forked from tinygrad/tinygrad
SkoshandGitHub
78aa147b39
* Some progress on yolov3 * Removed some debugging comments… Also, the forward pass eats all RAM for some reason * forward pass almost runs * forward pass runs almost * forward pass runs, now we gotta load the weights * loading weights works * fetches config and weights * everything kind of works, postprocessing of output still needs to be implemented, temp_process_results kind of works, but its kind of terrible, and not how things should be done * some changes * fixed some bugs in the forward pass and load_weights function, now outputs more correct values, however some values are still loaded incorrectly * Something is wrong with the forward pass, Conv2d tests added * forward pass almost outputs correct values, gotta fix one more thign * yolo works * some final changes * reverting changes * removed dataloader * fixed some indentation * comment out failing test, somehow it fails CI even though it passes on my computer… * fixed wrong probabilities * added webcam option to YOLO, now just need to add bounding boxes and speed it up * some progress towards adding bounding boxes * trying to speed up yolo layer on GPU, still faster on CPU but with 30GB ram usage * Faster inference times, bounding boxes added correctly, webcam works, but is slow, and there is a memory leak when running on CPU... Also added tinygrads output on the classic dog image * removed some debugging print statements * updated result image * something weird is going on, mean op on GPU tensor randomly faults, copying a tensor from GPU->CPU takes 10+ seconds…
33 lines
1.4 KiB
Python
33 lines
1.4 KiB
Python
from tinygrad.tensor import Tensor
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class BatchNorm2D:
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def __init__(self, sz, eps=1e-5, track_running_stats=False, training=False, momentum=0.1):
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self.eps, self.track_running_stats, self.training, self.momentum = eps, track_running_stats, training, momentum
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self.weight, self.bias = Tensor.ones(sz), Tensor.zeros(sz)
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self.running_mean, self.running_var = Tensor.zeros(sz, requires_grad=False), Tensor.ones(sz, requires_grad=False)
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self.num_batches_tracked = Tensor.zeros(1, requires_grad=False)
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def __call__(self, x):
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if self.track_running_stats or self.training:
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batch_mean = x.mean(axis=(0,2,3))
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y = (x - batch_mean.reshape(shape=[1, -1, 1, 1]))
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batch_var = (y*y).mean(axis=(0,2,3))
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if self.track_running_stats:
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self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * batch_mean
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self.running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
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if self.num_batches_tracked is None: self.num_batches_tracked = Tensor.zeros(1, requires_grad=False)
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self.num_batches_tracked += 1
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if self.training:
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return self.normalize(x, batch_mean, batch_var)
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return self.normalize(x, self.running_mean, self.running_var)
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def normalize(self, x, mean, var):
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x = (x - mean.reshape(shape=[1, -1, 1, 1])) * self.weight.reshape(shape=[1, -1, 1, 1])
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return x.div(var.add(self.eps).reshape(shape=[1, -1, 1, 1])**0.5) + self.bias.reshape(shape=[1, -1, 1, 1])
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