mirror of
https://github.com/zebrajr/pytorch.git
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a63e7314f3
Summary: Add Conv and Pool operators with dimensions. Reviewed By: bddppq Differential Revision: D5588614 fbshipit-source-id: 2552c40dc3ca180a6ab51817d60f0b85b97885d5
263 lines
9.0 KiB
Python
263 lines
9.0 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, 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, 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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"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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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])
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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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@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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)
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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(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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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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