Move pruning and quant API from paddleslim.dygraph to paddleslim (#633) (#642)

paddleslim/__init__.py

@@ -24,3 +24,8 @@ from paddleslim import dygraph
 __all__ = [
     'models', 'prune', 'nas', 'analysis', 'dist', 'quant', 'pantheon', 'dygraph'
 ]
+
+from paddleslim.dygraph import *
+__all__ += dygraph.__all__
+from paddleslim.analysis import *
+__all__ += analysis.__all__

paddleslim/analysis/flops.py

@@ -18,7 +18,34 @@ from ..core import GraphWrapper, dygraph2program
 __all__ = ["flops", "dygraph_flops"]
 
 
-def flops(program, only_conv=True, detail=False):
+def flops(model, inputs=None, dtypes=None, only_conv=True, detail=False):
+    """
+    Compute the FLOPs of nn.Layer of paddle.Program.
+    Args:
+      model(paddle.nn.Layer|paddle.static.Program): The target model.
+      inputs(list): It is only used when model is instance of 'paddle.nn.Layer'. 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.
+    """
+    if isinstance(model, paddle.static.Program):
+        return _static_flops(model, only_conv=only_conv, detail=detail)
+    elif isinstance(model, paddle.nn.Layer):
+        return dygraph_flops(
+            model, inputs, dtypes=dtypes, only_conv=only_conv, detail=detail)
+
+
+def _static_flops(program, only_conv=True, detail=False):
     """Get FLOPs of target graph.
 
     Args:

paddleslim/dygraph/__init__.py

-from . import var_group
-from .var_group import *
-from . import l1norm_pruner
-from .l1norm_pruner import *
-from . import pruner
-from .pruner import *
-from . import filter_pruner
-from .filter_pruner import *
-from . import l2norm_pruner
-from .l2norm_pruner import *
-from . import fpgm_pruner
-from .fpgm_pruner import *
-
 __all__ = []
-
-__all__ += var_group.__all__
-__all__ += l1norm_pruner.__all__
-__all__ += l2norm_pruner.__all__
-__all__ += fpgm_pruner.__all__
-__all__ += pruner.__all__
-__all__ += filter_pruner.__all__
-
 from .quant import *
 __all__ += quant.__all__
+from .prune import *
+__all__ += prune.__all__

paddleslim/dygraph/prune/__init__.py

+from . import var_group
+from .var_group import *
+from . import l1norm_pruner
+from .l1norm_pruner import *
+from . import pruner
+from .pruner import *
+from . import filter_pruner
+from .filter_pruner import *
+from . import l2norm_pruner
+from .l2norm_pruner import *
+from . import fpgm_pruner
+from .fpgm_pruner import *
+
+__all__ = []
+
+__all__ += var_group.__all__
+__all__ += l1norm_pruner.__all__
+__all__ += l2norm_pruner.__all__
+__all__ += fpgm_pruner.__all__
+__all__ += pruner.__all__
+__all__ += filter_pruner.__all__

paddleslim/dygraph/filter_pruner.py → paddleslim/dygraph/prune/filter_pruner.py

@@ -4,11 +4,11 @@ import numpy as np
 import pickle
 import copy
 import paddle
-from ..common import get_logger
+from paddleslim.common import get_logger
 from .var_group import *
 from .pruning_plan import *
 from .pruner import Pruner
-from ..analysis import dygraph_flops as flops
+from paddleslim.analysis import dygraph_flops as flops
 from .var_group import VarGroup
 
 __all__ = ['Status', 'FilterPruner']

paddleslim/dygraph/fpgm_pruner.py → paddleslim/dygraph/prune/fpgm_pruner.py

 import logging
 import numpy as np
 import paddle
-from ..common import get_logger
+from paddleslim.common import get_logger
 from .var_group import *
 from .pruning_plan import *
 from .filter_pruner import FilterPruner

paddleslim/dygraph/l1norm_pruner.py → paddleslim/dygraph/prune/l1norm_pruner.py

 import logging
 import numpy as np
 import paddle
-from ..common import get_logger
+from paddleslim.common import get_logger
 from .var_group import *
 from .pruning_plan import *
 from .filter_pruner import FilterPruner

paddleslim/dygraph/l2norm_pruner.py → paddleslim/dygraph/prune/l2norm_pruner.py

 import logging
 import numpy as np
 import paddle
-from ..common import get_logger
+from paddleslim.common import get_logger
 from .var_group import *
 from .pruning_plan import *
 from .filter_pruner import FilterPruner

paddleslim/dygraph/pruner.py → paddleslim/dygraph/prune/pruner.py

@@ -3,7 +3,7 @@ import pickle
 import numpy as np
 import logging
 from .pruning_plan import PruningPlan
-from ..common import get_logger
+from paddleslim.common import get_logger
 
 __all__ = ["Pruner"]
 

paddleslim/dygraph/pruning_plan.py → paddleslim/dygraph/prune/pruning_plan.py

@@ -2,7 +2,7 @@ import paddle
 import collections
 import numpy as np
 import logging
-from ..common import get_logger
+from paddleslim.common import get_logger
 from paddle.fluid import core
 _logger = get_logger(__name__, level=logging.INFO)
 

paddleslim/dygraph/var_group.py → paddleslim/dygraph/prune/var_group.py

@@ -2,9 +2,9 @@ import numpy as np
 import logging
 import paddle
 from paddle.fluid.dygraph import TracedLayer
-from ..core import GraphWrapper, dygraph2program
-from ..prune import collect_convs
-from ..common import get_logger
+from paddleslim.core import GraphWrapper, dygraph2program
+from paddleslim.prune import collect_convs
+from paddleslim.common import get_logger
 
 __all__ = ["VarGroup"]
 

tests/dygraph/test_flops.py

@@ -3,7 +3,7 @@ sys.path.append("../../")
 import unittest
 import numpy as np
 import paddle
-from paddleslim.analysis import dygraph_flops as flops
+from paddleslim import flops
 from paddle.vision.models import mobilenet_v1, resnet50
 from paddle.nn import Conv2D, Layer
 
@@ -16,7 +16,7 @@ class TestFlops(unittest.TestCase):
 
     def runTest(self):
         net = self._net(pretrained=False)
-        FLOPs = flops(net, (1, 3, 32, 32))
+        FLOPs = flops(net, (1, 3, 32, 32), only_conv=False)
         self.assertTrue(FLOPs == self._gt)
 
 
@@ -54,7 +54,7 @@ class TestFLOPsCase1(unittest.TestCase):
             "y": paddle.to_tensor(y),
             "z": "test"
         }
-        FLOPs = flops(net, [inputs])
+        FLOPs = flops(net, [inputs], only_conv=False)
         self.assertTrue(FLOPs == 59184)
 
 
@@ -67,9 +67,10 @@ class TestFLOPsCase2(unittest.TestCase):
         y = np.random.uniform(-1, 1, y_shape).astype('float32')
 
         inputs = [paddle.to_tensor(x), paddle.to_tensor(y)]
-        FLOPs1 = flops(net, inputs)
+        FLOPs1 = flops(net, inputs, only_conv=False)
         shapes = [x_shape, y_shape]
-        FLOPs2 = flops(net, shapes, dtypes=["float32", "float32"])
+        FLOPs2 = flops(
+            net, shapes, dtypes=["float32", "float32"], only_conv=False)
         self.assertTrue(FLOPs1 == FLOPs2)
 
 

tests/dygraph/test_prune.py

@@ -16,7 +16,7 @@ sys.path.append("../../")
 import unittest
 import paddle
 import paddle.fluid as fluid
-from paddleslim.dygraph import L1NormFilterPruner
+from paddleslim import L1NormFilterPruner
 from paddle.vision.models import mobilenet_v1, resnet50
 from paddleslim.prune import Pruner
 

tests/test_dygraph_pruning_plan.py

@@ -2,7 +2,7 @@ import sys
 sys.path.append("../")
 import unittest
 import numpy as np
-from paddleslim.dygraph.pruning_plan import PruningPlan, PruningMask
+from paddleslim.dygraph.prune.pruning_plan import PruningPlan, PruningMask
 
 
 class TestPruningPlan(unittest.TestCase):

tests/test_flops.py

@@ -15,7 +15,7 @@ import sys
 sys.path.append("../")
 import unittest
 import paddle.fluid as fluid
-from paddleslim.analysis import flops
+from paddleslim import flops
 from layers import conv_bn_layer
 from static_case import StaticCase