提交 28ee9ac1 编写于 作者: L LielinJiang

fix test model

上级 46158530
......@@ -20,6 +20,8 @@ import unittest
import os
import cv2
import numpy as np
import tempfile
import shutil
import paddle
from paddle import fluid
......@@ -36,14 +38,6 @@ from hapi.download import get_weights_path_from_url
class LeNetDygraph(fluid.dygraph.Layer):
"""LeNet model from
`"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.`_
Args:
num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer
will not be defined. Default: 10.
classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
"""
def __init__(self, num_classes=10, classifier_activation='softmax'):
super(LeNetDygraph, self).__init__()
......@@ -137,6 +131,15 @@ class TestEvaluatePredict(unittest.TestCase):
low_level_lenet_dygraph_train(self.lenet_dygraph, train_dataloader)
self.acc1 = low_level_dynamic_evaluate(self.lenet_dygraph,
val_dataloader)
self.save_dir = tempfile.mkdtemp()
self.weight_path = os.path.join(self.save_dir, 'lenet')
fluid.dygraph.save_dygraph(self.lenet_dygraph.state_dict(), self.weight_path)
fluid.disable_dygraph()
def tearDown(self):
shutil.rmtree(self.save_dir)
def evaluate(self, dynamic):
fluid.enable_dygraph(self.device) if dynamic else None
......@@ -144,67 +147,44 @@ class TestEvaluatePredict(unittest.TestCase):
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
if fluid.in_dygraph_mode():
feed_list = None
else:
feed_list = [x.forward() for x in inputs + labels]
self.train_dataloader = fluid.io.DataLoader(
self.train_dataset,
places=self.device,
batch_size=64,
feed_list=feed_list)
self.val_dataloader = fluid.io.DataLoader(
val_dataloader = fluid.io.DataLoader(
self.val_dataset,
places=self.device,
batch_size=64,
feed_list=feed_list)
self.test_dataloader = fluid.io.DataLoader(
self.test_dataset,
places=self.device,
batch_size=64,
feed_list=feed_list)
return_list=True)
model = LeNet()
model.load_dict(self.lenet_dygraph.state_dict())
model.load(self.weight_path)
model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
result = model.evaluate(self.val_dataloader)
result = model.evaluate(val_dataloader)
np.testing.assert_allclose(result['acc'], self.acc1)
if fluid.in_dygraph_mode():
fluid.disable_dygraph()
def predict(self, dynamic):
fluid.enable_dygraph(self.device) if dynamic else None
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
if fluid.in_dygraph_mode():
feed_list = None
else:
feed_list = [x.forward() for x in inputs + labels]
self.train_dataloader = fluid.io.DataLoader(
self.train_dataset,
places=self.device,
batch_size=64,
feed_list=feed_list)
self.val_dataloader = fluid.io.DataLoader(
self.val_dataset,
places=self.device,
batch_size=64,
feed_list=feed_list)
self.test_dataloader = fluid.io.DataLoader(
test_dataloader = fluid.io.DataLoader(
self.test_dataset,
places=self.device,
batch_size=64,
feed_list=feed_list)
return_list=True)
model = LeNet()
model.load_dict(self.lenet_dygraph.state_dict())
model.load(self.weight_path)
model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
output = model.predict(self.test_dataloader, stack_outputs=True)
output = model.predict(test_dataloader, stack_outputs=True)
np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
......@@ -212,6 +192,9 @@ class TestEvaluatePredict(unittest.TestCase):
np.testing.assert_allclose(acc, self.acc1)
if fluid.in_dygraph_mode():
fluid.disable_dygraph()
def test_evaluate_dygraph(self):
self.evaluate(True)
......
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