def __init__(
self,
output_size,
scales,
sampling_ratio,
pooler_type,
canonical_box_size=224,
canonical_level=4,
):
"""
Args:
output_size (int, tuple[int] or list[int]): output size of the pooled region,
e.g., 14 x 14. If tuple or list is given, the length must be 2.
scales (list[float]): The scale for each low-level pooling op relative to
the input image. For a feature map with stride s relative to the input
image, scale is defined as a 1 / s. The stride must be power of 2.
When there are multiple scales, they must form a pyramid, i.e. they must be
a monotically decreasing geometric sequence with a factor of 1/2.
sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op.
pooler_type (string): Name of the type of pooling operation that should be applied.
For instance, "ROIPool" or "ROIAlignV2".
canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). The default
is heuristically defined as 224 pixels in the FPN paper (based on ImageNet
pre-training).
canonical_level (int): The feature map level index from which a canonically-sized box
should be placed. The default is defined as level 4 (stride=16) in the FPN paper,
i.e., a box of size 224x224 will be placed on the feature with stride=16.
The box placement for all boxes will be determined from their sizes w.r.t
canonical_box_size. For example, a box whose area is 4x that of a canonical box
should be used to pool features from feature level ``canonical_level+1``.
Note that the actual input feature maps given to this module may not have
sufficiently many levels for the input boxes. If the boxes are too large or too
small for the input feature maps, the closest level will be used.
"""
super().__init__()
if isinstance(output_size, int):
output_size = (output_size, output_size)
assert len(output_size) == 2
assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
self.output_size = output_size
if pooler_type == "ROIAlign":
self.level_poolers = nn.ModuleList(
ROIAlign(
output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False
)
for scale in scales
)
elif pooler_type == "ROIAlignV2":
self.level_poolers = nn.ModuleList(
ROIAlign(
output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True
)
for scale in scales
)
elif pooler_type == "ROIPool":
self.level_poolers = nn.ModuleList(
RoIPool(output_size, spatial_scale=scale) for scale in scales
)
elif pooler_type == "ROIAlignRotated":
self.level_poolers = nn.ModuleList(
ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio)
for scale in scales
)
else:
raise ValueError("Unknown pooler type: {}".format(pooler_type))
# Map scale (defined as 1 / stride) to its feature map level under the
# assumption that stride is a power of 2.
min_level = -(math.log2(scales[0]))
max_level = -(math.log2(scales[-1]))
assert math.isclose(min_level, int(min_level)) and math.isclose(
max_level, int(max_level)
), "Featuremap stride is not power of 2!"
self.min_level = int(min_level)
self.max_level = int(max_level)
assert (
len(scales) == self.max_level - self.min_level + 1
), "[ROIPooler] Sizes of input featuremaps do not form a pyramid!"
assert 0 <= self.min_level and self.min_level <= self.max_level
self.canonical_level = canonical_level
assert canonical_box_size > 0
self.canonical_box_size = canonical_box_size
def create_roi_pool(self, scale): # 32 resnet18
return RoIPool(self.pool_size, scale)
def _roi_pool_layer(self, bottom, rois):
return RoIPool((cfg.POOLING_SIZE, cfg.POOLING_SIZE),
1.0 / 16.0)(bottom, rois)
def create_roi_pool(self):
# see #64
if self.backbone.__name__ == 'resnet18':
return RoIPool(self.pool_size, 1 / 32)
else:
raise NotImplementedError
import torch from torchvision.ops import RoIPool img = torch.randn(size=(1, 3, 100, 100)) box = torch.tensor([10., 10., 20., 20.]).unsqueeze(0) #box2 = torch.tensor([30.,30.,70.,70.]).unsqueeze(0) boxes = [box] #print(boxes) pooler = RoIPool(output_size=3, spatial_scale=1.) #print(pooler) out_tsr = pooler(img, boxes) print(out_tsr.size())
def __init__(self, bn=False, num_classes=10):
super(ori, self).__init__()
self.num_classes = num_classes
self.base_layer = nn.Sequential(
Conv2d(1, 16, 9, same_padding=True, NL='prelu', bn=bn),
Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn))
self.hl_prior = nn.Sequential(
Conv2d(32, 16, 9, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(32, 32, 7, same_padding=True, NL='prelu', bn=bn),
Conv2d(32, 32, 7, same_padding=True, NL='prelu', bn=bn))
self.roi_pool = RoIPool([16, 16], 1 / 4.0)
self.hl_prior_conv2d = Conv2d(32,
16,
1,
same_padding=True,
NL='prelu',
bn=bn)
self.bbx_pred = nn.Sequential(FC(16 * 16 * 16, 512, NL='prelu'),
FC(512, 256, NL='prelu'),
FC(256, self.num_classes, NL='prelu'))
# generate dense map
self.den_stage_1 = nn.Sequential(
Conv2d(32, 32, 7, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(32, 64, 5, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(64, 32, 5, same_padding=True, NL='prelu', bn=bn),
Conv2d(32, 32, 5, same_padding=True, NL='prelu', bn=bn))
self.den_stage_DULR = nn.Sequential(
convDU(in_out_channels=32, kernel_size=(1, 9)),
convLR(in_out_channels=32, kernel_size=(9, 1)))
self.den_stage_2 = nn.Sequential(
Conv2d(64, 64, 3, same_padding=True, NL='prelu', bn=bn),
Conv2d(64, 32, 3, same_padding=True, NL='prelu', bn=bn),
nn.ConvTranspose2d(32,
16,
4,
stride=2,
padding=1,
output_padding=0,
bias=True), nn.PReLU(),
nn.ConvTranspose2d(16,
8,
4,
stride=2,
padding=1,
output_padding=0,
bias=True), nn.PReLU())
# generrate seg map
self.seg_stage = nn.Sequential(
Conv2d(32, 32, 1, same_padding=True, NL='prelu', bn=bn),
Conv2d(32, 64, 3, same_padding=True, NL='prelu', bn=bn),
Conv2d(64, 32, 3, same_padding=True, NL='prelu', bn=bn),
nn.ConvTranspose2d(32,
16,
4,
stride=2,
padding=1,
output_padding=0,
bias=True), nn.PReLU(),
nn.ConvTranspose2d(16,
8,
4,
stride=2,
padding=1,
output_padding=0,
bias=True), nn.PReLU())
self.seg_pred = Conv2d(8, 2, 1, same_padding=True, NL='relu', bn=bn)
self.trans_den = Conv2d(8, 8, 1, same_padding=True, NL='relu', bn=bn)
self.den_pred = Conv2d(16, 1, 1, same_padding=True, NL='relu', bn=bn)
# initialize_weights(self.modules())
weights_normal_init(self.base_layer, self.hl_prior, self.hl_prior_conv2d, self.bbx_pred, self.den_stage_1, \
self.den_stage_DULR, self.den_stage_2, self.trans_den, self.den_pred)
initialize_weights(self.seg_stage, self.seg_pred)
def __init__(
self,
output_size,
scales,
sampling_ratio,
pooler_type,
canonical_box_size=224,
canonical_level=4,
):
"""
Args:
output_size (int, tuple[int] or list[int]): output size of the pooled region,
e.g., 14 x 14. If tuple or list is given, the length must be 2.
scales (list[float]): The scale for each low-level pooling op relative to
the input image. For a feature map with stride s relative to the input
image, scale is defined as a 1 / s.
sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op.
pooler_type (string): Name of the type of pooling operation that should be applied.
For instance, "ROIPool" or "ROIAlignV2".
canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). The default
is heuristically defined as 224 pixels in the FPN paper (based on ImageNet
pre-training).
canonical_level (int): The feature map level index on which a canonically-sized box
should be placed. The default is defined as level 4 in the FPN paper.
"""
super().__init__()
if isinstance(output_size, int):
output_size = (output_size, output_size)
assert len(output_size) == 2
assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
self.output_size = output_size
if pooler_type == "ROIAlign":
self.level_poolers = nn.ModuleList(
ROIAlign(
output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False
)
for scale in scales
)
elif pooler_type == "ROIAlignV2":
self.level_poolers = nn.ModuleList(
ROIAlign(
output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True
)
for scale in scales
)
elif pooler_type == "ROIPool":
self.level_poolers = nn.ModuleList(
RoIPool(output_size, spatial_scale=scale) for scale in scales
)
elif pooler_type == "ROIAlignRotated":
self.level_poolers = nn.ModuleList(
ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio)
for scale in scales
)
else:
raise ValueError("Unknown pooler type: {}".format(pooler_type))
# Map scale (defined as 1 / stride) to its feature map level under the
# assumption that stride is a power of 2.
min_level = -math.log2(scales[0])
max_level = -math.log2(scales[-1])
assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level))
self.min_level = int(min_level)
self.max_level = int(max_level)
assert 0 < self.min_level and self.min_level <= self.max_level
assert self.min_level <= canonical_level and canonical_level <= self.max_level
self.canonical_level = canonical_level
assert canonical_box_size > 0
self.canonical_box_size = canonical_box_size
def roi_pool_layer(bottom, rois):
return RoIPool((opt.pooling_size, opt.pooling_size),
1.0 / opt.feat_stride)(bottom, rois)
def __init__(self,
input_dim=(16, 32),
pred_input_dim=(32, 32),
pred_inter_dim=(32, 32),
cpu=False):
super().__init__(input_dim, pred_input_dim, pred_inter_dim)
# _r for reference, _t for test
# in: 36x36x16 out: 36x36x16
self.conv3_1r = conv(input_dim[0], 16, kernel_size=3, stride=1)
# in: 36x36x16 out: 36x36x32
self.conv3_1t = conv(input_dim[0], 32, kernel_size=3, stride=1)
# in: 36x36x32 out: 36x36x32
self.conv3_2t = conv(32, pred_input_dim[0], kernel_size=3, stride=1)
if cpu:
self.prroi_pool3r = RoIPool((3, 3), 1 / 8)
self.prroi_pool3t = RoIPool((5, 5), 1 / 8)
else:
# in: 36x36x16 out:3x3x16
self.prroi_pool3r = PrRoIPool2D(3, 3, 1 / 8)
# in: 36x36x32 out:5x5x32
self.prroi_pool3t = PrRoIPool2D(5, 5, 1 / 8)
# in: 3x3x16 out:1x1x32
self.fc3_1r = conv(16, 32, kernel_size=3, stride=1, padding=0)
# in: 18x18x32 out: 18x18x32
self.conv4_1r = conv(input_dim[1], 32, kernel_size=3, stride=1)
# in: 18x18x32 out: 18x18x32
self.conv4_1t = conv(input_dim[1], 32, kernel_size=3, stride=1)
# in: 18x18x32 out: 18x18x32
self.conv4_2t = conv(32, pred_input_dim[1], kernel_size=3, stride=1)
if cpu:
self.prroi_pool4r = RoIPool((1, 1), 1 / 16)
self.prroi_pool4t = RoIPool((3, 3), 1 / 16)
else:
# in: 18x18x32 out:1x1x32
self.prroi_pool4r = PrRoIPool2D(1, 1, 1 / 16)
# in: 18x18x32 out: 3x3x32
self.prroi_pool4t = PrRoIPool2D(3, 3, 1 / 16)
# in: 1x1x64 out: 1x1x32
self.fc34_3r = conv(32 + 32,
pred_input_dim[0],
kernel_size=1,
stride=1,
padding=0)
# in: 1x1x64 out: 1x1x32
self.fc34_4r = conv(32 + 32,
pred_input_dim[1],
kernel_size=1,
stride=1,
padding=0)
# in: 5x5x32 out: 1x1x32
self.fc3_rt = LinearBlock(pred_input_dim[0], pred_inter_dim[0], 5)
# in: 3x3x32 out: 1x1x32
self.fc4_rt = LinearBlock(pred_input_dim[1], pred_inter_dim[1], 3)
# in: 1x1x64 out: 1x1x1
self.iou_predictor = nn.Linear(pred_inter_dim[0] + pred_inter_dim[1],
1,
bias=True)
# Init weights
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(
m, nn.ConvTranspose2d) or isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight.data, mode='fan_in')
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
# In earlier versions batch norm parameters was initialized with default initialization,
# which changed in pytorch 1.2. In 1.1 and earlier the weight was set to U(0,1).
# So we use the same initialization here.
# m.weight.data.fill_(1)
m.weight.data.uniform_()
m.bias.data.zero_()
def __init__(self,
num_classes=2,
in_channels=3,
arch='resnet101',
output_stride=16,
bn_momentum=0.9,
freeze_bn=False,
noStyle=False,
noGlobal=False,
noLocal=False,
pretrained=False,
patch_size=16,
patch_num=4,
patch_out_channel=False,
pross_num=28,
**kwargs):
super(DeepLab, self).__init__(**kwargs)
self.model_name = 'deeplabv3plus_' + arch
# Setup arch
if arch == 'resnet18':
NotImplementedError('resnet18 backbone is not implemented yet.')
elif arch == 'resnet34':
NotImplementedError('resnet34 backbone is not implemented yet.')
elif arch == 'resnet50':
self.backbone = ResNet.resnet50(bn_momentum, pretrained)
if in_channels != 3:
self.backbone.conv1 = nn.Conv2d(in_channels,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
elif arch == 'resnet101':
self.backbone = ResNet.resnet101(bn_momentum, pretrained)
if in_channels != 3:
self.backbone.conv1 = nn.Conv2d(in_channels,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.encoder = Encoder(bn_momentum, output_stride)
self.decoder = Decoder(num_classes, bn_momentum)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.avgpool1 = nn.AdaptiveAvgPool2d((1, 1))
self.patch_num = patch_num
self.roi_pool = RoIPool((patch_size, patch_size),
1.0) #RoIAlign((1,1),1.0,4)
self.patch_size = patch_size
self.pross_num = pross_num
self.noStyle = noStyle
self.noGlobal = noGlobal
self.noLocal = noLocal
# projection head
'''
self.proj = nn.Sequential(
nn.Conv2d(256, 256, 1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 10, 1, bias=True)
)
'''
if self.noStyle:
self.proj = nn.Sequential(nn.Linear(256, 256), nn.ReLU(),
nn.Linear(256, num_classes))
else:
self.proj = nn.Sequential(nn.Linear(256 * 2, 256), nn.ReLU(),
nn.Linear(256, num_classes))
if not patch_out_channel:
patch_out_channel = num_classes
self.proj1 = nn.Sequential(nn.Linear(num_classes, num_classes),
nn.ReLU(),
nn.Linear(num_classes, patch_out_channel))
