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misc.py
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import matplotlib
matplotlib.use('Agg')
import json
import torch
import misc as ms
import models
import datasets
import test
import os
def set_gpu(gpu_id):
if gpu_id is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = "%d" % gpu_id
def create_dirs(fname):
if "/" not in fname:
return
if not os.path.exists(os.path.dirname(fname)):
try:
os.makedirs(os.path.dirname(fname))
except OSError:
pass
def save_json(fname, data):
create_dirs(fname)
with open(fname, "w") as json_file:
json.dump(data, json_file, indent=4, sort_keys=True)
def load_json(fname):
with open(fname, "r") as json_file:
d = json.load(json_file)
return d
def copy_models(exp_dict, path_dst):
history = load_history(exp_dict)
# src_model, src_opt = ms.load_model_src(exp_dict)
# tgt_model, tgt_opt, disc, disc_opt = ms.load_model_tgt(exp_dict)
# create_dirs(path_dst + "/tmp")
# torch.save(src_model.state_dict(), path_dst+"/model_src.pth")
# torch.save(src_opt.state_dict(), path_dst+"/opt_src.pth")
# torch.save(tgt_model.state_dict(), path_dst+"/model_tgt.pth")
# torch.save(tgt_opt.state_dict(), path_dst+"/opt_tgt.pth")
# torch.save(disc.state_dict(), path_dst+"/disc.pth")
# torch.save(disc_opt.state_dict(), path_dst+"/disc_opt.pth")
ms.save_json(path_dst + "/history.json", history)
print("copied...")
def test_latest_model(exp_dict, verbose=1):
history = load_history(exp_dict)
src_trainloader, _ = ms.load_src_loaders(exp_dict)
_, tgt_valloader = ms.load_tgt_loaders(exp_dict)
src_model, src_opt = ms.load_model_src(exp_dict)
tgt_model, tgt_opt, _, _ = ms.load_model_tgt(exp_dict)
acc_tgt = test.validate(src_model, tgt_model, src_trainloader,
tgt_valloader)
if verbose:
print("====================="
"\nAcc of model at epoch {}: {}\n"
"=====================".format(history["tgt_train"][-1]["epoch"],
acc_tgt))
return acc_tgt
def load_src_loaders(exp_dict):
train_loader = datasets.get_loader(
exp_dict["src_dataset"],
"train",
batch_size=exp_dict["src_batch_size"])
val_loader = datasets.get_loader(
exp_dict["src_dataset"], "val", batch_size=exp_dict["src_batch_size"])
n_train = len(train_loader.dataset)
n_test = len(val_loader.dataset)
name = type(train_loader.dataset).__name__
print("Source ({}): train set: {} - val set: {}".format(
name, n_train, n_test))
return train_loader, val_loader
def load_tgt_loaders(exp_dict):
train_loader = datasets.get_loader(
exp_dict["tgt_dataset"],
"train",
batch_size=exp_dict["tgt_batch_size"])
val_loader = datasets.get_loader(
exp_dict["tgt_dataset"], "val", batch_size=exp_dict["tgt_batch_size"])
name = type(train_loader.dataset).__name__
n_train = len(train_loader.dataset)
n_test = len(val_loader.dataset)
print("Target ({}): train set: {} - val set: {}".format(
name, n_train, n_test))
return train_loader, val_loader
def load_history(exp_dict):
name_history = exp_dict["path"] + "/history.json"
if not os.path.exists(name_history) or (exp_dict["reset_src"]
and exp_dict["reset_tgt"]):
history = {"src_train": [{"epoch": 0}]}
history["tgt_train"] = [{"epoch": 0, "acc_tgt": -1}]
print("History from scratch...")
else:
history = ms.load_json(name_history)
print("Loaded history {}".format(name_history))
if exp_dict["reset_tgt"]:
history["tgt_train"] = [{"epoch": 0, "acc_tgt": -1}]
print("Resetting target training...")
return history
def save_model_src(exp_dict, history, model_src, opt_src):
save_json(exp_dict["path"] + "/history.json", history)
torch.save(model_src.state_dict(), exp_dict["path"] + "/model_src.pth")
torch.save(opt_src.state_dict(), exp_dict["path"] + "/opt_src.pth")
print("Saved Source...")
def save_model_tgt(exp_dict, history, model_tgt, opt_tgt, disc, disc_opt):
save_json(exp_dict["path"] + "/history.json", history)
torch.save(model_tgt.state_dict(), exp_dict["path"] + "/model_tgt.pth")
torch.save(opt_tgt.state_dict(), exp_dict["path"] + "/opt_tgt.pth")
torch.save(disc.state_dict(), exp_dict["path"] + "/disc.pth")
torch.save(disc_opt.state_dict(), exp_dict["path"] + "/disc_opt.pth")
print("Saved Target...")
def load_model_src(exp_dict):
src_model, src_opt = models.get_model(exp_dict["src_model"],
exp_dict["n_outputs"])
name_model = exp_dict["path"] + "/model_src.pth"
name_opt = exp_dict["path"] + "/opt_src.pth"
if os.path.exists(name_model) and not exp_dict["reset_src"]:
src_model.load_state_dict(torch.load(name_model))
src_opt.load_state_dict(torch.load(name_opt))
print("Loading saved {}".format(name_model))
else:
print("Loading source models from scratch..")
return src_model, src_opt
def load_model_tgt(exp_dict):
tgt_model, tgt_opt = models.get_model(exp_dict["tgt_model"],
exp_dict["n_outputs"])
disc, disc_opt = models.get_model("disc", exp_dict["n_outputs"])
name_model = exp_dict["path"] + "/model_tgt.pth"
name_opt = exp_dict["path"] + "/opt_tgt.pth"
name_disc = exp_dict["path"] + "/disc.pth"
name_disc_opt = exp_dict["path"] + "/disc_opt.pth"
if os.path.exists(name_model) and not exp_dict["reset_tgt"]:
tgt_model.load_state_dict(torch.load(name_model))
tgt_opt.load_state_dict(torch.load(name_opt))
disc.load_state_dict(torch.load(name_disc))
disc_opt.load_state_dict(torch.load(name_disc_opt))
print("Loading saved {}".format(name_model))
else:
print("Loading target models from scratch..")
return tgt_model, tgt_opt, disc, disc_opt