def get_imagenet_loaders(train_path,
test_path,
transform_train=None,
transform_test=None):
torch.manual_seed(hyperparams.hyperparameter_defaults['seed'])
train_transform = Compose([
#Resize(32, 32, interpolation=1, always_apply=True, p=1),
Cutout(num_holes=1,
max_h_size=8,
max_w_size=8,
always_apply=True,
p=1,
fill_value=[0.485 * 255, 0.456 * 255, 0.406 * 255]),
IAAFliplr(p=0.5),
#MotionBlur(blur_limit=7, always_apply=True, p=1),
RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
always_apply=True,
p=1),
#IAAPerspective(scale=(0.05, 0.1), keep_size=True, always_apply=False, p=0.5),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensor()
])
test_transform = Compose([
#MotionBlur(blur_limit=7, always_apply=True, p=1),
#Resize(32, 32, interpolation=1, always_apply=True, p=1),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensor()
])
train_data, test_data = imgnetloader.generate_timgnet_train_test_data(
"/content/t2/", 0.7, train_transform, test_transform)
print(train_data.transform, test_data.transform)
kwargs = {'num_workers': 2, 'pin_memory': True}
trainloader = torch.utils.data.DataLoader(
train_data,
batch_size=hyperparams.hyperparameter_defaults['batch_size'],
shuffle=True,
**kwargs)
testloader = torch.utils.data.DataLoader(
test_data,
batch_size=hyperparams.hyperparameter_defaults['batch_size'],
shuffle=True,
**kwargs)
return trainloader, testloader
def __init__(self, train=True):
if train:
self.albumentations_transform = Compose([
PadIfNeeded(min_height=40, min_width=40, always_apply=True, p=1.0),
RandomCrop(height=32, width=32, always_apply=True, p=1.0),
IAAFliplr(p=0.5),
Cutout(num_holes=1, max_h_size=8, max_w_size=8, p=0.5),
Normalize(mean=[0.49139968, 0.48215841, 0.44653091], std=[0.24703223, 0.24348513, 0.26158784],),
ToTensor()
])
else:
self.albumentations_transform = Compose([
Normalize(mean=[0.49139968, 0.48215841, 0.44653091], std=[0.24703223, 0.24348513, 0.26158784],),
ToTensor()
])
def get_default_transforms_cifar10():
torch.manual_seed(hyperparams.hyperparameter_defaults['seed'])
patch_size = 2
transform_train = Compose([
#Cutout(num_holes=1,max_h_size=16,max_w_size=16,always_apply=True,p=1,fill_value=[0.5268*255, 0.5267*255, 0.5328*255]),
Cutout(num_holes=1,
max_h_size=16,
max_w_size=16,
always_apply=True,
p=1,
fill_value=[0.4819 * 255, 0.4713 * 255, 0.4409 * 255]),
IAAFliplr(p=0.5),
#MotionBlur(blur_limit=7, always_apply=True, p=1),
RandomBrightnessContrast(brightness_limit=0.2,
contrast_limit=0.2,
always_apply=True,
p=1),
#MultiplicativeNoise(multiplier=1.5, p=1),
#InvertImg(p=0.5),
#HorizontalFlip(p=1),
Normalize(
mean=[0.4914, 0.4826, 0.44653],
std=[0.24703, 0.24349, 0.26519],
),
# Normalize(
# mean=[0.5268, 0.5267, 0.5328],
# std=[0.3485, 0.3444, 0.3447],
# ),
ToTensor()
])
transform_test = Compose([
Normalize(
mean=[0.4914, 0.4826, 0.44653],
std=[0.24703, 0.24349, 0.26519],
),
ToTensor()
])
return transform_train, transform_test
def transform(image, mask, image_name, mask_name):
x, y = image, mask
rand = random.uniform(0, 1)
if (rand > 0.5):
images_name = [f"{image_name}"]
masks_name = [f"{mask_name}"]
images_aug = [x]
masks_aug = [y]
it = iter(images_name)
it2 = iter(images_aug)
imagedict = dict(zip(it, it2))
it = iter(masks_name)
it2 = iter(masks_aug)
masksdict = dict(zip(it, it2))
return imagedict, masksdict
mask_density = np.count_nonzero(y)
## Augmenting only images with Gloms
if (mask_density > 0):
try:
h, w, c = x.shape
except Exception as e:
image = image[:-1]
x, y = image, mask
h, w, c = x.shape
aug = Blur(p=1, blur_limit=3)
augmented = aug(image=x, mask=y)
x0 = augmented['image']
y0 = augmented['mask']
# aug = CenterCrop(p=1, height=32, width=32)
# augmented = aug(image=x, mask=y)
# x1 = augmented['image']
# y1 = augmented['mask']
## Horizontal Flip
aug = HorizontalFlip(p=1)
augmented = aug(image=x, mask=y)
x2 = augmented['image']
y2 = augmented['mask']
aug = VerticalFlip(p=1)
augmented = aug(image=x, mask=y)
x3 = augmented['image']
y3 = augmented['mask']
# aug = Normalize(p=1)
# augmented = aug(image=x, mask=y)
# x4 = augmented['image']
# y4 = augmented['mask']
aug = Transpose(p=1)
augmented = aug(image=x, mask=y)
x5 = augmented['image']
y5 = augmented['mask']
aug = RandomGamma(p=1)
augmented = aug(image=x, mask=y)
x6 = augmented['image']
y6 = augmented['mask']
## Optical Distortion
aug = OpticalDistortion(p=1, distort_limit=2, shift_limit=0.5)
augmented = aug(image=x, mask=y)
x7 = augmented['image']
y7 = augmented['mask']
## Grid Distortion
aug = GridDistortion(p=1)
augmented = aug(image=x, mask=y)
x8 = augmented['image']
y8 = augmented['mask']
aug = RandomGridShuffle(p=1)
augmented = aug(image=x, mask=y)
x9 = augmented['image']
y9 = augmented['mask']
aug = HueSaturationValue(p=1)
augmented = aug(image=x, mask=y)
x10 = augmented['image']
y10 = augmented['mask']
# aug = PadIfNeeded(p=1)
# augmented = aug(image=x, mask=y)
# x11 = augmented['image']
# y11 = augmented['mask']
aug = RGBShift(p=1)
augmented = aug(image=x, mask=y)
x12 = augmented['image']
y12 = augmented['mask']
## Random Brightness
aug = RandomBrightness(p=1)
augmented = aug(image=x, mask=y)
x13 = augmented['image']
y13 = augmented['mask']
## Random Contrast
aug = RandomContrast(p=1)
augmented = aug(image=x, mask=y)
x14 = augmented['image']
y14 = augmented['mask']
#aug = MotionBlur(p=1)
#augmented = aug(image=x, mask=y)
# x15 = augmented['image']
# y15 = augmented['mask']
aug = MedianBlur(p=1, blur_limit=5)
augmented = aug(image=x, mask=y)
x16 = augmented['image']
y16 = augmented['mask']
aug = GaussianBlur(p=1, blur_limit=3)
augmented = aug(image=x, mask=y)
x17 = augmented['image']
y17 = augmented['mask']
aug = GaussNoise(p=1)
augmented = aug(image=x, mask=y)
x18 = augmented['image']
y18 = augmented['mask']
aug = GlassBlur(p=1)
augmented = aug(image=x, mask=y)
x19 = augmented['image']
y19 = augmented['mask']
aug = CLAHE(clip_limit=1.0,
tile_grid_size=(8, 8),
always_apply=False,
p=1)
augmented = aug(image=x, mask=y)
x20 = augmented['image']
y20 = augmented['mask']
aug = ChannelShuffle(p=1)
augmented = aug(image=x, mask=y)
x21 = augmented['image']
y21 = augmented['mask']
aug = ToGray(p=1)
augmented = aug(image=x, mask=y)
x22 = augmented['image']
y22 = augmented['mask']
aug = ToSepia(p=1)
augmented = aug(image=x, mask=y)
x23 = augmented['image']
y23 = augmented['mask']
aug = JpegCompression(p=1)
augmented = aug(image=x, mask=y)
x24 = augmented['image']
y24 = augmented['mask']
aug = ImageCompression(p=1)
augmented = aug(image=x, mask=y)
x25 = augmented['image']
y25 = augmented['mask']
aug = Cutout(p=1)
augmented = aug(image=x, mask=y)
x26 = augmented['image']
y26 = augmented['mask']
# aug = CoarseDropout(p=1, max_holes=8, max_height=32, max_width=32)
# augmented = aug(image=x, mask=y)
# x27 = augmented['image']
# y27 = augmented['mask']
# aug = ToFloat(p=1)
# augmented = aug(image=x, mask=y)
# x28 = augmented['image']
# y28 = augmented['mask']
aug = FromFloat(p=1)
augmented = aug(image=x, mask=y)
x29 = augmented['image']
y29 = augmented['mask']
## Random Brightness and Contrast
aug = RandomBrightnessContrast(p=1)
augmented = aug(image=x, mask=y)
x30 = augmented['image']
y30 = augmented['mask']
aug = RandomSnow(p=1)
augmented = aug(image=x, mask=y)
x31 = augmented['image']
y31 = augmented['mask']
aug = RandomRain(p=1)
augmented = aug(image=x, mask=y)
x32 = augmented['image']
y32 = augmented['mask']
aug = RandomFog(p=1)
augmented = aug(image=x, mask=y)
x33 = augmented['image']
y33 = augmented['mask']
aug = RandomSunFlare(p=1)
augmented = aug(image=x, mask=y)
x34 = augmented['image']
y34 = augmented['mask']
aug = RandomShadow(p=1)
augmented = aug(image=x, mask=y)
x35 = augmented['image']
y35 = augmented['mask']
aug = Lambda(p=1)
augmented = aug(image=x, mask=y)
x36 = augmented['image']
y36 = augmented['mask']
aug = ChannelDropout(p=1)
augmented = aug(image=x, mask=y)
x37 = augmented['image']
y37 = augmented['mask']
aug = ISONoise(p=1)
augmented = aug(image=x, mask=y)
x38 = augmented['image']
y38 = augmented['mask']
aug = Solarize(p=1)
augmented = aug(image=x, mask=y)
x39 = augmented['image']
y39 = augmented['mask']
aug = Equalize(p=1)
augmented = aug(image=x, mask=y)
x40 = augmented['image']
y40 = augmented['mask']
aug = Posterize(p=1)
augmented = aug(image=x, mask=y)
x41 = augmented['image']
y41 = augmented['mask']
aug = Downscale(p=1)
augmented = aug(image=x, mask=y)
x42 = augmented['image']
y42 = augmented['mask']
aug = MultiplicativeNoise(p=1)
augmented = aug(image=x, mask=y)
x43 = augmented['image']
y43 = augmented['mask']
aug = FancyPCA(p=1)
augmented = aug(image=x, mask=y)
x44 = augmented['image']
y44 = augmented['mask']
# aug = MaskDropout(p=1)
# augmented = aug(image=x, mask=y)
# x45 = augmented['image']
# y45 = augmented['mask']
aug = GridDropout(p=1)
augmented = aug(image=x, mask=y)
x46 = augmented['image']
y46 = augmented['mask']
aug = ColorJitter(p=1)
augmented = aug(image=x, mask=y)
x47 = augmented['image']
y47 = augmented['mask']
## ElasticTransform
aug = ElasticTransform(p=1,
alpha=120,
sigma=512 * 0.05,
alpha_affine=512 * 0.03)
augmented = aug(image=x, mask=y)
x50 = augmented['image']
y50 = augmented['mask']
aug = CropNonEmptyMaskIfExists(p=1, height=22, width=32)
augmented = aug(image=x, mask=y)
x51 = augmented['image']
y51 = augmented['mask']
aug = IAAAffine(p=1)
augmented = aug(image=x, mask=y)
x52 = augmented['image']
y52 = augmented['mask']
# aug = IAACropAndPad(p=1)
# augmented = aug(image=x, mask=y)
# x53 = augmented['image']
# y53 = augmented['mask']
aug = IAAFliplr(p=1)
augmented = aug(image=x, mask=y)
x54 = augmented['image']
y54 = augmented['mask']
aug = IAAFlipud(p=1)
augmented = aug(image=x, mask=y)
x55 = augmented['image']
y55 = augmented['mask']
aug = IAAPerspective(p=1)
augmented = aug(image=x, mask=y)
x56 = augmented['image']
y56 = augmented['mask']
aug = IAAPiecewiseAffine(p=1)
augmented = aug(image=x, mask=y)
x57 = augmented['image']
y57 = augmented['mask']
aug = LongestMaxSize(p=1)
augmented = aug(image=x, mask=y)
x58 = augmented['image']
y58 = augmented['mask']
aug = NoOp(p=1)
augmented = aug(image=x, mask=y)
x59 = augmented['image']
y59 = augmented['mask']
# aug = RandomCrop(p=1, height=22, width=22)
# augmented = aug(image=x, mask=y)
# x61 = augmented['image']
# y61 = augmented['mask']
# aug = RandomResizedCrop(p=1, height=22, width=20)
# augmented = aug(image=x, mask=y)
# x63 = augmented['image']
# y63 = augmented['mask']
aug = RandomScale(p=1)
augmented = aug(image=x, mask=y)
x64 = augmented['image']
y64 = augmented['mask']
# aug = RandomSizedCrop(p=1, height=22, width=20, min_max_height = [32,32])
# augmented = aug(image=x, mask=y)
# x66 = augmented['image']
# y66 = augmented['mask']
# aug = Resize(p=1, height=22, width=20)
# augmented = aug(image=x, mask=y)
# x67 = augmented['image']
# y67 = augmented['mask']
aug = Rotate(p=1)
augmented = aug(image=x, mask=y)
x68 = augmented['image']
y68 = augmented['mask']
aug = ShiftScaleRotate(p=1)
augmented = aug(image=x, mask=y)
x69 = augmented['image']
y69 = augmented['mask']
aug = SmallestMaxSize(p=1)
augmented = aug(image=x, mask=y)
x70 = augmented['image']
y70 = augmented['mask']
images_aug.extend([
x, x0, x2, x3, x5, x6, x7, x8, x9, x10, x12, x13, x14, x16, x17,
x18, x19, x20, x21, x22, x23, x24, x25, x26, x29, x30, x31, x32,
x33, x34, x35, x36, x37, x38, x39, x40, x41, x42, x43, x44, x46,
x47, x50, x51, x52, x54, x55, x56, x57, x58, x59, x64, x68, x69,
x70
])
masks_aug.extend([
y, y0, y2, y3, y5, y6, y7, y8, y9, y10, y12, y13, y14, y16, y17,
y18, y19, y20, y21, y22, y23, y24, y25, y26, y29, y30, y31, y32,
y33, y34, y35, y36, y37, y38, y39, y40, y41, y42, y43, y44, y46,
y47, y50, y51, y52, y54, y55, y56, y57, y58, y59, y64, y68, y69,
y70
])
idx = -1
images_name = []
masks_name = []
for i, m in zip(images_aug, masks_aug):
if idx == -1:
tmp_image_name = f"{image_name}"
tmp_mask_name = f"{mask_name}"
else:
tmp_image_name = f"{image_name}_{smalllist[idx]}"
tmp_mask_name = f"{mask_name}_{smalllist[idx]}"
images_name.extend(tmp_image_name)
masks_name.extend(tmp_mask_name)
idx += 1
it = iter(images_name)
it2 = iter(images_aug)
imagedict = dict(zip(it, it2))
it = iter(masks_name)
it2 = iter(masks_aug)
masksdict = dict(zip(it, it2))
return imagedict, masksdict
def main():
device = torch.device("cuda" if not hyperparams.
hyperparameter_defaults['no_cuda'] else "cpu")
hyperparams.hyperparameter_defaults['run_name'] = fileutils.rand_run_name()
transform_train = Compose([
PadIfNeeded(min_height=40, min_width=40, always_apply=True,
p=1.0), #,value=(0,0,0), border_mode=0),
RandomCrop(height=32, width=32, p=1),
#Flip(p=0.5),
IAAFliplr(p=0.5),
Cutout(num_holes=1,
max_h_size=8,
max_w_size=8,
always_apply=True,
p=1,
fill_value=[0.4914 * 255, 0.4826 * 255, 0.44653 * 255]),
Normalize(
mean=[0.4914, 0.4826, 0.44653],
std=[0.24703, 0.24349, 0.26519],
),
ToTensor()
])
trainloader, testloader = dataloader.get_train_test_dataloader_cifar10(
transform_train=transform_train)
print("Initializing datasets and dataloaders")
#model_new = basemodelclass.ResNet18(hyperparams.hyperparameter_defaults['dropout'])
model_new = basemodelclass.S11ResNet()
wandb_run_init = wandb.init(
config=hyperparams.hyperparameter_defaults,
project=hyperparams.hyperparameter_defaults['project'])
wandb.watch_called = False
config = wandb.config
print(config)
wandb.watch(model_new, log="all")
#trainloader, testloader = dataloader.get_train_test_dataloader_cifar10()
optimizer = optim.SGD(model_new.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
#optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss
#scheduler = None
cycle_momentum = True if config.cycle_momentum == "True" else False
print("Momentum cycling set to {}".format(cycle_momentum))
#scheduler = CyclicLR(optimizer, base_lr=config.lr*0.01, max_lr=config.lr, mode='triangular', gamma=1., cycle_momentum=True,step_size_up=2000)#, scale_fn='triangular',step_size_up=200)
scheduler = OneCycleLR(optimizer,
config.ocp_max_lr,
epochs=config.epochs,
cycle_momentum=cycle_momentum,
steps_per_epoch=len(trainloader),
base_momentum=config.momentum,
max_momentum=0.95,
pct_start=0.208,
anneal_strategy=config.anneal_strategy,
div_factor=config.div_factor,
final_div_factor=config.final_div_factor)
#scheduler =CyclicLR(optimizer, base_lr=config.lr*0.01, max_lr=config.lr, mode='triangular', gamma=1., cycle_momentum=True,step_size_up=2000)#, scale_fn='triangular',step_size_up=200)
#scheduler = StepLR(optimizer, step_size=config.sched_lr_step, gamma=config.sched_lr_gamma)
#scheduler = MultiStepLR(optimizer, milestones=[10,20], gamma=config.sched_lr_gamma)
#scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.2, patience=2, verbose=True, threshold=0.0001)
#scheduler = traintest.MyOwnReduceLROnPlateau(optimizer, mode='min', factor=0.2, patience=2, verbose=True, threshold=0.0001)
#scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=config.factor, patience=4, verbose=True, threshold=config.lr_decay_threshold)
final_model_path = traintest.execute_model(
model_new,
hyperparams.hyperparameter_defaults,
trainloader,
testloader,
device,
dataloader.classes,
wandb=wandb,
optimizer_in=optimizer,
scheduler=scheduler,
prev_saved_model=saved_model_path,
criterion=criterion,
save_best=True,
lars_mode=False,
batch_step=True)
"""
def __init__(self,transforms):
self.transforms = transforms
def __call__(self, img):
img = np.array(img)
img = self.transforms(image=img)["image"]
return img
# Albumentations Transformations
transform_train_albu = Compose([
PadIfNeeded(value=4),
RandomCrop(height=32, width=32), #, always_apply=True
HorizontalFlip(), # p=0.2
VerticalFlip(), # p=0.0
IAAFliplr(p=0.2),
Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010), always_apply=True),
Cutout(num_holes=1, max_h_size=8, max_w_size=8, fill_value=[0.4914, 0.4822, 0.4465], p=0.3),
ToTensorV2(always_apply=True)
])
transform_test_albu = Compose([
#ToTensor(),
Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010)),
ToTensorV2(always_apply=True)
])
transform_test_albu = AlbuCompose(transform_test_albu)
transform_train_albu = AlbuCompose(transform_train_albu)