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trainval.py
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trainval.py
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from haven import haven_chk as hc
from haven import haven_results as hr
from haven import haven_wizard as hw
from haven import haven_utils as hu
import torch
import torchvision
import tqdm
import pandas as pd
import pprint
import itertools
import os
import pylab as plt
import exp_configs
import time
import numpy as np
from src import models
from src import datasets
from src import utils as ut
import argparse
from torch.backends import cudnn
from torch.utils.data import DataLoader
cudnn.benchmark = True
def trainval(exp_dict, savedir, args):
"""
exp_dict: dictionary defining the hyperparameters of the experiment
savedir: the directory where the experiment will be saved
args: arguments passed through the command line
"""
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=args.datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=args.datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# test set
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="test",
datadir=args.datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(val_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=args.num_workers)
test_loader = DataLoader(test_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=args.num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set, device=args.device)
# model.opt = optimizers.get_optim(exp_dict['opt'], model)
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
model.waiting = 0
model.val_score_best = -np.inf
train_sampler = torch.utils.data.RandomSampler(
train_set, replacement=True,
num_samples=2*len(test_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
collate_fn=ut.collate_fn,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=args.num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(savedir, "images"),
n_images=3)
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate the model
val_dict = model.val_on_loader(val_loader)
score_dict["val_score"] = val_dict["val_score"]
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = e
score_dict["waiting"] = model.waiting
model.waiting += 1
# Add to score_list and save checkpoint
score_list += [score_dict]
# Save Best Checkpoint
score_df = pd.DataFrame(score_list)
if score_dict["val_score"] >= model.val_score_best:
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(savedir, "images"),
n_images=3)
score_dict.update(test_dict)
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"), score_list)
# score_df.to_csv(os.path.join(savedir, "score_best_df.csv"))
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
model.waiting = 0
model.val_score_best = score_dict["val_score"]
print("Saved Best: %s" % savedir)
# Report & Save
score_df = pd.DataFrame(score_list)
# score_df.to_csv(os.path.join(savedir, "score_df.csv"))
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
if model.waiting > 100:
break
print('Experiment completed et epoch %d' % e)
if __name__ == '__main__':
# 9. Launch experiments using magic command
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--exp_group_list', nargs="+",
help='Define which exp groups to run.')
parser.add_argument('-sb', '--savedir_base', default=None,
help='Define the base directory where the experiments will be saved.')
parser.add_argument('-d', '--datadir')
parser.add_argument("-r", "--reset", default=0, type=int,
help='Reset or resume the experiment.')
parser.add_argument("--device", default='cuda')
parser.add_argument("-j", "--job_scheduler", default=None,
help='Run jobs in cluster.')
parser.add_argument("-p", "--python_binary_path", default='python')
parser.add_argument("--num_workers", type=int, default=0)
args, others = parser.parse_known_args()
# Load job config to run things on cluster
jc = None
if os.path.exists('job_config.py'):
import job_config
jc = job_config.JOB_CONFIG
hw.run_wizard(func=trainval, exp_groups=exp_configs.EXP_GROUPS,
savedir_base=args.savedir_base,
reset=args.reset,
python_binary_path=args.python_binary_path,
job_config=jc, args=args, use_threads=True,
results_fname='results.ipynb')