Fix api docs easeof use (PaddlePaddle#740)

demo/dygraph/unstructured_pruning/evaluate.py

@@ -33,7 +33,7 @@ def compress(args):
     test_reader = None
     if args.data == "imagenet":
         import imagenet_reader as reader
-        val_dataset = reader.ImageNetDataset(data_dir='/data', mode='val')
+        val_dataset = reader.ImageNetDataset(mode='val')
         class_dim = 1000
     elif args.data == "cifar10":
         normalize = T.Normalize(
@@ -47,13 +47,12 @@ def compress(args):
 
     places = paddle.static.cuda_places(
     ) if args.use_gpu else paddle.static.cpu_places()
-    batch_size_per_card = int(args.batch_size / len(places))
     valid_loader = paddle.io.DataLoader(
         val_dataset,
         places=places,
         drop_last=False,
         return_list=True,
-        batch_size=batch_size_per_card,
+        batch_size=args.batch_size,
         shuffle=False,
         use_shared_memory=True)
 
@@ -70,15 +69,12 @@ def test(epoch):
             y_data = paddle.to_tensor(data[1])
             if args.data == 'cifar10':
                 y_data = paddle.unsqueeze(y_data, 1)
-            end_time = time.time()
 
             logits = model(x_data)
             loss = F.cross_entropy(logits, y_data)
             acc_top1 = paddle.metric.accuracy(logits, y_data, k=1)
             acc_top5 = paddle.metric.accuracy(logits, y_data, k=5)
-
-            acc_top1_ns.append(acc_top1.numpy())
-            acc_top5_ns.append(acc_top5.numpy())
+            end_time = time.time()
             if batch_id % args.log_period == 0:
                 _logger.info(
                     "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".

demo/dygraph/unstructured_pruning/train.py

@@ -23,6 +23,7 @@
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('batch_size',       int,  64 * 4,                 "Minibatch size.")
+add_arg('batch_size_for_validation',       int,  64,                 "Minibatch size for validation.")
 add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
 add_arg('lr',               float,  0.1,               "The learning rate used to fine-tune pruned model.")
 add_arg('lr_strategy',      str,  "piecewise_decay",   "The learning rate decay strategy.")
@@ -121,7 +122,7 @@ def compress(args):
         places=place,
         drop_last=False,
         return_list=True,
-        batch_size=64,
+        batch_size=args.batch_size_for_validation,
         shuffle=False,
         use_shared_memory=True)
     step_per_epoch = int(
@@ -146,15 +147,12 @@ def test(epoch):
             y_data = paddle.to_tensor(data[1])
             if args.data == 'cifar10':
                 y_data = paddle.unsqueeze(y_data, 1)
-            end_time = time.time()
 
             logits = model(x_data)
             loss = F.cross_entropy(logits, y_data)
             acc_top1 = paddle.metric.accuracy(logits, y_data, k=1)
             acc_top5 = paddle.metric.accuracy(logits, y_data, k=5)
-
-            acc_top1_ns.append(acc_top1.numpy())
-            acc_top5_ns.append(acc_top5.numpy())
+            end_time = time.time()
             if batch_id % args.log_period == 0:
                 _logger.info(
                     "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".

demo/unstructured_prune/evaluate.py

@@ -20,7 +20,7 @@
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('batch_size',       int,  64*12,                 "Minibatch size.")
+add_arg('batch_size',       int,  64,                 "Minibatch size.")
 add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
 add_arg('model',            str,  "MobileNet",                "The target model.")
 add_arg('pruned_model', str,  "models",                "Whether to use pretrained model.")
@@ -44,8 +44,8 @@ def compress(args):
         image_shape = "1,28,28"
     elif args.data == "imagenet":
         import imagenet_reader as reader
-        train_dataset = reader.ImageNetDataset(data_dir='/data', mode='train')
-        val_dataset = reader.ImageNetDataset(data_dir='/data', mode='val')
+        train_dataset = reader.ImageNetDataset(mode='train')
+        val_dataset = reader.ImageNetDataset(mode='val')
         class_dim = 1000
         image_shape = "3,224,224"
     else:
@@ -71,7 +71,6 @@ def compress(args):
         use_shared_memory=True,
         batch_size=batch_size_per_card,
         shuffle=False)
-    step_per_epoch = int(np.ceil(len(train_dataset) * 1. / args.batch_size))
 
     # model definition
     model = models.__dict__[args.model]()
@@ -103,12 +102,7 @@ def test(epoch, program):
         for batch_id, data in enumerate(valid_loader):
             start_time = time.time()
             acc_top1_n, acc_top5_n = exe.run(
-                program,
-                feed={
-                    "image": data[0].get('image'),
-                    "label": data[0].get('label'),
-                },
-                fetch_list=[acc_top1.name, acc_top5.name])
+                program, feed=data, fetch_list=[acc_top1.name, acc_top5.name])
             end_time = time.time()
             if batch_id % args.log_period == 0:
                 _logger.info(

demo/unstructured_prune/train.py

@@ -20,6 +20,7 @@
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('batch_size',       int,  64 * 4,                 "Minibatch size.")
+add_arg('batch_size_for_validation',       int,  64,                 "Minibatch size for validation.")
 add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
 add_arg('model',            str,  "MobileNet",                "The target model.")
 add_arg('pretrained_model', str,  "../pretrained_model/MobileNetV1_pretrained",                "Whether to use pretrained model.")
@@ -123,7 +124,7 @@ def compress(args):
         drop_last=False,
         return_list=False,
         use_shared_memory=True,
-        batch_size=batch_size_per_card,
+        batch_size=args.batch_size_for_validation,
         shuffle=False)
     step_per_epoch = int(np.ceil(len(train_dataset) * 1. / args.batch_size))
 

docs/zh_cn/api_cn/dygraph/pruners/unstructured_pruner.rst

@@ -7,7 +7,7 @@ UnstructuredPruner
 .. py:class:: paddleslim.UnstructuredPruner(model, mode, threshold=0.01, ratio=0.3, skip_params_func=None)
 
 
-`源代码 <https://github.com/minghaoBD/PaddleSlim/blob/update_unstructured_pruning_docs/paddleslim/dygraph/prune/unstructured_pruner.py>`_
+`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/dygraph/prune/unstructured_pruner.py>`_
 
 对于神经网络中的参数进行非结构化稀疏。非结构化稀疏是指，根据某些衡量指标，将不重要的参数置0。其不按照固定结构剪裁（例如一个通道等），这是和结构化剪枝的主要区别。
 
@@ -23,11 +23,16 @@ UnstructuredPruner
 
 **示例代码：**
 
-此示例不能直接运行，因为需要定义和加载模型，详细用法请参考 `这里 <https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning>`_
-
 .. code-block:: python
 
+  import paddle
   from paddleslim import UnstructuredPruner
+  from paddle.vision.models import LeNet as net
+  import numpy as np
+
+  place = paddle.set_device('cpu')
+  model = net(num_classes=10)
+
   pruner = UnstructuredPruner(model, mode='ratio', ratio=0.5)
 
 ..
@@ -38,13 +43,19 @@ UnstructuredPruner
 
   **示例代码：**
 
-  此示例不能直接运行，因为需要定义和加载模型，详细用法请参考 `这里 <https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning>`_
-
   .. code-block:: python
 
     from paddleslim import UnstructuredPruner
+    from paddle.vision.models import LeNet as net
+    import numpy as np
+
+    place = paddle.set_device('cpu')
+    model = net(num_classes=10)
     pruner = UnstructuredPruner(model, mode='ratio', ratio=0.5)
+
+    print(pruner.threshold)
     pruner.step()
+    print(pruner.threshold) # 可以看出，这里的threshold和上面打印的不同，这是因为step函数根据设定的ratio更新了threshold数值，便于剪裁操作。
 
   ..
 
@@ -54,13 +65,23 @@ UnstructuredPruner
 
   **示例代码：**
 
-  此示例不能直接运行，因为需要定义和加载模型，详细用法请参考 `这里 <https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning>`_
-
   .. code-block:: python
 
     from paddleslim import UnstructuredPruner
-    pruner = UnstructuredPruner(model, mode='ratio', ratio=0.5)
+    from paddle.vision.models import LeNet as net
+    import numpy as np
+
+    place = paddle.set_device('cpu')
+    model = net(num_classes=10)
+    pruner = UnstructuredPruner(model, mode='threshold', threshold=0.5)
+
+    density = UnstructuredPruner.total_sparse(model)
+    print(density)
+    model(paddle.to_tensor(
+                np.random.uniform(0, 1, [16, 1, 28, 28]), dtype='float32'))
     pruner.update_params()
+    density = UnstructuredPruner.total_sparse(model)
+    print(density) # 可以看出，这里打印的模型稠密度与上述不同，这是因为update_params()函数置零了所有绝对值小于0.5的权重。
 
   ..
 
@@ -78,13 +99,17 @@ UnstructuredPruner
 
   **示例代码：**
 
-  此示例不能直接运行，因为需要定义和加载模型，详细用法请参考 `这里 <https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning>`_
-
   .. code-block:: python
 
     from paddleslim import UnstructuredPruner
-    density = UnstructuredPruner.total_sparse(model)
+    from paddle.vision.models import LeNet as net
+    import numpy as np
 
+    place = paddle.set_device('cpu')
+    model = net(num_classes=10)
+    density = UnstructuredPruner.total_sparse(model)
+    print(density)
+    
   ..
 
   .. py:method:: paddleslim.UnstructuredPruner.summarize_weights(model, ratio=0.1)
@@ -102,12 +127,17 @@ UnstructuredPruner
 
   **示例代码：**
 
-  此示例不能直接运行，因为需要定义和加载模型，详细用法请参考 `这里 <https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning>`_
-
   .. code-block:: python
 
     from paddleslim import UnstructuredPruner
+    from paddle.vision.models import LeNet as net
+    import numpy as np
+
+    place = paddle.set_device('cpu')
+    model = net(num_classes=10)
     pruner = UnstructuredPruner(model, mode='ratio', ratio=0.5)
-    threshold = pruner.summarize_weights(model, ratio=0.1)
+
+    threshold = pruner.summarize_weights(model, 0.5)
+    print(threshold)
 
   ..