def train(fold):
training_data_path = TRAINING_DATA_PATH
df = pd.read_csv(TRAIN_FOLDS)
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
model = ResNext101_64x4d(pretrained="imagenet")
model.to(DEVICE)
train_images = df_train.image_name.values.tolist()
train_images = [
os.path.join(training_data_path, i + ".jpg") for i in train_images
]
train_targets = df_train.target.values
valid_images = df_valid.image_name.values.tolist()
valid_images = [
os.path.join(training_data_path, i + ".jpg") for i in valid_images
]
valid_targets = df_valid.target.values
train_loader = train_dataloader(images=train_images, targets=train_targets)
valid_loader = valid_dataloader(images=valid_images, targets=valid_targets)
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
threshold=0.001,
mode="max")
es = EarlyStopping(patience=3, mode="max")
for epoch in range(EPOCHS):
train_loss = Engine.train(train_loader,
model,
optimizer,
device=DEVICE)
predictions, valid_loss = Engine.evaluate(valid_loader,
model,
device=DEVICE)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(valid_targets, predictions)
print(f"Epoch = {epoch}, AUC = {auc}")
scheduler.step(auc)
es(auc,
model,
model_path=os.path.join(MODEL_PATH, f"model_fold_{fold}.bin"))
if es.early_stop:
print("Early stopping")
break
class SEResnext50_32x4dLearner(ActiveLearner):
def __init__(self, device=0, logger_name=None):
super().__init__(device=device)
self.model = SEResnext50_32x4d(pretrained="imagenet")
self.model.to(device)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-4)
self.Engine = Engine(self.model, self.optimizer, device)
self.logger = logging.getLogger(logger_name)
def inference(self, dataset, bs=64):
loader = torch.utils.data.DataLoader(
dataset, batch_size=bs, shuffle=False, num_workers=4
)
predictions = self.Engine.predict(loader)
predictions = np.vstack((predictions)).ravel()
probabilities = 1 / (1 + np.exp(-predictions))
probabilities = np.stack([probabilities, 1-probabilities], axis=1)
return {'class_probabilities': probabilities}
def fit(self, train_dataset, epochs=50, train_bs=32, **kwargs):
labeled_targets = [x['targets'].numpy()
for x in tqdm.tqdm(train_dataset)]
train_target_distrib = pd.value_counts(labeled_targets)
self.logger.info('Targets labeled distribution :')
self.logger.info(train_target_distrib)
if self.cuda_available:
self.model.cuda()
self.model.train()
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_bs, shuffle=True, num_workers=4
)
optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-4)
for epoch in tqdm.tqdm(range(epochs)):
train_loss = self.Engine.train(train_loader)
return {'target_distribution': train_target_distrib}
def score(self, valid_dataset, batch_size=64):
self.model.eval()
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=batch_size, shuffle=False, num_workers=4
)
# valid_loss = self.Engine.evaluate(valid_loader)
# print(f'Validation loss : {valid_loss:.3f}')
predictions = self.Engine.predict(valid_loader)
predictions = np.vstack((predictions)).ravel()
valid_targets = valid_dataset.targets
auc = metrics.roc_auc_score(valid_targets, predictions)
print(f"AUC = {auc:.3f}")
return {'auc': auc}
augmentations=aug,
).fetch(batch_size=16, num_workers=4, drop_last=False, shuffle=True, tpu=False)
valid_loader = ClassificationDataLoader(
image_paths=valid_images,
targets=valid_targets,
resize=(128, 128),
augmentations=aug,
).fetch(batch_size=16, num_workers=4, drop_last=False, shuffle=False, tpu=False)
optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.6)
eng = Engine(model, optimizer, device=args.device)
for epoch in range(args.epochs):
train_loss = eng.train(train_loader)
valid_loss = eng.evaluate(valid_loader)
print(f"{epoch}, Train Loss={train_loss} Valid Loss={valid_loss}")
test_df = pd.read_csv(os.path.join(args.data_path, "test.csv"))
images = test_df.image_id.values.tolist()
images = [os.path.join(args.data_path, "images", i + ".jpg") for i in images]
targets = np.zeros((len(images), 4))
aug = albumentations.Compose(
[albumentations.Normalize(mean, std, max_pixel_value=255.0, always_apply=True)]
)
test_loader = ClassificationDataLoader(
image_paths=images, targets=targets, resize=(128, 128), augmentations=aug
).fetch(batch_size=16, num_workers=4, drop_last=False, shuffle=False, tpu=False)
def train(fold):
#config
train_path = img_path + "/train3/"
df = pd.read_csv(data_path + "/train_folds.csv")
device = "cuda" if torch.cuda.is_available() else "cpu"
epochs = 1
train_bs = 32
valid_bs = 32
#df
df_train = df[df.fold != fold].reset_index(drop=True)
df_valid = df[df.fold == fold].reset_index(drop=True)
#Aug
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
train_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
albumentations.ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=15),
albumentations.VerticalFlip(p=0.5),
albumentations.HorizontalFlip(p=0.5),
])
valid_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
#Dataset
train_imgs = df_train.image_name.values.tolist()
train_imgs = [img_path + "/train3/" + file + ".jpg" for file in train_imgs]
train_targets = df_train.target.values
valid_imgs = df_valid.image_name.values.tolist()
valid_imgs = [img_path + "/train3/" + file + ".jpg" for file in valid_imgs]
valid_targets = df_valid.target.values
train_dataset = ClassificationLoader(
image_paths=train_imgs,
targets=train_targets,
resize=None,
augmentations=train_aug,
)
valid_dataset = ClassificationLoader(
image_paths=valid_imgs,
targets=valid_targets,
resize=None,
augmentations=valid_aug,
)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_bs,
shuffle=True,
num_workers=4)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=valid_bs,
shuffle=True,
num_workers=4)
# model
model = SEResnext50_32x4d(pretrained=True)
model = model.to(device)
model_path = save_path + f"/200712_fold_{fold}.bin"
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
threshold=0.001,
mode="max")
es = EarlyStopping(patience=3, mode="max")
for epoch in range(epochs):
train_loss = Engine.train(train_loader,
model,
optimizer,
device=device)
preds, valid_loss = Engine.evaluate(valid_loader, model, device=device)
preds = np.vstack(preds).ravel()
auc = metrics.roc_auc_score(valid_targets, preds)
print(f"Epoch {epoch}, AUC {auc}")
scheduler.step(auc)
es(auc, model, model_path=model_path)
if es.early_stop:
print("early stop")
break
def train(fold):
training_data_path = "../input/siic-isic-224x224-images/train/"
df = pd.read_csv("/kaggle/working/train_folds.csv")
device = "cuda"
epochs = 50
train_bs = 32
valid_bs = 16
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
model = SEResnext50_32x4d(pretrained="imagenet")
model.to(device)
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
train_aug = albumentations.Compose(
[
albumentations.Normalize(mean, std, max_pixel_value=255.0, always_apply=True),
albumentations.ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.1, rotate_limit=15),
albumentations.Flip(p=0.5)
]
)
valid_aug = albumentations.Compose(
[
albumentations.Normalize(mean, std, max_pixel_value=255.0, always_apply=True)
]
)
train_images = df_train.image_name.values.tolist()
train_images = [os.path.join(training_data_path, i + ".png") for i in train_images]
train_targets = df_train.target.values
valid_images = df_valid.image_name.values.tolist()
valid_images = [os.path.join(training_data_path, i + ".png") for i in valid_images]
valid_targets = df_valid.target.values
train_dataset = ClassificationLoader(
image_paths=train_images,
targets=train_targets,
resize=None,
augmentations=train_aug,
)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_bs, shuffle=True, num_workers=4
)
valid_dataset = ClassificationLoader(
image_paths=valid_images,
targets=valid_targets,
resize=None,
augmentations=valid_aug,
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=valid_bs, shuffle=False, num_workers=4
)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
patience=3,
threshold=0.001,
mode="max"
)
es = EarlyStopping(patience=5, mode="max")
for epoch in range(epochs):
train_loss = Engine.train(train_loader, model, optimizer, device=device)
predictions, valid_loss = Engine.evaluate(
valid_loader, model, device=device
)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(valid_targets, predictions)
print(f"Epoch = {epoch}, AUC = {auc}")
scheduler.step(auc)
es(auc, model, model_path=f"model_fold_{fold}.bin")
if es.early_stop:
print("Early stopping")
break
def train():
df = pd.read_csv(DATA_PATH + "images_labeled.csv")
X_train, X_test, y_train, y_test = train_test_split(df.image.values,
df.label.values,
test_size=0.3,
random_state=42,
shuffle=True)
train_aug = albumentations.Compose([
albumentations.Normalize(mean=MEAN,
std=STD,
max_pixel_value=255.0,
always_apply=True)
])
valid_aug = albumentations.Compose([
albumentations.Normalize(mean=MEAN,
std=STD,
max_pixel_value=255.0,
always_apply=True)
])
train_images = [os.path.join(DATA_PATH, filename) for filename in X_train]
valid_images = [os.path.join(DATA_PATH, filename) for filename in X_test]
train_dataset = ClassificationDataset(
image_paths=train_images,
targets=y_train,
resize=None,
augmentations=train_aug,
)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=TRAIN_BS,
shuffle=True,
num_workers=4)
valid_dataset = ClassificationDataset(
image_paths=valid_images,
targets=y_test,
resize=None,
augmentations=valid_aug,
)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=VALID_BS,
shuffle=False,
num_workers=4)
model = CV_Model(pretrained="imagenet")
model.to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
mode="max")
es = EarlyStopping(patience=5, mode="max")
for epoch in range(EPOCHS):
engine = Engine(model=model, optimizer=optimizer, device=DEVICE)
engine.train(train_loader)
predictions = engine.predict(valid_loader)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(y_test, predictions)
print(f"Epoch = {epoch}, AUC = {auc}")
scheduler.step(auc)
es(auc, model, model_path=os.path.join(MODEL_PATH, "model.bin"))
if es.early_stop:
print("Early stopping")
break
def train(fold):
# train image path
training_data_path = input("Enter the train data path (resized image): ")
# csv data path that was created from folds
fold_csv_path = input("Enter the train_fold.csv file path: ")
df = pd.read_csv(fold_csv_path)
model_path = "model/"
device = "cuda"
epochs = 30
train_batch_size = 32
valid_batch_size = 16
mean = (0.485, 0.456, 0.225)
standard_deviation = (0.229, 0.224, 0.225)
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
# normalize images
train_aug = albumentations.Compose([
albumentations.Normalize(mean,
std=standard_deviation,
max_pixel_value=255.0,
always_apply=True)
])
valid_aug = albumentations.Compose([
albumentations.Normalize(mean,
std=standard_deviation,
max_pixel_value=255.0,
always_apply=True)
])
# train image mapping
train_images = df_train.image_name.values.tolist()
train_images = [
os.path.join(training_data_path, i + ".jpg") for i in train_images
]
train_targets = df_train.target.values
valid_images = df_train.image_name.values.tolist()
valid_images = [
os.path.join(training_data_path, i + ".jpg") for i in valid_images
]
valid_targets = df_valid.target.values
# create train loader
train_dataset = ClassificationLoader(image_paths=train_images,
targets=train_targets,
resize=None,
augmentations=train_aug)
# train loader
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=4)
# create valid dataset
valid_dataset = ClassificationLoader(image_paths=valid_images,
targets=valid_targets,
resize=None,
augmentations=valid_aug)
# validation data loader
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=valid_batch_size,
shuffle=False,
num_workers=4)
# import model
model = SEResNext(pretrained='imagenet')
model.to(device)
#
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
# dynamic learning rate reducing based on validation measurements.
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
# https://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau
optimizer,
patience=4,
mode='max',
)
# use apex for mixed precision training
# amp: Automatic Mixed Precision
model, optimizer = amp.initialize(model,
optimizer,
opt_level='01',
verbosity=0)
# earlystopping
es = EarlyStopping(patience=5, mode='max')
# train the train data
# use thr wtfml module for calculating loss and evaluation
for epoch in range(epochs):
training_loss = Engine.train(train_loader,
model,
optimizer,
device,
fp16=True)
predictions, valid_loss = Engine.evaluate(train_loader, model,
optimizer, device)
predictions = np.vstack((predictions).ravel())
auc = metrics.roc_auc_score(valid_targets, predictions)
scheduler.step(auc)
print(f"epoch = {epoch}, auc= {auc}")
es(auc, model, os.path.join(model_path, f"model{fold}.bin"))
if es.early_stop:
print("Early Stopping")
break
def train(fold):
training_data_path = "/home/abhishek/workspace/melanoma/input/jpeg/train224/"
model_path = "/home/abhishek/workspace/melanoma-deep-learning"
df = pd.read_csv("/home/abhishek/workspace/melanoma/input/train_folds.csv")
device = "cuda"
epochs = 50
train_bs = 32
valid_bs = 16
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
train_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
valid_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
train_images = df_train.image_name.values.tolist()
train_images = [
os.path.join(training_data_path, i + ".jpg") for i in train_images
]
train_targets = df_train.target.values
valid_images = df_valid.image_name.values.tolist()
valid_images = [
os.path.join(training_data_path, i + ".jpg") for i in valid_images
]
valid_targets = df_valid.target.values
train_dataset = ClassificationLoader(image_paths=train_images,
targets=train_targets,
resize=None,
augmentations=train_aug)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_bs,
shuffle=True,
num_workers=4)
valid_dataset = ClassificationLoader(image_paths=valid_images,
targets=valid_targets,
resize=None,
augmentations=valid_aug)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=valid_bs,
shuffle=False,
num_workers=4)
model = SEResNext50_32x4d(pretrained="imagenet")
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
mode="max")
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
es = EarlyStopping(patience=5, mode="max")
for epoch in range(epochs):
training_loss = Engine.train(train_loader,
model,
optimizer,
device,
fp16=True)
predictions, valid_loss = Engine.evaluate(train_loader, model,
optimizer, device)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(valid_targets, predictions)
scheduler.step(auc)
print(f"epoch={epoch}, auc={auc}")
es(auc, model, os.path.join(model_path, f"model{fold}.bin"))
if es.early_stop:
print("early stopping")
break
resize=(128, 128),
augmentations=aug,
).fetch(batch_size=16,
num_workers=4,
drop_last=False,
shuffle=False,
tpu=False)
optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=15,
gamma=0.6)
for epoch in range(args.epochs):
train_loss = Engine.train(train_loader,
model,
optimizer,
device=args.device)
predictions, valid_loss = Engine.evaluate(valid_loader,
model,
device=args.device)
predictions = np.vstack((predictions))
roc_1 = metrics.roc_auc_score(valid_targets[:, 0], predictions[:, 0])
roc_2 = metrics.roc_auc_score(valid_targets[:, 1], predictions[:, 1])
roc_3 = metrics.roc_auc_score(valid_targets[:, 2], predictions[:, 2])
roc_4 = metrics.roc_auc_score(valid_targets[:, 3], predictions[:, 3])
mean_roc = (roc_1 + roc_2 + roc_3 + roc_4) / 4
print(
f"Epoch={epoch}, Train Loss={train_loss} Valid Loss={valid_loss}, Mean ROC AUC={mean_roc}"
)
test_df = pd.read_csv(os.path.join(args.data_path, "test.csv"))
def train(fold):
training_data_path = ''
model_path = ""
df = pd.read_csv("/.../train_folds.csv")
device = "cuda"
epochs = 50
train_bs = 32 #train batch size
valid_bs = 16
#normalize image pixel values
mean = (0.485, 0.456, 0.406) #these values are for this model
std = (0.229, 0.224, 0.225)
df_train = df[df.kfold != fold].reset_index(
drop=True) #absolutely removes the previous index
df_valid = df[df.kfold == fold].reset_index(drop=True)
#for image augmentation
train_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
valid_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
train_images = df_train.image_name.values.tolist()
train_images = [
os.path.join(training_data_path, i + '.jpg') for i in train_images
]
train_targets = df_train.target.values
valid_images = df_valid.image_name.values.tolist()
valid_images = [
os.path.join(training_data_path, i + '.jpg') for i in valid_images
]
valid_targets = df_valid.target.values
train_dataset = ClassificationLoader(image_paths=train_images,
targets=train_targets,
resize=None,
augmentation=train_aug)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_bs,
shuffle=True,
num_workers=4)
valid_dataset = ClassificationLoader(image_paths=valid_images,
targets=valid_targets,
resize=None,
augmentation=valid_aug)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=valid_bs,
shuffle=True,
num_workers=4)
model = SEResNext50_32x4d(pretrained='imagenet')
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( # reduce learning rate if it plateaus at any level
optimizer,
patience=3,
mode=
"max" #max because we'll be using scheduler on AUC(area under ROC curve)
)
model, optimizer = amp.initialize( #apex is used for mixed precision training, it trains faster with less memory
model,
optimizer,
opt_level="01",
verbosity=0)
es = EarlyStopping(patience=5, mode="max")
for epoch in range(epochs):
training_loss = Engine.train(train_loader,
model,
optimizer,
device,
fp16=True)
predictions, valid_loss = Engine.evaluate(train_loader, model,
optimizer, device)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(
valid_targets, predictions
) #this is why valid_data should not be shuffled as opposed to training data
scheduler.step(auc)
print("epoch={}, auc={}".format(epoch, auc))
es(auc, model, os.path.join(model_path, f"model{fold}.bin"))
if es.early_stop:
print('early stopping')
break
def train(fold):
training_data_path = "/content/train_images/"
df = pd.read_csv("train_folds.csv")
model_path = "/content/checkpoints/"
device = "cpu"
epochs = 10
train_bs = 32
val_bs = 16
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
df_train = df[df.kfold != fold].reset_index(drop=True)
df_val = df[df.kfold == fold].reset_index(drop=True)
train_aug = albumentations.Compose(
[
albumentations.Normalize(mean, std, max_pixel_value=255, always_apply=True)
]
)
val_aug = albumentations.Compose(
[
albumentations.Normalize(mean, std, max_pixel_value=255, always_apply=True)
]
)
train_images = df_train.image_name.values.tolist()
train_images = [os.path.join(training_data_path, i + ".jpg") for i in train_images]
train_targets = df_train.target.values
val_images = df_val.image_name.values.tolist()
val_images = [os.path.join(training_data_path, i + ".jpg") for i in val_images]
val_targets = df_train.target.values
train_dataset = ClassificationLoader(
image_paths= train_images,
targets = train_targets,
resize=None,
augmentations=train_aug
)
train_loder = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_bs,
shuffle=False,
num_workers=4
)
val_dataset = ClassificationLoader(
image_paths= val_images,
targets = val_targets,
resize=None,
augmentations=val_aug
)
val_loder = torch.utils.data.DataLoader(
val_dataset,
batch_size=val_bs,
num_workers=4
)
model = SEResNext50_32x4d(pretrained='imagenet')
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr= 1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
patience=3,
mode="max"
)
es = EarlyStopping(patience=5, mode= "max")
predictions = []
for epoch in range(epochs):
training_loss = Engine.train(train_loder, model, optimizer, device=device)
val_loss = engine.evaluate(val_loder)
predictions = np.vstack((engine.predict(val_loder))).ravel()
auc = metrics.roc_auc_score(val_targets, predictions)
scheduler.step(auc)
print(f"epoch = {epoch}, auc = {auc}")
es(auc, model, model_path)
if es.early_stop:
print("early stopping")
break
def train(fold):
args = get_args()
with open(args.config) as file:
config_file = yaml.load(file, Loader=yaml.FullLoader)
wandb.init(
project="siim2020",
entity="siim_melanoma",
# name=f"20200718-effb0-adamw-consineaneal-{fold}",
name=f"2017-2018-rexnet-test-{fold}",
#name=f"swav-test-{fold}",
#name=f"RAdam-b6-384x384-{fold}"
)
config = wandb.config # Initialize config
config.update(config_file)
device = config.device
model_path = config.model_path.format(fold)
seed_everything(config.seed)
df = pd.read_csv(config.train_csv_fold)
df_train = df[df.kfold != fold].reset_index(drop=True)
df_train["image_name"] = config.training_data_path + df_train[
"image_name"] + ".jpg"
if config.supplement_data["use_supplement"]:
print(f"training shape before merge {df_train.shape}")
df_supplement = pd.read_csv(config.supplement_data["csv_file"])
df_supplement = df_supplement[df_supplement["tfrecord"] % 2 == 0]
df_supplement = df_supplement[df_supplement["target"] == 1]
df_supplement["image_name"] = (config.supplement_data["file_path"] +
df_supplement["image_name"] + ".jpg")
df_train = pd.concat([df_train, df_supplement]).reset_index(drop=True)
df_train = df_train.sample(
frac=1, random_state=config.seed).reset_index(drop=True)
del df_supplement
print(f"training shape after merge {df_train.shape}")
df_valid = df[df.kfold == fold].reset_index(drop=True)
df_valid["image_name"] = config.training_data_path + df_valid[
"image_name"] + ".jpg"
if config.use_metadata:
df_train, meta_features = get_meta_feature(df_train)
df_valid, _ = get_meta_feature(df_valid)
else:
meta_features = None
model = get_model(
config.model_backbone,
config.model_name,
config.num_classes,
config.input_size,
config.use_metadata,
meta_features,
)
model = model.to(config.device)
print("watching model")
wandb.watch(model, log="all")
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
train_aug = albumentations.Compose([
AdvancedHairAugmentation(hairs_folder="../input/melanoma-hairs/"),
# albumentations.augmentations.transforms.CenterCrop(64, 64, p=0.8),
albumentations.augmentations.transforms.RandomBrightnessContrast(),
albumentations.augmentations.transforms.HueSaturationValue(),
# Microscope(p=0.4),
albumentations.augmentations.transforms.RandomResizedCrop(
config.input_size, config.input_size, scale=(0.7, 1.0), p=0.4),
albumentations.augmentations.transforms.VerticalFlip(p=0.4),
albumentations.augmentations.transforms.Cutout(p=0.3), # doesnt work
albumentations.ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=15),
albumentations.Flip(p=0.5),
RandomAugMix(severity=7, width=7, alpha=5, p=0.3),
# albumentations.augmentations.transforms.Resize(
# config.input_size, config.input_size, p=1
# ),
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
valid_aug = albumentations.Compose([
albumentations.Normalize(mean,
std,
max_pixel_value=255.0,
always_apply=True),
])
train_images = df_train.image_name.values.tolist()
# train_images = [
# os.path.join(config.training_data_path, i + ".jpg") for i in train_images
# ]
train_targets = df_train.target.values
valid_images = df_valid.image_name.values.tolist()
# valid_images = [
# os.path.join(config.training_data_path, i + ".jpg") for i in valid_images
# ]
valid_targets = df_valid.target.values
train_dataset = ClassificationDataset(
image_paths=train_images,
targets=train_targets,
resize=None,
augmentations=train_aug,
meta_features=meta_features,
df_meta_features=df_train,
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.batch_size,
# num_workers=4,
num_workers=1,
pin_memory=True,
shuffle=True,
#sampler=BalanceClassSampler(labels=train_targets, mode="upsampling"),
drop_last=True,
)
valid_dataset = ClassificationDataset(
image_paths=valid_images,
targets=valid_targets,
resize=None,
augmentations=valid_aug,
meta_features=meta_features,
df_meta_features=df_valid,
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=config.test_batch_size,
shuffle=False,
# num_workers=4,
num_workers=1,
pin_memory=True,
# drop_last=True
)
#optimizer = torch.optim.AdamW(model.parameters(), lr=config.lr)
optimizer = RAdam(model.parameters(), lr=config.lr)
if config.swa["use_swa"]:
optimizer = SWA(optimizer, swa_start=12, swa_freq=1)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=2,
threshold=0.0001,
mode="max")
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
# optimizer, len(train_loader) * config.epochs
# )
#scheduler = torch.optim.lr_scheduler.CyclicLR(
# optimizer,
# base_lr=config.lr / 10,
# max_lr=config.lr * 100,
# mode="triangular2",
# cycle_momentum=False,
#)
#scheduler = torch.optim.lr_scheduler.OneCycleLR(
# optimizer, max_lr=3e-3, steps_per_epoch=len(train_loader), epochs=config.epochs
#)
es = EarlyStopping(patience=6, mode="max")
if config.fp16:
print("************* using fp16 *************")
scaler = GradScaler()
else:
scaler = False
for epoch in range(config.epochs):
train_loss = Engine.train(
train_loader,
model,
optimizer,
device=config.device,
wandb=wandb,
accumulation_steps=config.accumulation_steps,
fp16=config.fp16,
scaler=scaler,
)
predictions, valid_loss = Engine.evaluate(
valid_loader,
model,
device=config.device,
wandb=wandb,
epoch=epoch,
upload_image=False,
use_sigmoid=True,
)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(valid_targets, predictions)
print(f"Epoch = {epoch}, AUC = {auc}")
wandb.log({
"valid_auc": auc,
})
scheduler.step(auc)
es(auc, model, model_path=model_path)
if es.early_stop:
print("Early stopping")
break
if config.swa["use_swa"]:
print("saving the model using SWA")
optimizer.swap_swa_sgd()
torch.save(model.state_dict(), config.swa["model_path"].format(fold))
evaluate_for_best_epoch(
fold,
model_path,
config.device,
valid_loader,
config.model_name,
valid_targets,
"final",
meta_features=meta_features,
)
if config.swa["use_swa"]:
model_path = config.swa["model_path"].format(fold)
evaluate_for_best_epoch(
fold,
model_path,
config.device,
valid_loader,
config.model_name,
valid_targets,
"swa",
meta_features=meta_features,
)
def train(fold):
training_data_path = "/home/sushi/code/Kaggle/Melanoma-Detection-/input/jpeg/train224"
df = pd.read_csv(
"/home/sushi/code/Kaggle/Melanoma-Detection-/input/train_folds.csv")
model_path = "/home/sushi/code/Kaggle/Melanoma-Detection-/models"
device = "cuda"
epochs = 50
train_bs = 32
valid_bs = 16
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
train_aug = albumentations.Compose(
albumentations.Normalize(always_apply=True))
valid_aug = albumentations.Compose(
albumentations.Normalize(always_apply=True))
train_images = df_train.image_name.values.tolist()
train_images = [
os.path.join(training_data_path, i + ".jpg") for i in train_images
]
train_targets = df_train.targets.values
valid_images = df_valid.image_name.values.tolist()
valid_images = [
os.path.join(training_data_path, i + "jpg") for i in valid_images
]
valid_targets = df_valid.target.values
train_dataset = ClassificationLoader(image_paths=train_images,
targets=train_targets,
resize=None,
augmentations=train_aug)
valid_dataset = ClassificationLoader(image_paths=valid_images,
targets=train_targets,
resize=None,
augmentations=valid_aug)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_bs,
shuffle=False,
num_workers=4)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=train_bs,
shuffle=False,
num_workers=4)
model = SEResNext50_32x4d(pretrained="imagenet")
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
mode="max")
#model,optimizer = amp.initialize(
# model,
# optimizer,
# opt_level ="01",
# verbosity=0
#)
es = EarlyStopping(patience=5, mode="max")
for epoch in range(epochs):
training_loss = Engine.train(train_loader,
model,
optimizer,
device,
fp16=True)
predictions, valid_loss = Engine.evaluate(
train_loader,
model,
optimizer,
device,
)
predictions = np.vstack((predictions)).ravel()
auc = metrics.roc_auc_score(valid_targets, predictions)
scheduler.step(auc)
print(f"epoch={epoch}, auc:{auc}")
es(auc, model, model_path)
if es.early_stop:
print("early stopping")
break