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a3a6ab60cd
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9598 The "max_length" should be passed to UnPackSegmentsOp if "max_length" is given when calling PackSegmentsOp. Reviewed By: jerryzh168 Differential Revision: D8919799 fbshipit-source-id: 8c97aa717b69177b8a5d5d56892817d488853840
314 lines
10 KiB
Python
314 lines
10 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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from __future__ import unicode_literals
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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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from hypothesis import given
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from hypothesis import strategies as st
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import numpy as np
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import time
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class TestTensorPackOps(hu.HypothesisTestCase):
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def pack_segments_ref(self, return_presence_mask=False, max_length=None):
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def pack_segments_ref(lengths, data, max_length=max_length):
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arr = []
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constant_values = 0
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if data.dtype.char == 'S':
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constant_values = ''
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if max_length is not None:
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assert(max_length > np.max(lengths))
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else:
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max_length = np.max(lengths)
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for idx in range(np.size(lengths)):
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chunk = data[np.sum(lengths[:idx]):np.sum(lengths[:idx + 1])]
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pad_length = max_length - lengths[idx]
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# ((0, pad_length), (0, 0)) says add pad_length rows of padding
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# below chunk and 0 rows of padding elsewhere
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arr.append(
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np.pad(
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chunk, ((0, pad_length), (0, 0)),
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mode=str("constant"),
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constant_values=constant_values
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)
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)
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result = [arr]
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if return_presence_mask:
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presence_arr = []
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for length in lengths:
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pad_length = max_length - length
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presence_arr.append(
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np.pad(
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np.ones((length), dtype=np.bool), ((0, pad_length)),
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mode=str("constant")
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)
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)
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result.append(presence_arr)
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return result
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return pack_segments_ref
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@given(
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num_seq=st.integers(10, 100),
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cell_size=st.integers(1, 10),
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**hu.gcs
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)
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def test_pack_with_max_length_ops(self, num_seq, cell_size, gc, dc):
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# create data
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lengths = np.arange(num_seq, dtype=np.int32) + 1
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num_cell = np.sum(lengths)
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data = np.zeros(num_cell * cell_size, dtype=np.float32)
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left = np.cumsum(np.arange(num_seq) * cell_size)
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right = np.cumsum(lengths * cell_size)
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for i in range(num_seq):
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data[left[i]:right[i]] = i + 1.0
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data.resize(num_cell, cell_size)
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print("\nnum seq:{}, num cell: {}, cell size:{}\n".format(
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num_seq, num_cell, cell_size)
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+ "=" * 60
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)
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# run test
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max_length = num_seq + 1
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'], max_length=max_length)
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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start = time.time()
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[lengths, data, max_length],
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reference=self.pack_segments_ref(max_length=max_length),
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)
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end = time.time()
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print("{} used time: {}".format(gc, end - start).replace('\n', ' '))
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with core.DeviceScope(gc):
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments',
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['l', 'd'],
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['t'],
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max_length=max_length,
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device_option=gc))
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workspace.RunOperatorOnce(core.CreateOperator(
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'UnpackSegments',
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['l', 't'],
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['newd'],
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max_length=max_length,
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device_option=gc))
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assert(workspace.FetchBlob('t').shape[1] == max_length)
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assert((workspace.FetchBlob('newd') == workspace.FetchBlob('d')).all())
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@given(
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num_seq=st.integers(10, 500),
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cell_size=st.integers(1, 10),
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**hu.gcs
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)
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def test_pack_ops(self, num_seq, cell_size, gc, dc):
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# create data
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lengths = np.arange(num_seq, dtype=np.int32) + 1
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num_cell = np.sum(lengths)
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data = np.zeros(num_cell * cell_size, dtype=np.float32)
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left = np.cumsum(np.arange(num_seq) * cell_size)
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right = np.cumsum(lengths * cell_size)
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for i in range(num_seq):
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data[left[i]:right[i]] = i + 1.0
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data.resize(num_cell, cell_size)
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print("\nnum seq:{}, num cell: {}, cell size:{}\n".format(
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num_seq, num_cell, cell_size)
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+ "=" * 60
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)
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# run test
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'])
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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start = time.time()
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[lengths, data],
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reference=self.pack_segments_ref(),
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)
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end = time.time()
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print("{} used time: {}".format(gc, end - start).replace('\n', ' '))
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with core.DeviceScope(gc):
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments',
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['l', 'd'],
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['t'],
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device_option=gc))
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workspace.RunOperatorOnce(core.CreateOperator(
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'UnpackSegments',
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['l', 't'],
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['newd'],
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device_option=gc))
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assert((workspace.FetchBlob('newd') == workspace.FetchBlob('d')).all())
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@given(
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**hu.gcs_cpu_only
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)
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def test_pack_ops_str(self, gc, dc):
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# GPU does not support string. Test CPU implementation only.
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workspace.FeedBlob('l', np.array([1, 2, 3], dtype=np.int64))
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strs = np.array([
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["a", "a"],
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["b", "b"],
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["bb", "bb"],
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["c", "c"],
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["cc", "cc"],
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["ccc", "ccc"]],
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dtype='|S')
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workspace.FeedBlob('d', strs)
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments',
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['l', 'd'],
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['t'],
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device_option=gc))
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workspace.RunOperatorOnce(core.CreateOperator(
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'UnpackSegments',
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['l', 't'],
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['newd'],
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device_option=gc))
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assert((workspace.FetchBlob('newd') == workspace.FetchBlob('d')).all())
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def test_pad_minf(self):
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workspace.FeedBlob('l', np.array([1, 2, 3], dtype=np.int32))
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workspace.FeedBlob(
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'd',
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np.array([
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[1.0, 1.1],
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[2.0, 2.1],
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[2.2, 2.2],
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[3.0, 3.1],
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[3.2, 3.3],
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[3.4, 3.5]],
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dtype=np.float32))
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'], pad_minf=True))
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workspace.RunOperatorOnce(core.CreateOperator(
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'Exp', ['t'], ['r']
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))
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result = workspace.FetchBlob('t')
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assert(result[0, -1, 0] < -1000.0)
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# The whole point of padding with -inf is that when we exponentiate it
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# then it should be zero.
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exponentiated = workspace.FetchBlob('r')
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assert(exponentiated[0, -1, 0] == 0.0)
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@given(**hu.gcs_cpu_only)
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def test_presence_mask(self, gc, dc):
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lengths = np.array([1, 2, 3], dtype=np.int32)
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data = np.array(
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[
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[1.0, 1.0], [2.0, 2.0], [2.0, 2.0], [3.0, 3.0], [3.0, 3.0],
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[3.0, 3.0]
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],
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dtype=np.float32
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)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t', 'p'], return_presence_mask=True
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)
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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inputs = [lengths, data]
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=inputs,
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reference=self.pack_segments_ref(return_presence_mask=True),
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)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t', 'p'], return_presence_mask=True
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)
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('t')
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expected_output_shape = (3, 3, 2)
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self.assertEquals(output.shape, expected_output_shape)
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presence_mask = workspace.FetchBlob('p')
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expected_presence_mask = np.array(
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[[True, False, False], [True, True, False], [True, True, True]],
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dtype=np.bool
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)
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self.assertEqual(presence_mask.shape, expected_presence_mask.shape)
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np.testing.assert_array_equal(presence_mask, expected_presence_mask)
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def test_presence_mask_empty(self):
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lengths = np.array([], dtype=np.int32)
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data = np.array([], dtype=np.float32)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t', 'p'], return_presence_mask=True
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)
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('p')
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expected_output_shape = (0, 0)
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self.assertEquals(output.shape, expected_output_shape)
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@given(**hu.gcs_cpu_only)
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def test_out_of_bounds(self, gc, dc):
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# Copy pasted from test_pack_ops but with 3 changed to 4
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lengths = np.array([1, 2, 4], dtype=np.int32)
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data = np.array([
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[1.0, 1.0],
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[2.0, 2.0],
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[2.0, 2.0],
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[3.0, 3.0],
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[3.0, 3.0],
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[3.0, 3.0]], dtype=np.float32)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'])
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inputs = [lengths, data]
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self.assertRunOpRaises(
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device_option=gc,
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op=op,
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inputs=inputs,
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exception=RuntimeError
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)
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@given(**hu.gcs_cpu_only)
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def test_under_bounds(self, gc, dc):
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# Copy pasted from test_pack_ops but with 3 changed to 2
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lengths = np.array([1, 2, 2], dtype=np.int32)
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data = np.array([
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[1.0, 1.0],
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[2.0, 2.0],
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[2.0, 2.0],
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[3.0, 3.0],
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[3.0, 3.0],
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[3.0, 3.0]], dtype=np.float32)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'])
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inputs = [lengths, data]
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self.assertRunOpRaises(
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device_option=gc,
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op=op,
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inputs=inputs,
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exception=RuntimeError
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)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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