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‎imageai/Detection/keras_retinanet/callbacks/__init__.py‎

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from .common import *

‎imageai/Detection/keras_retinanet/callbacks/coco.py‎

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"""
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Copyright 2017-2018 Fizyr (https://fizyr.com)
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import keras
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from ..utils.coco_eval import evaluate_coco
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class CocoEval(keras.callbacks.Callback):
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def __init__(self, generator, threshold=0.05):
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self.generator = generator
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self.threshold = threshold
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super(CocoEval, self).__init__()
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def on_epoch_end(self, epoch, logs={}):
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evaluate_coco(self.generator, self.model, self.threshold)

‎imageai/Detection/keras_retinanet/callbacks/common.py‎

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import keras.callbacks
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class RedirectModel(keras.callbacks.Callback):
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"""Callback which wraps another callback, but executed on a different model.
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# Arguments
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callback: callback to wrap.
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model: model to use when executing callbacks.
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# Example
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```python
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model = keras.models.load_model('model.h5')
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model_checkpoint = ModelCheckpoint(filepath='snapshot.h5')
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parallel_model = multi_gpu_model(model, gpus=2)
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parallel_model.fit(X_train, Y_train, callbacks=[RedirectModel(model_checkpoint, model)])
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```
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"""
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def __init__(self,
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callback,
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model):
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super(RedirectModel, self).__init__()
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self.callback = callback
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self.redirect_model = model
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def on_epoch_begin(self, epoch, logs=None):
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self.callback.on_epoch_begin(epoch, logs=logs)
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def on_epoch_end(self, epoch, logs=None):
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self.callback.on_epoch_end(epoch, logs=logs)
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def on_batch_begin(self, batch, logs=None):
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self.callback.on_batch_begin(batch, logs=logs)
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def on_batch_end(self, batch, logs=None):
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self.callback.on_batch_end(batch, logs=logs)
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def on_train_begin(self, logs=None):
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# overwrite the model with our custom model
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self.callback.set_model(self.redirect_model)
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self.callback.on_train_begin(logs=logs)
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def on_train_end(self, logs=None):
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self.callback.on_train_end(logs=logs)

‎imageai/Detection/keras_retinanet/callbacks/eval.py‎

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"""
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Copyright 2017-2018 Fizyr (https://fizyr.com)
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import keras
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from ..utils.eval import evaluate
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class Evaluate(keras.callbacks.Callback):
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def __init__(self, generator, iou_threshold=0.5, score_threshold=0.05, max_detections=100, save_path=None, tensorboard=None, verbose=1):
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""" Evaluate a given dataset using a given model at the end of every epoch during training.
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# Arguments
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generator : The generator that represents the dataset to evaluate.
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iou_threshold : The threshold used to consider when a detection is positive or negative.
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score_threshold : The score confidence threshold to use for detections.
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max_detections : The maximum number of detections to use per image.
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save_path : The path to save images with visualized detections to.
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tensorboard : Instance of keras.callbacks.TensorBoard used to log the mAP value.
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verbose : Set the verbosity level, by default this is set to 1.
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"""
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self.generator = generator
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self.iou_threshold = iou_threshold
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self.score_threshold = score_threshold
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self.max_detections = max_detections
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self.save_path = save_path
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self.tensorboard = tensorboard
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self.verbose = verbose
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super(Evaluate, self).__init__()
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def on_epoch_end(self, epoch, logs={}):
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# run evaluation
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average_precisions = evaluate(
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self.generator,
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self.model,
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iou_threshold=self.iou_threshold,
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score_threshold=self.score_threshold,
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max_detections=self.max_detections,
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save_path=self.save_path
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)
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self.mean_ap = sum(average_precisions.values()) / len(average_precisions)
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if self.tensorboard is not None and self.tensorboard.writer is not None:
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import tensorflow as tf
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summary = tf.Summary()
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summary_value = summary.value.add()
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summary_value.simple_value = self.mean_ap
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summary_value.tag = "mAP"
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self.tensorboard.writer.add_summary(summary, epoch)
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if self.verbose == 1:
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for label, average_precision in average_precisions.items():
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print(self.generator.label_to_name(label), '{:.4f}'.format(average_precision))
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print('mAP: {:.4f}'.format(self.mean_ap))

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