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"""
Leras.
like lighter keras.
This is my lightweight neural network library written from scratch
based on pure tensorflow without keras.
Provides:
+ full freedom of tensorflow operations without keras model's restrictions
+ easy model operations like in PyTorch, but in graph mode (no eager execution)
+ convenient and understandable logic
Reasons why we cannot import tensorflow or any tensorflow.sub modules right here:
1) program is changing env variables based on DeviceConfig before import tensorflow
2) multiprocesses will import tensorflow every spawn
NCHW speed up training for 10-20%.
"""
import os
import sys
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from pathlib import Path
import numpy as np
from core.interact import interact as io
from .device import Devices
class nn():
current_DeviceConfig = None
tf = None
tf_sess = None
tf_sess_config = None
tf_default_device_name = None
data_format = None
conv2d_ch_axis = None
conv2d_spatial_axes = None
floatx = None
@staticmethod
def initialize(device_config=None, floatx="float32", data_format="NHWC"):
if nn.tf is None:
if device_config is None:
device_config = nn.getCurrentDeviceConfig()
nn.setCurrentDeviceConfig(device_config)
# Manipulate environment variables before import tensorflow
first_run = False
if len(device_config.devices) != 0:
if sys.platform[0:3] == 'win':
# Windows specific env vars
if all( [ x.name == device_config.devices[0].name for x in device_config.devices ] ):
devices_str = "_" + device_config.devices[0].name.replace(' ','_')
else:
devices_str = ""
for device in device_config.devices:
devices_str += "_" + device.name.replace(' ','_')
compute_cache_path = Path(os.environ['APPDATA']) / 'NVIDIA' / ('ComputeCache' + devices_str)
if not compute_cache_path.exists():
first_run = True
compute_cache_path.mkdir(parents=True, exist_ok=True)
os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
if first_run:
io.log_info("Caching GPU kernels...")
import tensorflow
tf_version = tensorflow.version.VERSION
#if tf_version is None:
# tf_version = tensorflow.version.GIT_VERSION
if tf_version[0] == 'v':
tf_version = tf_version[1:]
if tf_version[0] == '2':
tf = tensorflow.compat.v1
else:
tf = tensorflow
import logging
# Disable tensorflow warnings
tf_logger = logging.getLogger('tensorflow')
tf_logger.setLevel(logging.ERROR)
if tf_version[0] == '2':
tf.disable_v2_behavior()
nn.tf = tf
# Initialize framework
import core.leras.ops
import core.leras.layers
import core.leras.initializers
import core.leras.optimizers
import core.leras.models
import core.leras.archis
# Configure tensorflow session-config
if len(device_config.devices) == 0:
config = tf.ConfigProto(device_count={'GPU': 0})
nn.tf_default_device_name = '/CPU:0'
else:
nn.tf_default_device_name = f'/{device_config.devices[0].tf_dev_type}:0'
config = tf.ConfigProto()
config.gpu_options.visible_device_list = ','.join([str(device.index) for device in device_config.devices])
config.gpu_options.force_gpu_compatible = True
config.gpu_options.allow_growth = True
nn.tf_sess_config = config
if nn.tf_sess is None:
nn.tf_sess = tf.Session(config=nn.tf_sess_config)
if floatx == "float32":
floatx = nn.tf.float32
elif floatx == "float16":
floatx = nn.tf.float16
else:
raise ValueError(f"unsupported floatx {floatx}")
nn.set_floatx(floatx)
nn.set_data_format(data_format)
@staticmethod
def initialize_main_env():
Devices.initialize_main_env()
@staticmethod
def set_floatx(tf_dtype):
"""
set default float type for all layers when dtype is None for them
"""
nn.floatx = tf_dtype
@staticmethod
def set_data_format(data_format):
if data_format != "NHWC" and data_format != "NCHW":
raise ValueError(f"unsupported data_format {data_format}")
nn.data_format = data_format
if data_format == "NHWC":
nn.conv2d_ch_axis = 3
nn.conv2d_spatial_axes = [1,2]
elif data_format == "NCHW":
nn.conv2d_ch_axis = 1
nn.conv2d_spatial_axes = [2,3]
@staticmethod
def get4Dshape ( w, h, c ):
"""
returns 4D shape based on current data_format
"""
if nn.data_format == "NHWC":
return (None,h,w,c)
else:
return (None,c,h,w)
@staticmethod
def to_data_format( x, to_data_format, from_data_format):
if to_data_format == from_data_format:
return x
if to_data_format == "NHWC":
return np.transpose(x, (0,2,3,1) )
elif to_data_format == "NCHW":
return np.transpose(x, (0,3,1,2) )
else:
raise ValueError(f"unsupported to_data_format {to_data_format}")
@staticmethod
def getCurrentDeviceConfig():
if nn.current_DeviceConfig is None:
nn.current_DeviceConfig = DeviceConfig.BestGPU()
return nn.current_DeviceConfig
@staticmethod
def setCurrentDeviceConfig(device_config):
nn.current_DeviceConfig = device_config
@staticmethod
def reset_session():
if nn.tf is not None:
if nn.tf_sess is not None:
nn.tf.reset_default_graph()
nn.tf_sess.close()
nn.tf_sess = nn.tf.Session(config=nn.tf_sess_config)
@staticmethod
def close_session():
if nn.tf_sess is not None:
nn.tf.reset_default_graph()
nn.tf_sess.close()
nn.tf_sess = None
@staticmethod
def ask_choose_device_idxs(choose_only_one=False, allow_cpu=True, suggest_best_multi_gpu=False, suggest_all_gpu=False):
devices = Devices.getDevices()
if len(devices) == 0:
return []
all_devices_indexes = [device.index for device in devices]
if choose_only_one:
suggest_best_multi_gpu = False
suggest_all_gpu = False
if suggest_all_gpu:
best_device_indexes = all_devices_indexes
elif suggest_best_multi_gpu:
best_device_indexes = [device.index for device in devices.get_equal_devices(devices.get_best_device()) ]
else:
best_device_indexes = [ devices.get_best_device().index ]
best_device_indexes = ",".join([str(x) for x in best_device_indexes])
io.log_info ("")
if choose_only_one:
io.log_info ("Choose one GPU idx.")
else:
io.log_info ("Choose one or several GPU idxs (separated by comma).")
io.log_info ("")
if allow_cpu:
io.log_info ("[CPU] : CPU")
for device in devices:
io.log_info (f" [{device.index}] : {device.name}")
io.log_info ("")
while True:
try:
if choose_only_one:
choosed_idxs = io.input_str("Which GPU index to choose?", best_device_indexes)
else:
choosed_idxs = io.input_str("Which GPU indexes to choose?", best_device_indexes)
if allow_cpu and choosed_idxs.lower() == "cpu":
choosed_idxs = []
break
choosed_idxs = [ int(x) for x in choosed_idxs.split(',') ]
if choose_only_one:
if len(choosed_idxs) == 1:
break
else:
if all( [idx in all_devices_indexes for idx in choosed_idxs] ):
break
except:
pass
io.log_info ("")
return choosed_idxs
class DeviceConfig():
@staticmethod
def ask_choose_device(*args, **kwargs):
return nn.DeviceConfig.GPUIndexes( nn.ask_choose_device_idxs(*args,**kwargs) )
def __init__ (self, devices=None):
devices = devices or []
if not isinstance(devices, Devices):
devices = Devices(devices)
self.devices = devices
self.cpu_only = len(devices) == 0
@staticmethod
def BestGPU():
devices = Devices.getDevices()
if len(devices) == 0:
return nn.DeviceConfig.CPU()
return nn.DeviceConfig([devices.get_best_device()])
@staticmethod
def WorstGPU():
devices = Devices.getDevices()
if len(devices) == 0:
return nn.DeviceConfig.CPU()
return nn.DeviceConfig([devices.get_worst_device()])
@staticmethod
def GPUIndexes(indexes):
if len(indexes) != 0:
devices = Devices.getDevices().get_devices_from_index_list(indexes)
else:
devices = []
return nn.DeviceConfig(devices)
@staticmethod
def CPU():
return nn.DeviceConfig([])
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