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@@ -19,15 +19,14 @@ from collections import OrderedDict
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import paddle
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import paddle.nn.functional as F
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from paddle.static import InputSpec
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+import paddleseg
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import paddlex
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-from paddlex.cv.nets.paddleseg import models
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from paddlex.cv.transforms import arrange_transforms
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from paddlex.utils import get_single_card_bs
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import paddlex.utils.logging as logging
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from .base import BaseModel
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from .utils import seg_metrics as metrics
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from paddlex.utils.checkpoint import seg_pretrain_weights_dict
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-from paddlex.cv.nets.paddleseg.cvlibs import manager
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from paddlex.cv.transforms import Decode
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__all__ = ["UNet", "DeepLabV3P", "FastSCNN", "HRNet", "BiSeNetV2"]
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@@ -41,7 +40,7 @@ class BaseSegmenter(BaseModel):
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**params):
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self.init_params = locals()
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super(BaseSegmenter, self).__init__('segmenter')
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- if not hasattr(models, model_name):
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+ if not hasattr(paddleseg.models, model_name):
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raise Exception("ERROR: There's no model named {}.".format(
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model_name))
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self.model_name = model_name
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@@ -54,8 +53,8 @@ class BaseSegmenter(BaseModel):
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def build_net(self, **params):
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# TODO: when using paddle.utils.unique_name.guard,
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# DeepLabv3p and HRNet will raise a error
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- net = models.__dict__[self.model_name](num_classes=self.num_classes,
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- **params)
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+ net = paddleseg.models.__dict__[self.model_name](
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+ num_classes=self.num_classes, **params)
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return net
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def get_test_inputs(self, image_shape):
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@@ -101,15 +100,16 @@ class BaseSegmenter(BaseModel):
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if isinstance(self.use_mixed_loss, bool):
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if self.use_mixed_loss:
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losses = [
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- manager.LOSSES['CrossEntropyLoss'](),
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- manager.LOSSES['LovaszSoftmaxLoss']()
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+ paddleseg.models.CrossEntropyLoss(),
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+ paddleseg.models.LovaszSoftmaxLoss()
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]
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coef = [.8, .2]
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loss_type = [
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- manager.LOSSES['MixedLoss'](losses=losses, coef=coef)
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+ paddleseg.models.MixedLoss(
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+ losses=losses, coef=coef),
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]
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else:
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- loss_type = [manager.LOSSES['CrossEntropyLoss']()]
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+ loss_type = [paddleseg.models.CrossEntropyLoss()]
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else:
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losses, coef = list(zip(*self.use_mixed_loss))
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if not set(losses).issubset(
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@@ -117,9 +117,10 @@ class BaseSegmenter(BaseModel):
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raise ValueError(
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"Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
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)
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- losses = [manager.LOSSES[loss]() for loss in losses]
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+ losses = [getattr(paddleseg.models, loss)() for loss in losses]
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loss_type = [
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- manager.LOSSES['MixedLoss'](losses=losses, coef=list(coef))
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+ paddleseg.models.MixedLoss(
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+ losses=losses, coef=list(coef))
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]
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if self.model_name == 'FastSCNN':
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loss_type *= 2
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@@ -447,7 +448,8 @@ class DeepLabV3P(BaseSegmenter):
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raise ValueError(
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"backbone: {} is not supported. Please choose one of "
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"('ResNet50_vd', 'ResNet101_vd')".format(backbone))
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- backbone = manager.BACKBONES[backbone](output_stride=output_stride)
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+ backbone = getattr(paddleseg.models, backbone)(
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+ output_stride=output_stride)
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params = {
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'backbone': backbone,
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'backbone_indices': backbone_indices,
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@@ -486,7 +488,7 @@ class HRNet(BaseSegmenter):
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"width={} is not supported, please choose from [18, 48]".
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format(width))
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self.backbone_name = 'HRNet_W{}'.format(width)
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- backbone = manager.BACKBONES[self.backbone_name](
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+ backbone = getattr(paddleseg.models, self.backbone_name)(
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align_corners=align_corners)
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params = {'backbone': backbone, 'align_corners': align_corners}
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will-jl944