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train_sketch_ringview.py
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train_sketch_ringview.py
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import torch
from torch.utils.data import DataLoader
from pytorch_metric_learning.losses import NTXentLoss, CrossBatchMemory
from ringnet.dataset import SHREC23_Rings_RenderOnly_ImageQuery
from ringnet.models import Base3DObjectRingsExtractor
from common.models import ResNetExtractor, MLP
from common.test import test_loop
from common.train import train_loop
batch_size = 2
latent_dim = 128
epoch = 10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
obj_extractor = Base3DObjectRingsExtractor(
nheads=4,
dropout=0.1,
nrings=3,
)
obj_embedder = MLP(obj_extractor,latent_dim=latent_dim).to(device)
query_extractor = ResNetExtractor()
query_embedder = MLP(query_extractor,latent_dim=latent_dim).to(device)
train_ds = SHREC23_Rings_RenderOnly_ImageQuery(
'data/csv/train_skt.csv', 'data/SketchANIMAR2023/3D_Model_References/generated_models', 'data/SketchANIMAR2023/Train/SketchQuery_Train', [1, 3, 5])
train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, collate_fn=train_ds.collate_fn)
test_ds = SHREC23_Rings_RenderOnly_ImageQuery(
'data/csv/test_skt.csv', 'data/SketchANIMAR2023/3D_Model_References/generated_models', 'data/SketchANIMAR2023/Train/SketchQuery_Train', [1, 3, 5])
test_dl = DataLoader(test_ds, batch_size=batch_size, shuffle=True, num_workers=4, collate_fn=test_ds.collate_fn)
contra_loss = NTXentLoss()
cbm_query = CrossBatchMemory(contra_loss, latent_dim, 128)
cbm_object = CrossBatchMemory(contra_loss, latent_dim, 128)
# Set optimizers
optimizer1 = torch.optim.Adam(obj_embedder.parameters(), lr=0.00001, weight_decay=0.0001)
optimizer2 = torch.optim.Adam(query_embedder.parameters(), lr=0.00001, weight_decay=0.0001)
for e in range(epoch):
print(f'Epoch {e+1}/{epoch}:')
loss = train_loop(obj_embedder = obj_embedder, query_embedder = query_embedder,
obj_input='object_ims', query_input='query_ims',
cbm_query=cbm_query, cbm_object=cbm_object,
obj_optimizer=optimizer1, query_optimizer=optimizer2,
dl=train_dl,
device=device)
print(f'Loss: {loss:.4f}')
test_loop(obj_embedder = obj_embedder, query_embedder = query_embedder,
obj_input='object_ims', query_input='query_ims',
dl=test_dl,
dimension=latent_dim,
device=device)