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[Test-driven] Recognize Digit: update mnist test cases with the new API syntax. (#10507)
* Update the mnist test cases with the new API syntax. * Turn on the tests for MNIST * delete the test files got merged accidently. * Enable the mnist tests. ready for test driven development. * Comment out the infer first This is to confirm that the Trainer.train is working * Add CMake file to include the tests * Make the train program only return avg_cost for now * Update the tests to use the latest syntax
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-60
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5 files changed

+111
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lines changed

‎python/paddle/fluid/tests/book/CMakeLists.txt

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@@ -5,3 +5,5 @@ string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
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foreach(src ${TEST_OPS})
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py_test(${src} SRCS ${src}.py)
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endforeach()
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add_subdirectory(high-level-api)

‎python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

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@@ -0,0 +1,9 @@
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file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
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string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
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# default test
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foreach(src ${TEST_OPS})
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py_test(${src} SRCS ${src}.py)
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endforeach()
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add_subdirectory(recognize_digits)

‎python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt

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@@ -0,0 +1,7 @@
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file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
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string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
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# default test
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foreach(src ${TEST_OPS})
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py_test(${src} SRCS ${src}.py)
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endforeach()

‎python/paddle/fluid/tests/book/high-level-api/recognize_digits/notest_recognize_digits_conv.py renamed to ‎python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py

Lines changed: 47 additions & 30 deletions
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,6 @@
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import math
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import sys
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import os
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import paddle.v2.dataset as dataset
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BATCH_SIZE = 64
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@@ -54,47 +53,65 @@ def train_program():
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predict = inference_program()
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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acc = fluid.layers.accuracy(input=predict, label=label)
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return avg_cost, acc
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# acc = fluid.layers.accuracy(input=predict, label=label)
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# return avg_cost, acc
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return avg_cost
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def train(use_cuda, save_dirname):
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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optimizer = fluid.optimizer.Adam(learning_rate=0.001)
65-
trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)
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trainer = fluid.Trainer(
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train_func=train_program,
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infer_func=inference_program,
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place=place,
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optimizer=optimizer)
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6771
def event_handler(event):
68-
if isinstance(event, fluid.EndIteration):
69-
avg_cost, acc = event.values
70-
print("avg_cost: %s" % avg_cost)
71-
print("acc : %s" % acc)
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73-
if (event.batch_id + 1) % 10 == 0:
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test_metrics = trainer.test(reader=dataset.mnist.test())
75-
avg_cost_set = test_metrics[0]
76-
acc_set = test_metrics[1]
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78-
# get test acc and loss
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acc = numpy.array(acc_set).mean()
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avg_cost = numpy.array(avg_cost_set).mean()
81-
if float(acc) > 0.2: # Smaller value to increase CI speed
82-
trainer.save_params(save_dirname)
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else:
84-
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
85-
event.batch_id + 1, float(avg_cost), float(acc)))
86-
if math.isnan(float(avg_cost)):
87-
sys.exit("got NaN loss, training failed.")
72+
if isinstance(event, fluid.EndEpochEvent):
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# if (event.epoch + 1) % 10 == 0:
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# trainer.save_params(save_dirname)
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trainer.save_inference_model(save_dirname)
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# TODO: Uncomment this part once we are sure that .train is working
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# test_reader = paddle.batch(
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# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
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# test_metrics = trainer.test(reader=test_reader)
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# avg_cost_set = test_metrics[0]
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# acc_set = test_metrics[1]
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#
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# # get test acc and loss
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# acc = numpy.array(acc_set).mean()
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# avg_cost = numpy.array(avg_cost_set).mean()
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#
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# print("avg_cost: %s" % avg_cost)
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# print("acc : %s" % acc)
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#
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# if float(acc) > 0.2: # Smaller value to increase CI speed
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# trainer.save_params(save_dirname)
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# else:
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# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
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# event.epoch + 1, float(avg_cost), float(acc)))
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# if math.isnan(float(avg_cost)):
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# sys.exit("got NaN loss, training failed.")
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train_reader = paddle.batch(
100+
paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=500),
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batch_size=BATCH_SIZE)
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89104
trainer.train(
90-
reader=dataset.mnist.train(), num_pass=100, event_handler=event_handler)
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num_epochs=1,
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event_handler=event_handler,
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reader=train_reader,
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feed_order=['img', 'label'])
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93111
def infer(use_cuda, save_dirname=None):
94112
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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96-
inferencer = fluid.Inferencer(
97-
inference_program, param_path=save_dirname, place=place)
114+
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
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batch_size = 1
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tensor_img = numpy.random.uniform(-1.0, 1.0,
@@ -114,5 +131,5 @@ def main(use_cuda):
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if __name__ == '__main__':
117-
for use_cuda in (False, True):
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main(use_cuda=use_cuda)
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# for use_cuda in (False, True):
135+
main(use_cuda=False)

‎python/paddle/fluid/tests/book/high-level-api/recognize_digits/notest_recognize_digits_mlp.py renamed to ‎python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py

Lines changed: 46 additions & 30 deletions
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,6 @@
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import math
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import sys
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import os
24-
import paddle.v2.dataset as dataset
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2625
BATCH_SIZE = 64
2726

@@ -41,47 +40,64 @@ def train_program():
4140
predict = inference_program()
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cost = fluid.layers.cross_entropy(input=predict, label=label)
4342
avg_cost = fluid.layers.mean(cost)
44-
acc = fluid.layers.accuracy(input=predict, label=label)
45-
return avg_cost, acc
43+
# acc = fluid.layers.accuracy(input=predict, label=label)
44+
# return avg_cost, acc
45+
return avg_cost
4646

4747

4848
def train(use_cuda, save_dirname):
4949
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
50-
5150
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
52-
trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)
51+
52+
trainer = fluid.Trainer(
53+
train_func=train_program,
54+
infer_func=inference_program,
55+
place=place,
56+
optimizer=optimizer)
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5458
def event_handler(event):
55-
if isinstance(event, fluid.EndIteration):
56-
avg_cost, acc = event.values
57-
print("avg_cost: %s" % avg_cost)
58-
print("acc : %s" % acc)
59-
60-
if (event.batch_id + 1) % 10 == 0:
61-
test_metrics = trainer.test(reader=dataset.mnist.test())
62-
avg_cost_set = test_metrics[0]
63-
acc_set = test_metrics[1]
64-
65-
# get test acc and loss
66-
acc = numpy.array(acc_set).mean()
67-
avg_cost = numpy.array(avg_cost_set).mean()
68-
if float(acc) > 0.2: # Smaller value to increase CI speed
69-
trainer.save_params(save_dirname)
70-
else:
71-
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
72-
event.batch_id + 1, float(avg_cost), float(acc)))
73-
if math.isnan(float(avg_cost)):
74-
sys.exit("got NaN loss, training failed.")
59+
if isinstance(event, fluid.EndEpochEvent):
60+
# if (event.epoch + 1) % 10 == 0:
61+
trainer.save_inference_model(save_dirname)
62+
63+
# TODO: Uncomment this part once we are sure that .train is working
64+
# test_reader = paddle.batch(
65+
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
66+
# test_metrics = trainer.test(reader=test_reader)
67+
# avg_cost_set = test_metrics[0]
68+
# acc_set = test_metrics[1]
69+
#
70+
# # get test acc and loss
71+
# acc = numpy.array(acc_set).mean()
72+
# avg_cost = numpy.array(avg_cost_set).mean()
73+
#
74+
# print("avg_cost: %s" % avg_cost)
75+
# print("acc : %s" % acc)
76+
#
77+
# if float(acc) > 0.2: # Smaller value to increase CI speed
78+
# trainer.save_params(save_dirname)
79+
# else:
80+
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
81+
# event.epoch + 1, float(avg_cost), float(acc)))
82+
# if math.isnan(float(avg_cost)):
83+
# sys.exit("got NaN loss, training failed.")
84+
85+
train_reader = paddle.batch(
86+
paddle.reader.shuffle(
87+
paddle.dataset.mnist.train(), buf_size=500),
88+
batch_size=BATCH_SIZE)
7589

7690
trainer.train(
77-
reader=dataset.mnist.train(), num_pass=100, event_handler=event_handler)
91+
num_epochs=1,
92+
event_handler=event_handler,
93+
reader=train_reader,
94+
feed_order=['img', 'label'])
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7996

8097
def infer(use_cuda, save_dirname=None):
8198
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
8299

83-
inferencer = fluid.Inferencer(
84-
inference_program, param_path=save_dirname, place=place)
100+
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
85101

86102
batch_size = 1
87103
tensor_img = numpy.random.uniform(-1.0, 1.0,
@@ -101,5 +117,5 @@ def main(use_cuda):
101117

102118

103119
if __name__ == '__main__':
104-
for use_cuda in (False, True):
105-
main(use_cuda=use_cuda)
120+
# for use_cuda in (False, True):
121+
main(use_cuda=False)

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