def train_autoencoder_deep_fashion():
"""Trains the autoencoder for DeepFashion."""
print("=============================================================")
print("================ Train AE with DeepFashion ==================")
print("=============================================================\n")
encoder = CifarNet(input_channels=3, num_classes=50)
encoder = encoder.to(DEVICE)
decoder = Decoder(input_channels=64, num_classes=50, out_channels=3)
decoder = decoder.to(DEVICE)
parameters = list(encoder.parameters()) + list(decoder.parameters())
loss_fn = nn.MSELoss()
# Observe that all parameters are being optimized
optimizer = torch.optim.Adam(parameters, lr=LEARNING_RATE_TRAIN)
# Decay LR by a factor of GAMMA
scheduler = torch.optim.lr_scheduler.StepLR(optimizer=optimizer,
step_size=STEP_SIZE_TRAIN,
gamma=GAMMA)
return train(encoder, decoder, loss_fn, optimizer, scheduler, EPOCHS,
train_loader_deep_fashion)
def train_exemplar_cnn_deep_fashion():
"""Trains the exemplar cnn model."""
print("============================================================")
print("============ Train ExemplarCNN with DeepFashion ============")
print("============================================================\n")
# number of predicted classes = number of training images
model = CifarNet(input_channels=3, num_classes=len(train_loader_deep_fashion.dataset))
model = model.to(DEVICE)
loss_fn = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
parameters = model.parameters()
optimizer = torch.optim.Adam(parameters, lr=LEARNING_RATE_TRAIN)
# Decay LR by a factor of GAMMA
scheduler = torch.optim.lr_scheduler.StepLR(optimizer=optimizer, step_size=STEP_SIZE_TRAIN, gamma=GAMMA)
return train(model, loss_fn, optimizer, scheduler, EPOCHS, train_loader_deep_fashion)
def train_rotation_net_deep_fashion():
"""Trains the rotation model."""
print("============================================================")
print("========== Train Rotation Model with DeepFashion ===========")
print("============================================================\n")
model = CifarNet(input_channels=3, num_classes=4)
model = model.to(DEVICE)
loss_fn = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE_TRAIN)
# Decay LR by a factor of GAMMA
scheduler = torch.optim.lr_scheduler.StepLR(optimizer=optimizer,
step_size=STEP_SIZE_TRAIN,
gamma=GAMMA)
return train(model, loss_fn, optimizer, scheduler, EPOCHS,
train_loader_deep_fashion, val_loader_deep_fashion)
def train_supervised_deep_fashion():
"""Trains the supervised model."""
print("============================================================")
print("============= Supervised Training DeepFashion ==============")
print("============================================================\n")
df_supervised_model = CifarNet(input_channels=3, num_classes=50)
df_supervised_model = df_supervised_model.to(DEVICE)
loss_fn = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer = torch.optim.Adam(df_supervised_model.parameters(),
lr=LEARNING_RATE_TRAIN)
# Decay LR by a factor of GAMMA
scheduler = torch.optim.lr_scheduler.StepLR(optimizer=optimizer,
step_size=STEP_SIZE_TRAIN,
gamma=GAMMA)
return fine_tune(df_supervised_model, loss_fn, optimizer, scheduler,
EPOCHS, train_loader_deep_fashion,
val_loader_deep_fashion)