def from_name(name: str, in_channels: int, num_classes: int,
saving_path: str) -> EfficientNet:
return EfficientNetWrapper(EfficientNet.from_name(
name, in_channels=in_channels, num_classes=num_classes),
saving_path=saving_path)
default=0.1,
help='set decay rate for learning rate')
parser.add_argument('--smooth', action='store_true', help='use smoothout')
args = parser.parse_args()
model_file = args.network + "_cifar10.ckpt"
fig_file = args.network + "_cifar10.png"
network = NET.index(args.network)
if network <= 1 or args.use32:
transform = transforms.ToTensor()
elif network <= 7:
transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor()])
elif network <= 11:
size = EfficientNet.get_image_size('efficientnet-b{}'.format(network - 8))
transform = transforms.Compose(
[transforms.Resize((size, size)),
transforms.ToTensor()])
else:
sys.exit(1)
# prepare data
train_Data = dsets.CIFAR10(root='../data_cifar10',
train=True,
transform=transform,
download=False)
test_data = dsets.CIFAR10(root='../data_cifar10',
train=False,
transform=transform,
def EfficientNetB7():
return EfficientNet.from_pretrained('efficientnet-b7', num_classes=data.c)
# train
pth_map = {
'efficientnet-b0': 'efficientnet-b0-355c32eb.pth',
'efficientnet-b1': 'efficientnet-b1-f1951068.pth',
'efficientnet-b2': 'efficientnet-b2-8bb594d6.pth',
'efficientnet-b3': 'efficientnet-b3-5fb5a3c3.pth',
'efficientnet-b4': 'efficientnet-b4-6ed6700e.pth',
'efficientnet-b5': 'efficientnet-b5-b6417697.pth',
'efficientnet-b6': 'efficientnet-b6-c76e70fd.pth',
'efficientnet-b7': 'efficientnet-b7-dcc49843.pth',
}
# 自动下载到本地预训练
# model = EfficientNet.from_pretrained('efficientnet-b0')
# 离线加载预训练，需要事先下载好
model_ft = EfficientNet.from_name(net_name)
net_weight = 'eff_weights/' + pth_map[net_name]
state_dict = torch.load(net_weight)
model_ft.load_state_dict(state_dict)
# 修改全连接层
num_ftrs = model_ft._fc.in_features
model_ft._fc = nn.Linear(num_ftrs, class_num)
criterion = nn.CrossEntropyLoss()
if use_gpu:
model_ft = model_ft.cuda()
criterion = criterion.cuda()
optimizer = optim.SGD((model_ft.parameters()),
lr=lr,
train_dataloader = DataLoader(dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=8,
drop_last=True,
sampler=AgrinetDatasetSampler(dataset))
#train_dataloader = DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=8, drop_last=True)
val_dataloader = DataLoader(dataset_val,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=1)
## -- LOAD MODEL -- ##
backbone = EfficientNet.from_pretrained('efficientnet-b7',
num_classes=cfg.num_class)
model = AudioClassifier(backbone, cfg.resample_freq, window_size,
hop_size, mel_bins, fmin, fmax,
cfg.num_class).to(cfg.device)
optimizer = torch.optim.Adam(
model.parameters(), lr=1e-3, amsgrad=False
) # torch.optim.SGD(model.parameters(), lr=1e-3, momentum=5e-4, nesterov=True)#
reducer = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.5,
patience=7,
verbose=True,
min_lr=1e-6)
best_score_bce = np.inf
best_score_ce = np.inf
def __init__(self):
super().__init__()
self.net = EfficientNet.from_pretrained('efficientnet-b7')
n_features = self.net._fc.in_features
self.net._fc = nn.Linear(in_features=n_features, out_features=1, bias=True)
def __init__(self, net_version, num_classes):
super(Net, self).__init__()
self.backbone = EfficientNet.from_pretrained('efficientnet-' +
net_version)
self.backbone._fc = nn.Sequential(nn.Linear(1280, num_classes), )
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import time
import copy
import random
import torchvision
from sklearn.model_selection import train_test_split
from torch.utils.data import Subset
from torchvision import transforms, datasets
from efficientnet_pytorch import EfficientNet
model_name = 'efficientnet-b7' # b5
image_size = EfficientNet.get_image_size(model_name)
print(image_size)
model = EfficientNet.from_pretrained(model_name, num_classes=2)
## 데이타 로드!!
batch_size = 128
random_seed = 555
random.seed(random_seed)
torch.manual_seed(random_seed)
## make dataset
data_path = 'shape/' # class 별 폴더로 나누어진걸 확 가져와서 라벨도 달아준다
president_dataset = datasets.ImageFolder(
data_path,
transforms.Compose([
transforms.Resize((224, 224)),
def __init__(self, name: str):
super().__init__()
self.model = EfficientNet.from_pretrained(name)
self.classifier = nn.Linear(1280, 4)
def initialize_model(model_name, feature_extract=False, use_pretrained=False):
'''
params:
-------
model_name: name of the model to train
num_classes: number of classes
feature_extract: default = False; start with a pretrained model and only
update the final layer weights from which we derive predictions
use_pretrained: default = False; update all of the model’s
parameters for our new task (retrain)
'''
num_classes = len(config.CLASS_NAMES)
# all input size of 224
if model_name == "resnet18":
model_ft = models.resnet18(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
elif model_name == "resnet34":
model_ft = models.resnet34(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
elif model_name == "resnet152":
model_ft = models.resnet152(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
elif model_name == "alexnet":
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
elif model_name == "vgg16":
model_ft = models.vgg16_bn(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
elif model_name == "vgg19":
model_ft = models.vgg19_bn(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
elif model_name == "squeezenet":
model_ft = models.squeezenet1_1(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(
512, num_classes, kernel_size=(7, 7), stride=(2, 2)
)
# model_ft.num_classes = num_classes
elif model_name == "densenet169":
model_ft = models.densenet169(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
elif model_name == "densenet201":
model_ft = models.densenet201(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
elif model_name == "efficient":
model_ft = EfficientNet.from_name("efficientnet-b0")
else:
print("Invalid model name, exiting...")
exit()
return model_ft
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('running in device: ', end='')
print(device)
if not os.path.exists(params.model_dir):
os.mkdir(params.model_dir)
f_id = None
if args.record == 'true':
loss_filename = params.model_dir + 'training_loss.txt'
f_id = open(loss_filename, 'w')
# model = models.resnet50(pretrained=True) # type:nn.Module
# num_ftrs = model.fc.in_features
# model.fc = nn.Linear(num_ftrs, params.num_classes)
# model = nets.resnext101_elastic(num_classes=params.num_classes) # type:nn.Module
model = EfficientNet.from_pretrained('efficientnet-b5', num_classes=params.num_classes,
elastic=True if args.elastic == 'true' else False,
cbam=True if args.cbam == 'true' else False)
print('model type: {}'.format(type(model)))
if args.hard_sample_mining == 'true':
print('use hard sample mining strategy')
criterion = nn.CrossEntropyLoss(reduction='none')
else:
criterion = nn.CrossEntropyLoss()
if args.label_smooth == 'true':
print('use label smooth method')
criterion = label_smooth.LabelSmoothSoftmaxCE()
# optimizer = torch.optim.SGD(model.parameters(), params.lr if args.model_path is None else 0.001,
# momentum=params.momentum, weight_decay=params.weight_decay)
optimizer = torch.optim.Adam(model.parameters(), 1e-3, betas=(0.9, 0.999), eps=1e-9)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.7, patience=3, verbose=True)
def __init__(self):
super(EfficientNetB4, self).__init__()
self.efficientnet = EfficientNet.from_pretrained('efficientnet-b4')
def get_net():
net = EfficientNet.from_pretrained('efficientnet-b5', num_classes=4)
return net
if model_num == 152:
model = torch.load('models/resnet152.model').to(device)
elif model_num == 101:
model = models.resnet101().to(device)
model.fc = nn.Linear(2048, 42).to(device)
else:
model = models.resnet152().to(device)
model.fc = nn.Linear(2048, 42).to(device)
elif model_type == 'd':
model = models.densenet169().to(device)
model.classifier = nn.Linear(1664, 42).to(device)
elif model_type == 'e':
if model_num == 1:
model = EfficientNet.from_pretrained('efficientnet-b1',
num_classes=42).to(device)
elif model_num == 3:
model = EfficientNet.from_pretrained('efficientnet-b3',
num_classes=42).to(device)
elif model_num == 4:
model = EfficientNet.from_pretrained('efficientnet-b4',
num_classes=42).to(device)
elif model_num == 5:
model = EfficientNet.from_pretrained('efficientnet-b5',
num_classes=42).to(device)
elif model_num == 7:
model = EfficientNet.from_pretrained('efficientnet-b7',
num_classes=42).to(device)
elif model_type == 'v':
model = models.vgg16_bn().to(device)
def trial_process(self,
trial,
optimizer,
learning_rate,
horizontal_flip,
horizontal_shift_ratio,
vertical_shift_ratio,
random_erasing):
self.best_pred=0.0
self.start_run(trial)
# mlflowにtrialごとの情報をロギング
self.log_trial(trial)
self.model = EfficientNet.from_pretrained(self.args.backbone)
# Unfreeze model weights
for param in self.model.parameters():
param.requires_grad = True
num_ftrs = self.model._fc.in_features
self.model._fc = nn.Linear(num_ftrs, self.args.nclass)
if self.args.smry_viz:
from torchinfo import summary
from torchviz import make_dot
dummy_image=torch.zeros((2, 3, 32,32))
dummy_output=self.model(dummy_image)
make_dot(dummy_output,params=dict(self.model.named_parameters())).render("torchviz", format="png")
summary(self.model, (1,3, 32,32))
import sys;sys.exit()
if self.args.cuda:
self.model = self.model.to('cuda')
if optimizer=='SGD':
self.optimizer = optim.SGD(self.model.parameters(), lr=learning_rate)
elif optimizer=='Adam':
self.optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
pipeline = [
T.ToTensor(),
T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
if strtobool(horizontal_flip) == 1:
pipeline.append(T.RandomHorizontalFlip(p=0.5))
pipeline.append(T.RandomAffine(0,translate=(horizontal_shift_ratio,vertical_shift_ratio)))
if strtobool(random_erasing) == 1:
pipeline.append(T.RandomErasing())
transform = T.Compose(pipeline)
train_set = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
self.train_loader = torch.utils.data.DataLoader(train_set, batch_size=self.args.batch_size,
shuffle=True, num_workers=self.args.workers)
val_set = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
self.val_loader = torch.utils.data.DataLoader(val_set, batch_size=self.args.batch_size,
shuffle=False, num_workers=self.args.workers)
self.criterion = nn.CrossEntropyLoss()
for epoch in range(self.args.start_epoch, self.args.epochs):
self.training(epoch)
if not self.args.no_val and epoch % self.args.eval_interval == (self.args.eval_interval - 1):
best_score=self.validating(epoch)
self.end_run()
# scoring by best
return 1.0 - best_score
def main(config):
# For Reproducibility #
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(config.seed)
# Weights and Plots Path #
paths = [config.weights_path, config.plots_path]
for path in paths:
make_dirs(path)
# Prepare Data Loader #
if config.dataset == 'cifar':
train_loader, val_loader, test_loader = cifar_loader(
config.num_classes, config.batch_size)
input_size = 32
# Prepare Networks #
if config.model == 'vit':
model = VisionTransformer(in_channels=config.in_channels,
embed_dim=config.embed_dim,
patch_size=config.patch_size,
num_layers=config.num_layers,
num_heads=config.num_heads,
mlp_dim=config.mlp_dim,
dropout=config.drop_out,
input_size=input_size,
num_classes=config.num_classes).to(device)
elif config.model == 'efficient':
model = EfficientNet.from_name(
'efficientnet-b0', num_classes=config.num_classes).to(device)
elif config.model == 'resnet':
model = resnet34(pretrained=False).to(device)
model.fc = nn.Linear(config.mlp_dim, config.num_classes).to(device)
else:
raise NotImplementedError
# Weight Initialization #
if not config.model == 'efficient':
if config.init == 'normal':
model.apply(init_weights_normal)
elif config.init == 'xavier':
model.apply(init_weights_xavier)
elif config.init == 'he':
model.apply(init_weights_kaiming)
else:
raise NotImplementedError
# Train #
if config.phase == 'train':
# Loss Function #
criterion = nn.CrossEntropyLoss()
# Optimizers #
if config.num_classes == 10:
optimizer = torch.optim.Adam(model.parameters(),
lr=config.lr,
betas=(0.5, 0.999))
optimizer_scheduler = get_lr_scheduler(config.lr_scheduler,
optimizer)
elif config.num_classes == 100:
optimizer = torch.optim.SGD(model.parameters(),
lr=config.lr,
momentum=0.9,
weight_decay=5e-4)
optimizer_scheduler = get_lr_scheduler('step', optimizer)
# Constants #
best_top1_acc = 0
# Lists #
train_losses, val_losses = list(), list()
train_top1_accs, train_top5_accs = list(), list()
val_top1_accs, val_top5_accs = list(), list()
# Train and Validation #
print("Training {} has started.".format(model.__class__.__name__))
for epoch in range(config.num_epochs):
# Train #
train_loss, train_top1_acc, train_top5_acc = train(
train_loader, model, optimizer, criterion, epoch, config)
# Validation #
val_loss, val_top1_acc, val_top5_acc = validate(
val_loader, model, criterion, epoch, config)
# Add items to Lists #
train_losses.append(train_loss)
val_losses.append(val_loss)
train_top1_accs.append(train_top1_acc)
train_top5_accs.append(train_top5_acc)
val_top1_accs.append(val_top1_acc)
val_top5_accs.append(val_top5_acc)
# If Best Top 1 Accuracy #
if val_top1_acc > best_top1_acc:
best_top1_acc = max(val_top1_acc, best_top1_acc)
# Save Models #
print("The best model is saved!")
torch.save(
model.state_dict(),
os.path.join(
config.weights_path,
'BEST_{}_{}_{}.pkl'.format(model.__class__.__name__,
str(config.dataset).upper(),
config.num_classes)))
print("Best Top 1 Accuracy {:.2f}%\n".format(best_top1_acc))
# Optimizer Scheduler #
optimizer_scheduler.step()
# Plot Losses and Accuracies #
losses = (train_losses, val_losses)
accs = (train_top1_accs, train_top5_accs, val_top1_accs, val_top5_accs)
plot_metrics(losses, accs, config.plots_path, model, config.dataset,
config.num_classes)
print("Training {} using {} {} finished.".format(
model.__class__.__name__,
str(config.dataset).upper(), config.num_classes))
# Test #
elif config.phase == 'test':
test(test_loader, model, config)
else:
raise NotImplementedError
def main():
# if GPU is availale, use GPU
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print("Use " + str(device))
# create dataset
file_list = None
for path, dirs, files in os.walk(test_path, topdown=False):
file_list = list(files)
# preprocessing steps
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
test_dataset = Leaf_test_Dataset(file_list, test_path, transform)
test_loader = DataLoader(dataset=test_dataset, batch_size=batchSize)
print("Start testing:")
# net model
eff_models = []
for model_path in eff_model_paths:
eff_net = EfficientNet.from_name('efficientnet-b4')
eff_net._fc = nn.Linear(eff_net._fc.in_features, 5)
eff_net.load_state_dict(torch.load(model_path))
eff_net = eff_net.to(device)
eff_net.eval()
eff_models.append(eff_net)
preds = []
result = None
with torch.no_grad():
batch_num = len(test_loader)
for index, image in enumerate(test_loader):
image = image.to(device)
eff_result = []
for eff_net in eff_models:
output = eff_net(image)
output = output.to('cpu')
pred = output.argmax(dim=1, keepdim=True).flatten()
eff_result.append(pred)
if len(preds) == 0:
preds = np.dstack(eff_result)[0]
else:
preds = np.vstack([preds, np.dstack(eff_result)[0]])
# start train combine model
df = pd.read_csv(pred_train_csv)
# 移除全错选项
# get the pred acc for this line
def get_acc(pred_csv, index):
label = pred_csv.loc[index, 'label']
acc = 0
if pred_csv.loc[index, 'pred_0'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_1'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_2'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_3'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_4'] == label:
acc += 0.2
return round(acc, 1)
delete_index = []
for index in range(len(df)):
acc = get_acc(df, index)
# remove noise data
if acc <= 0:
delete_index.append(index)
df = df.drop(delete_index)
df = df.reset_index(drop=True)
X = np.array(df[["pred_0", "pred_1", "pred_2", "pred_3", "pred_4"]])
y = np.array(df[["label"]]).flatten()
from sklearn.neural_network import MLPClassifier
# Neural Network
nn = MLPClassifier(max_iter=2000)
nn.fit(X, y)
result = nn.predict(preds)
pred_result = pd.concat([
pd.DataFrame(file_list, columns=['image_id']),
pd.DataFrame(result, columns=['label'])
],
axis=1)
pred_result.to_csv(output_path + "submission.csv", index=False, sep=',')
print("Done.")
from __future__ import absolute_import, division, print_function, unicode_literals
import json
from PIL import Image
import torch
from torchvision import transforms
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')
image_path = 'examples/simple/img.jpg'
labels_map = 'examples/simple/labels_map.txt'
# Preprocess image
tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),])
img = tfms(Image.open(image_path)).unsqueeze(0)
print(img.shape) # torch.Size([1, 3, 224, 224])
# Load ImageNet class names
labels_map = json.load(open(labels_map))
labels_map = [labels_map[str(i)] for i in range(1000)]
# Classify
model.eval()
with torch.no_grad():
outputs = model(img)
# Print predictions
print('-----')
for idx in torch.topk(outputs, k=5).indices.squeeze(0).tolist():
def __init__(self, config):
super(FrameWork, self).__init__()
self.inputs = None
self.labels = None
self.config = config
if torch.cuda.is_available() and self.config['System']['GPU'] and int(
self.config['System']['GPU_ID']) > -1:
self.device = torch.device('cuda:{}'.format(
self.config['System']['GPU_ID']))
else:
self.device = torch.device('cpu')
self.image_channel = self.config['Model']['ImageChannel']
self.resize = self.config['Model']['RESIZE']
self.cnn_type = self.config['Train']['CNN']['NAME']
self.paramters = []
if self.cnn_type == CNN.MobileNetV2.value:
from torchvision.models import mobilenet_v2
self.cnn = mobilenet_v2().features
self.cnn[0][0] = torch.nn.Conv2d(int(self.image_channel),
32, (3, 3),
stride=(2, 2),
padding=(1, 1),
bias=False)
self.cnn.to(device=self.device)
self.paramters.append({'params': self.cnn.parameters()})
self.out_size = 1280
elif self.cnn_type == CNN.EfficientNetb0.value:
from efficientnet_pytorch import EfficientNet
self.cnn = EfficientNet.from_name('efficientnet-b0')
self.cnn._conv_stem = torch.nn.Conv2d(int(self.image_channel),
32,
kernel_size=3,
stride=2,
bias=False)
self.cnn.to(device=self.device)
self.paramters.append({'params': self.cnn.parameters()})
self.out_size = 1280
else:
raise CnnNotFoundError("CNN Name not found!")
rnn = self.config['Train']['RNN']['NAME']
self.hidden_num = int(self.config['Train']['LSTM']['HIDDEN_NUM'])
dropout = int(self.config['Train']['LSTM']['DROPOUT'])
if rnn == RNN.LSTM.value:
self.lstm = nn.LSTM(input_size=self.out_size,
hidden_size=self.hidden_num,
num_layers=2,
bidirectional=True,
dropout=dropout)
self.lstm.to(device=self.device)
self.paramters.append({'params': self.lstm.parameters()})
else:
raise RnnNotFoundError("RNN Name not found!")
self.charset = self.config['Model']['CharSet']
self.charset = json.loads(self.charset)
self.charset_len = len(self.charset)
self.fc = nn.Linear(in_features=self.hidden_num * 2,
out_features=self.charset_len)
self.fc.to(device=self.device)
self.paramters.append({'params': self.fc.parameters()})
self.ctc_loss = nn.CTCLoss(blank=0, reduction='mean')
self.ctc_loss.to(device=self.device)
self.paramters.append({'params': self.ctc_loss.parameters()})
optimizer = self.config['Train']['OPTIMIZER']
self.lr = self.config['Train']['LR']
if optimizer == OPTIMIZER.Momentum.value:
self.optimizer = optim.SGD(self.paramters,
lr=self.lr,
momentum=0.9)
elif optimizer == OPTIMIZER.Adma.value:
self.optimizer = optim.Adam(self.paramters,
lr=self.lr,
betas=(0.9, 0.99))
else:
raise OptimizerNotFoundError("Optimizer Name not found!")
self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer,
gamma=0.98)
def EfficientNetB4(num_classes=8):
model = efficientnet.from_name('efficientnet-b4')
model._fc = nn.Linear(model._fc.in_features, num_classes)
return model
def __init__(self, cnn_encoder):
super(EncoderCNNefficientnet, self).__init__()
self.cnn = EfficientNet.from_pretrained(cnn_encoder)
for param in self.cnn.parameters():
param.requires_grad = True
def __init__(self, opt):
super(PCB_Effi, self).__init__()
self.opt = opt
self.model = EfficientNet.from_pretrained('efficientnet-b0')
self.avgpool = nn.AdaptiveAvgPool2d((self.opt.nparts, 1))
self.dropout = nn.Dropout(p=0.5)
self.feature_dim = self.model._fc.in_features
if self.opt.single_cls:
name = 'classifier'
setattr(
self, name,
ClassBlock(self.opt.nparts * self.feature_dim,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
else:
for i in range(self.opt.nparts):
name = 'classifierA' + str(i)
setattr(
self, name,
ClassBlock(self.feature_dim,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
for i in range(self.opt.nparts - 1):
name = 'classifierB' + str(i)
setattr(
self, name,
ClassBlock(2 * 1280,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
for i in range(self.opt.nparts - 1):
name = 'classifierB' + str(i + self.opt.nparts - 1)
setattr(
self, name,
ClassBlock(2 * 1280,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
for i in range(self.opt.nparts - 2):
name = 'classifierC' + str(i)
setattr(
self, name,
ClassBlock(3 * 1280,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
for i in range(self.opt.nparts - 2):
name = 'classifierC' + str(i + self.opt.nparts - 2)
setattr(
self, name,
ClassBlock(3 * 1280,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
for i in range(self.opt.nparts - 3):
name = 'classifierD' + str(i)
setattr(
self, name,
ClassBlock(4 * 1280,
self.opt.nclasses,
droprate=0.5,
relu=False,
bnorm=True,
num_bottleneck=256))
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--seed', type=int, default=42)
args = parser.parse_args()
torch.manual_seed(args.seed)
device = args.device
use_ensemble_model_session = [{'session':'team_27/airush1/372', 'checkpoint':'1'} #'efficientnet-b4'
, {'session':'team_27/airush1/354', 'checkpoint':'9'} #se_resnext50_32x4d
,{'session':'team_27/airush1/377', 'checkpoint':'14'} ]#'nasnetamobile'
modelA = EfficientNet.from_name('efficientnet-b4', override_params={'num_classes': args.output_size})
modelB = make_model('se_resnext50_32x4d', num_classes=args.output_size, pretrained=False, pool=nn.AdaptiveAvgPool2d(1))
modelC = make_model('nasnetamobile', num_classes=args.output_size, pretrained=False, pool=nn.AdaptiveAvgPool2d(1))
# DONOTCHANGE: They are reserved for nsml
bind_model(modelA)
re_train_info = use_ensemble_model_session[0]#'efficientnet-b4'
nsml.load(checkpoint=re_train_info['checkpoint'], session=re_train_info['session'])
bind_model(modelB)
re_train_info = use_ensemble_model_session[1]#'se_resnext50_32x4d'
nsml.load(checkpoint=re_train_info['checkpoint'], session=re_train_info['session'])
bind_model(modelC)
re_train_info = use_ensemble_model_session[2]#'nasnetamobile'
nsml.load(checkpoint=re_train_info['checkpoint'], session=re_train_info['session'])
model = MyEnsembleTTA(modelA,modelB,modelC)
model = fuse_bn_recursively(model)
def train():
data_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.CenterCrop((200, 200)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.3),
transforms.RandomRotation(30),
transforms.RandomAffine(degrees=0, translate=(0.2, 0.2)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
#transforms.Lambda(lambda img: img * 2.0 - 1.0)
])
train_set = IMAGE_Dataset(Path(DATASET_ROOT_train), data_transform)
data_loader = DataLoader(dataset=train_set,
batch_size=16,
shuffle=True,
num_workers=1)
model = EfficientNet.from_pretrained('efficientnet-b7')
model = model.cuda(CUDA_DEVICES)
model.train()
best_model_params = copy.deepcopy(model.state_dict())
best_acc = 0.0
num_epochs = 200
criterion = nn.CrossEntropyLoss()
stepsize = 20
base_lr = 0.001
max_lr = 0.01
base_mm = 0.8
max_mm = 0.99
for epoch in range(num_epochs):
#newlr = get_triangular_lr(epoch,stepsize,base_lr,max_lr)
#mm=get_dynamic_momentum(epoch,stepsize,base_mm,max_mm)
optimizer = torch.optim.SGD(params=model.parameters(),
lr=0.001,
momentum=0.9)
print(f'Epoch: {epoch + 1}/{num_epochs}')
print('-' * len(f'Epoch: {epoch + 1}/{num_epochs}'))
training_loss = 0.0
training_corrects = 0
for i, (inputs, labels) in enumerate(data_loader):
inputs = Variable(inputs.cuda(CUDA_DEVICES))
labels = Variable(labels.cuda(CUDA_DEVICES))
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
training_loss += loss.item() * inputs.size(0)
# print(training_loss)
#revise loss.data[0]-->loss.item()
training_corrects += torch.sum(preds == labels.data)
#print(f'training_corrects: {training_corrects}')
training_loss = training_loss / len(train_set)
training_acc = training_corrects.double() / len(train_set)
print(
f'Training loss: {training_loss:.4f}\taccuracy: {training_acc:.4f}\n'
)
test_acc = test(model)
if test_acc > best_acc:
best_acc = test_acc
best_model_params = copy.deepcopy(model.state_dict())
model.load_state_dict(best_model_params)
torch.save(model, f'model-{best_acc:.02f}-best_train_acc.pth')
def __init__(self,
model_name='efficientnet-b3',
n_channels=3,
n_classes=None,
pretrained=False):
super(ENet, self).__init__()
model_encoder = {
# 'efficientnet-b0': {
# 'model': EfficientNet.from_name('efficientnet-b0'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b0', advprop=True),
# 'out_channels': 1280
# },
# 'efficientnet-b1': {
# 'model': EfficientNet.from_name('efficientnet-b1'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b1', advprop=True),
# 'out_channels': 1280
# },
# 'efficientnet-b2': {
# 'model': EfficientNet.from_name('efficientnet-b2'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b2', advprop=True),
# 'out_channels': 1408
# },
'efficientnet-b3': {
'model':
EfficientNet.from_name('efficientnet-b3'),
'pretrained_model':
EfficientNet.from_pretrained('efficientnet-b3', advprop=True),
'out_channels':
1536
},
# 'efficientnet-b4': {
# 'model': EfficientNet.from_name('efficientnet-b4'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b4', advprop=True),
# 'out_channels': 1792
# },
# 'efficientnet-b5': {
# 'model': EfficientNet.from_name('efficientnet-b5'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b5', advprop=True),
# 'out_channels': 2048
# },
# 'efficientnet-b6': {
# 'model': EfficientNet.from_name('efficientnet-b6'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b6', advprop=True),
# 'out_channels': 2304
# },
# 'efficientnet-b7': {
# 'model': EfficientNet.from_name('efficientnet-b7'),
# 'pretrained_model': EfficientNet.from_pretrained('efficientnet-b7', advprop=True),
# 'out_channels': 2560
# },
}
if pretrained:
self.model = model_encoder[model_name]['pretrained_model']
else:
self.model = model_encoder[model_name]['model']
self.out_channels = model_encoder[model_name]['out_channels']
self.model._conv_stem.in_channels = n_channels
conv_stem = self.model._conv_stem
if n_channels <= 3:
self.model._conv_stem.weight.data[:, :
n_channels, :, :] = conv_stem.weight.data[:, :
n_channels, :, :]
else:
self.model._conv_stem.weight.data[:, :
3, :, :] = conv_stem.weight.data
self.model._conv_stem.weight.data[:, 3:
n_channels, :, :] = conv_stem.weight.data[:, :int(
n_channels - 3), :, :]
self.model._avg_pooling = GeM()
self.model._fc = nn.Sequential(
nn.BatchNorm1d(self.out_channels,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True))
self.model._fc.out_channels = self.out_channels
self._fc_gr = nn.Sequential(
nn.BatchNorm1d(self.out_channels,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True),
nn.Linear(in_features=self.out_channels,
out_features=n_classes[0],
bias=True))
self._fc_vo = nn.Sequential(
nn.BatchNorm1d(self.out_channels,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True),
nn.Linear(in_features=self.out_channels,
out_features=n_classes[1],
bias=True))
self._fc_co = nn.Sequential(
nn.BatchNorm1d(self.out_channels,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True),
nn.Linear(in_features=self.out_channels,
out_features=n_classes[2],
bias=True))
def predict_class_probabilities(options):
"""Predicts class probabilities and optionally evaluates accuracy, precision,
recall and f1 score if labels are provided
Args:
options: parsed arguments
"""
# Initialize model
num_classes = 196
num_attentions = 64
if options.feature_net_name == 'resnet152cbam':
feature_net = resnet152_cbam(pretrained=True)
elif options.feature_net_name == 'efficientnetb3':
feature_net = EfficientNet.from_pretrained('efficientnet-b3')
elif options.feature_net_name == 'inception':
feature_net = inception_v3(pretrained=True)
else:
raise RuntimeError('Invalid model name')
net = WSDAN(num_classes=num_classes, M=num_attentions, net=feature_net)
# Load ckpt and get state_dict
checkpoint = torch.load(options.ckpt_path)
state_dict = checkpoint['state_dict']
# Load weights
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(options.ckpt_path))
# load feature center
feature_center = checkpoint['feature_center'].to(torch.device(device))
logging.info('feature_center loaded from {}'.format(options.ckpt_path))
# Use cuda
cudnn.benchmark = True
net.to(torch.device(device))
net = nn.DataParallel(net)
# Load dataset
dataset, data_loader, image_list = prepare_dataloader(options)
# Default Parameters
theta_c = 0.5
crop_size = image_size # size of cropped images for 'See Better'
# metrics initialization
batches = 0
epoch_loss = 0
epoch_acc = np.array([0, 0, 0], dtype='float') # top - 1, 3, 5
loss = nn.CrossEntropyLoss()
start_time = time.time()
net.eval()
y_pred_average = np.zeros((len(dataset), 196))
with torch.no_grad():
for i, sample in enumerate(tqdm(data_loader)):
if options.do_eval:
X, y = sample
y = y.to(torch.device(device))
else:
X = sample
X = X.to(torch.device(device))
# Raw Image
y_pred_raw, feature_matrix, attention_map = net(X)
# Object Localization and Refinement
crop_mask = F.upsample_bilinear(
attention_map, size=(X.size(2), X.size(3))) > theta_c
crop_images = []
for batch_index in range(crop_mask.size(0)):
nonzero_indices = torch.nonzero(crop_mask[batch_index, 0, ...])
height_min = nonzero_indices[:, 0].min()
height_max = nonzero_indices[:, 0].max()
width_min = nonzero_indices[:, 1].min()
width_max = nonzero_indices[:, 1].max()
crop_images.append(
F.upsample_bilinear(X[batch_index:batch_index + 1, :,
height_min:height_max,
width_min:width_max],
size=crop_size))
crop_images = torch.cat(crop_images, dim=0)
y_pred_crop, _, _ = net(crop_images)
y_pred = (y_pred_raw + y_pred_crop) / 2
y_pred_average[i * options.batch_size:(i + 1) *
options.batch_size] = y_pred.cpu().numpy()
batches += 1
if options.do_eval:
# loss
batch_loss = loss(y_pred, y)
epoch_loss += batch_loss.item()
# metrics: top-1, top-3, top-5 error
epoch_acc += accuracy(y_pred, y, topk=(1, 3, 5))
end_time = time.time()
if options.do_eval:
epoch_loss /= batches
epoch_acc /= batches
logging.info(
'Valid: Loss %.5f, Accuracy: Top-1 %.4f, Top-3 %.4f, Top-5 %.4f, Time %3.2f'
% (epoch_loss, epoch_acc[0], epoch_acc[1], epoch_acc[2],
end_time - start_time))
ground_truth = [sample[1] for sample in dataset.samples]
precision, recall, f1, _ = precision_recall_fscore_support(
ground_truth, np.argmax(y_pred_average, axis=1), average='macro')
logging.info(
f'Precision: {precision}, Recall: {recall}, Macro F1: {f1}')
y_pred_average = softmax(y_pred_average, axis=1)
save_predictions(image_list,
y_pred_average,
options,
ground_truth=ground_truth)
else:
save_predictions(image_list, y_pred_average, options)
def get_net():
net = EfficientNet.from_pretrained('efficientnet-b5')
net._fc = nn.Linear(in_features=2048, out_features=4, bias=True)
return net
training_generator = data.DataLoader(training_set, **params)
# validation data generator
validation_set = Dataset(os.path.join(BASE_VAL_PATH,
'regular-fundus-validation',
'regular-fundus-validation.csv'),
BASE_VAL_PATH,
transform=transform_train)
validation_generator = data.DataLoader(validation_set, **params)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = EfficientNet.from_pretrained('efficientnet-b0', num_classes=5)
model.to(device)
print(summary(model, input_size=(3, 512, 512)))
PATH_SAVE = '../Weights/'
if (not os.path.exists(PATH_SAVE)):
os.mkdir(PATH_SAVE)
criterion = nn.CrossEntropyLoss()
lr_decay = 0.99
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Eye to create an 5x5 tensor
eye = torch.eye(5).to(device)
valset = JoinDataset_val()
val_loader = DataLoader(dataset=valset, batch_size=64, shuffle=True)
dataloders = {'train': train_loader, 'val': val_loader}
# Trained model의 Weights를 사용하기 위하여 경로 지정
weight_path = 'advprop_efficientnet-b0.pth'
trained_weights_path = os.path.join(
'../input/efficientnetpytorch/EfficientNet-PyTorch/efficientnet_weights',
weight_path)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
# model 생성
model = EfficientNet.from_name('efficientnet-b0')
model.load_state_dict(
torch.load(trained_weights_path, map_location=torch.device(device)))
model._fc = nn.Linear(model._fc.in_features, out_features=1)
# # Layer를 지정하여 해당 위치까지 Weights를 고정시킴 (Transfer Learning 시 사용)
# for i,param in enumerate(model.parameters()):
# param.requires_grad = False
# print(i)
# if i == 50:
# break
# # FC Layer에 추가적으로 Dense를 더함 (Transfer Learning 시 사용)
# fc = nn.Sequential(
# nn.Linear(1280, 512),
# nn.ReLU(),
def main():
model_choice = input()
num_bands = int(input())
if model_choice == 'Resnet-18':
model = models.resnet18(pretrained=True)
num_feat = model.fc.in_features
model.fc = nn.Linear(num_feat, 4)
model.conv1 = nn.Sequential(
nn.ConvTranspose2d(num_bands, 32, 2, 1, 2, bias=False),
nn.Conv2d(32, 3, 1, bias=False), nn.ReLU(inplace=True),
nn.Conv2d(3, 64, 7, 2, 3, bias=False))
model.to(device)
elif model_choice == 'Resnet-50':
model = models.resnet50(pretrained=True)
num_feat = model.fc.in_features
model.fc = nn.Linear(num_feat, 4)
model.conv1 = nn.Sequential(
nn.ConvTranspose2d(num_bands, 32, 2, 1, 2, bias=False),
nn.Conv2d(32, 3, 1, bias=False), nn.ReLU(inplace=True),
nn.Conv2d(3, 64, 7, 2, 3, bias=False))
model.to(device)
elif model_choice == 'EffcientNet-B4':
model = EfficientNet.from_pretrained('efficientnet-b4', num_classes=4)
model = model.to(device)
base_opt = torch.optim.Adam(model.parameters(), 1.) ## base optimizer
## Set up the CLR
step_size = 2
max_lr = 1e-4
base_lr = 1e-5
cyclic_lr = triangular_lr(step_size, base_lr, max_lr)
scheduler = LambdaLR(base_opt, cyclic_lr)
## Setup the SWA
opt = torchcontrib.optim.SWA(base_opt)
## Training Loop
n_epochs = 40
train_loader, valid_loader = load_data(weighted=False)
valid_acc_max = -np.Inf
train_losses, valid_losses = [], []
val_acc = []
idx = 0
betas = [0.99, 0.99999]
for epoch in range(1, n_epochs + 1):
train_loss = 0.0 ## running losses
valid_loss = 0.0
if epoch > 25 and idx == 0:
idx += 1
valid_acc_max = -np.Inf
correct, total = acc_by_class(valid_loader, model)
beta = betas[idx]
effective_num = 1.0 - np.power(beta, cls_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
cls_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
criterion = LDAMLoss(cls_num_list=cls_num_list,
weight=per_cls_weights) ## Loss
model.train() ## training mode
for data, labels in train_loader:
data, labels = data.to(device), labels.to(device)
batch_size = data.size(0)
opt.zero_grad() ## clear the gradient
output = model(data) ## get the output
loss = criterion(output, labels) ## get the output
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
1) ## Gradinet clipping
opt.step()
train_loss += loss.item() * batch_size
model.eval()
for data, labels in valid_loader:
data, labels = data.to(device), labels.to(device)
opt.zero_grad()
output = model(data)
loss = criterion(output, labels)
valid_loss += loss.item() * batch_size
train_loss /= len(train_loader.sampler)
valid_loss /= len(valid_loader.sampler)
scheduler.step()
valid_acc = accuracy(valid_loader, model)
valid_losses.append(valid_loss)
train_losses.append(train_loss)
val_acc.append(valid_acc)
print(
'Epoch : {} \tTraining Loss : {} \tValidation Loss :{} \tValidation Acc : {}'
.format(epoch, train_loss, valid_loss, valid_acc))
print('-' * 100)
if valid_acc >= valid_acc_max:
print('Validation Acc. increased ({:.6f} --> {:.6f})'.format(
valid_acc_max, valid_acc))
valid_acc_max = valid_acc