def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
if args.pretrained_model != None:
args.restore_from = pretrianed_models_dict[args.pretrained_model]
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(VOCDataSet(args.data_dir, args.data_list, crop_size=(505, 505), mean=IMG_MEAN, scale=False, mirror=False),
batch_size=1, shuffle=False, pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(505, 505), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize()
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd'%(index))
image, label, size, name = batch
size = size[0].numpy()
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
output = output[:,:size[0],:size[1]]
gt = np.asarray(label[0].numpy()[:size[0],:size[1]], dtype=np.int)
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result.txt')
get_iou(data_list, args.num_classes, filename)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
if args.pretrained_model != None:
args.restore_from[i] = pretrianed_models_dict[args.pretrained_model] # if there is pretrained model, restore_from will be changed
if args.restore_from[i][:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from[i])
else:
saved_state_dict = torch.load(args.restore_from[i]) ##VOC_25000
model.load_state_dict(saved_state_dict)
model.eval() #evaluation mode
model.cuda(gpu0)
testloader = data.DataLoader(VOCDataSet(args.data_dir, args.data_list, crop_size=(505, 505), mean=IMG_MEAN, scale=False, mirror=False),
batch_size=1, shuffle=False, pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(505, 505), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize() # colorize!
with torch.no_grad():### added for 0.4
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd'%(index))
image, label, size, name = batch # size >> tensor([[366, 500, 3]])
size = size[0].numpy() ## [366 500 3]
output = model(image.cuda(args.gpu))
output = interp(output).cpu().data[0].numpy()# 21,505,505
output = output[:,:size[0],:size[1]] # 21,366,500
gt = np.asarray(label[0].numpy()[:size[0],:size[1]], dtype=np.int) # size of each image is diff
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(colorize(output).transpose(1, 2, 0), 'RGB') # colorize the output
color_file.save(filename)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result'+args.restore_from[i][-10:-4]+'.txt')
get_iou(data_list, args.num_classes, filename)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save):
os.makedirs(args.save)
if args.model == 'Deeplab':
model = Res_Deeplab(num_classes=args.num_classes)
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
saved_state_dict = torch.load(args.restore_from)
### for running different versions of pytorch
model_dict = model.state_dict()
saved_state_dict = {k: v for k, v in saved_state_dict.items() if k in model_dict}
model_dict.update(saved_state_dict)
###
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),
batch_size=1, shuffle=False, pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(1024, 2048), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(1024, 2048), mode='bilinear')
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % index)
image, _, name = batch
if args.model == 'Deeplab':
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
elif args.model == 'DeeplabVGG':
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
output_col = colorize_mask(output)
output = Image.fromarray(output)
name = name[0].split('/')[-1]
output.save('%s/%s' % (args.save, name))
output_col.save('%s/%s_color.png' % (args.save, name.split('.')[0]))
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
if args.pretrained_model != None:
args.restore_from = pretrianed_models_dict[args.pretrained_model]
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.eval()
# model.cuda(gpu0)
testloader = data.DataLoader(VOCDataSet(args.data_dir, args.data_list, crop_size=(505, 505), mean=IMG_MEAN, scale=False, mirror=False),
batch_size=1, shuffle=False, pin_memory=True)
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize()
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd'%(index))
image, label, size, name = batch
size = size[0].numpy()
# output = model(Variable(image, volatile=True).cuda(gpu0))
output = model(Variable(image, volatile=True).cpu())
output = interp(output).cpu().data[0].numpy()
output = output[:,:size[0],:size[1]]
gt = np.asarray(label[0].numpy()[:size[0],:size[1]], dtype=np.int)
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result.txt')
get_iou(data_list, args.num_classes, filename)
def eval(pth, cityscapes_eval_dir, i_iter):
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if args.model == 'ResNet':
model = Res_Deeplab(num_classes=args.num_classes)
saved_state_dict = torch.load(pth)
elif args.model == 'VGG':
model = DeeplabVGG(num_classes=args.num_classes)
saved_state_dict = torch.load(pth)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
cityscapesloader = data.DataLoader(cityscapesDataSet(
args.cityscapes_data_dir,
args.cityscapes_data_list,
crop_size=(1024, 512),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=1,
shuffle=False,
pin_memory=True)
interp = nn.Upsample(size=(1024, 2048),
mode='bilinear',
align_corners=True)
for index, batch in enumerate(cityscapesloader):
with torch.no_grad():
if index % 100 == 0:
print('%d processd' % index)
image, _, name = batch
output = model(Variable(image).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
output_col = colorize_mask(output)
output = Image.fromarray(output)
name = name[0].split('/')[-1]
output.save('%s/%s' % (cityscapes_eval_dir, name))
output_col.save('%s/%s_color.png' %
(cityscapes_eval_dir, name.split('.')[0]))
if i_iter == 0:
break
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
# if args.pretrained_model != None:
# args.restore_from = pretrianed_models_dict[args.pretrained_model]
#
# if args.restore_from[:4] == 'http' :
# saved_state_dict = model_zoo.load_url(args.restore_from)
# else:
# saved_state_dict = torch.load(args.restore_from)
#model.load_state_dict(saved_state_dict)
model = Res_Deeplab(num_classes=args.num_classes)
#model.load_state_dict(torch.load('/data/wyc/AdvSemiSeg/snapshots/VOC_15000.pth'))
state_dict=torch.load('/data1/wyc/AdvSemiSeg/snapshots/VOC_t_concat_pred_img_15000.pth')
from model.discriminator_pred_concat_img import FCDiscriminator
model_D = FCDiscriminator(num_classes=args.num_classes)
state_dict_d = torch.load('/data1/wyc/AdvSemiSeg/snapshots/VOC_t_concat_pred_img_15000_D.pth')
# original saved file with DataParallel
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
new_params = model.state_dict().copy()
for name, param in new_params.items():
print (name)
if name in new_state_dict and param.size() == new_state_dict[name].size():
new_params[name].copy_(new_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.eval()
model.cuda()
new_state_dict_d = OrderedDict()
for k, v in state_dict_d.items():
name = k[7:] # remove `module.`
new_state_dict_d[name] = v
new_params_d = model_D.state_dict().copy()
for name, param in new_params_d.items():
print (name)
if name in new_state_dict_d and param.size() == new_state_dict_d[name].size():
new_params_d[name].copy_(new_state_dict_d[name])
print('copy {}'.format(name))
model_D.load_state_dict(new_params_d)
model_D.eval()
model_D.cuda()
testloader = data.DataLoader(VOCDataSet(args.data_dir, args.data_list, crop_size=(505, 505), mean=IMG_MEAN, scale=False, mirror=False),
batch_size=1, shuffle=False, pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(505, 505), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize()
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd'%(index))
image, label, size, name = batch
size = size[0].numpy()
output = model(Variable(image, volatile=True).cuda())
image_d=Variable(image, volatile=True).cuda()
output=interp(output)
output_dout = output.clone()
output_pred = F.softmax(output, dim=1).cpu().data[0].numpy()
label2=label[0].numpy()
output = output.cpu().data[0].numpy()
output = output[:,:size[0],:size[1]]
gt = np.asarray(label[0].numpy()[:size[0],:size[1]], dtype=np.int)
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
#"""pred result"""
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
#"""the area of the pred which is wrong"""
output_mistake=np.zeros(output.shape)
semi_ignore_mask_correct = (output == gt)
#semi_ignore_mask_255=(gt==255)
output_mistake[semi_ignore_mask_correct] = 255
#output_mistake[semi_ignore_mask_255] = 255
output_mistake = np.expand_dims(output_mistake, axis=2)
filename2 = os.path.join('/data1/wyc/AdvSemiSeg/pred_mis/', '{}.png'.format(name[0]))
cv2.imwrite(filename2, output_mistake)
#"""dis confidence map decide line of pred map"""
D_out = interp(model_D(torch.cat([F.softmax(output_dout, dim=1),F.sigmoid(image_d)],1)))#67
D_out_sigmoid = (F.sigmoid(D_out).data[0].cpu().numpy())
D_out_sigmoid = D_out_sigmoid[:, :size[0], :size[1]]
semi_ignore_mask_dout0 = (D_out_sigmoid < 0.0001)
semi_ignore_mask_dout255 = (D_out_sigmoid >= 0.0001)
D_out_sigmoid[semi_ignore_mask_dout0] = 0
D_out_sigmoid[semi_ignore_mask_dout255] = 255
filename2 = os.path.join('/data1/wyc/AdvSemiSeg/confidence_line/', '{}.png'.format(name[0]))#0 black 255 white
cv2.imwrite(filename2,D_out_sigmoid.transpose(1, 2, 0))
#""""pred max decide line of pred map"""
# id2 = np.argmax(output_pred, axis=0)
# map=np.zeros([1,id2.shape[0],id2.shape[1]])
# for i in range(id2.shape[0]):
# for j in range(id2.shape[1]):
# map[0][i][j]=output_pred[id2[i][j]][i][j]
# semi_ignore_mask2 = (map < 0.999999)
# semi_ignore_mask3 = (map >= 0.999999)
# map[semi_ignore_mask2] = 0
# map[semi_ignore_mask3] = 255
# map = map[:, :size[0], :size[1]]
# filename2 = os.path.join('/data1/wyc/AdvSemiSeg/pred_line/', '{}.png'.format(name[0]))#0 black 255 white
# cv2.imwrite(filename2,map.transpose(1, 2, 0))
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result.txt')
get_iou(data_list, args.num_classes, filename)
def main():
# 将参数的input_size 映射到整数，并赋值，从字符串转换到整数二元组
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = False
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
# 确保模型中参数的格式与要加载的参数相同
# 返回一个字典，保存着module的所有状态（state）；parameters和persistent buffers都会包含在字典中，字典的key就是parameter和buffer的 names。
new_params = model.state_dict().copy()
for name, param in new_params.items():
# print (name)
if name in saved_state_dict and param.size() == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
# print('copy {}'.format(name))
model.load_state_dict(new_params)
# 设置为训练模式
model.train()
cudnn.benchmark = True
model.cuda(gpu)
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True)
else:
# sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = list(range(train_dataset_size)) # ?
np.random.shuffle(train_ids)
pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) # 写入文件
train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # ???
# labels for adversarial training
pred_label = 0
gt_label = 1
for i_iter in range(args.num_steps):
print("Iter:", i_iter)
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size):
# train G
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if args.lambda_semi > 0 and i_iter >= args.semi_start:
try:
_, batch = next(trainloader_remain_iter)
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = next(trainloader_remain_iter)
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda(gpu)
# images = Variable(images).cpu()
pred = interp(model(images))
D_out = interp(model_D(F.softmax(pred)))
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1)
# produce ignore mask
semi_ignore_mask = (D_out_sigmoid < args.mask_T)
semi_gt = pred.data.cpu().numpy().argmax(axis=1)
semi_gt[semi_ignore_mask] = 255
semi_ratio = 1.0 - float(semi_ignore_mask.sum()) / semi_ignore_mask.size
print('semi ratio: {:.4f}'.format(semi_ratio))
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
loss_semi = args.lambda_semi * loss_calc(pred, semi_gt, args.gpu)
loss_semi = loss_semi / args.iter_size
loss_semi.backward()
loss_semi_value += loss_semi.data.cpu().numpy()[0] / args.lambda_semi
else:
loss_semi = None
# train with source
try:
_, batch = next(trainloader_iter)
except:
trainloader_iter = enumerate(trainloader)
_, batch = next(trainloader_iter)
images, labels, _, _ = batch
images = Variable(images).cuda(gpu)
# images = Variable(images).cpu()
ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
loss_seg = loss_calc(pred, labels, args.gpu)
D_out = interp(model_D(F.softmax(pred)))
loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask))
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
# proper normalization
loss = loss / args.iter_size
loss.backward()
loss_seg_value += loss_seg.data.cpu().numpy()[0] / args.iter_size
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0] / args.iter_size
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
D_out = interp(model_D(F.softmax(pred)))
loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
# train with gt
# get gt labels
try:
_, batch = next(trainloader_gt_iter)
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = next(trainloader_gt_iter)
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
# D_gt_v = Variable(one_hot(labels_gt)).cpu()
ignore_mask_gt = (labels_gt.numpy() == 255)
D_out = interp(model_D(D_gt_v))
loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
optimizer.step()
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir))
print(
'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}'.format(
i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '.pth'))
torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth'))
torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def evaluate(arch, dataset, ignore_label, restore_from, pretrained_model,
save_dir, device):
import argparse
import scipy
from scipy import ndimage
import cv2
import numpy as np
import sys
from collections import OrderedDict
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
from model.deeplab import Res_Deeplab
from model.unet import unet_resnet50
from model.deeplabv3 import resnet101_deeplabv3
from dataset.voc_dataset import VOCDataSet
from PIL import Image
import matplotlib.pyplot as plt
pretrianed_models_dict = {
'semi0.125':
'http://vllab1.ucmerced.edu/~whung/adv-semi-seg/AdvSemiSegVOC0.125-03c6f81c.pth',
'semi0.25':
'http://vllab1.ucmerced.edu/~whung/adv-semi-seg/AdvSemiSegVOC0.25-473f8a14.pth',
'semi0.5':
'http://vllab1.ucmerced.edu/~whung/adv-semi-seg/AdvSemiSegVOC0.5-acf6a654.pth',
'advFull':
'http://vllab1.ucmerced.edu/~whung/adv-semi-seg/AdvSegVOCFull-92fbc7ee.pth'
}
class VOCColorize(object):
def __init__(self, n=22):
self.cmap = color_map(22)
self.cmap = torch.from_numpy(self.cmap[:n])
def __call__(self, gray_image):
size = gray_image.shape
color_image = np.zeros((3, size[0], size[1]), dtype=np.uint8)
for label in range(0, len(self.cmap)):
mask = (label == gray_image)
color_image[0][mask] = self.cmap[label][0]
color_image[1][mask] = self.cmap[label][1]
color_image[2][mask] = self.cmap[label][2]
# handle void
mask = (255 == gray_image)
color_image[0][mask] = color_image[1][mask] = color_image[2][
mask] = 255
return color_image
def color_map(N=256, normalized=False):
def bitget(byteval, idx):
return ((byteval & (1 << idx)) != 0)
dtype = 'float32' if normalized else 'uint8'
cmap = np.zeros((N, 3), dtype=dtype)
for i in range(N):
r = g = b = 0
c = i
for j in range(8):
r = r | (bitget(c, 0) << 7 - j)
g = g | (bitget(c, 1) << 7 - j)
b = b | (bitget(c, 2) << 7 - j)
c = c >> 3
cmap[i] = np.array([r, g, b])
cmap = cmap / 255 if normalized else cmap
return cmap
def get_iou(data_list,
class_num,
ignore_label,
class_names,
save_path=None):
from multiprocessing import Pool
from utils.evaluation import EvaluatorIoU
evaluator = EvaluatorIoU(class_num)
for truth, prediction in data_list:
evaluator.sample(truth, prediction, ignore_value=ignore_label)
per_class_iou = evaluator.score()
mean_iou = per_class_iou.mean()
for i, (class_name, iou) in enumerate(zip(class_names, per_class_iou)):
print('class {:2d} {:12} IU {:.2f}'.format(i, class_name, iou))
print('meanIOU: ' + str(mean_iou) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, (class_name,
iou) in enumerate(zip(class_names, per_class_iou)):
f.write('class {:2d} {:12} IU {:.2f}'.format(
i, class_name, iou) + '\n')
f.write('meanIOU: ' + str(mean_iou) + '\n')
def show_all(gt, pred):
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, axes = plt.subplots(1, 2)
ax1, ax2 = axes
colormap = [(0, 0, 0), (0.5, 0, 0), (0, 0.5, 0), (0.5, 0.5, 0),
(0, 0, 0.5), (0.5, 0, 0.5), (0, 0.5, 0.5), (0.5, 0.5, 0.5),
(0.25, 0, 0), (0.75, 0, 0), (0.25, 0.5, 0), (0.75, 0.5, 0),
(0.25, 0, 0.5), (0.75, 0, 0.5), (0.25, 0.5, 0.5),
(0.75, 0.5, 0.5), (0, 0.25, 0), (0.5, 0.25, 0),
(0, 0.75, 0), (0.5, 0.75, 0), (0, 0.25, 0.5)]
cmap = colors.ListedColormap(colormap)
bounds = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21
]
norm = colors.BoundaryNorm(bounds, cmap.N)
ax1.set_title('gt')
ax1.imshow(gt, cmap=cmap, norm=norm)
ax2.set_title('pred')
ax2.imshow(pred, cmap=cmap, norm=norm)
plt.show()
torch_device = torch.device(device)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if dataset == 'pascal_aug':
ds = VOCDataSet()
else:
print('Dataset {} not yet supported'.format(dataset))
return
if arch == 'deeplab2':
model = Res_Deeplab(num_classes=ds.num_classes)
elif arch == 'unet_resnet50':
model = unet_resnet50(num_classes=ds.num_classes)
elif arch == 'resnet101_deeplabv3':
model = resnet101_deeplabv3(num_classes=ds.num_classes)
else:
print('Architecture {} not supported'.format(arch))
return
if pretrained_model is not None:
restore_from = pretrianed_models_dict[pretrained_model]
if restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(restore_from)
else:
saved_state_dict = torch.load(restore_from)
model.load_state_dict(saved_state_dict)
model.eval()
model = model.to(torch_device)
ds_val_xy = ds.val_xy(crop_size=(505, 505),
scale=False,
mirror=False,
mean=model.MEAN,
std=model.STD)
testloader = data.DataLoader(ds_val_xy,
batch_size=1,
shuffle=False,
pin_memory=True)
data_list = []
colorize = VOCColorize()
with torch.no_grad():
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % (index))
image, label, size, name = batch
size = size[0].numpy()
image = torch.tensor(image, dtype=torch.float, device=torch_device)
output = model(image)
output = output.cpu().data[0].numpy()
output = output[:, :size[0], :size[1]]
gt = np.asarray(label[0].numpy()[:size[0], :size[1]], dtype=np.int)
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(
colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(save_dir, 'result.txt')
get_iou(data_list, ds.num_classes, ignore_label, ds.class_names,
filename)
def main():
# prepare
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
# init D
model_D = detector.FlawDetector(in_channels=24)
# model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
else:
#sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = [_ for _ in range(0, train_dataset_size)]
np.random.shuffle(train_ids)
pickle.dump(train_ids,
open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb'))
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=3,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
minimum_loss = detector.MinimumCriterion()
detector_loss = detector.FlawDetectorCriterion()
# bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
for i_iter in range(args.num_steps):
if i_iter > 0 and i_iter % 1000 == 0:
val(model, args.gpu)
model.train()
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
loss_semi_adv_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size):
# train G
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if (args.lambda_semi > 0 or args.lambda_semi_adv > 0
) and i_iter >= args.semi_start_adv:
try:
_, batch = trainloader_remain_iter.__next__()
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = trainloader_remain_iter.__next__()
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
pred_remain = pred.detach()
D_out = model_D(images, pred)
ignore_mask_remain = np.zeros(D_out.shape).astype(np.bool)
loss_semi_adv = args.lambda_semi_adv * minimum_loss(
D_out) # ke: SSL loss for unlabeled data
loss_semi_adv = loss_semi_adv
#loss_semi_adv.backward()
loss_semi_adv_value += loss_semi_adv.data.cpu().numpy(
) / args.iter_size
loss_semi_adv.backward()
loss_semi_value = 0
else:
loss_semi = None
loss_semi_adv = None
# train with source (labeled data)
try:
_, batch = trainloader_iter.__next__()
except:
trainloader_iter = enumerate(trainloader)
_, batch = trainloader_iter.__next__()
images, labels, _, _ = batch
images = Variable(images).cuda(args.gpu)
ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
loss_seg = loss_calc(pred, labels, args.gpu)
D_out = interp(model_D(images, pred))
loss_adv_pred = args.lambda_adv_pred * minimum_loss(
D_out) # ke: SSL loss for labeled data
loss = loss_seg + loss_adv_pred
# proper normalization
loss = loss / args.iter_size
loss.backward()
loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy(
) / args.iter_size
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred from labeled data
pred = pred.detach()
D_out = interp(model_D(images, pred))
detect_gt = detector.generate_flaw_detector_gt(
pred,
labels.view(labels.shape[0], 1, labels.shape[1],
labels.shape[2]).cuda(args.gpu), NUM_CLASSES,
IGNORE_LABEL)
loss_D = detector_loss(D_out, detect_gt)
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()
# # train with gt
# # get gt labels
# try:
# _, batch = trainloader_gt_iter.__next__()
# except:
# trainloader_gt_iter = enumerate(trainloader_gt)
# _, batch = trainloader_gt_iter.__next__()
# _, labels_gt, _, _ = batch
# D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
# ignore_mask_gt = (labels_gt.numpy() == 255)
# D_out = interp(model_D(D_gt_v))
# loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt))
# loss_D = loss_D/args.iter_size/2
# loss_D.backward()
# loss_D_value += loss_D.data.cpu().numpy()
optimizer.step()
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir))
print(
'iter = {0:8d}/{1:8d}, loss_seg = {2:.6f}, loss_adv_l = {3:.6f}, loss_D = {4:.6f}, loss_semi = {5:.6f}, loss_adv_u = {6:.6f}'
.format(i_iter, args.num_steps, loss_seg_value,
loss_adv_pred_value, loss_D_value, loss_semi_value,
loss_semi_adv_value))
if i_iter >= args.num_steps - 1:
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
def main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters (weights)
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(
args.restore_from
) ## http://vllab1.ucmerced.edu/~whung/adv-semi-seg/resnet101COCO-41f33a49.pth
else:
saved_state_dict = torch.load(args.restore_from)
#checkpoint = torch.load(args.restore_from)_
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy() # state_dict() is current model
for name, param in new_params.items():
#print (name) # 'conv1.weight, name:param(value), dict
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
#print('copy {}'.format(name))
model.load_state_dict(new_params)
#model.load_state_dict(checkpoint['state_dict'])
#optimizer.load_state_dict(args.checkpoint['optim_dict'])
model.train(
) # https://pytorch.org/docs/stable/nn.html, Sets the module in training mode.
model.cuda(args.gpu) ##
cudnn.benchmark = True # This flag allows you to enable the inbuilt cudnn auto-tuner to find the best algorithm to use for your hardware
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
#args.restore_from_D = 'snapshots/linear2/VOC_25000_D.pth'
if args.restore_from_D is not None: # None
model_D.load_state_dict(torch.load(args.restore_from_D))
# checkpoint_D = torch.load(args.restore_from_D)
# model_D.load_state_dict(checkpoint_D['state_dict'])
# optimizer_D.load_state_dict(checkpoint_D['optim_dict'])
model_D.train()
model_D.cuda(args.gpu)
if USECALI:
model_cali = ModelWithTemperature(model, model_D)
model_cali.cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
random.seed(args.random_seed)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_remain = VOCDataSet(args.data_dir,
args.data_list_remain,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_dataset_size_remain = len(train_dataset_remain)
print train_dataset_size
print train_dataset_size_remain
train_gt_dataset = VOCGTDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
if args.partial_data is None: #if not partial, load all
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
else:
#sample partial data
#args.partial_data = 0.125
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else: #args.partial_id is none
train_ids = range(train_dataset_size)
train_ids_remain = range(train_dataset_size_remain)
np.random.shuffle(train_ids) #shuffle!
np.random.shuffle(train_ids_remain)
pickle.dump(train_ids,
open(osp.join(args.snapshot_dir, 'train_id.pkl'),
'wb')) #randomly suffled ids
#sampler
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:]) # 0~1/8,
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids_remain[:])
train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:])
# train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) # 0~1/8
# train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:]) # used as unlabeled, 7/8
# train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
#train loader
trainloader = data.DataLoader(
train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=3,
pin_memory=True) # multi-process data loading
trainloader_remain = data.DataLoader(train_dataset_remain,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=3,
pin_memory=True)
# trainloader_remain = data.DataLoader(train_dataset,
# batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3,
# pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
# model.optim_paramters(args) = list(dict1, dict2), dict1 >> 'lr' and 'params'
# print(type(model.optim_parameters(args)[0]['params'])) # generator
#print(model.state_dict()['coeff'][0]) #confirmed
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
#optimizer.add_param_group({"params":model.coeff}) # assign new coefficient to the optimizer
#print(len(optimizer.param_groups))
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad() #initialize
if USECALI:
optimizer_cali = optim.LBFGS([model_cali.temperature],
lr=0.01,
max_iter=50)
optimizer_cali.zero_grad()
nll_criterion = BCEWithLogitsLoss().cuda() # BCE!!
ece_criterion = ECELoss().cuda()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(
size=(input_size[1], input_size[0]), mode='bilinear'
) # okay it automatically change to functional.interpolate
# 321, 321
if version.parse(torch.__version__) >= version.parse('0.4.0'): #0.4.1
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
semi_ratio_sum = 0
semi_sum = 0
loss_seg_sum = 0
loss_adv_sum = 0
loss_vat_sum = 0
l_seg_sum = 0
l_vat_sum = 0
l_adv_sum = 0
logits_list = []
labels_list = []
#https: // towardsdatascience.com / understanding - pytorch -with-an - example - a - step - by - step - tutorial - 81fc5f8c4e8e
for i_iter in range(args.num_steps):
loss_seg_value = 0 # L_seg
loss_adv_pred_value = 0 # 0.01 L_adv
loss_D_value = 0 # L_D
loss_semi_value = 0 # 0.1 L_semi
loss_semi_adv_value = 0 # 0.001 L_adv
loss_vat_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter) #changing lr by iteration
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size):
###################### train G!!!###########################
############################################################
# don't accumulate grads in D
for param in model_D.parameters(
): # <class 'torch.nn.parameter.Parameter'>, convolution weights
param.requires_grad = False # do not update gradient of D (freeze) while G
######### do unlabeled first!! 0.001 L_adv + 0.1 L_semi ###############
# lambda_semi, lambda_adv for unlabeled
if (args.lambda_semi > 0 or args.lambda_semi_adv > 0
) and i_iter >= args.semi_start_adv:
try:
_, batch = trainloader_remain_iter.next(
) #remain = unlabeled
print(trainloader_remain_iter.next())
except:
trainloader_remain_iter = enumerate(
trainloader_remain) # impose counters
_, batch = trainloader_remain_iter.next()
# only access to img
images, _, _, _ = batch # <class 'torch.Tensor'>
images = Variable(images).cuda(
args.gpu) # <class 'torch.Tensor'>
pred = interp(
model(images)) # S(X), pred <class 'torch.Tensor'>
pred_remain = pred.detach(
) #use detach() when attempting to remove a tensor from a computation graph, will be used for D
# https://discuss.pytorch.org/t/clone-and-detach-in-v0-4-0/16861
# The difference is that detach refers to only a given variable on which it's called.
# torch.no_grad affects all operations taking place within the with statement. >> for context,
# requires_grad is for tensor
# pred >> (8,21,321,321), L_adv
D_out = interp(
model_D(F.softmax(pred))
) # D(S(X)), confidence, 8,1,321,321, not detached, there was not dim
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(
axis=1) # (8,321,321) 0~1
# 0.001 L_adv!!!!
ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(
np.bool) # no ignore_mask for unlabeled adv
loss_semi_adv = args.lambda_semi_adv * bce_loss(
D_out, make_D_label(gt_label,
ignore_mask_remain)) #gt_label =1,
# -log(D(S(X)))
loss_semi_adv = loss_semi_adv / args.iter_size #normalization
loss_semi_adv_value += loss_semi_adv.data.cpu().numpy(
) / args.lambda_semi_adv
##--- visualization, pred(8,21,321,321), D_out_sigmoid(8,321,321)
"""
if i_iter % 1000 == 0:
vpred = pred.transpose(1, 2).transpose(2, 3).contiguous() # (8,321,321,21)
vpred = vpred.view(-1, 21) # (8*321*321, 21)
vlogsx = F.log_softmax(vpred) # torch.Tensor
vsemi_gt = pred.data.cpu().numpy().argmax(axis=1)
vsemi_gt = Variable(torch.FloatTensor(vsemi_gt).long()).cuda(gpu)
vlogsx = vlogsx.gather(1, vsemi_gt.view(-1, 1))
sx = F.softmax(vpred).gather(1, vsemi_gt.view(-1, 1))
vD_out_sigmoid = Variable(torch.FloatTensor(D_out_sigmoid)).cuda(gpu).view(-1, 1)
vlogsx = (vlogsx*(2.5*vD_out_sigmoid+0.5))
vlogsx = -vlogsx.squeeze(dim=1)
sx = sx.squeeze(dim=1)
vD_out_sigmoid = vD_out_sigmoid.squeeze(dim=1)
dsx = vD_out_sigmoid.data.cpu().detach().numpy()
vlogsx = vlogsx.data.cpu().detach().numpy()
sx = sx.data.cpu().detach().numpy()
plt.clf()
plt.figure(figsize=(15, 5))
plt.subplot(131)
plt.ylim(0, 0.004)
plt.scatter(dsx, vlogsx, s = 0.1) # variable requires grad cannot call numpy >> detach
plt.xlabel('D(S(X))')
plt.ylabel('Loss_Semi per Pixel')
plt.subplot(132)
plt.scatter(dsx, vlogsx, s = 0.1) # variable requires grad cannot call numpy >> detach
plt.xlabel('D(S(X))')
plt.ylabel('Loss_Semi per Pixel')
plt.subplot(133)
plt.scatter(dsx, sx, s=0.1)
plt.xlabel('D(S(X))')
plt.ylabel('S(x)')
plt.savefig('/home/eungyo/AdvSemiSeg/plot/' + str(i_iter) + '.png')
"""
if args.lambda_semi <= 0 or i_iter < args.semi_start:
loss_semi_adv.backward()
loss_semi_value = 0
else:
semi_gt = pred.data.cpu().numpy().argmax(
axis=1
) # pred=S(X) ((8,21,321,321)), semi_gt is not one-hot, 8,321,321
#(8, 321, 321)
if not USECALI:
semi_ignore_mask = (
D_out_sigmoid < args.mask_T
) # both (8,321,321) 0~1threshold!, numpy
semi_gt[
semi_ignore_mask] = 255 # Yhat, ignore pixel becomes 255
semi_ratio = 1.0 - float(semi_ignore_mask.sum(
)) / semi_ignore_mask.size # ignored pixels / H*W
print('semi ratio: {:.4f}'.format(semi_ratio))
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
confidence = torch.FloatTensor(
D_out_sigmoid) ## added, only pred is on cuda
loss_semi = args.lambda_semi * weighted_loss_calc(
pred, semi_gt, args.gpu, confidence)
else:
semi_ratio = 1
semi_gt = (torch.FloatTensor(semi_gt)) # (8,321,321)
confidence = torch.FloatTensor(
F.sigmoid(
model_cali.temperature_scale(D_out.view(
-1))).data.cpu().numpy()) # (8*321*321,)
loss_semi = args.lambda_semi * calibrated_loss_calc(
pred, semi_gt, args.gpu, confidence, accuracies,
n_bin
) # L_semi = Yhat * log(S(X)) # loss_calc(pred, semi_gt, args.gpu)
# pred(8,21,321,321)
if semi_ratio != 0:
loss_semi = loss_semi / args.iter_size
loss_semi_value += loss_semi.data.cpu().numpy(
) / args.lambda_semi
if args.method == 'vatent' or args.method == 'vat':
#v_loss = vat_loss(model, images, pred, eps=args.epsilon[i]) # R_vadv
weighted_v_loss = weighted_vat_loss(
model,
images,
pred,
confidence,
eps=args.epsilon)
if args.method == 'vatent':
#v_loss += entropy_loss(pred) # R_cent (conditional entropy loss)
weighted_v_loss += weighted_entropy_loss(
pred, confidence)
v_loss = weighted_v_loss / args.iter_size
loss_vat_value += v_loss.data.cpu().numpy()
loss_semi_adv += args.alpha * v_loss
loss_vat_sum += loss_vat_value
if i_iter % 100 == 0 and sub_i == 4:
l_vat_sum = loss_vat_sum / 100
if i_iter == 0:
l_vat_sum = l_vat_sum * 100
loss_vat_sum = 0
loss_semi += loss_semi_adv
loss_semi.backward(
) # 0.001 L_adv + 0.1 L_semi, backward == back propagation
else:
loss_semi = None
loss_semi_adv = None
###########train with source (labeled data)############### L_ce + 0.01 * L_adv
try:
_, batch = trainloader_iter.next()
except:
trainloader_iter = enumerate(trainloader) # safe coding
_, batch = trainloader_iter.next() #counter, batch
images, labels, _, _ = batch # also get labels images(8,321,321)
images = Variable(images).cuda(args.gpu)
ignore_mask = (
labels.numpy() == 255
) # ignored pixels == 255 >> 1, yes ignored mask for labeled data
pred = interp(model(images)) # S(X), 8,21,321,321
loss_seg = loss_calc(pred, labels,
args.gpu) # -Y*logS(X)= L_ce, not detached
if USED:
softsx = F.softmax(pred, dim=1)
D_out = interp(model_D(softsx)) # D(S(X)), L_adv
loss_adv_pred = bce_loss(
D_out, make_D_label(
gt_label,
ignore_mask)) # both 8,1,321,321, gt_label = 1
# L_adv = -log(D(S(X)), make_D_label is all 1 except ignored_region
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
if USECALI:
if (args.lambda_semi > 0 or args.lambda_semi_adv > 0
) and i_iter >= args.semi_start_adv:
with torch.no_grad():
_, prediction = torch.max(softsx, 1)
labels_mask = (
(labels > 0) *
(labels != 255)) | (prediction.data.cpu() > 0)
labels = labels[labels_mask]
prediction = prediction[labels_mask]
fake_mask = (labels.data.cpu().numpy() !=
prediction.data.cpu().numpy())
real_label = make_conf_label(
1, fake_mask
) # (10*321*321, ) 0 or 1 (fake or real)
logits = D_out.squeeze(dim=1)
logits = logits[labels_mask]
logits_list.append(logits) # initialize
labels_list.append(real_label)
if (i_iter * args.iter_size * args.batch_size + sub_i +
1) % train_dataset_size == 0:
logits = torch.cat(logits_list).cuda(
) # overall 5000 images in val, #logits >> 5000,100, (1464*321*321,)
labels = torch.cat(labels_list).cuda()
before_temperature_nll = nll_criterion(
logits, labels).item() ####modify
before_temperature_ece, _, _ = ece_criterion(
logits, labels) # (1464*321*321,)
before_temperature_ece = before_temperature_ece.item(
)
print('Before temperature - NLL: %.3f, ECE: %.3f' %
(before_temperature_nll,
before_temperature_ece))
def eval():
loss_cali = nll_criterion(
model_cali.temperature_scale(logits),
labels)
loss_cali.backward()
return loss_cali
optimizer_cali.step(
eval) # just one backward >> not 50 iterations
after_temperature_nll = nll_criterion(
model_cali.temperature_scale(logits),
labels).item()
after_temperature_ece, accuracies, n_bin = ece_criterion(
model_cali.temperature_scale(logits), labels)
after_temperature_ece = after_temperature_ece.item(
)
print('Optimal temperature: %.3f' %
model_cali.temperature.item())
print(
'After temperature - NLL: %.3f, ECE: %.3f' %
(after_temperature_nll, after_temperature_ece))
logits_list = []
labels_list = []
else:
loss = loss_seg
# proper normalization
loss = loss / args.iter_size
loss.backward()
loss_seg_sum += loss_seg / args.iter_size
if USED:
loss_adv_sum += loss_adv_pred
if i_iter % 100 == 0 and sub_i == 4:
l_seg_sum = loss_seg_sum / 100
if USED:
l_adv_sum = loss_adv_sum / 100
if i_iter == 0:
l_seg_sum = l_seg_sum * 100
l_adv_sum = l_adv_sum * 100
loss_seg_sum = 0
loss_adv_sum = 0
loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size
if USED:
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy(
) / args.iter_size
##################### train D!!!###########################
###########################################################
# bring back requires_grad
if USED:
for param in model_D.parameters():
param.requires_grad = True # before False.
############# train with pred S(X)############# labeled + unlabeled
pred = pred.detach(
) #orginally only use labeled data, freeze S(X) when train D,
# We do train D with the unlabeled data. But the difference is quite small
if args.D_remain: #default true
pred = torch.cat(
(pred, pred_remain), 0
) # pred_remain(unlabeled S(x)) is detached 16,21,321,321
ignore_mask = np.concatenate(
(ignore_mask, ignore_mask_remain),
axis=0) # 16,321,321
D_out = interp(
model_D(F.softmax(pred, dim=1))
) # D(S(X)) 16,1,321,321 # softmax(pred,dim=1) for 0.4, not nessesary
loss_D = bce_loss(D_out,
make_D_label(pred_label,
ignore_mask)) # pred_label = 0
# -log(1-D(S(X)))
loss_D = loss_D / args.iter_size / 2 # iter_size = 1, /2 because there is G and D
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()
################## train with gt################### only labeled
#VOCGT and VOCdataset can be reduced to one dataset in this repo.
# get gt labels Y
#print "before train gt"
try:
print(trainloader_gt_iter.next()) # len 732
_, batch = trainloader_gt_iter.next()
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = trainloader_gt_iter.next()
#print "train with gt?"
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) #one_hot
ignore_mask_gt = (labels_gt.numpy() == 255
) # same as ignore_mask (8,321,321)
#print "finish"
D_out = interp(model_D(D_gt_v)) # D(Y)
loss_D = bce_loss(D_out,
make_D_label(gt_label,
ignore_mask_gt)) # log(D(Y))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()
optimizer.step()
if USED:
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir)) #snapshot
print(
'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.6f}, loss_semi = {5:.6f}, loss_semi_adv = {6:.3f}, loss_vat = {7: .5f}'
.format(i_iter, args.num_steps, loss_seg_value,
loss_adv_pred_value, loss_D_value, loss_semi_value,
loss_semi_adv_value, loss_vat_value))
# L_ce L_adv for labeled L_D L_semi L_adv for unlabeled
#loss_adv should be inversely proportional to the loss_D if they are seeing the same data.
# loss_adv_p is essentially the inverse loss of loss_D. We expect them to achieve a good balance during the adversarial training
# loss_D is around 0.2-0.5 >> good
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '_D.pth'))
#torch.save(state, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth.tar'))
#torch.save(state_D, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth.tar'))
break
if i_iter % 100 == 0 and sub_i == 4: #loss_seg_value
wdata = "iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.6f}, loss_semi = {5:.8f}, loss_semi_adv = {6:.3f}, l_vat_sum = {7: .5f}, loss_label = {8: .4}\n".format(
i_iter, args.num_steps, l_seg_sum, l_adv_sum, loss_D_value,
loss_semi_value, loss_semi_adv_value, l_vat_sum,
l_seg_sum + 0.01 * l_adv_sum)
#wdata2 = "{0:8d} {1:s} {2:s} {3:s} {4:s} {5:s} {6:s} {7:s} {8:s}\n".format(i_iter,str(model.coeff[0])[8:14],str(model.coeff[1])[8:14],str(model.coeff[2])[8:14],str(model.coeff[3])[8:14],str(model.coeff[4])[8:14],str(model.coeff[5])[8:14],str(model.coeff[6])[8:14],str(model.coeff[7])[8:14])
if i_iter == 0:
f2 = open("/home/eungyo/AdvSemiSeg/snapshots/log.txt", 'w')
f2.write(wdata)
f2.close()
#f3 = open("/home/eungyo/AdvSemiSeg/snapshots/coeff.txt", 'w')
#f3.write(wdata2)
#f3.close()
else:
f1 = open("/home/eungyo/AdvSemiSeg/snapshots/log.txt", 'a')
f1.write(wdata)
f1.close()
#f4 = open("/home/eungyo/AdvSemiSeg/snapshots/coeff.txt", 'a')
#f4.write(wdata2)
#f4.close()
if i_iter % args.save_pred_every == 0 and i_iter != 0: # 5000
print('taking snapshot ...')
#state = {'epoch':i_iter, 'state_dict':model.state_dict(),'optim_dict':optimizer.state_dict()}
#state_D = {'epoch':i_iter, 'state_dict': model_D.state_dict(), 'optim_dict': optimizer_D.state_dict()}
#torch.save(state, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth.tar'))
#torch.save(state_D, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth.tar'))
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def train(log_file, arch, dataset, batch_size, iter_size, num_workers,
partial_data, partial_data_size, partial_id, ignore_label, crop_size,
eval_crop_size, is_training, learning_rate, learning_rate_d,
supervised, lambda_adv_pred, lambda_semi, lambda_semi_adv, mask_t,
semi_start, semi_start_adv, d_remain, momentum, not_restore_last,
num_steps, power, random_mirror, random_scale, random_seed,
restore_from, restore_from_d, eval_every, save_snapshot_every,
snapshot_dir, weight_decay, device):
settings = locals().copy()
import cv2
import torch
import torch.nn as nn
from torch.utils import data, model_zoo
import numpy as np
import pickle
import torch.optim as optim
import torch.nn.functional as F
import scipy.misc
import sys
import os
import os.path as osp
import pickle
from model.deeplab import Res_Deeplab
from model.unet import unet_resnet50
from model.deeplabv3 import resnet101_deeplabv3
from model.discriminator import FCDiscriminator
from utils.loss import CrossEntropy2d, BCEWithLogitsLoss2d
from utils.evaluation import EvaluatorIoU
from dataset.voc_dataset import VOCDataSet
import logger
torch_device = torch.device(device)
import time
if log_file != '' and log_file != 'none':
if os.path.exists(log_file):
print('Log file {} already exists; exiting...'.format(log_file))
return
with logger.LogFile(log_file if log_file != 'none' else None):
if dataset == 'pascal_aug':
ds = VOCDataSet(augmented_pascal=True)
elif dataset == 'pascal':
ds = VOCDataSet(augmented_pascal=False)
else:
print('Dataset {} not yet supported'.format(dataset))
return
print('Command: {}'.format(sys.argv[0]))
print('Arguments: {}'.format(' '.join(sys.argv[1:])))
print('Settings: {}'.format(', '.join([
'{}={}'.format(k, settings[k])
for k in sorted(list(settings.keys()))
])))
print('Loaded data')
def loss_calc(pred, label):
"""
This function returns cross entropy loss for semantic segmentation
"""
# out shape batch_size x channels x h x w -> batch_size x channels x h x w
# label shape h x w x 1 x batch_size -> batch_size x 1 x h x w
label = label.long().to(torch_device)
criterion = CrossEntropy2d()
return criterion(pred, label)
def lr_poly(base_lr, iter, max_iter, power):
return base_lr * ((1 - float(iter) / max_iter)**(power))
def adjust_learning_rate(optimizer, i_iter):
lr = lr_poly(learning_rate, i_iter, num_steps, power)
optimizer.param_groups[0]['lr'] = lr
if len(optimizer.param_groups) > 1:
optimizer.param_groups[1]['lr'] = lr * 10
def adjust_learning_rate_D(optimizer, i_iter):
lr = lr_poly(learning_rate_d, i_iter, num_steps, power)
optimizer.param_groups[0]['lr'] = lr
if len(optimizer.param_groups) > 1:
optimizer.param_groups[1]['lr'] = lr * 10
def one_hot(label):
label = label.numpy()
one_hot = np.zeros((label.shape[0], ds.num_classes, label.shape[1],
label.shape[2]),
dtype=label.dtype)
for i in range(ds.num_classes):
one_hot[:, i, ...] = (label == i)
#handle ignore labels
return torch.tensor(one_hot,
dtype=torch.float,
device=torch_device)
def make_D_label(label, ignore_mask):
ignore_mask = np.expand_dims(ignore_mask, axis=1)
D_label = np.ones(ignore_mask.shape) * label
D_label[ignore_mask] = ignore_label
D_label = torch.tensor(D_label,
dtype=torch.float,
device=torch_device)
return D_label
h, w = map(int, eval_crop_size.split(','))
eval_crop_size = (h, w)
h, w = map(int, crop_size.split(','))
crop_size = (h, w)
# create network
if arch == 'deeplab2':
model = Res_Deeplab(num_classes=ds.num_classes)
elif arch == 'unet_resnet50':
model = unet_resnet50(num_classes=ds.num_classes)
elif arch == 'resnet101_deeplabv3':
model = resnet101_deeplabv3(num_classes=ds.num_classes)
else:
print('Architecture {} not supported'.format(arch))
return
# load pretrained parameters
if restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(restore_from)
else:
saved_state_dict = torch.load(restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
model.load_state_dict(new_params)
model.train()
model = model.to(torch_device)
# init D
model_D = FCDiscriminator(num_classes=ds.num_classes)
if restore_from_d is not None:
model_D.load_state_dict(torch.load(restore_from_d))
model_D.train()
model_D = model_D.to(torch_device)
print('Built model')
if snapshot_dir is not None:
if not os.path.exists(snapshot_dir):
os.makedirs(snapshot_dir)
ds_train_xy = ds.train_xy(crop_size=crop_size,
scale=random_scale,
mirror=random_mirror,
range01=model.RANGE01,
mean=model.MEAN,
std=model.STD)
ds_train_y = ds.train_y(crop_size=crop_size,
scale=random_scale,
mirror=random_mirror,
range01=model.RANGE01,
mean=model.MEAN,
std=model.STD)
ds_val_xy = ds.val_xy(crop_size=eval_crop_size,
scale=False,
mirror=False,
range01=model.RANGE01,
mean=model.MEAN,
std=model.STD)
train_dataset_size = len(ds_train_xy)
if partial_data_size != -1:
if partial_data_size > partial_data_size:
print('partial-data-size > |train|: exiting')
return
if partial_data == 1.0 and (partial_data_size == -1 or
partial_data_size == train_dataset_size):
trainloader = data.DataLoader(ds_train_xy,
batch_size=batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_gt = data.DataLoader(ds_train_y,
batch_size=batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_remain = None
print('|train|={}'.format(train_dataset_size))
print('|val|={}'.format(len(ds_val_xy)))
else:
#sample partial data
if partial_data_size != -1:
partial_size = partial_data_size
else:
partial_size = int(partial_data * train_dataset_size)
if partial_id is not None:
train_ids = pickle.load(open(partial_id))
print('loading train ids from {}'.format(partial_id))
else:
rng = np.random.RandomState(random_seed)
train_ids = list(rng.permutation(train_dataset_size))
if snapshot_dir is not None:
pickle.dump(train_ids,
open(osp.join(snapshot_dir, 'train_id.pkl'), 'wb'))
print('|train supervised|={}'.format(partial_size))
print('|train unsupervised|={}'.format(train_dataset_size -
partial_size))
print('|val|={}'.format(len(ds_val_xy)))
print('supervised={}'.format(list(train_ids[:partial_size])))
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(ds_train_xy,
batch_size=batch_size,
sampler=train_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain = data.DataLoader(ds_train_xy,
batch_size=batch_size,
sampler=train_remain_sampler,
num_workers=3,
pin_memory=True)
trainloader_gt = data.DataLoader(ds_train_y,
batch_size=batch_size,
sampler=train_gt_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
testloader = data.DataLoader(ds_val_xy,
batch_size=1,
shuffle=False,
pin_memory=True)
print('Data loaders ready')
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(learning_rate),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=learning_rate_d,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
print('Built optimizer')
# labels for adversarial training
pred_label = 0
gt_label = 1
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_mask_accum = 0
loss_semi_value = 0
loss_semi_adv_value = 0
t1 = time.time()
print('Training for {} steps...'.format(num_steps))
for i_iter in range(num_steps + 1):
model.train()
model.freeze_batchnorm()
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(iter_size):
# train G
if not supervised:
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if not supervised and (lambda_semi > 0 or lambda_semi_adv > 0 ) and i_iter >= semi_start_adv and \
trainloader_remain is not None:
try:
_, batch = next(trainloader_remain_iter)
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = next(trainloader_remain_iter)
# only access to img
images, _, _, _ = batch
images = images.float().to(torch_device)
pred = model(images)
pred_remain = pred.detach()
D_out = model_D(F.softmax(pred, dim=1))
D_out_sigmoid = F.sigmoid(
D_out).data.cpu().numpy().squeeze(axis=1)
ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(
np.bool)
loss_semi_adv = lambda_semi_adv * bce_loss(
D_out, make_D_label(gt_label, ignore_mask_remain))
loss_semi_adv = loss_semi_adv / iter_size
#loss_semi_adv.backward()
loss_semi_adv_value += float(
loss_semi_adv) / lambda_semi_adv
if lambda_semi <= 0 or i_iter < semi_start:
loss_semi_adv.backward()
loss_semi_value = 0
else:
# produce ignore mask
semi_ignore_mask = (D_out_sigmoid < mask_t)
semi_gt = pred.data.cpu().numpy().argmax(axis=1)
semi_gt[semi_ignore_mask] = ignore_label
semi_ratio = 1.0 - float(
semi_ignore_mask.sum()) / semi_ignore_mask.size
loss_semi_mask_accum += float(semi_ratio)
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
loss_semi = lambda_semi * loss_calc(pred, semi_gt)
loss_semi = loss_semi / iter_size
loss_semi_value += float(loss_semi) / lambda_semi
loss_semi += loss_semi_adv
loss_semi.backward()
else:
loss_semi = None
loss_semi_adv = None
# train with source
try:
_, batch = next(trainloader_iter)
except:
trainloader_iter = enumerate(trainloader)
_, batch = next(trainloader_iter)
images, labels, _, _ = batch
images = images.float().to(torch_device)
ignore_mask = (labels.numpy() == ignore_label)
pred = model(images)
loss_seg = loss_calc(pred, labels)
if supervised:
loss = loss_seg
else:
D_out = model_D(F.softmax(pred, dim=1))
loss_adv_pred = bce_loss(
D_out, make_D_label(gt_label, ignore_mask))
loss = loss_seg + lambda_adv_pred * loss_adv_pred
loss_adv_pred_value += float(loss_adv_pred) / iter_size
# proper normalization
loss = loss / iter_size
loss.backward()
loss_seg_value += float(loss_seg) / iter_size
if not supervised:
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
if d_remain:
pred = torch.cat((pred, pred_remain), 0)
ignore_mask = np.concatenate(
(ignore_mask, ignore_mask_remain), axis=0)
D_out = model_D(F.softmax(pred, dim=1))
loss_D = bce_loss(D_out,
make_D_label(pred_label, ignore_mask))
loss_D = loss_D / iter_size / 2
loss_D.backward()
loss_D_value += float(loss_D)
# train with gt
# get gt labels
try:
_, batch = next(trainloader_gt_iter)
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = next(trainloader_gt_iter)
_, labels_gt, _, _ = batch
D_gt_v = one_hot(labels_gt)
ignore_mask_gt = (labels_gt.numpy() == ignore_label)
D_out = model_D(D_gt_v)
loss_D = bce_loss(D_out,
make_D_label(gt_label, ignore_mask_gt))
loss_D = loss_D / iter_size / 2
loss_D.backward()
loss_D_value += float(loss_D)
optimizer.step()
optimizer_D.step()
sys.stdout.write('.')
sys.stdout.flush()
if i_iter % eval_every == 0 and i_iter != 0:
model.eval()
with torch.no_grad():
evaluator = EvaluatorIoU(ds.num_classes)
for index, batch in enumerate(testloader):
image, label, size, name = batch
size = size[0].numpy()
image = image.float().to(torch_device)
output = model(image)
output = output.cpu().data[0].numpy()
output = output[:, :size[0], :size[1]]
gt = np.asarray(label[0].numpy()[:size[0], :size[1]],
dtype=np.int)
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2),
dtype=np.int)
evaluator.sample(gt, output, ignore_value=ignore_label)
sys.stdout.write('+')
sys.stdout.flush()
per_class_iou = evaluator.score()
mean_iou = per_class_iou.mean()
loss_seg_value /= eval_every
loss_adv_pred_value /= eval_every
loss_D_value /= eval_every
loss_semi_mask_accum /= eval_every
loss_semi_value /= eval_every
loss_semi_adv_value /= eval_every
sys.stdout.write('\n')
t2 = time.time()
print(
'iter = {:8d}/{:8d}, took {:.3f}s, loss_seg = {:.6f}, loss_adv_p = {:.6f}, loss_D = {:.6f}, loss_semi_mask_rate = {:.3%} loss_semi = {:.6f}, loss_semi_adv = {:.3f}'
.format(i_iter, num_steps, t2 - t1, loss_seg_value,
loss_adv_pred_value, loss_D_value,
loss_semi_mask_accum, loss_semi_value,
loss_semi_adv_value))
for i, (class_name,
iou) in enumerate(zip(ds.class_names, per_class_iou)):
print('class {:2d} {:12} IU {:.2f}'.format(
i, class_name, iou))
print('meanIOU: ' + str(mean_iou) + '\n')
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
loss_semi_mask_accum = 0
loss_semi_adv_value = 0
t1 = t2
if snapshot_dir is not None and i_iter % save_snapshot_every == 0 and i_iter != 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
osp.join(snapshot_dir, 'VOC_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth'))
if snapshot_dir is not None:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(snapshot_dir, 'VOC_' + str(num_steps) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(snapshot_dir, 'VOC_' + str(num_steps) + '_D.pth'))
def main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
np.random.seed(args.random_seed)
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
print(name)
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
# load dataset
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
else:
#sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(train_ids,
open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb'))
# labeled data
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=3,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=3,
pin_memory=True)
# unlabeled data
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# loss/bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear')
for i_iter in range(args.num_steps):
loss_seg_value = 0
loss_unlabeled_seg_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
for sub_i in range(args.iter_size):
# train Segmentation
# train with labeled images
try:
_, batch = trainloader_iter.next()
except:
trainloader_iter = enumerate(trainloader)
_, batch = trainloader_iter.next()
images, labels, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
# computing loss
loss_seg = loss_calc(pred, labels, args.gpu)
# proper normalization
loss = loss_seg / args.iter_size
loss.backward()
loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size
# train with unlabeled
if args.lambda_semi > 0 and i_iter >= args.semi_start:
try:
_, batch = trainloader_remain_iter.next()
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = trainloader_remain_iter.next()
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
semi_gt = pred.data.cpu().numpy().argmax(axis=1)
semi_gt = torch.FloatTensor(semi_gt)
loss_unlabeled_seg = args.lambda_semi * loss_calc(
pred, semi_gt, args.gpu)
loss_unlabeled_seg = loss_unlabeled_seg / args.iter_size
loss_unlabeled_seg.backward()
loss_unlabeled_seg_value += loss_unlabeled_seg.data.cpu(
).numpy() / args.lambda_semi
else:
if args.lambda_semi > 0 and i_iter < args.semi_start:
loss_unlabeled_seg_value = 0
else:
loss_unlabeled_seg_value = None
optimizer.step()
print('exp = {}'.format(args.snapshot_dir))
print(
'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_unlabeled_seg = {3:.3f} '
.format(i_iter, args.num_steps, loss_seg_value,
loss_unlabeled_seg_value))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(
args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_' +
str(args.lambda_semi) + '_' + str(args.random_seed) +
'.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
osp.join(
args.snapshot_dir,
'VOC_' + str(i_iter) + '_' + str(args.lambda_semi) + '_' +
str(args.random_seed) + '.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
print(name)
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=16,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=16,
pin_memory=True)
else:
#sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(train_ids,
open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb'))
train_sampler_all = data.sampler.SubsetRandomSampler(train_ids)
train_gt_sampler_all = data.sampler.SubsetRandomSampler(train_ids)
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader_all = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler_all,
num_workers=16,
pin_memory=True)
trainloader_gt_all = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler_all,
num_workers=16,
pin_memory=True)
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=16,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=16,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=16,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
trainloader_all_iter = iter(trainloader_all)
trainloader_iter = iter(trainloader)
trainloader_gt_iter = iter(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
#y_real_, y_fake_ = Variable(torch.ones(args.batch_size, 1).cuda()), Variable(torch.zeros(args.batch_size, 1).cuda())
for i_iter in range(args.num_steps):
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_fm_value = 0
loss_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size):
# train G
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# train with source
try:
batch = next(trainloader_iter)
except:
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
images, labels, _, _ = batch
images = Variable(images).cuda(args.gpu)
#ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
loss_seg = loss_calc(pred, labels, args.gpu)
loss_seg.backward()
loss_seg_value += loss_seg.data.cpu().numpy()[0] / args.iter_size
if i_iter >= args.adv_start:
#fm loss calc
try:
batch = next(trainloader_all_iter)
except:
trainloader_iter = iter(trainloader_all)
batch = next(trainloader_all_iter)
images, labels, _, _ = batch
images = Variable(images).cuda(args.gpu)
#ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
_, D_out_y_pred = model_D(F.softmax(pred))
trainloader_gt_iter = iter(trainloader_gt)
batch = next(trainloader_gt_iter)
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
#ignore_mask_gt = (labels_gt.numpy() == 255)
_, D_out_y_gt = model_D(D_gt_v)
fm_loss = torch.mean(
torch.abs(
torch.mean(D_out_y_gt, 0) -
torch.mean(D_out_y_pred, 0)))
loss = loss_seg + args.lambda_fm * fm_loss
# proper normalization
fm_loss.backward()
#loss_seg_value += loss_seg.data.cpu().numpy()[0]/args.iter_size
loss_fm_value += fm_loss.data.cpu().numpy()[0] / args.iter_size
loss_value += loss.data.cpu().numpy()[0] / args.iter_size
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
D_out_z, _ = model_D(F.softmax(pred))
y_fake_ = Variable(torch.zeros(D_out_z.size(0), 1).cuda())
loss_D_fake = criterion(D_out_z, y_fake_)
# train with gt
# get gt labels
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
#ignore_mask_gt = (labels_gt.numpy() == 255)
D_out_z_gt, _ = model_D(D_gt_v)
#D_out = interp(D_out_x)
y_real_ = Variable(torch.ones(D_out_z_gt.size(0), 1).cuda())
loss_D_real = criterion(D_out_z_gt, y_real_)
loss_D = loss_D_fake + loss_D_real
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
optimizer.step()
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir))
print('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_D = {3:.3f}'.
format(i_iter, args.num_steps, loss_seg_value, loss_D_value))
print('fm_loss: ', loss_fm_value, ' g_loss: ', loss_value)
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
# LD build for summary
# training_summary = tf.summary.FileWriter(os.path.join(SUMMARY_DIR, 'train'))
# val_summary = tf.summary.FileWriter(os.path.join(SUMMARY_DIR, 'val'))
# dice_placeholder = tf.placeholder(tf.float32, [], name='dice')
# loss_placeholder = tf.placeholder(tf.float32, [], name='loss')
# # image_placeholder = tf.placeholder(tf.float32, [400*2, 400*2], name='image')
# # prediction_placeholder = tf.placeholder(tf.float32, [400*2, 400*2], name='prediction')
# tf.summary.scalar('dice', dice_placeholder)
# tf.summary.scalar('loss', loss_placeholder)
# # tf.summary.image('image', image_placeholder, max_outputs=1)
# # tf.summary.image('prediction', prediction_placeholder, max_outputs=1)
# summary_op = tf.summary.merge_all()
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# sess = tf.Session(config=config)
perfix_name = 'Liver'
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
print(name)
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
# LD delete
'''
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(args.gpu)
'''
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
# LD ADD start
from dataset.LiverDataset.liver_dataset import LiverDataset
user_name = 'ld'
validation_interval = 800
max_steps = 1000000000
batch_size = 5
n_neighboringslices = 1
input_size = 400
output_size = 400
slice_type = 'axial'
oversample = False
# reset_counter = args.reset_counter
label_of_interest = 1
label_required = 0
magic_number = 26.91
max_slice_tries_val = 0
max_slice_tries_train = 2
fuse_labels = True
apply_crop = False
train_data_dir = "/home/" + user_name + "/Documents/dataset/ISBI2017/media/nas/01_Datasets/CT/LITS/Training_Batch_2"
test_data_dir = "/home/" + user_name + "/Documents/dataset/ISBI2017/media/nas/01_Datasets/CT/LITS/Training_Batch_1"
train_dataset = LiverDataset(data_dir=train_data_dir,
slice_type=slice_type,
n_neighboringslices=n_neighboringslices,
input_size=input_size,
oversample=oversample,
label_of_interest=label_of_interest,
label_required=label_required,
max_slice_tries=max_slice_tries_train,
fuse_labels=fuse_labels,
apply_crop=apply_crop,
interval=validation_interval,
is_training=True,
batch_size=batch_size,
data_augmentation=True)
val_dataset = LiverDataset(data_dir=test_data_dir,
slice_type=slice_type,
n_neighboringslices=n_neighboringslices,
input_size=input_size,
oversample=oversample,
label_of_interest=label_of_interest,
label_required=label_required,
max_slice_tries=max_slice_tries_val,
fuse_labels=fuse_labels,
apply_crop=apply_crop,
interval=validation_interval,
is_training=False,
batch_size=batch_size)
# LD ADD end
# LD delete
'''
train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True)
else:
#sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = range(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb'))
train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
'''
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# LD delete
'''
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9,0.99))
optimizer_D.zero_grad()
'''
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size, input_size), mode='bilinear')
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(input_size, input_size),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(input_size, input_size), mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
loss_list = []
for i_iter in range(iter_start, args.num_steps):
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
loss_semi_adv_value = 0
num_prediction = 0
num_ground_truth = 0
num_intersection = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
# LD delete
'''
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
'''
for sub_i in range(args.iter_size):
# train G
# LD delete
'''
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv :
try:
_, batch = trainloader_remain_iter.next()
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = trainloader_remain_iter.next()
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
pred_remain = pred.detach()
D_out = interp(model_D(F.softmax(pred)))
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1)
ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(np.bool)
loss_semi_adv = args.lambda_semi_adv * bce_loss(D_out, make_D_label(gt_label, ignore_mask_remain))
loss_semi_adv = loss_semi_adv/args.iter_size
#loss_semi_adv.backward()
loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()[0]/args.lambda_semi_adv
if args.lambda_semi <= 0 or i_iter < args.semi_start:
loss_semi_adv.backward()
loss_semi_value = 0
else:
# produce ignore mask
semi_ignore_mask = (D_out_sigmoid < args.mask_T)
semi_gt = pred.data.cpu().numpy().argmax(axis=1)
semi_gt[semi_ignore_mask] = 255
semi_ratio = 1.0 - float(semi_ignore_mask.sum())/semi_ignore_mask.size
print('semi ratio: {:.4f}'.format(semi_ratio))
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
loss_semi = args.lambda_semi * loss_calc(pred, semi_gt, args.gpu)
loss_semi = loss_semi/args.iter_size
loss_semi_value += loss_semi.data.cpu().numpy()[0]/args.lambda_semi
loss_semi += loss_semi_adv
loss_semi.backward()
else:
loss_semi = None
loss_semi_adv = None
'''
# train with source
# LD delete
'''
try:
_, batch = trainloader_iter.next()
except:
trainloader_iter = enumerate(trainloader)
_, batch = trainloader_iter.next()
images, labels, _, _ = batch
images = Variable(images).cuda(args.gpu)
'''
batch_image, batch_label = train_dataset.get_next_batch()
batch_image = np.transpose(batch_image, axes=(0, 3, 1, 2))
batch_image = np.concatenate(
[batch_image, batch_image, batch_image], axis=1)
# print('Shape: ', np.shape(batch_image))
batch_image_torch = torch.Tensor(batch_image)
images = Variable(batch_image_torch).cuda(args.gpu)
# LD delete
# ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
pred_ny = pred.data.cpu().numpy()
pred_ny = np.transpose(pred_ny, axes=(0, 2, 3, 1))
pred_label_ny = np.squeeze(np.argmax(pred_ny, axis=3))
# prepare for dice
# print('Shape of gt is: ', np.shape(batch_label))
# print('Shape of pred is: ', np.shape(pred_ny))
# print('Shape of pred_label is: ', np.shape(pred_label_ny))
num_prediction += np.sum(np.asarray(pred_label_ny, np.uint8))
num_ground_truth += np.sum(np.asarray(batch_label >= 1, np.uint8))
num_intersection += np.sum(
np.asarray(
np.logical_and(batch_label >= 1, pred_label_ny >= 1),
np.uint8))
# num_intersection += np.sum(np.asarray(batch_label >= 1, np.uint8) == np.asarray(pred_label_ny, np.uint8))
loss_seg = loss_calc(pred, batch_label, args.gpu)
# LD delete
'''
D_out = interp(model_D(F.softmax(pred)))
loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask))
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
'''
loss = loss_seg
# print('Loss is: ', loss)
# proper normalization
loss = loss / args.iter_size
loss.backward()
# print('Loss of numpy is: ', loss_seg.data.cpu().numpy())
# print('Loss of numpy of zero is: ', loss_seg.data.cpu().numpy())
loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size
loss_list.append(loss_seg_value)
# loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0]/args.iter_size
# train D
# LD delete
'''
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
if args.D_remain:
pred = torch.cat((pred, pred_remain), 0)
ignore_mask = np.concatenate((ignore_mask,ignore_mask_remain), axis = 0)
D_out = interp(model_D(F.softmax(pred)))
loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask))
loss_D = loss_D/args.iter_size/2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
# train with gt
# get gt labels
try:
_, batch = trainloader_gt_iter.next()
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = trainloader_gt_iter.next()
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
ignore_mask_gt = (labels_gt.numpy() == 255)
D_out = interp(model_D(D_gt_v))
loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt))
loss_D = loss_D/args.iter_size/2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
'''
optimizer.step()
# optimizer_D.step()
dice = (2 * num_intersection + 1e-7) / (num_prediction +
num_ground_truth + 1e-7)
print('exp = {}'.format(args.snapshot_dir))
print('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}'.format(
i_iter, args.num_steps, loss_seg_value))
print(
'dice: %.4f, num_prediction: %d, num_ground_truth: %d, num_intersection: %d'
% (dice, num_prediction, num_ground_truth, num_intersection))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir,
perfix_name + str(args.num_steps) + '.pth'))
# torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, perfix_name +str(args.num_steps)+'_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
# torch.save(model.state_dict(), osp.join(args.snapshot_dir, perfix_name + str(i_iter)+'.pth'))
save_model(model, args.snapshot_dir, perfix_name, i_iter, 2)
# torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, perfix_name +str(i_iter)+'_D.pth'))
# if i_iter % UPDATE_TENSORBOARD_INTERVAL and i_iter != 0:
# # update tensorboard
# feed_dict = {
# dice_placeholder: dice,
# loss_placeholder: np.mean(loss_list)
# }
# summery_value = sess.run(summary_op, feed_dict)
# training_summary.add_summary(summery_value, i_iter)
# training_summary.flush()
#
# # for validation
# val_num_prediction = 0
# val_num_ground_truth = 0
# val_num_intersection = 0
# loss_list = []
#
# for _ in range(VAL_EXECUTE_TIMES):
# batch_image, batch_label = val_dataset.get_next_batch()
# batch_image = np.transpose(batch_image, axes=(0, 3, 1, 2))
# batch_image = np.concatenate([batch_image, batch_image, batch_image], axis=1)
# # print('Shape: ', np.shape(batch_image))
# batch_image_torch = torch.Tensor(batch_image)
# images = Variable(batch_image_torch).cuda(args.gpu)
#
# # LD delete
# # ignore_mask = (labels.numpy() == 255)
# pred = interp(model(images))
# pred_ny = pred.data.cpu().numpy()
# pred_ny = np.transpose(pred_ny, axes=(0, 2, 3, 1))
# pred_label_ny = np.squeeze(np.argmax(pred_ny, axis=3))
# val_num_prediction += np.sum(np.asarray(pred_label_ny, np.uint8))
# val_num_ground_truth += np.sum(np.asarray(batch_label >= 1, np.uint8))
# val_num_intersection += np.sum(np.asarray(np.logical_and(batch_label >= 1, pred_label_ny >= 1), np.uint8))
#
# loss_seg = loss_calc(pred, batch_label, args.gpu)
# loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size
# loss_list.append(loss_seg)
# dice = (2 * val_num_intersection + 1e-7) / (val_num_prediction + val_num_ground_truth + 1e-7)
# feed_dict = {
# dice_placeholder: dice,
# loss_placeholder: np.mean(loss_list)
# }
# summery_value = sess.run(summary_op, feed_dict)
# val_summary.add_summary(summery_value, i_iter)
# val_summary.flush()
# loss_list = []
training_summary.close()
val_summary.close()
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
print("Evaluating model")
print(args.restore_from)
print("classifier model")
print(args.restore_from_classifier)
print("sigmoid threshold")
print(args.sigmoid_threshold)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
model_cls = Res_Deeplab_class(num_classes=args.num_classes,
mode=6,
latent_vars=args.latent_vars)
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict, strict=False)
model.eval()
model.cuda(gpu0)
saved_state_dict = torch.load(args.restore_from_classifier)
model_cls.load_state_dict(saved_state_dict, strict=False)
model_cls.eval()
model_cls.cuda(gpu0)
testloader = data.DataLoader(VOCDataSet(args.data_dir,
args.data_list,
crop_size=(505, 505),
mean=IMG_MEAN,
scale=False,
mirror=False),
batch_size=1,
shuffle=False,
pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(505, 505),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize()
combo_matrix = np.zeros((args.num_classes, args.latent_vars + 1),
dtype=np.float32)
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % (index))
image, label, size, name = batch
size = size[0].numpy()
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
output = output[:, :size[0], :size[1]]
cls_pred = F.sigmoid(
model_cls(Variable(image, volatile=True).cuda(gpu0)))
cls_pred = cls_pred.cpu().data.numpy()[0]
gt = np.asarray(label[0].numpy()[:size[0], :size[1]], dtype=np.int)
#gt_classes = np.unique(gt).tolist()
for clsID in range(1, args.num_classes):
if cls_pred[clsID - 1] < args.sigmoid_threshold:
output[clsID, :, :] = -1000000000
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(
colorize(output).transpose(1, 2, 0), 'RGB')
#color_file.save(filename)
#filename = os.path.join(args.save_dir, '{}_lv.png'.format(name[0]))
#color_file = Image.fromarray(colorize(output_lv).transpose(1, 2, 0), 'RGB')
#color_file.save(filename)
filename_gt = os.path.join(args.save_dir, '{}_gt.png'.format(name[0]))
color_file_gt = Image.fromarray(colorize(gt).transpose(1, 2, 0), 'RGB')
#color_file_gt.save(filename_gt)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(
args.save_dir,
args.restore_from.split('/')[-1][:-4] + '_with_classifier_result.txt')
confusion_matrix = get_iou(data_list, args.num_classes, filename)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
# if args.pretrained_model != None:
# args.restore_from = pretrianed_models_dict[args.pretrained_model]
#
# if args.restore_from[:4] == 'http' :
# saved_state_dict = model_zoo.load_url(args.restore_from)
# else:
# saved_state_dict = torch.load(args.restore_from)
#model.load_state_dict(saved_state_dict)
model = Res_Deeplab(num_classes=args.num_classes)
#model.load_state_dict(torch.load('/data/wyc/AdvSemiSeg/snapshots/VOC_15000.pth'))#70.7
state_dict = torch.load(
'/data1/wyc/AdvSemiSeg/snapshots/VOC_t_baseline_1adv_mul_20000.pth'
) #baseline707 adv 709 nadv 705()*2#n adv0.694
# state_dict = torch.load(
# '/home/wyc/VOC_t_baseline_nadv2_20000.pth') # baseline707 adv 709 nadv 705()*2
# original saved file with DataParallel
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
new_params = model.state_dict().copy()
for name, param in new_params.items():
print(name)
if name in new_state_dict and param.size(
) == new_state_dict[name].size():
new_params[name].copy_(new_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(VOCDataSet(args.data_dir,
args.data_list,
crop_size=(505, 505),
mean=IMG_MEAN,
scale=False,
mirror=False),
batch_size=1,
shuffle=False,
pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(505, 505),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize()
tag = 0
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % (index))
image, label, size, name = batch
size = size[0].numpy()
output = model(Variable(image, volatile=True).cuda(gpu0))
pred = interp(output)
pred01 = F.softmax(pred, dim=1)
output = interp(output).cpu().data[0].numpy()
image = Variable(image).cuda()
pred_re = F.softmax(pred, dim=1).repeat(1, 3, 1, 1)
indices_1 = torch.index_select(image, 1,
Variable(torch.LongTensor([0])).cuda())
indices_2 = torch.index_select(image, 1,
Variable(torch.LongTensor([1])).cuda())
indices_3 = torch.index_select(image, 1,
Variable(torch.LongTensor([2])).cuda())
img_re = torch.cat([
indices_1.repeat(1, 21, 1, 1),
indices_2.repeat(1, 21, 1, 1),
indices_3.repeat(1, 21, 1, 1),
], 1)
mul_img = pred_re * img_re
for i_l in range(label.shape[0]):
label_set = np.unique(label[i_l]).tolist()
for ls in label_set:
if ls != 0 and ls != 255:
ls = int(ls)
img_p = torch.cat([
mul_img[i_l][ls].unsqueeze(0).unsqueeze(0),
mul_img[i_l][ls + 21].unsqueeze(0).unsqueeze(0),
mul_img[i_l][ls + 21 + 21].unsqueeze(0).unsqueeze(0)
], 1)
imgs = img_p.squeeze()
imgs = imgs.transpose(0, 1)
imgs = imgs.transpose(1, 2)
imgs = imgs.data.cpu().numpy()
img_ori = image[0]
img_ori = img_ori.squeeze()
img_ori = img_ori.transpose(0, 1)
img_ori = img_ori.transpose(1, 2)
img_ori = img_ori.data.cpu().numpy()
pred_ori = pred01[0][ls]
pred_ori = pred_ori.data.cpu().numpy()
pred_0 = pred_ori.copy()
pred_ori = pred_ori
size = pred_ori.shape
color_image = np.zeros((3, size[0], size[1]),
dtype=np.uint8)
for i in range(size[0]):
for j in range(size[1]):
if pred_0[i][j] > 0.995:
color_image[0][i][j] = 0
color_image[1][i][j] = 255
color_image[2][i][j] = 0
elif pred_0[i][j] > 0.9:
color_image[0][i][j] = 255
color_image[1][i][j] = 0
color_image[2][i][j] = 0
elif pred_0[i][j] > 0.7:
color_image[0][i][j] = 0
color_image[1][i][j] = 0
color_image[2][i][j] = 255
color_image = color_image.transpose((1, 2, 0))
# print pred_ori.shape
cv2.imwrite(
osp.join('/data1/wyc/AdvSemiSeg/vis/img_pred',
name[0] + '.png'), imgs)
cv2.imwrite(
osp.join('/data1/wyc/AdvSemiSeg/vis/image',
name[0] + '.png'), img_ori)
cv2.imwrite(
osp.join('/data1/wyc/AdvSemiSeg/vis/pred',
name[0] + '.png'), color_image)
output = output[:, :size[0], :size[1]]
gt = np.asarray(label[0].numpy()[:size[0], :size[1]], dtype=np.int)
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(
colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result.txt')
get_iou(data_list, args.num_classes, filename)
def main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
np.random.seed(args.random_seed)
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
print(name)
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.train()
model.cuda(args.gpu)
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
# load dataset
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
else:
#sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(train_ids,
open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb'))
# labeled data
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=3,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=3,
pin_memory=True)
# unlabeled data
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
for i_iter in range(args.num_steps):
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_D_ul_value = 0
loss_semi_value = 0
loss_semi_adv_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
# creating 2nd discriminator as a copy of the 1st one
if i_iter == args.discr_split:
model_D_ul = FCDiscriminator(num_classes=args.num_classes)
model_D_ul.load_state_dict(net_D.state_dict())
model_D_ul.train()
model_D_ul.cuda(args.gpu)
optimizer_D_ul = optim.Adam(model_D_ul.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
# start training 2nd discriminator after specified number of steps
if i_iter >= args.discr_split:
optimizer_D_ul.zero_grad()
adjust_learning_rate_D(optimizer_D_ul, i_iter)
for sub_i in range(args.iter_size):
# train Segmentation
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# don't accumulate grads in D_ul, in case split has already been made
if i_iter >= args.discr_split:
for param in model_D_ul.parameters():
param.requires_grad = False
# do semi-supervised training first
if args.lambda_semi_adv > 0 and i_iter >= args.semi_start_adv:
try:
_, batch = trainloader_remain_iter.next()
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = trainloader_remain_iter.next()
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
pred_remain = pred.detach()
# choose discriminator depending on the iteration
if i_iter >= args.discr_split:
D_out = interp(model_D_ul(F.softmax(pred)))
else:
D_out = interp(model_D(F.softmax(pred)))
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(
axis=1)
ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(
np.bool)
# adversarial loss
loss_semi_adv = args.lambda_semi_adv * bce_loss(
D_out, make_D_label(gt_label, ignore_mask_remain,
args.gpu))
loss_semi_adv = loss_semi_adv / args.iter_size
# true loss value without multiplier
loss_semi_adv_value += loss_semi_adv.data.cpu().numpy(
) / args.lambda_semi_adv
loss_semi_adv.backward()
else:
loss_semi = None
loss_semi_adv = None
# train with labeled images
try:
_, batch = trainloader_iter.next()
except:
trainloader_iter = enumerate(trainloader)
_, batch = trainloader_iter.next()
images, labels, _, _ = batch
images = Variable(images).cuda(args.gpu)
ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
D_out = interp(model_D(F.softmax(pred)))
# computing loss
loss_seg = loss_calc(pred, labels, args.gpu)
loss_adv_pred = bce_loss(
D_out, make_D_label(gt_label, ignore_mask, args.gpu))
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
# proper normalization
loss = loss / args.iter_size
loss.backward()
loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy(
) / args.iter_size
# train D and D_ul
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
if i_iter >= args.discr_split:
for param in model_D_ul.parameters():
param.requires_grad = True
# train D with pred
pred = pred.detach()
# before split, traing D with both labeled and unlabeled
if args.D_remain and i_iter < args.discr_split and (
args.lambda_semi > 0 or args.lambda_semi_adv > 0):
pred = torch.cat((pred, pred_remain), 0)
ignore_mask = np.concatenate((ignore_mask, ignore_mask_remain),
axis=0)
D_out = interp(model_D(F.softmax(pred)))
loss_D = bce_loss(D_out,
make_D_label(pred_label, ignore_mask, args.gpu))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()
# train D_ul with pred on unlabeled
if i_iter >= args.discr_split and (args.lambda_semi > 0
or args.lambda_semi_adv > 0):
D_ul_out = interp(model_D_ul(F.softmax(pred_remain)))
loss_D_ul = bce_loss(
D_ul_out,
make_D_label(pred_label, ignore_mask_remain, args.gpu))
loss_D_ul = loss_D_ul / args.iter_size / 2
loss_D_ul.backward()
loss_D_ul_value += loss_D_ul.data.cpu().numpy()
# get gt labels
try:
_, batch = trainloader_gt_iter.next()
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = trainloader_gt_iter.next()
images_gt, labels_gt, _, _ = batch
images_gt = Variable(images_gt).cuda(args.gpu)
with torch.no_grad():
pred_l = interp(model(images_gt))
# train D with gt
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
ignore_mask_gt = (labels_gt.numpy() == 255)
D_out = interp(model_D(D_gt_v))
loss_D = bce_loss(D_out,
make_D_label(gt_label, ignore_mask_gt, args.gpu))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()
# train D_ul with pseudo_gt (gt are substituted for pred)
if i_iter >= args.discr_split:
D_ul_out = interp(model_D_ul(F.softmax(pred_l)))
loss_D_ul = bce_loss(
D_ul_out, make_D_label(gt_label, ignore_mask_gt, args.gpu))
loss_D_ul = loss_D_ul / args.iter_size / 2
loss_D_ul.backward()
loss_D_ul_value += loss_D_ul.data.cpu().numpy()
optimizer.step()
optimizer_D.step()
if i_iter >= args.discr_split:
optimizer_D_ul.step()
print('exp = {}'.format(args.snapshot_dir))
print(
'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_D_ul={5:.3f}, loss_semi = {6:.3f}, loss_semi_adv = {7:.3f}'
.format(i_iter, args.num_steps, loss_seg_value,
loss_adv_pred_value, loss_D_value, loss_D_ul_value,
loss_semi_value, loss_semi_adv_value))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
net.state_dict(),
osp.join(
args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_' +
str(args.random_seed) + '.pth'))
torch.save(
net_D.state_dict(),
osp.join(
args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_' +
str(args.random_seed) + '_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
torch.save(
net.state_dict(),
osp.join(
args.snapshot_dir, 'VOC_' + str(i_iter) + '_' +
str(args.random_seed) + '.pth'))
torch.save(
net_D.state_dict(),
osp.join(
args.snapshot_dir, 'VOC_' + str(i_iter) + '_' +
str(args.random_seed) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
# if args.pretrained_model != None:
# args.restore_from = pretrianed_models_dict[args.pretrained_model]
#
# if args.restore_from[:4] == 'http' :
# saved_state_dict = model_zoo.load_url(args.restore_from)
# else:
# saved_state_dict = torch.load(args.restore_from)
#model.load_state_dict(saved_state_dict)
model = Res_Deeplab(num_classes=args.num_classes)
#model.load_state_dict(torch.load('/data/wyc/AdvSemiSeg/snapshots/VOC_15000.pth'))#70.7
state_dict = torch.load(
'/data1/wyc/AdvSemiSeg/snapshots/VOC_t_baseline_1adv_mul_new_two_patch2_20000.pth'
) #baseline707 adv 709 nadv 705()*2#n adv0.694
# state_dict = torch.load(
# '/home/wyc/VOC_t_baseline_nadv2_20000.pth') # baseline707 adv 709 nadv 705()*2
# original saved file with DataParallel
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
new_params = model.state_dict().copy()
for name, param in new_params.items():
print(name)
if name in new_state_dict and param.size(
) == new_state_dict[name].size():
new_params[name].copy_(new_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(VOCDataSet(args.data_dir,
args.data_list,
crop_size=(505, 505),
mean=IMG_MEAN,
scale=False,
mirror=False),
batch_size=1,
shuffle=False,
pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(505, 505),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(505, 505), mode='bilinear')
data_list = []
colorize = VOCColorize()
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % (index))
image, label, size, name = batch
size = size[0].numpy()
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
output = output[:, :size[0], :size[1]]
gt = np.asarray(label[0].numpy()[:size[0], :size[1]], dtype=np.int)
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
filename = os.path.join(args.save_dir, '{}.png'.format(name[0]))
color_file = Image.fromarray(
colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
# show_all(gt, output)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result.txt')
get_iou(data_list, args.num_classes, filename)
def main():
"""Create the model and start the training."""
w, h = map(int, args.input_size_source.split(','))
input_size_source = (w, h)
w, h = map(int, args.input_size_target.split(','))
input_size_target = (w, h)
cudnn.enabled = True
# Create network
if args.model == 'ResNet':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
new_params = model.state_dict().copy()
for i in saved_state_dict:
# Scale.layer5.conv2d_list.3.weight
i_parts = i.split('.')
# print i_parts
if not args.num_classes == 19 or not i_parts[1] == 'layer5':
new_params['.'.join(i_parts[1:])] = saved_state_dict[i]
# print i_parts
model.load_state_dict(new_params)
# model.load_state_dict(saved_state_dict)
elif args.model == 'VGG':
model = DeeplabVGG(num_classes=args.num_classes,
pretrained=True,
vgg16_caffe_path=args.restore_from)
# saved_state_dict = torch.load(args.restore_from)
# model.load_state_dict(saved_state_dict)
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
#Discrimintator setting
model_D = FCDiscriminator(num_classes=args.num_classes)
model_D.train()
model_D.cuda(args.gpu)
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
bce_loss = torch.nn.BCEWithLogitsLoss()
# labels for adversarial training
source_adv_label = 0
target_adv_label = 1
#Dataloader
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
trainloader = data.DataLoader(GTA5DataSet(args.translated_data_dir,
args.data_list,
max_iters=args.num_steps *
args.iter_size * args.batch_size,
crop_size=input_size_source,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
trainloader_iter = enumerate(trainloader)
style_trainloader = data.DataLoader(GTA5DataSet(
args.stylized_data_dir,
args.data_list,
max_iters=args.num_steps * args.iter_size * args.batch_size,
crop_size=input_size_source,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
style_trainloader_iter = enumerate(style_trainloader)
if STAGE == 1:
targetloader = data.DataLoader(cityscapesDataSet(
args.data_dir_target,
args.data_list_target,
max_iters=args.num_steps * args.iter_size * args.batch_size,
crop_size=input_size_target,
mean=IMG_MEAN,
set=args.set),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
targetloader_iter = enumerate(targetloader)
else:
#Dataloader for self-training
targetloader = data.DataLoader(cityscapesDataSetLabel(
args.data_dir_target,
args.data_list_target,
max_iters=args.num_steps * args.iter_size * args.batch_size,
crop_size=input_size_target,
mean=IMG_MEAN,
set=args.set,
label_folder='Path to generated pseudo labels'),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
targetloader_iter = enumerate(targetloader)
interp = nn.Upsample(size=(input_size_source[1], input_size_source[0]),
mode='bilinear',
align_corners=True)
interp_target = nn.Upsample(size=(input_size_target[1],
input_size_target[0]),
mode='bilinear',
align_corners=True)
# load checkpoint
model, model_D, optimizer, start_iter = load_checkpoint(
model,
model_D,
optimizer,
filename=args.snapshot_dir + 'checkpoint_' + CHECKPOINT + '.pth.tar')
for i_iter in range(start_iter, args.num_steps):
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
#train segementation network
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# train with source
if STAGE == 1:
if i_iter % 2 == 0:
_, batch = next(trainloader_iter)
else:
_, batch = next(style_trainloader_iter)
else:
_, batch = next(trainloader_iter)
image_source, label, _, _ = batch
image_source = Variable(image_source).cuda(args.gpu)
pred_source = model(image_source)
pred_source = interp(pred_source)
loss_seg_source = loss_calc(pred_source, label, args.gpu)
loss_seg_source_value = loss_seg_source.item()
loss_seg_source.backward()
if STAGE == 2:
# train with target
_, batch = next(targetloader_iter)
image_target, target_label, _, _ = batch
image_target = Variable(image_target).cuda(args.gpu)
pred_target = model(image_target)
pred_target = interp_target(pred_target)
#target segmentation loss
loss_seg_target = loss_calc(pred_target,
target_label,
gpu=args.gpu)
loss_seg_target.backward()
# optimize
optimizer.step()
if STAGE == 1:
# train with target
_, batch = next(targetloader_iter)
image_target, _, _ = batch
image_target = Variable(image_target).cuda(args.gpu)
pred_target = model(image_target)
pred_target = interp_target(pred_target)
#output-level adversarial training
D_output_target = model_D(F.softmax(pred_target))
loss_adv = bce_loss(
D_output_target,
Variable(
torch.FloatTensor(D_output_target.data.size()).fill_(
source_adv_label)).cuda(args.gpu))
loss_adv = loss_adv * args.lambda_adv
loss_adv.backward()
#train discriminator
for param in model_D.parameters():
param.requires_grad = True
pred_source = pred_source.detach()
pred_target = pred_target.detach()
D_output_source = model_D(F.softmax(pred_source))
D_output_target = model_D(F.softmax(pred_target))
loss_D_source = bce_loss(
D_output_source,
Variable(
torch.FloatTensor(D_output_source.data.size()).fill_(
source_adv_label)).cuda(args.gpu))
loss_D_target = bce_loss(
D_output_target,
Variable(
torch.FloatTensor(D_output_target.data.size()).fill_(
target_adv_label)).cuda(args.gpu))
loss_D_source = loss_D_source / 2
loss_D_target = loss_D_target / 2
loss_D_source.backward()
loss_D_target.backward()
#optimize
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir))
print('iter = {0:8d}/{1:8d}, loss_seg_source = {2:.5f}'.format(
i_iter, args.num_steps, loss_seg_source_value))
if i_iter % args.save_pred_every == 0:
print('taking snapshot ...')
state = {
'iter': i_iter,
'model': model.state_dict(),
'model_D': model_D.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(
state,
osp.join(args.snapshot_dir,
'checkpoint_' + str(i_iter) + '.pth.tar'))
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir, 'GTA5_D_' + str(i_iter) + '.pth'))
cityscapes_eval_dir = osp.join(args.cityscapes_eval_dir,
str(i_iter))
if not os.path.exists(cityscapes_eval_dir):
os.makedirs(cityscapes_eval_dir)
eval(osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth'),
cityscapes_eval_dir, i_iter)
iou19, iou13, iou = compute_mIoU(cityscapes_eval_dir, i_iter)
outputfile = open(args.output_file, 'a')
outputfile.write(
str(i_iter) + '\t' + str(iou19) + '\t' +
str(iou.replace('\n', ' ')) + '\n')
outputfile.close()
def main():
# 将参数的input_size 映射到整数，并赋值，从字符串转换到整数二元组
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = False
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
# 确保模型中参数的格式与要加载的参数相同
# 返回一个字典，保存着module的所有状态（state）；parameters和persistent buffers都会包含在字典中，字典的key就是parameter和buffer的 names。
new_params = model.state_dict().copy()
for name, param in new_params.items():
# print (name)
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
# print('copy {}'.format(name))
model.load_state_dict(new_params)
# 设置为训练模式
model.train()
cudnn.benchmark = True
model.cuda(gpu)
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
else:
# sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = list(range(train_dataset_size)) # ?
np.random.shuffle(train_ids)
pickle.dump(train_ids,
open(osp.join(args.snapshot_dir, 'train_id.pkl'),
'wb')) # 写入文件
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=3,
pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=3,
pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]),
mode='bilinear') # ???
# labels for adversarial training
pred_label = 0
gt_label = 1
for i_iter in range(args.num_steps):
print("Iter:", i_iter)
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size):
# train G
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if args.lambda_semi > 0 and i_iter >= args.semi_start:
try:
_, batch = next(trainloader_remain_iter)
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = next(trainloader_remain_iter)
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda(gpu)
# images = Variable(images).cpu()
pred = interp(model(images))
D_out = interp(model_D(F.softmax(pred)))
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(
axis=1)
# produce ignore mask
semi_ignore_mask = (D_out_sigmoid < args.mask_T)
semi_gt = pred.data.cpu().numpy().argmax(axis=1)
semi_gt[semi_ignore_mask] = 255
semi_ratio = 1.0 - float(
semi_ignore_mask.sum()) / semi_ignore_mask.size
print('semi ratio: {:.4f}'.format(semi_ratio))
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
loss_semi = args.lambda_semi * loss_calc(
pred, semi_gt, args.gpu)
loss_semi = loss_semi / args.iter_size
loss_semi.backward()
loss_semi_value += loss_semi.data.cpu().numpy(
)[0] / args.lambda_semi
else:
loss_semi = None
# train with source
try:
_, batch = next(trainloader_iter)
except:
trainloader_iter = enumerate(trainloader)
_, batch = next(trainloader_iter)
images, labels, _, _ = batch
images = Variable(images).cuda(gpu)
# images = Variable(images).cpu()
ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
loss_seg = loss_calc(pred, labels, args.gpu)
D_out = interp(model_D(F.softmax(pred)))
loss_adv_pred = bce_loss(D_out,
make_D_label(gt_label, ignore_mask))
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
# proper normalization
loss = loss / args.iter_size
loss.backward()
loss_seg_value += loss_seg.data.cpu().numpy()[0] / args.iter_size
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy(
)[0] / args.iter_size
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
D_out = interp(model_D(F.softmax(pred)))
loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
# train with gt
# get gt labels
try:
_, batch = next(trainloader_gt_iter)
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = next(trainloader_gt_iter)
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
# D_gt_v = Variable(one_hot(labels_gt)).cpu()
ignore_mask_gt = (labels_gt.numpy() == 255)
D_out = interp(model_D(D_gt_v))
loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt))
loss_D = loss_D / args.iter_size / 2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
optimizer.step()
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir))
print(
'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}'
.format(i_iter, args.num_steps, loss_seg_value,
loss_adv_pred_value, loss_D_value, loss_semi_value))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir,
'VOC_' + str(args.num_steps) + '_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in currendt model (caffe-like)
new_params = model.state_dict().copy()
for name, param in new_params.items():
print (name)
if name in saved_state_dict and param.size() == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
print('copy {}'.format(name))
model.load_state_dict(new_params)
model.train()
model=nn.DataParallel(model)
model.cuda()
cudnn.benchmark = True
# init D
model_D = FCDiscriminator(num_classes=args.num_classes)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D = nn.DataParallel(model_D)
model_D.train()
model_D.cuda()
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
if args.partial_data is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True)
else:
#sample partial data
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print('loading train ids from {}'.format(args.partial_id))
else:
train_ids = list(range(train_dataset_size))
np.random.shuffle(train_ids)
pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb'))
train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.module.optim_parameters(args),
lr=args.learning_rate, momentum=args.momentum,weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9,0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
for i_iter in range(args.num_steps):
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
loss_semi_adv_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size):
# train G
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv :
try:
_, batch = trainloader_remain_iter.next()
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = trainloader_remain_iter.next()
# only access to img
images, _, _, _ = batch
images = Variable(images).cuda()
pred = interp(model(images))
pred_remain = pred.detach()
mask1=F.softmax(pred,dim=1).data.cpu().numpy()
id2 = np.argmax(mask1, axis=1)#10, 321, 321)
D_out = interp(model_D(F.softmax(pred,dim=1)))
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1)
ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(np.bool)
loss_semi_adv = args.lambda_semi_adv * bce_loss(D_out, make_D_label(gt_label, ignore_mask_remain))
loss_semi_adv = loss_semi_adv/args.iter_size
#loss_semi_adv.backward()
loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()[0]/args.lambda_semi_adv
if args.lambda_semi <= 0 or i_iter < args.semi_start:
loss_semi_adv.backward()
loss_semi_value = 0
else:
# produce ignore mask
semi_ignore_mask = (D_out_sigmoid < args.mask_T)
#print semi_ignore_mask.shape 10,321,321
map2 = np.zeros([pred.size()[0], id2.shape[1], id2.shape[2]])
for k in range(pred.size()[0]):
for i in range(id2.shape[1]):
for j in range(id2.shape[2]):
map2[k][i][j] = mask1[k][id2[k][i][j]][i][j]
semi_ignore_mask = (map2 < 0.999999)
semi_gt = pred.data.cpu().numpy().argmax(axis=1)
semi_gt[semi_ignore_mask] = 255
semi_ratio = 1.0 - float(semi_ignore_mask.sum())/semi_ignore_mask.size
print('semi ratio: {:.4f}'.format(semi_ratio))
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
loss_semi = args.lambda_semi * loss_calc(pred, semi_gt)
loss_semi = loss_semi/args.iter_size
loss_semi_value += loss_semi.data.cpu().numpy()[0]/args.lambda_semi
loss_semi += loss_semi_adv
loss_semi.backward()
else:
loss_semi = None
loss_semi_adv = None
# train with source
try:
_, batch = trainloader_iter.next()
except:
trainloader_iter = enumerate(trainloader)
_, batch = trainloader_iter.next()
images, labels, _, _ = batch
images = Variable(images).cuda()
ignore_mask = (labels.numpy() == 255)
pred = interp(model(images))
loss_seg = loss_calc(pred, labels)
D_out = interp(model_D(F.softmax(pred,dim=1)))
loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask))
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
# proper normalization
loss = loss/args.iter_size
loss.backward()
loss_seg_value += loss_seg.data.cpu().numpy()[0]/args.iter_size
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0]/args.iter_size
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
if args.D_remain:
pred = torch.cat((pred, pred_remain), 0)
ignore_mask = np.concatenate((ignore_mask,ignore_mask_remain), axis = 0)
D_out = interp(model_D(F.softmax(pred,dim=1)))
loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask))
loss_D = loss_D/args.iter_size/2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
# train with gt
# get gt labels
try:
_, batch = trainloader_gt_iter.next()
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = trainloader_gt_iter.next()
_, labels_gt, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda()
ignore_mask_gt = (labels_gt.numpy() == 255)
D_out = interp(model_D(D_gt_v))
loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt))
loss_D = loss_D/args.iter_size/2
loss_D.backward()
loss_D_value += loss_D.data.cpu().numpy()[0]
optimizer.step()
optimizer_D.step()
print('exp = {}'.format(args.snapshot_dir))
print('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}, loss_semi_adv = {6:.3f}'.format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value))
if i_iter >= args.num_steps-1:
print( 'save model ...')
torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(args.num_steps)+'.pth'))
torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(args.num_steps)+'_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter!=0:
print ('taking snapshot ...')
torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(i_iter)+'.pth'))
torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(i_iter)+'_D.pth'))
end = timeit.default_timer()
print(end-start,'seconds')
def main():
h, w = list(map(int, args.input_size.split(','))) # 321, 321
input_size = (h, w)
cudnn.enabled = True
# create network
model = Res_Deeplab(num_classes=args.num_classes) # num_classes = 21
# load pretrained parameters
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
new_params = model.state_dict().copy()
for name, param in list(new_params.items()):
# print(name)
if name in saved_state_dict and param.size() == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
# print(('copy {}'.format(name)))
model.load_state_dict(new_params)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
# init D
model_D = FCDiscriminator(num_classes=args.num_classes) # num_classes = 21，全卷积判别模型
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
if not os.path.exists('logs/'):
os.makedirs('logs/')
now_time = datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
log_file = 'logs/' + now_time + '.txt'
file = open(log_file, 'w') # 保存loss
train_dataset = VOCDataSet(args.data_dir, args.data_list, args.label_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, args.label_list, crop_size=input_size,
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
if args.partial_data is None: # 使用全部数据
trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, # batch_size = 10
num_workers=5, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=5, pin_memory=True)
else:
# sample partial data 部分数据
partial_size = int(args.partial_data * train_dataset_size)
if args.partial_id is not None:
train_ids = pickle.load(open(args.partial_id))
print(('loading train ids from {}'.format(args.partial_id)))
else:
train_ids = list(range(train_dataset_size))
np.random.shuffle(train_ids)
pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) # 将train_ids写入train_id.pkl
train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset, # 数据集中采样输入
batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True)
trainloader_gt = data.DataLoader(train_gt_dataset,
batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True)
trainloader_remain_iter = enumerate(trainloader_remain)
trainloader_iter = enumerate(trainloader)
trainloader_gt_iter = enumerate(trainloader_gt)
# implement model.optim_parameters(args) to handle different models' lr setting
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99))
optimizer_D.zero_grad()
# loss/ bilinear upsampling
bce_loss = BCEWithLogitsLoss2d()
# interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear')
# labels for adversarial training
pred_label = 0
gt_label = 1
best_loss = 1
best_epoch = 0
for i_iter in range(args.num_steps): # num_steps = 20000
loss_seg_value = 0
loss_adv_pred_value = 0
loss_D_value = 0
loss_semi_value = 0
loss_semi_adv_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
for sub_i in range(args.iter_size): # iter_size = 1
# train G
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# do semi first
if (args.lambda_semi > 0 or args.lambda_semi_adv > 0) and i_iter >= args.semi_start_adv:
try:
_, batch = next(trainloader_remain_iter)
except:
trainloader_remain_iter = enumerate(trainloader_remain)
_, batch = next(trainloader_remain_iter)
# only access to img 无标签数据
images, _, _, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
pred_remain = pred.detach() # 返回一个新的Variable，不具有grade
D_out = interp(model_D(F.softmax(pred)))
D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1)
ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(np.bool)
loss_semi_adv = args.lambda_semi_adv * bce_loss(D_out, make_D_label(gt_label, ignore_mask_remain))
loss_semi_adv = loss_semi_adv/args.iter_size
# loss_semi_adv.backward()
# print('bug,', loss_semi_adv.data.cpu().numpy())
# loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()[0]/args.lambda_semi_adv
loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()/args.lambda_semi_adv
if args.lambda_semi <= 0 or i_iter < args.semi_start:
loss_semi_adv.backward()
loss_semi_value = 0
else:
# produce ignore mask
semi_ignore_mask = (D_out_sigmoid < args.mask_T) # mask_T = 0.2，阈值
semi_gt = pred.data.cpu().numpy().argmax(axis=1) # 返回维度为1上的最大值的下标
semi_gt[semi_ignore_mask] = 255
semi_ratio = 1.0 - float(semi_ignore_mask.sum())/semi_ignore_mask.size # 被忽略的点占的比重
print(('semi ratio: {:.4f}'.format(semi_ratio)))
if semi_ratio == 0.0:
loss_semi_value += 0
else:
semi_gt = torch.FloatTensor(semi_gt)
loss_semi = args.lambda_semi * loss_calc(pred, semi_gt, args.gpu)
loss_semi = loss_semi/args.iter_size
# loss_semi_value += loss_semi.data.cpu().numpy()[0]/args.lambda_semi
loss_semi_value += loss_semi.data.cpu().numpy()/args.lambda_semi
loss_semi += loss_semi_adv
loss_semi.backward()
else:
loss_semi = None
loss_semi_adv = None
# train with source
try:
_, batch = next(trainloader_iter)
except:
trainloader_iter = enumerate(trainloader)
_, batch = next(trainloader_iter)
images, labels, _, _, _ = batch # 有标签数据
images = Variable(images).cuda(args.gpu)
ignore_mask = (labels.numpy() == 255)
pred = interp(model(images)) # interp上采样
loss_seg = loss_calc(pred, labels, args.gpu) # 语义分割的cross entropy loss
# loss_seg_NLL = loss_NLL(pred, labels, args.gpu) # 语义分割的NLLLoss
D_out = interp(model_D(F.softmax(pred))) # 得到判别模型输出的判别图
loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask))
loss = loss_seg + args.lambda_adv_pred * loss_adv_pred
# proper normalization
loss = loss/args.iter_size
loss.backward()
# loss_seg_value += loss_seg.data.cpu().numpy()[0]/args.iter_size
# loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0]/args.iter_size
loss_seg_value += loss_seg.data.cpu().numpy()/args.iter_size
# loss_seg_value += loss_seg_NLL.data.cpu().numpy()/args.iter_size
loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()/args.iter_size
# train D
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred = pred.detach()
if args.D_remain:
pred = torch.cat((pred, pred_remain), 0)
ignore_mask = np.concatenate((ignore_mask,ignore_mask_remain), axis=0)
D_out = interp(model_D(F.softmax(pred)))
loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask))
loss_D = loss_D/args.iter_size/2
loss_D.backward()
# loss_D_value += loss_D.data.cpu().numpy()[0]
loss_D_value += loss_D.data.cpu().numpy()
# train with gt
# get gt labels
try:
_, batch = next(trainloader_gt_iter)
except:
trainloader_gt_iter = enumerate(trainloader_gt)
_, batch = next(trainloader_gt_iter)
_, labels_gt, _, _, _ = batch
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) # 每个类别一张label图，batch * class * h * w
ignore_mask_gt = (labels_gt.numpy() == 255)
D_out = interp(model_D(D_gt_v)) # ground_truth输入判别模型
loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt))
loss_D = loss_D/args.iter_size/2
loss_D.backward()
# loss_D_value += loss_D.data.cpu().numpy()[0]
loss_D_value += loss_D.data.cpu().numpy()
optimizer.step()
optimizer_D.step()
print(('exp = {}'.format(args.snapshot_dir)))
print(('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, '
'loss_semi = {5:.3f}, loss_semi_adv = {6:.3f}'.format(i_iter, args.num_steps, loss_seg_value,
loss_adv_pred_value, loss_D_value, loss_semi_value,
loss_semi_adv_value)))
file.write('{0} {1} {2} {3} {4}\n'.format(loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value,
loss_semi_adv_value))
if loss_seg_value < best_loss: # 保存最优模型，删除次优模型
# print('loss:', loss_seg_value, 'best:', best_loss)
torch.save(model.state_dict(), osp.join(args.snapshot_dir,
'VOC_epoch_{0}_seg_loss_{1}.pth'.format(i_iter+1, loss_seg_value)))
torch.save(model_D.state_dict(), osp.join(args.snapshot_dir,
'VOC_epoch_{0}_seg_loss_{1}_D.pth'.format(i_iter+1, loss_seg_value)))
delete_models(best_epoch + 1, best_loss)
best_loss = loss_seg_value
best_epoch = i_iter
if i_iter >= args.num_steps-1: # num_step = 20000
print('save model ...')
torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_'+str(args.num_steps)+'.pth'))
torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_'+str(args.num_steps)+'_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0: # save_pred_every = 5000
print('taking snapshot ...')
torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(i_iter)+'.pth'))
torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(i_iter)+'_D.pth'))
end = timeit.default_timer()
print(end-start, 'seconds')
file.close()