks(name),
|
|
validation_split=validation_split,
|
|
validation_data=validation_data,
|
|
shuffle=shuffle,
|
|
class_weight=class_weight,
|
|
sample_weight=sample_weight,
|
|
initial_epoch=initial_epoch,
|
|
steps_per_epoch=steps_per_epoch,
|
|
validation_steps=validation_steps,
|
|
validation_freq=validation_freq,
|
|
max_queue_size=max_queue_size,
|
|
workers=workers,
|
|
use_multiprocessing=use_multiprocessing,
|
|
)
|
|
|
|
return TensorflowTrainResponse(model=model, model_name=name, epochs=ret.epoch, history=ret.history)
|
|
|
|
@action
|
|
def evaluate(self,
|
|
model: str,
|
|
inputs: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]],
|
|
outputs: Optional[Union[str, np.ndarray, Iterable]] = None,
|
|
batch_size: Optional[int] = None,
|
|
verbose: int = 1,
|
|
sample_weight: Optional[Union[np.ndarray, Iterable]] = None,
|
|
steps: Optional[int] = None,
|
|
max_queue_size: int = 10,
|
|
workers: int = 1,
|
|
use_multiprocessing: bool = False) -> Union[Dict[str, float], List[float]]:
|
|
"""
|
|
Returns the loss value and metrics values for the model in test model.
|
|
|
|
:param model: Name of the model. It can be a folder name stored under ``<workdir>/models``, or an absolute path
|
|
to a model directory or file (Tensorflow directories, Protobuf models and HDF5 files are supported).
|
|
:param inputs: Input data. It can be:
|
|
|
|
- A numpy array (or array-like), or a list of arrays in case the model has multiple inputs.
|
|
- A TensorFlow tensor, or a list of tensors in case the model has multiple inputs.
|
|
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
|
|
- A ``tf.data`` dataset. Should return a tuple of either ``(inputs, targets)`` or
|
|
``(inputs, targets, sample_weights)``.
|
|
- A generator or ``keras.utils.Sequence`` returning ``(inputs, targets)`` or
|
|
``(inputs, targets, sample weights)``.
|
|
- A string that points to a file. Supported formats:
|
|
|
|
- CSV with header (``.csv`` extension``)
|
|
- Numpy raw or compressed files (``.npy`` or ``.npz`` extension)
|
|
- Image files
|
|
- An HTTP URL pointing to one of the file types listed above
|
|
- Directories with images. If ``inputs`` points to a directory of images then the following
|
|
conventions are followed:
|
|
|
|
- The folder must contain exactly as many subfolders as the output units of your model. If
|
|
the model has ``output_labels`` then those subfolders should be named as the output labels.
|
|
Each subfolder will contain training examples that match the associated label (e.g.
|
|
``positive`` will contain all the positive images and ``negative`` all the negative images).
|
|
- ``outputs`` doesn't have to be specified.
|
|
|
|
|
|
:param outputs: Target data. Like the input data `x`, it can be a numpy array (or array-like) or TensorFlow tensor(s).
|
|
It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely).
|
|
If `x` is a dataset, generator, or `keras.utils.Sequence` instance, `y` should not be specified
|
|
(since targets will be obtained from `x`).
|
|
|
|
:param batch_size: Number of samples per gradient update. If unspecified, ``batch_size`` will default to 32.
|
|
Do not specify the ``batch_size`` if your data is in the form of symbolic tensors, datasets,
|
|
generators, or ``keras.utils.Sequence`` instances (since they generate batches).
|
|
|
|
:param verbose: Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
|
|
Note that the progress bar is not particularly useful when
|
|
logged to a file, so verbose=2 is recommended when not running
|
|
interactively (eg, in a production environment).
|
|
|
|
:param sample_weight: Optional iterable/numpy array of weights for the training samples, used for weighting
|
|
the loss function (during training only). You can either pass a flat (1D) numpy array/iterable with the
|
|
same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data,
|
|
you can pass a 2D array with shape ``(samples, sequence_length)``, to apply a different weight to every
|
|
time step of every sample. In this case you should make sure to specify ``sample_weight_mode="temporal"``
|
|
in ``compile()``. This argument is not supported when ``x`` is a dataset, generator, or
|
|
``keras.utils.Sequence`` instance, instead provide the sample_weights as the third element of ``x``.
|
|
|
|
:param steps: Total number of steps (batches of samples) before declaring the evaluation round finished.
|
|
Ignored with the default value of ``None``. If x is a ``tf.data`` dataset and ``steps`` is None, 'evaluate'
|
|
will run until the dataset is exhausted. This argument is not supported with array inputs.
|
|
|
|
:param max_queue_size: Used for generator or ``keras.utils.Sequence`` input only. Maximum size for the generator
|
|
queue. If unspecified, ``max_queue_size`` will default to 10.
|
|
|
|
:param workers: Used for generator or ``keras.utils.Sequence`` input only. Maximum number of processes
|
|
to spin up when using process-based threading. If unspecified, ``workers`` will default to 1. If 0, will
|
|
execute the generator on the main thread.
|
|
|
|
:param use_multiprocessing: Used for generator or ``keras.utils.Sequence`` input only. If ``True``,
|
|
use process-based threading. If unspecified, ``use_multiprocessing`` will default to ``False``.
|
|
Note that because this implementation relies on multiprocessing, you should not pass non-picklable
|
|
arguments to the generator as they can't be passed easily to children processes.
|
|
|
|
:return: ``{test_metric: metric_value}`` dictionary if the ``metrics_names`` of the model are specified,
|
|
otherwise a list with the result test metrics (loss is usually the first value).
|
|
"""
|
|
|
|
name = model
|
|
model = self._load_model(model)
|
|
inputs, outputs = self._get_dataset(inputs, outputs, model)
|
|
|
|
ret = model.evaluate(
|
|
x=inputs,
|
|
y=outputs,
|
|
batch_size=batch_size,
|
|
verbose=verbose,
|
|
sample_weight=sample_weight,
|
|
steps=steps,
|
|
callbacks=self._generate_callbacks(name),
|
|
max_queue_size=max_queue_size,
|
|
workers=workers,
|
|
use_multiprocessing=use_multiprocessing
|
|
)
|
|
|
|
ret = ret if isinstance(ret, list) else [ret]
|
|
if not model.metrics_names:
|
|
return ret
|
|
|
|
return {model.metrics_names[i]: value for i, value in enumerate(ret)}
|
|
|
|
@action
|
|
def predict(self,
|
|
model: str,
|
|
inputs: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]],
|
|
batch_size: Optional[int] = None,
|
|
verbose: int = 0,
|
|
steps: Optional[int] = None,
|
|
max_queue_size: int = 10,
|
|
workers: int = 1,
|
|
use_multiprocessing: bool = False) -> TensorflowPredictResponse:
|
|
"""
|
|
Generates output predictions for the input samples.
|
|
|
|
:param model: Name of the model. It can be a folder name stored under ``<workdir>/models``, or an absolute path
|
|
to a model directory or file (Tensorflow directories, Protobuf models and HDF5 files are supported).
|
|
:param inputs: Input data. It can be:
|
|
|
|
- A numpy array (or array-like), or a list of arrays in case the model has multiple inputs.
|
|
- A TensorFlow tensor, or a list of tensors in case the model has multiple inputs.
|
|
- A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
|
|
- A ``tf.data`` dataset. Should return a tuple of either ``(inputs, targets)`` or
|
|
``(inputs, targets, sample_weights)``.
|
|
- A generator or ``keras.utils.Sequence`` returning ``(inputs, targets)`` or
|
|
``(inputs, targets, sample weights)``.
|
|
- A string that points to a file. Supported formats:
|
|
|
|
- CSV with header (``.csv`` extension``)
|
|
- Numpy raw or compressed files (``.npy`` or ``.npz`` extension)
|
|
- Image files
|
|
- An HTTP URL pointing to one of the file types listed above
|
|
|
|
:param batch_size: Number of samples per gradient update. If unspecified, ``batch_size`` will default to 32.
|
|
Do not specify the ``batch_size`` if your data is in the form of symbolic tensors, datasets,
|
|
generators, or ``keras.utils.Sequence`` instances (since they generate batches).
|
|
|
|
:param verbose: Verbosity mode, 0 or 1.
|
|
|
|
:param steps: Total number of steps (batches of samples) before declaring the prediction round finished.
|
|
Ignored with the default value of ``None``. If x is a ``tf.data`` dataset and ``steps`` is None, ``predict``
|
|
will run until the input dataset is exhausted.
|
|
|
|
:param max_queue_size: Integer. Used for generator or ``keras.utils.Sequence`` input only. Maximum size for
|
|
the generator queue (default: 10).
|
|
|
|
:param workers: Used for generator or ``keras.utils.Sequence`` input only. Maximum number of processes
|
|
to spin up when using process-based threading. If unspecified, ``workers`` will default to 1. If 0, will
|
|
execute the generator on the main thread.
|
|
|
|
:param use_multiprocessing: Used for generator or ``keras.utils.Sequence`` input only. If ``True``,
|
|
use process-based threading. If unspecified, ``use_multiprocessing`` will default to ``False``.
|
|
Note that because this implementation relies on multiprocessing, you should not pass non-picklable
|
|
arguments to the generator as they can't be passed easily to children processes.
|
|
|
|
:return: :class:`platypush.message.response.tensorflow.TensorflowPredictResponse`. Format:
|
|
|
|
- For regression models with no output labels specified: ``outputs`` will contain the output vector:
|
|
|
|
.. code-block:: json
|
|
|
|
{
|
|
"outputs": [[3.1415]]
|
|
}
|
|
|
|
- For regression models with output labels specified: ``outputs`` will be a list of ``{label -> value}``
|
|
maps:
|
|
|
|
.. code-block:: json
|
|
|
|
{
|
|
"outputs": [
|
|
{
|
|
"x": 42.0,
|
|
"y": 43.0
|
|
}
|
|
]
|
|
}
|
|
|
|
- For neural networks: ``outputs`` will contain the list of the output vector like in the case of
|
|
regression, and ``predictions`` will store the list of ``argmax`` (i.e. the index of the output unit with the
|
|
highest value) or their labels, if the model has output labels:
|
|
|
|
.. code-block:: json
|
|
|
|
{
|
|
"predictions": [
|
|
"positive"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"positive": 0.998,
|
|
"negative": 0.002
|
|
}
|
|
]
|
|
}
|
|
|
|
"""
|
|
name = model
|
|
model = self._load_model(model)
|
|
inputs = self._get_data(inputs, model)
|
|
if isinstance(inputs, np.ndarray) and \
|
|
len(model.inputs[0].shape) == len(inputs.shape) + 1 and \
|
|
(model.inputs[0].shape[0] is None or model.inputs[0].shape[0].value is None):
|
|
inputs = np.asarray([inputs])
|
|
|
|
ret = model.predict(
|
|
inputs,
|
|
batch_size=batch_size,
|
|
verbose=verbose,
|
|
steps=steps,
|
|
callbacks=self._generate_callbacks(name),
|
|
max_queue_size=max_queue_size,
|
|
workers=workers,
|
|
use_multiprocessing=use_multiprocessing
|
|
)
|
|
|
|
return TensorflowPredictResponse(model=model, model_name=name, prediction=ret,
|
|
output_labels=model.output_labels)
|
|
|
|
@action
|
|
def save(self, model: str, overwrite: bool = True, **opts) -> None:
|
|
"""
|
|
Save a model in memory to the filesystem. The model files will be stored under
|
|
``<WORKDIR>/models/<model_name>``.
|
|
|
|
:param model: Model name.
|
|
:param overwrite: Overwrite the model files if they already exist.
|
|
:param opts: Extra options to be passed to ``Model.save()``.
|
|
"""
|
|
model_name = model
|
|
model_dir = None
|
|
|
|
if os.path.isdir(os.path.join(self._models_dir, model_name)) or model_name in self.models:
|
|
model_dir = os.path.join(self._models_dir, model_name)
|
|
else:
|
|
model_file = os.path.abspath(os.path.expanduser(model_name))
|
|
if os.path.isfile(model_file):
|
|
model_dir = str(pathlib.Path(model_file).parent)
|
|
elif os.path.isdir(model_file):
|
|
model_dir = model_file
|
|
|
|
model = self.models.get(model_name, self.models.get(model_dir))
|
|
assert model, 'No such model loaded: {}'.format(model_name)
|
|
pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True)
|
|
|
|
with self._lock_model(model_name):
|
|
labels = {}
|
|
labels_file = os.path.join(model_dir, 'labels.json')
|
|
|
|
if hasattr(model, 'input_labels') and model.input_labels:
|
|
labels['input'] = model.input_labels
|
|
if hasattr(model, 'output_labels') and model.output_labels:
|
|
if hasattr(labels, 'input'):
|
|
labels['output'] = model.output_labels
|
|
else:
|
|
labels = model.output_labels
|
|
|
|
if labels:
|
|
with open(labels_file, 'w') as f:
|
|
json.dump(labels, f)
|
|
|
|
model.save(model_name if os.path.isfile(model_name) else model_dir, overwrite=overwrite, options=opts)
|
|
|
|
|
|
# vim:sw=4:ts=4:et:
|