Update relu_fn for better memory consumption

README.md

@@ -1,5 +1,9 @@
 # EfficientNet PyTorch
 
+### Update (October 08, 2019)
+
+This update changes activation function implementation to more memory-efficient. For more details please refer to: https://github.com/lukemelas/EfficientNet-PyTorch/issues/18
+
 ### Update (July 31, 2019)
 
 _Upgrade the pip package with_ `pip install --upgrade efficientnet-pytorch`

efficientnet_pytorch/utils.py

@@ -12,7 +12,6 @@
 from torch.nn import functional as F
 from torch.utils import model_zoo
 
-
 ########################################################################
 ############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
 ########################################################################
@@ -24,21 +23,37 @@
     'num_classes', 'width_coefficient', 'depth_coefficient',
     'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size'])
 
-
 # Parameters for an individual model block
 BlockArgs = collections.namedtuple('BlockArgs', [
     'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
     'expand_ratio', 'id_skip', 'stride', 'se_ratio'])
 
-
 # Change namedtuple defaults
 GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
 BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
 
 
-def relu_fn(x):
-    """ Swish activation function """
-    return x * torch.sigmoid(x)
+class SwishImplementation(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, i):
+        result = i * torch.sigmoid(i)
+        ctx.save_for_backward(i)
+        return result
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        i = ctx.saved_variables[0]
+        sigmoid_i = torch.sigmoid(i)
+        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
+
+
+class Swish(nn.Module):
+    @staticmethod
+    def forward(x):
+        return SwishImplementation.apply(x)
+
+
+relu_fn = Swish()
 
 
 def round_filters(filters, global_params):
@@ -84,11 +99,13 @@ def get_same_padding_conv2d(image_size=None):
     else:
         return partial(Conv2dStaticSamePadding, image_size=image_size)
 
+
 class Conv2dDynamicSamePadding(nn.Conv2d):
     """ 2D Convolutions like TensorFlow, for a dynamic image size """
+
     def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
         super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
-        self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]]*2
+        self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
 
     def forward(self, x):
         ih, iw = x.size()[-2:]
@@ -98,12 +115,13 @@ def forward(self, x):
         pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
         pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
         if pad_h > 0 or pad_w > 0:
-            x = F.pad(x, [pad_w//2, pad_w - pad_w//2, pad_h//2, pad_h - pad_h//2])
+            x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
         return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
 
 
 class Conv2dStaticSamePadding(nn.Conv2d):
     """ 2D Convolutions like TensorFlow, for a fixed image size"""
+
     def __init__(self, in_channels, out_channels, kernel_size, image_size=None, **kwargs):
         super().__init__(in_channels, out_channels, kernel_size, **kwargs)
         self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
@@ -128,7 +146,7 @@ def forward(self, x):
 
 
 class Identity(nn.Module):
-    def __init__(self,):
+    def __init__(self, ):
         super(Identity, self).__init__()
 
     def forward(self, input):
@@ -286,6 +304,7 @@ def get_model_params(model_name, override_params):
     'efficientnet-b7': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b7-dcc49843.pth',
 }
 
+
 def load_pretrained_weights(model, model_name, load_fc=True):
     """ Loads pretrained weights, and downloads if loading for the first time. """
     state_dict = model_zoo.load_url(url_map[model_name])