[cherrypick-2.0-beta] Add summary (#26990)

* add summary api

python/paddle/__init__.py

@@ -269,5 +269,6 @@ from . import static
 # high-level api
 from .hapi import Model
 from .hapi import callbacks
+from .hapi import summary
 import paddle.text
 import paddle.vision

python/paddle/hapi/__init__.py

@@ -14,14 +14,15 @@
 
 from . import logger
 from . import callbacks
+from . import model_summary
 
 from . import model
 from .model import *
-
+from .model_summary import summary
 from .dygraph_layer_patch import monkey_patch_layer
 
 logger.setup_logger()
 
-__all__ = ['callbacks'] + model.__all__
+__all__ = ['callbacks'] + model.__all__ + ['summary']
 
 monkey_patch_layer()

python/paddle/hapi/model.py

@@ -47,10 +47,10 @@ from paddle.io import DataLoader, Dataset, DistributedBatchSampler
 from paddle.fluid.executor import scope_guard, Executor
 from paddle.fluid.dygraph.layers import Layer
 from paddle.metric import Metric
-
 from paddle.static import InputSpec as Input
 
 from .callbacks import config_callbacks
+from .model_summary import summary
 
 __all__ = ['Model', ]
 
@@ -1828,6 +1828,49 @@ class Model(object):
             return logs, outputs
         return logs
 
+    def summary(self, input_size=None, batch_size=None, dtype=None):
+        """Prints a string summary of the network.
+
+        Args:
+            input_size (tuple|InputSpec|list[tuple|InputSpec], optional): size of input tensor. 
+                    if not set, input_size will get from ``self._inputs`` if network only have 
+                    one input, input_size can be tuple or InputSpec. if model have multiple 
+                    input, input_size must be a list which contain every input's shape. 
+                    Default: None.
+            batch_size (int, optional): batch size of input tensor, Default: None.
+            dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
+
+        Returns:
+            Dict: a summary of the network including total params and total trainable params.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+              from paddle.static import InputSpec
+
+              dynamic = True
+              device = paddle.set_device('cpu')
+              paddle.disable_static(device) if dynamic else None
+           
+              input = InputSpec([None, 1, 28, 28], 'float32', 'image')
+              label = InputSpec([None, 1], 'int64', 'label')
+           
+              model = paddle.Model(paddle.vision.LeNet(classifier_activation=None),
+                  input, label)
+              optim = paddle.optimizer.Adam(
+                  learning_rate=0.001, parameters=model.parameters())
+              model.prepare(
+                  optim,
+                  paddle.nn.CrossEntropyLoss())
+
+              params_info = model.summary()
+              print(params_info)
+
+        """
+
+        return summary(self.network, self._inputs, batch_size, dtype)
+
     def _verify_spec(self, specs, is_input=False):
         out_specs = []
 

python/paddle/hapi/model_summary.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+import paddle
+import paddle.nn as nn
+from paddle.static import InputSpec
+
+from collections import OrderedDict
+
+__all__ = ['summary']
+
+
+def summary(net, input_size, batch_size=None, dtypes=None):
+    """Prints a string summary of the network.
+
+    Args:
+        net (Layer): the network which must be a subinstance of Layer.
+        input_size (tuple|InputSpec|list[tuple|InputSpec]): size of input tensor. if model only 
+                    have one input, input_size can be tuple or InputSpec. if model
+                    have multiple input, input_size must be a list which contain 
+                    every input's shape.
+        batch_size (int, optional): batch size of input tensor, Default: None.
+        dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
+
+    Returns:
+        Dict: a summary of the network including total params and total trainable params.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn as nn
+
+            class LeNet(nn.Layer):
+                def __init__(self, num_classes=10):
+                    super(LeNet, self).__init__()
+                    self.num_classes = num_classes
+                    self.features = nn.Sequential(
+                        nn.Conv2d(
+                            1, 6, 3, stride=1, padding=1),
+                        nn.ReLU(),
+                        nn.MaxPool2d(2, 2),
+                        nn.Conv2d(
+                            6, 16, 5, stride=1, padding=0),
+                        nn.ReLU(),
+                        nn.MaxPool2d(2, 2))
+
+                    if num_classes > 0:
+                        self.fc = nn.Sequential(
+                            nn.Linear(400, 120),
+                            nn.Linear(120, 84),
+                            nn.Linear(
+                                84, 10))
+
+                def forward(self, inputs):
+                    x = self.features(inputs)
+
+                    if self.num_classes > 0:
+                        x = paddle.flatten(x, 1)
+                        x = self.fc(x)
+                    return x
+
+            lenet = LeNet()
+
+            params_info = paddle.summary(lenet, (1, 28, 28))
+            print(params_info)
+
+    """
+    if isinstance(input_size, InputSpec):
+        _input_size = tuple(input_size.shape[1:])
+        if batch_size is None:
+            batch_size = input_size.shape[0]
+    elif isinstance(input_size, list):
+        _input_size = []
+        for item in input_size:
+            assert isinstance(item,
+                              (list, InputSpec)), 'When input_size is list, \
+            expect item in input_size is a tuple or InputSpec, but got {}'.format(
+                                  type(item))
+
+            if isinstance(item, InputSpec):
+                _input_size.append(tuple(item.shape[1:]))
+                if batch_size is None:
+                    batch_size = item.shape[0]
+            else:
+                _input_size.append(item)
+    else:
+        _input_size = input_size
+
+    if batch_size is None:
+        batch_size = -1
+
+    result, params_info = summary_string(net, _input_size, batch_size, dtypes)
+    print(result)
+
+    return params_info
+
+
+def summary_string(model, input_size, batch_size=-1, dtypes=None):
+    if dtypes == None:
+        dtypes = ['float32'] * len(input_size)
+
+    summary_str = ''
+
+    depth = len(list(model.sublayers()))
+
+    def register_hook(module):
+        def hook(module, input, output):
+            class_name = str(module.__class__).split(".")[-1].split("'")[0]
+
+            try:
+                module_idx = int(module._full_name.split('_')[-1])
+            except:
+                module_idx = len(summary)
+
+            m_key = "%s-%i" % (class_name, module_idx + 1)
+            summary[m_key] = OrderedDict()
+            summary[m_key]["input_shape"] = list(input[0].shape)
+            summary[m_key]["input_shape"][0] = batch_size
+            if isinstance(output, (list, tuple)):
+                summary[m_key]["output_shape"] = [[-1] + list(o.shape)[1:]
+                                                  for o in output]
+            else:
+                summary[m_key]["output_shape"] = list(output.shape)
+                summary[m_key]["output_shape"][0] = batch_size
+
+            params = 0
+            if hasattr(module, "weight"):
+                params += np.prod(module.weight.shape)
+                summary[m_key]["trainable"] = module.weight.trainable or (
+                    not module.weight.stop_gradient)
+            if hasattr(module, "bias"):
+                params += np.prod(module.bias.shape)
+            summary[m_key]["nb_params"] = params
+
+        if (not isinstance(module, nn.Sequential) and
+                not isinstance(module, nn.LayerList) and
+            (not (module == model) or depth < 1)):
+
+            hooks.append(module.register_forward_post_hook(hook))
+
+    if isinstance(input_size, tuple):
+        input_size = [input_size]
+
+    x = [
+        paddle.rand(
+            [2] + list(in_size), dtype=dtype)
+        for in_size, dtype in zip(input_size, dtypes)
+    ]
+
+    # create properties
+    summary = OrderedDict()
+    hooks = []
+
+    # register hook
+    model.apply(register_hook)
+
+    # make a forward pass
+    model(*x)
+
+    # remove these hooks
+    for h in hooks:
+        h.remove()
+
+    table_width = 80
+    summary_str += "-" * table_width + "\n"
+    line_new = "{:>15} {:>20} {:>20} {:>15}".format(
+        "Layer (type)", "Input Shape", "Output Shape", "Param #")
+    summary_str += line_new + "\n"
+    summary_str += "=" * table_width + "\n"
+    total_params = 0
+    total_output = 0
+    trainable_params = 0
+    for layer in summary:
+        # input_shape, output_shape, trainable, nb_params
+        line_new = "{:>15} {:>20} {:>20} {:>15}".format(
+            layer,
+            str(summary[layer]["input_shape"]),
+            str(summary[layer]["output_shape"]),
+            "{0:,}".format(summary[layer]["nb_params"]), )
+        total_params += summary[layer]["nb_params"]
+
+        total_output += np.prod(summary[layer]["output_shape"])
+        if "trainable" in summary[layer]:
+            if summary[layer]["trainable"] == True:
+                trainable_params += summary[layer]["nb_params"]
+        summary_str += line_new + "\n"
+
+    # assume 4 bytes/number (float on cuda).
+    total_input_size = abs(
+        np.prod(sum(input_size, ())) * batch_size * 4. / (1024**2.))
+    total_output_size = abs(2. * total_output * 4. /
+                            (1024**2.))  # x2 for gradients
+    total_params_size = abs(total_params * 4. / (1024**2.))
+    total_size = total_params_size + total_output_size + total_input_size
+
+    summary_str += "=" * table_width + "\n"
+    summary_str += "Total params: {0:,}".format(total_params) + "\n"
+    summary_str += "Trainable params: {0:,}".format(trainable_params) + "\n"
+    summary_str += "Non-trainable params: {0:,}".format(total_params -
+                                                        trainable_params) + "\n"
+    summary_str += "-" * table_width + "\n"
+    summary_str += "Input size (MB): %0.2f" % total_input_size + "\n"
+    summary_str += "Forward/backward pass size (MB): %0.2f" % total_output_size + "\n"
+    summary_str += "Params size (MB): %0.2f" % total_params_size + "\n"
+    summary_str += "Estimated Total Size (MB): %0.2f" % total_size + "\n"
+    summary_str += "-" * table_width + "\n"
+    # return summary
+    return summary_str, {
+        'total_params': total_params,
+        'trainable_params': trainable_params
+    }

python/paddle/tests/test_model.py

@@ -499,6 +499,26 @@ class TestModelFunction(unittest.TestCase):
             self.assertTrue(params[0].shape[1] == 10)
             fluid.disable_dygraph() if dynamic else None
 
+    def test_summary(self):
+        def _get_param_from_state_dict(state_dict):
+            params = 0
+            for k, v in state_dict.items():
+                params += np.prod(v.numpy().shape)
+            return params
+
+        for dynamic in [True, False]:
+            device = paddle.set_device('cpu')
+            fluid.enable_dygraph(device) if dynamic else None
+            net = MyModel()
+            inputs = [InputSpec([None, 20], 'float32', 'x')]
+            model = Model(net, inputs)
+            model.prepare()
+            params_info = model.summary()
+            gt_params = _get_param_from_state_dict(net.state_dict())
+
+            np.testing.assert_allclose(params_info['total_params'], gt_params)
+            print(params_info)
+
     def test_export_deploy_model(self):
         for dynamic in [True, False]:
             fluid.enable_dygraph() if dynamic else None

python/paddle/utils/__init__.py

@@ -17,6 +17,7 @@ from .profiler import ProfilerOptions
 from .profiler import Profiler
 from .profiler import get_profiler
 from .deprecated import deprecated
+
 from . import download
 
 __all__ = ['dump_config', 'Ploter', 'deprecated', 'download']