def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
if isTrain:
augmentors = [
GoogleNetResize(),
imgaug.RandomOrderAug( # Remove these augs if your CPU is not fast enough
[imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(0.1,
eigval=np.asarray(
[0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]
)]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
if isTrain:
augmentors = [
GoogleNetResize(),
imgaug.RandomOrderAug([
JohnAug(),
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else:
round2pow2 = lambda x: 2**(x - 1).bit_length()
augmentors = [
imgaug.ResizeShortestEdge(round2pow2(IMAGE_SIZE), cv2.INTER_CUBIC),
imgaug.CenterCrop((IMAGE_SIZE, IMAGE_SIZE)),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
if isTrain:
"""
Sec 5.1:
We use scale and aspect ratio data augmentation [35] as
in [12]. The network input image is a 224×224 pixel random
crop from an augmented image or its horizontal flip.
"""
augmentors = [
GoogleNetResize(),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
if isTrain:
augmentors = [
GoogleNetResize(),
# It's OK to remove the following augs if your CPU is not fast enough.
# Removing brightness/contrast/saturation does not have a significant effect on accuracy.
# Removing lighting leads to a tiny drop in accuracy.
imgaug.RandomOrderAug([
# imgaug.BrightnessScale((0.6, 1.4), clip=False),
# imgaug.Contrast((0.6, 1.4), clip=False),
# imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((DEFAULT_IMAGE_SHAPE, DEFAULT_IMAGE_SHAPE)),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images.
"""
if isTrain:
augmentors = [
GoogleNetResize(),
imgaug.RandomOrderAug([
imgaug.Brightness(30, clip=False),
imgaug.Contrast((0.8, 1.2), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion
imgaug.Lighting(0.1,
eigval=[0.2175, 0.0188, 0.0045][::-1],
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Clip(),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255]. # 残差网络增强图像
"""
if isTrain: # 如果训练数据的话
augmentors = [
GoogleNetResize(), # 定义好了crop_area_fraction等参数
imgaug.
RandomOrderAug( # GPU不行的话就把这部分删除Remove these augs if your CPU is not fast enough #imgaug是一个图像增强库
[
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) *
255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else: # 如果不是训练数据的话
augmentors = [
imgaug.ResizeShortestEdge(
256, cv2.INTER_CUBIC), # 在保持纵横比的同时，将最短边的大小调整为某个数字。
imgaug.CenterCrop((224, 224)), # 在中间裁剪图像
]
return augmentors
def get_augmentations(is_train):
if is_train:
augmentors = [
GoogleNetResize(crop_area_fraction=0.76,
target_shape=224), # TODO : 76% or 49%?
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=True),
imgaug.Contrast((0.6, 1.4), clip=True),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
]
return augmentors
def fbresnet_augmentor(isTrain, target_shape=224):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
if isTrain:
augmentors = [
GoogleNetResize(crop_area_fraction=0.32,
target_shape=target_shape),
# GoogleNetResize(target_shape=target_shape),
imgaug.
RandomOrderAug([ # imgaug.BrightnessScale((0.6, 1.4), clip=False),
# imgaug.Contrast((0.6, 1.4), clip=False),
# imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(int(256 / 224 * target_shape),
cv2.INTER_CUBIC),
imgaug.CenterCrop((target_shape, target_shape)),
]
return augmentors
def small_augmentor():
augmentors = [
GoogleNetResize(),
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]),
imgaug.Flip(horiz=True),
]
return augmentors
def fbresnet_augmentor(isTrain):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
interpolation = cv2.INTER_LINEAR
if isTrain:
"""
Sec 5.1:
We use scale and aspect ratio data augmentation [35] as
in [12]. The network input image is a 224×224 pixel random
crop from an augmented image or its horizontal flip.
"""
augmentors = [
imgaug.GoogleNetRandomCropAndResize(interp=interpolation),
# It's OK to remove the following augs if your CPU is not fast enough.
# Removing brightness/contrast/saturation does not have a significant effect on accuracy.
# Removing lighting leads to a tiny drop in accuracy.
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), rgb=False, clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(0.1,
eigval=np.asarray(
[0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]
)]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, interp=interpolation),
imgaug.CenterCrop((224, 224)),
]
return augmentors
def prepare_video(args):
data_root, video_path, video_width, video_height, video_length, video_downsample_ratio, video_index, batch_size, shared_mem_idx, is_training, is_ucf101, is_imagenet, is_zipped = args
augs = [
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=True),
imgaug.Lighting(0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')),
]
random.shuffle(augs)
video_mem = np.frombuffer(shared_mem[shared_mem_idx],
np.ctypeslib.ctypes.c_float)
video_mem = video_mem.reshape(
(batch_size, video_length, video_height, video_width, 3))
pathgen = lambda x: os.path.join(data_root, str(video_path), x)
frames = None
if os.path.isdir(pathgen('')):
frames = sorted(os.listdir(pathgen('')))
else:
frames = [os.path.join(data_root, str(video_path))]
crop_frames = is_training
flip_frames = bool(
random.getrandbits(1)) and is_training and (is_ucf101 or is_imagenet)
add_noise = bool(random.getrandbits(1)) and is_training and not is_training
# choose a random time to start the video
num_frames = len(frames) // video_downsample_ratio
t_offset = 0
stride_offset = 0
if is_training and num_frames > video_length:
t_offset = random.choice(range(num_frames - video_length))
stride_offset = random.choice(range(video_downsample_ratio))
num_frames = min(len(frames) // video_downsample_ratio, video_length)
assert num_frames != 0, 'num frames in video cannot be 0: {}'.format(
video_path)
round2pow2 = lambda x: 2**(x - 1).bit_length()
pow2_width = round2pow2(video_width)
pow2_height = round2pow2(video_height)
crop_margin_x = pow2_width - video_width
crop_margin_y = pow2_height - video_height
x1 = random.choice(list(range(crop_margin_x)))
y1 = random.choice(list(range(crop_margin_y)))
x2 = pow2_width - (crop_margin_x - x1)
y2 = pow2_height - (crop_margin_y - y1)
rotation_angle = random.choice(list(range(-10, 10,
1))) if is_training else 0
video_mem[video_index, :, :, :] = 0
for i in range(num_frames):
image_idx = video_downsample_ratio * (i + t_offset)
image_idx = min(image_idx + stride_offset, len(frames))
image = None
if is_imagenet and is_zipped:
fname = pathgen(frames[image_idx])
jpeg_filename = os.path.basename(fname)
jpeg_dirname = os.path.basename(os.path.dirname(fname))
zip_filepath = os.path.dirname(fname) + '.zip'
f = zipfile.ZipFile(zip_filepath, 'r')
compress_jpeg = io.BytesIO(
f.read(os.path.join(jpeg_dirname, jpeg_filename)))
image = Image.open(compress_jpeg)
else:
image = Image.open(pathgen(
frames[image_idx])) # in RGB order by default
image = image.convert('RGB')
#image = image.convert('L') # convert to YUV and grab Y-component
if crop_frames:
image = image.resize((pow2_width, pow2_height), PIL.Image.BICUBIC)
image = image.crop(box=(x1, y1, x2, y2))
else:
image = image.resize((video_width, video_height),
PIL.Image.BICUBIC)
if flip_frames:
image = image.transpose(PIL.Image.FLIP_LEFT_RIGHT)
if rotation_angle != 0:
image = image.rotate(rotation_angle)
image = np.asarray(image, dtype=np.uint8)
assert image.shape == (
video_width, video_height,
3), 'cropped image must be {} but was {}'.format(
(video_width, video_height, 3), image.shape)
if is_imagenet:
if is_training:
for a in augs:
a.reset_state()
image = a.augment(image)
image = np.asarray(image, dtype=np.float32)
image = image * (1.0 / 255)
mean = np.asarray([0.485, 0.456, 0.406])
std = np.asarray([0.229, 0.224, 0.225])
image = (image - mean) / std
else:
image = np.asarray(image, dtype=np.uint32)
image = image - 116 # center on mean value of 116 (as computed in preprocessing step)
image = np.clip(image, -128, 128)
image = np.asarray(image, dtype=np.float32)
if add_noise:
noise = np.random.normal(loc=0,
scale=5,
size=(video_height, video_width, 3))
image = image + noise
image = np.clip(image, -128, 128)
video_mem[video_index, i, :, :, :] = image
return {'num_frames': num_frames, 'video_path': video_path}
def fbresnet_augmentor(isTrain, crop_method, color_augmentation):
"""
Augmentor used in fb.resnet.torch, for BGR images in range [0,255].
"""
execution_lst = []
if isTrain:
augmentors = [
# 1. crop_method
# a) GoogleNetResize
GoogleNetResize(),
# b) ShortestEdgeResize
imgaug.ResizeShortestEdge(256),
# c) GlobalWarp
imgaug.Resize(226), # NOTE: for CAM generation
imgaug.RandomCrop((224, 224)),
# d) CAMCrop
# (when CAMCrop is set, the output from the original DataFlow has already been cropped)
# 2. color_augmentation
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
#
if crop_method == 'GoogleNetResize':
print(
'--> perform GoogleNetResize cropping method during the training pipeline'
)
execution_lst.extend([0])
elif crop_method == 'ShortestEdgeResize':
print(
'--> perform ShortestEdgeResize cropping method during the training pipeline'
)
execution_lst.extend([1, 3])
elif crop_method == 'GlobalWarp':
print(
'--> perform GlobalWarp cropping method during the training pipeline'
)
execution_lst.extend([2, 3])
elif crop_method == 'CAMCrop':
# enable CAMCrop @ 20171124
print(
'*** Perform CAMCrop to better the training dynamics and the results ***'
)
if color_augmentation:
print(
'--> perform color augmentation during the training pipeline')
execution_lst.extend([4])
else:
print(
'--> discard the color jittering process during the training pipeline'
)
# perform mirror reflection augmentation anyway
execution_lst.extend([5])
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
imgaug.RandomCrop((224, 224)),
]
if crop_method == 'RandomCrop':
execution_lst.extend([0, 2])
elif crop_method == 'CenterCrop':
execution_lst.extend([0, 1])
return [
item_ for id_, item_ in enumerate(augmentors) if id_ in execution_lst
]
