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train.py
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train.py
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# -*- coding: utf-8 -*-
from __future__ import print_function, division
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import datasets, transforms
from folder import ImageFolder
import torch.backends.cudnn as cudnn
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
# from PIL import Image
import copy
import time
import os
from model import two_view_net, three_view_net
from random_erasing import RandomErasing
from autoaugment import ImageNetPolicy, CIFAR10Policy
import yaml
from shutil import copyfile
from utils import update_average, get_model_list, load_network, save_network, make_weights_for_balanced_classes
from pytorch_metric_learning import losses, miners # pip install pytorch-metric-learning
from circle_loss import CircleLoss, convert_label_to_similarity
version = torch.__version__
# fp16
try:
from apex.fp16_utils import *
from apex import amp, optimizers
except ImportError: # will be 3.x series
print(
'This is not an error. If you want to use low precision, i.e., fp16, please install the apex with cuda support (https://github.com/NVIDIA/apex) and update pytorch to 1.0')
######################################################################
# Options
# --------
parser = argparse.ArgumentParser(description='Training')
parser.add_argument('--gpu_ids', default='0', type=str, help='gpu_ids: e.g. 0 0,1,2 0,2')
parser.add_argument('--name', default='two_view', type=str, help='output model name')
parser.add_argument('--resume', action='store_true', help='use resume trainning')
#data
parser.add_argument('--data_dir', default='./data/train', type=str, help='training dir path')
parser.add_argument('--extra_Google', action='store_true', help='using extra noise Google')
parser.add_argument('--train_all', action='store_true', help='use all training data')
parser.add_argument('--color_jitter', action='store_true', help='use color jitter in training')
parser.add_argument('--batchsize', default=8, type=int, help='batchsize')
parser.add_argument('--pad', default=10, type=int, help='padding')
parser.add_argument('--h', default=384, type=int, help='height')
parser.add_argument('--w', default=384, type=int, help='width')
parser.add_argument('--views', default=2, type=int, help='the number of views')
parser.add_argument('--erasing_p', default=0, type=float, help='Random Erasing probability, in [0,1]')
parser.add_argument('--DA', action='store_true', help='use Color Data Augmentation')
#backbone
parser.add_argument('--share', action='store_true', help='share weight between different view')
parser.add_argument('--droprate', default=0.5, type=float, help='drop rate')
parser.add_argument('--pool', default='avg', type=str, help='pool avg')
parser.add_argument('--stride', default=2, type=int, help='stride')
parser.add_argument('--use_dense', action='store_true', help='use densenet121')
parser.add_argument('--use_NAS', action='store_true', help='use NAS')
#optimizer
parser.add_argument('--warm_epoch', default=0, type=int, help='the first K epoch that needs warm up')
parser.add_argument('--lr', default=0.01, type=float, help='learning rate')
parser.add_argument('--moving_avg', default=1.0, type=float, help='moving average')
parser.add_argument('--fp16', action='store_true',
help='use float16 instead of float32, which will save about 50% memory')
# extra losses (default is cross-entropy loss. You can fuse different losses for further performance boost.)
parser.add_argument('--arcface', action='store_true', help='use ArcFace loss')
parser.add_argument('--circle', action='store_true', help='use Circle loss')
parser.add_argument('--cosface', action='store_true', help='use CosFace loss')
parser.add_argument('--contrast', action='store_true', help='use contrast loss')
parser.add_argument('--triplet', action='store_true', help='use triplet loss')
parser.add_argument('--lifted', action='store_true', help='use lifted loss')
parser.add_argument('--sphere', action='store_true', help='use sphere loss')
parser.add_argument('--loss_merge', action='store_true', help='combine perspectives to calculate losses')
opt = parser.parse_args()
if opt.resume:
model, opt, start_epoch = load_network(opt.name, opt)
else:
start_epoch = 0
fp16 = opt.fp16
data_dir = opt.data_dir
name = opt.name
str_ids = opt.gpu_ids.split(',')
gpu_ids = []
for str_id in str_ids:
gid = int(str_id)
if gid >= 0:
gpu_ids.append(gid)
# set gpu ids
if len(gpu_ids) > 0:
torch.cuda.set_device(gpu_ids[0])
cudnn.benchmark = True
######################################################################
# Load Data
# ---------
#
transform_train_list = [
# transforms.RandomResizedCrop(size=(opt.h, opt.w), scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC)
transforms.Resize((opt.h, opt.w), interpolation=3),
transforms.Pad(opt.pad, padding_mode='edge'),
transforms.RandomCrop((opt.h, opt.w)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_satellite_list = [
transforms.Resize((opt.h, opt.w), interpolation=3),
transforms.Pad(opt.pad, padding_mode='edge'),
transforms.RandomAffine(90),
transforms.RandomCrop((opt.h, opt.w)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_val_list = [
transforms.Resize(size=(opt.h, opt.w), interpolation=3), # Image.BICUBIC
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
if opt.erasing_p > 0:
transform_train_list = transform_train_list + [RandomErasing(probability=opt.erasing_p, mean=[0.0, 0.0, 0.0])]
if opt.color_jitter:
transform_train_list = [transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1,
hue=0)] + transform_train_list
transform_satellite_list = [transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1,
hue=0)] + transform_satellite_list
if opt.DA:
transform_train_list = [ImageNetPolicy()] + transform_train_list
print(transform_train_list)
data_transforms = {
'train': transforms.Compose(transform_train_list),
'val': transforms.Compose(transform_val_list),
'satellite': transforms.Compose(transform_satellite_list)}
train_all = ''
if opt.train_all:
train_all = '_all'
image_datasets = {}
image_datasets['satellite'] = datasets.ImageFolder(os.path.join(data_dir, 'satellite'),
data_transforms['satellite'])
image_datasets['street'] = datasets.ImageFolder(os.path.join(data_dir, 'street'),
data_transforms['train'])
image_datasets['drone'] = datasets.ImageFolder(os.path.join(data_dir, 'drone'),
data_transforms['train'])
image_datasets['google'] = ImageFolder(os.path.join(data_dir, 'google'),
# google contain empty subfolder, so we overwrite the Folder
data_transforms['train'])
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
shuffle=True, num_workers=2, pin_memory=True) # 8 workers may work faster
for x in ['satellite', 'street', 'drone', 'google']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['satellite', 'street', 'drone', 'google']}
class_names = image_datasets['street'].classes
print(dataset_sizes)
use_gpu = torch.cuda.is_available()
######################################################################
# Training the model
# ------------------
#
# Now, let's write a general function to train a model. Here, we will
# illustrate:
#
# - Scheduling the learning rate
# - Saving the best model
#
# In the following, parameter ``scheduler`` is an LR scheduler object from
# ``torch.optim.lr_scheduler``.
y_loss = {} # loss history
y_loss['train'] = []
y_loss['val'] = []
y_err = {}
y_err['train'] = []
y_err['val'] = []
def train_model(model, model_test, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
# best_model_wts = model.state_dict()
# best_acc = 0.0
warm_up = 0.1 # We start from the 0.1*lrRate
warm_iteration = round(dataset_sizes['satellite'] / opt.batchsize) * opt.warm_epoch # first 5 epoch
if opt.arcface:
criterion_arcface = losses.ArcFaceLoss(num_classes=opt.nclasses, embedding_size=512)
if opt.cosface:
criterion_cosface = losses.CosFaceLoss(num_classes=opt.nclasses, embedding_size=512)
if opt.circle:
criterion_circle = CircleLoss(m=0.25, gamma=32) # gamma = 64 may lead to a better result.
if opt.triplet:
miner = miners.MultiSimilarityMiner()
criterion_triplet = losses.TripletMarginLoss(margin=0.3)
if opt.lifted:
criterion_lifted = losses.GeneralizedLiftedStructureLoss(neg_margin=1, pos_margin=0)
if opt.contrast:
criterion_contrast = losses.ContrastiveLoss(pos_margin=0, neg_margin=1)
if opt.sphere:
criterion_sphere = losses.SphereFaceLoss(num_classes=opt.nclasses, embedding_size=512, margin=4)
for epoch in range(num_epochs - start_epoch):
epoch = epoch + start_epoch
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train']:
if phase == 'train':
model.train(True) # Set model to training mode
else:
model.train(False) # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0.0
running_corrects2 = 0.0
running_corrects3 = 0.0
# Iterate over data.
for data, data2, data3, data4 in zip(dataloaders['satellite'], dataloaders['street'], dataloaders['drone'],
dataloaders['google']):
# get the inputs
inputs, labels = data
inputs2, labels2 = data2
inputs3, labels3 = data3
inputs4, labels4 = data4
now_batch_size, c, h, w = inputs.shape
if now_batch_size < opt.batchsize: # skip the last batch
continue
if use_gpu:
inputs = Variable(inputs.cuda().detach())
inputs2 = Variable(inputs2.cuda().detach())
inputs3 = Variable(inputs3.cuda().detach())
labels = Variable(labels.cuda().detach())
labels2 = Variable(labels2.cuda().detach())
labels3 = Variable(labels3.cuda().detach())
if opt.extra_Google:
inputs4 = Variable(inputs4.cuda().detach())
labels4 = Variable(labels4.cuda().detach())
else:
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward
if phase == 'val':
with torch.no_grad():
outputs, outputs2 = model(inputs, inputs2)
else:
if opt.views == 2:
outputs, outputs2 = model(inputs, inputs2)
elif opt.views == 3:
if opt.extra_Google:
outputs, outputs2, outputs3, outputs4 = model(inputs, inputs2, inputs3, inputs4)
else:
outputs, outputs2, outputs3 = model(inputs, inputs2, inputs3)
return_feature = opt.arcface or opt.cosface or opt.circle or opt.triplet or opt.contrast or opt.lifted or opt.sphere
if opt.views == 2:
_, preds = torch.max(outputs.data, 1)
_, preds2 = torch.max(outputs2.data, 1)
loss = criterion(outputs, labels) + criterion(outputs2, labels2)
elif opt.views == 3:
if return_feature:
logits, ff = outputs
logits2, ff2 = outputs2
logits3, ff3 = outputs3
fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)
fnorm2 = torch.norm(ff2, p=2, dim=1, keepdim=True)
fnorm3 = torch.norm(ff3, p=2, dim=1, keepdim=True)
ff = ff.div(fnorm.expand_as(ff)) # 8*512,tensor
ff2 = ff2.div(fnorm2.expand_as(ff2))
ff3 = ff3.div(fnorm3.expand_as(ff3))
loss = criterion(logits, labels) + criterion(logits2, labels2) + criterion(logits3, labels3)
_, preds = torch.max(logits.data, 1)
_, preds2 = torch.max(logits2.data, 1)
_, preds3 = torch.max(logits3.data, 1)
# Multiple perspectives are combined to calculate losses, please join ''--loss_merge'' in run.sh
if opt.loss_merge:
ff_all = torch.cat((ff, ff2, ff3), dim=0)
labels_all = torch.cat((labels, labels2, labels3), dim=0)
if opt.extra_Google:
logits4, ff4 = outputs4
fnorm4 = torch.norm(ff4, p=2, dim=1, keepdim=True)
ff4 = ff4.div(fnorm4.expand_as(ff4))
loss = criterion(logits, labels) + criterion(logits2, labels2) + criterion(logits3, labels3) +criterion(logits4, labels4)
if opt.loss_merge:
ff_all = torch.cat((ff_all, ff4), dim=0)
labels_all = torch.cat((labels_all, labels4), dim=0)
if opt.arcface:
if opt.loss_merge:
loss += criterion_arcface(ff_all, labels_all)
else:
loss += criterion_arcface(ff, labels) + criterion_arcface(ff2, labels2) + criterion_arcface(ff3, labels3) # /now_batch_size
if opt.extra_Google:
loss += criterion_arcface(ff4, labels4) # /now_batch_size
if opt.cosface:
if opt.loss_merge:
loss += criterion_cosface(ff_all, labels_all)
else:
loss += criterion_cosface(ff, labels) + criterion_cosface(ff2, labels2) + criterion_cosface(ff3, labels3) # /now_batch_size
if opt.extra_Google:
loss += criterion_cosface(ff4, labels4) # /now_batch_size
if opt.circle:
if opt.loss_merge:
loss += criterion_circle(*convert_label_to_similarity(ff_all, labels_all)) / now_batch_size
else:
loss += criterion_circle(*convert_label_to_similarity(ff, labels)) / now_batch_size + criterion_circle(*convert_label_to_similarity(ff2, labels2)) / now_batch_size + criterion_circle(*convert_label_to_similarity(ff3, labels3)) / now_batch_size
if opt.extra_Google:
loss += criterion_circle(*convert_label_to_similarity(ff4, labels4)) / now_batch_size
if opt.triplet:
if opt.loss_merge:
hard_pairs_all = miner(ff_all, labels_all)
loss += criterion_triplet(ff_all, labels_all, hard_pairs_all)
else:
hard_pairs = miner(ff, labels)
hard_pairs2 = miner(ff2, labels2)
hard_pairs3 = miner(ff3, labels3)
loss += criterion_triplet(ff, labels, hard_pairs) + criterion_triplet(ff2, labels2, hard_pairs2) + criterion_triplet(ff3, labels3, hard_pairs3)# /now_batch_size
if opt.extra_Google:
hard_pairs4 = miner(ff4, labels4)
loss += criterion_triplet(ff4, labels4, hard_pairs4)
if opt.lifted:
if opt.loss_merge:
loss += criterion_lifted(ff_all, labels_all)
else:
loss += criterion_lifted(ff, labels) + criterion_lifted(ff2, labels2) + criterion_lifted(ff3, labels3) # /now_batch_size
if opt.extra_Google:
loss += criterion_lifted(ff4, labels4)
if opt.contrast:
if opt.loss_merge:
loss += criterion_contrast(ff_all, labels_all)
else:
loss += criterion_contrast(ff, labels) + criterion_contrast(ff2,labels2) + criterion_contrast(ff3, labels3) # /now_batch_size
if opt.extra_Google:
loss += criterion_contrast(ff4, labels4)
if opt.sphere:
if opt.loss_merge:
loss += criterion_sphere(ff_all, labels_all) / now_batch_size
else:
loss += criterion_sphere(ff, labels) / now_batch_size + criterion_sphere(ff2, labels2) / now_batch_size + criterion_sphere(ff3, labels3) / now_batch_size
if opt.extra_Google:
loss += criterion_sphere(ff4, labels4)
else:
_, preds = torch.max(outputs.data, 1)
_, preds2 = torch.max(outputs2.data, 1)
_, preds3 = torch.max(outputs3.data, 1)
if opt.loss_merge:
outputs_all = torch.cat((outputs, outputs2, outputs3), dim=0)
labels_all = torch.cat((labels, labels2, labels3), dim=0)
if opt.extra_Google:
outputs_all = torch.cat((outputs_all, outputs4), dim=0)
labels_all = torch.cat((labels_all, labels4), dim=0)
loss = 4*criterion(outputs_all, labels_all)
else:
loss = criterion(outputs, labels) + criterion(outputs2, labels2) + criterion(outputs3, labels3)
if opt.extra_Google:
loss += criterion(outputs4, labels4)
# backward + optimize only if in training phase
if epoch < opt.warm_epoch and phase == 'train':
warm_up = min(1.0, warm_up + 0.9 / warm_iteration)
loss *= warm_up
if phase == 'train':
if fp16: # we use optimier to backward loss
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
##########
if opt.moving_avg < 1.0:
update_average(model_test, model, opt.moving_avg)
# statistics
if int(version[0]) > 0 or int(version[2]) > 3: # for the new version like 0.4.0, 0.5.0 and 1.0.0
running_loss += loss.item() * now_batch_size
else: # for the old version like 0.3.0 and 0.3.1
running_loss += loss.data[0] * now_batch_size
running_corrects += float(torch.sum(preds == labels.data))
running_corrects2 += float(torch.sum(preds2 == labels2.data))
if opt.views == 3:
running_corrects3 += float(torch.sum(preds3 == labels3.data))
epoch_loss = running_loss / dataset_sizes['satellite']
epoch_acc = running_corrects / dataset_sizes['satellite']
epoch_acc2 = running_corrects2 / dataset_sizes['satellite']
if opt.views == 2:
print('{} Loss: {:.4f} Satellite_Acc: {:.4f} Street_Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc,
epoch_acc2))
elif opt.views == 3:
epoch_acc3 = running_corrects3 / dataset_sizes['satellite']
print('{} Loss: {:.4f} Satellite_Acc: {:.4f} Street_Acc: {:.4f} Drone_Acc: {:.4f}'.format(phase,
epoch_loss,
epoch_acc,
epoch_acc2,
epoch_acc3))
y_loss[phase].append(epoch_loss)
y_err[phase].append(1.0 - epoch_acc)
# deep copy the model
if phase == 'train':
scheduler.step()
last_model_wts = model.state_dict()
if epoch % 20 == 19:
save_network(model, opt.name, epoch)
# draw_curve(epoch)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
# print('Best val Acc: {:4f}'.format(best_acc))
# save_network(model_test, opt.name+'adapt', epoch)
return model
######################################################################
# Draw Curve
# ---------------------------
x_epoch = []
fig = plt.figure()
ax0 = fig.add_subplot(121, title="loss")
ax1 = fig.add_subplot(122, title="top1err")
def draw_curve(current_epoch):
x_epoch.append(current_epoch)
ax0.plot(x_epoch, y_loss['train'], 'bo-', label='train')
ax0.plot(x_epoch, y_loss['val'], 'ro-', label='val')
ax1.plot(x_epoch, y_err['train'], 'bo-', label='train')
ax1.plot(x_epoch, y_err['val'], 'ro-', label='val')
if current_epoch == 0:
ax0.legend()
ax1.legend()
fig.savefig(os.path.join('./model', name, 'train.jpg'))
######################################################################
# Finetuning the convnet
# ----------------------
#
# Load a pretrainied model and reset final fully connected layer.
#
return_feature = opt.arcface or opt.cosface or opt.circle or opt.triplet or opt.contrast or opt.lifted or opt.sphere
if opt.views == 2:
model = two_view_net(len(class_names), droprate=opt.droprate, stride=opt.stride, pool=opt.pool,
share_weight=opt.share, circle=return_feature)
elif opt.views == 3:
model = three_view_net(len(class_names), droprate=opt.droprate, stride=opt.stride, pool=opt.pool,
share_weight=opt.share, circle=return_feature)
opt.nclasses = len(class_names)
print(model)
# For resume:
if start_epoch >= 40:
opt.lr = opt.lr * 0.1
ignored_params = list(map(id, model.classifier.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params, model.parameters())
optimizer_ft = optim.SGD([
{'params': base_params, 'lr': 0.1 * opt.lr},
{'params': model.classifier.parameters(), 'lr': opt.lr}
], weight_decay=5e-4, momentum=0.9, nesterov=True)
# Decay LR by a factor of 0.1 every 40 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=80, gamma=0.1)
######################################################################
# Train and evaluate
# ^^^^^^^^^^^^^^^^^^
#
# It should take around 1-2 hours on GPU.
#
dir_name = os.path.join('./model', name)
if not opt.resume:
if not os.path.isdir(dir_name):
os.mkdir(dir_name)
# record every run
copyfile('train.py', dir_name + '/train.py')
copyfile('./model.py', dir_name + '/model.py')
# save opts
with open('%s/opts.yaml' % dir_name, 'w') as fp:
yaml.dump(vars(opt), fp, default_flow_style=False)
# model to gpu
model = model.cuda()
if fp16:
model, optimizer_ft = amp.initialize(model, optimizer_ft, opt_level="O1")
criterion = nn.CrossEntropyLoss()
if opt.moving_avg < 1.0:
model_test = copy.deepcopy(model)
num_epochs = 140
else:
model_test = None
num_epochs = 120
model = train_model(model, model_test, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=num_epochs)