move Loss to loss.py

examples/bert/bert_classifier.py

@@ -18,7 +18,8 @@ from hapi.metrics import Accuracy
 from hapi.configure import Config
 from hapi.text.bert import BertEncoder
 from paddle.fluid.dygraph import Linear, Layer
-from hapi.model import set_device, Model, SoftmaxWithCrossEntropy, Input
+from hapi.model import set_device, Model, Input
+from hapi.loss import SoftmaxWithCrossEntropy
 import hapi.text.tokenizer.tokenization as tokenization
 from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
 

examples/bert_leveldb/bert_classifier.py

@@ -18,7 +18,8 @@ from hapi.metrics import Accuracy
 from hapi.configure import Config
 from hapi.text.bert import BertEncoder
 from paddle.fluid.dygraph import Linear, Layer
-from hapi.model import set_device, Model, SoftmaxWithCrossEntropy, Input
+from hapi.model import set_device, Model, Input
+from hapi.loss import SoftmaxWithCrossEntropy
 import hapi.text.tokenizer.tokenization as tokenization
 from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
 

examples/bmn/modeling.py

@@ -17,8 +17,9 @@ from paddle.fluid import ParamAttr
 import numpy as np
 import math
 
-from hapi.model import Model, Loss
-from hapi.download import get_weights_path
+from hapi.model import Model
+from hapi.loss import Loss
+from hapi.download import get_weights_path_from_url
 
 __all__ = ["BMN", "BmnLoss", "bmn"]
 
@@ -459,7 +460,7 @@ def bmn(tscale,
     model = BMN(tscale, dscale, prop_boundary_ratio, num_sample,
                 num_sample_perbin)
     if pretrained:
-        weight_path = get_weights_path(*(pretrain_infos['bmn']))
+        weight_path = get_weights_path_from_url(*(pretrain_infos['bmn']))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
         model.load(weight_path)

examples/cyclegan/cyclegan.py

@@ -19,7 +19,8 @@ from __future__ import print_function
 import numpy as np
 
 import paddle.fluid as fluid
-from hapi.model import Model, Loss
+from hapi.model import Model
+from hapi.loss import Loss
 
 from layers import ConvBN, DeConvBN
 

examples/image_classification/main.py

@@ -27,7 +27,8 @@ import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
 from paddle.io import BatchSampler, DataLoader
 
-from hapi.model import CrossEntropy, Input, set_device
+from hapi.model import Input, set_device
+from hapi.loss import CrossEntropy
 from hapi.distributed import DistributedBatchSampler
 from hapi.metrics import Accuracy
 import hapi.vision.models as models

examples/ocr/seq2seq_attn.py

@@ -20,7 +20,8 @@ import paddle.fluid.layers as layers
 from paddle.fluid.layers import BeamSearchDecoder
 
 from hapi.text import RNNCell, RNN, DynamicDecode
-from hapi.model import Model, Loss
+from hapi.model import Model
+from hapi.loss import Loss
 
 
 class ConvBNPool(fluid.dygraph.Layer):

examples/sequence_tagging/train.py

@@ -28,14 +28,14 @@ import numpy as np
 work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.append(os.path.join(work_dir, "../"))
 
-
 from hapi.metrics import Metric
-from hapi.model import Model, Input, Loss, set_device
+from hapi.model import Model, Input, set_device
+from hapi.loss import Loss
 from hapi.text.text import SequenceTagging
 
 from utils.check import check_gpu, check_version
 from utils.configure import PDConfig
-from reader import LacDataset, create_lexnet_data_generator, create_dataloader 
+from reader import LacDataset, create_lexnet_data_generator, create_dataloader
 
 import paddle.fluid as fluid
 from paddle.fluid.optimizer import AdamOptimizer
@@ -65,19 +65,19 @@ class SeqTagging(Model):
         self.bigru_num = args.bigru_num
         self.batch_size = args.batch_size
         self.init_bound = 0.1
-        self.length=length
+        self.length = length
 
         self.sequence_tagging = SequenceTagging(
-                        vocab_size=self.vocab_size,
-                        num_labels=self.num_labels,
-                        batch_size=self.batch_size,
-                        word_emb_dim=self.word_emb_dim,
-                        grnn_hidden_dim=self.grnn_hidden_dim,
-                        emb_learning_rate=self.emb_lr,
-                        crf_learning_rate=self.crf_lr,
-                        bigru_num=self.bigru_num,
-                        init_bound=self.init_bound,
-                        length=self.length)
+            vocab_size=self.vocab_size,
+            num_labels=self.num_labels,
+            batch_size=self.batch_size,
+            word_emb_dim=self.word_emb_dim,
+            grnn_hidden_dim=self.grnn_hidden_dim,
+            emb_learning_rate=self.emb_lr,
+            crf_learning_rate=self.crf_lr,
+            bigru_num=self.bigru_num,
+            init_bound=self.init_bound,
+            length=self.length)
 
     def forward(self, *inputs):
         """
@@ -85,10 +85,10 @@ class SeqTagging(Model):
         """
         word = inputs[0]
         lengths = inputs[1]
-        if self.mode_type == "train" or self.mode_type == "test": 
+        if self.mode_type == "train" or self.mode_type == "test":
             target = inputs[2]
             outputs = self.sequence_tagging(word, lengths, target)
-        else: 
+        else:
             outputs = self.sequence_tagging(word, lengths)
         return outputs
 
@@ -156,7 +156,7 @@ class ChunkEval(Metric):
             int(math.ceil((num_labels - 1) / 2.0)), "IOB")
         self.reset()
 
-    def add_metric_op(self, *args): 
+    def add_metric_op(self, *args):
         crf_decode = args[0]
         lengths = args[2]
         label = args[3]
@@ -207,13 +207,18 @@ def main(args):
     place = set_device(args.device)
     fluid.enable_dygraph(place) if args.dynamic else None
 
-    inputs = [Input([None, None], 'int64', name='words'),
-              Input([None], 'int64', name='length'), 
-              Input([None, None], 'int64', name='target')]
+    inputs = [
+        Input(
+            [None, None], 'int64', name='words'), Input(
+                [None], 'int64', name='length'), Input(
+                    [None, None], 'int64', name='target')
+    ]
 
     labels = [Input([None, None], 'int64', name='labels')]
 
-    feed_list = None if args.dynamic else [x.forward() for x in inputs + labels]
+    feed_list = None if args.dynamic else [
+        x.forward() for x in inputs + labels
+    ]
     dataset = LacDataset(args)
     train_path = args.train_file
     test_path = args.test_file
@@ -263,7 +268,7 @@ if __name__ == '__main__':
     args = PDConfig(yaml_file="sequence_tagging.yaml")
     args.build()
     args.Print()
-    
+
     use_gpu = True if args.device == "gpu" else False
     check_gpu(use_gpu)
     check_version()

examples/tsm/main.py

@@ -22,7 +22,8 @@ import numpy as np
 from paddle import fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
 
-from hapi.model import Model, CrossEntropy, Input, set_device
+from hapi.model import Model, Input, set_device
+from hapi.loss import CrossEntropy
 from hapi.metrics import Accuracy
 
 from modeling import tsm_resnet50
@@ -33,11 +34,10 @@ from transforms import *
 
 def make_optimizer(step_per_epoch, parameter_list=None):
     boundaries = [e * step_per_epoch for e in [40, 60]]
-    values = [FLAGS.lr * (0.1 ** i) for i in range(len(boundaries) + 1)]
+    values = [FLAGS.lr * (0.1**i) for i in range(len(boundaries) + 1)]
 
     learning_rate = fluid.layers.piecewise_decay(
-        boundaries=boundaries,
-        values=values)
+        boundaries=boundaries, values=values)
     optimizer = fluid.optimizer.Momentum(
         learning_rate=learning_rate,
         regularization=fluid.regularizer.L2Decay(1e-4),
@@ -51,29 +51,27 @@ def main():
     device = set_device(FLAGS.device)
     fluid.enable_dygraph(device) if FLAGS.dynamic else None
 
-    train_transform = Compose([GroupScale(),
-                               GroupMultiScaleCrop(),
-                               GroupRandomCrop(),
-                               GroupRandomFlip(),
-                               NormalizeImage()])
+    train_transform = Compose([
+        GroupScale(), GroupMultiScaleCrop(), GroupRandomCrop(),
+        GroupRandomFlip(), NormalizeImage()
+    ])
     train_dataset = KineticsDataset(
-            file_list=os.path.join(FLAGS.data, 'train_10.list'),
-            pickle_dir=os.path.join(FLAGS.data, 'train_10'),
-            label_list=os.path.join(FLAGS.data, 'label_list'),
-            transform=train_transform)
-    val_transform = Compose([GroupScale(),
-                             GroupCenterCrop(),
-                             NormalizeImage()])
+        file_list=os.path.join(FLAGS.data, 'train_10.list'),
+        pickle_dir=os.path.join(FLAGS.data, 'train_10'),
+        label_list=os.path.join(FLAGS.data, 'label_list'),
+        transform=train_transform)
+    val_transform = Compose(
+        [GroupScale(), GroupCenterCrop(), NormalizeImage()])
     val_dataset = KineticsDataset(
-            file_list=os.path.join(FLAGS.data, 'val_10.list'),
-            pickle_dir=os.path.join(FLAGS.data, 'val_10'),
-            label_list=os.path.join(FLAGS.data, 'label_list'),
-            mode='val',
-            transform=val_transform)
+        file_list=os.path.join(FLAGS.data, 'val_10.list'),
+        pickle_dir=os.path.join(FLAGS.data, 'val_10'),
+        label_list=os.path.join(FLAGS.data, 'label_list'),
+        mode='val',
+        transform=val_transform)
 
     pretrained = FLAGS.eval_only and FLAGS.weights is None
-    model = tsm_resnet50(num_classes=train_dataset.num_classes,
-                         pretrained=pretrained)
+    model = tsm_resnet50(
+        num_classes=train_dataset.num_classes, pretrained=pretrained)
 
     step_per_epoch = int(len(train_dataset) / FLAGS.batch_size \
                          / ParallelEnv().nranks)
@@ -116,7 +114,9 @@ def main():
 if __name__ == '__main__':
     parser = argparse.ArgumentParser("CNN training on TSM")
     parser.add_argument(
-        "--data", type=str, default='dataset/kinetics',
+        "--data",
+        type=str,
+        default='dataset/kinetics',
         help="path to dataset root directory")
     parser.add_argument(
         "--device", type=str, default='gpu', help="device to use, gpu or cpu")

examples/tsm/modeling.py

@@ -18,7 +18,7 @@ from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
 
 from hapi.model import Model
-from hapi.download import get_weights_path
+from hapi.download import get_weights_path_from_url
 
 __all__ = ["TSM_ResNet", "tsm_resnet50"]
 
@@ -122,6 +122,7 @@ class TSM_ResNet(Model):
         seg_num (int): segment number of each video sample. Default 8.
         num_classes (int): video class number. Default 400.
     """
+
     def __init__(self, num_layers=50, seg_num=8, num_classes=400):
         super(TSM_ResNet, self).__init__()
 
@@ -136,7 +137,11 @@ class TSM_ResNet(Model):
         num_filters = [64, 128, 256, 512]
 
         self.conv = ConvBNLayer(
-            num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu')
         self.pool2d_max = Pool2D(
             pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
 
@@ -193,7 +198,7 @@ def _tsm_resnet(num_layers, seg_num=8, num_classes=400, pretrained=True):
         assert num_layers in pretrain_infos.keys(), \
                 "TSM-ResNet{} do not have pretrained weights now, " \
                 "pretrained should be set as False".format(num_layers)
-        weight_path = get_weights_path(*(pretrain_infos[num_layers]))
+        weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
         model.load(weight_path)

examples/yolov3/modeling.py

@@ -20,8 +20,9 @@ from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
-from hapi.model import Model, Loss
-from hapi.download import get_weights_path
+from hapi.model import Model
+from hapi.loss import Loss
+from hapi.download import get_weights_path_from_url
 from hapi.vision.models import darknet53
 
 __all__ = ['YoloLoss', 'YOLOv3', 'yolov3_darknet53']
@@ -315,7 +316,7 @@ def _yolov3_darknet(num_layers=53,
         assert num_layers in pretrain_infos.keys(), \
                 "YOLOv3-DarkNet{} do not have pretrained weights now, " \
                 "pretrained should be set as False".format(num_layers)
-        weight_path = get_weights_path(*(pretrain_infos[num_layers]))
+        weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
         model.load(weight_path)