Merge pull request #93 from heavengate/update_tsm

update TSM use new APIS

tsm/infer.py

@@ -19,8 +19,8 @@ import os
 import argparse
 import numpy as np
 
-from paddle.incubate.hapi.model import Input, set_device
-from paddle.incubate.hapi.vision.transforms import Compose
+import paddle
+from paddle.vision.transforms import Compose
 
 from check import check_gpu, check_version
 from modeling import tsm_resnet50
@@ -33,8 +33,8 @@ logger = logging.getLogger(__name__)
 
 
 def main():
-    device = set_device(FLAGS.device)
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    device = paddle.set_device(FLAGS.device)
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     transform = Compose([GroupScale(), GroupCenterCrop(), NormalizeImage()])
     dataset = KineticsDataset(
@@ -47,9 +47,7 @@ def main():
     model = tsm_resnet50(
         num_classes=len(labels), pretrained=FLAGS.weights is None)
 
-    inputs = [Input([None, 8, 3, 224, 224], 'float32', name='image')]
-
-    model.prepare(inputs=inputs, device=FLAGS.device)
+    model.prepare()
 
     if FLAGS.weights is not None:
         model.load(FLAGS.weights, reset_optimizer=True)

tsm/main.py

@@ -19,13 +19,11 @@ import os
 import argparse
 import numpy as np
 
+import paddle
 from paddle import fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
 
-from paddle.incubate.hapi.model import Model, Input, set_device
-from paddle.incubate.hapi.loss import CrossEntropy
-from paddle.incubate.hapi.metrics import Accuracy
-from paddle.incubate.hapi.vision.transforms import Compose
+from paddle.vision.transforms import Compose
 
 from modeling import tsm_resnet50
 from check import check_gpu, check_version
@@ -50,8 +48,8 @@ def make_optimizer(step_per_epoch, parameter_list=None):
 
 
 def main():
-    device = set_device(FLAGS.device)
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    device = paddle.set_device(FLAGS.device)
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     train_transform = Compose([
         GroupScale(), GroupMultiScaleCrop(), GroupRandomCrop(),
@@ -79,16 +77,10 @@ def main():
                          / ParallelEnv().nranks)
     optim = make_optimizer(step_per_epoch, model.parameters())
 
-    inputs = [Input([None, 8, 3, 224, 224], 'float32', name='image')]
-    labels = [Input([None, 1], 'int64', name='label')]
-
     model.prepare(
-        optim,
-        CrossEntropy(),
-        metrics=Accuracy(topk=(1, 5)),
-        inputs=inputs,
-        labels=labels,
-        device=FLAGS.device)
+        optimizer=optim,
+        loss=paddle.nn.CrossEntropyLoss(),
+        metrics=paddle.metric.Accuracy(topk=(1, 5)))
 
     if FLAGS.eval_only:
         if FLAGS.weights is not None:

tsm/modeling.py

@@ -13,12 +13,13 @@
 #limitations under the License.
 
 import math
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
 
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.download import get_weights_path_from_url
+from paddle.static import InputSpec
+from paddle.utils.download import get_weights_path_from_url
 
 __all__ = ["TSM_ResNet", "tsm_resnet50"]
 
@@ -112,7 +113,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
         return y
 
 
-class TSM_ResNet(Model):
+class TSM_ResNet(fluid.dygraph.Layer):
     """
     TSM network with ResNet as backbone
 
@@ -193,7 +194,10 @@ class TSM_ResNet(Model):
 
 
 def _tsm_resnet(num_layers, seg_num=8, num_classes=400, pretrained=True):
-    model = TSM_ResNet(num_layers, seg_num, num_classes)
+    inputs = [InputSpec([None, 8, 3, 224, 224], 'float32', name='image')]
+    labels = [InputSpec([None, 1], 'int64', name='label')]
+    net = TSM_ResNet(num_layers, seg_num, num_classes)
+    model = paddle.Model(net, inputs, labels)
     if pretrained:
         assert num_layers in pretrain_infos.keys(), \
                 "TSM-ResNet{} do not have pretrained weights now, " \
@@ -201,6 +205,8 @@ def _tsm_resnet(num_layers, seg_num=8, num_classes=400, pretrained=True):
         weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
+        # weight_dict, _ = fluid.load_dygraph(weight_path)
+        # model.set_dict(weight_dict)
         model.load(weight_path)
     return model