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[Test-driven] Recognize Digit: update mnist test cases with the new API syntax. #10507

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Merged
jetfuel merged 8 commits into from
May 11, 2018
Merged

[Test-driven] Recognize Digit: update mnist test cases with the new API syntax. #10507

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53c0b1a
Update the mnist test cases with the new API syntax.
jetfuel May 8, 2018
22dd983
Turn on the tests for MNIST
jetfuel May 9, 2018
c1687d5
delete the test files got merged accidently.
jetfuel May 9, 2018
5539026
Enable the mnist tests. ready for test driven development.
jetfuel May 9, 2018
06db9af
Comment out the infer first
jetfuel May 9, 2018
fbb8063
Add CMake file to include the tests
jetfuel May 10, 2018
3c58b00
Make the train program only return avg_cost for now
jetfuel May 10, 2018
1cde8a1
Update the tests to use the latest syntax
jetfuel May 11, 2018
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2 changes: 2 additions & 0 deletions python/paddle/fluid/tests/book/CMakeLists.txt
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Expand Up @@ -5,3 +5,5 @@ string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()

add_subdirectory(high-level-api)
9 changes: 9 additions & 0 deletions python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
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@@ -0,0 +1,9 @@
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")

# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()

add_subdirectory(recognize_digits)
7 changes: 7 additions & 0 deletions python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt
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@@ -0,0 +1,7 @@
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")

# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
77 changes: 47 additions & 30 deletions ...ze_digits/notest_recognize_digits_conv.py → ...nize_digits/test_recognize_digits_conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@
import math
import sys
import os
import paddle.v2.dataset as dataset

BATCH_SIZE = 64

Expand Down Expand Up @@ -54,47 +53,65 @@ def train_program():
predict = inference_program()
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
acc = fluid.layers.accuracy(input=predict, label=label)
return avg_cost, acc
# acc = fluid.layers.accuracy(input=predict, label=label)
# return avg_cost, acc
return avg_cost


def train(use_cuda, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

optimizer = fluid.optimizer.Adam(learning_rate=0.001)
trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)

trainer = fluid.Trainer(
train_func=train_program,
infer_func=inference_program,
place=place,
optimizer=optimizer)

def event_handler(event):
if isinstance(event, fluid.EndIteration):
avg_cost, acc = event.values
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)

if (event.batch_id + 1) % 10 == 0:
test_metrics = trainer.test(reader=dataset.mnist.test())
avg_cost_set = test_metrics[0]
acc_set = test_metrics[1]

# get test acc and loss
acc = numpy.array(acc_set).mean()
avg_cost = numpy.array(avg_cost_set).mean()
if float(acc) > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.batch_id + 1, float(avg_cost), float(acc)))
if math.isnan(float(avg_cost)):
sys.exit("got NaN loss, training failed.")
if isinstance(event, fluid.EndEpochEvent):
# if (event.epoch + 1) % 10 == 0:
# trainer.save_params(save_dirname)
trainer.save_inference_model(save_dirname)

# TODO: Uncomment this part once we are sure that .train is working
# test_reader = paddle.batch(
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
# test_metrics = trainer.test(reader=test_reader)
# avg_cost_set = test_metrics[0]
# acc_set = test_metrics[1]
#
# # get test acc and loss
# acc = numpy.array(acc_set).mean()
# avg_cost = numpy.array(avg_cost_set).mean()
#
# print("avg_cost: %s" % avg_cost)
# print("acc : %s" % acc)
#
# if float(acc) > 0.2: # Smaller value to increase CI speed
# trainer.save_params(save_dirname)
# else:
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
# event.epoch + 1, float(avg_cost), float(acc)))
# if math.isnan(float(avg_cost)):
# sys.exit("got NaN loss, training failed.")

train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)

trainer.train(
reader=dataset.mnist.train(), num_pass=100, event_handler=event_handler)
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['img', 'label'])


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

inferencer = fluid.Inferencer(
inference_program, param_path=save_dirname, place=place)
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)

batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
Expand All @@ -114,5 +131,5 @@ def main(use_cuda):


if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
# for use_cuda in (False, True):
main(use_cuda=False)
76 changes: 46 additions & 30 deletions ...ize_digits/notest_recognize_digits_mlp.py → ...gnize_digits/test_recognize_digits_mlp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@
import math
import sys
import os
import paddle.v2.dataset as dataset

BATCH_SIZE = 64

Expand All @@ -41,47 +40,64 @@ def train_program():
predict = inference_program()
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
acc = fluid.layers.accuracy(input=predict, label=label)
return avg_cost, acc
# acc = fluid.layers.accuracy(input=predict, label=label)
# return avg_cost, acc
return avg_cost


def train(use_cuda, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

optimizer = fluid.optimizer.Adam(learning_rate=0.001)
trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)

trainer = fluid.Trainer(
train_func=train_program,
infer_func=inference_program,
place=place,
optimizer=optimizer)

def event_handler(event):
if isinstance(event, fluid.EndIteration):
avg_cost, acc = event.values
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)

if (event.batch_id + 1) % 10 == 0:
test_metrics = trainer.test(reader=dataset.mnist.test())
avg_cost_set = test_metrics[0]
acc_set = test_metrics[1]

# get test acc and loss
acc = numpy.array(acc_set).mean()
avg_cost = numpy.array(avg_cost_set).mean()
if float(acc) > 0.2: # Smaller value to increase CI speed
trainer.save_params(save_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.batch_id + 1, float(avg_cost), float(acc)))
if math.isnan(float(avg_cost)):
sys.exit("got NaN loss, training failed.")
if isinstance(event, fluid.EndEpochEvent):
# if (event.epoch + 1) % 10 == 0:
trainer.save_inference_model(save_dirname)

# TODO: Uncomment this part once we are sure that .train is working
# test_reader = paddle.batch(
# paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
# test_metrics = trainer.test(reader=test_reader)
# avg_cost_set = test_metrics[0]
# acc_set = test_metrics[1]
#
# # get test acc and loss
# acc = numpy.array(acc_set).mean()
# avg_cost = numpy.array(avg_cost_set).mean()
#
# print("avg_cost: %s" % avg_cost)
# print("acc : %s" % acc)
#
# if float(acc) > 0.2: # Smaller value to increase CI speed
# trainer.save_params(save_dirname)
# else:
# print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
# event.epoch + 1, float(avg_cost), float(acc)))
# if math.isnan(float(avg_cost)):
# sys.exit("got NaN loss, training failed.")

train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)

trainer.train(
reader=dataset.mnist.train(), num_pass=100, event_handler=event_handler)
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['img', 'label'])


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

inferencer = fluid.Inferencer(
inference_program, param_path=save_dirname, place=place)
inferencer = fluid.Inferencer(param_path=save_dirname, place=place)

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


if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
# for use_cuda in (False, True):
main(use_cuda=False)