def pack_model(inputs, train = True, back_network = 'resnet50',
#num_classes=81,
num_classes=91,
base_anchors=9,
weight_decay=0.00005,
**kwargs
):
# Reshape the input image, batch size 1 supported
image = inputs['images']
ih = inputs['height']
iw = inputs['width']
im_shape = tf.shape(image)
#image = tf.Print(image, [im_shape], message = 'shape', summarize = 4)
image = tf.reshape(image, (im_shape[0], im_shape[1], im_shape[2], 3))
image = tf.cast(image, tf.float32)
image_height = ih
image_width = iw
num_instances = inputs['num_objects']
gt_boxes = inputs['bboxes']
#gt_boxes = tf.reshape(gt_boxes, [num_instances, 4])
#labels = inputs['labels']
#labels = tf.reshape(labels, [num_instances, 1])
#gt_boxes = tf.concat([gt_boxes, tf.cast(labels, tf.float64)], 1)
#gt_boxes = tf.Print(gt_boxes, [tf.shape(gt_boxes)], message = 'Box shape', summarize = 4)
# Build the basic network
logits, end_points, pyramid_map = network.get_network(back_network, image,
weight_decay=weight_decay)
# Build the pyramid
pyramid = pyramid_network.build_pyramid(pyramid_map, end_points)
# Build the heads
outputs = \
pyramid_network.build_heads(pyramid, image_height, image_width, num_classes, base_anchors,
is_training=train, gt_boxes=gt_boxes)
return {'outputs': outputs, 'pyramid': pyramid}, {'network': back_network}
pyramid = pyramid_network.build_pyramid('resnet50', end_points)
# for p in pyramid:
# print (p, pyramid[p])
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
for p in pyramid:
summaries.add(tf.summary.histogram('pyramid/hist/' + p,
pyramid[p]))
summaries.add(
tf.summary.scalar('pyramid/means/' + p,
tf.reduce_mean(tf.abs(pyramid[p]))))
outputs = pyramid_network.build_heads(pyramid,
ih,
iw,
num_classes=81,
base_anchors=9,
is_training=True,
gt_boxes=gt_boxes)
## losses
loss, losses, batch_info = pyramid_network.build_losses(
pyramid,
outputs,
gt_boxes,
gt_masks,
num_classes=81,
base_anchors=9,
rpn_box_lw=0.1,
rpn_cls_lw=0.2,
refined_box_lw=2.0,
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tensorflow as tf
import tensorflow.contrib.slim as slim
from libs.logs.log import LOG
import libs.nets.resnet_v1 as resnet_v1
resnet50 = resnet_v1.resnet_v1_50
images = tf.placeholder(tf.float32, [16, 224, 224, 3], name='image')
logits, end_points = resnet50(images, 1000, is_training=False)
end_points['inputs'] = images
for x in sorted(end_points.keys()):
print (x, end_points[x].name, end_points[x].shape)
import libs.nets.pyramid_network as pyramid_network
pyramid = pyramid_network.build_pyramid('resnet50', end_points)
for p in pyramid:
print (p, pyramid[p])
outputs = pyramid_network.build_heads(pyramid, num_classes=81, base_anchors=15, is_training=True)
outputs = pyramid_network.build_losses(pyramid, outputs)