def main():
# model = Model(cf.segment_class, cf.level_class, cf.image_scale)
# batch_size = 16
dtype = torch.cuda.FloatTensor
weights_file = "NYU_ResNet-UpProj.npy"
print("Loading model......")
model = Model()
#resnet = torchvision.models.resnet50(pretrained=True)
resnet = torchvision.models.resnet50()
resnet.load_state_dict(torch.load('/home/xuqingyao/Multi_CNN/model/resnet50.pth'))
#resnet.load_state_dict(torch.load('/home/xpfly/nets/ResNet/resnet50-19c8e357.pth'))
print("resnet50 loaded.")
resnet50_pretrained_dict = resnet.state_dict()
model.load_state_dict(load_weights(model, weights_file, dtype))
"""
print('\nresnet50 keys:\n')
for key, value in resnet50_pretrained_dict.items():
print(key, value.size())
"""
#model_dict = model.state_dict()
"""
print('\nmodel keys:\n')
for key, value in model_dict.items():
print(key, value.size())
print("resnet50.dict loaded.")
"""
# load pretrained weights
#resnet50_pretrained_dict = {k: v for k, v in resnet50_pretrained_dict.items() if k in model_dict}
print("resnet50_pretrained_dict loaded.")
"""
print('\nresnet50_pretrained keys:\n')
for key, value in resnet50_pretrained_dict.items():
print(key, value.size())
"""
#model_dict.update(resnet50_pretrained_dict)
print("model_dict updated.")
"""
print('\nupdated model dict keys:\n')
for key, value in model_dict.items():
print(key, value.size())
"""
#model.load_state_dict(model_dict)
print("model_dict loaded.")
# print("========== data has been load! ==========")
if torch.cuda.is_available():
model.cuda()
else:
print("No cuda QAQ")
trainer = Trainer(model, torch.optim.Adam(model.parameters(), lr=0.001), epoch=600, use_cuda=torch.cuda.is_available())
trainer.train()
def predict(X, model_id, weights_id):
"""Output predictions for input samples using selected trained model.
Args:
X (numpy.ndarray): Input data to use for predictions.
model_id (str): Model identifier in string format.
weights_id (tuple): Weights file identifier in tuple of strings format.
The tuple should be of the form: (model_id, iteration_id).
Returns:
numpy.ndarray: Predictions.
"""
model = models.load_model(model_id)
model_nn = weights.load_weights(weights_id[0], weights_id[1])
single_image = False
if len(X.shape) == 2:
single_image = True
X = np.array([X])
X = model.pack_data(X)
logger.debug(
"after pack: min(X)={0}, max(X)={1}, avg(X)={2}, var(X)={3}".format(
np.min(X), np.max(X), np.average(X), np.var(X)
)
)
pred = model_nn.predict(X)
logger.debug(
"after predict: min(X)={0}, max(X)={1}, avg(X)={2}, var(X)={3}".format(
np.min(pred), np.max(pred), np.average(pred), np.var(pred)
)
)
pred = model.unpack_data(pred)
if single_image is True:
pred = np.squeeze(pred)
return pred
shuffle=True,
drop_last=True)
val_loader = data_utils.DataLoader(val_dataset,
batch_size,
shuffle=True,
drop_last=True)
test_loader = data_utils.DataLoader(test_dataset,
batch_size,
shuffle=True,
drop_last=True)
model = Model(ResidualBlock, UpProj_Block, batch_size)
model.type(dtype)
# Loading pretrained weights
model.load_state_dict(load_weights(model, weights_file, dtype))
loss_fn = torch.nn.NLLLoss2d().type(dtype)
# Uncomment When transfer learning the model parameters of the semantic segmentation branch
# for name, param in model.named_parameters():
# if name.startswith('up_conv5') or\
# name.startswith('conv4') or\
# name.startswith('bn4') or\
# name.startswith('conv5') or\
# name.startswith('bn5'):
# param.requires_grad = True
# else:
# param.requires_grad = False
# Uncomment when fine tuning the model by allowing backpropogation through all layers of the model
def train(
model_id, ds, ds_test, ds_val=None,
revision_id=None, seed=None, flags=[], options={}
):
"""Train and save neural network model.
Args:
model_id (str): Model identifier.
ds (datasets.Dataset): Dataset object with loaded data.
ds_test (datasets.Dataset): Dataset object to be used for testing.
ds_val (datasets.Dataset): Dataset object to be used for validation.
If None is passed, validation is not performed.
revision_id (str): Revision identifier of the model to be updated.
If None is passed, a new model will be created.
seed (int): Random number generator seed.
flags (list): A flag is set if it is present in the passed list.
List of possible flags:
sanity-test: Flag for testing mode (model not saved).
no-save: Do not save this model.
no-metrics: Do not evaluate this model.
no-early-stopping: Always train the specfied number of epochs.
Otherwise training stops when loss begins to stagnate.
options (dict): Additional training options. Is intersected with the
_train_def_options dictionary.
Returns:
tensorflow.keras.Model: Trained model.
"""
options = {**train_def_options, **options}
# Load model definition
model = models.load_model(model_id)
if seed is None:
seed = set_seed()
# Set up dataset properties
for i in [ds, ds_test, ds_val]:
if i is None:
continue
i.rs = np.random.default_rng(seed=seed)
i.batch_size = options['batch_size']
i.shuffle_on_epoch_end = True
i.apply(model.pack_data)
# Save some dataset statistics to debug
batch0 = ds[0]
logger.debug("Statistics of first data batch:")
logger.debug("X.shape={0}".format(batch0[0].shape))
logger.debug("min(X)={0}, max(X)={1}, avg(X)={2}, var(X)={3}".format(
np.min(batch0[0]), np.max(batch0[0]),
np.average(batch0[0]), np.var(batch0[0])
))
logger.debug("Y.shape={0}".format(batch0[1].shape))
logger.debug("min(Y)={0}, max(Y)={1}, avg(Y)={2}, var(Y)={3}".format(
np.min(batch0[1]), np.max(batch0[1]),
np.average(batch0[1]), np.var(batch0[1])
))
# Load a model to add to or set up a new one
if ('sanity-test' not in flags and 'no-save' not in flags
and revision_id is not None
and weights.weights_exist(model_id, revision_id)):
logger.info(
'Pre-trained weights found. Loading iteration {0}.'.format(
revision_id
)
)
model_nn = weights.load_weights(model_id, revision_id)
else:
logger.info(
'Pre-trained weights not used. Building model from scratch.')
input_shape = batch0[0][0].shape
logger.info(
'Using input shape: {0}.'.format(input_shape))
model_nn = model.build(options['learning_rate'], input_shape)
# Define callbacks
callbacks = []
callbacks.append(tf.keras.callbacks.TensorBoard(
log_dir=lib.logger.tensorboard_log_path(model_id),
write_graph=True,
write_images=True
))
if 'no-early-stopping' not in flags and hasattr(model, 'es_callback'):
callbacks.append(model.es_callback)
# if 'sanity-test' not in flags:
# callbacks.append(tf.keras.callbacks.ModelCheckpoint(
# os.path.join(
# options['checkpoint_dir'], options['checkpoint_file']),
# monitor='val_loss',
# save_best_only=True, # checkpoint only when `val_loss` improves
# save_freq='epoch',
# verbose=1
# ))
# Train the autoencoder model
model_nn.fit(
x=ds,
validation_data=ds_val,
epochs=options['epochs'],
callbacks=callbacks,
verbose=2
)
# Evaluate
metrics = []
if 'no-metrics' not in flags:
logger.info('Evaluating model.')
metrics = model_nn.evaluate(
x=ds_test,
verbose=0
)
model.metrics(metrics, logger)
# Save model to weights directory
if 'sanity-test' not in flags and 'no-save' not in flags:
if revision_id is None:
weights_id = weights.available(model_id, str(seed))
else:
weights_id = weights.available(model_id, revision_id)
weights_path = weights.path(weights_id[0], weights_id[1])
logger.info('Saving model to `{0}`.'.format(weights_path))
model_nn.save(weights_path)
return model_nn, metrics
def main():
batch_size = 32
data_path = 'nyu_depth_v2_labeled.mat'
learning_rate = 1.0e-5
monentum = 0.9
weight_decay = 0.0005
num_epochs = 50
resume_from_file = False
# 1.Load data
train_lists, val_lists, test_lists = load_split()
print("Loading data......")
train_loader = torch.utils.data.DataLoader(NyuDepthLoader(
data_path, train_lists),
batch_size=batch_size,
shuffle=True,
drop_last=True)
val_loader = torch.utils.data.DataLoader(NyuDepthLoader(
data_path, val_lists),
batch_size=batch_size,
shuffle=True,
drop_last=True)
test_loader = torch.utils.data.DataLoader(NyuDepthLoader(
data_path, test_lists),
batch_size=batch_size,
shuffle=True,
drop_last=True)
print(train_loader)
# 2.Load model
print("Loading model......")
model = FCRN(batch_size)
resnet = torchvision.models.resnet50(pretrained=True)
#resnet = torchvision.models.resnet50()
#resnet.load_state_dict(torch.load('/home/pengfei/data/nets/ResNet/resnet50-19c8e357.pth'))
#resnet.load_state_dict(torch.load('/home/xpfly/nets/ResNet/resnet50-19c8e357.pth'))
print("resnet50 loaded.")
resnet50_pretrained_dict = resnet.state_dict()
model.load_state_dict(load_weights(model, weights_file, dtype))
"""
print('\nresnet50 keys:\n')
for key, value in resnet50_pretrained_dict.items():
print(key, value.size())
"""
#model_dict = model.state_dict()
"""
print('\nmodel keys:\n')
for key, value in model_dict.items():
print(key, value.size())
print("resnet50.dict loaded.")
"""
# load pretrained weights
#resnet50_pretrained_dict = {k: v for k, v in resnet50_pretrained_dict.items() if k in model_dict}
print("resnet50_pretrained_dict loaded.")
"""
print('\nresnet50_pretrained keys:\n')
for key, value in resnet50_pretrained_dict.items():
print(key, value.size())
"""
#model_dict.update(resnet50_pretrained_dict)
print("model_dict updated.")
"""
print('\nupdated model dict keys:\n')
for key, value in model_dict.items():
print(key, value.size())
"""
#model.load_state_dict(model_dict)
print("model_dict loaded.")
model = model.cuda()
# 3.Loss
loss_fn = torch.nn.MSELoss().cuda()
print("loss_fn set.")
# 5.Train
best_val_err = 1.0e3
# validate
model.eval()
num_correct, num_samples = 0, 0
loss_local = 0
with torch.no_grad():
for input, depth in val_loader:
input_var = Variable(input.type(dtype))
depth_var = Variable(depth.type(dtype))
output = model(input_var)
input_rgb_image = input_var[0].data.permute(
1, 2, 0).cpu().numpy().astype(np.uint8)
input_gt_depth_image = depth_var[0][0].data.cpu().numpy().astype(
np.float32)
pred_depth_image = output[0].data.squeeze().cpu().numpy().astype(
np.float32)
input_gt_depth_image /= np.max(input_gt_depth_image)
pred_depth_image /= np.max(pred_depth_image)
plot.imsave('input_rgb_epoch_0.png', input_rgb_image)
plot.imsave('gt_depth_epoch_0.png',
input_gt_depth_image,
cmap="viridis")
plot.imsave('pred_depth_epoch_0.png',
pred_depth_image,
cmap="viridis")
# depth_var = depth_var[:, 0, :, :]
# loss_fn_local = torch.nn.MSELoss()
loss_local += loss_fn(output, depth_var)
num_samples += 1
err = float(loss_local) / num_samples
print('val_error before train:', err)
start_epoch = 0
resume_file = 'checkpoint.pth.tar'
if resume_from_file:
if os.path.isfile(resume_file):
print("=> loading checkpoint '{}'".format(resume_file))
checkpoint = torch.load(resume_file)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
resume_file, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(resume_file))
df_loss = pd.DataFrame(columns=['train_loss', 'val_loss'])
for epoch in range(num_epochs):
# 4.Optim
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=monentum)
# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=monentum, weight_decay=weight_decay)
print("optimizer set.")
print('Starting train epoch %d / %d' %
(start_epoch + epoch + 1, num_epochs))
model.train()
running_loss = 0
count = 0
epoch_loss = 0
epoch_df_loss = []
#for i, (input, depth) in enumerate(train_loader):
for input, depth in train_loader:
# input, depth = data
#input_var = input.cuda()
#depth_var = depth.cuda()
input_var = Variable(input.type(dtype))
depth_var = Variable(depth.type(dtype))
output = model(input_var)
loss = loss_fn(output, depth_var)
print('loss:', loss.data.cpu().item())
count += 1
running_loss += loss.data.cpu().numpy()
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss = running_loss / count
epoch_df_loss += [epoch_loss]
print('epoch loss:', epoch_loss)
# validate
model.eval()
num_correct, num_samples = 0, 0
loss_local = 0
with torch.no_grad():
for input, depth in val_loader:
input_var = Variable(input.type(dtype))
depth_var = Variable(depth.type(dtype))
output = model(input_var)
input_rgb_image = input_var[0].data.permute(
1, 2, 0).cpu().numpy().astype(np.uint8)
input_gt_depth_image = depth_var[0][0].data.cpu().numpy(
).astype(np.float32)
pred_depth_image = output[0].data.squeeze().cpu().numpy(
).astype(np.float32)
input_gt_depth_image /= np.max(input_gt_depth_image)
pred_depth_image /= np.max(pred_depth_image)
plot.imsave(
'input_rgb_epoch_{}.png'.format(start_epoch + epoch + 1),
input_rgb_image)
plot.imsave('gt_depth_epoch_{}.png'.format(start_epoch +
epoch + 1),
input_gt_depth_image,
cmap="viridis")
plot.imsave('pred_depth_epoch_{}.png'.format(start_epoch +
epoch + 1),
pred_depth_image,
cmap="viridis")
# depth_var = depth_var[:, 0, :, :]
# loss_fn_local = torch.nn.MSELoss()
loss_local += loss_fn(output, depth_var)
num_samples += 1
err = float(loss_local) / num_samples
epoch_df_loss += [err]
print('val_error:', err)
df_loss.loc[epoch] = epoch_df_loss
if err < best_val_err:
best_val_err = err
torch.save(
{
'epoch': start_epoch + epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, 'checkpoint.pth.tar')
if epoch % 10 == 0:
learning_rate = learning_rate * 0.6
df_loss.to_csv('./loss.csv')
def main():
batch_size = 16
data_path = './data/nyu_depth_v2_labeled.mat'
learning_rate = 1.0e-4
monentum = 0.9
weight_decay = 0.0005
num_epochs = 100
# 1.Load data
train_lists, val_lists, test_lists = load_split()
print("Loading data...")
train_loader = torch.utils.data.DataLoader(NyuDepthLoader(data_path, train_lists),
batch_size=batch_size, shuffle=False, drop_last=True)
val_loader = torch.utils.data.DataLoader(NyuDepthLoader(data_path, val_lists),
batch_size=batch_size, shuffle=True, drop_last=True)
test_loader = torch.utils.data.DataLoader(NyuDepthLoader(data_path, test_lists),
batch_size=batch_size, shuffle=True, drop_last=True)
print(train_loader)
# 2.Load model
print("Loading model...")
model = FCRN(batch_size)
model.load_state_dict(load_weights(model, weights_file, dtype)) #加载官方参数，从tensorflow转过来
#加载训练模型
resume_from_file = False
resume_file = './model/model_300.pth'
if resume_from_file:
if os.path.isfile(resume_file):
checkpoint = torch.load(resume_file)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("loaded checkpoint '{}' (epoch {})"
.format(resume_file, checkpoint['epoch']))
else:
print("can not find!")
model = model.cuda()
# 3.Loss
# 官方MSE
# loss_fn = torch.nn.MSELoss()
# 自定义MSE
# loss_fn = loss_mse()
# 论文的loss,the reverse Huber
loss_fn = loss_huber()
print("loss_fn set...")
# 4.Optim
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
print("optimizer set...")
# 5.Train
best_val_err = 1.0e-4
start_epoch = 0
for epoch in range(num_epochs):
print('Starting train epoch %d / %d' % (start_epoch + epoch + 1, num_epochs + start_epoch))
model.train()
running_loss = 0
count = 0
epoch_loss = 0
for input, depth in train_loader:
input_var = Variable(input.type(dtype))
depth_var = Variable(depth.type(dtype))
output = model(input_var)
loss = loss_fn(output, depth_var)
print('loss: %f' % loss.data.cpu().item())
count += 1
running_loss += loss.data.cpu().numpy()
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss = running_loss / count
print('epoch loss:', epoch_loss)
# validate
model.eval()
num_correct, num_samples = 0, 0
loss_local = 0
with torch.no_grad():
for input, depth in val_loader:
input_var = Variable(input.type(dtype))
depth_var = Variable(depth.type(dtype))
output = model(input_var)
if num_epochs == epoch + 1:
# 关于保存的测试图片可以参考 loader 的写法
# input_rgb_image = input_var[0].data.permute(1, 2, 0).cpu().numpy().astype(np.uint8)
input_rgb_image = input[0].data.permute(1, 2, 0)
input_gt_depth_image = depth_var[0][0].data.cpu().numpy().astype(np.float32)
pred_depth_image = output[0].data.squeeze().cpu().numpy().astype(np.float32)
input_gt_depth_image /= np.max(input_gt_depth_image)
pred_depth_image /= np.max(pred_depth_image)
plot.imsave('./result/input_rgb_epoch_{}.png'.format(start_epoch + epoch + 1), input_rgb_image)
plot.imsave('./result/gt_depth_epoch_{}.png'.format(start_epoch + epoch + 1), input_gt_depth_image, cmap="viridis")
plot.imsave('./result/pred_depth_epoch_{}.png'.format(start_epoch + epoch + 1), pred_depth_image, cmap="viridis")
loss_local += loss_fn(output, depth_var)
num_samples += 1
err = float(loss_local) / num_samples
print('val_error: %f' % err)
if err < best_val_err or epoch == num_epochs - 1:
best_val_err = err
torch.save({
'epoch': start_epoch + epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, './model/model_' + str(start_epoch + epoch + 1) + '.pth')
if epoch % 10 == 0:
learning_rate = learning_rate * 0.8
def get_tf_implementation(weights_path, all_layer_outs=False):
print("Making Structure")
tf_model = model.make_deng_tf_test(verbose=True, all_layer_outs=all_layer_outs)
print("Loading Weights")
tf_model = weights.load_weights(tf_model, weights_path, verbose=True)
return tf_model