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util.py
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import numpy as np
import os,sys,time
import shutil
import datetime
import torch
import torch.nn.functional as torch_F
import ipdb
import types
import termcolor
import socket
import contextlib
from easydict import EasyDict as edict
# convert to colored strings
def red(message,**kwargs): return termcolor.colored(str(message),color="red",attrs=[k for k,v in kwargs.items() if v is True])
def green(message,**kwargs): return termcolor.colored(str(message),color="green",attrs=[k for k,v in kwargs.items() if v is True])
def blue(message,**kwargs): return termcolor.colored(str(message),color="blue",attrs=[k for k,v in kwargs.items() if v is True])
def cyan(message,**kwargs): return termcolor.colored(str(message),color="cyan",attrs=[k for k,v in kwargs.items() if v is True])
def yellow(message,**kwargs): return termcolor.colored(str(message),color="yellow",attrs=[k for k,v in kwargs.items() if v is True])
def magenta(message,**kwargs): return termcolor.colored(str(message),color="magenta",attrs=[k for k,v in kwargs.items() if v is True])
def grey(message,**kwargs): return termcolor.colored(str(message),color="grey",attrs=[k for k,v in kwargs.items() if v is True])
def get_time(sec):
d = int(sec//(24*60*60))
h = int(sec//(60*60)%24)
m = int((sec//60)%60)
s = int(sec%60)
return d,h,m,s
def add_datetime(func):
def wrapper(*args,**kwargs):
datetime_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(grey("[{}] ".format(datetime_str),bold=True),end="")
return func(*args,**kwargs)
return wrapper
def add_functionname(func):
def wrapper(*args,**kwargs):
print(grey("[{}] ".format(func.__name__),bold=True))
return func(*args,**kwargs)
return wrapper
def pre_post_actions(pre=None,post=None):
def func_decorator(func):
def wrapper(*args,**kwargs):
if pre: pre()
retval = func(*args,**kwargs)
if post: post()
return retval
return wrapper
return func_decorator
debug = ipdb.set_trace
class Log:
def __init__(self): pass
def process(self,pid):
print(grey("Process ID: {}".format(pid),bold=True))
def title(self,message):
print(yellow(message,bold=True,underline=True))
def status(self,message):
print(green(message,bold=False,underline=False))
def warning(self,message):
print(red(message,bold=True,underline=False))
def info(self,message):
print(magenta(message,bold=True))
def options(self,opt,level=0):
for key,value in sorted(opt.items()):
if isinstance(value,(dict,edict)):
print(" "*level+cyan("* ")+green(key)+":")
self.options(value,level+1)
else:
print(" "*level+cyan("* ")+green(key)+":",yellow(value))
def loss_train(self,opt,ep,lr,loss,timer):
if not opt.max_epoch: return
message = grey("[train] ",bold=True)
message += "epoch {}/{}".format(cyan(ep,bold=True),opt.max_epoch)
message += ", lr:{}".format(yellow("{:.2e}".format(lr),bold=True))
message += ", loss:{}".format(red("{:.3e}".format(loss),bold=True))
message += ", time:{}".format(blue("{0}-{1:02d}:{2:02d}:{3:02d}".format(*get_time(timer.elapsed)),bold=True))
message += " (ETA:{})".format(blue("{0}-{1:02d}:{2:02d}:{3:02d}".format(*get_time(timer.arrival))))
print(message)
def loss_val(self,opt,loss):
message = grey("[val] ",bold=True)
message += "loss:{}".format(red("{:.3e}".format(loss),bold=True))
print(message)
log = Log()
def update_timer(opt,timer,ep,it_per_ep):
if not opt.max_epoch: return
momentum = 0.99
timer.elapsed = time.time()-timer.start
timer.it = timer.it_end-timer.it_start
# compute speed with moving average
timer.it_mean = timer.it_mean*momentum+timer.it*(1-momentum) if timer.it_mean is not None else timer.it
timer.arrival = timer.it_mean*it_per_ep*(opt.max_epoch-ep)
# move tensors to device in-place
def move_to_device(X,device):
if isinstance(X,dict):
for k,v in X.items():
X[k] = move_to_device(v,device)
elif isinstance(X,list):
for i,e in enumerate(X):
X[i] = move_to_device(e,device)
elif isinstance(X,tuple) and hasattr(X,"_fields"): # collections.namedtuple
dd = X._asdict()
dd = move_to_device(dd,device)
return type(X)(**dd)
elif isinstance(X,torch.Tensor):
return X.to(device=device)
return X
def to_dict(D,dict_type=dict):
D = dict_type(D)
for k,v in D.items():
if isinstance(v,dict):
D[k] = to_dict(v,dict_type)
return D
def get_child_state_dict(state_dict,key):
return { ".".join(k.split(".")[1:]): v for k,v in state_dict.items() if k.startswith("{}.".format(key)) }
def restore_checkpoint(opt,model,load_name=None,resume=False):
assert((load_name is None)==(resume is not False)) # resume can be True/False or epoch numbers
if resume:
load_name = "{0}/model.ckpt".format(opt.output_path) if resume is True else \
"{0}/model/{1}.ckpt".format(opt.output_path,resume)
checkpoint = torch.load(load_name,map_location=opt.device)
# load individual (possibly partial) children modules
for name,child in model.graph.named_children():
child_state_dict = get_child_state_dict(checkpoint["graph"],name)
if child_state_dict:
print("restoring {}...".format(name))
child.load_state_dict(child_state_dict)
for key in model.__dict__:
if key.split("_")[0] in ["optim","sched"] and key in checkpoint and resume:
print("restoring {}...".format(key))
getattr(model,key).load_state_dict(checkpoint[key])
if resume:
ep,it = checkpoint["epoch"],checkpoint["iter"]
if resume is not True: assert(resume==(ep or it))
print("resuming from epoch {0} (iteration {1})".format(ep,it))
else: ep,it = None,None
return ep,it
def save_checkpoint(opt,model,ep,it,latest=False,children=None):
os.makedirs("{0}/model".format(opt.output_path),exist_ok=True)
if children is not None:
graph_state_dict = { k: v for k,v in model.graph.state_dict().items() if k.startswith(children) }
else: graph_state_dict = model.graph.state_dict()
checkpoint = dict(
epoch=ep,
iter=it,
graph=graph_state_dict,
)
for key in model.__dict__:
if key.split("_")[0] in ["optim","sched"]:
checkpoint.update({ key: getattr(model,key).state_dict() })
torch.save(checkpoint,"{0}/model.ckpt".format(opt.output_path))
if not latest:
shutil.copy("{0}/model.ckpt".format(opt.output_path),
"{0}/model/{1}.ckpt".format(opt.output_path,ep or it)) # if ep is None, track it instead
def restore_checkpoint_kilo(opt,model,network_id, load_name=None,resume=False):
assert((load_name is None)==(resume is not False)) # resume can be True/False or epoch numbers
if resume:
load_name = "{0}/model_{1}.ckpt".format(opt.output_path, network_id) if resume is True else \
"{0}/model_{1}/{2}.ckpt".format(opt.output_path,network_id, resume)
checkpoint = torch.load(load_name,map_location=opt.device)
# load individual (possibly partial) children modules
for name,child in model.graph.named_children():
child_state_dict = get_child_state_dict(checkpoint["graph"],name)
if child_state_dict:
print("restoring {}...".format(name))
child.load_state_dict(child_state_dict)
for key in model.__dict__:
if key.split("_")[0] in ["optim","sched"] and key in checkpoint and resume:
print("restoring {}...".format(key))
getattr(model,key).load_state_dict(checkpoint[key])
if resume:
ep,it = checkpoint["epoch"],checkpoint["iter"]
if resume is not True: assert(resume==(ep or it))
print("resuming from epoch {0} (iteration {1})".format(ep,it))
else: ep,it = None,None
return ep,it
def restore_checkpoint_kilo_v2(opt,model,network_id, grid_network_id, load_name=None,resume=False):
assert((load_name is None)==(resume is not False)) # resume can be True/False or epoch numbers
if resume:
load_name = "{0}/model_{1}_{2}.ckpt".format(opt.output_path, network_id, grid_network_id) if resume is True else \
"{0}/model_{1}_{2}/{3}.ckpt".format(opt.output_path,network_id, grid_network_id, resume)
checkpoint = torch.load(load_name,map_location=opt.device)
# load individual (possibly partial) children modules
for name,child in model.graph.named_children():
child_state_dict = get_child_state_dict(checkpoint["graph"],name)
if child_state_dict:
print("restoring {}...".format(name))
child.load_state_dict(child_state_dict)
for key in model.__dict__:
if key.split("_")[0] in ["optim","sched"] and key in checkpoint and resume:
print("restoring {}...".format(key))
getattr(model,key).load_state_dict(checkpoint[key])
if resume:
ep,it = checkpoint["epoch"],checkpoint["iter"]
if resume is not True: assert(resume==(ep or it))
print("resuming from epoch {0} (iteration {1})".format(ep,it))
else: ep,it = None,None
return ep,it
def save_checkpoint_kilo(opt,model,network_ind, ep,it, latest=False,children=None):
os.makedirs("{0}/model_{1}".format(opt.output_path, network_ind),exist_ok=True)
if children is not None:
graph_state_dict = { k: v for k,v in model.graph.state_dict().items() if k.startswith(children) }
else: graph_state_dict = model.graph.state_dict()
checkpoint = dict(
epoch=ep,
iter=it,
graph=graph_state_dict,
)
for key in model.__dict__:
if key.split("_")[0] in ["optim","sched"]:
checkpoint.update({ key: getattr(model,key).state_dict() })
torch.save(checkpoint,"{0}/model_{1}.ckpt".format(opt.output_path, network_ind))
if not latest:
shutil.copy("{0}/model_{1}.ckpt".format(opt.output_path, network_ind),
"{0}/model_{1}/{2}.ckpt".format(opt.output_path,network_ind, ep or it)) # if ep is None, track it instead
def save_checkpoint_kilo_v2(opt,model,network_ind,grid_network_ind, ep,it, latest=False,children=None):
os.makedirs("{0}/model_{1}_{2}".format(opt.output_path, network_ind, grid_network_ind),exist_ok=True)
if children is not None:
graph_state_dict = { k: v for k,v in model.graph.state_dict().items() if k.startswith(children) }
else: graph_state_dict = model.graph.state_dict()
checkpoint = dict(
epoch=ep,
iter=it,
graph=graph_state_dict,
)
for key in model.__dict__:
if key.split("_")[0] in ["optim","sched"]:
checkpoint.update({ key: getattr(model,key).state_dict() })
torch.save(checkpoint,"{0}/model_{1}_{2}.ckpt".format(opt.output_path, network_ind, grid_network_ind))
if not latest:
shutil.copy("{0}/model_{1}_{2}.ckpt".format(opt.output_path, network_ind, grid_network_ind),
"{0}/model_{1}_{2}/{3}.ckpt".format(opt.output_path,network_ind, grid_network_ind, ep or it)) # if ep is None, track it instead
def check_socket_open(hostname,port):
s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
is_open = False
try:
s.bind((hostname,port))
except socket.error:
is_open = True
finally:
s.close()
return is_open
def get_layer_dims(layers):
# return a list of tuples (k_in,k_out)
return list(zip(layers[:-1],layers[1:]))
@contextlib.contextmanager
def suppress(stdout=False,stderr=False):
with open(os.devnull,"w") as devnull:
if stdout: old_stdout,sys.stdout = sys.stdout,devnull
if stderr: old_stderr,sys.stderr = sys.stderr,devnull
try: yield
finally:
if stdout: sys.stdout = old_stdout
if stderr: sys.stderr = old_stderr
def colorcode_to_number(code):
ords = [ord(c) for c in code[1:]]
ords = [n-48 if n<58 else n-87 for n in ords]
rgb = (ords[0]*16+ords[1],ords[2]*16+ords[3],ords[4]*16+ords[5])
return rgb