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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
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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import collections
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core, dyndep, workspace
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from hypothesis import given
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dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
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workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
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def mse(x, xh):
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d = (x - xh).reshape(-1)
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return 0 if len(d) == 0 else np.sqrt(np.matmul(d, d.transpose())) / len(d)
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class FullyConnectedFP16Test(hu.HypothesisTestCase):
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@given(
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input_channels=st.integers(128, 256),
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output_channels=st.integers(128, 256),
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batch_size=st.integers(128, 256),
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empty_batch=st.booleans(),
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**hu.gcs_cpu_only
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)
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def test_fully_connected(self, input_channels, output_channels, batch_size, empty_batch, gc, dc):
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if empty_batch:
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batch_size = 0
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W = np.random.randn(output_channels, input_channels).astype(np.float32)
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X = np.random.randn(batch_size, input_channels).astype(np.float32)
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b = np.random.randn(output_channels).astype(np.float32)
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Output = collections.namedtuple("Output", ["Y", "engine", "order"])
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order = "NHWC"
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net = core.Net("test_net")
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engine = "FAKE_FP16"
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fc = core.CreateOperator(
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"FC", ["X", "W", "b"], ["Y"], order=order, engine=engine, device_option=gc
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)
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net.Proto().op.extend([fc])
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self.ws.create_blob("X").feed(X, device_option=gc)
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self.ws.create_blob("W").feed(W, device_option=gc)
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self.ws.create_blob("b").feed(b, device_option=gc)
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self.ws.run(net)
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output = Output(Y=self.ws.blobs["Y"].fetch(), engine=engine, order=order)
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# Mimic the quantization in python
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Wh = W.astype(np.float16)
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Xh = X.astype(np.float16)
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bh = b.astype(np.float16)
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bbh = np.outer(np.ones(batch_size, dtype=np.float16), bh)
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assert bbh.dtype == np.float16
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Yrefh = np.matmul(Xh, Wh.transpose()) + bbh
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assert Yrefh.dtype == np.float16
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bb = np.outer(np.ones(batch_size, dtype=np.float32), b)
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Yref = np.matmul(X, W.transpose()) + bb
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assert Yref.dtype == np.float32
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# The error between plain->quantized, and plain->python_quantized
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# should be very close
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mse_c2 = mse(Yref, output.Y)
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mse_py = mse(Yref, Yrefh)
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print(np.abs(mse_c2 - mse_py))
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assert np.isclose(mse_c2, mse_py, atol=1e-3), np.abs(mse_c2 - mse_py)
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