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