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import torch.nn as nn
class MultiTaskModel(nn.Module):
def __init__(self, base_net, task_heads, device):
super().__init__()
self.base_net = base_net
self.task_heads = nn.ModuleList(task_heads)
self.device = device
def forward(self, x):
base_output = self.base_net(x)
# Forward pass through task-specific heads
task_outputs = [head(base_output) for head in self.task_heads]
return task_outputs
def predict(self, x):
base_output = self.base_net(x)
# Forward pass through task-specific heads
task_outputs = [head.predict(base_output) for head in self.task_heads]
return task_outputs
def accuracy(self, predictions, targets):
accuracies = [head.accuracy(prediction, target) for head, prediction, target in zip(
self.task_heads, predictions, targets)]
return accuracies
def recall(self, predictions, targets):
recalls = [head.recall(prediction, target) for head, prediction, target in zip(
self.task_heads, predictions, targets)]
return recalls
def precision(self, predictions, targets):
precisions = [head.precision(prediction, target) for head, prediction, target in zip(
self.task_heads, predictions, targets)]
return precisions |