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train.py
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train.py
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"""
File: train.py
Author: Nrupatunga
Email: nrupatunga.s@byjus.com
Github: https://github.com/nrupatunga
Description: Training scripts for goturn
"""
import argparse
import random
import sys
from collections import OrderedDict
from multiprocessing import Manager
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.backends.cudnn as cudnn
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.core.lightning import LightningModule
from torch.utils.data import DataLoader
from torch_lr_finder import LRFinder
from loguru import logger
try:
from goturn.dataloaders.goturndataloader import GoturnDataloader
from goturn.helper.vis_utils import Visualizer
from goturn.network.network import GoturnNetwork
from goturn.helper.BoundingBox import BoundingBox
from goturn.helper.draw_util import draw
from goturn.optimizer.caffeSGD import CaffeSGD
except ImportError:
logger.error('Please run $source settings.sh from root directory')
sys.exit(1)
class GoturnTrain(LightningModule):
"""Docstring for GoturnTrain. """
def __init__(self, hparams, dbg=False):
'''
Pytorch lightning module for training goturn tracker.
@hparams: all the argparse arguments for training
@dbg: boolean for switching on visualizer
'''
logger.info('=' * 15)
logger.info('GOTURN TRACKER')
logger.info('=' * 15)
super(GoturnTrain, self).__init__()
self.__set_seed(hparams.seed)
self.hparams = hparams
logger.info('Setting up the network...')
# network with pretrained model
self._model = GoturnNetwork(self.hparams.pretrained_model)
self._dbg = dbg
if dbg:
self._viz = Visualizer(port=8097)
def __freeze(self):
"""Freeze the model features layer
"""
features_layer = self._model._net
for param in features_layer.parameters():
param.requires_grad = False
def _set_conv_layer(self, conv_layers, param_dict):
for layer in conv_layers.modules():
if type(layer) == torch.nn.modules.conv.Conv2d:
param_dict.append({'params': layer.weight,
'lr': 0,
'weight_decay': self.hparams.wd})
param_dict.append({'params': layer.bias,
'lr': 0,
'weight_decay': 0})
return param_dict
def __set_lr(self):
'''set learning rate for classifier layer'''
param_dict = []
if 1:
conv_layers = self._model._net_1
param_dict = self._set_conv_layer(conv_layers, param_dict)
conv_layers = self._model._net_2
param_dict = self._set_conv_layer(conv_layers, param_dict)
regression_layer = self._model._classifier
for layer in regression_layer.modules():
if type(layer) == torch.nn.modules.linear.Linear:
param_dict.append({'params': layer.weight,
'lr': 10 * self.hparams.lr,
'weight_decay': self.hparams.wd})
param_dict.append({'params': layer.bias,
'lr': 20 * self.hparams.lr,
'weight_decay': 0})
return param_dict
def find_lr(self):
"""finding suitable learning rate """
model = self._model
params = self.__set_lr()
criterion = torch.nn.L1Loss(size_average=False)
optimizer = CaffeSGD(params,
lr=1e-8,
momentum=self.hparams.momentum,
weight_decay=self.hparams.wd)
lr_finder = LRFinder(model, optimizer, criterion, device="cuda")
trainloader = self.train_dataloader()
lr_finder.range_test(trainloader, start_lr=1e-7, end_lr=1,
num_iter=500)
lr_finder.plot()
def __set_seed(self, SEED):
''' set all the seeds for reproducibility '''
logger.info('Settings seed = {}'.format(SEED))
torch.manual_seed(SEED)
np.random.seed(SEED)
random.seed(SEED)
cudnn.deterministic = True
@staticmethod
def add_model_specific_args(parent_parser):
''' These are specific parameters for the sample generator '''
ap = argparse.ArgumentParser(parents=[parent_parser])
ap.add_argument('--min_scale', type=float,
default=-0.4,
help='min scale')
ap.add_argument('--max_scale', type=float,
default=0.4,
help='max scale')
ap.add_argument('--lamda_shift', type=float, default=5)
ap.add_argument('--lamda_scale', type=int, default=15)
return ap
def configure_optimizers(self):
"""Configure optimizers"""
logger.info('Configuring optimizer: SGD with lr = {}, momentum = {}'.format(self.hparams.lr, self.hparams.momentum))
params = self.__set_lr()
optimizer = CaffeSGD(params,
lr=self.hparams.lr,
momentum=self.hparams.momentum,
weight_decay=self.hparams.wd)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=self.hparams.lr_step,
gamma=self.hparams.gamma)
return [optimizer], [scheduler]
@pl.data_loader
def train_dataloader(self):
"""train dataloader"""
logger.info('===' * 20)
logger.info('Loading dataset for training, please wait...')
logger.info('===' * 20)
imagenet_path = self.hparams.imagenet_path
alov_path = self.hparams.alov_path
mean_file = None
manager = Manager()
objGoturn = GoturnDataloader(imagenet_path, alov_path,
mean_file=mean_file,
images_p=manager.list(),
targets_p=manager.list(),
bboxes_p=manager.list(),
val_ratio=0.005,
isTrain=True, dbg=False)
train_loader = DataLoader(objGoturn,
batch_size=self.hparams.batch_size, shuffle=True,
num_workers=6,
collate_fn=objGoturn.collate)
return train_loader
@pl.data_loader
def val_dataloader(self):
"""validation dataloader"""
logger.info('===' * 20)
logger.info('Loading dataset for Validation, please wait...')
logger.info('===' * 20)
imagenet_path = self.hparams.imagenet_path
alov_path = self.hparams.alov_path
mean_file = None
manager = Manager()
objGoturn = GoturnDataloader(imagenet_path, alov_path,
mean_file=mean_file,
images_p=manager.list(),
targets_p=manager.list(),
bboxes_p=manager.list(),
val_ratio=0.005,
isTrain=False, dbg=False)
val_loader = DataLoader(objGoturn,
batch_size=self.hparams.batch_size, shuffle=True,
num_workers=6,
collate_fn=objGoturn.collate)
return val_loader
def forward(self, prev, curr):
"""forward function
"""
pred_bb = self._model(prev.float(), curr.float())
return pred_bb
def vis_images(self, prev, curr, gt_bb, pred_bb, prefix='train'):
def unnormalize(image, mean):
image = np.transpose(image, (1, 2, 0)) + mean
image = image.astype(np.float32)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
for i in range(0, prev.shape[0]):
# _mean = np.load(self.hparams.mean_file)
_mean = np.array([104, 117, 123])
prev_img = prev[i].cpu().detach().numpy()
curr_img = curr[i].cpu().detach().numpy()
prev_img = unnormalize(prev_img, _mean)
curr_img = unnormalize(curr_img, _mean)
gt_bb_i = BoundingBox(*gt_bb[i].cpu().detach().numpy().tolist())
gt_bb_i.unscale(curr_img)
curr_img = draw.bbox(curr_img, gt_bb_i, color=(255, 0, 255))
pred_bb_i = BoundingBox(*pred_bb[i].cpu().detach().numpy().tolist())
pred_bb_i.unscale(curr_img)
curr_img = draw.bbox(curr_img, pred_bb_i)
out = np.concatenate((prev_img[np.newaxis, ...], curr_img[np.newaxis, ...]), axis=0)
out = np.transpose(out, [0, 3, 1, 2])
self._viz.plot_images_np(out, title='sample_{}'.format(i),
env='goturn_{}'.format(prefix))
def training_step(self, batch, batch_idx):
"""Training step
@batch: current batch data
@batch_idx: current batch index
"""
curr, prev, gt_bb = batch
pred_bb = self.forward(prev, curr)
loss = torch.nn.L1Loss(size_average=False)(pred_bb.float(), gt_bb.float())
if self.trainer.use_dp:
loss = loss.unsqueeze(0)
if self._dbg:
if batch_idx % 1000 == 0:
d = {'loss': loss.item()}
iters = (self.trainer.num_training_batches - 1) * self.current_epoch + batch_idx
self._viz.plot_curves(d, iters, title='Train', ylabel='train_loss')
if batch_idx % 1000 == 0:
self.vis_images(prev, curr, gt_bb, pred_bb)
tqdm_dict = {'batch_loss': loss}
output = OrderedDict({'loss': loss,
'progress_bar': tqdm_dict,
'log': tqdm_dict})
return output
def validation_step(self, batch, batch_idx):
"""validation step
@batch: current batch data
@batch_idx: current batch index
"""
curr, prev, gt_bb = batch
pred_bb = self.forward(prev, curr)
loss = torch.nn.L1Loss(size_average=False)(pred_bb, gt_bb.float())
if self.trainer.use_dp:
loss = loss.unsqueeze(0)
if self._dbg:
if batch_idx % 100 == 0:
d = {'loss': loss.item()}
iters = (self.trainer.num_val_batches - 1) * self.current_epoch + batch_idx
self._viz.plot_curves(d, iters, title='Validation', ylabel='val_loss')
if batch_idx % 1000 == 0:
self.vis_images(prev, curr, gt_bb, pred_bb, prefix='val')
tqdm_dict = {'val_loss': loss}
output = OrderedDict({'val_loss': loss,
'progress_bar': tqdm_dict,
'log': tqdm_dict})
return output
def validation_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
return {'val_loss': avg_loss}
def get_args():
""" These are common arguments such as
1. Path to dataset (imagenet and alov)
2. Architecture, learning rate, batch size
3. Optimizers: learning rate, momentum, weight decay, learning step,
gamma
4. Seed for reproducibility
5. save path for the model
"""
ap = argparse.ArgumentParser(add_help=False,
description='Arguments for training Goturn Tracker')
ap.add_argument('--gpus', type=int, default=1,
help='number of gpus, 0: means no gpu, -1 to use all \
gpus, 1 = use one gpu, 2 = use two gpus')
# Data settings
ap.add_argument('--imagenet_path', type=str,
required=True, help='path to imagenet folder, this \
folder shoud have images and gt folder')
ap.add_argument('--alov_path', type=str,
required=True, help='path to ALOV folder, this \
folder should have images and gt folder')
# architecture and hyperparameters
ap.add_argument('--arch', default='alexnet',
choices={'alexnet'}, help='model architecture, \
default: alexnet, currently only alexnet is \
supported')
ap.add_argument('--pretrained_model',
default='../goturn/models/pretrained/alexnet.pth.tar',
help='Path to pretrained model')
ap.add_argument('--epochs', default=90,
type=int, help='number of total epochs to run')
ap.add_argument('--batch_size', default=3,
type=int, help='number of images per batch')
# Optimizer settings
ap.add_argument('--lr', default=1e-6, type=float,
help='initial learning rate', dest='lr')
ap.add_argument('--momentum', default=0.9, type=float, help='momentum')
ap.add_argument('--wd', default=5e-4, type=float, help='weight decay (default: 5e-4)',
dest='wd')
ap.add_argument('--lr_step', default=1, type=int,
help='Number of epoch after which we change the learning rate',
dest='lr_step')
ap.add_argument('--gamma', default=0.1, type=float,
help='multiplicative factor for learning rate',
dest='gamma')
# reproducibility
ap.add_argument('--seed', type=int, default=42, help='seed value')
ap.add_argument('--seed', type=int, default=800, help='seed value')
# save path
ap.add_argument('--save_path', default=".", type=str, help='path to save output')
# goturn specific arguments
ap = GoturnTrain.add_model_specific_args(ap)
return ap.parse_args()
def read_images_dbg(idx):
idx = idx + 1
_mean = np.array([104, 117, 123])
images = []
target = []
bbox = []
parent_path = '/media/nthere/datasets/goturn_samples/0{}'.format(idx)
gt_path = '{}/gt.txt'.format(parent_path)
with open(gt_path) as f:
for i, line in enumerate(f):
prev_path = '{}/Image{}_curr.png'.format(parent_path, i)
curr_path = '{}/Image{}_target.png'.format(parent_path, i)
prev = cv2.imread(prev_path) - _mean
prev = np.transpose(prev, axes=(2, 0, 1))
curr = cv2.imread(curr_path) - _mean
curr = np.transpose(curr, axes=(2, 0, 1))
gt = line.strip().split(',')[0:4]
gt = [float(p) for p in gt]
images.append(prev)
target.append(curr)
bbox.append(gt)
images = torch.from_numpy(np.stack(images)).to('cuda:0')
targets = torch.from_numpy(np.stack(target)).to('cuda:0')
bboxes = torch.from_numpy(np.stack(bbox)).to('cuda:0')
return images, targets, bboxes
def main(hparams):
hparams = get_args()
model = GoturnTrain(hparams, dbg=True)
# ckpt_resume_path = './caffenet-dbg-2/_ckpt_epoch_1.ckpt'
ckpt_cb = ModelCheckpoint(filepath=hparams.save_path, save_top_k=-1,
save_weights_only=False)
trainer = Trainer(default_save_path=hparams.save_path,
gpus=[0, ], min_nb_epochs=hparams.epochs,
accumulate_grad_batches=1,
train_percent_check=1,
# resume_from_checkpoint=ckpt_resume_path,
checkpoint_callback=ckpt_cb,
val_percent_check=1, profiler=True)
trainer.fit(model)
if __name__ == "__main__":
main(get_args())