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train.py
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train.py
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horse_changed = False # True # False
import os
import shutil
import argparse
import numpy as np
from tqdm import tqdm
import mxnet as mx
from mxnet import gluon, autograd
from mxnet.gluon.data.vision import transforms
import gluoncv
from gluoncv.loss import *
from gluoncv.utils import LRScheduler
from gluoncv.model_zoo.segbase import *
from gluoncv.model_zoo import get_model
from gluoncv.utils.parallel import *
from gluoncv.data import get_segmentation_dataset
import sys
sys.setrecursionlimit(10000)
print('>>> set up maximum recursion depth')
print_shape = True
import pickle
def parse_args():
"""Training Options for Segmentation Experiments"""
parser = argparse.ArgumentParser(description='MXNet Gluon \
Segmentation')
# model and dataset
parser.add_argument('--outdir', type=str, default='outdir',
help='outdir (default: outdir)')
parser.add_argument('--scoredir', type=str, default='scoredir',
help='scoredir (default: scoredir)')
parser.add_argument('--save_name', type=str, default='checkpoint',
help='name for saving parameters (default: checkpoint)')
parser.add_argument('--model', type=str, default='fcn',
help='model name (default: fcn)')
parser.add_argument('--backbone', type=str, default='resnet50',
help='backbone name (default: resnet50)')
parser.add_argument('--dataset', type=str, default='pascal_voc',
help='dataset name (default: pascal)')
parser.add_argument('--workers', type=int, default=16,
metavar='N', help='dataloader threads')
parser.add_argument('--base-size', type=int, default=520,
help='base image size')
parser.add_argument('--crop-size', type=int, default=480,
help='crop image size')
parser.add_argument('--train-split', type=str, default='train',
help='dataset train split (default: train)')
# training hyper params
parser.add_argument('--aux', action='store_true', default= False,
help='Auxiliary loss')
parser.add_argument('--aux-weight', type=float, default=0.5, # default=0.5
help='auxiliary loss weight')
parser.add_argument('--epochs', type=int, default=30, metavar='N', # 50
help='number of epochs to train (default: 50)')
parser.add_argument('--start_epoch', type=int, default=0,
metavar='N', help='start epochs (default:0)')
parser.add_argument('--batch-size', type=int, default=16,
metavar='N', help='input batch size for \
training (default: 16)')
parser.add_argument('--test-batch-size', type=int, default=16,
metavar='N', help='input batch size for \
testing (default: 32)')
parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',
help='learning rate (default: 1e-3)')
parser.add_argument('--momentum', type=float, default=0.9, # default=0.9
metavar='M', help='momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=1e-4, # default=1e-4
metavar='M', help='w-decay (default: 1e-4)')
parser.add_argument('--no-wd', action='store_true',
help='whether to remove weight decay on bias, \
and beta/gamma for batchnorm layers.')
# cuda and logging
parser.add_argument('--no-cuda', action='store_true', default=
False, help='disables CUDA training')
parser.add_argument('--ngpus', type=int,
default=len(mx.test_utils.list_gpus()),
help='number of GPUs (default: 4)')
parser.add_argument('--kvstore', type=str, default='device',
help='kvstore to use for trainer/module.')
parser.add_argument('--dtype', type=str, default='float32',
help='data type for training. default is float32')
# checking point
parser.add_argument('--resume', type=str, default=None,
help='put the path to resuming file if needed')
parser.add_argument('--checkname', type=str, default='default',
help='set the checkpoint name')
parser.add_argument('--model-zoo', type=str, default=None,
help='evaluating on model zoo model')
# evaluation only
parser.add_argument('--eval', action='store_true', default= False,
help='evaluation only')
parser.add_argument('--no-val', action='store_true', default= False,
help='skip validation during training')
# synchronized Batch Normalization
parser.add_argument('--syncbn', action='store_true', default= False,
help='using Synchronized Cross-GPU BatchNorm')
# the parser
args = parser.parse_args()
# handle contexts
if args.no_cuda:
print('Using CPU')
args.kvstore = 'local'
args.ctx = [mx.cpu(0)]
else:
print('Number of GPUs:', args.ngpus)
args.ctx = [mx.gpu(i) for i in range(args.ngpus)]
# Synchronized BatchNorm
args.norm_layer = mx.gluon.contrib.nn.SyncBatchNorm if args.syncbn \
else mx.gluon.nn.BatchNorm
args.norm_kwargs = {'num_devices': args.ngpus} if args.syncbn else {}
print(args)
return args
class Trainer(object):
def __init__(self, args):
self.args = args
self.two_model = False ##
self.semi = False
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
# transforms.Normalize([0, 0, 0], [1, 1, 1]), # ([0, 0, 0], [1, 1, 1])
# transforms.Normalize([0], [1]), # this is for 1 channel: ([0], [1]) ([556.703], [482.175])
])
# dataset and dataloader
data_kwargs = {'transform': input_transform, 'base_size': args.base_size,
'crop_size': args.crop_size}
trainset = get_segmentation_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
valset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
self.train_data = gluon.data.DataLoader(trainset,
args.batch_size,
shuffle=True,
last_batch='rollover',
num_workers=args.workers)
self.eval_data = gluon.data.DataLoader(valset,
args.batch_size, # args.test_batch_size, [horse changed this]
last_batch='rollover',
num_workers=args.workers)
# create network
if args.model_zoo is not None:
print('get model from the zoo.')
model = get_model(args.model_zoo, pretrained=True)
if self.two_model:
self.model2 = get_model(args.model_zoo, pretrained=True) ## 2nd identical model
else:
print('create model.')
model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
norm_layer=args.norm_layer,
norm_kwargs=args.norm_kwargs,
aux=args.aux,
crop_size=args.crop_size,
pretrained=False)
if self.two_model:
self.model2 = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
norm_layer=args.norm_layer,
norm_kwargs=args.norm_kwargs,
aux=args.aux,
crop_size=args.crop_size,
pretrained=False)
model.cast(args.dtype)
if self.two_model:
self.model2.cast(args.dtype)
# print(model) # don't print model
# print(help(model.collect_params))
# >>> Notice here <<<
# model.initialize() # horse ref: https://discuss.mxnet.io/t/object-detection-transfer-learning/2477/2
''' '''
self.net = DataParallelModel(model, args.ctx, args.syncbn)
self.evaluator = DataParallelModel(SegEvalModel(model), args.ctx)
if self.two_model:
self.evaluator2 = DataParallelModel(SegEvalModel(self.model2), args.ctx)
# resume checkpoint if needed
if args.resume is not None:
if os.path.isfile(args.resume):
if not horse_changed:
model.load_parameters(args.resume, ctx=args.ctx)
if horse_changed:
model.load_parameters(args.resume, ctx=args.ctx, allow_missing=True, ignore_extra=True)
else:
raise RuntimeError("=> no checkpoint found at '{}'" \
.format(args.resume))
'''
self.net = DataParallelModel(model, args.ctx, args.syncbn)
self.evaluator = DataParallelModel(SegEvalModel(model), args.ctx)
'''
# create criterion
criterion = MixSoftmaxCrossEntropyLoss(args.aux, aux_weight=args.aux_weight)
self.criterion = DataParallelCriterion(criterion, args.ctx, args.syncbn)
# optimizer and lr scheduling
self.lr_scheduler = LRScheduler(mode='poly',
baselr=args.lr,
niters=len(self.train_data),
nepochs=args.epochs)
kv = mx.kv.create(args.kvstore)
optimizer_params = {'lr_scheduler': self.lr_scheduler,
'wd':args.weight_decay,
'momentum': args.momentum}
if args.dtype == 'float16':
optimizer_params['multi_precision'] = True
if args.no_wd:
for k, v in self.net.module.collect_params('.*beta|.*gamma|.*bias').items():
v.wd_mult = 0.0
self.optimizer = gluon.Trainer(self.net.module.collect_params(),
'sgd',
optimizer_params,
kvstore = kv)
# evaluation metrics
self.metric = gluoncv.utils.metrics.SegmentationMetric(trainset.num_class)
def training(self, epoch):
if self.two_model:
if self.two_model:
self.model2.load_parameters('runs/pascal_voc/deeplab/HVSMR/res50_backup.params', ctx=args.ctx) # args.resume
self.model2.cast(args.dtype)
self.evaluator2 = DataParallelModel(SegEvalModel(self.model2), args.ctx)
if horse_changed:
print('>>> start training.') # [horse]
tbar = tqdm(self.train_data)
train_loss = 0.0
alpha = 0.2
for i, (data, target) in enumerate(tbar):
self.lr_scheduler.update(i, epoch)
with autograd.record(True):
# >>>>>>>>>>>>>>>>>>>>
global print_shape
if print_shape:
print('>>> data of one batch:')
print(data.shape, target.shape) # horse
'''
with open('have_a_look.pkl', 'wb') as fo:
pickle.dump(data.asnumpy(), fo)
pickle.dump(target.asnumpy(), fo)
'''
for ii in range(data.shape[1]):
one_sample = data[0,ii,:,:].asnumpy()
s_mean = np.mean(one_sample.flatten())
s_std = np.std(one_sample.flatten())
s_min = min(one_sample.flatten())
s_max = max(one_sample.flatten())
print('dim | mean | std | min | max', ii, s_mean, s_std, s_min, s_max)
print_shape = False
# >>>>>>>>>>>>>>>>>>>>
outputs = self.net(data.astype(args.dtype, copy=False))
# print('outputs:', len(outputs[0]), outputs[0][0].shape) # [horse]
# print('target:', target.shape)
# outputs: 2 (14, 3, 250, 250)
# target: (14, 250, 250)
# +++++ +++++ +++++
_outputs = outputs
_target = mx.ndarray.reshape(target, shape=(-3,-2)) # to be (batch_size*NUM_SEQ, 250, 250)
# +++++ +++++ +++++
# losses = self.criterion(outputs, target)
losses = self.criterion(_outputs, _target)
mx.nd.waitall()
autograd.backward(losses)
self.optimizer.step(self.args.batch_size)
for loss in losses:
train_loss += loss.asnumpy()[0] / len(losses)
tbar.set_description('Epoch %d, training loss %.3f'%\
(epoch, train_loss/(i+1)))
mx.nd.waitall()
# save every epoch
save_checkpoint(self.net.module, self.args, False)
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
if not horse_changed:
tbar = tqdm(self.train_data)
train_loss = 0.0
alpha = 0.2
for i, (data, target) in enumerate(tbar):
self.lr_scheduler.update(i, epoch)
with autograd.record(True):
outputs = self.net(data.astype(args.dtype, copy=False))
# print('target:', target.shape) # target: (4, 480, 480)
## print('target sum before:', [i.sum() for i in target.asnumpy()]) # target sum: [389344.0, 0.0, 0.0, 188606.0]
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
if self.semi:
pos = np.where(np.array([i.sum() for i in target.asnumpy()])==0)[0]
## print('pos',pos)
if len(pos) != 0:
data2 = data[pos,:,:,:]
_outputs = self.evaluator2(data2.astype(args.dtype, copy=False))
_outputs = [x[0] for x in _outputs]
label_generated = np.zeros((len(pos),target.shape[1],target.shape[2]))
for k in range(len(pos)):
## print(_outputs[0].shape)
label_slice = labeler_random(_outputs[0].asnumpy()[k,0:3,:,:],
crop_size=target.shape[1],
prob_cut=0.46)
label_generated[k,:,:] = label_slice
target[pos,:,:] = mx.nd.array(label_generated)
## print('target sum after:', [i.sum() for i in target.asnumpy()])
'''
if True:
# print('targets and outputs shape:', len(outputs), outputs[0].shape) # outputs: 1 (18, 3, 250, 250); targets: 1 (18, 250, 250)
for sample in range(2):
mx2img(data[sample,:,:,:], str(sample)+'.jpg')
mx2img(target[sample,:,:], str(sample)+'.png')
'''
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
losses = self.criterion(outputs, target)
mx.nd.waitall()
autograd.backward(losses)
self.optimizer.step(self.args.batch_size)
for loss in losses:
train_loss += loss.asnumpy()[0] / len(losses)
tbar.set_description('Epoch %d, training loss %.3f'%\
(epoch, train_loss/(i+1)))
mx.nd.waitall()
# save every epoch
save_checkpoint(self.net.module, self.args, False)
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
''' <- this is backup
if not horse_changed:
tbar = tqdm(self.train_data)
train_loss = 0.0
alpha = 0.2
for i, (data, target) in enumerate(tbar):
self.lr_scheduler.update(i, epoch)
with autograd.record(True):
outputs = self.net(data.astype(args.dtype, copy=False))
losses = self.criterion(outputs, target)
mx.nd.waitall()
autograd.backward(losses)
self.optimizer.step(self.args.batch_size)
for loss in losses:
train_loss += loss.asnumpy()[0] / len(losses)
tbar.set_description('Epoch %d, training loss %.3f'%\
(epoch, train_loss/(i+1)))
mx.nd.waitall()
# save every epoch
save_checkpoint(self.net.module, self.args, False)
# ++++++++++ ++++++++++ ++++++++++ ++++++++++ ++++++++++
'''
def validation(self, epoch):
if not horse_changed:
output_to_see = False # False # [horse added]
output_score_map = False # [horse added]
#total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
self.metric.reset()
tbar = tqdm(self.eval_data)
output_index = 0 # [horse added]
for i, (data, target) in enumerate(tbar):
# print('target', target)
outputs = self.evaluator(data.astype(args.dtype, copy=False))
outputs = [x[0] for x in outputs]
# print(outputs)
'''
if i == 50:
with open('have_a_look.pkl', 'wb') as fo:
pickle.dump(outputs[0].asnumpy(),fo)
'''
targets = mx.gluon.utils.split_and_load(target, args.ctx, even_split=False)
# ++++++++++ ++++++++++ ++++++++++
if output_to_see:
# print('targets and outputs shape:', len(outputs), outputs[0].shape) # outputs: 1 (18, 3, 250, 250); targets: 1 (18, 250, 250)
output_prefix = 'outdir_tosee'
if not os.path.exists(output_prefix):
os.makedirs(output_prefix)
batch_size = self.args.batch_size
crop_size = self.args.crop_size
for sample in range(batch_size):
path = os.path.join(output_prefix, str(output_index)+'.png')
mx2img(outputs[0][sample, :,:,:], path)
output_index += 1
# ++++++++++ ++++++++++ ++++++++++
if output_score_map:
score_map_dir = 'scoredir_tosee' # args.scoredir
if not os.path.exists(score_map_dir):
os.makedirs(score_map_dir)
batch_size = self.args.batch_size
for sample in range(batch_size):
# score_map_name = os.path.splitext(impath)[0] + '.pkl'
# score_map_path = os.path.join(score_map_dir, score_map_name)
score_map_path = os.path.join(score_map_dir, str(output_index)+'.pkl')
with open(score_map_path, 'wb') as fo:
pickle.dump(outputs[0].asnumpy()[sample,0:3,:,:], fo)
output_index += 1
self.metric.update(targets, outputs)
'''
pixAcc, mIoU = self.metric.get()
tbar.set_description('Epoch %d, validation pixAcc: %.3f, mIoU: %.3f'%\
(epoch, pixAcc, mIoU))
'''
pixAcc, mIoU, dice = self.metric.get() # [horse changed]
tbar.set_description('Epoch %d, validation pixAcc: %.3f, mIoU: %.3f, dice: %.3f, %.3f, %.3f'%\
(epoch, pixAcc, mIoU, dice[0], dice[1], dice[2]))
mx.nd.waitall()
if horse_changed:
output_to_see = True # False
#total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
self.metric.reset()
tbar = tqdm(self.eval_data)
output_index = 0
for i, (data, target) in enumerate(tbar):
# print('target', target)
outputs = self.evaluator(data.astype(args.dtype, copy=False))
outputs = [x[0] for x in outputs]
_target = mx.ndarray.reshape(target, shape=(-3,-2))
targets = mx.gluon.utils.split_and_load(_target, args.ctx, even_split=False)
# ++++++++++ ++++++++++ ++++++++++
if output_to_see:
# print('targets and outputs shape:', len(outputs), outputs[0].shape) # outputs: 1 (18, 3, 250, 250); targets: 1 (18, 250, 250)
output_prefix = 'outdir_seq'
batch_size = self.args.batch_size
crop_size = self.args.crop_size
NUM_SEQ = int(outputs[0].shape[0] / batch_size)
# print(batch_size, NUM_SEQ, crop_size)
outputs_out = mx.ndarray.reshape(outputs[0], shape=(batch_size, NUM_SEQ, 3, crop_size, crop_size)) # 3 is the class number not image channel, just for convenience
targets_out = mx.ndarray.reshape(targets[0], shape=(batch_size, NUM_SEQ, crop_size, crop_size))
for sample in range(batch_size):
for seq in range(NUM_SEQ):
path = os.path.join(output_prefix, str(output_index)+'_'+str(seq)+'.png')
path_mask = os.path.join(output_prefix, str(output_index)+'_gt_'+str(seq)+'.png')
mx2img(outputs_out[sample, seq, :,:,:], path)
mx2img(targets_out[sample, seq, :,:], path_mask)
output_index += 1
# ++++++++++ ++++++++++ ++++++++++
self.metric.update(targets, outputs)
'''
pixAcc, mIoU = self.metric.get()
tbar.set_description('Epoch %d, validation pixAcc: %.3f, mIoU: %.3f'%\
(epoch, pixAcc, mIoU))
'''
pixAcc, mIoU, dice = self.metric.get() # [horse changed]
tbar.set_description('Epoch %d, validation pixAcc: %.3f, mIoU: %.3f, dice: %.3f, %.3f, %.3f'%\
(epoch, pixAcc, mIoU, dice[0], dice[1], dice[2]))
mx.nd.waitall()
# break
def save_checkpoint(net, args, is_best=False): # [horse added params name]
"""Save Checkpoint"""
directory = "runs/%s/%s/%s/" % (args.dataset, args.model, args.checkname)
if not os.path.exists(directory):
os.makedirs(directory)
# filename='checkpoint.params'
filename = args.save_name+'.params'
filename = directory + filename
net.save_parameters(filename)
if is_best:
shutil.copyfile(filename, directory + 'model_best.params')
def save_checkpoint_old(net, args, is_best=False):
"""Save Checkpoint"""
directory = "runs/%s/%s/%s/" % (args.dataset, args.model, args.checkname)
if not os.path.exists(directory):
os.makedirs(directory)
filename='checkpoint.params'
filename = directory + filename
net.save_parameters(filename)
if is_best:
shutil.copyfile(filename, directory + 'model_best.params')
from PIL import Image
def mx2img(a, path):
r"""
save mxnet array to image
"""
# print(img.shape)
if len(a.shape) == 3:
img = a.asnumpy()[0:3,:,:] # convert to numpy array
img = img.transpose((1, 2, 0)) # Move channel to the last dimension
result = Image.fromarray((img * 255).astype(np.uint8)) # 10 here and 100 below are for illustration
else:
img = a.asnumpy()
result = Image.fromarray((img * 100).astype(np.uint8))
result.save(path)
def labeler_random(pred,crop_size=480,prob_cut=0.5):
"""
pred: prediction matrix (3, 480, 480)
input: numpy; output: numpy.
"""
pred[pred<0] = 0
pred /= pred[0,:,:]+pred[1,:,:]+pred[2,:,:]
# generate random mask
random_mask = np.random.rand(crop_size,crop_size)
# return this
label = np.zeros((crop_size,crop_size))
# random labeler
label[random_mask<pred[0,:,:]] = 0
label[random_mask>(pred[0,:,:]+pred[1,:,:])] = 2
label[np.logical_and(random_mask>pred[0,:,:],random_mask<(pred[0,:,:]+pred[1,:,:]))] = 1
# high prob
# prob_cut = 0.5
label[pred[0,:,:]>prob_cut] = 0
label[pred[1,:,:]>prob_cut] = 1
label[pred[2,:,:]>prob_cut] = 2
return label
if __name__ == "__main__":
args = parse_args()
trainer = Trainer(args)
if args.eval:
print('Evaluating model: ', args.resume)
trainer.validation(args.start_epoch)
else:
print('Starting Epoch:', args.start_epoch)
print('Total Epochs:', args.epochs)
for epoch in range(args.start_epoch, args.epochs):
trainer.training(epoch)
if not trainer.args.no_val:
trainer.validation(epoch)