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Lev Zlotnik authored
Add an example of compressing OD pytorch models. In this example we compress torchvision's object detection models - FasterRCNN / MaskRCNN / KeypointRCNN. We've modified the reference code for object detection to allow easy compression scheduling with YAML configuration.
Lev Zlotnik authoredAdd an example of compressing OD pytorch models. In this example we compress torchvision's object detection models - FasterRCNN / MaskRCNN / KeypointRCNN. We've modified the reference code for object detection to allow easy compression scheduling with YAML configuration.
logging.conf 1.11 KiB
[formatters]
keys: simple, time_simple
[handlers]
keys: console, file
[loggers]
keys: root, app_cfg, distiller.thinning, apputils.model_summaries
[formatter_simple]
format: %(message)s
[formatter_time_simple]
format: %(asctime)s - %(message)s
[handler_console]
class: StreamHandler
propagate: 0
args: []
formatter: simple
[handler_file]
class: FileHandler
mode: 'w'
args=('%(logfilename)s', 'w')
formatter: time_simple
[logger_root]
level: INFO
propagate: 1
handlers: console, file
[logger_app_cfg]
# Use this logger to log the application configuration and execution environment
level: DEBUG
qualname: app_cfg
propagate: 0
handlers: file
# Example of adding a module-specific logger
# Do not forget to add distiller.thinning to the list of keys in section [loggers]
[logger_distiller.thinning]
level: INFO
qualname: distiller.thinning
propagate: 0
handlers: console, file
# Example of adding a module-specific logger
# Do not forget to add apputils.model_summaries to the list of keys in section [loggers]
[logger_apputils.model_summaries]
level: INFO
qualname: apputils.model_summaries
propagate: 0
handlers: console, file