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@ -23,15 +23,16 @@ 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 paddlers.models.ppseg as paddleseg |
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import paddlers.rs_models.seg as cmseg |
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import paddlers |
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from paddlers.utils import get_single_card_bs, DisablePrint |
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import paddlers.models.ppseg as ppseg |
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import paddlers.rs_models.seg as cmseg |
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import paddlers.utils.logging as logging |
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from paddlers.models import seg_losses |
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from paddlers.transforms import Resize, decode_image |
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from paddlers.utils import get_single_card_bs, DisablePrint |
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from paddlers.utils.checkpoint import seg_pretrain_weights_dict |
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from .base import BaseModel |
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from .utils import seg_metrics as metrics |
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from paddlers.utils.checkpoint import seg_pretrain_weights_dict |
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from paddlers.transforms import Resize, decode_image |
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__all__ = ["UNet", "DeepLabV3P", "FastSCNN", "HRNet", "BiSeNetV2", "FarSeg"] |
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@ -41,19 +42,20 @@ class BaseSegmenter(BaseModel): |
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model_name, |
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num_classes=2, |
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use_mixed_loss=False, |
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losses=None, |
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**params): |
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self.init_params = locals() |
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if 'with_net' in self.init_params: |
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del self.init_params['with_net'] |
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super(BaseSegmenter, self).__init__('segmenter') |
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if not hasattr(paddleseg.models, model_name) and \ |
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if not hasattr(ppseg.models, model_name) and \ |
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not hasattr(cmseg, model_name): |
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raise ValueError("ERROR: There is no model named {}.".format( |
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model_name)) |
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self.model_name = model_name |
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self.num_classes = num_classes |
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self.use_mixed_loss = use_mixed_loss |
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self.losses = None |
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self.losses = losses |
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self.labels = None |
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if params.get('with_net', True): |
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params.pop('with_net', None) |
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@ -63,9 +65,8 @@ 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 = dict(paddleseg.models.__dict__, |
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**cmseg.__dict__)[self.model_name]( |
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num_classes=self.num_classes, **params) |
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net = dict(ppseg.models.__dict__, **cmseg.__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 _fix_transforms_shape(self, image_shape): |
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@ -143,7 +144,7 @@ class BaseSegmenter(BaseModel): |
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origin_shape = [label.shape[-2:]] |
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pred = self._postprocess( |
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pred, origin_shape, transforms=inputs[2])[0] # NCHW |
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intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area( |
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intersect_area, pred_area, label_area = ppseg.utils.metrics.calculate_area( |
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pred, label, self.num_classes) |
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outputs['intersect_area'] = intersect_area |
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outputs['pred_area'] = pred_area |
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@ -161,16 +162,13 @@ 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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paddleseg.models.CrossEntropyLoss(), |
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paddleseg.models.LovaszSoftmaxLoss() |
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seg_losses.CrossEntropyLoss(), |
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seg_losses.LovaszSoftmaxLoss() |
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] |
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coef = [.8, .2] |
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loss_type = [ |
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paddleseg.models.MixedLoss( |
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losses=losses, coef=coef), |
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] |
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loss_type = [seg_losses.MixedLoss(losses=losses, coef=coef), ] |
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else: |
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loss_type = [paddleseg.models.CrossEntropyLoss()] |
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loss_type = [seg_losses.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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@ -178,11 +176,8 @@ 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 = [getattr(paddleseg.models, loss)() for loss in losses] |
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loss_type = [ |
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paddleseg.models.MixedLoss( |
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losses=losses, coef=list(coef)) |
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] |
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losses = [getattr(seg_losses, loss)() for loss in losses] |
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loss_type = [seg_losses.MixedLoss(losses=losses, coef=list(coef))] |
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if self.model_name == 'FastSCNN': |
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loss_type *= 2 |
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loss_coef = [1.0, 0.4] |
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@ -475,13 +470,13 @@ class BaseSegmenter(BaseModel): |
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pred_area_all = pred_area_all + pred_area |
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label_area_all = label_area_all + label_area |
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conf_mat_all.append(conf_mat) |
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class_iou, miou = paddleseg.utils.metrics.mean_iou( |
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class_iou, miou = ppseg.utils.metrics.mean_iou( |
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intersect_area_all, pred_area_all, label_area_all) |
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# TODO 确认是按oacc还是macc |
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class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all, |
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pred_area_all) |
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kappa = paddleseg.utils.metrics.kappa(intersect_area_all, pred_area_all, |
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label_area_all) |
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class_acc, oacc = ppseg.utils.metrics.accuracy(intersect_area_all, |
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pred_area_all) |
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kappa = ppseg.utils.metrics.kappa(intersect_area_all, pred_area_all, |
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label_area_all) |
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category_f1score = metrics.f1_score(intersect_area_all, pred_area_all, |
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label_area_all) |
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eval_metrics = OrderedDict( |
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@ -613,15 +608,15 @@ class BaseSegmenter(BaseModel): |
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ysize = int(height - yoff) |
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im = src_data.ReadAsArray(int(xoff), int(yoff), xsize, |
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ysize).transpose((1, 2, 0)) |
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# fill |
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# Fill |
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h, w = im.shape[:2] |
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im_fill = np.zeros( |
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(block_size[1], block_size[0], bands), dtype=im.dtype) |
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im_fill[:h, :w, :] = im |
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# predict |
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# Predict |
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pred = self.predict(im_fill, |
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transforms)["label_map"].astype("uint8") |
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# overlap |
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# Overlap |
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rd_block = band.ReadAsArray(int(xoff), int(yoff), xsize, ysize) |
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mask = (rd_block == pred[:h, :w]) | (rd_block == 255) |
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temp = pred[:h, :w].copy() |
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@ -778,12 +773,18 @@ class BaseSegmenter(BaseModel): |
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raise TypeError( |
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"`transforms.arrange` must be an ArrangeSegmenter object.") |
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def set_losses(self, losses, weights=None): |
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if weights is None: |
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weights = [1. for _ in range(len(losses))] |
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self.losses = {'types': losses, 'coef': weights} |
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class UNet(BaseSegmenter): |
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def __init__(self, |
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input_channel=3, |
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num_classes=2, |
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use_mixed_loss=False, |
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losses=None, |
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use_deconv=False, |
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align_corners=False, |
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**params): |
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@ -796,6 +797,7 @@ class UNet(BaseSegmenter): |
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input_channel=input_channel, |
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num_classes=num_classes, |
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use_mixed_loss=use_mixed_loss, |
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losses=losses, |
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**params) |
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@ -805,6 +807,7 @@ class DeepLabV3P(BaseSegmenter): |
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num_classes=2, |
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backbone='ResNet50_vd', |
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use_mixed_loss=False, |
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losses=None, |
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output_stride=8, |
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backbone_indices=(0, 3), |
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aspp_ratios=(1, 12, 24, 36), |
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@ -818,7 +821,7 @@ class DeepLabV3P(BaseSegmenter): |
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"{'ResNet50_vd', 'ResNet101_vd'}.".format(backbone)) |
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if params.get('with_net', True): |
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with DisablePrint(): |
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backbone = getattr(paddleseg.models, backbone)( |
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backbone = getattr(ppseg.models, backbone)( |
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input_channel=input_channel, output_stride=output_stride) |
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else: |
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backbone = None |
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@ -833,6 +836,7 @@ class DeepLabV3P(BaseSegmenter): |
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model_name='DeepLabV3P', |
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num_classes=num_classes, |
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use_mixed_loss=use_mixed_loss, |
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losses=losses, |
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**params) |
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@ -840,6 +844,7 @@ class FastSCNN(BaseSegmenter): |
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def __init__(self, |
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num_classes=2, |
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use_mixed_loss=False, |
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losses=None, |
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align_corners=False, |
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**params): |
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params.update({'align_corners': align_corners}) |
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@ -847,6 +852,7 @@ class FastSCNN(BaseSegmenter): |
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model_name='FastSCNN', |
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num_classes=num_classes, |
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use_mixed_loss=use_mixed_loss, |
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losses=losses, |
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**params) |
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@ -855,6 +861,7 @@ class HRNet(BaseSegmenter): |
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num_classes=2, |
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width=48, |
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use_mixed_loss=False, |
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losses=None, |
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align_corners=False, |
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**params): |
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if width not in (18, 48): |
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@ -864,7 +871,7 @@ class HRNet(BaseSegmenter): |
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self.backbone_name = 'HRNet_W{}'.format(width) |
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if params.get('with_net', True): |
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with DisablePrint(): |
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backbone = getattr(paddleseg.models, self.backbone_name)( |
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backbone = getattr(ppseg.models, self.backbone_name)( |
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align_corners=align_corners) |
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else: |
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backbone = None |
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@ -874,6 +881,7 @@ class HRNet(BaseSegmenter): |
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model_name='FCN', |
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num_classes=num_classes, |
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use_mixed_loss=use_mixed_loss, |
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losses=losses, |
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**params) |
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self.model_name = 'HRNet' |
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@ -882,6 +890,7 @@ class BiSeNetV2(BaseSegmenter): |
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def __init__(self, |
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num_classes=2, |
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use_mixed_loss=False, |
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losses=None, |
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align_corners=False, |
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**params): |
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params.update({'align_corners': align_corners}) |
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@ -889,13 +898,19 @@ class BiSeNetV2(BaseSegmenter): |
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model_name='BiSeNetV2', |
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num_classes=num_classes, |
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use_mixed_loss=use_mixed_loss, |
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losses=losses, |
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**params) |
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class FarSeg(BaseSegmenter): |
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def __init__(self, num_classes=2, use_mixed_loss=False, **params): |
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def __init__(self, |
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num_classes=2, |
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use_mixed_loss=False, |
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losses=None, |
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**params): |
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super(FarSeg, self).__init__( |
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model_name='FarSeg', |
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num_classes=num_classes, |
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use_mixed_loss=use_mixed_loss, |
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losses=losses, |
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**params) |
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Bobholamovic
2 years ago
