train_cvusa.py

# -*- coding: utf-8 -*-

from __future__ import print_function, division

import argparse
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
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
import math
from shutil import copyfile
from utils import update_average, get_model_list, load_network, save_network, make_weights_for_balanced_classes

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='usa_two_view', type=str, help='output model name')
parser.add_argument('--pool',default='avg', type=str, help='pool avg')
parser.add_argument('--data_dir',default='./data/cvpr2017_cvusa/train',type=str, help='training dir path')
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('--stride', default=2, type=int, help='stride')
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('--use_dense', action='store_true', help='use densenet121' )
parser.add_argument('--use_vgg16', action='store_true', help='use VGG16' )
parser.add_argument('--use_NAS', action='store_true', help='use NAS' )
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('--droprate', default=0.5, type=float, help='drop rate')
parser.add_argument('--DA', action='store_true', help='use Color Data Augmentation' )
parser.add_argument('--resume', action='store_true', help='use resume trainning' )
parser.add_argument('--share', action='store_true', help='share weight between different view' )
parser.add_argument('--extra_Google', action='store_true', help='using extra noise Google' )
parser.add_argument('--fp16', action='store_true', help='use float16 instead of float32, which will save about 50% memory' )
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'])

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']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['satellite', 'street']}
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

    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 in zip(dataloaders['satellite'], dataloaders['street']) :
                # get the inputs
                inputs, labels = data
                inputs2, labels2 = data2
                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())
                    labels = Variable(labels.cuda().detach())
                    labels2 = Variable(labels2.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)
                _, preds = torch.max(outputs.data, 1)
                _, preds2 = torch.max(outputs2.data, 1)
                
                if opt.views == 2:
                    loss = criterion(outputs, labels) + criterion(outputs2, labels2)
                elif opt.views == 3:
                    _, preds3 = torch.max(outputs3.data, 1)
                    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.
#

if opt.views == 2:
    model = two_view_net(len(class_names), droprate = opt.droprate, stride = opt.stride, pool = opt.pool, share_weight = opt.share, VGG16=opt.use_vgg16)
elif opt.views == 3:
    model = three_view_net(len(class_names), droprate = opt.droprate, stride = opt.stride, pool = opt.pool, share_weight = opt.share, VGG16=opt.use_vgg16)

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)