def create_models(num_classes, p):
# create "base" model (no NMS)
image = keras.layers.Input((None, None, 3))
if p['resnet'] == 101:
model = ResNet101RetinaNet(image, num_classes=num_classes, weights=p['weights'], nms=False)
elif p['resnet'] == 152:
model = ResNet152RetinaNet(image, num_classes=num_classes, weights=p['weights'], nms=False)
else: # 50
model = ResNet50RetinaNet(image, num_classes=num_classes, weights=p['weights'], nms=False)
training_model = model
# append NMS for prediction only
classification = model.outputs[1]
detections = model.outputs[2]
boxes = keras.layers.Lambda(lambda x: x[:, :, :4])(detections)
detections = layers.NonMaximumSuppression(name='nms')([boxes, classification, detections])
prediction_model = keras.models.Model(inputs=model.inputs, outputs=model.outputs[:2] + [detections])
# compile model
training_model.compile(
loss={
'regression' : losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr = p['learning-rate'], clipnorm=0.001),
metrics = []
)
return model, training_model, prediction_model
def create_models(backbone_retinanet,
backbone,
num_classes,
weights,
multi_gpu=0,
freeze_backbone=False):
modifier = freeze_model if freeze_backbone else None
# Keras recommends initialising a multi-gpu model on the CPU to ease weight sharing, and to prevent OOM errors.
# optionally wrap in a parallel model
if multi_gpu > 1:
with tf.device('/cpu:0'):
model = model_with_weights(backbone_retinanet(num_classes,
backbone=backbone,
nms=False,
modifier=modifier),
weights=weights,
skip_mismatch=True)
training_model = multi_gpu_model(model, gpus=multi_gpu)
# append NMS for prediction only
classification = model.outputs[1]
detections = model.outputs[2]
boxes = keras.layers.Lambda(lambda x: x[:, :, :4])(detections)
detections = layers.NonMaximumSuppression(name='nms')(
[boxes, classification, detections])
prediction_model = keras.models.Model(inputs=model.inputs,
outputs=model.outputs[:2] +
[detections])
else:
model = model_with_weights(backbone_retinanet(num_classes,
backbone=backbone,
nms=True,
modifier=modifier),
weights=weights,
skip_mismatch=True)
training_model = model
prediction_model = model
# compile model
training_model.compile(loss={
'regression': losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5,
clipnorm=0.001))
return model, training_model, prediction_model
def create_models(num_classes, weights='imagenet', multi_gpu=0):
# create "base" model (no NMS)
image = keras.layers.Input((None, None, 3))
# Keras recommends initialising a multi-gpu model on the CPU to ease weight sharing, and to prevent OOM errors.
# optionally wrap in a parallel model
if multi_gpu > 1:
with tf.device('/cpu:0'):
model = ResNet50RetinaNet(image,
num_classes=num_classes,
weights=weights,
nms=False)
training_model = multi_gpu_model(model, gpus=multi_gpu)
else:
model = ResNet50RetinaNet(image,
num_classes=num_classes,
weights=weights,
nms=False)
training_model = model
# append NMS for prediction only
classification = model.outputs[1]
detections = model.outputs[2]
boxes = keras.layers.Lambda(lambda x: x[:, :, :4])(detections)
detections = layers.NonMaximumSuppression(name='nms')(
[boxes, classification, detections])
prediction_model = keras.models.Model(inputs=model.inputs,
outputs=model.outputs[:2] +
[detections])
# compile model
training_model.compile(loss={
'regression': losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5,
clipnorm=0.001))
return model, training_model, prediction_model
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
import numpy as np
from keras_retinanet import layers
import json
path_to_model = sys.argv[1]
model = keras.models.load_model(path_to_model, custom_objects=custom_objects)
#transfer to predict model
classification = model.outputs[1]
detections = model.outputs[2]
boxes = keras.layers.Lambda(lambda x: x[:, :, :4],
name='lambda_before_output')(detections)
detections = layers.NonMaximumSuppression(name='nms')(
[boxes, classification, detections])
prediction_model = keras.models.Model(inputs=model.inputs,
outputs=model.outputs[:2] + [detections])
model = prediction_model
test_files = glob('deploy/test/*/*_image.jpg')
result_dict = {}
#01bb984a-d3f1-4ba5-b0c0-5242533efa4d/0000
count = 0
all_num = len(test_files)
for test_file in test_files: