Refine pruning code for detection (#554) (#600)

paddleslim/analysis/flops.py

@@ -14,7 +14,7 @@
 import paddle
 import numpy as np
 import paddle.jit as jit
-from ..core import GraphWrapper
+from ..core import GraphWrapper, dygraph2program
 
 __all__ = ["flops", "dygraph_flops"]
 
@@ -83,11 +83,27 @@ def _graph_flops(graph, only_conv=True, detail=False):
         return flops
 
 
-def dygraph_flops(model, input_shape, only_conv=False, detail=False):
+def dygraph_flops(model, inputs, dtypes=None, only_conv=False, detail=False):
+    """
+    Compute the FLOPs of nn.Layer.
+    Args:
+      model(nn.Layer): The target model.
+      inputs(list): The dummy inputs used for 'model.forward'. It can be:
+                      1. list<int>|tuple<int>: means 'model.forward' accepts
+                         only one variable as argument and the shape of
+                         variable is 'inputs'.
+                      2. list<list<list>>: means 'model.forward' accepts multiple
+                         variables as arguments and the shapes of variables is 'inputs'.
+                      3. others: 'inputs' will be used as argument list by calling
+                         'model.forward(*inputs)'.
+      dtypes(str|list<str>): It only used when 'inputs' is shape or shapes that means
+                      data type of each input. None means all the inputs is 'float32'.
+                      Default: None.
+      only_conv(bool): Just return number of mul-adds in convolution and FC layer if `only_conv` is true.
+                         default: True.
+      detail(bool): Whether to return detail of each convolution layer.
+    """
 
-    data = np.ones(tuple(input_shape)).astype("float32")
-    in_var = paddle.to_tensor(data)
-    _, traced = paddle.jit.TracedLayer.trace(model, [in_var])
-    program = traced.program
+    program = dygraph2program(model, inputs)
     graph = GraphWrapper(program)
     return _graph_flops(graph, only_conv=only_conv, detail=detail)

paddleslim/core/__init__.py

@@ -12,7 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .graph_wrapper import GraphWrapper, VarWrapper, OpWrapper
-from .registry import Registry
+from ..core import graph_wrapper
+from .graph_wrapper import *
+from ..core import registry
+from .registry import *
+from ..core import dygraph
+from .dygraph import *
 
-__all__ = ['GraphWrapper', 'VarWrapper', 'OpWrapper', 'Registry']
+__all__ = []
+__all__ += graph_wrapper.__all__
+__all__ += registry.__all__
+__all__ += dygraph.__all__

paddleslim/core/dygraph.py

+import paddle
+import collections
+import logging
+import numpy as np
+from paddle.fluid.framework import _dygraph_tracer, dygraph_only, _dygraph_guard
+from paddle.fluid.dygraph.base import program_desc_tracing_guard
+from paddle.fluid.dygraph.layers import Layer
+from paddle.fluid.framework import Block, ParamBase, Program, Variable
+from ..common import get_logger
+
+__all__ = ["dygraph2program"]
+
+_logger = get_logger(__name__, level=logging.INFO)
+
+
+def _is_shape(values):
+    if not isinstance(values, (list, tuple)):
+        return False
+    for v in values:
+        if not isinstance(v, int):
+            return False
+    return True
+
+
+def _is_shapes(values):
+    if not isinstance(values, (list, tuple)):
+        return False
+    for v in values:
+        if not _is_shape(v):
+            return False
+    return True
+
+
+def _create_tensors(shapes, dtypes=None):
+    if dtypes is not None:
+        assert len(shapes) == len(
+            dtypes
+        ), "Length of shapes and dtypes must be same. But get len(shapes): {}; len(dtypes): {}; shapes: {}; dtypes: {}".format(
+            len(shapes), len(dtypes), shapes, dtypes)
+    else:
+        dtypes = len(shapes) * ['float32']
+    tensors = []
+    for shape, dtype in zip(shapes, dtypes):
+        data = np.ones(tuple(shape)).astype(dtype)
+        tensors.append(paddle.to_tensor(data))
+    return tensors
+
+
+def extract_vars(inputs):
+    """
+    Extract a list of variables from inputs.
+    Args:
+        inputs(Variable | list<Object> | dict): 
+    """
+    vars = []
+    if isinstance(inputs, Variable):
+        vars = [inputs]
+    elif isinstance(inputs, dict):
+        for _key, _value in inputs.items():
+            if isinstance(_value, Variable):
+                vars.append(_value)
+            else:
+                _logger.warn(
+                    f"Variable is excepted, but get an element with type({type(_value)}) from inputs whose type is dict. And the key of element is {_key}."
+                )
+    elif isinstance(inputs, (tuple, list)):
+        for _value in inputs:
+            vars.extend(extract_vars(_value))
+    if len(vars) == 0:
+        _logger.warn(f"Extract none variables from inputs.")
+    return vars
+
+
+def to_variables(inputs):
+    """
+    Find and rename variables. Find np.ndarray and convert it to variable.
+    """
+    if isinstance(inputs, Variable) or isinstance(inputs, np.ndarray):
+        return paddle.fluid.dygraph.to_variable(inputs)
+    elif isinstance(inputs, dict):
+        ret = {}
+        for _key in inputs:
+            ret[_key] = to_variables(inputs[_key])
+        return inputs
+    elif isinstance(inputs, list):
+        ret = []
+        for _value in inputs:
+            ret.append(to_variables(_value))
+        return ret
+
+
+@dygraph_only
+def dygraph2program(layer,
+                    inputs,
+                    feed_prefix='feed_',
+                    fetch_prefix='fetch_',
+                    tmp_prefix='t_',
+                    extract_inputs_fn=None,
+                    extract_outputs_fn=None,
+                    dtypes=None):
+    assert isinstance(layer, Layer)
+
+    extract_inputs_fn = extract_inputs_fn if extract_inputs_fn is not None else extract_vars
+    extract_outputs_fn = extract_outputs_fn if extract_outputs_fn is not None else extract_vars
+    tracer = _dygraph_tracer()._get_program_desc_tracer()
+
+    with program_desc_tracing_guard(True):
+
+        if _is_shape(inputs):
+            shapes = [inputs]
+            inputs = _create_tensors(shapes, dtypes=dtypes)
+            input_var_list = inputs
+        elif _is_shapes(inputs):
+            inputs = _create_tensors(inputs, dtypes=dtypes)
+            input_var_list = inputs
+        else:
+            inputs = to_variables(inputs)
+            input_var_list = extract_inputs_fn(inputs)
+        original_outputs = layer(*inputs)
+        # 'original_outputs' may be dict, so we should convert it to list of varibles.
+        # And should not create new varibles in 'extract_vars'.
+        out_var_list = extract_outputs_fn(original_outputs)
+        program_desc, feed_names, fetch_names, parameters = tracer.create_program_desc(
+            input_var_list, feed_prefix, out_var_list, fetch_prefix, tmp_prefix)
+        tracer.reset()
+
+    with _dygraph_guard(None):
+        program = Program()
+        program.desc = program_desc
+        program.blocks = [Block(program, 0)]
+        program._sync_with_cpp()
+    return program

paddleslim/core/graph_wrapper.py

@@ -397,9 +397,3 @@ class GraphWrapper(object):
         # Infer the remain ops in topological order.
         for op in head_op:
             recursive_infer(op, infer=True)
-
-    def update_groups_of_conv(self):
-        for op in self.ops():
-            if 'conv2d' in op.type() and op.attr('groups') >= op.inputs(
-                    'Filter')[0].shape()[0]:
-                op.set_attr('groups', op.inputs('Filter')[0].shape()[0])

paddleslim/dygraph/filter_pruner.py

@@ -47,24 +47,34 @@ class FilterPruner(Pruner):
 
     Args:
         model(paddle.nn.Layer): The target model to be pruned.
-        input_shape(list<int>): The input shape of model. It is used to trace the graph of the model.
+        inputs(list<int>): The inputs of model. It will be use in calling 'model.forward(inputs)'.
         sen_file(str, optional): The absolute path of file that stores computed sensitivities. If it is
                               set rightly, 'FilterPruner::sensitive' function can not be called anymore
                               in next step. Default: None.
     
     """
 
-    def __init__(self, model, input_shape, sen_file=None):
-        super(FilterPruner, self).__init__(model, input_shape)
+    def __init__(self, model, inputs, sen_file=None):
+        super(FilterPruner, self).__init__(model, inputs)
         self._status = Status(sen_file)
         # sensitive and var_group are just used in filter pruning
-        self.var_group = VarGroup(model, input_shape)
+        self.var_group = VarGroup(model, inputs)
+
+        # skip vars in:
+        # 1. depthwise conv2d layer
+        self.skip_vars = []
+        for sub_layer in model.sublayers():
+            if isinstance(
+                    sub_layer,
+                    paddle.nn.layer.conv.Conv2D) and sub_layer._groups > 1:
+                for param in sub_layer.parameters():
+                    self.skip_vars.append(param.name)
 
     def sensitive(self,
                   eval_func=None,
                   sen_file=None,
                   target_vars=None,
-                  skip_vars=None):
+                  skip_vars=[]):
         """
         Compute or get sensitivities of model in current pruner. It will return a cached sensitivities when all the arguments are "None".
 
@@ -88,7 +98,7 @@ class FilterPruner(Pruner):
           eval_func(function, optional): The function to evaluate the model in current pruner. This function should have an empy arguments list and return a score with type "float32". Default: None.
           sen_file(str, optional): The absolute path of file to save sensitivities into local filesystem. Default: None.
           target_vars(list, optional): The names of tensors whose sensitivity will be computed. "None" means all weights in convolution layer will be computed. Default: None.
-          skip_vars(list, optional): The names of tensors whose sensitivity won't be computed. "None" means skip nothing. Default: None.
+          skip_vars(list, optional): The names of tensors whose sensitivity won't be computed. Default: [].
     
         Returns:
            dict: A dict storing sensitivities.       
@@ -102,6 +112,7 @@ class FilterPruner(Pruner):
         if not self._status.is_ckp:
             return self._status
 
+        skip_vars.extend(self.skip_vars)
         self._cal_sensitive(
             self.model,
             eval_func,
@@ -186,9 +197,9 @@ class FilterPruner(Pruner):
         Returns:
             tuple: A tuple with format ``(ratios, pruned_flops)`` . "ratios" is a dict whose key is name of tensor and value is ratio to be pruned. "pruned_flops" is the ratio of total pruned FLOPs in the model.
         """
-        base_flops = flops(self.model, self.input_shape)
+        base_flops = flops(self.model, self.inputs)
 
-        _logger.info("Base FLOPs: {}".format(base_flops))
+        _logger.debug("Base FLOPs: {}".format(base_flops))
         low = 0.
         up = 1.0
         history = set()
@@ -200,8 +211,7 @@ class FilterPruner(Pruner):
             if align is not None:
                 ratios = self._round_to(ratios, dims=dims, factor=align)
             plan = self.prune_vars(ratios, axis=dims)
-            _logger.debug("pruning plan: {}".format(plan))
-            c_flops = flops(self.model, self.input_shape)
+            c_flops = flops(self.model, self.inputs)
             _logger.debug("FLOPs after pruning: {}".format(c_flops))
             c_pruned_flops = (base_flops - c_flops) / base_flops
             plan.restore(self.model)
@@ -304,7 +314,11 @@ class FilterPruner(Pruner):
             plan: An instance of PruningPlan that can be applied on model by calling 'plan.apply(model)'.
 
         """
-
+        if var_name in self.skip_vars:
+            _logger.warn(
+                f"{var_name} is skiped beacause it is not support for pruning derectly."
+            )
+            return
         if isinstance(pruned_dims, int):
             pruned_dims = [pruned_dims]
         group = self.var_group.find_group(var_name, pruned_dims)
@@ -315,29 +329,52 @@ class FilterPruner(Pruner):
             for param in sub_layer.parameters(include_sublayers=False):
                 if param.name in group:
                     group_dict[param.name] = group[param.name]
-                    group_dict[param.name].update({
-                        'layer': sub_layer,
-                        'var': param,
-                        'value': np.array(param.value().get_tensor())
-                    })
+                    # Varibales can be pruned on multiple axies.
+                    for _item in group_dict[param.name]:
+                        _item.update({
+                            'layer': sub_layer,
+                            'var': param,
+                            'value': np.array(param.value().get_tensor())
+                        })
                     _logger.debug(f"set value of {param.name} into group")
 
         mask = self.cal_mask(var_name, pruned_ratio, group_dict)
         for _name in group_dict:
-            dims = group_dict[_name]['pruned_dims']
-            stride = group_dict[_name]['stride']
-            var_shape = group_dict[_name]['var'].shape
-            if isinstance(dims, int):
-                dims = [dims]
-
-            current_mask = mask.repeat(stride[0]) if stride[0] > 1 else mask
-
-            assert len(current_mask) == var_shape[dims[
-                0]], "The length of current_mask must be equal to the size of dimension to be pruned on."
-
-            plan.add(_name, PruningMask(dims, current_mask, pruned_ratio))
+            # Varibales can be pruned on multiple axies. 
+            for _item in group_dict[_name]:
+                dims = _item['pruned_dims']
+                transforms = _item['transforms']
+                var_shape = _item['var'].shape
+                if isinstance(dims, int):
+                    dims = [dims]
+                for trans in transforms:
+                    mask = self._transform_mask(mask, trans)
+                current_mask = mask
+                assert len(current_mask) == var_shape[dims[
+                    0]], f"The length of current_mask must be equal to the size of dimension to be pruned on. But get: len(current_mask): {len(current_mask)}; var_shape: {var_shape}; dims: {dims}; var name: {_name}; len(mask): {len(mask)}"
+                plan.add(_name, PruningMask(dims, current_mask, pruned_ratio))
         if apply == "lazy":
             plan.apply(self.model, lazy=True)
         elif apply == "impretive":
             plan.apply(self.model, lazy=False)
         return plan
+
+    def _transform_mask(self, mask, transform):
+        src_start = transform['src_start']
+        src_end = transform['src_end']
+        target_start = transform['target_start']
+        target_end = transform['target_end']
+        target_len = transform['target_len']
+        stride = transform['stride']
+        mask = mask[src_start:src_end]
+        mask = mask.repeat(stride) if stride > 1 else mask
+
+        dst_mask = np.ones([target_len])
+        # for depthwise conv2d with:
+        # input shape:  (1, 4, 32, 32)
+        # filter shape: (32, 1, 3, 3)
+        # groups: 4
+        # if we pruning input channels by 50%(from 4 to 2), the output channel should be 50% * 4 * 8.
+        expand = int((target_end - target_start) / len(mask))
+        dst_mask[target_start:target_end] = list(mask) * expand
+        return dst_mask

paddleslim/dygraph/fpgm_pruner.py

@@ -12,13 +12,15 @@ _logger = get_logger(__name__, logging.INFO)
 
 
 class FPGMFilterPruner(FilterPruner):
-    def __init__(self, model, input_shape, sen_file=None):
-        super(FPGMFilterPruner, self).__init__(
-            model, input_shape, sen_file=sen_file)
+    def __init__(self, model, inputs, sen_file=None):
+        super(FPGMFilterPruner, self).__init__(model, inputs, sen_file=sen_file)
 
     def cal_mask(self, var_name, pruned_ratio, group):
-        value = group[var_name]['value']
-        pruned_dims = group[var_name]['pruned_dims']
+        for _item in group[var_name]:
+            if _item['pruned_dims'] == [0]:
+                value = _item['value']
+                pruned_dims = _item['pruned_dims']
+
         assert (pruned_dims == [0])
 
         dist_sum_list = []

paddleslim/dygraph/l1norm_pruner.py

@@ -12,13 +12,15 @@ _logger = get_logger(__name__, logging.INFO)
 
 
 class L1NormFilterPruner(FilterPruner):
-    def __init__(self, model, input_shape, sen_file=None):
+    def __init__(self, model, inputs, sen_file=None):
         super(L1NormFilterPruner, self).__init__(
-            model, input_shape, sen_file=sen_file)
+            model, inputs, sen_file=sen_file)
 
     def cal_mask(self, var_name, pruned_ratio, group):
-        value = group[var_name]['value']
-        pruned_dims = group[var_name]['pruned_dims']
+        for _item in group[var_name]:
+            if _item['pruned_dims'] == [0]:
+                value = _item['value']
+                pruned_dims = _item['pruned_dims']
         reduce_dims = [
             i for i in range(len(value.shape)) if i not in pruned_dims
         ]

paddleslim/dygraph/l2norm_pruner.py

@@ -12,13 +12,16 @@ _logger = get_logger(__name__, logging.INFO)
 
 
 class L2NormFilterPruner(FilterPruner):
-    def __init__(self, model, input_shape, sen_file=None):
+    def __init__(self, model, inputs, sen_file=None):
         super(L2NormFilterPruner, self).__init__(
-            model, input_shape, sen_file=sen_file)
+            model, inputs, sen_file=sen_file)
 
     def cal_mask(self, var_name, pruned_ratio, group):
-        value = group[var_name]['value']
-        pruned_dims = group[var_name]['pruned_dims']
+        # find information of pruning on output channels
+        for _item in group[var_name]:
+            if _item['pruned_dims'] == [0]:
+                value = _item['value']
+                pruned_dims = _item['pruned_dims']
         reduce_dims = [
             i for i in range(len(value.shape)) if i not in pruned_dims
         ]

paddleslim/dygraph/pruner.py

@@ -19,9 +19,9 @@ class Pruner(object):
         
     """
 
-    def __init__(self, model, input_shape):
+    def __init__(self, model, inputs):
         self.model = model
-        self.input_shape = input_shape
+        self.inputs = inputs
         self._var_shapes = {}
         for var in model.parameters():
             self._var_shapes[var.name] = var.shape
@@ -53,5 +53,5 @@ class Pruner(object):
             global_plan.apply(self.model, lazy=True)
         elif apply == "impretive":
             global_plan.apply(self.model, lazy=False)
-
+        self.plan = global_plan
         return global_plan

paddleslim/dygraph/pruning_plan.py

@@ -28,7 +28,7 @@ class PruningMask():
         if self._mask is not None:
             assert len(self._mask.shape) == len(
                 value
-            ), "The length of value must be same with shape of mask in current PruningMask instance."
+            ), "The length of value must be same with length of mask's shape in current PruningMask instance."
         self._dims = list(value)
 
     @property
@@ -37,11 +37,6 @@ class PruningMask():
 
     @mask.setter
     def mask(self, value):
-        assert (isinstance(value, PruningMask))
-        if self._dims is not None:
-            assert len(self._mask.shape) == len(
-                value
-            ), "The length of value must be same with shape of mask in current PruningMask instance."
         self._mask = value
 
     def __str__(self):
@@ -71,13 +66,21 @@ class PruningPlan():
         self._pruned_flops = value
 
     def add(self, var_name, pruning_mask):
+
         assert (isinstance(pruning_mask, PruningMask))
         if var_name not in self._masks:
             self._masks[var_name] = []
-        self._masks[var_name].append(pruning_mask)
         if var_name not in self._dims:
             self._dims[var_name] = []
-        self._dims[var_name].append(pruning_mask.dims)
+
+        if pruning_mask.dims in self._dims[var_name]:
+            for _mask in self._masks[var_name]:
+                if pruning_mask.dims == _mask.dims:
+                    _mask.mask = list(
+                        np.array(_mask.mask) | np.array(pruning_mask.mask))
+        else:
+            self._masks[var_name].append(pruning_mask)
+            self._dims[var_name].append(pruning_mask.dims)
 
     @property
     def masks(self):
@@ -87,8 +90,7 @@ class PruningPlan():
         assert (isinstance(plan, PruningPlan))
         for var_name in plan.masks:
             for mask in plan.masks[var_name]:
-                if not self.contains(var_name, mask.dims):
-                    self.add(var_name, mask)
+                self.add(var_name, mask)
 
     def contains(self, var_name, dims=None):
         return (var_name in self._dims) and (dims is None or
@@ -172,7 +174,6 @@ class PruningPlan():
                         bool_mask = mask.astype(bool)
                         pruned_value = np.apply_along_axis(
                             lambda data: data[bool_mask], dims[0], value)
-
                         p = t_value._place()
                         if p.is_cpu_place():
                             place = paddle.CPUPlace()
@@ -184,14 +185,19 @@ class PruningPlan():
                             place = paddle.CUDAPlace(p.gpu_device_id())
 
                         t_value.set(pruned_value, place)
-                        if isinstance(sub_layer, paddle.nn.layer.conv.Conv2D):
-                            if sub_layer._groups > 1 and pruned_value.shape[
-                                    1] == 1:  # depthwise conv2d
-                                _logger.debug(
-                                    "Update groups of depthwise conv2d form {} to {}".
-                                    format(sub_layer._groups,
-                                           pruned_value.shape[0]))
-                                sub_layer._groups = pruned_value.shape[0]
+                        if isinstance(
+                                sub_layer, paddle.nn.layer.conv.Conv2D
+                        ) and sub_layer._groups > 1 and len(param.shape) == 4:
+                            assert param.shape[
+                                1] == 1, "It just supports depthwise conv2d when groups > 1."
+                            new_groups = int(bool_mask.sum() *
+                                             sub_layer._groups / len(bool_mask))
+                            _logger.debug(
+                                "Update groups of depthwise conv2d form {} to {}".
+                                format(sub_layer._groups, new_groups))
+                            sub_layer._origin_groups = sub_layer._groups
+                            sub_layer._groups = new_groups
+
                     # for training
                     if param.trainable:
                         param.clear_gradient()
@@ -218,11 +224,6 @@ class PruningPlan():
                         place = paddle.CUDAPlace(p.gpu_device_id())
 
                     t_value.set(np.array(t_backup).astype("float32"), place)
-
-                    if isinstance(sub_layer, paddle.nn.layer.conv.Conv2D):
-                        if sub_layer._groups > 1:
-                            _logger.debug(
-                                "Update groups of conv form {} to {}".format(
-                                    sub_layer._groups, t_value.shape()[0]))
-                            sub_layer._groups = t_value.shape()[0]
+                    if "_origin_groups" in sub_layer.__dict__:
+                        sub_layer._groups = sub_layer._origin_groups
                     del sub_layer._buffers[backup_name]

paddleslim/dygraph/var_group.py

@@ -2,7 +2,7 @@ import numpy as np
 import logging
 import paddle
 from paddle.fluid.dygraph import TracedLayer
-from ..core import GraphWrapper
+from ..core import GraphWrapper, dygraph2program
 from ..prune import collect_convs
 from ..common import get_logger
 
@@ -12,33 +12,43 @@ _logger = get_logger(__name__, level=logging.INFO)
 
 
 class VarGroup():
-    def __init__(self, model, input_shape):
+    """
+    A tool used to parse dygraph and store information of variables' relationship.
+    Args:
+      - model(nn.Layer): The dygraph to be parsed.
+      - inputs(Variable|list|dict): The dummy inputs of target model. It will be used in calling `model.forward(inputs)`.
+    """
+
+    def __init__(self, model, inputs):
         self.groups = []
-        self._parse_model(model, input_shape)
+        self._parse_model(model, inputs)
 
     def _to_dict(self, group):
         ret = {}
-        for _name, _axis, _stride in group:
+        for _name, _axis, _transforms in group:
             if isinstance(_axis, int):
-                _axis = [_axis]  # TODO: fix
-            ret[_name] = {'pruned_dims': _axis, 'stride': _stride}
+                _axis = [_axis]
+            if _name not in ret:
+                ret[_name] = []
+            # Variable can be pruned on multiple axies.
+            ret[_name].append({'pruned_dims': _axis, 'transforms': _transforms})
         return ret
 
     def find_group(self, var_name, axis):
         for group in self.groups:
             for _name, _axis, _stride in group:
                 if isinstance(_axis, int):
-                    _axis = [_axis]  # TODO: fix
+                    _axis = [_axis]
                 if _name == var_name and _axis == axis:
                     return self._to_dict(group)
 
-    def _parse_model(self, model, input_shape):
-        _logger.debug("Parsing model with input: {}".format(input_shape))
-        data = np.ones(tuple(input_shape)).astype("float32")
-        in_var = paddle.to_tensor(data)
+    def _parse_model(self, model, inputs):
+        _logger.debug("Parsing model with input: {}".format(inputs))
+
         model.eval()
-        out_dygraph, static_layer = TracedLayer.trace(model, inputs=[in_var])
-        graph = GraphWrapper(static_layer.program)
+        program = dygraph2program(model, inputs=inputs)
+
+        graph = GraphWrapper(program)
 
         visited = {}
         for name, param in model.named_parameters():

paddleslim/prune/group_param.py

@@ -79,7 +79,7 @@ def collect_convs(params, graph, visited={}):
                          pruned_params=pruned_params,
                          visited=visited)
 
-        walker.prune(param, pruned_axis=0, pruned_idx=[0])
+        walker.prune(param, pruned_axis=0, pruned_idx=[])
         groups.append(pruned_params)
     visited = set()
     uniq_groups = []
@@ -96,5 +96,4 @@ def collect_convs(params, graph, visited={}):
             simple_group.append((param, axis, pruned_idx))
         if not repeat_group:
             uniq_groups.append(simple_group)
-
     return uniq_groups

paddleslim/prune/idx_selector.py

@@ -59,11 +59,9 @@ def default_idx_selector(group, ratio):
 
     pruned_num = int(round(len(sorted_idx) * ratio))
     pruned_idx = sorted_idx[:pruned_num]
-
     idxs = []
-    for name, axis, score, offsets in group:
-        r_idx = [i + offsets[0] for i in pruned_idx]
-        idxs.append((name, axis, r_idx))
+    for name, axis, score, transforms in group:
+        idxs.append((name, axis, pruned_idx, transforms))
     return idxs
 
 
@@ -112,6 +110,6 @@ def optimal_threshold(group, ratio):
     pruned_idx = np.squeeze(np.argwhere(score < th))
 
     idxs = []
-    for name, axis, score, _ in group:
-        idxs.append((name, axis, pruned_idx))
+    for name, axis, score, transforms in group:
+        idxs.append((name, axis, pruned_idx, transforms))
     return idxs

paddleslim/prune/prune_io.py

@@ -10,6 +10,7 @@ __all__ = ["save_model", "load_model"]
 _logger = get_logger(__name__, level=logging.INFO)
 
 _SHAPES_FILE = "__shapes__"
+_GROUPS_FILE = "__groups__"
 
 
 def save_model(exe, graph, dirname):
@@ -39,6 +40,17 @@ def save_model(exe, graph, dirname):
         json.dump(shapes, f)
         _logger.info("Save shapes of weights into {}".format(SHAPES_FILE))
 
+    groups = {}
+    for op in graph.ops():
+        if 'conv2d' in op.type():
+            filter_name = op.inputs('Filter')[0].name()
+            groups[filter_name] = op.attr('groups')
+
+    GROUPS_FILE = os.path.join(dirname, _GROUPS_FILE)
+    with open(GROUPS_FILE, "w") as f:
+        json.dump(groups, f)
+        _logger.info("Save groups of cnov2d into {}".format(GROUPS_FILE))
+
 
 def load_model(exe, graph, dirname):
     """
@@ -53,7 +65,6 @@ def load_model(exe, graph, dirname):
                                               paddle.static.Program) else graph
 
     SHAPES_FILE = os.path.join(dirname, _SHAPES_FILE)
-    _logger.info("Load shapes of weights from {}".format(SHAPES_FILE))
     with open(SHAPES_FILE, "r") as f:
         shapes = json.load(f)
         for param_name, shape in shapes.items():
@@ -62,9 +73,17 @@ def load_model(exe, graph, dirname):
                 param.set_shape(shape)
             else:
                 _logger.info('{} is not loaded'.format(param_name))
-
     _logger.info("Load shapes of weights from {}".format(SHAPES_FILE))
+
+    GROUPS_FILE = os.path.join(dirname, _GROUPS_FILE)
+    with open(GROUPS_FILE, "r") as f:
+        groups = json.load(f)
+        for op in graph.ops():
+            if 'conv2d' in op.type():
+                filter_name = op.inputs('Filter')[0].name()
+                op.set_attr('groups', groups[filter_name])
+    _logger.info("Load groups of conv2d from {}".format(GROUPS_FILE))
+
     paddle.static.load(program=graph.program, model_path=dirname, executor=exe)
-    graph.update_groups_of_conv()
     graph.infer_shape()
     _logger.info("Load weights from {}".format(dirname))

paddleslim/prune/prune_walker.py

@@ -65,6 +65,15 @@ class PruneWorker(object):
             self.visited[pruned_axis][key] = True
             return True
 
+    def _visit_and_search(self, var, axis, transforms):
+        self._visit(var, axis)
+        pre_ops = var.inputs()
+        for op in pre_ops:
+            self._prune_op(op, var, axis, transforms)
+        next_ops = var.outputs()
+        for op in next_ops:
+            self._prune_op(op, var, axis, transforms)
+
     def _prune(self, var, pruned_axis, pruned_idx):
         raise NotImplementedError('Abstract method.')
 
@@ -85,6 +94,9 @@ class PruneWorker(object):
             cls = PRUNE_WORKER.get("default_walker")
         _logger.debug("\nfrom: {}\nto: {}\npruned_axis: {}; var: {}".format(
             self.op, op, pruned_axis, var.name()))
+        _logger.debug(
+            f"visit {op.type()} by var [{var.name()}] on axis [{pruned_axis}];\t visited={self.visited}\n"
+        )
         walker = cls(op, pruned_params=self.pruned_params, visited=self.visited)
         walker.prune(var, pruned_axis, pruned_idx)
 
@@ -170,11 +182,6 @@ class conv2d(PruneWorker):
                 self.pruned_params.append(
                     (self.op.inputs("Bias")[0], channel_axis, pruned_idx))
 
-            output_var = self.op.outputs("Output")[0]
-            next_ops = output_var.outputs()
-            for op in next_ops:
-                self._prune_op(op, output_var, channel_axis, pruned_idx)
-
 
 @PRUNE_WORKER.register
 class conv2d_transpose(PruneWorker):
@@ -250,6 +257,12 @@ class batch_norm(PruneWorker):
             self._prune_op(op, out_var, pruned_axis, pruned_idx)
 
 
+@PRUNE_WORKER.register
+class sync_batch_norm(batch_norm):
+    def __init__(self, op, pruned_params, visited):
+        super(sync_batch_norm, self).__init__(op, pruned_params, visited)
+
+
 class elementwise_op(PruneWorker):
     def __init__(self, op, pruned_params, visited):
         super(elementwise_op, self).__init__(op, pruned_params, visited)
@@ -269,9 +282,12 @@ class elementwise_op(PruneWorker):
                 in_var = self.op.inputs(name)[0]
                 if len(in_var.shape()) == 1 and in_var.shape()[0] == 1:
                     continue
-                pre_ops = in_var.inputs()
-                for op in pre_ops:
-                    self._prune_op(op, in_var, actual_axis, pruned_idx)
+
+                # for bias
+                if name == "Y" and actual_axis >= 0 and not (
+                        len(in_var.shape()) == 1 and in_var.shape()[0] == 1):
+                    self.pruned_params.append((in_var, actual_axis, pruned_idx))
+                self._visit_and_search(in_var, actual_axis, pruned_idx)
 
         else:
             if var in self.op.inputs("X"):
@@ -287,24 +303,17 @@ class elementwise_op(PruneWorker):
                                                in_var.shape()[0] == 1):
                     self.pruned_params.append(
                         (in_var, y_pruned_axis, pruned_idx))
-                    pre_ops = in_var.inputs()
-                    for op in pre_ops:
-                        self._prune_op(op, in_var, y_pruned_axis, pruned_idx)
+                    self._visit_and_search(in_var, y_pruned_axis, pruned_idx)
             elif var in self.op.inputs("Y"):
                 in_var = self.op.inputs("X")[0]
                 if len(in_var.shape()) != len(var.shape()):
                     assert (len(var.shape()) < len(in_var.shape()))
                     pruned_axis = pruned_axis + axis
                 if pruned_axis <= len(in_var.shape()):
-                    pre_ops = in_var.inputs()
-                    for op in pre_ops:
-                        self._prune_op(op, in_var, pruned_axis, pruned_idx)
+                    self._visit_and_search(in_var, pruned_axis, pruned_idx)
 
-        out_var = self.op.outputs("Out")[0]
-        self._visit(out_var, pruned_axis)
-        next_ops = out_var.outputs()
-        for op in next_ops:
-            self._prune_op(op, out_var, pruned_axis, pruned_idx)
+            out_var = self.op.outputs("Out")[0]
+            self._visit_and_search(out_var, pruned_axis, pruned_idx)
 
 
 @PRUNE_WORKER.register
@@ -447,53 +456,70 @@ class concat(PruneWorker):
     def __init__(self, op, pruned_params, visited):
         super(concat, self).__init__(op, pruned_params, visited)
 
-    def _prune(self, var, pruned_axis, pruned_idx):
-        idx = []
+    def _prune(self, var, pruned_axis, transforms):
         axis = self.op.attr("axis")
         if var in self.op.outputs("Out"):
+            self._visit(var, pruned_axis)
             start = 0
             if axis == pruned_axis:
                 for _, in_var in enumerate(self.op.inputs("X")):
                     idx = []
-                    for i in pruned_idx:
-                        r_idx = i - start
-                        if r_idx < in_var.shape()[pruned_axis] and r_idx >= 0:
-                            idx.append(r_idx)
+                    transoform = {
+                        'src_start': start,
+                        'src_end': start + in_var.shape()[pruned_axis],
+                        'target_start': 0,
+                        'target_end': in_var.shape()[pruned_axis],
+                        'target_len': in_var.shape()[pruned_axis],
+                        'stride': 1
+                    }
                     start += in_var.shape()[pruned_axis]
 
+                    self._visit(in_var, pruned_axis)
                     pre_ops = in_var.inputs()
                     for op in pre_ops:
-                        self._prune_op(op, in_var, pruned_axis, idx)
-                idx = pruned_idx[:]
+                        self._prune_op(op, in_var, pruned_axis,
+                                       transforms + [transoform])
             else:
                 for _, in_var in enumerate(self.op.inputs("X")):
+                    self._visit(in_var, pruned_axis)
                     pre_ops = in_var.inputs()
                     for op in pre_ops:
-                        self._prune_op(op, in_var, pruned_axis, pruned_idx)
+                        self._prune_op(op, in_var, pruned_axis, transforms)
         elif var in self.op.inputs("X"):
+            self._visit(var, pruned_axis)
             if axis == pruned_axis:
                 idx = []
-                start = 0
+                target_start = 0
                 for v in self.op.inputs("X"):
-                    if v.name() == var.name():
-                        idx = [i + start for i in pruned_idx]
+                    if v.name() != var.name():
+                        target_start += v.shape()[pruned_axis]
                     else:
-                        start += v.shape()[pruned_axis]
-
+                        break
+                target_end = target_start + v.shape()[pruned_axis]
                 out_var = self.op.outputs("Out")[0]
-                self._visit(out_var, pruned_axis)
                 next_ops = out_var.outputs()
-                for op in next_ops:
-                    self._prune_op(op, out_var, pruned_axis, idx, visited={})
-            else:
-                for v in self.op.inputs("X"):
-                    for op in v.inputs():
-                        self._prune_op(op, v, pruned_axis, pruned_idx)
-                out_var = self.op.outputs("Out")[0]
+
+                transform = {
+                    'src_start': 0,
+                    'src_end': var.shape()[pruned_axis],
+                    'target_start': target_start,
+                    'target_end': target_end,
+                    'target_len': out_var.shape()[pruned_axis],
+                    'stride': 1
+                }
+
                 self._visit(out_var, pruned_axis)
-                next_ops = out_var.outputs()
                 for op in next_ops:
-                    self._prune_op(op, out_var, pruned_axis, pruned_idx)
+                    # The output of concat can be visited repeatedly
+                    c_visited = {}
+                    self._prune_op(
+                        op,
+                        out_var,
+                        pruned_axis,
+                        transforms + [transform],
+                        visited=c_visited)
+                    # Add nodes searched from concat into global visited array.
+                    self.visited.update(c_visited)
 
 
 @PRUNE_WORKER.register
@@ -501,8 +527,14 @@ class depthwise_conv2d(PruneWorker):
     def __init__(self, op, pruned_params, visited={}):
         super(depthwise_conv2d, self).__init__(op, pruned_params, visited)
 
-    def _prune(self, var, pruned_axis, pruned_idx):
+    def _prune(self, var, pruned_axis, transforms):
+        assert var not in self.op.inputs(
+            "Filter"), "Unsupport for pruning depthwise conv2d directly."
+        assert var not in self.op.outputs(
+            "Output"
+        ), "Unsupport for pruning output of depthwise conv2d directly."
         data_format = self.op.attr("data_format")
+        groups = self.op.attr("groups")
         channel_axis = 1
         if data_format == "NHWC":
             channel_axis = 3
@@ -510,60 +542,28 @@ class depthwise_conv2d(PruneWorker):
             assert pruned_axis == channel_axis, "The Input of conv2d can only be pruned at channel axis, but got {}".format(
                 pruned_axis)
 
+            groups = var.shape()[channel_axis]
             filter_var = self.op.inputs("Filter")[0]
-            self.pruned_params.append((filter_var, 0, pruned_idx))
+            transform = {
+                'src_start': 0,
+                'src_end': var.shape()[pruned_axis],
+                'target_start': 0,
+                'target_end': filter_var.shape()[0],
+                'target_len': filter_var.shape()[0],
+                'stride': 1
+            }
+
+            self.pruned_params.append((filter_var, 0, transforms + [transform]))
             self._visit(filter_var, 0)
 
             for op in filter_var.outputs():
-                self._prune_op(op, filter_var, 0, pruned_idx)
+                self._prune_op(op, filter_var, 0, transforms + [transform])
 
             output_var = self.op.outputs("Output")[0]
             next_ops = output_var.outputs()
             for op in next_ops:
-                self._prune_op(op, output_var, channel_axis, pruned_idx)
-
-        elif var in self.op.inputs("Filter"):
-            assert pruned_axis in [0]
-            if pruned_axis == 0:
-                if len(self.op.inputs("Bias")) > 0:
-                    self.pruned_params.append(
-                        (self.op.inputs("Bias"), channel_axis, pruned_idx))
-
-                self.pruned_params.append((var, 0, pruned_idx))
-
-                for op in var.outputs():
-                    self._prune_op(op, var, 0, pruned_idx)
-
-                output_var = self.op.outputs("Output")[0]
-                self._visit(output_var, channel_axis)
-                next_ops = output_var.outputs()
-                for op in next_ops:
-                    self._prune_op(op, output_var, channel_axis, pruned_idx)
-            for op in var.outputs():
-                self._prune_op(op, var, pruned_axis, pruned_idx)
-        elif var in self.op.outputs("Output"):
-            assert pruned_axis == channel_axis
-            filter_var = self.op.inputs("Filter")[0]
-            self.pruned_params.append((filter_var, 0, pruned_idx))
-            self._visit(filter_var, 0)
-
-            for op in filter_var.outputs():
-                self._prune_op(op, filter_var, 0, pruned_idx)
-
-            if len(self.op.inputs("Bias")) > 0:
-                self.pruned_params.append(
-                    (self.op.inputs("Bias")[0], channel_axis, pruned_idx))
-
-            in_var = self.op.inputs("Input")[0]
-            self._visit(in_var, channel_axis)
-            pre_ops = in_var.inputs()
-            for op in pre_ops:
-                self._prune_op(op, in_var, channel_axis, pruned_idx)
-
-            output_var = self.op.outputs("Output")[0]
-            next_ops = output_var.outputs()
-            for op in next_ops:
-                self._prune_op(op, output_var, channel_axis, pruned_idx)
+                self._prune_op(op, output_var, channel_axis,
+                               transforms + [transform])
 
 
 @PRUNE_WORKER.register
@@ -679,7 +679,7 @@ class flatten_contiguous_range(PruneWorker):
         super(flatten_contiguous_range, self).__init__(op, pruned_params,
                                                        visited)
 
-    def _prune(self, var, pruned_axis, pruned_idx):
+    def _prune(self, var, pruned_axis, transforms):
         start_axis = self.op.attr("start_axis")
         stop_axis = self.op.attr("stop_axis")
         if var in self.op.inputs("X"):
@@ -687,7 +687,6 @@ class flatten_contiguous_range(PruneWorker):
             in_var = self.op.inputs("X")[0]
             stride = 1
             out_pruned_axis = pruned_axis
-            out_pruned_idx = pruned_idx
             if pruned_axis >= start_axis and pruned_axis <= stop_axis:
                 out_pruned_axis = start_axis
                 for i in range(pruned_axis + 1, stop_axis + 1):
@@ -697,7 +696,8 @@ class flatten_contiguous_range(PruneWorker):
 
             self._visit(in_var, pruned_axis)
             self._visit(out_var, out_pruned_axis)
-
+            transform = {'stride': stride}
             next_ops = out_var.outputs()
             for op in next_ops:
-                self._prune_op(op, out_var, out_pruned_axis, [stride])
+                self._prune_op(op, out_var, out_pruned_axis,
+                               transforms + [transform])

paddleslim/prune/pruner.py

@@ -85,8 +85,8 @@ class Pruner():
         param_backup = {} if param_backup else None
         param_shape_backup = {} if param_shape_backup else None
 
-        visited = {}
         pruned_params = []
+        visited = {}
         for param, ratio in zip(params, ratios):
             _logger.info("pruning: {}".format(param))
             if graph.var(param) is None:
@@ -98,28 +98,19 @@ class Pruner():
                                   visited)[0]  # [(name, axis, pruned_idx)]
             if group is None or len(group) == 0:
                 continue
-            if only_graph and self.idx_selector.__name__ == "default_idx_selector":
-
-                param_v = graph.var(param)
-                pruned_num = int(round(param_v.shape()[0] * ratio))
-                pruned_idx = [0] * pruned_num
-                for name, axis, _ in group:
-                    pruned_params.append((name, axis, pruned_idx))
-
-            else:
-                assert ((not self.pruned_weights),
-                        "The weights have been pruned once.")
-                group_values = []
-                for name, axis, pruned_idx in group:
-                    var = scope.find_var(name)
-                    if var is not None:
-                        values = np.array(var.get_tensor())
-                        group_values.append((name, values, axis, pruned_idx))
-
-                scores = self.criterion(
-                    group_values, graph)  # [(name, axis, score, pruned_idx)]
-
-                pruned_params.extend(self.idx_selector(scores, ratio))
+            assert ((not self.pruned_weights),
+                    "The weights have been pruned once.")
+            group_values = []
+            for name, axis, pruned_idx in group:
+                var = scope.find_var(name)
+                if var is not None:
+                    values = np.array(var.get_tensor())
+                    group_values.append((name, values, axis, pruned_idx))
+
+            scores = self.criterion(group_values,
+                                    graph)  # [(name, axis, score, pruned_idx)]
+            g = self._transform(self.idx_selector(scores, ratio))
+            pruned_params.extend(g)
 
         merge_pruned_params = {}
         for param, pruned_axis, pruned_idx in pruned_params:
@@ -128,7 +119,6 @@ class Pruner():
             if pruned_axis not in merge_pruned_params[param]:
                 merge_pruned_params[param][pruned_axis] = []
             merge_pruned_params[param][pruned_axis].append(pruned_idx)
-
         for param_name in merge_pruned_params:
             for pruned_axis in merge_pruned_params[param_name]:
                 pruned_idx = np.concatenate(merge_pruned_params[param_name][
@@ -138,12 +128,26 @@ class Pruner():
                     _logger.debug("{}\t{}\t{}\t{}".format(
                         param.name(), pruned_axis,
                         param.shape()[pruned_axis], len(pruned_idx)))
+                    origin_shape = copy.deepcopy(param.shape())
                     if param_shape_backup is not None:
-                        origin_shape = copy.deepcopy(param.shape())
                         param_shape_backup[param.name()] = origin_shape
                     new_shape = list(param.shape())
                     new_shape[pruned_axis] -= len(pruned_idx)
                     param.set_shape(new_shape)
+                    # update groups of depthwise conv2d
+                    for op in param.outputs():
+                        if op.type() in ["conv2d", "depthwise_conv2d"
+                                         ] and op.attr("groups") > 1:
+                            assert origin_shape[
+                                1] == 1, "Only support for depthwise when groups > 1."
+                            new_groups = int(
+                                op.attr("groups") * new_shape[pruned_axis] /
+                                origin_shape[pruned_axis])
+                            _logger.debug(
+                                f"change groups of conv({param.name()}) from {op.attr('groups')} to {new_groups}; origin_shape: {origin_shape}; new_shape: {new_shape}"
+                            )
+                            op.set_attr("groups", new_groups)
+
                 if not only_graph:
                     param_t = scope.find_var(param.name()).get_tensor()
                     if param_backup is not None and (
@@ -156,16 +160,35 @@ class Pruner():
                             pruned_idx,
                             pruned_axis=pruned_axis,
                             lazy=lazy)
+                        param_t.set(pruned_param, place)
                     except IndexError as e:
                         _logger.error("Pruning {}, but get [{}]".format(
                             param.name(), e))
 
-                    param_t.set(pruned_param, place)
-        graph.update_groups_of_conv()
         graph.infer_shape()
         self.pruned_weights = (not only_graph)
         return graph.program, param_backup, param_shape_backup
 
+    def _transform(self, group):
+        ret = []
+        for name, axis, pruned_idx, transforms in group:
+            src = pruned_idx
+            for trans in transforms:
+                src_start = trans['src_start']
+                src_end = trans['src_end']
+                target_start = trans['target_start']
+                target_end = trans['target_end']
+                target = []
+                for idx in src:
+                    if idx >= src_start and idx < src_end:
+                        idx -= src_start
+                        idx += target_start
+                        if idx < target_end:
+                            target.append(idx)
+                src = target
+            ret.append((name, axis, src))
+        return ret
+
     def _prune_tensor(self, tensor, pruned_idx, pruned_axis, lazy=False):
         """
         Pruning a array by indexes on given axis.

tests/dygraph/test_flops.py

 import sys
 sys.path.append("../../")
 import unittest
+import numpy as np
+import paddle
 from paddleslim.analysis import dygraph_flops as flops
 from paddle.vision.models import mobilenet_v1, resnet50
+from paddle.nn import Conv2D, Layer
 
 
 class TestFlops(unittest.TestCase):
@@ -17,9 +20,64 @@ class TestFlops(unittest.TestCase):
         self.assertTrue(FLOPs == self._gt)
 
 
+class Net1(Layer):
+    def __init__(self):
+        super(Net1, self).__init__()
+        self.conv1 = Conv2D(3, 2, 3)
+        self.conv2 = Conv2D(3, 2, 3)
+
+    def forward(self, inputs):
+        assert isinstance(inputs, dict)
+        x = inputs["x"]
+        y = inputs["y"]
+        return {"x": self.conv1(x), "y": self.conv2(y), "dummy": "dummy"}
+
+
+class Net2(Net1):
+    def __init__(self):
+        super(Net2, self).__init__()
+
+    def forward(self, x, y):
+        return [self.conv1(x), self.conv2(y), "dummy"]
+
+
+class TestFLOPsCase1(unittest.TestCase):
+    def runTest(self):
+        x_shape = (1, 3, 32, 32)
+        y_shape = (1, 3, 16, 16)
+        net = Net1()
+        x = np.random.uniform(-1, 1, x_shape).astype('float32')
+        y = np.random.uniform(-1, 1, y_shape).astype('float32')
+
+        inputs = {
+            "x": paddle.to_tensor(x),
+            "y": paddle.to_tensor(y),
+            "z": "test"
+        }
+        FLOPs = flops(net, [inputs])
+        self.assertTrue(FLOPs == 59184)
+
+
+class TestFLOPsCase2(unittest.TestCase):
+    def runTest(self):
+        x_shape = (1, 3, 32, 32)
+        y_shape = (1, 3, 16, 16)
+        net = Net2()
+        x = np.random.uniform(-1, 1, x_shape).astype('float32')
+        y = np.random.uniform(-1, 1, y_shape).astype('float32')
+
+        inputs = [paddle.to_tensor(x), paddle.to_tensor(y)]
+        FLOPs1 = flops(net, inputs)
+        shapes = [x_shape, y_shape]
+        FLOPs2 = flops(net, shapes, dtypes=["float32", "float32"])
+        self.assertTrue(FLOPs1 == FLOPs2)
+
+
 def add_cases(suite):
     suite.addTest(TestFlops(net=mobilenet_v1, gt=11792896.0))
     suite.addTest(TestFlops(net=resnet50, gt=83872768.0))
+    suite.addTest(TestFLOPsCase1())
+    suite.addTest(TestFLOPsCase2())
 
 
 def load_tests(loader, standard_tests, pattern):

tests/dygraph/test_prune.py

@@ -47,6 +47,7 @@ class TestPrune(unittest.TestCase):
         shapes = {}
         for param in model.parameters():
             shapes[param.name] = param.shape
+        pruner.restore()
         return shapes
 
     def static_prune(self, net, ratios):

tests/layers.py

@@ -11,6 +11,7 @@
 # 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 paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 
@@ -23,7 +24,8 @@ def conv_bn_layer(input,
                   groups=1,
                   act=None,
                   bias=False,
-                  use_cudnn=True):
+                  use_cudnn=True,
+                  sync_bn=False):
     conv = fluid.layers.conv2d(
         input=input,
         num_filters=num_filters,
@@ -37,11 +39,19 @@ def conv_bn_layer(input,
         name=name + "_out",
         use_cudnn=use_cudnn)
     bn_name = name + "_bn"
-    return fluid.layers.batch_norm(
-        input=conv,
-        act=act,
-        name=bn_name + '_output',
-        param_attr=ParamAttr(name=bn_name + '_scale'),
-        bias_attr=ParamAttr(bn_name + '_offset'),
-        moving_mean_name=bn_name + '_mean',
-        moving_variance_name=bn_name + '_variance', )
+    if sync_bn:
+        bn = paddle.nn.SyncBatchNorm(
+            num_filters,
+            weight_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(name=bn_name + '_offset'),
+            name=bn_name)
+        return bn(conv)
+    else:
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            name=bn_name + '_output',
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance', )

tests/test_dygraph_pruning_plan.py

+import sys
+sys.path.append("../")
+import unittest
+import numpy as np
+from paddleslim.dygraph.pruning_plan import PruningPlan, PruningMask
+
+
+class TestPruningPlan(unittest.TestCase):
+    def testAdd(self):
+        plan = PruningPlan()
+        mask = PruningMask([0], [0, 0, 1], 0.33)
+        plan.add("a", mask)
+        mask = PruningMask([0], [0, 1, 0], 0.33)
+        plan.add("a", mask)
+        a_mask = plan.masks["a"]
+        self.assertTrue(len(a_mask) == 1)
+        self.assertTrue(a_mask[0].mask == [0, 1, 1])
+        self.assertTrue(a_mask[0].dims == [0])
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_group_param.py

@@ -41,14 +41,30 @@ class TestPrune(StaticCase):
             sum2 = conv4 + sum1
             conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
             conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
-        groups = collect_convs(
+        collected_groups = collect_convs(
             ["conv1_weights", "conv2_weights", "conv3_weights"], main_program)
-        while [] in groups:
-            groups.remove([])
-        print(groups)
-        self.assertTrue(len(groups) == 2)
-        self.assertTrue(len(groups[0]) == 20)
-        self.assertTrue(len(groups[1]) == 6)
+        while [] in collected_groups:
+            collected_groups.remove([])
+        print(collected_groups)
+
+        params = set([
+            param.name for param in main_program.all_parameters()
+            if "weights" in param.name
+        ])
+
+        expected_groups = [[('conv1_weights', 0), ('conv2_weights', 1),
+                            ('conv2_weights', 0), ('conv3_weights', 1),
+                            ('conv4_weights', 0), ('conv5_weights', 1)],
+                           [('conv3_weights', 0), ('conv4_weights', 1)]]
+
+        self.assertTrue(len(collected_groups) == len(expected_groups))
+        for _collected, _expected in zip(collected_groups, expected_groups):
+            for _name, _axis, _ in _collected:
+                if _name in params:
+                    self.assertTrue((_name, _axis) in _expected)
+            for _name, _axis in _expected:
+                if _name in params:
+                    self.assertTrue((_name, _axis, []) in _collected)
 
 
 if __name__ == '__main__':

tests/test_prune_op.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 sys
+sys.path.append("../")
+import unittest
+from static_case import StaticCase
+import paddle.fluid as fluid
+from paddleslim.prune import Pruner
+from static_case import StaticCase
+from layers import conv_bn_layer
+
+
+class TestPrune(StaticCase):
+    def test_concat(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        #                                  X              
+        # conv1   conv2-->concat         conv3-->sum-->out
+        #     |            ^ |                    ^
+        #     |____________| |____________________|
+        #
+        with fluid.program_guard(main_program, startup_program):
+            input = fluid.data(name="image", shape=[None, 3, 16, 16])
+            conv1 = conv_bn_layer(input, 8, 3, "conv1")
+            conv2 = conv_bn_layer(input, 8, 3, "conv2", sync_bn=True)
+            tmp = fluid.layers.concat([conv1, conv2], axis=1)
+            conv3 = conv_bn_layer(input, 16, 3, "conv3", bias=None)
+            out = conv3 + tmp
+
+        shapes = {}
+        for param in main_program.global_block().all_parameters():
+            shapes[param.name] = param.shape
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        scope = fluid.Scope()
+        exe.run(startup_program, scope=scope)
+        pruner = Pruner()
+        # test backward search of concat
+        pruned_program, _, _ = pruner.prune(
+            main_program,
+            scope,
+            params=["conv3_weights"],
+            ratios=[0.5],
+            place=place,
+            lazy=False,
+            only_graph=True,
+            param_backup=None,
+            param_shape_backup=None)
+        shapes = {
+            "conv3_weights": (8, 3, 3, 3),
+            "conv2_weights": (4, 3, 3, 3),
+            "conv1_weights": (4, 3, 3, 3)
+        }
+        for param in pruned_program.global_block().all_parameters():
+            if "weights" in param.name and "conv2d" in param.name:
+                self.assertTrue(shapes[param.name] == param.shape)
+
+        # test forward search of concat
+        pruned_program, _, _ = pruner.prune(
+            main_program,
+            scope,
+            params=["conv1_weights", "conv2_weights"],
+            ratios=[0.5, 0.5],
+            place=place,
+            lazy=False,
+            only_graph=False,
+            param_backup=None,
+            param_shape_backup=None)
+
+        shapes = {
+            "conv1_weights": (4, 3, 3, 3),
+            "conv1_bn_scale": (4, ),
+            "conv1_bn_variance": (4, ),
+            "conv1_bn_mean": (4, ),
+            "conv1_bn_offset": (4, ),
+            "conv2_weights": (4, 3, 3, 3),
+            "sync_batch_norm_0.w_0": (4, ),
+            "sync_batch_norm_0.w_1": (4, ),
+            "conv2_bn_scale": (4, ),
+            "conv2_bn_offset": (4, ),
+            "conv3_weights": (8, 3, 3, 3),
+            "conv3_bn_mean": (8, ),
+            "conv3_bn_offset": (8, ),
+            "conv3_bn_scale": (8, ),
+            "conv3_bn_variance": (8, ),
+            "conv3_out.b_0": (8, ),
+        }
+
+        for param in pruned_program.global_block().all_parameters():
+            if "weights" in param.name and "conv2d" in param.name:
+                self.assertTrue(shapes[param.name] == param.shape)
+
+
+if __name__ == '__main__':
+    unittest.main()