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import torch |
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from torch import nn |
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class Loss(object): |
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def __call__(self, *args, **kwargs): |
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raise NotImplementedError |
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class DummyLoss(Loss): |
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def __init__(self): |
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self.loss = nn.CrossEntropyLoss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss(logits, targets) |
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class DummyK400Loss(Loss): |
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"""dummy k400 loss for MViT.""" |
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def __init__(self): |
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self.loss = nn.CrossEntropyLoss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss( |
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logits, torch.randint(0, 400, (logits.size(0),), device=logits.device)) |
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class CrossEntropy(Loss): |
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def __init__(self): |
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self.loss = nn.CrossEntropyLoss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss(logits.reshape(-1, logits.size(-1)), targets.reshape(-1)) |
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class ArgmaxCrossEntropy(Loss): |
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def __init__(self): |
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self.loss = nn.CrossEntropyLoss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss(logits, targets.argmax(dim=1)) |
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class BCE(Loss): |
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def __init__(self): |
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self.loss = nn.BCEWithLogitsLoss() |
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def __call__(self, logits, targets, **kwargs): |
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targets = targets.squeeze(0) |
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return self.loss(logits, targets) |
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class NLGLoss(Loss): |
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def __init__(self): |
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self.loss = nn.CrossEntropyLoss() |
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def __call__(self, logits, text_label, **kwargs): |
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targets = text_label[text_label != -100] |
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return self.loss(logits, targets) |
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class MSE(Loss): |
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def __init__(self): |
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self.loss = nn.MSELoss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss(logits, targets) |
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class L1(Loss): |
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def __init__(self): |
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self.loss = nn.L1Loss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss(logits, targets) |
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class SmoothL1(Loss): |
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def __init__(self): |
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self.loss = nn.SmoothL1Loss() |
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def __call__(self, logits, targets, **kwargs): |
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return self.loss(logits, targets) |
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