mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
13f38ab79d
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15997 Add count_include_pad to average_pool_gradient_op Reviewed By: houseroad Differential Revision: D13648339 fbshipit-source-id: 205cb2acb32dc24a85256b628298b1a11f0ffa2c
405 lines
14 KiB
Python
405 lines
14 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from hypothesis import assume, given, settings
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import hypothesis.strategies as st
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import os
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import unittest
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from caffe2.python import core, utils, workspace
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import caffe2.python.hip_test_util as hiputl
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import caffe2.python.hypothesis_test_util as hu
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class TestPooling(hu.HypothesisTestCase):
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# CUDNN does NOT support different padding values and we skip it
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@given(stride_h=st.integers(1, 3),
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stride_w=st.integers(1, 3),
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pad_t=st.integers(0, 3),
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pad_l=st.integers(0, 3),
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pad_b=st.integers(0, 3),
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pad_r=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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order=st.sampled_from(["NCHW", "NHWC"]),
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op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool",
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"MaxPool2D", "AveragePool2D"]),
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**hu.gcs)
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def test_pooling_separate_stride_pad(self, stride_h, stride_w,
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pad_t, pad_l, pad_b,
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pad_r, kernel, size,
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input_channels,
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batch_size, order,
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op_type,
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gc, dc):
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assume(np.max([pad_t, pad_l, pad_b, pad_r]) < kernel)
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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stride_h=stride_h,
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stride_w=stride_w,
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pad_t=pad_t,
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pad_l=pad_l,
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pad_b=pad_b,
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pad_r=pad_r,
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kernel=kernel,
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order=order,
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32)
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if order == "NCHW":
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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if 'MaxPool' not in op_type:
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self.assertGradientChecks(gc, op, [X], 0, [0])
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# This test is to check if CUDNN works for bigger batch size or not
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@unittest.skipIf(not os.getenv('CAFFE2_DEBUG'),
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"This is a test that reproduces a cudnn error. If you "
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"want to run it, set env variable CAFFE2_DEBUG=1.")
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@given(**hu.gcs_gpu_only)
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def test_pooling_big_batch(self, gc, dc):
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op = core.CreateOperator(
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"AveragePool",
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["X"],
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["Y"],
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stride=1,
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kernel=7,
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pad=0,
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order="NHWC",
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engine="CUDNN",
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)
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X = np.random.rand(70000, 7, 7, 81).astype(np.float32)
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self.assertDeviceChecks(dc, op, [X], [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(1, 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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order=st.sampled_from(["NCHW", "NHWC"]),
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op_type=st.sampled_from(["MaxPool", "AveragePool",
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"MaxPool1D", "AveragePool1D"]),
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**hu.gcs)
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def test_pooling_1d(self, stride, pad, kernel, size, input_channels,
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batch_size, order, op_type, gc, dc):
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assume(pad < kernel)
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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strides=[stride],
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kernels=[kernel],
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pads=[pad, pad],
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order=order,
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engine="",
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)
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X = np.random.rand(
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batch_size, size, input_channels).astype(np.float32)
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if order == "NCHW":
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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if 'MaxPool' not in op_type:
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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, 2),
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kernel=st.integers(1, 6),
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size=st.integers(3, 5),
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input_channels=st.integers(1, 3),
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batch_size=st.integers(1, 3),
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order=st.sampled_from(["NCHW", "NHWC"]),
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op_type=st.sampled_from(["MaxPool", "AveragePool",
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"MaxPool3D", "AveragePool3D"]),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_pooling_3d(self, stride, pad, kernel, size, input_channels,
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batch_size, order, op_type, engine, gc, dc):
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assume(pad < kernel)
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assume(size + pad + pad >= kernel)
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# Currently MIOpen Pooling only supports 2d pooling
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if hiputl.run_in_hip(gc, dc):
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assume(engine != "CUDNN")
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# some case here could be calculated with global pooling, but instead
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# calculated with general implementation, slower but should still
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# be corect.
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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strides=[stride] * 3,
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kernels=[kernel] * 3,
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pads=[pad] * 6,
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order=order,
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engine=engine,
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)
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X = np.random.rand(
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batch_size, size, size, size, input_channels).astype(np.float32)
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if order == "NCHW":
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
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if 'MaxPool' not in op_type:
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self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
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@given(kernel=st.integers(3, 6),
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size=st.integers(3, 5),
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input_channels=st.integers(1, 3),
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batch_size=st.integers(1, 3),
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order=st.sampled_from(["NCHW", "NHWC"]),
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op_type=st.sampled_from(["MaxPool", "AveragePool",
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"MaxPool3D", "AveragePool3D"]),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_global_pooling_3d(self, kernel, size, input_channels,
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batch_size, order, op_type, engine, gc, dc):
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# Currently MIOpen Pooling only supports 2d pooling
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if hiputl.run_in_hip(gc, dc):
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assume(engine != "CUDNN")
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# pad and stride ignored because they will be infered in global_pooling
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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kernels=[kernel] * 3,
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order=order,
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global_pooling=True,
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engine=engine,
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)
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X = np.random.rand(
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batch_size, size, size, size, input_channels).astype(np.float32)
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if order == "NCHW":
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
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if 'MaxPool' not in op_type:
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self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
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@unittest.skipIf(not workspace.has_gpu_support, "No GPU support")
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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(1, 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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**hu.gcs_gpu_only)
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def test_pooling_with_index(self, stride, pad, kernel, size,
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input_channels, batch_size, gc, dc):
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assume(pad < kernel)
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op = core.CreateOperator(
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"MaxPoolWithIndex",
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["X"],
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["Y", "Y_index"],
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stride=stride,
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kernel=kernel,
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pad=pad,
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order="NCHW",
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deterministic=1,
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32)
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# transpose due to order = NCHW
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(sz=st.integers(1, 20),
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batch_size=st.integers(1, 4),
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engine=st.sampled_from(["", "CUDNN"]),
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op_type=st.sampled_from(["AveragePool", "AveragePool2D"]),
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**hu.gcs)
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@settings(max_examples=3, timeout=10)
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def test_global_avg_pool_nchw(self, op_type, sz, batch_size, engine, gc, dc):
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''' Special test to stress the fast path of NCHW average pool '''
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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stride=1,
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kernel=sz,
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pad=0,
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order="NCHW",
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engine=engine,
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)
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X = np.random.rand(
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batch_size, 3, sz, sz).astype(np.float32)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@given(sz=st.integers(1, 20),
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batch_size=st.integers(1, 4),
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engine=st.sampled_from(["", "CUDNN"]),
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op_type=st.sampled_from(["MaxPool", "MaxPool2D"]),
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**hu.gcs)
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@settings(max_examples=3, timeout=10)
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def test_global_max_pool_nchw(self, op_type, sz,
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batch_size, engine, gc, dc):
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''' Special test to stress the fast path of NCHW max pool '''
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# CuDNN 5 does not support deterministic max pooling.
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assume(workspace.GetCuDNNVersion() >= 6000 or engine != "CUDNN")
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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stride=1,
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kernel=sz,
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pad=0,
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order="NCHW",
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engine=engine,
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deterministic=1,
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)
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np.random.seed(1234)
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X = np.random.rand(
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batch_size, 3, sz, sz).astype(np.float32)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=1e-4)
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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(1, 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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order=st.sampled_from(["NCHW", "NHWC"]),
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op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool",
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"MaxPool2D", "AveragePool2D"]),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_pooling(self, stride, pad, kernel, size,
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input_channels, batch_size,
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order, op_type, engine, gc, dc):
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assume(pad < kernel)
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if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
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assume(order == "NCHW" and op_type != "LpPool")
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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stride=stride,
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kernel=kernel,
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pad=pad,
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order=order,
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engine=engine,
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32)
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if order == "NCHW":
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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if 'MaxPool' not in op_type:
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@given(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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order=st.sampled_from(["NCHW", "NHWC"]),
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op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool"]),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_global_pooling(self, size, input_channels, batch_size,
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order, op_type, engine, gc, dc):
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# CuDNN 5 does not support deterministic max pooling.
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assume(workspace.GetCuDNNVersion() >= 6000 or op_type != "MaxPool")
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if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
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assume(order == "NCHW" and op_type != "LpPool")
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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order=order,
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engine=engine,
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global_pooling=True,
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32)
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if order == "NCHW":
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X = utils.NHWC2NCHW(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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if 'MaxPool' not in op_type:
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@given(op_type=st.sampled_from(["MaxPool", "MaxPoolND"]),
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dim=st.integers(1, 3),
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N=st.integers(1, 3),
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C=st.integers(1, 3),
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D=st.integers(3, 5),
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H=st.integers(3, 5),
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W=st.integers(3, 5),
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kernel=st.integers(1, 3),
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stride=st.integers(1, 3),
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pad=st.integers(0, 2),
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order=st.sampled_from(["NCHW", "NHWC"]),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_max_pool_grad(
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self, op_type, dim, N, C, D, H, W, kernel, stride, pad, order,
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engine, gc, dc):
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assume(pad < kernel)
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assume(dim > 1 or engine == "")
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if hiputl.run_in_hip(gc, dc):
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if dim != 2:
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assume(engine != "CUDNN")
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elif engine == "CUDNN":
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assume(order == "NCHW")
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if op_type.endswith("ND"):
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op_type = op_type.replace("N", str(dim))
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op = core.CreateOperator(
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op_type,
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["X"],
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["Y"],
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kernels=[kernel] * dim,
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strides=[stride] * dim,
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pads=[pad] * dim * 2,
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order=order,
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engine=engine,
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)
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if dim == 1:
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size = W
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dims = [N, C, W]
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axes = [0, 2, 1]
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elif dim == 2:
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size = H * W
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dims = [N, C, H, W]
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axes = [0, 2, 3, 1]
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else:
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size = D * H * W
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dims = [N, C, D, H, W]
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axes = [0, 2, 3, 4, 1]
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X = np.zeros((N * C, size)).astype(np.float32)
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for i in range(N * C):
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X[i, :] = np.arange(size, dtype=np.float32) / size
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np.random.shuffle(X[i, :])
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X = X.reshape(dims)
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if order == "NHWC":
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X = np.transpose(X, axes)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(
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gc, op, [X], 0, [0], threshold=5e-2, stepsize=1e-3)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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