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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv import ConfigDict
from mmcv.cnn import build_plugin_layer
from mmdet.models.plugins import DropBlock
def test_dropblock():
feat = torch.rand(1, 1, 11, 11)
drop_prob = 1.0
dropblock = DropBlock(drop_prob, block_size=11, warmup_iters=0)
out_feat = dropblock(feat)
assert (out_feat == 0).all() and out_feat.shape == feat.shape
drop_prob = 0.5
dropblock = DropBlock(drop_prob, block_size=5, warmup_iters=0)
out_feat = dropblock(feat)
assert out_feat.shape == feat.shape
# drop_prob must be (0,1]
with pytest.raises(AssertionError):
DropBlock(1.5, 3)
# block_size cannot be an even number
with pytest.raises(AssertionError):
DropBlock(0.5, 2)
# warmup_iters cannot be less than 0
with pytest.raises(AssertionError):
DropBlock(0.5, 3, -1)
def test_pixel_decoder():
base_channels = 64
pixel_decoder_cfg = ConfigDict(
dict(
type='PixelDecoder',
in_channels=[base_channels * 2**i for i in range(4)],
feat_channels=base_channels,
out_channels=base_channels,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU')))
self = build_plugin_layer(pixel_decoder_cfg)[1]
img_metas = [{}, {}]
feats = [
torch.rand((2, base_channels * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
for i in range(4)
]
mask_feature, memory = self(feats, img_metas)
assert (memory == feats[-1]).all()
assert mask_feature.shape == feats[0].shape
def test_transformer_encoder_pixel_decoder():
base_channels = 64
pixel_decoder_cfg = ConfigDict(
dict(
type='TransformerEncoderPixelDecoder',
in_channels=[base_channels * 2**i for i in range(4)],
feat_channels=base_channels,
out_channels=base_channels,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiheadAttention',
embed_dims=base_channels,
num_heads=8,
attn_drop=0.1,
proj_drop=0.1,
dropout_layer=None,
batch_first=False),
ffn_cfgs=dict(
embed_dims=base_channels,
feedforward_channels=base_channels * 8,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.1,
dropout_layer=None,
add_identity=True),
operation_order=('self_attn', 'norm', 'ffn', 'norm'),
norm_cfg=dict(type='LN'),
init_cfg=None,
batch_first=False),
init_cfg=None),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=base_channels // 2,
normalize=True)))
self = build_plugin_layer(pixel_decoder_cfg)[1]
img_metas = [{
'batch_input_shape': (128, 160),
'img_shape': (120, 160, 3),
}, {
'batch_input_shape': (128, 160),
'img_shape': (125, 160, 3),
}]
feats = [
torch.rand((2, base_channels * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
for i in range(4)
]
mask_feature, memory = self(feats, img_metas)
assert memory.shape[-2:] == feats[-1].shape[-2:]
assert mask_feature.shape == feats[0].shape
def test_msdeformattn_pixel_decoder():
base_channels = 64
pixel_decoder_cfg = ConfigDict(
dict(
type='MSDeformAttnPixelDecoder',
in_channels=[base_channels * 2**i for i in range(4)],
strides=[4, 8, 16, 32],
feat_channels=base_channels,
out_channels=base_channels,
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention',
embed_dims=base_channels,
num_heads=8,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=False,
norm_cfg=None,
init_cfg=None),
ffn_cfgs=dict(
type='FFN',
embed_dims=base_channels,
feedforward_channels=base_channels * 4,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True)),
operation_order=('self_attn', 'norm', 'ffn', 'norm')),
init_cfg=None),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=base_channels // 2,
normalize=True),
init_cfg=None), )
self = build_plugin_layer(pixel_decoder_cfg)[1]
feats = [
torch.rand((2, base_channels * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
for i in range(4)
]
mask_feature, multi_scale_features = self(feats)
assert mask_feature.shape == feats[0].shape
assert len(multi_scale_features) == 3
multi_scale_features = multi_scale_features[::-1]
for i in range(3):
assert multi_scale_features[i].shape[-2:] == feats[i + 1].shape[-2:]
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