def __init__(self,
num_classes: int = 19,
backbone_indices: Tuple[int] = (-1, ),
channels: int = None,
align_corners: bool = False,
pretrained: str = None):
super(FCN, self).__init__()
self.backbone = HRNet_W48()
backbone_channels = [
self.backbone.feat_channels[i] for i in backbone_indices
]
self.head = FCNHead(num_classes, backbone_indices, backbone_channels,
channels)
self.align_corners = align_corners
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory, 'model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
def __init__(self,
num_classes: int = 21,
backbone_indices: Tuple[int] = (0, 3),
aspp_ratios: Tuple[int] = (1, 12, 24, 36),
aspp_out_channels: int = 256,
align_corners=False,
pretrained: str = None):
super(DeepLabV3PResnet50, self).__init__()
self.backbone = ResNet50_vd()
backbone_channels = [
self.backbone.feat_channels[i] for i in backbone_indices
]
self.head = DeepLabV3PHead(num_classes, backbone_indices,
backbone_channels, aspp_ratios,
aspp_out_channels, align_corners)
self.align_corners = align_corners
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory,
'deeplabv3p_model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
def __init__(self,
num_classes: int = 19,
align_corners: bool = False,
use_deconv: bool = False,
pretrained: str = None):
super(UNet, self).__init__()
self.encode = Encoder()
self.decode = Decoder(align_corners, use_deconv=use_deconv)
self.cls = self.conv = nn.Conv2D(in_channels=64,
out_channels=num_classes,
kernel_size=3,
stride=1,
padding=1)
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory, 'model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
def __init__(self,
num_classes: int = 19,
backbone_indices: List[int] = [0],
ocr_mid_channels: int = 512,
ocr_key_channels: int = 256,
align_corners: bool = False,
pretrained: str = None):
super(OCRNetHRNetW18, self).__init__()
self.backbone = HRNet_W18()
self.backbone_indices = backbone_indices
in_channels = [
self.backbone.feat_channels[i] for i in backbone_indices
]
self.head = OCRHead(num_classes=num_classes,
in_channels=in_channels,
ocr_mid_channels=ocr_mid_channels,
ocr_key_channels=ocr_key_channels)
self.align_corners = align_corners
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory, 'model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
def __init__(self,
num_classes: int = 19,
align_corners: bool = False,
pretrained: str = None):
super(FastSCNN, self).__init__()
self.learning_to_downsample = LearningToDownsample(32, 48, 64)
self.global_feature_extractor = GlobalFeatureExtractor(
in_channels=64,
block_channels=[64, 96, 128],
out_channels=128,
expansion=6,
num_blocks=[3, 3, 3],
align_corners=True)
self.feature_fusion = FeatureFusionModule(64, 128, 128, align_corners)
self.classifier = Classifier(128, num_classes)
self.align_corners = align_corners
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory,
'fastscnn_model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
def __init__(self,
num_classes: int = 19,
lambd: float = 0.25,
align_corners: bool = False,
pretrained: str = None):
super(BiSeNetV2, self).__init__()
C1, C2, C3 = 64, 64, 128
db_channels = (C1, C2, C3)
C1, C3, C4, C5 = int(C1 * lambd), int(C3 * lambd), 64, 128
sb_channels = (C1, C3, C4, C5)
mid_channels = 128
self.db = DetailBranch(db_channels)
self.sb = SemanticBranch(sb_channels)
self.bga = BGA(mid_channels, align_corners)
self.aux_head1 = SegHead(C1, C1, num_classes)
self.aux_head2 = SegHead(C3, C3, num_classes)
self.aux_head3 = SegHead(C4, C4, num_classes)
self.aux_head4 = SegHead(C5, C5, num_classes)
self.head = SegHead(mid_channels, mid_channels, num_classes)
self.align_corners = align_corners
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory, 'bisenet_model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
def __init__(self,
num_classes: int = 19,
stem_channels: Tuple[int] = (16, 24, 32, 48),
ch_list: Tuple[int] = (64, 96, 160, 224, 320),
grmul: float = 1.7,
gr: Tuple[int] = (10, 16, 18, 24, 32),
n_layers: Tuple[int] = (4, 4, 8, 8, 8),
align_corners: bool = False,
pretrained: str = None):
super(HarDNet, self).__init__()
self.align_corners = align_corners
self.pretrained = pretrained
encoder_blks_num = len(n_layers)
decoder_blks_num = encoder_blks_num - 1
encoder_in_channels = stem_channels[3]
self.stem = nn.Sequential(
layers.ConvBNReLU(3,
stem_channels[0],
kernel_size=3,
bias_attr=False),
layers.ConvBNReLU(stem_channels[0],
stem_channels[1],
kernel_size=3,
bias_attr=False),
layers.ConvBNReLU(stem_channels[1],
stem_channels[2],
kernel_size=3,
stride=2,
bias_attr=False),
layers.ConvBNReLU(stem_channels[2],
stem_channels[3],
kernel_size=3,
bias_attr=False))
self.encoder = Encoder(encoder_blks_num, encoder_in_channels, ch_list,
gr, grmul, n_layers)
skip_connection_channels = self.encoder.get_skip_channels()
decoder_in_channels = self.encoder.get_out_channels()
self.decoder = Decoder(decoder_blks_num, decoder_in_channels,
skip_connection_channels, gr, grmul, n_layers,
align_corners)
self.cls_head = nn.Conv2D(in_channels=self.decoder.get_out_channels(),
out_channels=num_classes,
kernel_size=1)
self.transforms = T.Compose([T.Normalize()])
if pretrained is not None:
model_dict = paddle.load(pretrained)
self.set_dict(model_dict)
print("load custom parameters success")
else:
checkpoint = os.path.join(self.directory, 'model.pdparams')
model_dict = paddle.load(checkpoint)
self.set_dict(model_dict)
print("load pretrained parameters success")
