diff --git a/paddlex/__init__.py b/paddlex/__init__.py index de762df7ad7dc01670e795b93f709bb23a08f1c8..972210bdb80c445e59d4a8ed10418ee988bd353c 100644 --- a/paddlex/__init__.py +++ b/paddlex/__init__.py @@ -53,4 +53,4 @@ log_level = 2 from . import interpret -__version__ = '1.0.2.github' +__version__ = '1.0.4' diff --git a/paddlex/cv/models/deeplabv3p.py b/paddlex/cv/models/deeplabv3p.py index a253aa5d1d8c005c7903b57a9b9b36da45982d78..3127bd8549ae221f7f7604613bba2e1437b93605 100644 --- a/paddlex/cv/models/deeplabv3p.py +++ b/paddlex/cv/models/deeplabv3p.py @@ -190,11 +190,6 @@ class DeepLabv3p(BaseAPI): if mode == 'train': self.optimizer.minimize(model_out) outputs['loss'] = model_out - elif mode == 'eval': - outputs['loss'] = model_out[0] - outputs['pred'] = model_out[1] - outputs['label'] = model_out[2] - outputs['mask'] = model_out[3] else: outputs['pred'] = model_out[0] outputs['logit'] = model_out[1] @@ -336,18 +331,26 @@ class DeepLabv3p(BaseAPI): for step, data in tqdm.tqdm( enumerate(data_generator()), total=total_steps): images = np.array([d[0] for d in data]) - labels = np.array([d[1] for d in data]) + + _, _, im_h, im_w = images.shape + labels = list() + for d in data: + padding_label = np.zeros( + (1, im_h, im_w)).astype('int64') + self.ignore_index + padding_label[:, :im_h, :im_w] = d[1] + labels.append(padding_label) + labels = np.array(labels) + num_samples = images.shape[0] if num_samples < batch_size: num_pad_samples = batch_size - num_samples pad_images = np.tile(images[0:1], (num_pad_samples, 1, 1, 1)) images = np.concatenate([images, pad_images]) feed_data = {'image': images} - outputs = self.exe.run( - self.parallel_test_prog, - feed=feed_data, - fetch_list=list(self.test_outputs.values()), - return_numpy=True) + outputs = self.exe.run(self.parallel_test_prog, + feed=feed_data, + fetch_list=list(self.test_outputs.values()), + return_numpy=True) pred = outputs[0] if num_samples < batch_size: pred = pred[0:num_samples] @@ -364,8 +367,7 @@ class DeepLabv3p(BaseAPI): metrics = OrderedDict( zip(['miou', 'category_iou', 'macc', 'category_acc', 'kappa'], - [miou, category_iou, macc, category_acc, - conf_mat.kappa()])) + [miou, category_iou, macc, category_acc, conf_mat.kappa()])) if return_details: eval_details = { 'confusion_matrix': conf_mat.confusion_matrix.tolist() @@ -394,10 +396,9 @@ class DeepLabv3p(BaseAPI): transforms=self.test_transforms, mode='test') im, im_info = self.test_transforms(im_file) im = np.expand_dims(im, axis=0) - result = self.exe.run( - self.test_prog, - feed={'image': im}, - fetch_list=list(self.test_outputs.values())) + result = self.exe.run(self.test_prog, + feed={'image': im}, + fetch_list=list(self.test_outputs.values())) pred = result[0] pred = np.squeeze(pred).astype('uint8') logit = result[1] @@ -413,6 +414,6 @@ class DeepLabv3p(BaseAPI): pred = pred[0:h, 0:w] logit = logit[0:h, 0:w, :] else: - raise Exception("Unexpected info '{}' in im_info".format( - info[0])) + raise Exception("Unexpected info '{}' in im_info".format(info[ + 0])) return {'label_map': pred, 'score_map': logit} diff --git a/paddlex/cv/nets/segmentation/deeplabv3p.py b/paddlex/cv/nets/segmentation/deeplabv3p.py index 08dad240c0b28d6e6e13845dcc0c9148c442014f..60a34d0128bf271d42fa8658100318ce05e31812 100644 --- a/paddlex/cv/nets/segmentation/deeplabv3p.py +++ b/paddlex/cv/nets/segmentation/deeplabv3p.py @@ -135,7 +135,8 @@ class DeepLabv3p(object): param_attr = fluid.ParamAttr( name=name_scope + 'weights', regularizer=None, - initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.06)) + initializer=fluid.initializer.TruncatedNormal( + loc=0.0, scale=0.06)) with scope('encoder'): channel = 256 with scope("image_pool"): @@ -151,8 +152,8 @@ class DeepLabv3p(object): padding=0, param_attr=param_attr)) input_shape = fluid.layers.shape(input) - image_avg = fluid.layers.resize_bilinear( - image_avg, input_shape[2:]) + image_avg = fluid.layers.resize_bilinear(image_avg, + input_shape[2:]) with scope("aspp0"): aspp0 = bn_relu( @@ -244,7 +245,8 @@ class DeepLabv3p(object): param_attr = fluid.ParamAttr( name=name_scope + 'weights', regularizer=None, - initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.06)) + initializer=fluid.initializer.TruncatedNormal( + loc=0.0, scale=0.06)) with scope('decoder'): with scope('concat'): decode_shortcut = bn_relu( @@ -326,9 +328,6 @@ class DeepLabv3p(object): if self.mode == 'train': inputs['label'] = fluid.data( dtype='int32', shape=[None, 1, None, None], name='label') - elif self.mode == 'eval': - inputs['label'] = fluid.data( - dtype='int32', shape=[None, 1, None, None], name='label') return inputs def build_net(self, inputs): @@ -351,7 +350,8 @@ class DeepLabv3p(object): name=name_scope + 'weights', regularizer=fluid.regularizer.L2DecayRegularizer( regularization_coeff=0.0), - initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01)) + initializer=fluid.initializer.TruncatedNormal( + loc=0.0, scale=0.01)) with scope('logit'): with fluid.name_scope('last_conv'): logit = conv( diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py index 004f60f071e50087fb69198fcdffb91345e1427a..6dc4ea7b95d876ae896c77395ab155bec1727a8a 100644 --- a/paddlex/cv/transforms/cls_transforms.py +++ b/paddlex/cv/transforms/cls_transforms.py @@ -494,13 +494,14 @@ class ComposedClsTransforms(Compose): std=[0.229, 0.224, 0.225]): width = crop_size if isinstance(crop_size, list): - if shape[0] != shape[1]: + if crop_size[0] != crop_size[1]: raise Exception( - "In classifier model, width and height should be equal") + "In classifier model, width and height should be equal, please modify your parameter `crop_size`" + ) width = crop_size[0] if width % 32 != 0: raise Exception( - "In classifier model, width and height should be multiple of 32, e.g 224、256、320...." + "In classifier model, width and height should be multiple of 32, e.g 224、256、320...., please modify your parameter `crop_size`" ) if mode == 'train': @@ -513,7 +514,7 @@ class ComposedClsTransforms(Compose): else: # 验证/预测时的transforms transforms = [ - ReiszeByShort(short_size=int(width * 1.14)), + ResizeByShort(short_size=int(width * 1.14)), CenterCrop(crop_size=width), Normalize( mean=mean, std=std) ] diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index d6e302443bb1ebe64c08533f145577bdcd5bb2cb..07b2785a5e6194b9d7c2cd6ab02ea15c7af693ab 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -1280,7 +1280,7 @@ class ComposedRCNNTransforms(Compose): Padding(coarsest_stride=32) ] - super(RCNNTransforms, self).__init__(transforms) + super(ComposedRCNNTransforms, self).__init__(transforms) class ComposedYOLOTransforms(Compose): @@ -1338,4 +1338,4 @@ class ComposedYOLOTransforms(Compose): target_size=width, interp='CUBIC'), Normalize( mean=mean, std=std) ] - super(YOLOTransforms, self).__init__(transforms) + super(ComposedYOLOTransforms, self).__init__(transforms) diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index 764dcd69bb3859ed59295898ec6a1876a73d25e7..884ab482e1d48ce377ebaac13679b8863a6d0955 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/paddlex/cv/transforms/seg_transforms.py @@ -1091,7 +1091,7 @@ class ArrangeSegmenter(SegTransform): return (im, ) -class ComposedTransforms(Compose): +class ComposedSegTransforms(Compose): """ 语义分割模型(UNet/DeepLabv3p)的图像处理流程，具体如下 训练阶段： 1. 随机对图像以0.5的概率水平翻转 @@ -1113,7 +1113,7 @@ class ComposedTransforms(Compose): train_crop_size=[769, 769], mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]): - if self.mode == 'train': + if mode == 'train': # 训练时的transforms，包含数据增强 transforms = [ RandomHorizontalFlip(prob=0.5), ResizeStepScaling(), @@ -1122,6 +1122,6 @@ class ComposedTransforms(Compose): ] else: # 验证/预测时的transforms - transforms = [transforms.Normalize(mean=mean, std=std)] + transforms = [Resize(512), Normalize(mean=mean, std=std)] super(ComposedSegTransforms, self).__init__(transforms) diff --git a/setup.py b/setup.py index a044495c902f6b754a69265c5020d7dbda992b14..bba199719ce65075f8a61b965bca49f026406c91 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ long_description = "PaddleX. A end-to-end deeplearning model development toolkit setuptools.setup( name="paddlex", - version='1.0.2', + version='1.0.4', author="paddlex", author_email="paddlex@baidu.com", description=long_description,