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https://github.com/zebrajr/pytorch.git
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82198831e7
Summary: In D5681122 - when routing to global maxpool and average pool, the condition is not correct. see T24876217 for discussion Reviewed By: Yangqing Differential Revision: D6665466 fbshipit-source-id: dcb5b4686249e6ee8e1e976ab66b003ef09b32fd
388 lines
15 KiB
Python
388 lines
15 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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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, workspace
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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 = X.transpose((0, 3, 1, 2))
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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 = X.transpose((0, 2, 1))
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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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# 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 = X.transpose((0, 4, 1, 2, 3))
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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.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
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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_global_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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# Used to determine if we can use global pooling for average or max pooling
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# the assumptions here are:
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# 1. kernel can be greater than input dim, but always smaller than dim + pads
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# on both sides, ie.
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# dim.H + pad_t + pad_b >= kernel.H
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# dim.W + pad_l + pad_r >= kernel.W
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# dim.D + pad_f + pad_e >= kernel.D (f = front e = end)
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# 2. padding applied to both sides of the input dim
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# 3. pooling are applied by first align kernel with one side of padding, then
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# shifting kernel for a stride distance towards the other side of padding
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# 4. kernel continue shifts by stride distance until when one more stride is
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# applied, kernel will go beyond input dim plus padding.
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# So it is possible if stride value is large, some input dim elements will
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# not be covered. consider these cases:
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#
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# case 1:
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# kernel = 4, dim = 3, pad_l = 2, pad_r = 2, stride = 4
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# when kernel is applied for the first time, pad_l and dim upto 2
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# is covered then we have 1 unit left of dim and pad_r not covered, but
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# because stride is 4, shift kernel by 4 will go beyond pad_r, we should not
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# apply another kernel, the out_size will be 1, and some element (last of
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# dim) is ignored, therefore we can not use global pooling
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#
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# case 2:
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# k = 4, dim = 3, pad_l = 1, pad_r = 2, stride = 1
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# after kernel applied first time, pad_l and dim and 1st pad_r element all
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# covered, shift kernel by stride move it to the end of pad_r, covering dim +
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# pad_r, not beyond pad_r, so we should apply the kernel for a second time.
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# out_size = 2 and we should not use global pooling either because dim is
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# covered twice.
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#
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# case 3:
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# k = 4, dim = 3, pad_l = 1, pad_r = 1, stride = 2
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# first kernel apply cover all dim, but can not shift by stride because
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# kernel go beyond pad_r so kernel is only applied once and cover entire dim
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# this is the only case we can use global pooling.
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#
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# Summary: use global pooling when all dim is covered and only covered once
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assume(kernel >= size)
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assume(kernel + stride > size + pad + pad)
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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 = X.transpose((0, 4, 1, 2, 3))
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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.assertDeviceChecks(dc, op, [X], [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 = X.transpose((0, 3, 1, 2))
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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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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 = X.transpose((0, 3, 1, 2))
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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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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 = X.transpose((0, 3, 1, 2))
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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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if __name__ == "__main__":
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import unittest
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unittest.main()
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