FM / ideep

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adam_op_test.py	3212	0644	edit dl rm
blobs_queue_db_test.py	4045	0644	edit dl rm
channel_shuffle_op_test.py	1286	0644	edit dl rm
concat_split_op_test.py	5532	0644	edit dl rm
convfusion_op_test.py	31919	0644	edit dl rm
conv_op_test.py	5666	0644	edit dl rm
conv_transpose_test.py	2595	0644	edit dl rm
copy_op_test.py	3074	0644	edit dl rm
dropout_op_test.py	2075	0644	edit dl rm
elementwise_sum_op_test.py	6317	0644	edit dl rm
expanddims_squeeze_op_test.py	4285	0644	edit dl rm
fc_op_test.py	11745	0644	edit dl rm
leaky_relu_op_test.py	2854	0644	edit dl rm
LRN_op_test.py	1195	0644	edit dl rm
moment_sgd_op_test.py	1777	0644	edit dl rm
operator_fallback_op_test.py	3450	0644	edit dl rm
order_switch_op_test.py	2299	0644	edit dl rm
pool_op_test.py	4296	0644	edit dl rm
pre_convert_test.py	4115	0644	edit dl rm
relu_op_test.py	3705	0644	edit dl rm
reshape_op_test.py	5919	0644	edit dl rm
shape_op_test.py	2631	0644	edit dl rm
sigmoid_op_test.py	775	0644	edit dl rm
softmax_op_test.py	932	0644	edit dl rm
spatial_bn_op_test.py	5237	0644	edit dl rm
test_ideep_net.py	4094	0644	edit dl rm
transform_ideep_net.py	11683	0644	edit dl rm
transpose_op_test.py	1284	0644	edit dl rm
weightedsum_op_test.py	1560	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/adam_op_test.py (3212B)

@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestAdamOps(hu.HypothesisTestCase):
    @given(inputs=hu.tensors(n=4),
           ITER=st.integers(min_value=0, max_value=10000),
           LR=st.floats(min_value=0.01, max_value=0.99,
                        allow_nan=False, allow_infinity=False),
           beta1=st.floats(min_value=0.01, max_value=0.99,
                           allow_nan=False, allow_infinity=False),
           beta2=st.floats(min_value=0.01, max_value=0.99,
                           allow_nan=False, allow_infinity=False),
           epsilon=st.floats(min_value=0.01, max_value=0.99,
                             allow_nan=False, allow_infinity=False),
           **mu.gcs)
    def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
        param, mom1, mom2, grad = inputs
        ITER = np.array([ITER], dtype=np.int64)
        LR = np.array([LR], dtype=np.float32)
        mom2 = np.absolute(mom2)
        op = core.CreateOperator(
            "Adam",
            ["param", "mom1", "mom2", "grad", "lr", "iter"],
            ["output_param", "output_mom1", "output_mom2"],
            beta1=beta1, beta2=beta2, epsilon=epsilon)
        # Iter lives on the CPU
        input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do}

self.assertDeviceChecks(
            dc, op,
            [param, mom1, mom2, grad, LR, ITER],
            [0],
            input_device_options=input_device_options,
            threshold=0.001)

@given(inputs=hu.tensors(n=4),
           ITER=st.integers(min_value=0, max_value=10000),
           LR=st.floats(min_value=0.01, max_value=0.99,
                        allow_nan=False, allow_infinity=False),
           beta1=st.floats(min_value=0.01, max_value=0.99,
                           allow_nan=False, allow_infinity=False),
           beta2=st.floats(min_value=0.01, max_value=0.99,
                           allow_nan=False, allow_infinity=False),
           epsilon=st.floats(min_value=0.01, max_value=0.99,
                             allow_nan=False, allow_infinity=False),
           **mu.gcs)
    def test_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
        param, mom1, mom2, grad = inputs
        ITER = np.array([ITER], dtype=np.int64)
        LR = np.array([LR], dtype=np.float32)
        mom2 = np.absolute(mom2)

op = core.CreateOperator(
            "Adam",
            ["param", "mom1", "mom2", "grad", "lr", "iter"],
            ["output_param", "output_mom1", "output_mom2", "output_grad"],
            beta1=beta1, beta2=beta2, epsilon=epsilon)

# Iter lives on the CPU
        input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do}

self.assertDeviceChecks(
            dc, op,
            [param, mom1, mom2, grad, LR, ITER],
            [0],
            input_device_options=input_device_options,
            threshold=0.001)

if __name__ == "__main__":
    unittest.main()

import numpy as np import hypothesis.strategies as st import unittest import caffe2.python.hypothesis_test_util as hu from caffe2.python import core, workspace from hypothesis import given import caffe2.python.ideep_test_util as mu @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") class TestAdamOps(hu.HypothesisTestCase): @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), **mu.gcs) def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc): param, mom1, mom2, grad = inputs ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) mom2 = np.absolute(mom2) op = core.CreateOperator( "Adam", ["param", "mom1", "mom2", "grad", "lr", "iter"], ["output_param", "output_mom1", "output_mom2"], beta1=beta1, beta2=beta2, epsilon=epsilon) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do} self.assertDeviceChecks( dc, op, [param, mom1, mom2, grad, LR, ITER], [0], input_device_options=input_device_options, threshold=0.001) @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), **mu.gcs) def test_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc): param, mom1, mom2, grad = inputs ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) mom2 = np.absolute(mom2) op = core.CreateOperator( "Adam", ["param", "mom1", "mom2", "grad", "lr", "iter"], ["output_param", "output_mom1", "output_mom2", "output_grad"], beta1=beta1, beta2=beta2, epsilon=epsilon) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do} self.assertDeviceChecks( dc, op, [param, mom1, mom2, grad, LR, ITER], [0], input_device_options=input_device_options, threshold=0.001) if __name__ == "__main__": unittest.main()