refactor efficientnet loading

accel/README

@@ -0,0 +1,6 @@
+This is where we scope out adding accelerators to tinygrad
+
+ane -- Apple Neural Engine, in the M1 + newer iPhones
+cherry -- Largely defunct custom hardware based on a RISC-V extension
+tpu -- Google's TPU, available for rent in Google Cloud
+

models/efficientnet.py

@@ -4,7 +4,16 @@ from tinygrad.tensor import Tensor
 from tinygrad.nn import BatchNorm2D
 from extra.utils import fetch, fake_torch_load
 
-USE_TORCH = False
+model_urls = {
+  0: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth",
+  1: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth",
+  2: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth",
+  3: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth",
+  4: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth",
+  5: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth",
+  6: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth",
+  7: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth"
+}
 
 class MBConvBlock:
   def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se):
@@ -126,26 +135,9 @@ class EfficientNet:
     #x = x.dropout(0.2)
     return x.dot(self._fc).add(self._fc_bias.reshape(shape=[1,-1]))
 
-  def load_weights_from_torch(self):
-    # load b0
-    # https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/utils.py#L551
-    if self.number == 0:
-      b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth")
-    elif self.number == 2:
-      b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth")
-    elif self.number == 4:
-      b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth")
-    elif self.number == 7:
-      b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth")
-    else:
-      raise Exception("no pretrained weights")
 
-    if USE_TORCH:
+  def load_weights_from_torch(self):
-      import io
+    b0 = fake_torch_load(fetch(model_urls[self.number]))
-      import torch
-      b0 = torch.load(io.BytesIO(b0))
-    else:
-      b0 = fake_torch_load(b0)
 
     for k,v in b0.items():
       for cat in ['_conv_head', '_conv_stem', '_depthwise_conv', '_expand_conv', '_fc', '_project_conv', '_se_reduce', '_se_expand']:
@@ -155,7 +147,7 @@ class EfficientNet:
 
       #print(k, v.shape)
       mv = _get_child(self, k)
-      vnp = v.numpy().astype(np.float32) if USE_TORCH else v.astype(np.float32)
+      vnp = v.astype(np.float32)
       vnp = vnp if k != '_fc' else vnp.T
       vnp = vnp if vnp.shape != () else np.array([vnp])
 

models/resnet.py

@@ -14,6 +14,7 @@ model_urls = {
 
 def load_from_pretrained(model, url):
   state_dict = load_state_dict_from_url(url, progress=True)
+  #state_dict = fake_torch_load(fetch(url))
   layers_not_loaded = []
   for k, v in state_dict.items():
     par_name = ['model']