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import torch
from torch import nn
from LLAVA_Biovil.biovil_t.pretrained import get_biovil_t_image_encoder
class ChexpertClassifier(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.biovil_encoder = get_biovil_t_image_encoder()
self.fc1 = nn.Linear(128 * 4 * 4, 512)
self.fc2 = nn.Linear(512, num_classes)
def forward(self, x):
x = self.biovil_encoder(x).projected_patch_embeddings
x = torch.nn.functional.avg_pool2d(x, 4)
x = x.view(x.shape[0], -1) # Flatten the tensor
x = torch.relu(self.fc1(x))
# x = self.biovil_encoder(x).img_embedding
return self.fc2(x)
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