refactor test organization (open-mmlab#440)

* refactor test organization

* fixed se layer

* update mmcv uper bound

mmseg/__init__.py

@@ -3,7 +3,7 @@
 from .version import __version__, version_info
 
 MMCV_MIN = '1.1.4'
-MMCV_MAX = '1.3.0'
+MMCV_MAX = '1.4.0'
 
 
 def digit_version(version_str):

mmseg/models/utils/__init__.py

@@ -1,10 +1,11 @@
 from .inverted_residual import InvertedResidual, InvertedResidualV3
 from .make_divisible import make_divisible
 from .res_layer import ResLayer
+from .se_layer import SELayer
 from .self_attention_block import SelfAttentionBlock
 from .up_conv_block import UpConvBlock
 
 __all__ = [
     'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual',
-    'UpConvBlock', 'InvertedResidualV3'
+    'UpConvBlock', 'InvertedResidualV3', 'SELayer'
 ]

tests/test_models/test_backbones/__init__.py

@@ -0,0 +1,3 @@
+from .utils import all_zeros, check_norm_state, is_block, is_norm
+
+__all__ = ['is_norm', 'is_block', 'all_zeros', 'check_norm_state']

tests/test_utils/test_inverted_residual_module.py → tests/test_models/test_backbones/test_blocks.py

@@ -2,7 +2,20 @@
 import pytest
 import torch
 
-from mmseg.models.utils import InvertedResidual, InvertedResidualV3
+from mmseg.models.utils import (InvertedResidual, InvertedResidualV3, SELayer,
+                                make_divisible)
+
+
+def test_make_divisible():
+    # test with min_value = None
+    assert make_divisible(10, 4) == 12
+    assert make_divisible(9, 4) == 12
+    assert make_divisible(1, 4) == 4
+
+    # test with min_value = 8
+    assert make_divisible(10, 4, 8) == 12
+    assert make_divisible(9, 4, 8) == 12
+    assert make_divisible(1, 4, 8) == 8
 
 
 def test_inv_residual():
@@ -118,3 +131,39 @@ def test_inv_residualv3():
     x = torch.randn(2, 32, 64, 64, requires_grad=True)
     output = inv_module(x)
     assert output.shape == (2, 40, 32, 32)
+
+
+def test_se_layer():
+    with pytest.raises(AssertionError):
+        # test act_cfg assertion.
+        SELayer(32, act_cfg=(dict(type='ReLU'), ))
+
+    # test config with channels = 16.
+    se_layer = SELayer(16)
+    assert se_layer.conv1.conv.kernel_size == (1, 1)
+    assert se_layer.conv1.conv.stride == (1, 1)
+    assert se_layer.conv1.conv.padding == (0, 0)
+    assert isinstance(se_layer.conv1.activate, torch.nn.ReLU)
+    assert se_layer.conv2.conv.kernel_size == (1, 1)
+    assert se_layer.conv2.conv.stride == (1, 1)
+    assert se_layer.conv2.conv.padding == (0, 0)
+    assert isinstance(se_layer.conv2.activate, mmcv.cnn.HSigmoid)
+
+    x = torch.rand(1, 16, 64, 64)
+    output = se_layer(x)
+    assert output.shape == (1, 16, 64, 64)
+
+    # test config with channels = 16, act_cfg = dict(type='ReLU').
+    se_layer = SELayer(16, act_cfg=dict(type='ReLU'))
+    assert se_layer.conv1.conv.kernel_size == (1, 1)
+    assert se_layer.conv1.conv.stride == (1, 1)
+    assert se_layer.conv1.conv.padding == (0, 0)
+    assert isinstance(se_layer.conv1.activate, torch.nn.ReLU)
+    assert se_layer.conv2.conv.kernel_size == (1, 1)
+    assert se_layer.conv2.conv.stride == (1, 1)
+    assert se_layer.conv2.conv.padding == (0, 0)
+    assert isinstance(se_layer.conv2.activate, torch.nn.ReLU)
+
+    x = torch.rand(1, 16, 64, 64)
+    output = se_layer(x)
+    assert output.shape == (1, 16, 64, 64)

tests/test_models/test_backbones/test_cgnet.py

@@ -0,0 +1,150 @@
+import pytest
+import torch
+
+from mmseg.models.backbones import CGNet
+from mmseg.models.backbones.cgnet import (ContextGuidedBlock,
+                                          GlobalContextExtractor)
+
+
+def test_cgnet_GlobalContextExtractor():
+    block = GlobalContextExtractor(16, 16, with_cp=True)
+    x = torch.randn(2, 16, 64, 64, requires_grad=True)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([2, 16, 64, 64])
+
+
+def test_cgnet_context_guided_block():
+    with pytest.raises(AssertionError):
+        # cgnet ContextGuidedBlock GlobalContextExtractor channel and reduction
+        # constraints.
+        ContextGuidedBlock(8, 8)
+
+    # test cgnet ContextGuidedBlock with checkpoint forward
+    block = ContextGuidedBlock(
+        16, 16, act_cfg=dict(type='PReLU'), with_cp=True)
+    assert block.with_cp
+    x = torch.randn(2, 16, 64, 64, requires_grad=True)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([2, 16, 64, 64])
+
+    # test cgnet ContextGuidedBlock without checkpoint forward
+    block = ContextGuidedBlock(32, 32)
+    assert not block.with_cp
+    x = torch.randn(3, 32, 32, 32)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([3, 32, 32, 32])
+
+    # test cgnet ContextGuidedBlock with down sampling
+    block = ContextGuidedBlock(32, 32, downsample=True)
+    assert block.conv1x1.conv.in_channels == 32
+    assert block.conv1x1.conv.out_channels == 32
+    assert block.conv1x1.conv.kernel_size == (3, 3)
+    assert block.conv1x1.conv.stride == (2, 2)
+    assert block.conv1x1.conv.padding == (1, 1)
+
+    assert block.f_loc.in_channels == 32
+    assert block.f_loc.out_channels == 32
+    assert block.f_loc.kernel_size == (3, 3)
+    assert block.f_loc.stride == (1, 1)
+    assert block.f_loc.padding == (1, 1)
+    assert block.f_loc.groups == 32
+    assert block.f_loc.dilation == (1, 1)
+    assert block.f_loc.bias is None
+
+    assert block.f_sur.in_channels == 32
+    assert block.f_sur.out_channels == 32
+    assert block.f_sur.kernel_size == (3, 3)
+    assert block.f_sur.stride == (1, 1)
+    assert block.f_sur.padding == (2, 2)
+    assert block.f_sur.groups == 32
+    assert block.f_sur.dilation == (2, 2)
+    assert block.f_sur.bias is None
+
+    assert block.bottleneck.in_channels == 64
+    assert block.bottleneck.out_channels == 32
+    assert block.bottleneck.kernel_size == (1, 1)
+    assert block.bottleneck.stride == (1, 1)
+    assert block.bottleneck.bias is None
+
+    x = torch.randn(1, 32, 32, 32)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 32, 16, 16])
+
+    # test cgnet ContextGuidedBlock without down sampling
+    block = ContextGuidedBlock(32, 32, downsample=False)
+    assert block.conv1x1.conv.in_channels == 32
+    assert block.conv1x1.conv.out_channels == 16
+    assert block.conv1x1.conv.kernel_size == (1, 1)
+    assert block.conv1x1.conv.stride == (1, 1)
+    assert block.conv1x1.conv.padding == (0, 0)
+
+    assert block.f_loc.in_channels == 16
+    assert block.f_loc.out_channels == 16
+    assert block.f_loc.kernel_size == (3, 3)
+    assert block.f_loc.stride == (1, 1)
+    assert block.f_loc.padding == (1, 1)
+    assert block.f_loc.groups == 16
+    assert block.f_loc.dilation == (1, 1)
+    assert block.f_loc.bias is None
+
+    assert block.f_sur.in_channels == 16
+    assert block.f_sur.out_channels == 16
+    assert block.f_sur.kernel_size == (3, 3)
+    assert block.f_sur.stride == (1, 1)
+    assert block.f_sur.padding == (2, 2)
+    assert block.f_sur.groups == 16
+    assert block.f_sur.dilation == (2, 2)
+    assert block.f_sur.bias is None
+
+    x = torch.randn(1, 32, 32, 32)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 32, 32, 32])
+
+
+def test_cgnet_backbone():
+    with pytest.raises(AssertionError):
+        # check invalid num_channels
+        CGNet(num_channels=(32, 64, 128, 256))
+
+    with pytest.raises(AssertionError):
+        # check invalid num_blocks
+        CGNet(num_blocks=(3, 21, 3))
+
+    with pytest.raises(AssertionError):
+        # check invalid dilation
+        CGNet(num_blocks=2)
+
+    with pytest.raises(AssertionError):
+        # check invalid reduction
+        CGNet(reductions=16)
+
+    with pytest.raises(AssertionError):
+        # check invalid num_channels and reduction
+        CGNet(num_channels=(32, 64, 128), reductions=(64, 129))
+
+    # Test CGNet with default settings
+    model = CGNet()
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(2, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size([2, 35, 112, 112])
+    assert feat[1].shape == torch.Size([2, 131, 56, 56])
+    assert feat[2].shape == torch.Size([2, 256, 28, 28])
+
+    # Test CGNet with norm_eval True and with_cp True
+    model = CGNet(norm_eval=True, with_cp=True)
+    with pytest.raises(TypeError):
+        # check invalid pretrained
+        model.init_weights(pretrained=8)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(2, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size([2, 35, 112, 112])
+    assert feat[1].shape == torch.Size([2, 131, 56, 56])
+    assert feat[2].shape == torch.Size([2, 256, 28, 28])

tests/test_models/test_backbones/test_fast_scnn.py

@@ -0,0 +1,31 @@
+import pytest
+import torch
+
+from mmseg.models.backbones import FastSCNN
+
+
+def test_fastscnn_backbone():
+    with pytest.raises(AssertionError):
+        # Fast-SCNN channel constraints.
+        FastSCNN(
+            3, (32, 48),
+            64, (64, 96, 128), (2, 2, 1),
+            global_out_channels=127,
+            higher_in_channels=64,
+            lower_in_channels=128)
+
+    # Test FastSCNN Standard Forward
+    model = FastSCNN()
+    model.init_weights()
+    model.train()
+    batch_size = 4
+    imgs = torch.randn(batch_size, 3, 512, 1024)
+    feat = model(imgs)
+
+    assert len(feat) == 3
+    # higher-res
+    assert feat[0].shape == torch.Size([batch_size, 64, 64, 128])
+    # lower-res
+    assert feat[1].shape == torch.Size([batch_size, 128, 16, 32])
+    # FFM output
+    assert feat[2].shape == torch.Size([batch_size, 128, 64, 128])

tests/test_models/test_backbones/test_mobilenet_v3.py

@@ -0,0 +1,66 @@
+import pytest
+import torch
+
+from mmseg.models.backbones import MobileNetV3
+
+
+def test_mobilenet_v3():
+    with pytest.raises(AssertionError):
+        # check invalid arch
+        MobileNetV3('big')
+
+    with pytest.raises(AssertionError):
+        # check invalid reduction_factor
+        MobileNetV3(reduction_factor=0)
+
+    with pytest.raises(ValueError):
+        # check invalid out_indices
+        MobileNetV3(out_indices=(0, 1, 15))
+
+    with pytest.raises(ValueError):
+        # check invalid frozen_stages
+        MobileNetV3(frozen_stages=15)
+
+    with pytest.raises(TypeError):
+        # check invalid pretrained
+        model = MobileNetV3()
+        model.init_weights(pretrained=8)
+
+    # Test MobileNetV3 with default settings
+    model = MobileNetV3()
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(2, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == (2, 16, 112, 112)
+    assert feat[1].shape == (2, 16, 56, 56)
+    assert feat[2].shape == (2, 576, 28, 28)
+
+    # Test MobileNetV3 with arch = 'large'
+    model = MobileNetV3(arch='large', out_indices=(1, 3, 16))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(2, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == (2, 16, 112, 112)
+    assert feat[1].shape == (2, 24, 56, 56)
+    assert feat[2].shape == (2, 960, 28, 28)
+
+    # Test MobileNetV3 with norm_eval True, with_cp True and frozen_stages=5
+    model = MobileNetV3(norm_eval=True, with_cp=True, frozen_stages=5)
+    with pytest.raises(TypeError):
+        # check invalid pretrained
+        model.init_weights(pretrained=8)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(2, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == (2, 16, 112, 112)
+    assert feat[1].shape == (2, 16, 56, 56)
+    assert feat[2].shape == (2, 576, 28, 28)

tests/test_models/test_backbones/test_resnest.py

@@ -0,0 +1,43 @@
+import pytest
+import torch
+
+from mmseg.models.backbones import ResNeSt
+from mmseg.models.backbones.resnest import Bottleneck as BottleneckS
+
+
+def test_resnest_bottleneck():
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        BottleneckS(64, 64, radix=2, reduction_factor=4, style='tensorflow')
+
+    # Test ResNeSt Bottleneck structure
+    block = BottleneckS(
+        64, 256, radix=2, reduction_factor=4, stride=2, style='pytorch')
+    assert block.avd_layer.stride == 2
+    assert block.conv2.channels == 256
+
+    # Test ResNeSt Bottleneck forward
+    block = BottleneckS(64, 16, radix=2, reduction_factor=4)
+    x = torch.randn(2, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([2, 64, 56, 56])
+
+
+def test_resnest_backbone():
+    with pytest.raises(KeyError):
+        # ResNeSt depth should be in [50, 101, 152, 200]
+        ResNeSt(depth=18)
+
+    # Test ResNeSt with radix 2, reduction_factor 4
+    model = ResNeSt(
+        depth=50, radix=2, reduction_factor=4, out_indices=(0, 1, 2, 3))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(2, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([2, 256, 56, 56])
+    assert feat[1].shape == torch.Size([2, 512, 28, 28])
+    assert feat[2].shape == torch.Size([2, 1024, 14, 14])
+    assert feat[3].shape == torch.Size([2, 2048, 7, 7])