mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
130 lines
3.6 KiB
Python
130 lines
3.6 KiB
Python
|
|
|
|
|
|
|
|
|
|
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"],
|
|
)
|
|
# go away from the origin point to avoid kink problems
|
|
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
|
|
# go away from the origin point to avoid kink problems
|
|
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()
|