def load_weights_from_torch(self):
# load b0
import torch
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"
)
else:
raise Exception("no pretrained weights")
b0 = torch.load(io.BytesIO(b0))
for k, v in b0.items():
if '_blocks.' in k:
k = "%s[%s].%s" % tuple(k.split(".", 2))
mk = "self." + k
#print(k, v.shape)
try:
mv = eval(mk)
except AttributeError:
try:
mv = eval(mk.replace(".weight", ""))
except AttributeError:
mv = eval(mk.replace(".bias", "_bias"))
vnp = v.numpy().astype(np.float32)
mv.data[:] = vnp if k != '_fc.weight' else vnp.T
if GPU:
mv.cuda_()
def fetch_mnist():
import gzip
parse = lambda dat: np.frombuffer(gzip.decompress(dat), dtype=np.uint8).copy()
X_train = parse(fetch("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28, 28))
Y_train = parse(fetch("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"))[8:]
X_test = parse(fetch("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28, 28))
Y_test = parse(fetch("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"))[8:]
return X_train, Y_train, X_test, Y_test
def load_cifar():
tt = tarfile.open(fileobj=io.BytesIO(
fetch('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz')),
mode='r:gz')
db = pickle.load(tt.extractfile('cifar-10-batches-py/data_batch_1'),
encoding="bytes")
X = db[b'data'].reshape((-1, 3, 32, 32))
Y = np.array(db[b'labels'])
return X, Y
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:
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:
k = "%s[%s].%s" % tuple(k.split(".", 2))
mk = "self."+k
#print(k, v.shape)
try:
mv = eval(mk)
except AttributeError:
try:
mv = eval(mk.replace(".weight", ""))
except AttributeError:
mv = eval(mk.replace(".bias", "_bias"))
vnp = v.numpy().astype(np.float32) if USE_TORCH else v
vnp = vnp if k != '_fc.weight' else vnp.T
if mv.shape == vnp.shape or vnp.shape == ():
mv.data[:] = vnp
else:
print("MISMATCH SHAPE IN %s, %r %r" % (k, mv.shape, vnp.shape))
vnp = v.numpy().astype(np.float32)
mv.data[:] = vnp if k != '_fc.weight' else vnp.T
if __name__ == "__main__":
# instantiate my net
model = EfficientNet()
model.load_weights_from_torch()
# load image and preprocess
from PIL import Image
if len(sys.argv) > 1:
url = sys.argv[1]
else:
url = "https://raw.githubusercontent.com/karpathy/micrograd/master/puppy.jpg"
img = Image.open(io.BytesIO(fetch(url)))
aspect_ratio = img.size[0] / img.size[1]
img = img.resize((int(224 * max(aspect_ratio, 1.0)),
int(224 * max(1.0 / aspect_ratio, 1.0))))
img = np.array(img)
y0, x0 = (np.asarray(img.shape)[:2] - 224) // 2
img = img[y0:y0 + 224, x0:x0 + 224]
img = np.moveaxis(img, [2, 0, 1], [0, 1, 2])
img = img.astype(np.float32).reshape(1, 3, 224, 224)
img /= 255.0
img -= np.array([0.485, 0.456, 0.406]).reshape((1, -1, 1, 1))
img /= np.array([0.229, 0.224, 0.225]).reshape((1, -1, 1, 1))
# if you want to look at the micrograd puppy
"""
mv = eval(mk)
except AttributeError:
try:
mv = eval(mk.replace(".weight", ""))
except AttributeError:
mv = eval(mk.replace(".bias", "_bias"))
vnp = v.numpy().astype(np.float32)
mv.data[:] = vnp if k != '_fc.weight' else vnp.T
if __name__ == "__main__":
# instantiate my net
model = EfficientNet()
model.load_weights_from_torch()
# load cat image
from PIL import Image
img = Image.open(
io.BytesIO(
fetch(
"https://c.files.bbci.co.uk/12A9B/production/_111434467_gettyimages-1143489763.jpg"
)))
img = img.resize((224, 224))
img = np.moveaxis(np.array(img), [2, 0, 1], [0, 1, 2])
img = img.astype(np.float32).reshape(1, 3, 224, 224)
print(img.shape, img.dtype)
# run the net
out = model.forward(Tensor(img))
print(np.argmax(out.data), np.max(out.data))
