def networks(network, **kwargs):
# ResNet
if 'resnet' in network and 'pre' not in network:
depth = int(network[6:])
return resnet(depth, **kwargs)
elif 'vgg' in network:
depth = int(network[3:5])
if 'bn' in network:
return vgg_bn(depth, **kwargs)
else:
return vgg(depth, **kwargs)
elif 'wideResNet' in network:
depth = int(network[10:12])
widen_factor = int(network[13:])
return wideResNet(depth, widen_factor, **kwargs)
elif 'preresnet' in network:
depth = int(network[9:])
return preresnet(depth, **kwargs)
elif 'pyramidnet' in network:
depth = int(network[10:])
return pyramidnet(depth, **kwargs)
def init_network(config, num_classes):
kwargs = {'depth': config.depth, 'num_classes': num_classes}
if config.network == 'vgg':
if 'avg_pool2d' in config:
kwargs.update({'avg_pool2d': config.avg_pool2d})
if 'batch_norm' in config:
kwargs.update({'batch_norm': config.batch_norm})
net = vgg(**kwargs)
elif config.network == 'resnet':
dataset = config.data_dir.split('/')[-1]
if 'cifar' in dataset.lower():
kwargs.update({'dataset': 'cifar'})
net = resnet(**kwargs)
elif config.network == 'reskipnet':
net = reskipnet(**kwargs)
else:
raise NotImplementedError
assert os.path.exists('{}/checkpoint/original'.format(config.result_dir)),\
'No checkpoint directory for original model!'
dataset = config.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, config.network, config.depth)
checkpoint_path, exp_name, epochs = get_best_checkpoint(
'{}/checkpoint/original/{}'.format(config.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
if torch.cuda.is_available():
net.cuda()
return net, exp_name, epochs
def __init__(self,
backbone_name='resnet18',
in_channels=3,
sc_channels=128,
load_pretrained=True,
norm_type='bn',
use_cbam=False):
super(ContextPath, self).__init__()
bias = not (norm_type == 'bn' or norm_type == 'gn')
self.backbone = resnet(name=backbone_name,
in_channels=in_channels,
load_pretrained=load_pretrained,
norm_type=norm_type,
use_cbam=use_cbam)
expansion = self.backbone.block.expansion
##self.ppm = PPM(in_channels=512*expansion, pool_sizes=[6, 3, 2, 1], mode='sum', norm_type=norm_type)
self.arm_32x = ARM(in_channels=512 * expansion,
out_channels=sc_channels,
norm_type=norm_type)
self.arm_16x = ARM(in_channels=256 * expansion,
out_channels=sc_channels,
norm_type=norm_type)
self.arm_8x = ARM(in_channels=128 * expansion,
out_channels=sc_channels,
norm_type=norm_type)
self.cbr_gap = ConvBatchNormRelu(in_channels=512 * expansion,
out_channels=sc_channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
norm_type=norm_type)
##self.cbr_ppm = ConvBatchNormRelu(in_channels=512*expansion, out_channels=sc_channels, kernel_size=1, stride=1, padding=0, bias=bias, norm_type=norm_type)
self.cbr_32x = ConvBatchNormRelu(in_channels=sc_channels,
out_channels=sc_channels,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type)
self.cbr_16x = ConvBatchNormRelu(in_channels=sc_channels,
out_channels=sc_channels,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type)
self.cbr_8x = ConvBatchNormRelu(in_channels=sc_channels,
out_channels=sc_channels,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type)
def main():
# Check that necessary paths exists, if not create them.
if not os.path.exists(MODEL_CHECKPOINT_PATH):
print_info('Creating model checkpoint path...')
os.makedirs(MODEL_CHECKPOINT_PATH)
# Get the dataset from the CSV, load it and preprocess the data.
print_info('Preparing training and validation data...')
train_img, train_labels, val_img, val_labels = get_training_val_data(
TRAINING_DATA_CSV_PATH)
# Plot the distribution.
# view_label_distribution(train_labels, title='Emotions in training set')
# view_label_distribution(val_labels, title='Emotions in validation set')
# Calculate class weights.
print_info('Calculating class weights...')
train_weights = calculate_class_weights(train_labels)
# Load and compile the model with the correct input shape and number of classes.
print_info('Loading model...')
input_shape = train_img[0].shape
num_of_classes = len(np.unique(train_labels))
model = resnet(input_shape, num_of_classes)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Calculate the number of steps taken during training.
training_steps = len(train_img) / BATCH_SIZE
# Add all callbacks in a list. Will be used during training.
save_path = MODEL_CHECKPOINT_PATH + 'weights_{epoch:03d}.hdf5'
checkpoint = ModelCheckpoint(filepath=save_path,
monitor='val_acc',
verbose=1,
save_best_only=True)
callbacks = [checkpoint]
# Create an ImageDataGenerator. Eventual augmentations are done here.
data_generator = ImageDataGenerator(rotation_range=20,
horizontal_flip=True,
width_shift_range=0.2,
height_shift_range=0.2)
# Train the model.
print_info('Starting training...')
model.fit_generator(data_generator.flow(train_img, train_labels),
steps_per_epoch=training_steps,
class_weight=train_weights,
epochs=EPOCHS,
validation_data=(val_img, val_labels),
shuffle=True,
callbacks=callbacks,
use_multiprocessing=False,
workers=multiprocessing.cpu_count())
def predict_agument(data, **kwargs):
"""
模型预测，单张图片复制
:param
data(tensor) -- 图片数据
:kwargs
:return(numpy)
预测结果
"""
# 选择运行的cpu或gpu
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 加载模型
models = []
for k, v in kwargs.items():
if k == '1':
model = MFFNet()
elif k == '2':
# 299 x 299
model = inception(pretrained = False)
elif k == '3':
model = resnet(pretrained = False)
elif k == '4':
model = densenet(pretrained = False)
elif k == '5':
model = senet(pretrained = False)
# 加载权重
model.load_state_dict(torch.load(v))
model = model.to(device)
# 测试模式
model.eval()
models.append(model)
# 使用平均策略获取集成模型输出
data = data.to(device)
sum = None
# 平均策略
for model in models:
output = model(data)
output = output.detach()
val = torch.zeros(7)
for i in range(output.size(0)):
val = val + output[i]
val = val / output.size(0)
if sum is None:
sum = val
else:
sum += val
val = sum / len(models)
_, a = torch.max(val, 0)
return a.item()
def main():
if "resnet" in args.model:
if args.model == "resnet18":
model, model_criterion, model_optimizer, model_scheduler = models.resnet(
18, args.learning_rate, args.momentum)
elif args.model == "resnet101":
model, model_criterion, model_optimizer, model_scheduler = models.resnet(
101, args.learning_rate, args.momentum)
elif args.model == "resnet152":
model, model_criterion, model_optimizer, model_scheduler = models.resnet(
152, args.learning_rate, args.momentum)
else:
print("Using default resnet model: resnet50")
model, model_criterion, model_optimizer, model_scheduler = models.resnet(
50, args.learning_rate, args.momentum)
model = train_resnet(model,
model_criterion,
model_optimizer,
model_scheduler,
epochs=args.epochs)
predict_model(model)
elif args.model == "chexnet":
model, model_criterion, model_optimizer = models.chexnet(
args.learning_rate, args.momentum, args.weight_decay)
model = train_chexnet(model,
model_criterion,
model_optimizer,
epochs=args.epochs,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay)
predict_model(model)
elif args.model == "squeezenet":
model, model_criterion, model_optimizer = models.squeezenet(
args.learning_rate, args.momentum, args.weight_decay)
model = train_squeezenet(model,
model_criterion,
model_optimizer,
num_epochs=args.epochs)
predict_model(model)
else:
print("Please input a valid model")
def run_dataset(file_path, checkpoint, idx_to_species, cuda):
# Load Data
input_size = (600, 600)
mean = [0.485, 0.456, 0.406][::-1]
std = [0.229, 0.224, 0.225][::-1]
data_transform = dataset.Compose([
dataset.ResizeCV(input_size),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
batch_size = 16
ds = dataset.SnakeDataset(file_path, data_transform)
dataloader = torch.utils.data.DataLoader(ds,
batch_size=batch_size,
shuffle=False,
num_workers=4)
# Load model & checkpoint
model = models.resnet(num_classes=45,
pretrained=True,
resnet_model='resnet18',
add_stn=False)
model = utils.load_model(checkpoint, model)
if cuda:
model = model.cuda()
model = model.eval()
submit = []
keys = ['filename']
for i in range(len(idx_to_species)):
keys += [idx_to_species[i]]
print(keys)
submit.append(keys)
for batch_idx, (x, _, names) in enumerate(dataloader):
print(batch_idx, '/', len(ds) / batch_size)
if cuda:
x = x.cuda()
outputs = model(x)
scores = F.softmax(outputs, dim=1).cpu().data.numpy()
for i, name in enumerate(names):
row = [os.path.basename(name)]
for j in range(scores[i].shape[0]):
row += [str(scores[i, j])]
submit.append(row)
submission = file_path.replace('.pkl', '.csv')
with open(submission, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
for row in submit:
writer.writerow(row)
def process():
input_path = generate_random_filename(upload_directory,"jpg")
output_path = generate_random_filename(upload_directory,"jpg")
try:
url = request.json["url"]
# phone: iphone, blackberr or sony
phone = request.json["phone"]
# resolution: orig,high,medium,small,tiny
resolution = request.json["resolution"]
download(url, input_path)
# get the specified image resolution
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = utils.get_specified_res(res_sizes, phone, resolution)
# create placeholders for input images
x_ = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
x_image = tf.reshape(x_, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
# generate enhanced image
enhanced = resnet(x_image)
with tf.Session(config=config) as sess:
saver = tf.train.Saver()
saver.restore(sess, "models_orig/" + phone + "_orig")
image = np.float16(misc.imresize(misc.imread(filename), res_sizes[phone])) / 255
image_crop = utils.extract_crop(image, resolution, phone, res_sizes)
image_crop_2d = np.reshape(image_crop, [1, IMAGE_SIZE])
enhanced_2d = sess.run(enhanced, feed_dict={x_: image_crop_2d})
enhanced_image = np.reshape(enhanced_2d, [IMAGE_HEIGHT, IMAGE_WIDTH, 3])
misc.imsave(filename, enhanced_image)
callback = send_file(output_path, mimetype='image/jpeg')
return callback, 200
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([
input_path,
output_path
])
def predict(data, **kwargs):
"""
模型预测，单张图片不复制
:param
data(tensor) -- 图片数据
:kwargs
:return(numpy)
预测结果
"""
# 选择运行的cpu或gpu
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 加载模型
models = []
for k, v in kwargs.items():
if k == '1':
model = MFFNet()
elif k == '2':
# 299 x 299
model = inception(pretrained = False)
elif k == '3':
model = resnet(pretrained = False)
elif k == '4':
model = densenet(pretrained = False)
elif k == '5':
model = senet(pretrained = False)
# 加载权重
model.load_state_dict(torch.load(v))
model = model.to(device)
# 测试模式
model.eval()
models.append(model)
# 使用平均策略获取集成模型输出
data = data.to(device)
output = None
# 平均策略
for model in models:
if output is None:
output = model(data).detach()
else:
output += model(data).detach()
output = output / len(models)
_, a = torch.max(output, 1)
a = a.cpu().detach().numpy()
# 预测结果
return a
def get_network(args, depth=10, width=10):
""" return given network
"""
if args.task == 'cifar10':
nclass = 10
elif args.task == 'cifar100':
nclass = 100
#Yang added none bn vggs
if args.net == 'vgg11':
if args.batch_norm:
net = vgg11_bn(num_classes=nclass)
else:
net = vgg11(num_classes=nclass)
elif args.net == 'vgg13':
if args.batch_norm:
net = vgg13_bn(num_classes=nclass)
else:
net = vgg13(num_classes=nclass)
elif args.net == 'vgg16':
if args.batch_norm:
net = vgg16_bn(num_classes=nclass)
else:
net = vgg16(num_classes=nclass)
elif args.net == 'vgg19':
if args.batch_norm:
net = vgg19_bn(num_classes=nclass)
else:
net = vgg19(num_classes=nclass)
elif args.net == 'resnet':
net = resnet(num_classes=nclass, depth=depth, width=width)
# elif args.net == 'resnet34':
# net = resnet34(num_classes=nclass)
# elif args.net == 'resnet50':
# net = resnet50(num_classes=nclass)
# elif args.net == 'resnet101':
# net = resnet101(num_classes=nclass)
# elif args.net == 'resnet152':
# net = resnet152(num_classes=nclass)
else:
print('the network name you have entered is not supported yet')
sys.exit()
if args.gpu: #use_gpu
net = net.cuda()
return net
def train():
print(lr)
train_size = len(train_loader)
total_loss = 0.0
total = 0.0
correct = 0.0
n = 0
for i, (inputs, targets) in enumerate(train_loader):
# Convert from list of 3D to 4D
inputs = np.stack(inputs, axis=1)
#plt.imshow(np.transpose(inputs[0][0], (1, 2, 0)))
#plt.show()
inputs = torch.from_numpy(inputs)
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
# compute output
outputs = resnet(inputs)
loss = criterion(outputs, targets)
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += (predicted.cpu() == targets.cpu()).sum()
total_loss += loss.item()
n += 1
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 5 == 0:
print("\tIter [%d/%d] Loss: %.4f" %
(i + 1, train_size, loss.item()))
avg_acc = 100 * correct / total
train_loss.append(total_loss / len(train_loader))
train_acc.append(avg_acc)
def eval(data_loader, is_test=False):
if is_test:
load_checkpoint()
# Eval
total = 0.0
correct = 0.0
total_loss = 0.0
n = 0
start = time.time()
for i, (inputs, targets) in enumerate(data_loader):
with torch.no_grad():
# Convert from list of 3D to 4D
inputs = np.stack(inputs, axis=1)
inputs = torch.from_numpy(inputs)
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
# compute output
outputs = resnet(inputs)
loss = criterion(outputs, targets)
total_loss += loss
n += 1
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += (predicted.cpu() == targets.cpu()).sum()
time_taken = time.time() - start
print('Time: ', time_taken)
print('Avg: ', time_taken / len(data_loader) * 1000, 'ms')
avg_acc = 100 * correct / total
avg_loss = total_loss / len(val_loader)
val_loss.append(avg_loss)
val_acc.append(avg_acc)
return avg_acc, avg_loss
def init_network(args, num_classes):
kwargs = {'depth': args.depth, 'num_classes': num_classes}
depth = str(args.depth)
if args.network == 'vgg':
kwargs.update({'avg_pool2d': args.avg_pool2d})
kwargs.update({'batch_norm': True})
net = vgg(**kwargs)
elif args.network == 'resnet':
dataset = args.data_dir.split('/')[-1]
if 'cifar' in dataset.lower():
kwargs.update({'dataset': 'cifar'})
net = resnet(**kwargs)
elif args.network == 'reskipnet':
net = reskipnet(**kwargs)
elif args.network == 'resnext':
dataset = args.data_dir.split('/')[-1]
if 'cifar' in dataset.lower():
kwargs.update({'dataset': 'cifar'})
kwargs.update({
'cardinality': args.cardinality,
'base_width': args.base_width
})
net = resnext(**kwargs)
depth = '{}_{}_{}d'.format(depth, args.cardinality, args.base_width)
else:
raise NotImplementedError
if args.resume:
dataset = args.data_dir.split('/')[-1]
path_to_add = '{}/{}/{}'.format(dataset, args.network, depth)
assert os.path.exists('{}/checkpoint/original/{}'.format(args.result_dir, path_to_add)),\
'No checkpoint directory for original model!'
checkpoint_path, _, previous_epochs = get_best_checkpoint(
'{}/checkpoint/original/{}'.format(args.result_dir, path_to_add))
checkpoint = torch.load(checkpoint_path, map_location='cpu')
net.load_state_dict(checkpoint)
previous_accuracy = checkpoint_path.split('_')[-2]
else:
previous_accuracy = 0
previous_epochs = 0
if torch.cuda.is_available():
net.cuda()
return net, float(previous_accuracy), previous_epochs, depth
def select_model(dataset,
model_name,
pretrained=False,
pretrained_models_path=None,
n_classes=10):
assert model_name in ['WRN-16-1', 'WRN-16-2', 'WRN-40-1', 'WRN-40-2', 'ResNet34', 'WRN-10-2', 'WRN-10-1']
if model_name=='WRN-16-1':
model = WideResNet(depth=16, num_classes=n_classes, widen_factor=1, dropRate=0)
elif model_name=='WRN-16-2':
model = WideResNet(depth=16, num_classes=n_classes, widen_factor=2, dropRate=0)
elif model_name=='WRN-40-1':
model = WideResNet(depth=40, num_classes=n_classes, widen_factor=1, dropRate=0)
elif model_name=='WRN-40-2':
model = WideResNet(depth=40, num_classes=n_classes, widen_factor=2, dropRate=0)
elif model_name == 'WRN-10-2':
model = WideResNet(depth=10, num_classes=n_classes, widen_factor=2, dropRate=0)
elif model_name == 'WRN-10-1':
model = WideResNet(depth=10, num_classes=n_classes, widen_factor=1, dropRate=0)
elif model_name=='ResNet34':
model = resnet(depth=32, num_classes=n_classes)
else:
raise NotImplementedError
if pretrained:
model_path = os.path.join(pretrained_models_path)
print('Loading Model from {}'.format(model_path))
checkpoint = torch.load(model_path, map_location='cpu')
print(checkpoint.keys())
model.load_state_dict(checkpoint['state_dict'])
#print("=> loaded checkpoint '{}' (epoch {}) (accuracy {})".format(model_path, checkpoint['epoch'], checkpoint['best_prec']))
print("=> loaded checkpoint '{}' (epoch {}) ".format(model_path, checkpoint['epoch']))
return model
def eval(data_loader, is_test=False):
if is_test:
load_checkpoint()
# Eval
total = 0.0
correct = 0.0
total_loss = 0.0
n = 0
for i, (inputs, targets) in enumerate(data_loader):
with torch.no_grad():
# Convert from list of 3D to 4D
inputs = np.stack(inputs, axis=1)
inputs = torch.from_numpy(inputs)
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
# compute output
outputs = resnet(inputs)
loss = criterion(outputs, targets)
total_loss += loss
n += 1
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += (predicted.cpu() == targets.cpu()).sum()
avg_test_acc = 100 * correct / total
avg_loss = total_loss / n
return avg_test_acc, avg_loss
def train():
train_size = len(train_loader)
for i, (inputs, targets) in enumerate(train_loader):
# Convert from list of 3D to 4D
inputs = np.stack(inputs, axis=1)
inputs = torch.from_numpy(inputs)
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
# compute output
outputs = resnet(inputs)
loss = criterion(outputs, targets)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % args.print_freq == 0:
print("\tIter [%d/%d] Loss: %.4f" %
(i + 1, train_size, loss.item()))
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('learning_rate', 0.01, 'Learning rate')
flags.DEFINE_integer('batch_size', 25, 'Batch size')
X_train, Y_train, X_test, Y_test = load_data()
batch_size = 128
X = tf.placeholder("float", [batch_size, 32, 32, 3])
Y = tf.placeholder("float", [batch_size, 10])
learning_rate = tf.placeholder("float", [])
net = models.resnet(X, 20)
cross_entropy = -tf.reduce_sum(Y*tf.log(net))
opt = tf.train.MomentumOptimizer(learning_rate, 0.9)
train_op = opt.minimize(cross_entropy)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
correct_prediction = tf.equal(tf.argmax(net, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
saver = tf.train.Saver()
for j in range (10):
help='depth of the resnet')
parser.add_argument('--percent', type=float, default=0.5,
help='scale sparse rate (default: 0.5)')
parser.add_argument('--model', default='./logs/checkpoint_resnet.pth.tar', type=str, metavar='PATH',
help='path to the model (default: none)')
parser.add_argument('--save', default='./logs', type=str, metavar='PATH',
help='path to save pruned model (default: none)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
args.cuda = False
if not os.path.exists(args.save):
os.makedirs(args.save)
model = resnet(depth=args.depth, dataset=args.dataset)
if args.cuda:
model.cuda()
if args.model:
if os.path.isfile(args.model):
print("=> loading checkpoint '{}'".format(args.model))
checkpoint = torch.load(args.model)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {}) Prec1: {:f}"
.format(args.model, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
loss_discrim1 = []
loss_generator1 = []
loss_mse1 = []
loss_psnr1 = []
loss_texture1 = []
loss_tv1 = []
discim_accuracy1 = []
# get processed enhanced image
for i in range(0, GPU_NUM):
with tf.device("/gpu:{}".format(i)), tf.variable_scope(
name_or_scope=tf.get_variable_scope(),
# re-using variables across GPUs.
reuse=True or (i > 0)):
print("Building graph on GPU:{}".format(i))
enhanced = models.resnet(phone_image[i])
# transform both dslr and enhanced images to grayscale
enhanced_gray = tf.reshape(tf.image.rgb_to_grayscale(enhanced),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
dslr_gray = tf.reshape(tf.image.rgb_to_grayscale(dslr_image[i]),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
# push randomly the enhanced or dslr image to an adversarial CNN-discriminator
adversarial_ = tf.multiply(
enhanced_gray, 1 - adv_[i]) + tf.multiply(dslr_gray, adv_[i])
adversarial_image = tf.reshape(adversarial_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 1])
torch.cuda.manual_seed(args.seed)
for arg in vars(args):
print(arg, getattr(args, arg))
if not os.path.isdir('checkpoint/'):
os.makedirs('checkpoint/')
# get dataset
train_loader, test_loader = getData(
name='cifar10', train_bs=args.batch_size, test_bs=args.test_batch_size)
# make sure to use cudnn.benchmark for second backprop
cudnn.benchmark = True
# get model and optimizer
model = resnet(num_classes=10, depth=args.depth).cuda()
print(model)
model = torch.nn.DataParallel(model)
print(' Total params: %.2fM' % (sum(p.numel()
for p in model.parameters()) / 1000000.0))
criterion = nn.CrossEntropyLoss()
if args.optimizer == 'sgd':
optimizer = optim.SGD(
model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
optimizer = optim.Adam(
model.parameters(),
def __init__(self,
n_classes=1,
backbone_name='resnet18',
in_channels=3,
load_pretrained=True,
norm_type='bn',
use_cbam=False,
p=0.1,
dam_scale=1):
super(unet, self).__init__()
bias = not (norm_type == 'bn' or norm_type == 'gn')
backbone_name = 'resnet18' if backbone_name is None else backbone_name
self.dropout = nn.Dropout2d(p=p)
self.backbone = resnet(name=backbone_name,
in_channels=in_channels,
load_pretrained=load_pretrained,
norm_type=norm_type,
use_cbam=use_cbam)
expansion = self.backbone.block.expansion
self.ppm = PPM(in_channels=512 * expansion,
pool_sizes=[6, 3, 2, 1],
mode='sum',
norm_type=norm_type)
self.c1br_16x = ConvBatchNormRelu(in_channels=256 * expansion,
out_channels=128 * expansion,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
norm_type=norm_type)
self.c1br_8x = ConvBatchNormRelu(in_channels=128 * expansion,
out_channels=64 * expansion,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
norm_type=norm_type)
self.c1br_4x = ConvBatchNormRelu(in_channels=64 * expansion,
out_channels=32 * expansion,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
norm_type=norm_type)
self.c1br_2x = ConvBatchNormRelu(in_channels=64 * expansion,
out_channels=16 * expansion,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
norm_type=norm_type)
self.c3br_ppm = ConvBatchNormRelu(in_channels=512 * expansion,
out_channels=128 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type)
self.c3br2_16x = nn.Sequential(
ConvBatchNormRelu(in_channels=256 * expansion,
out_channels=64 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
ConvBatchNormRelu(in_channels=64 * expansion,
out_channels=64 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
)
self.c3br2_8x = nn.Sequential(
ConvBatchNormRelu(in_channels=128 * expansion,
out_channels=32 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
ConvBatchNormRelu(in_channels=32 * expansion,
out_channels=32 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
)
self.c3br2_4x = nn.Sequential(
ConvBatchNormRelu(in_channels=64 * expansion,
out_channels=16 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
ConvBatchNormRelu(in_channels=16 * expansion,
out_channels=16 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
)
self.c3br2_2x = nn.Sequential(
ConvBatchNormRelu(in_channels=32 * expansion,
out_channels=8 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
ConvBatchNormRelu(in_channels=8 * expansion,
out_channels=8 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type),
)
#"""
self.pam = PAM(in_channels=32 * expansion,
reduction_ratio=2,
norm_type=norm_type,
scale=dam_scale)
self.cam = CAM(scale=dam_scale)
self.c3br_pam = ConvBatchNormRelu(in_channels=32 * expansion,
out_channels=32 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type)
self.c3br_cam = ConvBatchNormRelu(in_channels=32 * expansion,
out_channels=32 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type)
#"""
# (Auxiliary) Classifier
self.gamma_gap = nn.Parameter(torch.zeros(1))
self.aux_cls_gap = nn.Sequential(
ConvBatchNormRelu(in_channels=128 * expansion,
out_channels=32 * expansion,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
norm_type=norm_type), self.dropout,
nn.Conv2d(32 * expansion,
n_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.aux_cls_ppm = nn.Sequential(
ConvBatchNormRelu(in_channels=128 * expansion,
out_channels=32 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type), self.dropout,
nn.Conv2d(32 * expansion,
n_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.aux_cls_4x = nn.Sequential(
ConvBatchNormRelu(in_channels=32 * expansion,
out_channels=8 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type), self.dropout,
nn.Conv2d(8 * expansion,
n_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.cls = nn.Sequential(
ConvBatchNormRelu(in_channels=8 * expansion,
out_channels=8 * expansion,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
norm_type=norm_type), self.dropout,
nn.Conv2d(8 * expansion,
n_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self._init_weights(load_pretrained=load_pretrained)
def main(args):
run_idx = sys.argv[1]
# Paths
plotpath = path_prefix+'classifier/Plots/'
modelpath = path_prefix+'classifier/Models/'
# metadata
layers = ["EMB1", "EMB2", "EMB3", "TileBar0", "TileBar1", "TileBar2"]
cell_size_phi = [0.098, 0.0245, 0.0245, 0.1, 0.1, 0.1]
cell_size_eta = [0.0031, 0.025, 0.05, 0.1, 0.1, 0.2]
len_phi = [4, 16, 16, 4, 4, 4]
len_eta = [128, 16, 8, 4, 4, 2]
cell_shapes = {layers[i]:(len_eta[i],len_phi[i]) for i in range(len(layers))}
# Get the data.
inputpath = path_prefix+'data/pion/'
rootfiles = ["pi0", "piplus", "piminus"]
branches = ['runNumber', 'eventNumber', 'truthE', 'truthPt', 'truthEta', 'truthPhi', 'clusterIndex', 'nCluster', 'clusterE', 'clusterECalib', 'clusterPt', 'clusterEta', 'clusterPhi', 'cluster_nCells', 'cluster_sumCellE', 'cluster_ENG_CALIB_TOT', 'cluster_ENG_CALIB_OUT_T', 'cluster_ENG_CALIB_DEAD_TOT', 'cluster_EM_PROBABILITY', 'cluster_HAD_WEIGHT', 'cluster_OOC_WEIGHT', 'cluster_DM_WEIGHT', 'cluster_CENTER_MAG', 'cluster_FIRST_ENG_DENS', 'cluster_cell_dR_min', 'cluster_cell_dR_max', 'cluster_cell_dEta_min', 'cluster_cell_dEta_max', 'cluster_cell_dPhi_min', 'cluster_cell_dPhi_max', 'cluster_cell_centerCellEta', 'cluster_cell_centerCellPhi', 'cluster_cell_centerCellLayer', 'cluster_cellE_norm']
trees = {
rfile : ur.open(inputpath+rfile+".root")['ClusterTree']
for rfile in rootfiles
}
pdata = {
ifile : itree.pandas.df(branches, flatten=False)
for ifile, itree in trees.items()
}
# Selecting events -- making sure we have as much signal as background.
n_indices = {}
n_max = int(np.min(np.array([len(pdata[key]) for key in trees.keys()])))
rng = np.random.default_rng()
# If we have a piminus key, assume the dataset are piplus, piminus, pi0
if('piminus' in trees.keys()):
n_indices['piplus'] = int(np.ceil((n_max / 2)))
n_indices['piminus'] = int(np.floor((n_max / 2)))
n_indices['pi0'] = n_max
# Otherwise, assume we already have piplus (or piplus + piminus) and pi0, no merging needed
else: n_indices = {key:n_max for key in trees.keys}
indices = {key:rng.choice(len(pdata[key]), n_indices[key], replace=False) for key in trees.keys()}
# Make a boolean array version of our indices, since pandas is weird and doesn't handle non-bool indices?
bool_indices = {}
for key in pdata.keys():
bool_indices[key] = np.full(len(pdata[key]), False)
bool_indices[key][indices[key]] = True
# Apply the (bool) indices to pdata
for key in trees.keys():
pdata[key] = pdata[key][bool_indices[key]]
# prepare pcells -- immediately apply our selected indices
pcells = {
ifile : {
layer : mu.setupCells(itree, layer, indices = indices[ifile])
for layer in layers
}
for ifile, itree in trees.items()
}
# Now with the data extracted from the trees into pcells, we merge pdata and pcells as needed.
# Note the order in which we concatenate things: piplus -> piplus + piminus.
if('piminus' in trees.keys()):
# merge pdata
pdata['piplus'] = pdata['piplus'].append(pdata['piminus'])
del pdata['piminus']
# merge contents of pcells
for layer in layers:
pcells['piplus'][layer] = np.row_stack((pcells['piplus'][layer],pcells['piminus'][layer]))
del pcells['piminus']
# Now split things into training/validation/testing data.
training_dataset = ['pi0','piplus']
# create train/validation/test subsets containing 70%/10%/20%
# of events from each type of pion event
for p_index, plabel in enumerate(training_dataset):
mu.splitFrameTVT(pdata[plabel],trainfrac=0.7)
pdata[plabel]['label'] = p_index
# merge signal and background now
pdata_merged = pd.concat([pdata[ptype] for ptype in training_dataset])
pcells_merged = {
layer : np.concatenate([pcells[ptype][layer]
for ptype in training_dataset])
for layer in layers
}
plabels = np_utils.to_categorical(pdata_merged['label'],len(training_dataset))
# Tensorflow setup.
ngpu = 1
gpu_list = ["/gpu:"+str(i) for i in range(ngpu)]
strategy = tf.distribute.MirroredStrategy(devices=gpu_list)
ngpu = strategy.num_replicas_in_sync
print ('Number of devices: {}'.format(ngpu))
models = {}
model_history = {}
model_scores = {}
model_performance = {}
# Prepare the ResNet model.
tf.keras.backend.set_image_data_format('channels_last')
lr = 5e-5
input_shape = (128,16)
model_resnet = resnet(strategy, lr=lr)(input_shape)
# Minor extra data prep -- key names match those defined within resnet model in models.py!
pcells_merged_unflattened = {'input' + str(i):pcells_merged[key].reshape(tuple([-1] + list(cell_shapes[key]))) for i,key in enumerate(pcells_merged.keys())}
rn_train = {key:val[pdata_merged.train] for key,val in pcells_merged_unflattened.items()}
rn_valid = {key:val[pdata_merged.val] for key,val in pcells_merged_unflattened.items()}
rn_test = {key:val[pdata_merged.test] for key,val in pcells_merged_unflattened.items()}
nepochs = 10
batch_size = 20 * ngpu
verbose = 1 # 2 for a lot of printouts
model_key = 'resnet'
rn_dir = modelpath + 'resnet' # directory for saving ResNet
models[model_key] = model_resnet
# train+validate model
model_history[model_key] = models[model_key].fit(
x=rn_train,
y=plabels[pdata_merged.train],
validation_data=(
rn_valid,
plabels[pdata_merged.val]
),
epochs=nepochs,
batch_size=batch_size,
verbose=verbose
)
model_history[model_key] = model_history[model_key].history
# get overall performance metric
model_performance[model_key] = models[model_key].evaluate(
x=rn_test,
y=plabels[pdata_merged.test],
verbose=0
)
# get network scores for the dataset
model_scores[model_key] = models[model_key].predict(
pcells_merged_unflattened
)
try: os.makedirs(rn_dir)
except: pass
models[model_key].save(rn_dir + '/' + 'resnet_{}.h5'.format(run_idx))
with open(rn_dir + '/' + 'resnet_{}.history'.format(run_idx),'wb') as model_history_file:
pickle.dump(model_history[model_key], model_history_file)
print('Done.')
transform_train = transforms.Compose([
transforms.ToTensor(),
])
trainset = datasets.CIFAR10(root='../data',
train=True,
download=True,
transform=transform_train)
hessian_loader = torch.utils.data.DataLoader(trainset,
batch_size=128,
shuffle=True)
# get model and optimizer
model_list = {
'c1': c1_model(),
'ResNet': resnet(depth=args.depth),
}
model = model_list[args.arch].cuda()
model = torch.nn.DataParallel(model)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
########### training
if args.method == 'absa':
model, num_updates = hf_optm.absa(model,
train_loader,
hessian_loader,
test_loader,
criterion,
def trainer():
global load_thread
load_thread.start()
X = tf.placeholder("float", [None, 224, 224, 3])
Y = tf.placeholder("float", [None, n_classes])
# ResNet Models
pred = models.resnet(X, 56)
#net = models.resnet(X, 32)
# net = models.resnet(X, 44)
# net = models.resnet(X, 56)
cost = -tf.reduce_sum(Y * tf.log(pred))
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, Y))
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
info2 = tf.argmax(pred, 1)
# 初始化所有的共享变量
init = tf.initialize_all_variables()
# 开启一个训练
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
global step
global data_queue
global lr
lr = 1e-4
step = 0
#saver.restore(sess, "/home/jess/Disk1/ilsvrc12/tf_vgg_ccnn_model_iter27501.ckpt")
#step = 27501
# Keep training until reach max iterations
while step * batch_size < training_iters:
epcho1 = np.floor((step * batch_size) / glen1)
if (((step * batch_size) % glen1 < batch_size) & (epcho1 > 0)):
lr /= 10
batch_xs, batch_ys = data_queue.get()
# 获取批数据
sess.run(optimizer,
feed_dict={
X: batch_xs,
Y: batch_ys,
keep_prob: dropout,
learning_rate: lr
})
if (step % 2500 == 1):
save_path = saver.save(
sess,
"/home/jess/Disk1/ilsvrc12/tf_resnet_ccnn_model_iter" +
str(step) + ".ckpt",
global_step=step)
print("Model saved in file at iteration %d: %s" %
(step * batch_size, save_path))
if step % display_step == 1:
# 计算损失值
loss = sess.run(cost,
feed_dict={
X: batch_xs,
Y: batch_ys,
keep_prob: 1.
})
acc = sess.run(accuracy,
feed_dict={
X: batch_xs,
Y: batch_ys,
keep_prob: 1.
})
#info=sess.run(info2,feed_dict={X:batch_xs, keep_prob:1.})
#print(info)
print "Iter=" + str(step * batch_size) + "/epcho=" + str(
np.floor(
(step * batch_size) /
glen1)) + ", Loss= " + "{:.6f}".format(
loss) + ", Training Accuracy=" + "{:.5f}".format(
acc) + ", lr=" + str(lr)
step += 1
print "Optimization Finished!"
#saver.save(sess, 'jigsaw', global_step=step)
save_path = saver.save(
sess,
"/home/jess/Disk1/ilsvrc12/tf_resnet_ccnn_model.ckpt",
global_step=step)
print("Model saved in file: %s" % save_path)
# 计算测试精度
batch_xs, batch_ys = imagenet_batch(4096)
print "Testing Accuracy:", sess.run(accuracy,
feed_dict={
x: batch_xs,
y: batch_ys,
keep_prob: 1.
})
def stylize(initial,
initial_noiseblend,
content,
styles,
preserve_colors,
iterations,
content_weight,
content_weight_blend,
style_weight,
style_layer_weight_exp,
style_blend_weights,
tv_weight,
learning_rate,
beta1,
beta2,
epsilon,
pooling,
print_iterations=None,
checkpoint_iterations=None):
# Set parameters for training
lr = 100
content_coeff = 1
style_coeff = 1000000
tv_coeff = 0.000001
directory = '../resnet/cifar_10_progress/'
CONTENT_LAYERS = ['conv3', 'conv3_x']
STYLE_LAYERS = [
'conv1', 'conv2_x', 'conv2', 'conv3_x', 'conv3', 'conv4', 'conv4_x'
]
content_layers_weights = {}
for layer in CONTENT_LAYERS:
content_layers_weights[layer] = 1
style_layers_weights = {}
for style_layer in STYLE_LAYERS:
style_layers_weights[style_layer] = 1
shape = (1, ) + content.shape
style_shapes = [(1, ) + style.shape for style in styles]
content_features = {}
style_features = [{} for _ in styles]
# compute content features in feedforward mode
g = tf.Graph()
with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:
image = tf.placeholder('float', shape=shape)
net, _ = resnet(image, 20)
saver = tf.train.Saver()
saver.restore(sess, directory)
pre_content = np.array([content])
for layer in CONTENT_LAYERS:
content_features[layer] = net[layer].eval(
feed_dict={image: pre_content})
# compute style features in feedforward mode
for i in range(len(styles)):
g = tf.Graph()
with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:
image = tf.placeholder('float', shape=style_shapes[i])
net, layer_list = resnet(image, 20)
saver = tf.train.Saver()
saver.restore(sess, directory)
pre_style = np.array([styles[i]])
for k in range(len(layer_list)):
layer_list[k] = sess.run(layer_list[k],
feed_dict={image: pre_style})
for layer in STYLE_LAYERS:
features = net[layer].eval(feed_dict={image: pre_style})
features = np.reshape(features, (-1, features.shape[3]))
gram = np.matmul(features.T, features) / features.size
style_features[i][layer] = gram
initial_content_noise_coeff = 1.0 - initial_noiseblend
# make stylized image using backpropogation
g_net = tf.Graph()
with g_net.as_default():
if initial is None:
noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)
initial = tf.random_normal(shape) * 0.256
else:
initial = initial.astype('float32')
noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)
initial = (initial) * initial_content_noise_coeff + (
tf.random_normal(shape) *
0.256) * (1.0 - initial_content_noise_coeff)
#image = tf.Variable(initial)
image = tf.Variable(pre_content.astype('float32'))
net = resnet_m(image, 20, layer_list)
# content loss
content_loss = 0
content_losses = []
for content_layer in CONTENT_LAYERS:
content_losses.append(
content_layers_weights[content_layer] * content_weight *
(2 * tf.nn.l2_loss(net[content_layer] -
content_features[content_layer]) /
content_features[content_layer].size))
content_loss += reduce(tf.add, content_losses)
content_loss *= content_coeff
# style loss
style_loss = 0
style_losses = []
for style_layer in STYLE_LAYERS:
# TensorBoard
output = net[style_layer]
output_shape = int(output.get_shape()[3])
output_split = tf.split(output,
num_or_size_splits=output_shape,
axis=3)
feature_list = []
for j in range(output_shape):
feature_list.append(output_split[j])
output_show = tf.concat(feature_list, axis=0)
tf.summary.image(str(layer), output_show, 1000)
layer = net[style_layer]
_, height, width, number = map(lambda i: i.value,
layer.get_shape())
size = height * width * number
feats = tf.reshape(layer, (-1, number))
gram = tf.matmul(tf.transpose(feats), feats) / size
style_gram = style_features[0][style_layer]
style_loss = style_layers_weights[style_layer] * 2 * tf.nn.l2_loss(
gram - style_gram) / style_gram.size
style_loss *= style_coeff
# total variation denoising
tv_y_size = _tensor_size(image[:, 1:, :, :])
tv_x_size = _tensor_size(image[:, :, 1:, :])
tv_loss = tv_weight * 2 * (
(tf.nn.l2_loss(image[:, 1:, :, :] - image[:, :shape[1] - 1, :, :])
/ tv_y_size) +
(tf.nn.l2_loss(image[:, :, 1:, :] - image[:, :, :shape[2] - 1, :])
/ tv_x_size))
tv_loss *= tv_coeff
# overall loss
loss = content_loss + style_loss + tv_loss
# optimizer setup
train_step = tf.train.AdamOptimizer(lr, beta1, beta2,
epsilon).minimize(loss)
def print_progress():
stderr.write(' content loss: %g\n' % content_loss.eval())
stderr.write(' style loss: %g\n' % style_loss.eval())
stderr.write(' tv loss: %g\n' % tv_loss.eval())
stderr.write(' total loss: %g\n' % loss.eval())
merge_summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('./style_x/')
# optimization
best_loss = float('inf')
best = None
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
stderr.write('Optimization started...\n')
if (print_iterations and print_iterations != 0):
print_progress()
for i in range(iterations):
stderr.write('Iteration %4d/%4d\n' % (i + 1, iterations))
_ = sess.run([train_step])
print_progress()
last_step = (i == iterations - 1)
if last_step or (print_iterations
and i % print_iterations == 0):
print_progress()
if (checkpoint_iterations
and i % checkpoint_iterations == 0) or last_step:
this_loss = loss.eval()
if this_loss < best_loss:
best_loss = this_loss
best = image.eval()
img_out = best.reshape(shape[1:])
if preserve_colors and preserve_colors == True:
original_image = np.clip(content, 0, 255)
styled_image = np.clip(img_out, 0, 255)
# Luminosity transfer steps:
# 1. Convert stylized RGB->grayscale accoriding to Rec.601 luma (0.299, 0.587, 0.114)
# 2. Convert stylized grayscale into YUV (YCbCr)
# 3. Convert original image into YUV (YCbCr)
# 4. Recombine (stylizedYUV.Y, originalYUV.U, originalYUV.V)
# 5. Convert recombined image from YUV back to RGB
# 1
styled_grayscale = rgb2gray(styled_image)
styled_grayscale_rgb = gray2rgb(styled_grayscale)
# 2
styled_grayscale_yuv = np.array(
Image.fromarray(
styled_grayscale_rgb.astype(
np.uint8)).convert('YCbCr'))
# 3
original_yuv = np.array(
Image.fromarray(original_image.astype(
np.uint8)).convert('YCbCr'))
# 4
w, h, _ = original_image.shape
combined_yuv = np.empty((w, h, 3), dtype=np.uint8)
combined_yuv[..., 0] = styled_grayscale_yuv[..., 0]
combined_yuv[..., 1] = original_yuv[..., 1]
combined_yuv[..., 2] = original_yuv[..., 2]
# 5
img_out = np.array(
Image.fromarray(combined_yuv,
'YCbCr').convert('RGB'))
yield ((None if last_step else i), img_out)
res_sizes = utils.get_resolutions()
# get the specified image resolution
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = utils.get_specified_res(
res_sizes, phone, resolution)
# disable gpu if specified
config = tf.ConfigProto(
device_count={'GPU': 0}) if use_gpu == "false" else None
# create placeholders for input images
x_ = tf.placeholder(tf.float32, [None, None, 3])
#x_image = tf.reshape(x_, [-1, xsize[0], xsize[1], 3])
x_imge = tf.expand_dims(x_, axis=0)
# generate enhanced image
enhanced = resnet(x_imge)
'''
with tf.Session(config=config) as sess:
test_dir = dped_dir + phone.replace("_orig", "") + "/test_data/full_size_test_images/"
test_photos = [f for f in os.listdir(test_dir) if os.path.isfile(test_dir + f)]
if test_subset == "small":
# use five first images only
test_photos = test_photos[0:5]
if phone.endswith("_orig"):
# load pre-trained model
saver = tf.train.Saver()
saver.restore(sess, "models_orig/" + phone)
# placeholders for training data
print("Session initialized")
phone_ = tf.placeholder(tf.float32, [None, PATCH_SIZE], name="train-phone")
phone_image = tf.reshape(phone_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3], name="train-phone-img")
dslr_ = tf.placeholder(tf.float32, [None, PATCH_SIZE], name="train-dslr")
dslr_image = tf.reshape(dslr_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3], name="train-dslr-img")
adv_ = tf.placeholder(tf.float32, [None, 1], name="cointoss")
# get processed enhanced image
if convdeconv:
enhanced = models.convdeconv(phone_image, depth, parametric=parametric, s_conv=s_conv)
else:
enhanced = models.resnet(phone_image, kernel_size, depth, blocks, parametric=parametric, s_conv=s_conv)
# 2) content loss
with tf.name_scope("content_loss"):
CONTENT_LAYER = 'relu5_4'
enhanced_vgg = vgg.net(vgg_dir, vgg.preprocess(enhanced * 255))
dslr_vgg = vgg.net(vgg_dir, vgg.preprocess(dslr_image * 255))
content_size = utils._tensor_size(dslr_vgg[CONTENT_LAYER]) * batch_size
loss_content = 2 * tf.nn.l2_loss(enhanced_vgg[CONTENT_LAYER] - dslr_vgg[CONTENT_LAYER]) / content_size
tf.summary.scalar("loss_content", loss_content)
# transform both dslr and enhanced images to grayscale
train_on_gpu = check_gpu()
if not train_on_gpu:
print('Cuda is not available for traning.Traning on CPU.......')
else:
print('Cuda is available for traning. Traning on GPU.......')
model_list ={'chexnet': chexnet(),
'resnet' : resnet(num_of_classes = 2)}
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--lr',type=float,default=0.1)
arg('--n_epochs',type=int, default = 5)
arg('--batch-size',type=int, default= 32)
arg('--data_dir',type=str,default = 'chest_xray')
arg('--model',type=str, default ='chexnet',choices = model_list.keys())
arg('--root',type=str,default ='runs/debug', help = 'checkpoint root')
args = parser.parse_args()
train_loader= generate_trainloaders(data_dir= args.data_dir,
batch_size= args.batch_size)
data_slice=1.0,
return_path=True,
)
dataloader = data.DataLoader(
dataset,
batch_size=config["batch_size"],
shuffle=False,
num_workers=config["workers"],
)
if config["dataset_info"]:
utils.dataloader_info(dataloader)
# Initialize ship or no-ship detection network and then laod the weigths
print("Loading ship detection model...")
num_classes = 1
clf_net = models.resnet(config["clf_resnet"], num_classes)
print("Loading model weights from {}...".format(clf_checkpoint))
checkpoint = torch.load(clf_checkpoint, map_location=torch.device("cpu"))
clf_net.load_state_dict(checkpoint["model"])
print("Loading segmentation model...")
model_str = config["seg_model"].lower()
if model_str == "enet":
seg_net = models.ENet(num_classes)
elif model_str == "linknet":
seg_net = models.LinkNet(num_classes)
elif model_str == "linknet34":
seg_net = models.LinkNet(num_classes, 34)
elif model_str == "dilatedunet":
seg_net = models.DilatedUNet(classes=num_classes)
res_sizes = utils.get_resolutions()
# get the specified image resolution
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = utils.get_specified_res(
res_sizes, phone, resolution)
# disable gpu if specified
config = tf.ConfigProto(
device_count={'GPU': 0}) if use_gpu == "false" else None
# create placeholders for input images
x_ = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
x_image = tf.reshape(x_, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
# generate enhanced image
enhanced = resnet(x_image)
with tf.Session(config=config) as sess:
test_dir = dped_dir + 'Test/' + phone.replace("_orig", "") + '/Full/'
test_photos = [
f for f in os.listdir(test_dir) if os.path.isfile(test_dir + f)
]
if test_subset == "small":
# use five first images only
test_photos = test_photos[0:5]
if phone.endswith("_orig"):
# load pre-trained model
def test(test_path, agumentation, **kwargs):
"""
测试模型性能
:param
test_path(str) -- 测试集地址
agumentation(bool) -- 是否对单个图片多次复制
:kwargs
model(int) -- 模型
"""
# 设置超参数
if agumentation:
BATCH_SIZE = 1
else:
BATCH_SIZE = 32
# 选择运行的cpu或gpu
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义损失函数
# N / n，权重为各类别频率的倒数
weight = torch.Tensor([9., 1.5, 19.48, 30.62, 9.11, 86.86, 71.])
weight = weight.to(device)
criterion = nn.CrossEntropyLoss(weight=weight)
# 数据处理
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transform = transforms.Compose(
[transforms.RandomCrop(224),
transforms.ToTensor(), normalize])
# 加载数据
# 定义test_loader
test_dataset = SkinDiseaseDataset(test_path,
transforms=test_transform,
agumentation=agumentation)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE)
# 加载模型
if kwargs['model'] == 1:
model = MFFNet()
elif kwargs['model'] == 2:
# 299 x 299
model = inception(pretrained=False)
elif kwargs['model'] == 3:
model = resnet(pretrained=False)
elif kwargs['model'] == 4:
model = densenet(pretrained=False)
elif kwargs['model'] == 5:
model = senet(pretrained=False)
# 加载模型权重
model.load_state_dict(torch.load(CONFIG.best_model))
model = model.to(device)
# 测试模式
model.eval()
# 各类别预测正确个数
class_correct = list(0. for i in range(7))
# 各类别总个数
class_total = list(0. for i in range(7))
# 损失
sum_loss = 0.0
# 总预测正确个数
correct = 0
# 总个数
total = 0
# 总迭代次数
cnt = 0
# 测试集增强模式
if agumentation:
# 预测标签情况
x = []
# 真实标签情况
y = []
for data in test_loader:
cnt += 1
# 加载数据
image, label = data
image = image.view(-1, 3, 224, 224)
label = label[0]
image, label = image.to(device), label.to(device)
# 前向传播
output = model(image)
# 使用平均策略获取预测值
output = output.detach()
# 平均策略
val = None
for i in range(output.size(0)):
if val is None:
val = output[i]
else:
val = val + output[i]
val = val / output.size(0)
_, a = torch.max(val, 0)
# 统计各个类预测正确的个数
m = label.detach()
class_correct[m] += 1 if a == m else 0
class_total[m] += 1
# 统计预测正确总个数
correct += 1 if a == m else 0
x.append(a.item())
y.append(m.item())
# list转化为numpy
x = np.array(x)
y = np.array(y)
else:
# 预测标签情况
x = None
# 真实标签情况
y = None
for data in test_loader:
cnt += 1
# 加载数据
image, label = data
image, label = image.to(device), label.to(device)
# 前向传播
output = model(image)
loss = criterion(output, label)
# 计算loss和acc
sum_loss += loss.item()
_, a = torch.max(output.detach(), 1)
b = label.detach()
total += label.size(0)
correct += (a == b).sum()
# 预测和真实标签情况
if x is None:
x = a
y = b
else:
x = torch.cat((x, a))
y = torch.cat((y, b))
# 统计每个类别的正确预测情况
for i in range(label.size(0)):
m = b[i]
class_correct[m] += 1 if a[i] == m else 0
class_total[m] += 1
# tensor转化为numpy
x = x.cpu().detach().numpy()
y = y.cpu().detach().numpy()
# 打印结果
cm_plot_labels = ['MEL', 'NV', 'BCC', 'AKIEC', 'BKL', 'DF', 'VASC']
# 判断测试集是否增强
if agumentation:
# 打印acc
print("test_acc:%.2f%%\n" % (100 * correct / cnt))
else:
# 打印loss和acc
print("test_loss:%.2f test_acc:%.2f%%\n" %
(sum_loss / cnt, 100 * correct / total))
# 打印每个类别的acc
for i in range(7):
if class_total[i] > 0:
print('Test Accuracy of %5s: %.2f%% (%2d/%2d)' %
(cm_plot_labels[i], 100 * class_correct[i] / class_total[i],
class_correct[i], class_total[i]))
else:
print('Test Accuracy of %5s: N/A (no training examples)' %
cm_plot_labels[i])
print('')
# 计算混淆矩阵
cm = confusion_matrix(y, x)
print('')
# 计算BMC
balanced_multiclass_accuracy(cm)
print('')
# 可视化混淆矩阵
plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix')
print('')
# 打印分类报告
report = classification_report(y, x, target_names=cm_plot_labels)
print(report)
