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/pool_op_test.py (4296B)

@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class PoolTest(hu.HypothesisTestCase):
    @given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(3, 5),
           size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           method=st.sampled_from(["MaxPool", "AveragePool"]),
           **mu.gcs)
    @settings(deadline=10000)
    def test_pooling(self, stride, pad, kernel, size,
                         input_channels, batch_size,
                         method, gc, dc):
        assume(pad < kernel)
        op = core.CreateOperator(
            method,
            ["X"],
            ["Y"],
            stride=stride,
            pad=pad,
            kernel=kernel,
            device_option=dc[0],
        )
        X = np.random.rand(
            batch_size, input_channels, size, size
        ).astype(np.float32)

self.assertDeviceChecks(dc, op, [X], [0])

if 'MaxPool' not in method:
            self.assertGradientChecks(gc, op, [X], 0, [0])

@given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(3, 5),
           size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           method=st.sampled_from(["MaxPool", "AveragePool"]),
           **mu.gcs_cpu_ideep)
    def test_int8_pooling(self, stride, pad, kernel, size,
                         input_channels, batch_size,
                         method, gc, dc):
        assume(pad < kernel)
        pool_fp32 = core.CreateOperator(
            method,
            ["X"],
            ["Y"],
            stride=stride,
            pad=pad,
            kernel=kernel,
            device_option=dc[0]
        )
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32)

if X.min() >=0:
            scale = np.absolute(X).max() / 0xFF
            zero_point = 0
        else:
            scale = np.absolute(X).max() / 0x7F
            zero_point = 128

old_ws_name = workspace.CurrentWorkspace()
        workspace.SwitchWorkspace("_device_check_", True)

workspace.FeedBlob("X", X, dc[0])
        workspace.RunOperatorOnce(pool_fp32)
        Y = workspace.FetchBlob("Y")

workspace.ResetWorkspace()

sw2nhwc = core.CreateOperator(
            "NCHW2NHWC",
            ["Xi"],
            ["Xi_nhwc"],
            device_option=dc[1]
        )

quantize = core.CreateOperator(
            "Int8Quantize",
            ["Xi_nhwc"],
            ["Xi_quantized"],
            engine="DNNLOWP",
            device_option=dc[1],
            Y_zero_point=zero_point,
            Y_scale=scale,
        )

pool = core.CreateOperator(
            "Int8{}".format(method),
            ["Xi_quantized"],
            ["Y_quantized"],
            stride=stride,
            pad=pad,
            kernel=kernel,
            engine="DNNLOWP",
            device_option=dc[1],
        )

dequantize = core.CreateOperator(
            "Int8Dequantize",
            ["Y_quantized"],
            ["Y_nhwc"],
            engine="DNNLOWP",
            device_option=dc[1],
        )

sw2nchw = core.CreateOperator(
            "NHWC2NCHW",
            ["Y_nhwc"],
            ["Y_out"],
            device_option=dc[1]
        )

net = caffe2_pb2.NetDef()
        net.op.extend([sw2nhwc, quantize, pool, dequantize, sw2nchw])

workspace.FeedBlob("Xi", X, dc[1])
        workspace.RunNetOnce(net)
        Y_out = workspace.FetchBlob("Y_out")

MSE = np.square(np.subtract(Y, Y_out)).mean()
        if MSE > 0.005:
            print(Y.flatten())
            print(Y_out.flatten())
            print(np.max(np.abs(Y_out - Y)))
            print("MSE", MSE)
            self.assertTrue(False)

workspace.SwitchWorkspace(old_ws_name)

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

import unittest import hypothesis.strategies as st from hypothesis import assume, given, settings import numpy as np from caffe2.proto import caffe2_pb2 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 PoolTest(hu.HypothesisTestCase): @given(stride=st.integers(1, 3), pad=st.integers(0, 3), kernel=st.integers(3, 5), size=st.integers(7, 9), input_channels=st.integers(1, 3), batch_size=st.integers(1, 3), method=st.sampled_from(["MaxPool", "AveragePool"]), **mu.gcs) @settings(deadline=10000) def test_pooling(self, stride, pad, kernel, size, input_channels, batch_size, method, gc, dc): assume(pad < kernel) op = core.CreateOperator( method, ["X"], ["Y"], stride=stride, pad=pad, kernel=kernel, device_option=dc[0], ) X = np.random.rand( batch_size, input_channels, size, size ).astype(np.float32) self.assertDeviceChecks(dc, op, [X], [0]) if 'MaxPool' not in method: self.assertGradientChecks(gc, op, [X], 0, [0]) @given(stride=st.integers(1, 3), pad=st.integers(0, 3), kernel=st.integers(3, 5), size=st.integers(7, 9), input_channels=st.integers(1, 3), batch_size=st.integers(1, 3), method=st.sampled_from(["MaxPool", "AveragePool"]), **mu.gcs_cpu_ideep) def test_int8_pooling(self, stride, pad, kernel, size, input_channels, batch_size, method, gc, dc): assume(pad < kernel) pool_fp32 = core.CreateOperator( method, ["X"], ["Y"], stride=stride, pad=pad, kernel=kernel, device_option=dc[0] ) X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) if X.min() >=0: scale = np.absolute(X).max() / 0xFF zero_point = 0 else: scale = np.absolute(X).max() / 0x7F zero_point = 128 old_ws_name = workspace.CurrentWorkspace() workspace.SwitchWorkspace("_device_check_", True) workspace.FeedBlob("X", X, dc[0]) workspace.RunOperatorOnce(pool_fp32) Y = workspace.FetchBlob("Y") workspace.ResetWorkspace() sw2nhwc = core.CreateOperator( "NCHW2NHWC", ["Xi"], ["Xi_nhwc"], device_option=dc[1] ) quantize = core.CreateOperator( "Int8Quantize", ["Xi_nhwc"], ["Xi_quantized"], engine="DNNLOWP", device_option=dc[1], Y_zero_point=zero_point, Y_scale=scale, ) pool = core.CreateOperator( "Int8{}".format(method), ["Xi_quantized"], ["Y_quantized"], stride=stride, pad=pad, kernel=kernel, engine="DNNLOWP", device_option=dc[1], ) dequantize = core.CreateOperator( "Int8Dequantize", ["Y_quantized"], ["Y_nhwc"], engine="DNNLOWP", device_option=dc[1], ) sw2nchw = core.CreateOperator( "NHWC2NCHW", ["Y_nhwc"], ["Y_out"], device_option=dc[1] ) net = caffe2_pb2.NetDef() net.op.extend([sw2nhwc, quantize, pool, dequantize, sw2nchw]) workspace.FeedBlob("Xi", X, dc[1]) workspace.RunNetOnce(net) Y_out = workspace.FetchBlob("Y_out") MSE = np.square(np.subtract(Y, Y_out)).mean() if MSE > 0.005: print(Y.flatten()) print(Y_out.flatten()) print(np.max(np.abs(Y_out - Y))) print("MSE", MSE) self.assertTrue(False) workspace.SwitchWorkspace(old_ws_name) if __name__ == "__main__": unittest.main()