Add backward and default_cfg tests and fix a few issues found. Fix #153

tests/test_inference.py

@@ -1,19 +0,0 @@
-import pytest
-import torch
-
-from timm import list_models, create_model
-
-
-@pytest.mark.timeout(300)
-@pytest.mark.parametrize('model_name', list_models(exclude_filters='*efficientnet_l2*'))
-@pytest.mark.parametrize('batch_size', [1])
-def test_model_forward(model_name, batch_size):
-    """Run a single forward pass with each model"""
-    model = create_model(model_name, pretrained=False)
-    model.eval()
-
-    inputs = torch.randn((batch_size, *model.default_cfg['input_size']))
-    outputs = model(inputs)
-
-    assert outputs.shape[0] == batch_size
-    assert not torch.isnan(outputs).any(), 'Output included NaNs'

tests/test_models.py

@@ -0,0 +1,70 @@
+import pytest
+import torch
+
+from timm import list_models, create_model
+
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models())
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_forward(model_name, batch_size):
+    """Run a single forward pass with each model"""
+    model = create_model(model_name, pretrained=False)
+    model.eval()
+
+    input_size = model.default_cfg['input_size']
+    if any([x > 448 for x in input_size]):
+        # cap forward test at max res 448 * 448 to keep resource down
+        input_size = tuple([min(x, 448) for x in input_size])
+    inputs = torch.randn((batch_size, *input_size))
+    outputs = model(inputs)
+
+    assert outputs.shape[0] == batch_size
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'
+
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models(exclude_filters='dla*'))  # DLA models have an issue TBD
+@pytest.mark.parametrize('batch_size', [2])
+def test_model_backward(model_name, batch_size):
+    """Run a single forward pass with each model"""
+    model = create_model(model_name, pretrained=False, num_classes=42)
+    num_params = sum([x.numel() for x in model.parameters()])
+    model.eval()
+
+    input_size = model.default_cfg['input_size']
+    if any([x > 128 for x in input_size]):
+        # cap backward test at 128 * 128 to keep resource usage down
+        input_size = tuple([min(x, 128) for x in input_size])
+    inputs = torch.randn((batch_size, *input_size))
+    outputs = model(inputs)
+    outputs.mean().backward()
+    num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
+
+    assert outputs.shape[-1] == 42
+    assert num_params == num_grad, 'Some parameters are missing gradients'
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'
+
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models())
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_default_cfgs(model_name, batch_size):
+    """Run a single forward pass with each model"""
+    model = create_model(model_name, pretrained=False)
+    model.eval()
+    state_dict = model.state_dict()
+    cfg = model.default_cfg
+
+    classifier = cfg['classifier']
+    first_conv = cfg['first_conv']
+    pool_size = cfg['pool_size']
+    input_size = model.default_cfg['input_size']
+
+    if all([x <= 448 for x in input_size]):
+        # pool size only checked if default res <= 448 * 448 to keep resource down
+        input_size = tuple([min(x, 448) for x in input_size])
+        outputs = model.forward_features(torch.randn((batch_size, *input_size)))
+        assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
+    assert any([k.startswith(cfg['classifier']) for k in state_dict.keys()]), f'{classifier} not in model params'
+    assert any([k.startswith(cfg['first_conv']) for k in state_dict.keys()]), f'{first_conv} not in model params'

timm/models/dla.py

@@ -237,8 +237,11 @@ class DlaTree(nn.Module):
 
     def forward(self, x, residual=None, children=None):
         children = [] if children is None else children
-        bottom = self.downsample(x) if self.downsample else x
+        # FIXME the way downsample / project are used here and residual is passed to next level up
-        residual = self.project(bottom) if self.project else bottom
+        # the tree, the residual is overridden and some project weights are thus never used and
+        # have no gradients. This appears to be an issue with the original model / weights.
+        bottom = self.downsample(x) if self.downsample is not None else x
+        residual = self.project(bottom) if self.project is not None else bottom
         if self.level_root:
             children.append(bottom)
         x1 = self.tree1(x, residual)
@@ -354,7 +357,8 @@ def dla60_res2next(pretrained=None, num_classes=1000, in_chans=3, **kwargs):
 @register_model
 def dla34(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-34
     default_cfg = default_cfgs['dla34']
-    model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=DlaBasic, **kwargs)
+    model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=DlaBasic,
+                num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)

timm/models/gluon_xception.py

@@ -36,7 +36,7 @@ default_cfgs = {
         'url': '',
         'input_size': (3, 299, 299),
         'crop_pct': 0.875,
-        'pool_size': (10, 10),
+        'pool_size': (5, 5),
         'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN,
         'std': IMAGENET_DEFAULT_STD,

timm/models/hrnet.py

@@ -34,7 +34,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'conv1', 'classifier': 'fc',
+        'first_conv': 'conv1', 'classifier': 'classifier',
         **kwargs
     }
 

timm/models/inception_v3.py

@@ -15,7 +15,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
         'crop_pct': 0.875, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
-        'first_conv': 'conv1', 'classifier': 'fc',
+        'first_conv': 'Conv2d_1a_3x3', 'classifier': 'fc',
         **kwargs
     }
 

timm/models/mobilenetv3.py

@@ -21,7 +21,7 @@ __all__ = ['MobileNetV3']
 
 def _cfg(url='', **kwargs):
     return {
-        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
+        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (1, 1),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'conv_stem', 'classifier': 'classifier',

timm/models/nasnet.py

@@ -19,7 +19,7 @@ default_cfgs = {
         'mean': (0.5, 0.5, 0.5),
         'std': (0.5, 0.5, 0.5),
         'num_classes': 1001,
-        'first_conv': 'conv_0.conv',
+        'first_conv': 'conv0.conv',
         'classifier': 'last_linear',
     },
 }
@@ -612,7 +612,7 @@ def nasnetalarge(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     """NASNet-A large model architecture.
     """
     default_cfg = default_cfgs['nasnetalarge']
-    model = NASNetALarge(num_classes=1000, in_chans=in_chans, **kwargs)
+    model = NASNetALarge(num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(model, default_cfg, num_classes, in_chans)

timm/models/resnest.py

@@ -38,11 +38,14 @@ default_cfgs = {
     'resnest50d': _cfg(
         url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
     'resnest101e': _cfg(
-        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth', input_size=(3, 256, 256)),
+        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth',
+        input_size=(3, 256, 256), pool_size=(8, 8)),
     'resnest200e': _cfg(
-        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth', input_size=(3, 320, 320)),
+        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth',
+        input_size=(3, 320, 320), pool_size=(10, 10)),
     'resnest269e': _cfg(
-        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth', input_size=(3, 416, 416)),
+        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth',
+        input_size=(3, 416, 416), pool_size=(13, 13)),
     'resnest50d_4s2x40d': _cfg(
         url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_4s2x40d-41d14ed0.pth',
         interpolation='bicubic'),

timm/models/selecsls.py

@@ -26,7 +26,7 @@ __all__ = ['SelecSLS']  # model_registry will add each entrypoint fn to this
 def _cfg(url='', **kwargs):
     return {
         'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (3, 3),
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (4, 4),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'stem', 'classifier': 'fc',

timm/models/tresnet.py

@@ -28,7 +28,7 @@ def _cfg(url='', **kwargs):
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': (0, 0, 0), 'std': (1, 1, 1),
-        'first_conv': 'layer0.conv1', 'classifier': 'head.fc',
+        'first_conv': 'body.conv1', 'classifier': 'head.fc',
         **kwargs
     }
 
@@ -41,13 +41,13 @@ default_cfgs = {
     'tresnet_xl': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.pth'),
     'tresnet_m_448': _cfg(
-        input_size=(3, 448, 448),
+        input_size=(3, 448, 448), pool_size=(14, 14),
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.pth'),
     'tresnet_l_448': _cfg(
-        input_size=(3, 448, 448),
+        input_size=(3, 448, 448), pool_size=(14, 14),
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth'),
     'tresnet_xl_448': _cfg(
-        input_size=(3, 448, 448),
+        input_size=(3, 448, 448), pool_size=(14, 14),
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_448-8c1815de.pth')
 }
 

timm/models/xception.py

@@ -37,6 +37,7 @@ default_cfgs = {
     'xception': {
         'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth',
         'input_size': (3, 299, 299),
+        'pool_size': (10, 10),
         'crop_pct': 0.8975,
         'interpolation': 'bicubic',
         'mean': (0.5, 0.5, 0.5),