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https://github.com/zebrajr/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
98 lines
3.2 KiB
Python
98 lines
3.2 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 caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
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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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class DNNLowPConcatOpTest(hu.HypothesisTestCase):
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@given(
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dim1=st.integers(0, 256),
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dim2=st.integers(0, 256),
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axis=st.integers(0, 1),
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in_quantized=st.booleans(),
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out_quantized=st.booleans(),
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**hu.gcs_cpu_only
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)
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def test_dnnlowp_concat_int(
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self, dim1, dim2, axis, in_quantized, out_quantized, gc, dc
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):
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# X has scale 1, so exactly represented after quantization
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min_ = -100
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max_ = min_ + 255
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X = np.round(np.random.rand(dim1, dim2) * (max_ - min_) + min_)
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X = X.astype(np.float32)
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if dim1 >= 1 and dim2 >= 2:
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X[0, 0] = min_
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X[0, 1] = max_
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elif dim2 == 1:
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return
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# Y has scale 1/2, so exactly represented after quantization
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Y = np.round(np.random.rand(dim1, dim2) * 255 / 2 - 64)
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Y = Y.astype(np.float32)
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if dim1 >= 1 and dim2 >= 2:
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Y[0, 0] = -64
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Y[0, 1] = 127.0 / 2
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Output = collections.namedtuple("Output", ["Z", "op_type", "engine"])
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outputs = []
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op_engine_list = [
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("Concat", ""),
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("Concat", "DNNLOWP"),
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("Int8Concat", "DNNLOWP"),
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]
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for op_type, engine in op_engine_list:
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net = core.Net("test_net")
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do_quantize = "DNNLOWP" in engine and in_quantized
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do_dequantize = "DNNLOWP" in engine and out_quantized
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if do_quantize:
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quantize_x = core.CreateOperator(
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"Quantize", ["X"], ["X_q"], engine=engine, device_option=gc
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)
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quantize_y = core.CreateOperator(
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"Quantize", ["Y"], ["Y_q"], engine=engine, device_option=gc
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)
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net.Proto().op.extend([quantize_x, quantize_y])
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concat = core.CreateOperator(
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op_type,
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["X_q", "Y_q"] if do_quantize else ["X", "Y"],
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["Z_q" if do_dequantize else "Z", "split"],
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dequantize_output=not do_dequantize,
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engine=engine,
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device_option=gc,
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axis=axis,
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)
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net.Proto().op.extend([concat])
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if do_dequantize:
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dequantize = core.CreateOperator(
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"Dequantize", ["Z_q"], ["Z"], engine=engine, device_option=gc
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)
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net.Proto().op.extend([dequantize])
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self.ws.create_blob("X").feed(X, device_option=gc)
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self.ws.create_blob("Y").feed(Y, device_option=gc)
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self.ws.create_blob("split")
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self.ws.run(net)
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outputs.append(
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Output(Z=self.ws.blobs["Z"].fetch(), op_type=op_type, engine=engine)
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)
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check_quantized_results_close(outputs)
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