Adding B5-B7 weights.

Author: datumbox

references/classification/train.py

@@ -90,7 +90,7 @@ def load_data(traindir, valdir, args):
     elif args.model.startswith('efficientnet_'):
         sizes = {
             'b0': (256, 224), 'b1': (256, 240), 'b2': (288, 288), 'b3': (320, 300),
-            'b4': (384, 380), 'b5': (489, 456), 'b6': (561, 528), 'b7': (633, 600),
+            'b4': (384, 380), 'b5': (456, 456), 'b6': (528, 528), 'b7': (600, 600),
         }
         e_type = args.model.replace('efficientnet_', '')
         resize_size, crop_size = sizes[e_type]

test/expect/ModelTester.test_efficientnet_b5_expect.pkl

@@ -15,7 +15,7 @@
 
 
 __all__ = ["EfficientNet", "efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3",
-           "efficientnet_b4"]
+           "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7"]
 
 
 model_urls = {
@@ -25,6 +25,10 @@
     "efficientnet_b2": "https://download.pytorch.org/models/efficientnet_b2-rwightman.pth",
     "efficientnet_b3": "https://download.pytorch.org/models/efficientnet_b3-rwightman.pth",
     "efficientnet_b4": "https://download.pytorch.org/models/efficientnet_b4-rwightman.pth",
+    # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/
+    "efficientnet_b5": "https://download.pytorch.org/models/efficientnet_b5-lukemelas.pth",
+    "efficientnet_b6": "https://download.pytorch.org/models/efficientnet_b6-lukemelas.pth",
+    "efficientnet_b7": "https://download.pytorch.org/models/efficientnet_b7-lukemelas.pth",
 }
 
 
@@ -322,3 +326,45 @@ def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: A
     """
     inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs)
     return _efficientnet_model("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs)
+
+
+def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
+    """
+    Constructs a EfficientNet B5 architecture from
+    `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" <https://arxiv.org/abs/1905.11946>`_.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs)
+    return _efficientnet_model("efficientnet_b5", inverted_residual_setting, 0.4, pretrained, progress,
+                               norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs)
+
+
+def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
+    """
+    Constructs a EfficientNet B6 architecture from
+    `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" <https://arxiv.org/abs/1905.11946>`_.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs)
+    return _efficientnet_model("efficientnet_b6", inverted_residual_setting, 0.5, pretrained, progress,
+                               norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs)
+
+
+def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
+    """
+    Constructs a EfficientNet B7 architecture from
+    `"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" <https://arxiv.org/abs/1905.11946>`_.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs)
+    return _efficientnet_model("efficientnet_b7", inverted_residual_setting, 0.5, pretrained, progress,
+                               norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), **kwargs)