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# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import numpy as np
import torch
import torchvision.transforms as T
from torchvision import datasets
from megatron import get_args
from megatron.data.image_folder import ImageFolder
from megatron.data.autoaugment import ImageNetPolicy
from megatron.data.data_samplers import RandomSeedDataset
from PIL import Image, ImageFilter, ImageOps
class GaussianBlur(object):
"""
Apply Gaussian Blur to the PIL image.
"""
def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
self.prob = p
self.radius_min = radius_min
self.radius_max = radius_max
def __call__(self, img):
do_it = random.random() <= self.prob
if not do_it:
return img
return img.filter(
ImageFilter.GaussianBlur(
radius=random.uniform(self.radius_min, self.radius_max)
)
)
class Solarization(object):
"""
Apply Solarization to the PIL image.
"""
def __init__(self, p):
self.p = p
def __call__(self, img):
if random.random() < self.p:
return ImageOps.solarize(img)
else:
return img
class ClassificationTransform():
def __init__(self, image_size, train=True):
args = get_args()
assert args.fp16 or args.bf16
self.data_type = torch.half if args.fp16 else torch.bfloat16
if train:
self.transform = T.Compose([
T.RandomResizedCrop(image_size),
T.RandomHorizontalFlip(),
T.ColorJitter(0.4, 0.4, 0.4, 0.1),
ImageNetPolicy(),
T.ToTensor(),
T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
T.ConvertImageDtype(self.data_type)
])
else:
self.transform = T.Compose([
T.Resize(image_size),
T.CenterCrop(image_size),
T.ToTensor(),
T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
T.ConvertImageDtype(self.data_type)
])
def __call__(self, input):
output = self.transform(input)
return output
class InpaintingTransform():
def __init__(self, image_size, train=True):
args = get_args()
self.mask_factor = args.mask_factor
self.mask_type = args.mask_type
self.image_size = image_size
self.patch_size = args.patch_dim
self.mask_size = int(self.mask_factor*(image_size[0]/self.patch_size)*(image_size[1]/self.patch_size))
self.train = train
assert args.fp16 or args.bf16
self.data_type = torch.half if args.fp16 else torch.bfloat16
if self.train:
self.transform = T.Compose([
T.RandomResizedCrop(self.image_size),
T.RandomHorizontalFlip(),
T.ColorJitter(0.4, 0.4, 0.4, 0.1),
ImageNetPolicy(),
T.ToTensor(),
T.ConvertImageDtype(self.data_type)
])
else:
self.transform = T.Compose([
T.Resize(self.image_size, interpolation=2),
T.CenterCrop(self.image_size),
T.ToTensor(),
T.ConvertImageDtype(self.data_type)
])
def gen_mask(self, image_size, mask_size, mask_type, patch_size):
# output: mask as a list with indices for missing patches
action_list = [[0, 1], [0, -1], [1, 0], [-1, 0]]
assert image_size[0] == image_size[1]
img_size_patch = image_size[0] // patch_size
# drop masked patches
mask = torch.zeros((image_size[0], image_size[1]), dtype=torch.float)
if mask_type == 'random':
x = torch.randint(0, img_size_patch, ())
y = torch.randint(0, img_size_patch, ())
for i in range(mask_size):
r = torch.randint(0, len(action_list), ())
x = torch.clamp(x + action_list[r][0], min=0, max=img_size_patch - 1)
y = torch.clamp(y + action_list[r][1], min=0, max=img_size_patch - 1)
x_offset = x * patch_size
y_offset = y * patch_size
mask[x_offset:x_offset+patch_size, y_offset:y_offset+patch_size] = 1
else:
assert mask_type == 'row'
count = 0
for x in reversed(range(img_size_patch)):
for y in reversed(range(img_size_patch)):
if (count < mask_size):
count += 1
x_offset = x * patch_size
y_offset = y * patch_size
mask[x_offset:x_offset+patch_size, y_offset:y_offset+patch_size] = 1
return mask
def __call__(self, input):
trans_input = self.transform(input)
mask = self.gen_mask(self.image_size, self.mask_size,
self.mask_type, self.patch_size)
mask = mask.unsqueeze(dim=0)
return trans_input, mask
class DinoTransform(object):
def __init__(self, image_size, train=True):
args = get_args()
self.data_type = torch.half if args.fp16 else torch.bfloat16
flip_and_color_jitter = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.RandomApply(
[T.ColorJitter(brightness=0.4, contrast=0.4,
saturation=0.2, hue=0.1)],
p=0.8
),
T.RandomGrayscale(p=0.2),
])
if args.fp16 or args.bf16:
normalize = T.Compose([
T.ToTensor(),
T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
T.ConvertImageDtype(self.data_type)
])
else:
normalize = T.Compose([
T.ToTensor(),
T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# first global crop
scale_const = 0.4
self.global_transform1 = T.Compose([
T.RandomResizedCrop(image_size,
scale=(scale_const, 1),
interpolation=Image.BICUBIC),
flip_and_color_jitter,
GaussianBlur(1.0),
normalize
])
# second global crop
self.global_transform2 = T.Compose([
T.RandomResizedCrop(image_size,
scale=(scale_const, 1),
interpolation=Image.BICUBIC),
flip_and_color_jitter,
GaussianBlur(0.1),
Solarization(0.2),
normalize
])
# transformation for the local small crops
self.local_crops_number = args.dino_local_crops_number
self.local_transform = T.Compose([
T.RandomResizedCrop(args.dino_local_img_size,
scale=(0.05, scale_const),
interpolation=Image.BICUBIC),
flip_and_color_jitter,
GaussianBlur(p=0.5),
normalize
])
def __call__(self, image):
crops = []
crops.append(self.global_transform1(image))
crops.append(self.global_transform2(image))
for _ in range(self.local_crops_number):
crops.append(self.local_transform(image))
return crops
def build_train_valid_datasets(data_path, image_size=224):
args = get_args()
if args.vision_pretraining_type == 'classify':
train_transform = ClassificationTransform(image_size)
val_transform = ClassificationTransform(image_size, train=False)
elif args.vision_pretraining_type == 'inpaint':
train_transform = InpaintingTransform(image_size, train=False)
val_transform = InpaintingTransform(image_size, train=False)
elif args.vision_pretraining_type == 'dino':
train_transform = DinoTransform(image_size, train=True)
val_transform = ClassificationTransform(image_size, train=False)
else:
raise Exception('{} vit pretraining type is not supported.'.format(
args.vit_pretraining_type))
# training dataset
train_data_path = data_path[0] if len(data_path) <= 2 else data_path[2]
train_data = ImageFolder(
root=train_data_path,
transform=train_transform,
classes_fraction=args.classes_fraction,
data_per_class_fraction=args.data_per_class_fraction
)
train_data = RandomSeedDataset(train_data)
# validation dataset
val_data_path = data_path[1]
val_data = ImageFolder(
root=val_data_path,
transform=val_transform
)
val_data = RandomSeedDataset(val_data)
return train_data, val_data
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