ENET WORKS

examples/efficientnet.py

@@ -55,7 +55,7 @@ class MBConvBlock:
     x = self._bn2(x.conv2d(self._project_conv))
     if x.shape == inputs.shape:
       x = x.add(inputs)
-    return swish(x)
+    return x
 
 class EfficientNet:
   def __init__(self):
@@ -128,9 +128,11 @@ if __name__ == "__main__":
   else:
     url = "https://c.files.bbci.co.uk/12A9B/production/_111434467_gettyimages-1143489763.jpg"
   img = Image.open(io.BytesIO(fetch(url)))
-  img = img.resize((398, 224))
+  aspect_ratio = img.size[0] / img.size[1]
+  img = img.resize((int(224*aspect_ratio), 224))
   img = np.array(img)
-  img = img[:, 87:-87]
+  chapo = (img.shape[1]-224)//2
+  img = img[:, chapo:chapo+224]
   img = np.moveaxis(img, [2,0,1], [0,1,2])
   img = img.astype(np.float32).reshape(1,3,224,224)
   img /= 256
@@ -145,8 +147,9 @@ if __name__ == "__main__":
   """
 
   # category labels
-  lbls = fetch("https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt")
-  lbls = dict([(int(x.split(" ")[1]), x.split(" ")[2]) for x in lbls.decode('utf-8').split("\n")])
+  import ast
+  lbls = fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
+  lbls = ast.literal_eval(lbls.decode('utf-8'))
 
   # run the net
   import time