wehub-resource-sync
204 lines
7.7 KiB
Python
204 lines
7.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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enable_to_static_guard,
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test_ast_only,
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)
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from test_fetch_feed import Linear
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import paddle
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import paddle.nn.functional as F
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from paddle import base, nn
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from paddle.base import core
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from paddle.nn import BatchNorm
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from paddle.optimizer import Adam
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np.random.seed(2020)
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place = base.CUDAPlace(0) if base.is_compiled_with_cuda() else base.CPUPlace()
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class PrimeNet(paddle.nn.Layer):
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def __init__(self, data_layout='NCHW'):
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super().__init__()
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self.conv = nn.Conv2D(2, 4, (3, 3), bias_attr=False)
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self.bn = BatchNorm(4, act="relu", data_layout=data_layout)
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def forward(self, x):
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y = self.conv(x)
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out = self.bn(y)
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res = F.max_pool2d(out, kernel_size=2, stride=2, padding=0)
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return res
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def apply_to_static(net):
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return paddle.jit.to_static(net, backend=None)
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def forward_post_hook_for_prim_net(layer, input, output):
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return output * 2
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class TestDyToStaticSaveLoad(Dy2StTestBase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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self.model_path = os.path.join(
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self.temp_dir.name, "test_dy2stat_save_load"
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)
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_save_load_same_result(self):
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x_data = np.random.randn(30, 10, 32).astype('float32')
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batch_num = 3
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x = paddle.to_tensor(x_data)
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net = Linear(32, 64)
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adam = Adam(learning_rate=0.1, parameters=net.parameters())
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for i in range(batch_num):
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static_out, static_loss = net(x)
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# Update parameters
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static_loss.backward()
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adam.minimize(static_loss)
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net.clear_gradients()
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# Save parameters
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paddle.save(net.state_dict(), self.model_path + '.pdparams')
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# minimize() will update parameter, call net() to get output and avg_loss.
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# Switch into eval mode.
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net.eval()
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static_out, static_loss = net(x)
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# load parameters into dygraph
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dygraph_net = Linear(32, 64)
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# Load parameters
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model_dict = paddle.load(self.model_path + '.pdparams')
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dygraph_net.set_dict(model_dict)
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# Switch into eval mode.
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dygraph_net.eval()
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x = paddle.to_tensor(x_data)
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# predict output
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with enable_to_static_guard(False):
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dygraph_out, dygraph_loss = dygraph_net(x)
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np.testing.assert_allclose(
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dygraph_out.numpy(), static_out.numpy(), rtol=1e-05
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)
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np.testing.assert_allclose(
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dygraph_loss.numpy(), static_loss.numpy(), rtol=1e-05
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)
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def _compute_op_num(self, composite_program):
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comp_op_type_list = [
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op.name() for op in composite_program.program.global_block().ops
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]
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return comp_op_type_list
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@test_ast_only
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def test_save_load_prim(self):
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with base.dygraph.guard(place):
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self.x = paddle.randn([4, 2, 6, 6], dtype="float32")
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self.x.stop_gradient = False
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net = PrimeNet(data_layout="NCHW")
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core._set_prim_all_enabled(True)
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net.eval()
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static_net = apply_to_static(net)
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res = static_net(self.x)
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composite_program = static_net.forward.get_concrete_program(self.x)[
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1
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].train_program
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comp_op_type_list = self._compute_op_num(composite_program)
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self.assertNotIn("pd_op.batch_norm_", comp_op_type_list)
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self.assertNotIn("pd_op.relu", comp_op_type_list)
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self.assertNotIn("pd_op.pow", comp_op_type_list)
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self.assertNotIn("pd_op.expand_v2", comp_op_type_list)
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self.assertNotIn("pd_op.unsqueeze2", comp_op_type_list)
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self.assertNotIn("pd_op.reduce_mean", comp_op_type_list)
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self.assertNotIn("pd_op.batch_norm_grad", comp_op_type_list)
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self.assertNotIn("pd_op.relu_grad", comp_op_type_list)
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self.assertNotIn("pd_op.pow_grad", comp_op_type_list)
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self.assertNotIn("pd_op.expand_v2_grad", comp_op_type_list)
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self.assertNotIn("pd_op.unsqueeze2_grad", comp_op_type_list)
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self.assertNotIn("pd_op.reduce_mean_grad", comp_op_type_list)
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paddle.jit.save(static_net, self.model_path)
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load_func = paddle.jit.load(self.model_path)
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load_program = load_func.program()
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load_op_type_list = [
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op.name() for op in load_program.global_block().ops
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]
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new_res = load_func(self.x)
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self.assertIn("pd_op.conv2d", load_op_type_list)
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self.assertIn("pd_op.batch_norm_", load_op_type_list)
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self.assertIn("pd_op.relu", load_op_type_list)
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self.assertIn("pd_op.pool2d", load_op_type_list)
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np.testing.assert_allclose(res.numpy(), new_res.numpy(), rtol=1e-05)
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@test_ast_only
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def test_save_load_prim_with_hook(self):
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with base.dygraph.guard(place):
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self.x = paddle.randn([4, 2, 6, 6], dtype="float32")
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self.x.stop_gradient = False
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net = PrimeNet(data_layout="NCHW")
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net.register_forward_post_hook(forward_post_hook_for_prim_net)
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core._set_prim_all_enabled(True)
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net.eval()
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static_net = apply_to_static(net)
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res = static_net(self.x)
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composite_program = static_net.forward.get_concrete_program(self.x)[
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1
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].train_program
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comp_op_type_list = self._compute_op_num(composite_program)
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self.assertNotIn("pd_op.batch_norm_", comp_op_type_list)
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self.assertNotIn("pd_op.relu", comp_op_type_list)
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self.assertNotIn("pd_op.pow", comp_op_type_list)
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self.assertNotIn("pd_op.expand_v2", comp_op_type_list)
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self.assertNotIn("pd_op.unsqueeze2", comp_op_type_list)
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self.assertNotIn("pd_op.reduce_mean", comp_op_type_list)
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self.assertNotIn("pd_op.batch_norm_grad", comp_op_type_list)
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self.assertNotIn("pd_op.relu_grad", comp_op_type_list)
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self.assertNotIn("pd_op.pow_grad", comp_op_type_list)
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self.assertNotIn("pd_op.expand_v2_grad", comp_op_type_list)
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self.assertNotIn("pd_op.unsqueeze2_grad", comp_op_type_list)
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self.assertNotIn("pd_op.reduce_mean_grad", comp_op_type_list)
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self.assertNotIn("pd_op.multiply_grad", comp_op_type_list)
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paddle.jit.save(static_net, self.model_path)
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load_func = paddle.jit.load(self.model_path)
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load_program = load_func.program()
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load_op_type_list = [
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op.name() for op in load_program.global_block().ops
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]
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new_res = load_func(self.x)
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self.assertIn("pd_op.conv2d", load_op_type_list)
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self.assertIn("pd_op.batch_norm_", load_op_type_list)
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self.assertIn("pd_op.relu", load_op_type_list)
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self.assertIn("pd_op.pool2d", load_op_type_list)
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np.testing.assert_allclose(res.numpy(), new_res.numpy(), rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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