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https://github.com/tinygrad/tinygrad.git
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27845fd3a3
make it work out of the box for new users. the default configuration of train_efficientnet is to use the smaller cifar dataset. import datasets.imagenet tries to open imagenet_class_index.json and will fail, unless user has already downloaded it.
103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
import traceback
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import time
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from multiprocessing import Process, Queue
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import numpy as np
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from tqdm import trange
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from tinygrad.nn import optim
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from tinygrad.helpers import getenv
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from tinygrad.tensor import Tensor
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from datasets import fetch_cifar
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from models.efficientnet import EfficientNet
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class TinyConvNet:
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def __init__(self, classes=10):
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conv = 3
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inter_chan, out_chan = 8, 16 # for speed
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self.c1 = Tensor.uniform(inter_chan,3,conv,conv)
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self.c2 = Tensor.uniform(out_chan,inter_chan,conv,conv)
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self.l1 = Tensor.uniform(out_chan*6*6, classes)
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def forward(self, x):
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x = x.conv2d(self.c1).relu().max_pool2d()
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x = x.conv2d(self.c2).relu().max_pool2d()
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x = x.reshape(shape=[x.shape[0], -1])
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return x.dot(self.l1)
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if __name__ == "__main__":
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IMAGENET = getenv("IMAGENET")
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classes = 1000 if IMAGENET else 10
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TINY = getenv("TINY")
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TRANSFER = getenv("TRANSFER")
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if TINY:
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model = TinyConvNet(classes)
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elif TRANSFER:
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model = EfficientNet(getenv("NUM", 0), classes, has_se=True)
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model.load_from_pretrained()
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else:
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model = EfficientNet(getenv("NUM", 0), classes, has_se=False)
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parameters = optim.get_parameters(model)
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print("parameter count", len(parameters))
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optimizer = optim.Adam(parameters, lr=0.001)
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BS, steps = getenv("BS", 64 if TINY else 16), getenv("STEPS", 2048)
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print(f"training with batch size {BS} for {steps} steps")
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if IMAGENET:
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from datasets.imagenet import fetch_batch
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def loader(q):
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while 1:
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try:
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q.put(fetch_batch(BS))
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except Exception:
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traceback.print_exc()
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q = Queue(16)
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for i in range(2):
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p = Process(target=loader, args=(q,))
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p.daemon = True
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p.start()
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else:
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X_train, Y_train = fetch_cifar()
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Tensor.training = True
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for i in (t := trange(steps)):
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if IMAGENET:
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X, Y = q.get(True)
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else:
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samp = np.random.randint(0, X_train.shape[0], size=(BS))
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X, Y = X_train[samp], Y_train[samp]
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st = time.time()
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out = model.forward(Tensor(X.astype(np.float32), requires_grad=False))
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fp_time = (time.time()-st)*1000.0
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y = np.zeros((BS,classes), np.float32)
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y[range(y.shape[0]),Y] = -classes
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y = Tensor(y, requires_grad=False)
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loss = out.log_softmax().mul(y).mean()
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optimizer.zero_grad()
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st = time.time()
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loss.backward()
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bp_time = (time.time()-st)*1000.0
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st = time.time()
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optimizer.step()
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opt_time = (time.time()-st)*1000.0
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st = time.time()
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loss = loss.cpu().numpy()
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cat = np.argmax(out.cpu().numpy(), axis=1)
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accuracy = (cat == Y).mean()
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finish_time = (time.time()-st)*1000.0
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# printing
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t.set_description("loss %.2f accuracy %.2f -- %.2f + %.2f + %.2f + %.2f = %.2f" %
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(loss, accuracy,
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fp_time, bp_time, opt_time, finish_time,
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fp_time + bp_time + opt_time + finish_time))
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del out, y, loss
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