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Test split (#231)

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* Split tests

Split tests into "Test CPU" and "Test GPU".

Add test flag "TEST_DEVICES" which is a comma separated list of devices:
CPU,GPU,ANE

* Run tests based on provided TEST_DEVICES flag

By default will run all "CPU,GPU,ANE"

* fix bad quote

* Revert changes and use GPU=1

This is done through setting the default Tensor Device to Device.CPU of
GPU=1 is set.

Run GPU tests: GPU=1 pytest -s -v
This commit is contained in:
Liam
2021-01-01 15:19:03 +01:00
committed by GitHub
parent 4a7cf2e420
commit ebd72ff437
10 changed files with 137 additions and 213 deletions
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12
extra/training.py
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@@ -11,18 +11,16 @@ def sparse_categorical_crossentropy(out, Y):
# correct loss for NLL, torch NLL loss returns one per row
y[range(y.shape[0]),YY] = -1.0*num_classes
y = y.reshape(list(Y.shape)+[num_classes])
y = Tensor(y, device=out.device)
y = Tensor(y)
return out.mul(y).mean()
def train(model, X_train, Y_train, optim, steps, BS=128, device=Device.CPU, lossfn=sparse_categorical_crossentropy):
def train(model, X_train, Y_train, optim, steps, BS=128, lossfn=sparse_categorical_crossentropy):
Tensor.training = True
if device == Device.GPU: [x.gpu_() for x in get_parameters([model, optim])]
elif device == Device.ANE: [x.ane_() for x in get_parameters([model, optim])]
losses, accuracies = [], []
for i in (t := trange(steps, disable=os.getenv('CI') is not None)):
samp = np.random.randint(0, X_train.shape[0], size=(BS))
x = Tensor(X_train[samp], device=device)
x = Tensor(X_train[samp])
y = Y_train[samp]
# network
@@ -42,12 +40,12 @@ def train(model, X_train, Y_train, optim, steps, BS=128, device=Device.CPU, loss
accuracies.append(accuracy)
t.set_description("loss %.2f accuracy %.2f" % (loss, accuracy))
def evaluate(model, X_test, Y_test, num_classes=None, device=Device.CPU, BS=128):
def evaluate(model, X_test, Y_test, num_classes=None, BS=128):
Tensor.training = False
def numpy_eval(num_classes):
Y_test_preds_out = np.zeros(list(Y_test.shape)+[num_classes])
for i in trange(len(Y_test)//BS, disable=os.getenv('CI') is not None):
Y_test_preds_out[i*BS:(i+1)*BS] = model.forward(Tensor(X_test[i*BS:(i+1)*BS], device=device)).cpu().data
Y_test_preds_out[i*BS:(i+1)*BS] = model.forward(Tensor(X_test[i*BS:(i+1)*BS])).cpu().data
Y_test_preds = np.argmax(Y_test_preds_out, axis=-1)
return (Y_test == Y_test_preds).mean()