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/shape_op_test.py (2631B)

@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class ShapeTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 128),
           c=st.integers(1, 128),
           h=st.integers(1, 128),
           w=st.integers(1, 128),
           **mu.gcs)
    @settings(max_examples=10, deadline=None)
    def test_shape(self, n, c, h, w, gc, dc):
        op0 = core.CreateOperator(
            "Shape",
            ["X0"],
            ["Y0"],
            device_option=dc[0]
        )
        op1 = core.CreateOperator(
            "Shape",
            ["X1"],
            ["Y1"],
            device_option=dc[1]
        )
        X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
        workspace.FeedBlob('X0', X, dc[0])
        workspace.FeedBlob('X1', X, dc[1])
        workspace.RunOperatorOnce(op0)
        workspace.RunOperatorOnce(op1)
        Y0 = workspace.FetchBlob('Y0')
        Y1 = workspace.FetchBlob('Y1')

if not np.allclose(Y0, Y1, atol=0, rtol=0):
            print(Y1.flatten())
            print(Y0.flatten())
            print(np.max(np.abs(Y1 - Y0)))
            self.assertTrue(False)

@given(n=st.integers(1, 128),
           c=st.integers(1, 128),
           h=st.integers(1, 128),
           w=st.integers(1, 128),
           axes=st.lists(st.integers(0, 3), min_size=1, max_size=3),
           **mu.gcs)
    @settings(max_examples=10, deadline=None)
    def test_shape_with_axes(self, n, c, h, w, axes, gc, dc):
        axes = list(set(axes)).sort()
        op0 = core.CreateOperator(
            "Shape",
            ["X0"],
            ["Y0"],
            axes = axes,
            device_option=dc[0]
        )
        op1 = core.CreateOperator(
            "Shape",
            ["X1"],
            ["Y1"],
            axes = axes,
            device_option=dc[1]
        )
        X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
        workspace.FeedBlob('X0', X, dc[0])
        workspace.FeedBlob('X1', X, dc[1])
        workspace.RunOperatorOnce(op0)
        workspace.RunOperatorOnce(op1)
        Y0 = workspace.FetchBlob('Y0')
        Y1 = workspace.FetchBlob('Y1')

if not np.allclose(Y0, Y1, atol=0, rtol=0):
            print(Y1.flatten())
            print(Y0.flatten())
            print(np.max(np.abs(Y1 - Y0)))
            self.assertTrue(False)

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

import unittest import hypothesis.strategies as st from hypothesis import given, settings import numpy as np from caffe2.python import core, workspace import caffe2.python.hypothesis_test_util as hu import caffe2.python.ideep_test_util as mu @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") class ShapeTest(hu.HypothesisTestCase): @given(n=st.integers(1, 128), c=st.integers(1, 128), h=st.integers(1, 128), w=st.integers(1, 128), **mu.gcs) @settings(max_examples=10, deadline=None) def test_shape(self, n, c, h, w, gc, dc): op0 = core.CreateOperator( "Shape", ["X0"], ["Y0"], device_option=dc[0] ) op1 = core.CreateOperator( "Shape", ["X1"], ["Y1"], device_option=dc[1] ) X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5 workspace.FeedBlob('X0', X, dc[0]) workspace.FeedBlob('X1', X, dc[1]) workspace.RunOperatorOnce(op0) workspace.RunOperatorOnce(op1) Y0 = workspace.FetchBlob('Y0') Y1 = workspace.FetchBlob('Y1') if not np.allclose(Y0, Y1, atol=0, rtol=0): print(Y1.flatten()) print(Y0.flatten()) print(np.max(np.abs(Y1 - Y0))) self.assertTrue(False) @given(n=st.integers(1, 128), c=st.integers(1, 128), h=st.integers(1, 128), w=st.integers(1, 128), axes=st.lists(st.integers(0, 3), min_size=1, max_size=3), **mu.gcs) @settings(max_examples=10, deadline=None) def test_shape_with_axes(self, n, c, h, w, axes, gc, dc): axes = list(set(axes)).sort() op0 = core.CreateOperator( "Shape", ["X0"], ["Y0"], axes = axes, device_option=dc[0] ) op1 = core.CreateOperator( "Shape", ["X1"], ["Y1"], axes = axes, device_option=dc[1] ) X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5 workspace.FeedBlob('X0', X, dc[0]) workspace.FeedBlob('X1', X, dc[1]) workspace.RunOperatorOnce(op0) workspace.RunOperatorOnce(op1) Y0 = workspace.FetchBlob('Y0') Y1 = workspace.FetchBlob('Y1') if not np.allclose(Y0, Y1, atol=0, rtol=0): print(Y1.flatten()) print(Y0.flatten()) print(np.max(np.abs(Y1 - Y0))) self.assertTrue(False) if __name__ == "__main__": unittest.main()