import os
import time
import numpy as np
from efficientnet import EfficientNet
from tinygrad.tensor import Tensor
if __name__ == "__main__":
Tensor.default_gpu = os.getenv("GPU") is not None
model = EfficientNet()
BS = 4
img = np.zeros((BS, 3, 224, 224), dtype=np.float32)
st = time.time()
out = model.forward(Tensor(img))
et = time.time()
print("forward %.2f s" % (et - st))
Y = [0] * BS
y = np.zeros((BS, 1000), np.float32)
y[range(y.shape[0]), Y] = -1000.0
y = Tensor(y)
loss = out.logsoftmax().mul(y).mean()
st = time.time()
loss.backward()
et = time.time()
print("backward %.2f s" % (et - st))
def __init__(self):
self.l1 = Tensor.uniform(784, 128)
self.l2 = Tensor.uniform(128, 10)
def __init__(self, classes=10):
conv = 3
inter_chan, out_chan = 8, 16 # for speed
self.c1 = Tensor.uniform(inter_chan, 3, conv, conv)
self.c2 = Tensor.uniform(out_chan, inter_chan, conv, conv)
self.l1 = Tensor.uniform(out_chan * 6 * 6, classes)
def numpy_eval():
Y_test_preds_out = model.forward(
Tensor(X_test.reshape((-1, 28 * 28)).astype(np.float32),
gpu=gpu)).cpu()
Y_test_preds = np.argmax(Y_test_preds_out.data, axis=1)
return (Y_test == Y_test_preds).mean()
def __init__(self):
self.l1 = Tensor(layer_init_uniform(784, 128))
self.l2 = Tensor(layer_init_uniform(128, 10))
