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/operator_fallback_op_test.py (3450B)

@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestFallbackOps(hu.HypothesisTestCase):
    @given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(3, 5),
           size=st.integers(8, 10),
           input_channels=st.integers(1, 3),
           output_channels=st.integers(1, 5),
           batch_size=st.integers(1, 3),
           use_bias=st.booleans(),
           **mu.gcs)
    def test_in_place(self, stride, pad, kernel, size,
                             input_channels, output_channels,
                             batch_size, use_bias, gc, dc):
        # To expose fallback in-place potential issue, the fallback op
        # following ideep op must be run at least two iterations.
        conv = core.CreateOperator(
            "Conv",
            ["X", "w", "b"] if use_bias else ["X", "w"],
            ["Y"],
            stride=stride,
            pad=pad,
            kernel=kernel,
            device_option=dc[0]
        )
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32) - 0.5
        w = np.random.rand(output_channels, input_channels, kernel, kernel) \
            .astype(np.float32) - 0.5
        b = np.random.rand(output_channels).astype(np.float32) - 0.5

old_ws_name = workspace.CurrentWorkspace()
        workspace.SwitchWorkspace("_device_check_", True)
        workspace.FeedBlob('X', X, dc[0])
        workspace.FeedBlob('w', w, dc[0])
        workspace.FeedBlob('b', b, dc[0])
        workspace.RunOperatorOnce(conv)
        Y = workspace.FetchBlob('Y')

scale = np.random.randn(Y.shape[1]).astype(np.float32)
        bias = np.random.randn(Y.shape[1]).astype(np.float32)
        ac = core.CreateOperator(
            "AffineChannel",
            ["Y", "scale", "bias"],
            ["Y"],
            is_learnable=False,
            device_option=dc[0]
        )
        workspace.FeedBlob('scale', scale, dc[0])
        workspace.FeedBlob('bias', bias, dc[0])
        workspace.RunOperatorOnce(ac)
        workspace.RunOperatorOnce(conv)
        workspace.RunOperatorOnce(ac)
        Y0 = workspace.FetchBlob('Y')

workspace.ResetWorkspace()
        dev_net = caffe2_pb2.NetDef()
        conv_dev = caffe2_pb2.OperatorDef()
        conv_dev.CopyFrom(conv)
        conv_dev.device_option.CopyFrom(dc[1])
        ac_dev = caffe2_pb2.OperatorDef()
        ac_dev.CopyFrom(ac)
        ac_dev.device_option.CopyFrom(dc[1])
        dev_net.op.extend([conv_dev, ac_dev])
        workspace.FeedBlob('X', X, dc[1])
        workspace.FeedBlob('w', w, dc[1])
        workspace.FeedBlob('b', b, dc[1])
        workspace.FeedBlob('scale', scale, dc[1])
        workspace.FeedBlob('bias', bias, dc[1])
        workspace.RunNetOnce(dev_net)
        workspace.RunNetOnce(dev_net)
        Y1 = workspace.FetchBlob('Y')

if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
            print(Y1.flatten())
            print(Y0.flatten())
            print(np.max(np.abs(Y1 - Y0)))
            self.assertTrue(False)

workspace.SwitchWorkspace(old_ws_name)

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

import unittest import hypothesis.strategies as st from hypothesis import given import numpy as np from caffe2.python import core, workspace from caffe2.proto import caffe2_pb2 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 TestFallbackOps(hu.HypothesisTestCase): @given(stride=st.integers(1, 3), pad=st.integers(0, 3), kernel=st.integers(3, 5), size=st.integers(8, 10), input_channels=st.integers(1, 3), output_channels=st.integers(1, 5), batch_size=st.integers(1, 3), use_bias=st.booleans(), **mu.gcs) def test_in_place(self, stride, pad, kernel, size, input_channels, output_channels, batch_size, use_bias, gc, dc): # To expose fallback in-place potential issue, the fallback op # following ideep op must be run at least two iterations. conv = core.CreateOperator( "Conv", ["X", "w", "b"] if use_bias else ["X", "w"], ["Y"], stride=stride, pad=pad, kernel=kernel, device_option=dc[0] ) X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 w = np.random.rand(output_channels, input_channels, kernel, kernel) \ .astype(np.float32) - 0.5 b = np.random.rand(output_channels).astype(np.float32) - 0.5 old_ws_name = workspace.CurrentWorkspace() workspace.SwitchWorkspace("_device_check_", True) workspace.FeedBlob('X', X, dc[0]) workspace.FeedBlob('w', w, dc[0]) workspace.FeedBlob('b', b, dc[0]) workspace.RunOperatorOnce(conv) Y = workspace.FetchBlob('Y') scale = np.random.randn(Y.shape[1]).astype(np.float32) bias = np.random.randn(Y.shape[1]).astype(np.float32) ac = core.CreateOperator( "AffineChannel", ["Y", "scale", "bias"], ["Y"], is_learnable=False, device_option=dc[0] ) workspace.FeedBlob('scale', scale, dc[0]) workspace.FeedBlob('bias', bias, dc[0]) workspace.RunOperatorOnce(ac) workspace.RunOperatorOnce(conv) workspace.RunOperatorOnce(ac) Y0 = workspace.FetchBlob('Y') workspace.ResetWorkspace() dev_net = caffe2_pb2.NetDef() conv_dev = caffe2_pb2.OperatorDef() conv_dev.CopyFrom(conv) conv_dev.device_option.CopyFrom(dc[1]) ac_dev = caffe2_pb2.OperatorDef() ac_dev.CopyFrom(ac) ac_dev.device_option.CopyFrom(dc[1]) dev_net.op.extend([conv_dev, ac_dev]) workspace.FeedBlob('X', X, dc[1]) workspace.FeedBlob('w', w, dc[1]) workspace.FeedBlob('b', b, dc[1]) workspace.FeedBlob('scale', scale, dc[1]) workspace.FeedBlob('bias', bias, dc[1]) workspace.RunNetOnce(dev_net) workspace.RunNetOnce(dev_net) Y1 = workspace.FetchBlob('Y') if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01): print(Y1.flatten()) print(Y0.flatten()) print(np.max(np.abs(Y1 - Y0))) self.assertTrue(False) workspace.SwitchWorkspace(old_ws_name) if __name__ == "__main__": unittest.main()