forked from ccf-ai-infra/GPUCodeForces
28 lines
714 B
Python
28 lines
714 B
Python
# hingeembeddingloss_torch.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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BATCH_SIZE = 4096
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FEATURE_DIM = 512
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MARGIN = 1.0
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.criterion = nn.HingeEmbeddingLoss(margin=MARGIN, reduction='mean')
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def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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return self.criterion(input, target)
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def get_inputs():
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input_scores = torch.randn(BATCH_SIZE, FEATURE_DIM, dtype=torch.float32)
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target = torch.randint(0, 2, (BATCH_SIZE, FEATURE_DIM), dtype=torch.float32)
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target[target == 0] = -1
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return [input_scores, target]
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def get_init_inputs():
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return [] |