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# Copyright (c) OpenMMLab. All rights reserved.
"""pytest tests/test_forward.py."""
import copy
from os.path import dirname, exists, join
from unittest.mock import patch
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmengine.model.utils import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmengine.structures import PixelData
from mmengine.utils import is_list_of, is_tuple_of
from torch import Tensor
from mmseg.structures import SegDataSample
init_default_scope('mmseg')
def _demo_mm_inputs(batch_size=2, image_shapes=(3, 32, 32), num_classes=5):
"""Create a superset of inputs needed to run test or train batches.
Args:
batch_size (int): batch size. Default to 2.
image_shapes (List[tuple], Optional): image shape.
Default to (3, 128, 128)
num_classes (int): number of different labels a
box might have. Default to 10.
"""
if isinstance(image_shapes, list):
assert len(image_shapes) == batch_size
else:
image_shapes = [image_shapes] * batch_size
inputs = []
data_samples = []
for idx in range(batch_size):
image_shape = image_shapes[idx]
c, h, w = image_shape
image = np.random.randint(0, 255, size=image_shape, dtype=np.uint8)
mm_input = torch.from_numpy(image)
img_meta = {
'img_id': idx,
'img_shape': image_shape[1:],
'ori_shape': image_shape[1:],
'pad_shape': image_shape[1:],
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False,
'flip_direction': None,
}
data_sample = SegDataSample()
data_sample.set_metainfo(img_meta)
gt_semantic_seg = np.random.randint(
0, num_classes, (1, h, w), dtype=np.uint8)
gt_semantic_seg = torch.LongTensor(gt_semantic_seg)
gt_sem_seg_data = dict(data=gt_semantic_seg)
data_sample.gt_sem_seg = PixelData(**gt_sem_seg_data)
inputs.append(mm_input)
data_samples.append(data_sample)
return dict(inputs=inputs, data_samples=data_samples)
def _get_config_directory():
"""Find the predefined segmentor config directory."""
try:
# Assume we are running in the source mmsegmentation repo
repo_dpath = dirname(dirname(dirname(__file__)))
except NameError:
# For IPython development when this __file__ is not defined
import mmseg
repo_dpath = dirname(dirname(dirname(mmseg.__file__)))
config_dpath = join(repo_dpath, 'configs')
if not exists(config_dpath):
raise Exception('Cannot find config path')
return config_dpath
def _get_config_module(fname):
"""Load a configuration as a python module."""
from mmengine import Config
config_dpath = _get_config_directory()
config_fpath = join(config_dpath, fname)
config_mod = Config.fromfile(config_fpath)
return config_mod
def _get_segmentor_cfg(fname):
"""Grab configs necessary to create a segmentor.
These are deep copied to allow for safe modification of parameters without
influencing other tests.
"""
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
return model
def test_pspnet_forward():
_test_encoder_decoder_forward(
'pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py')
def test_fcn_forward():
_test_encoder_decoder_forward(
'fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py')
def test_deeplabv3_forward():
_test_encoder_decoder_forward(
'deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py')
def test_deeplabv3plus_forward():
_test_encoder_decoder_forward(
'deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py')
def test_gcnet_forward():
_test_encoder_decoder_forward(
'gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_ann_forward():
_test_encoder_decoder_forward(
'ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_ccnet_forward():
if not torch.cuda.is_available():
pytest.skip('CCNet requires CUDA')
_test_encoder_decoder_forward(
'ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_danet_forward():
_test_encoder_decoder_forward(
'danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_nonlocal_net_forward():
_test_encoder_decoder_forward(
'nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_upernet_forward():
_test_encoder_decoder_forward(
'upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py')
def test_hrnet_forward():
_test_encoder_decoder_forward(
'hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py')
def test_ocrnet_forward():
_test_encoder_decoder_forward(
'ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py')
def test_psanet_forward():
_test_encoder_decoder_forward(
'psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_sem_fpn_forward():
_test_encoder_decoder_forward(
'sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py')
def test_mobilenet_v2_forward():
_test_encoder_decoder_forward(
'mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py')
def test_dnlnet_forward():
_test_encoder_decoder_forward(
'dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def test_emanet_forward():
_test_encoder_decoder_forward(
'emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py')
def test_isanet_forward():
_test_encoder_decoder_forward(
'isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py')
def get_world_size(process_group):
return 1
def _check_input_dim(self, inputs):
pass
@patch('torch.nn.modules.batchnorm._BatchNorm._check_input_dim',
_check_input_dim)
@patch('torch.distributed.get_world_size', get_world_size)
def _test_encoder_decoder_forward(cfg_file):
model = _get_segmentor_cfg(cfg_file)
model['pretrained'] = None
model['test_cfg']['mode'] = 'whole'
from mmseg.models import build_segmentor
segmentor = build_segmentor(model)
segmentor.init_weights()
if isinstance(segmentor.decode_head, nn.ModuleList):
num_classes = segmentor.decode_head[-1].num_classes
else:
num_classes = segmentor.decode_head.num_classes
# batch_size=2 for BatchNorm
packed_inputs = _demo_mm_inputs(
batch_size=2, image_shapes=(3, 32, 32), num_classes=num_classes)
# convert to cuda Tensor if applicable
if torch.cuda.is_available():
segmentor = segmentor.cuda()
else:
segmentor = revert_sync_batchnorm(segmentor)
# Test forward train
data = segmentor.data_preprocessor(packed_inputs, True)
losses = segmentor.forward(**data, mode='loss')
assert isinstance(losses, dict)
packed_inputs = _demo_mm_inputs(
batch_size=1, image_shapes=(3, 32, 32), num_classes=num_classes)
data = segmentor.data_preprocessor(packed_inputs, False)
with torch.no_grad():
segmentor.eval()
# Test forward predict
batch_results = segmentor.forward(**data, mode='predict')
assert len(batch_results) == 1
assert is_list_of(batch_results, SegDataSample)
assert batch_results[0].pred_sem_seg.shape == (32, 32)
assert batch_results[0].seg_logits.data.shape == (num_classes, 32, 32)
assert batch_results[0].gt_sem_seg.shape == (32, 32)
# Test forward tensor
batch_results = segmentor.forward(**data, mode='tensor')
assert isinstance(batch_results, Tensor) or is_tuple_of(
batch_results, Tensor)
# Test forward predict without ground truth
data.pop('data_samples')
batch_results = segmentor.forward(**data, mode='predict')
assert len(batch_results) == 1
assert is_list_of(batch_results, SegDataSample)
assert batch_results[0].pred_sem_seg.shape == (32, 32)
# Test forward tensor without ground truth
batch_results = segmentor.forward(**data, mode='tensor')
assert isinstance(batch_results, Tensor) or is_tuple_of(
batch_results, Tensor)
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