Files
tinygrad/test/test_train.py
Guglielmo Camporese 2b7589db64 Added ResNet-{18, 34, 50, 101, 152} (#271)
* added resnets

* fix minor

* fix minor

* resnet in models

* added resnet test

* added resnet train test

* added linear, conv2d nn tests

* fix minor in extra/training

* resnet in models

* fix minor

* fix tolerance for linear in nn test

* fix eval, this causes cpu and gpu UT failing

* revert transformer test

* fix minor for CPU test

* improved model get_params for sequential layer

* fix minor for params counting

* commented broken ops tests

* improved train for resnet
2021-06-21 09:37:24 -07:00

54 lines
1.6 KiB
Python

import os
import unittest
import time
import tinygrad.optim as optim
import numpy as np
from tinygrad.tensor import Tensor
from extra.training import train
from extra.utils import get_parameters
from models.efficientnet import EfficientNet
from models.transformer import Transformer
from models.resnet import ResNet18, ResNet34, ResNet50
BS = int(os.getenv("BS", "4"))
def train_one_step(model,X,Y):
params = get_parameters(model)
pcount = 0
for p in params:
pcount += np.prod(p.shape)
optimizer = optim.Adam(params, lr=0.001)
print("stepping %r with %.1fM params bs %d" % (type(model), pcount/1e6, BS))
st = time.time()
train(model, X, Y, optimizer, steps=1, BS=BS)
et = time.time()-st
print("done in %.2f ms" % (et*1000.))
class TestTrain(unittest.TestCase):
def test_efficientnet(self):
model = EfficientNet(0)
X = np.zeros((BS,3,224,224), dtype=np.float32)
Y = np.zeros((BS), dtype=np.int32)
train_one_step(model,X,Y)
def test_transformer(self):
# this should be small GPT-2, but the param count is wrong
model = Transformer(syms=10, maxlen=6, layers=12, embed_dim=768, num_heads=12)
X = np.zeros((BS,6), dtype=np.float32)
Y = np.zeros((BS,6), dtype=np.int32)
train_one_step(model,X,Y)
def test_resnet(self):
X = np.zeros((BS, 3, 224, 224), dtype=np.float32)
Y = np.zeros((BS), dtype=np.int32)
for resnet_v in [ResNet18, ResNet34, ResNet50]:
model = resnet_v(num_classes=1000, pretrained=True)
train_one_step(model, X, Y)
def test_bert(self):
# TODO: write this
pass
if __name__ == '__main__':
unittest.main()