import numpy as np
import caffe2.python.models.resnet as resnet
import hypothesis.strategies as st
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.models.imagenet_trainer_test_utils as utils
class ResnetMemongerTest(hu.HypothesisTestCase):
@given(with_shapes=st.booleans(), **hu.gcs_cpu_only)
@settings(max_examples=2, deadline=None)
def test_resnet_shared_grads(self, with_shapes, gc, dc):
results = utils.test_shared_grads(
with_shapes,
resnet.create_resnet50,
'gpu_0/conv1_w',
'gpu_0/last_out_L1000'
)
self.assertTrue(results[0][0] < results[0][1])
np.testing.assert_almost_equal(results[1][0], results[1][1])
np.testing.assert_almost_equal(results[2][0], results[2][1])
def test_resnet_forward_only(self):
results = utils.test_forward_only(
resnet.create_resnet50,
'gpu_0/last_out_L1000'
)
self.assertTrue(results[0][0] < results[0][1])
self.assertTrue(results[1] < 7 and results[1] > 0)
np.testing.assert_almost_equal(results[2][0], results[2][1])
def test_resnet_forward_only_fast_simplenet(self):
'''
Test C++ memonger that is only for simple nets
'''
results = utils.test_forward_only_fast_simplenet(
resnet.create_resnet50,
'gpu_0/last_out_L1000'
)
self.assertTrue(results[0][0] < results[0][1])
self.assertTrue(results[1] < 4 and results[1] > 0)
np.testing.assert_almost_equal(results[2][0], results[2][1])
if __name__ == "__main__":
import unittest
import random
random.seed(2603)
workspace.GlobalInit([
'caffe2',
'--caffe2_log_level=0',
'--caffe2_print_blob_sizes_at_exit=0',
'--caffe2_gpu_memory_tracking=1'])
unittest.main()