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| | import unittest |
| | import hypothesis.strategies as st |
| | from hypothesis import 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 ReluTest(hu.HypothesisTestCase): |
| | @given(X=hu.tensor(), |
| | inplace=st.booleans(), |
| | **mu.gcs) |
| | @settings(deadline=1000) |
| | def test_relu(self, X, inplace, gc, dc): |
| | op = core.CreateOperator( |
| | "Relu", |
| | ["X"], |
| | ["Y"] if not inplace else ["X"], |
| | ) |
| | |
| | X += 0.02 * np.sign(X) |
| | X[X == 0.0] += 0.02 |
| |
|
| | self.assertDeviceChecks(dc, op, [X], [0]) |
| |
|
| | self.assertGradientChecks(gc, op, [X], 0, [0]) |
| |
|
| | @given(size=st.integers(7, 9), |
| | input_channels=st.integers(1, 3), |
| | batch_size=st.integers(1, 3), |
| | inplace=st.booleans(), |
| | **mu.gcs_cpu_ideep) |
| | @settings(max_examples=10, deadline=None) |
| | def test_int8_relu(self, size, input_channels, batch_size, inplace, gc, dc): |
| | relu_fp32 = core.CreateOperator( |
| | "Relu", |
| | ["X"], |
| | ["Y"] if not inplace else ["X"], |
| | device_option=dc[0] |
| | ) |
| |
|
| | X = np.random.rand( |
| | batch_size, input_channels, size, size).astype(np.float32) - 0.5 |
| | |
| | X += 0.02 * np.sign(X) |
| | X[X == 0.0] += 0.02 |
| |
|
| | 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(relu_fp32) |
| | Y = workspace.FetchBlob("X" if inplace else "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, |
| | ) |
| |
|
| | relu = core.CreateOperator( |
| | "Int8Relu", |
| | ["Xi_quantized"], |
| | ["Y_quantized"] if not inplace else ["Xi_quantized"], |
| | engine="DNNLOWP", |
| | device_option=dc[1], |
| | ) |
| |
|
| | dequantize = core.CreateOperator( |
| | "Int8Dequantize", |
| | ["Y_quantized"] if not inplace else ["Xi_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, relu, 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() |
| |
|
