load torch files without torch

extra/efficientnet.py

@@ -4,6 +4,71 @@ from tinygrad.tensor import Tensor
 from tinygrad.utils import fetch
 from tinygrad.nn import BatchNorm2D
 
+USE_TORCH = False
+
+def fake_torch_load(b0):
+  import io
+  import pickle
+  import struct
+
+  # convert it to a file
+  fb0 = io.BytesIO(b0)
+
+  # skip three junk pickles
+  pickle.load(fb0)
+  pickle.load(fb0)
+  pickle.load(fb0)
+
+  key_prelookup = {}
+
+  class HackTensor:
+    def __new__(cls, *args):
+      #print(args)
+      ident, storage_type, obj_key, location, obj_size, view_metadata = args[0]
+      assert ident == 'storage'
+
+      ret = np.zeros(obj_size, dtype=storage_type)
+      key_prelookup[obj_key] = (storage_type, obj_size, ret, args[2], args[3])
+      return ret
+
+  class MyPickle(pickle.Unpickler):
+    def find_class(self, module, name):
+      #print(module, name)
+      if name == 'FloatStorage':
+        return np.float32
+      if name == 'LongStorage':
+        return np.int64
+      if module == "torch._utils" or module == "torch":
+        return HackTensor
+      else:
+        return pickle.Unpickler.find_class(self, module, name)
+
+    def persistent_load(self, pid):
+      return pid
+
+  ret = MyPickle(fb0).load()
+
+  # create key_lookup
+  key_lookup = pickle.load(fb0)
+  key_real = [None] * len(key_lookup)
+  for k,v in key_prelookup.items():
+    key_real[key_lookup.index(k)] = v
+
+  # read in the actual data
+  for storage_type, obj_size, np_array, np_shape, np_strides in key_real:
+    ll = struct.unpack("Q", fb0.read(8))[0]
+    assert ll == obj_size
+    bytes_size = {np.float32: 4, np.int64: 8}[storage_type]
+    mydat = fb0.read(ll * bytes_size)
+    np_array[:] = np.frombuffer(mydat, storage_type)
+    np_array.shape = np_shape
+
+    # numpy stores its strides in bytes
+    real_strides = tuple([x*bytes_size for x in np_strides])
+    np_array.strides = real_strides
+
+  return ret
+
 class MBConvBlock:
   def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio):
     oup = expand_ratio * input_filters
@@ -123,8 +188,6 @@ class EfficientNet:
 
   def load_weights_from_torch(self, gpu):
     # load b0
-    import io
-    import torch
     # 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")
@@ -136,7 +199,13 @@ class EfficientNet:
       b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth")
     else:
       raise Exception("no pretrained weights")
-    b0 = torch.load(io.BytesIO(b0))
+
+    if USE_TORCH:
+      import io
+      import torch
+      b0 = torch.load(io.BytesIO(b0))
+    else:
+      b0 = fake_torch_load(b0)
 
     for k,v in b0.items():
       if '_blocks.' in k:
@@ -150,7 +219,7 @@ class EfficientNet:
           mv = eval(mk.replace(".weight", ""))
         except AttributeError:
           mv = eval(mk.replace(".bias", "_bias"))
-      vnp = v.numpy().astype(np.float32)
+      vnp = v.numpy().astype(np.float32) if USE_TORCH else v
       mv.data[:] = vnp if k != '_fc.weight' else vnp.T
       if gpu:
         mv.cuda_()