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bcf1518309
* Update all devices to be tested
ANE, CPU and OCL all now support all tests.
However tests are not currently passing on GPU and I cannot test on CPU.
Failing GPU test are not an issue caused by this update. Tests have not
been passing due to a missing "six" required installation.
OpenCL Tests have not been run since commit: 1a1c63a08b
devices have 3 types and are handle by a new DeviceTypes enum. (The goal
is to revert to Tensor.<type>, but this current setup allows for keyword
argument defaults: `device=DeviceType.CPU`)
All references to Tensor.GPU/CPU/ANE as been converted to the
corresponding `DeviceTypes` enum.
Refactor of the conversion code to allow for any device to any device
conversion.
* Add six dependency in requirements.txt
* Resolve failure to run tests
Move six into gpu required installs. Remove six from standard
installation.
* Remove repeated data conversion
* Refactor method names
Also reduce code with .to and .to_
* Dynamic device handlers
* Refactor DeviceTypes -> Device
* Add mem copy profiling back
* test_backward_pass_diamond_model passing
* Resolve Sum issue on GPU
* Revert batchnorm2d tests
* Update README with upadated API
* ANE testing with
* Last minute line gains
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import os
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import numpy as np
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from tqdm import trange
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from extra.utils import get_parameters
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from tinygrad.tensor import Tensor, GPU, Device
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def train(model, X_train, Y_train, optim, steps, num_classes=None, BS=128, device=Device.CPU, lossfn = lambda out,y: out.mul(y).mean()):
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if device == Device.GPU: [x.gpu_() for x in get_parameters([model, optim])]
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elif device == Device.ANE: [x.ane_() for x in get_parameters([model, optim])]
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if num_classes is None: num_classes = Y_train.max().astype(int)+1
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losses, accuracies = [], []
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for i in (t := trange(steps, disable=os.getenv('CI') is not None)):
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samp = np.random.randint(0, X_train.shape[0], size=(BS))
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x = Tensor(X_train[samp].reshape((-1, 28*28)).astype(np.float32), device=device)
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Y = Y_train[samp]
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y = np.zeros((len(samp),num_classes), np.float32)
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# correct loss for NLL, torch NLL loss returns one per row
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y[range(y.shape[0]),Y] = -1.0*num_classes
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y = Tensor(y, device=device)
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# network
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out = model.forward(x)
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# NLL loss function
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loss = lossfn(out, y)
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optim.zero_grad()
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loss.backward()
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optim.step()
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cat = np.argmax(out.cpu().data, axis=1)
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accuracy = (cat == Y).mean()
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# printing
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loss = loss.cpu().data
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losses.append(loss)
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accuracies.append(accuracy)
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t.set_description("loss %.2f accuracy %.2f" % (loss, accuracy))
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def evaluate(model, X_test, Y_test, num_classes=None, device=Device.CPU, BS=128):
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def numpy_eval(num_classes):
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Y_test_preds_out = np.zeros((len(Y_test),num_classes))
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for i in trange(len(Y_test)//BS, disable=os.getenv('CI') is not None):
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Y_test_preds_out[i*BS:(i+1)*BS] = model.forward(Tensor(X_test[i*BS:(i+1)*BS].reshape((-1, 28*28)).astype(np.float32), device=device)).cpu().data
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Y_test_preds = np.argmax(Y_test_preds_out, axis=1)
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return (Y_test == Y_test_preds).mean()
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if num_classes is None: num_classes = Y_test.max().astype(int)+1
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accuracy = numpy_eval(num_classes)
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print("test set accuracy is %f" % accuracy)
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return accuracy
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