def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts({
'nb_down_sample': 3,
'loss_weight': 1.0,
'share_model': False
}, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts({
'paths': 3,
'activation': 'incept',
},
super()._default_config())
def __init__(self,
name='summary',
tensors_to_summary=None,
inputs=None,
**config):
"""
Inputs:
tensors_to_summary: dict of name: SummaryItem
inputs: tensors required to run summary (will used for feeds)
"""
super().__init__(name, **config)
if tensors_to_summary is None:
tensors_to_summary = dict()
if inputs is None:
inputs = dict()
self._tensors = combine_dicts(tensors_to_summary,
self._default_summaries())
for k in inputs:
self.register_node(k, inputs[k])
self._summary_ops = list()
self.register_task('create_writer', self.__create_writer)
self.register_task('flush', self.flush)
self.register_main_task(self.summary)
if self.param('add_prefix'):
with tf.name_scope('summary'):
self.__add_all_ops()
else:
self.__add_all_ops()
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts({
'building_block': BuildingBlocks.STACKEDCONV,
'nb_layers': 10,
'filters': 32,
'boundary_crop': [4, 4]
}, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts(
{
'allow_growth': True,
'log_device_placement': False
},
super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
cfg = {
'method': 'mean',
'padding': 'same',
'keep_energy': False,
}
return combine_dicts(cfg, super()._default_config())
def _default_config(cls):
return combine_dicts(
{
'path': './summary',
'add_prefix': False,
'max_image': 3
},
super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
fmt = '/home/hongxwing/Datas/phantom/phantom.{}.tfrecord'
files = [fmt.format(i) for i in range(3)]
return combine_dicts({
'files': files,
'fields': 'sinogram',
'batch_size': 32
}, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
cfg = {
'keep_original': True,
'original_key': 'ds1x',
'rigister_output_with_prefix': False,
'with_shape_info': False
}
return combine_dicts(cfg, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts(
{
'kernel_size': 3,
'strides': (1, 1),
'padding': 'same',
'activation': 'basic'
},
super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
cfg = {
'lazy_create': False,
'reuse': None,
'register_inputs': True,
'register_outputs': True,
'simple_output': True,
'no_new_output_for_none_feeds': True,
'register_output_with_prefix': True,
}
return combine_dicts(cfg, super()._default_config())
def __init__(self,
name,
net,
tensors_to_summary=None,
inputs=None,
**config):
automatic_summary_tensors = dict()
automatic_keys = [NodeKeys.LOSS, NodeKeys.INFERENCE, NodeKeys.EVALUATE]
for k in automatic_keys:
if k in net.nodes:
automatic_summary_tensors[k] = SummaryItem(net[k])
if tensors_to_summary is None:
tensors_to_summary = dict()
super().__init__(
name, combine_dicts(tensors_to_summary, automatic_summary_tensors),
inputs, **config)
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts({'dtype': tf.float32}, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
cfg = {'input_shape': [14, 14, 1], 'label_shape': [28, 28, 1]}
return combine_dicts(cfg, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
cfg = {'activation': 'basic', 'padding': 'same', 'filters': 32}
return combine_dicts(cfg, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts({
'add_trainer': True,
'add_saver': True
}, super()._default_config())
def _default_config(cls):
from dxpy.collections.dicts import combine_dicts
return combine_dicts({'with_shape_info': False}, super()._default_config())
def __load_config(self, config_direct):
from .config import config as config_global
from dxpy.collections.dicts import combine_dicts
return combine_dicts(config_direct, config_global,
self._default_config())
