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Implemented mising encoding for labels

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-06-05 11:49:44 +02:00
parent 28abe6ae47
commit 292e5e3c95
1 changed files with 22 additions and 9 deletions
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27
src/twomartens/masterthesis/ssd.py
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@ -49,6 +49,7 @@ from twomartens.masterthesis.ssd_keras.keras_loss_function import keras_ssd_loss
from twomartens.masterthesis.ssd_keras.models import keras_ssd300
from twomartens.masterthesis.ssd_keras.models import keras_ssd300_dropout
from twomartens.masterthesis.ssd_keras.ssd_encoder_decoder import ssd_output_decoder
from twomartens.masterthesis.ssd_keras.ssd_encoder_decoder import ssd_input_encoder
K = tf.keras.backend
tfe = tf.contrib.eager
@ -72,9 +73,11 @@ class SSD:
"""
def __init__(self, mode: str, weights_path: Optional[str] = None) -> None:
self._model = keras_ssd300.ssd_300(image_size=IMAGE_SIZE, n_classes=N_CLASSES,
self._model, self.predictor_sizes = \
keras_ssd300.ssd_300(image_size=IMAGE_SIZE, n_classes=N_CLASSES,
mode=mode, iou_threshold=IOU_THRESHOLD, top_k=TOP_K,
scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05])
scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
return_predictor_sizes=True)
self.mode = mode
# load existing weights
@ -107,10 +110,14 @@ class DropoutSSD:
"""
def __init__(self, mode: str, weights_path: Optional[str] = None) -> None:
self._model = keras_ssd300_dropout.ssd_300_dropout(image_size=IMAGE_SIZE, n_classes=N_CLASSES,
self._model, self.predictor_sizes = \
keras_ssd300_dropout.ssd_300_dropout(image_size=IMAGE_SIZE,
n_classes=N_CLASSES,
dropout_rate=DROPOUT_RATE, mode=mode,
iou_threshold=IOU_THRESHOLD, top_k=TOP_K,
scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05])
iou_threshold=IOU_THRESHOLD,
top_k=TOP_K,
scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
return_predictor_sizes=True)
self.mode = mode
# load existing weights
@ -392,6 +399,10 @@ def train(dataset: tf.data.Dataset,
checkpoint = tf.train.Checkpoint(**checkpointables)
checkpoint.restore(latest_checkpoint)
# input encoder
input_encoder = ssd_input_encoder.SSDInputEncoder(IMAGE_SIZE[0], IMAGE_SIZE[1],
N_CLASSES, checkpointables['ssd'].predictor_sizes)
def _get_last_epoch(epoch_var: tf.Variable, **kwargs) -> int:
return int(epoch_var)
@ -406,7 +417,7 @@ def train(dataset: tf.data.Dataset,
for epoch in range(nr_epochs - previous_epochs):
_epoch = epoch + previous_epochs
outputs = _train_one_epoch(_epoch, dataset, **checkpointables)
outputs = _train_one_epoch(_epoch, dataset, input_encoder, **checkpointables)
if verbose:
print((
@ -427,6 +438,7 @@ def train(dataset: tf.data.Dataset,
def _train_one_epoch(epoch: int,
dataset: tf.data.Dataset,
input_encoder: ssd_input_encoder.SSDInputEncoder,
ssd: tf.keras.Model,
ssd_optimizer: tf.train.Optimizer,
global_step: tf.Variable,
@ -441,10 +453,11 @@ def _train_one_epoch(epoch: int,
# go through data set
for x, y in dataset:
encoded_ground_truth = input_encoder(y)
ssd_train_loss = _train_ssd_step(ssd=ssd,
optimizer=ssd_optimizer,
inputs=x,
ground_truth=y,
ground_truth=encoded_ground_truth,
global_step=global_step)
ssd_loss_avg(ssd_train_loss)
global_step.assign_add(1)