本文整理匯總了Python中utils.generate_pyramid_anchors方法的典型用法代碼示例。如果您正苦於以下問題：Python utils.generate_pyramid_anchors方法的具體用法？Python utils.generate_pyramid_anchors怎麽用？Python utils.generate_pyramid_anchors使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils的用法示例。
在下文中一共展示了utils.generate_pyramid_anchors方法的9個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_anchors
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def get_anchors(self, image_shape):
"""Returns anchor pyramid for the given image size."""
backbone_shapes = compute_backbone_shapes(self.config, image_shape)
# Cache anchors and reuse if image shape is the same
if not hasattr(self, "_anchor_cache"):
self._anchor_cache = {}
if not tuple(image_shape) in self._anchor_cache:
# Generate Anchors
a = utils.generate_pyramid_anchors(
self.config.RPN_ANCHOR_SCALES,
self.config.RPN_ANCHOR_RATIOS,
backbone_shapes,
self.config.BACKBONE_STRIDES,
self.config.RPN_ANCHOR_STRIDE)
# Keep a copy of the latest anchors in pixel coordinates because
# it's used in inspect_model notebooks.
# TODO: Remove this after the notebook are refactored to not use it
self.anchors = a
# Normalize coordinates
self._anchor_cache[tuple(image_shape)] = utils.norm_boxes(a, image_shape[:2])
return self._anchor_cache[tuple(image_shape)] 示例2: __init__
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def __init__(self, dataset, config, augmentation=False):
"""A data_generator that returns the image, mask and other ground truth for training."""
self.image_ids = np.copy(dataset.image_ids)
self.dataset = dataset
self.config = config
self.augmentation = augmentation
# Anchors
# [anchor_count, (z1, y1, x1, z2, y2, x2)]
self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES, config.RPN_ANCHOR_RATIOS,
compute_backbone_shapes(config, config.IMAGE_SHAPE),
config.BACKBONE_STRIDES, config.RPN_ANCHOR_STRIDE) 示例3: __init__
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def __init__(self, dataset, config, augment=True):
"""A generator that returns images and corresponding target class ids,
bounding box deltas, and masks.
dataset: The Dataset object to pick data from
config: The model config object
shuffle: If True, shuffles the samples before every epoch
augment: If True, applies image augmentation to images (currently only
horizontal flips are supported)
Returns a Python generator. Upon calling next() on it, the
generator returns two lists, inputs and outputs. The containtes
of the lists differs depending on the received arguments:
inputs list:
- images: [batch, H, W, C]
- image_metas: [batch, size of image meta]
- rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral)
- rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
- gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs
- gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)]
- gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width
are those of the image unless use_mini_mask is True, in which
case they are defined in MINI_MASK_SHAPE.
outputs list: Usually empty in regular training. But if detection_targets
is True then the outputs list contains target class_ids, bbox deltas,
and masks.
"""
self.b = 0 # batch item index
self.image_index = -1
self.image_ids = np.copy(dataset.image_ids)
self.error_count = 0
self.dataset = dataset
self.config = config
self.augment = augment
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE) 示例4: build
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def build(self, config):
"""Build Mask R-CNN architecture.
"""
# Image size must be dividable by 2 multiple times
h, w = config.IMAGE_SHAPE[:2]
if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):
raise Exception("Image size must be dividable by 2 at least 6 times "
"to avoid fractions when downscaling and upscaling."
"For example, use 256, 320, 384, 448, 512, ... etc. ")
# Build the shared convolutional layers.
# Bottom-up Layers
# Returns a list of the last layers of each stage, 5 in total.
# Don't create the thead (stage 5), so we pick the 4th item in the list.
resnet = ResNet("resnet101", stage5=True)
C1, C2, C3, C4, C5 = resnet.stages()
# Top-down Layers
# TODO: add assert to varify feature map sizes match what's in config
self.fpn = FPN(C1, C2, C3, C4, C5, out_channels=256)
# Generate Anchors
self.anchors = Variable(torch.from_numpy(utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)).float(), requires_grad=False)
if self.config.GPU_COUNT:
self.anchors = self.anchors.cuda()
# RPN
self.rpn = RPN(len(config.RPN_ANCHOR_RATIOS), config.RPN_ANCHOR_STRIDE, 256)
# FPN Classifier
self.classifier = Classifier(256, config.POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# FPN Mask
self.mask = Mask(256, config.MASK_POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# Fix batch norm layers
def set_bn_fix(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
for p in m.parameters(): p.requires_grad = False
self.apply(set_bn_fix) 示例5: build
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def build(self, config, test_flag=False):
"""Build 3D-Mask-RCNN architecture."""
# Image size must be dividable by 2 multiple times
h, w, d = config.IMAGE_SHAPE[:3]
if h / 16 != int(h / 16) or w / 16 != int(w / 16) or d / 16 != int(d / 16):
raise Exception("Image size must be dividable by 16. Use 256, 320, 512, ... etc.")
# Build the shared convolutional layers.
# Returns a list of the last layers of each stage, 3 in total.
if self.config.BACKBONE == "P3D19":
print("using P3D19 as backbone")
P3D_Resnet = backbone.P3D19(config=config)
else:
print("using P3D35 as backbone")
P3D_Resnet = backbone.P3D35(config=config)
C1, C2, C3 = P3D_Resnet.stages()
# Top-down Layers
self.fpn = FPN(C1, C2, C3, out_channels=config.TOP_DOWN_PYRAMID_SIZE, config=config)
# Generate Anchors
self.anchors = Variable(torch.from_numpy(utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
compute_backbone_shapes(
config, config.IMAGE_SHAPE),
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)).float(),
requires_grad=False)
if self.config.GPU_COUNT:
self.anchors = self.anchors.cuda()
# RPN
self.rpn = RPN(len(config.RPN_ANCHOR_RATIOS), config.RPN_ANCHOR_STRIDE, config.TOP_DOWN_PYRAMID_SIZE,
config.RPN_CONV_CHANNELS)
if self.config.STAGE != 'beginning':
for p in self.parameters():
p.requires_grad = False
# FPN Classifier
self.classifier = Classifier(config.TOP_DOWN_PYRAMID_SIZE, config.POOL_SIZE, config.IMAGE_SHAPE,
2, config.FPN_CLASSIFY_FC_LAYERS_SIZE, test_flag)
# FPN Mask
self.mask = Mask(1, config.MASK_POOL_SIZE, config.NUM_CLASSES, config.UNET_MASK_BRANCH_CHANNEL,
self.config.STAGE, test_flag)
if test_flag:
for p in self.parameters():
p.requires_grad = False
# Fix batch norm layers
def set_bn_fix(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
for p in m.parameters():
p.requires_grad = False
if not config.TRAIN_BN:
self.apply(set_bn_fix) 示例6: build
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def build(self, config, test_flag=False):
"""Build 3D-Mask-RCNN architecture."""
# Image size must be dividable by 2 multiple times
h, w, d = config.IMAGE_SHAPE[:3]
if h / 16 != int(h / 16) or w / 16 != int(w / 16) or d / 16 != int(d / 16):
raise Exception("Image size must be dividable by 16. Use 256, 320, 512, ... etc.")
# Build the shared convolutional layers.
# Returns a list of the last layers of each stage, 5 in total.
P3D_Resnet = backbone.P3D19(config=config)
C1, C2, C3 = P3D_Resnet.stages()
# Top-down Layers
self.fpn = FPN(C1, C2, C3, out_channels=config.TOP_DOWN_PYRAMID_SIZE, config=config)
# Generate Anchors
self.anchors = Variable(torch.from_numpy(utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
compute_backbone_shapes(
config, config.IMAGE_SHAPE),
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)).float(),
requires_grad=False)
if self.config.GPU_COUNT:
self.anchors = self.anchors.cuda()
# RPN
self.rpn = RPN(len(config.RPN_ANCHOR_RATIOS), config.RPN_ANCHOR_STRIDE, config.TOP_DOWN_PYRAMID_SIZE, config.RPN_CONV_CHANNELS)
# FPN Classifier
self.classifier = Classifier(config.TOP_DOWN_PYRAMID_SIZE, config.POOL_SIZE, config.IMAGE_SHAPE,
2, config.FPN_CLASSIFY_FC_LAYERS_SIZE, test_flag)
# FPN Mask
self.mask = Mask(1, config.MASK_POOL_SIZE, config.NUM_CLASSES, config.UNET_MASK_BRANCH_CHANNEL, self.config.STAGE, test_flag)
# Fix batch norm layers
def set_bn_fix(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
for p in m.parameters():
p.requires_grad = False
if not config.TRAIN_BN:
self.apply(set_bn_fix) 示例7: __init__
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def __init__(self, dataset, config, augment=True, augmentation = None):
"""A generator that returns images and corresponding target class ids,
bounding box deltas, and masks.
dataset: The Dataset object to pick data from
config: The model config object
shuffle: If True, shuffles the samples before every epoch
augment: If True, applies image augmentation to images (currently only
horizontal flips are supported)
Returns a Python generator. Upon calling next() on it, the
generator returns two lists, inputs and outputs. The containtes
of the lists differs depending on the received arguments:
inputs list:
- images: [batch, H, W, C]
- image_metas: [batch, size of image meta]
- rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral)
- rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
- gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs
- gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)]
- gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width
are those of the image unless use_mini_mask is True, in which
case they are defined in MINI_MASK_SHAPE.
outputs list: Usually empty in regular training. But if detection_targets
is True then the outputs list contains target class_ids, bbox deltas,
and masks.
"""
self.b = 0 # batch item index
self.image_index = -1
self.image_ids = np.copy(dataset.image_ids)
self.error_count = 0
self.dataset = dataset
self.config = config
self.augment = augment
self.augmentation = augmentation
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE) 示例8: build
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def build(self, config):
"""Build Mask R-CNN architecture.
"""
# Image size must be dividable by 2 multiple times
h, w = config.IMAGE_SHAPE[:2]
if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):
raise Exception("Image size must be dividable by 2 at least 6 times "
"to avoid fractions when downscaling and upscaling."
"For example, use 256, 320, 384, 448, 512, ... etc. ")
# Build the shared convolutional layers.
# Bottom-up Layers
# Returns a list of the last layers of each stage, 5 in total.
# Don't create the thead (stage 5), so we pick the 4th item in the list.
resnet = se_resnext101_32x4d(stage5=True)
C1, C2, C3, C4, C5 = resnet.stages()
# Top-down Layers
# TODO: add assert to varify feature map sizes match what's in config
self.fpn = FPN(C1, C2, C3, C4, C5, out_channels=256)
# Generate Anchors
self.anchors = Variable(torch.from_numpy(utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)).float(), requires_grad=False)
if self.config.GPU_COUNT:
self.anchors = self.anchors.cuda()
# RPN
self.rpn = RPN(len(config.RPN_ANCHOR_RATIOS), config.RPN_ANCHOR_STRIDE, 256)
# FPN Classifier
self.classifier = Classifier(256, config.POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# FPN Mask
self.mask = Mask(256, config.MASK_POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# Fix batch norm layers
def set_bn_fix(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
for p in m.parameters(): p.requires_grad = False
self.apply(set_bn_fix) 開發者ID:wwoody827，項目名稱:cvpr-2018-autonomous-driving-autopilot-solution，代碼行數:49，代碼來源:model_seresnext.py
示例9: build
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import generate_pyramid_anchors [as 別名]
def build(self, config):
"""Build Mask R-CNN architecture.
"""
# Image size must be dividable by 2 multiple times
h, w = config.IMAGE_SHAPE[:2]
if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6):
raise Exception("Image size must be dividable by 2 at least 6 times "
"to avoid fractions when downscaling and upscaling."
"For example, use 256, 320, 384, 448, 512, ... etc. ")
# Build the shared convolutional layers.
# Bottom-up Layers
# Returns a list of the last layers of each stage, 5 in total.
# Don't create the thead (stage 5), so we pick the 4th item in the list.
resnet = ResNext(stage5=True)
C1, C2, C3, C4, C5 = resnet.stages()
# Top-down Layers
# TODO: add assert to varify feature map sizes match what's in config
self.fpn = FPN(C1, C2, C3, C4, C5, out_channels=256)
# Generate Anchors
self.anchors = Variable(torch.from_numpy(utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
config.BACKBONE_SHAPES,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)).float(), requires_grad=False)
if self.config.GPU_COUNT:
self.anchors = self.anchors.cuda()
# RPN
self.rpn = RPN(len(config.RPN_ANCHOR_RATIOS), config.RPN_ANCHOR_STRIDE, 256)
# FPN Classifier
self.classifier = Classifier(256, config.POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# FPN Mask
self.mask = Mask(256, config.MASK_POOL_SIZE, config.IMAGE_SHAPE, config.NUM_CLASSES)
# Fix batch norm layers
def set_bn_fix(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
for p in m.parameters(): p.requires_grad = False
self.apply(set_bn_fix)