Spaces:
Runtime error
Runtime error
File size: 2,083 Bytes
06142a4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
import torch
import torch.nn as nn
import torchvision
from torchvision.models import resnet101
class BackBone(nn.Module):
def __init__(self, num_unfreeze_layers=3):
super(BackBone, self).__init__()
model = resnet101(weights='IMAGENET1K_V2', progress=True)
feature_maps = list(model.children())[:8]
# Adding an AdaptiveAvgPooling (batch_size, 2048, 8, 8) -> (batch_size, 2048, 1, 8)
feature_maps.append(nn.AdaptiveAvgPool2d((1, None)))
self.backbone = nn.Sequential(*feature_maps)
for layer in list(self.backbone.parameters())[-(num_unfreeze_layers+1):]:
layer.requires_grad = True
def forward(self, image):
return self.backbone(image)
class CRNN(nn.Module):
def __init__(self, vocab_size, hidden_size, n_layers, dropout=0.2, num_unfreeze_layers=3):
super(CRNN, self).__init__()
self.backbone = BackBone(num_unfreeze_layers=num_unfreeze_layers)
self.mapSeq = nn.Sequential(
nn.Linear(2048, 512),
nn.ReLU(),
nn.Dropout(p=dropout)
)
self.gru = nn.GRU(
input_size=512,
hidden_size=hidden_size,
num_layers=n_layers,
bidirectional=True,
batch_first=True,
dropout=dropout if n_layers > 1 else 0
)
self.layer_norm = nn.LayerNorm(hidden_size * 2)
# Dense layers
self.out = nn.Sequential(
nn.Linear(hidden_size * 2, vocab_size),
nn.LogSoftmax(dim=2)
)
def forward(self, x):
x = self.backbone(x)
# (batch_size, 2048, 1, 8) -> (batch_size, 8, 2048, 1)
x = x.permute(0, 3, 1, 2)
# flatten -> (batch_size, 8, 2048)
x = x.view(x.size(0), x.size(1), -1)
x = self.mapSeq(x)
x, _ = self.gru(x)
x = self.layer_norm(x)
x = self.out(x)
# (batch_size, 8, vocab_size) -> (8, batch_size, vocab_size)
x = x.permute(1, 0, 2)
return x |