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test_prune_op.py
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test_prune_op.py
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# 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
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)
class TestSplit(StaticCase):
def test_split(self):
main_program = fluid.Program()
startup_program = fluid.Program()
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, 4, 3, "conv2")
split_0, split_1 = paddle.split(conv1, 2, axis=1)
add = split_0 + conv2
out = conv_bn_layer(add, 4, 3, "conv3")
out1 = conv_bn_layer(split_1, 4, 4, "conv4")
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=["conv2_weights"],
ratios=[0.5],
place=place,
lazy=False,
only_graph=True,
param_backup=None,
param_shape_backup=None)
shapes = {
"conv1_weights": (6, 3, 3, 3),
"conv2_weights": (2, 3, 3, 3),
"conv3_weights": (4, 2, 3, 3),
"conv4_weights": (4, 4, 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)
class TestMul(StaticCase):
def test_mul(self):
main_program = fluid.Program()
startup_program = fluid.Program()
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")
fc_0 = paddle.fluid.layers.fc(conv1, size=10)
fc_1 = paddle.fluid.layers.fc(fc_0, size=10)
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=["conv1_weights"],
ratios=[0.5],
place=place,
lazy=False,
only_graph=True,
param_backup=None,
param_shape_backup=None)
shapes = {
"conv1_weights": (4, 3, 3, 3),
"fc_0.w_0": (1024, 10),
"fc_1.w_0": (10, 10)
}
for param in pruned_program.global_block().all_parameters():
if param.name in shapes.keys():
self.assertTrue(shapes[param.name] == param.shape)
if __name__ == '__main__':
unittest.main()