v2d / third-party /InterFormer /tests /test_sampler.py

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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch

from mmseg.models.decode_heads import FCNHead
from mmseg.structures import OHEMPixelSampler


def _context_for_ohem():
    return FCNHead(in_channels=32, channels=16, num_classes=19)


def _context_for_ohem_multiple_loss():
    return FCNHead(
        in_channels=32,
        channels=16,
        num_classes=19,
        loss_decode=[
            dict(type='CrossEntropyLoss', loss_name='loss_1'),
            dict(type='CrossEntropyLoss', loss_name='loss_2')
        ])


def test_ohem_sampler():

    with pytest.raises(AssertionError):
        # seg_logit and seg_label must be of the same size
        sampler = OHEMPixelSampler(context=_context_for_ohem())
        seg_logit = torch.randn(1, 19, 45, 45)
        seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
        sampler.sample(seg_logit, seg_label)

    # test with thresh
    sampler = OHEMPixelSampler(
        context=_context_for_ohem(), thresh=0.7, min_kept=200)
    seg_logit = torch.randn(1, 19, 45, 45)
    seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
    seg_weight = sampler.sample(seg_logit, seg_label)
    assert seg_weight.shape[0] == seg_logit.shape[0]
    assert seg_weight.shape[1:] == seg_logit.shape[2:]
    assert seg_weight.sum() > 200

    # test w.o thresh
    sampler = OHEMPixelSampler(context=_context_for_ohem(), min_kept=200)
    seg_logit = torch.randn(1, 19, 45, 45)
    seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
    seg_weight = sampler.sample(seg_logit, seg_label)
    assert seg_weight.shape[0] == seg_logit.shape[0]
    assert seg_weight.shape[1:] == seg_logit.shape[2:]
    assert seg_weight.sum() == 200

    # test multiple losses case
    with pytest.raises(AssertionError):
        # seg_logit and seg_label must be of the same size
        sampler = OHEMPixelSampler(context=_context_for_ohem_multiple_loss())
        seg_logit = torch.randn(1, 19, 45, 45)
        seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
        sampler.sample(seg_logit, seg_label)

    # test with thresh in multiple losses case
    sampler = OHEMPixelSampler(
        context=_context_for_ohem_multiple_loss(), thresh=0.7, min_kept=200)
    seg_logit = torch.randn(1, 19, 45, 45)
    seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
    seg_weight = sampler.sample(seg_logit, seg_label)
    assert seg_weight.shape[0] == seg_logit.shape[0]
    assert seg_weight.shape[1:] == seg_logit.shape[2:]
    assert seg_weight.sum() > 200

    # test w.o thresh in multiple losses case
    sampler = OHEMPixelSampler(
        context=_context_for_ohem_multiple_loss(), min_kept=200)
    seg_logit = torch.randn(1, 19, 45, 45)
    seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
    seg_weight = sampler.sample(seg_logit, seg_label)
    assert seg_weight.shape[0] == seg_logit.shape[0]
    assert seg_weight.shape[1:] == seg_logit.shape[2:]
    assert seg_weight.sum() == 200

	# Copyright (c) OpenMMLab. All rights reserved.
	import pytest
	import torch

	from mmseg.models.decode_heads import FCNHead
	from mmseg.structures import OHEMPixelSampler


	def _context_for_ohem():
	return FCNHead(in_channels=32, channels=16, num_classes=19)


	def _context_for_ohem_multiple_loss():
	return FCNHead(
	in_channels=32,
	channels=16,
	num_classes=19,
	loss_decode=[
	dict(type='CrossEntropyLoss', loss_name='loss_1'),
	dict(type='CrossEntropyLoss', loss_name='loss_2')
	])


	def test_ohem_sampler():

	with pytest.raises(AssertionError):
	# seg_logit and seg_label must be of the same size
	sampler = OHEMPixelSampler(context=_context_for_ohem())
	seg_logit = torch.randn(1, 19, 45, 45)
	seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
	sampler.sample(seg_logit, seg_label)

	# test with thresh
	sampler = OHEMPixelSampler(
	context=_context_for_ohem(), thresh=0.7, min_kept=200)
	seg_logit = torch.randn(1, 19, 45, 45)
	seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
	seg_weight = sampler.sample(seg_logit, seg_label)
	assert seg_weight.shape[0] == seg_logit.shape[0]
	assert seg_weight.shape[1:] == seg_logit.shape[2:]
	assert seg_weight.sum() > 200

	# test w.o thresh
	sampler = OHEMPixelSampler(context=_context_for_ohem(), min_kept=200)
	seg_logit = torch.randn(1, 19, 45, 45)
	seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
	seg_weight = sampler.sample(seg_logit, seg_label)
	assert seg_weight.shape[0] == seg_logit.shape[0]
	assert seg_weight.shape[1:] == seg_logit.shape[2:]
	assert seg_weight.sum() == 200

	# test multiple losses case
	with pytest.raises(AssertionError):
	# seg_logit and seg_label must be of the same size
	sampler = OHEMPixelSampler(context=_context_for_ohem_multiple_loss())
	seg_logit = torch.randn(1, 19, 45, 45)
	seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
	sampler.sample(seg_logit, seg_label)

	# test with thresh in multiple losses case
	sampler = OHEMPixelSampler(
	context=_context_for_ohem_multiple_loss(), thresh=0.7, min_kept=200)
	seg_logit = torch.randn(1, 19, 45, 45)
	seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
	seg_weight = sampler.sample(seg_logit, seg_label)
	assert seg_weight.shape[0] == seg_logit.shape[0]
	assert seg_weight.shape[1:] == seg_logit.shape[2:]
	assert seg_weight.sum() > 200

	# test w.o thresh in multiple losses case
	sampler = OHEMPixelSampler(
	context=_context_for_ohem_multiple_loss(), min_kept=200)
	seg_logit = torch.randn(1, 19, 45, 45)
	seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
	seg_weight = sampler.sample(seg_logit, seg_label)
	assert seg_weight.shape[0] == seg_logit.shape[0]
	assert seg_weight.shape[1:] == seg_logit.shape[2:]
	assert seg_weight.sum() == 200