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import numpy as np
import torch
from mmcv import ConfigDict
from torch import nn
from mmseg.models import BACKBONES, HEADS, build_segmentor
from mmseg.models.decode_heads.cascade_decode_head import BaseCascadeDecodeHead
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
def _demo_mm_inputs(input_shape=(1, 3, 8, 16), num_classes=10):
"""Create a superset of inputs needed to run test or train batches.
Args:
input_shape (tuple):
input batch dimensions
num_classes (int):
number of semantic classes
"""
(N, C, H, W) = input_shape
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
segs = rng.randint(
low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
img_metas = [{
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False,
'flip_direction': 'horizontal'
} for _ in range(N)]
mm_inputs = {
'imgs': torch.FloatTensor(imgs),
'img_metas': img_metas,
'gt_semantic_seg': torch.LongTensor(segs)
}
return mm_inputs
@BACKBONES.register_module()
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
def forward(self, x):
return [self.conv(x)]
@HEADS.register_module()
class ExampleDecodeHead(BaseDecodeHead):
def __init__(self):
super(ExampleDecodeHead, self).__init__(3, 3, num_classes=19)
def forward(self, inputs):
return self.cls_seg(inputs[0])
@HEADS.register_module()
class ExampleCascadeDecodeHead(BaseCascadeDecodeHead):
def __init__(self):
super(ExampleCascadeDecodeHead, self).__init__(3, 3, num_classes=19)
def forward(self, inputs, prev_out):
return self.cls_seg(inputs[0])
def _segmentor_forward_train_test(segmentor):
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
mm_inputs = _demo_mm_inputs(num_classes=num_classes)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_semantic_seg = mm_inputs['gt_semantic_seg']
# convert to cuda Tensor if applicable
if torch.cuda.is_available():
segmentor = segmentor.cuda()
imgs = imgs.cuda()
gt_semantic_seg = gt_semantic_seg.cuda()
# Test forward train
losses = segmentor.forward(
imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True)
assert isinstance(losses, dict)
# Test forward simple test
with torch.no_grad():
segmentor.eval()
# pack into lists
img_list = [img[None, :] for img in imgs]
img_meta_list = [[img_meta] for img_meta in img_metas]
segmentor.forward(img_list, img_meta_list, return_loss=False)
# Test forward aug test
with torch.no_grad():
segmentor.eval()
# pack into lists
img_list = [img[None, :] for img in imgs]
img_list = img_list + img_list
img_meta_list = [[img_meta] for img_meta in img_metas]
img_meta_list = img_meta_list + img_meta_list
segmentor.forward(img_list, img_meta_list, return_loss=False)
def test_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = ConfigDict(
type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
train_cfg=None,
test_cfg=dict(mode='whole'))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = ConfigDict(
type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
auxiliary_head=dict(type='ExampleDecodeHead'))
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = ConfigDict(
type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
auxiliary_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleDecodeHead')
])
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
def test_cascade_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
decode_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
])
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
decode_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
],
auxiliary_head=dict(type='ExampleDecodeHead'))
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
decode_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
],
auxiliary_head=[
dict(type='ExampleDecodeHead'),
dict(type='ExampleDecodeHead')
])
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
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