update test

mmcls/models/backbones/mobilenet_v2.py

@@ -2,8 +2,8 @@ import logging
 
 import torch.nn as nn
 import torch.utils.checkpoint as cp
-
+from mmcv.runner import load_checkpoint
-from ..runner import load_checkpoint
+t
 from .base_backbone import BaseBackbone
 from .weight_init import constant_init, kaiming_init
 
@@ -154,7 +154,7 @@ class MobileNetv2(BaseBackbone):
     def __init__(self,
                  widen_factor=1.,
                  activation=nn.ReLU6,
-                 out_indices=(0, 1, 2, 3, 4, 5, 6),
+                 out_indices=(0, 1, 2, 3, 4, 5, 6, 7),
                  frozen_stages=-1,
                  bn_eval=True,
                  bn_frozen=False,
@@ -177,6 +177,7 @@ class MobileNetv2(BaseBackbone):
         self.activation = activation(inplace=True)
 
         self.out_indices = out_indices
+        assert frozen_stages <= 7
         self.frozen_stages = frozen_stages
         self.bn_eval = bn_eval
         self.bn_frozen = bn_frozen

tests/test_backbone.py

@@ -1,11 +1,128 @@
+import pytest
 import torch
 import torch.nn as nn
+from torch.nn.modules import GroupNorm
+from torch.nn.modules.batchnorm import _BatchNorm
 
 from mmcls.models.backbones import MobileNetv2
+from mmcls.models.backbones.mobilenet_v2 import InvertedResidual
+
+
+def is_block(modules):
+    """Check if is ResNet building block."""
+    if isinstance(modules, (InvertedResidual,)):
+        return True
+    return False
+
+
+def is_norm(modules):
+    """Check if is one of the norms."""
+    if isinstance(modules, (GroupNorm, _BatchNorm)):
+        return True
+    return False
+
+
+def check_norm_state(modules, train_state):
+    """Check if norm layer is in correct train state."""
+    for mod in modules:
+        if isinstance(mod, _BatchNorm):
+            if mod.training != train_state:
+                return False
+    return True
+
+
+def test_mobilenetv2_invertedresidual():
+
+    with pytest.raises(AssertionError):
+        # stride must be in [1, 2]
+        InvertedResidual(64, 16,
+                         stride=3, expand_ratio=6)
+
+    # Test InvertedResidual with checkpoint forward, stride=1
+    block = InvertedResidual(64, 16,
+                             stride=1,
+                             expand_ratio=6)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 16, 56, 56])
+
+    # Test InvertedResidual with checkpoint forward, stride=2
+    block = InvertedResidual(64, 16,
+                             stride=2,
+                             expand_ratio=6)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 16, 28, 28])
+
+    # Test InvertedResidual with checkpoint forward
+    block = InvertedResidual(64, 16,
+                             stride=1,
+                             expand_ratio=6,
+                             with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 16, 56, 56])
+
+    # Test InvertedResidual with activation=nn.ReLU
+    block = InvertedResidual(64, 16,
+                             stride=1,
+                             expand_ratio=6,
+                             activation=nn.ReLU)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 16, 56, 56])
 
 
 def test_mobilenetv2_backbone():
-    # Test MobileNetv2 with widen_factor 1.0, activation nn.ReLU6
+    with pytest.raises(TypeError):
+        # pretrained must be a string path
+        model = MobileNetv2()
+        model.init_weights(pretrained=0)
+
+    with pytest.raises(AssertionError):
+        # frozen_stages must less than 7
+        MobileNetv2(frozen_stages=8)
+
+    # Test MobileNetv2
+    model = MobileNetv2()
+    model.init_weights()
+    model.train()
+    assert check_norm_state(model.modules(), False)
+
+    # Test MobileNetv2 with first stage frozen
+    frozen_stages = 1
+    model = MobileNetv2(frozen_stages=frozen_stages)
+    model.init_weights()
+    model.train()
+    assert model.bn1.training is False
+    for layer in [model.conv1, model.bn1]:
+        for param in layer.parameters():
+            assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'layer{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test MobileNetv2 with first stage frozen
+    model = MobileNetv2(bn_frozen=True)
+    model.init_weights()
+    model.train()
+    assert model.bn1.training is False
+
+    for i in range(1, 8):
+        layer = getattr(model, f'layer{i}')
+
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+                for params in mod.parameters():
+                    params.requires_grad = False
+
+    # Test MobileNetv2 forward with widen_factor=1.0
     model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6)
     model.init_weights()
     model.train()
@@ -20,3 +137,90 @@ def test_mobilenetv2_backbone():
     assert feat[4].shape == torch.Size([1, 96, 14, 14])
     assert feat[5].shape == torch.Size([1, 160, 7, 7])
     assert feat[6].shape == torch.Size([1, 320, 7, 7])
+
+    # Test MobileNetv2 forward with activation=nn.ReLU
+    model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 8
+    assert feat[0].shape == torch.Size([1, 16, 112, 112])
+    assert feat[1].shape == torch.Size([1, 24, 56, 56])
+    assert feat[2].shape == torch.Size([1, 32, 28, 28])
+    assert feat[3].shape == torch.Size([1, 64, 14, 14])
+    assert feat[4].shape == torch.Size([1, 96, 14, 14])
+    assert feat[5].shape == torch.Size([1, 160, 7, 7])
+    assert feat[6].shape == torch.Size([1, 320, 7, 7])
+
+    # Test MobileNetv2 with BatchNorm forward
+    model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6)
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 8
+    assert feat[0].shape == torch.Size([1, 16, 112, 112])
+    assert feat[1].shape == torch.Size([1, 24, 56, 56])
+    assert feat[2].shape == torch.Size([1, 32, 28, 28])
+    assert feat[3].shape == torch.Size([1, 64, 14, 14])
+    assert feat[4].shape == torch.Size([1, 96, 14, 14])
+    assert feat[5].shape == torch.Size([1, 160, 7, 7])
+    assert feat[6].shape == torch.Size([1, 320, 7, 7])
+
+    # Test MobileNetv2 with BatchNorm forward
+    model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6)
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 8
+    assert feat[0].shape == torch.Size([1, 16, 112, 112])
+    assert feat[1].shape == torch.Size([1, 24, 56, 56])
+    assert feat[2].shape == torch.Size([1, 32, 28, 28])
+    assert feat[3].shape == torch.Size([1, 64, 14, 14])
+    assert feat[4].shape == torch.Size([1, 96, 14, 14])
+    assert feat[5].shape == torch.Size([1, 160, 7, 7])
+    assert feat[6].shape == torch.Size([1, 320, 7, 7])
+
+    # Test MobileNetv2 with layers 1, 3, 5 out forward
+    model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6,
+                        out_indices=(0, 2, 4))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 16, 112, 112])
+    assert feat[1].shape == torch.Size([1, 32, 28, 28])
+    assert feat[2].shape == torch.Size([1, 96, 14, 14])
+
+    # Test MobileNetv2 with checkpoint forward
+    model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6,
+                        with_cp=True)
+    for m in model.modules():
+        if is_block(m):
+            assert m.with_cp
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 8
+    assert feat[0].shape == torch.Size([1, 16, 112, 112])
+    assert feat[1].shape == torch.Size([1, 24, 56, 56])
+    assert feat[2].shape == torch.Size([1, 32, 28, 28])
+    assert feat[3].shape == torch.Size([1, 64, 14, 14])
+    assert feat[4].shape == torch.Size([1, 96, 14, 14])
+    assert feat[5].shape == torch.Size([1, 160, 7, 7])
+    assert feat[6].shape == torch.Size([1, 320, 7, 7])