def _main():
# Set controller device and devices
# A one-gpu setup would be something like controller='/device:GPU:0' and gpu_devices=['/device:GPU:0']
gpu_devices = ['/device:GPU:0']
controller = '/device:CPU:0'
# Useful settings
batch_size = 4
img_size = (448, 256)
"""
# Dataset options
ds_opts = {
'batch_size': batch_size,
'verbose': True,
'in_memory': False, # True loads all samples upfront, False loads them on-demand
'crop_preproc': None, # None or (h, w), use (384, 768) for FlyingThings3D
'scale_preproc': img_size, # None or (h, w),
'type': 'noc', # ['clean' | 'final'] for MPISintel, ['noc' | 'occ'] for KITTI, 'into_future' for FlyingThings3D
'tb_test_imgs': False, # If True, make test images available to model in training mode
# Sampling and split options
'random_seed': 1337, # random seed used for sampling
'val_split': 0.01, # portion of data reserved for the validation split
# Augmentation options
'aug_type': 'heavy', # in [None, 'basic', 'heavy'] to add augmented data to training set
'aug_labels': True, # If True, augment both images and labels; otherwise, only augment images
'fliplr': 0.5, # Horizontally flip 50% of images
'flipud': 0.0, # Vertically flip 50% of images
'translate': (0., 0.), # Translate 50% of images by a value between -5 and +5 percent of original size on x- and y-axis independently
'scale': (0., 0.), # Scale 50% of images by a factor between 95 and 105 percent of original size
}
# Load train dataset
ds_1 = KITTIDataset(mode='train_with_val', ds_root=data_path + kitti12, options=ds_opts)
ds_2 = KITTIDataset(mode='train_with_val', ds_root=data_path + kitti15, options=ds_opts)
ds = MixedDataset(mode='train_with_val', datasets=[ds_1, ds_2], options=ds_opts)
"""
# Dataset options
ds_opts = {
'batch_size': batch_size,
'verbose': True,
'in_memory':
False, # True loads all samples upfront, False loads them on-demand
'crop_preproc':
None, # None or (h, w), use (384, 768) for FlyingThings3D
'scale_preproc': img_size, # None or (h, w),
# ['clean' | 'final'] for MPISintel, ['noc' | 'occ'] for KITTI, 'into_future' for FlyingThings3D
'type': 'into_future',
'tb_test_imgs':
False, # If True, make test images available to model in training mode
# Sampling and split options
'random_seed': 1337, # random seed used for sampling
'val_split': 0.01, # portion of data reserved for the validation split
# Augmentation options
'aug_type':
'heavy', # in [None, 'basic', 'heavy'] to add augmented data to training set
'aug_labels':
True, # If True, augment both images and labels; otherwise, only augment images
'fliplr': 0.5, # Horizontally flip 50% of images
'flipud': 0.0, # Vertically flip 50% of images
# Translate 50% of images by a value between -5 and +5 percent of original size on x- and y-axis independently
'translate': (0, 0),
'scale': (
0, 0
), # Scale 50% of images by a factor between 95 and 105 percent of original size
'train_imgs': _TRAIN_IMGS,
'train_flow': _TRAIN_FLOW,
'test_imgs': _TEST_IMGS,
}
ds = FlowDroNetDataset(mode='train_with_val',
ds_root=_DATASET_ROOT,
options=ds_opts)
# Display dataset configuration
ds.print_config()
"""
# Fine-Tuning options (Large)
nn_opts = {
'verbose': True,
'ckpt_path': ckpt_path, # original checkpoint to finetune
'ckpt_dir': save_path, # where finetuning checkpoints are stored
'max_to_keep': 10,
'x_dtype': tf.float32, # image pairs input type
'x_shape': [2, img_size[1], img_size[0], 3], # image pairs input shape [2, H, W, 3]
'y_dtype': tf.float32, # u,v flows output type
'y_shape': [img_size[1], img_size[0], 2], # u,v flows output shape [H, W, 2]
'train_mode': 'fine-tune', # in ['train', 'fine-tune']
'adapt_info': None, # if predicted flows are padded by the model, crop them back by to this size
'sparse_gt_flow': False, # if gt flows are sparse (KITTI), only compute average EPE where gt flows aren't (0., 0.)
# Logging/Snapshot params
'display_step': 10, # show progress every 100 training batches
'snapshot_step': 500, # save trained model every 1000 training batches
'val_step': 1000, # Test trained model on validation split every 1000 training batches
'val_batch_size': 100, # Use -1 to use entire validation split, or set number of val samples (0 disables it)
'tb_val_imgs': 'top_flow', # None, 'top_flow', or 'pyramid'; runs model on batch_size val images, log results
'tb_test_imgs': None, # None, 'top_flow', or 'pyramid'; runs trained model on batch_size test images, log results
# Multi-GPU config
# list devices on which to run the model's train ops (can be more than one GPU)
'gpu_devices': gpu_devices,
# controller device to put the model's variables on (usually, /cpu:0 or /gpu:0 -> try both!)
'controller': controller,
# Training config and hyper-params
'use_tf_data': False, # Set to True to get data from tf.data.Dataset; otherwise, use feed_dict with numpy
'use_mixed_precision': False, # Set to True to use mixed precision training (fp16 inputs)
'loss_scaler': 128., # Loss scaler (only used in mixed precision training)
'batch_size': batch_size * len(gpu_devices),
'lr_policy': 'multisteps', # choose between None, 'multisteps', and 'cyclic'; adjust the max_steps below too
# Multistep lr schedule
'init_lr': 1e-05, # initial learning rate
'max_steps': 25000, # max number of training iterations (i.e., batches to run)
'lr_boundaries': [5000, 10000, 15000, 20000], # step schedule boundaries
'lr_values': [1e-05, 5e-06, 2.5e-06, 1.25e-06, 6.25e-07], # step schedule values
# Cyclic lr schedule
'cyclic_lr_max': 2e-05, # maximum bound
'cyclic_lr_base': 1e-06, # min bound
'cyclic_lr_stepsize': 20000 * 8 / ds_opts['batch_size'], # step schedule values
# 'max_steps': 200000, # max number of training iterations
# Loss functions hyper-params
'loss_fn': 'loss_multiscale', # 'loss_robust' doesn't really work; the loss goes down but the EPE doesn't
'alphas': [0.32, 0.08, 0.02, 0.01, 0.005], # See 'Implementation details" on page 5 of ref PDF
'gamma': 0.0004, # See 'Implementation details" on page 5 of ref PDF
'q': 0.4, # See 'Implementation details" on page 5 of ref PDF
'epsilon': 0.01, # See 'Implementation details" on page 5 of ref PDF
# Model hyper-params
'pyr_lvls': 6, # number of feature levels in the flow pyramid
'flow_pred_lvl': 2, # which level to upsample to generate the final optical flow prediction
'search_range': 4, # cost volume search range
# if True, use model with dense connections (4705064 params w/o, 9374274 params with (no residual conn.))
'use_dense_cx': True,
# if True, use model with residual connections (4705064 params w/o, 6774064 params with (+2069000) (no dense conn.))
'use_res_cx': True,
}
"""
# Fine-Tuning options (Small)
nn_opts = {
'verbose': True,
'ckpt_path': ckpt_path, # original checkpoint to finetune
'ckpt_dir': save_path, # where finetuning checkpoints are stored
'max_to_keep': 10,
'x_dtype': tf.float32, # image pairs input type
'x_shape': [2, img_size[1], img_size[0],
3], # image pairs input shape [2, H, W, 3]
'y_dtype': tf.float32, # u,v flows output type
'y_shape': [img_size[1], img_size[0],
2], # u,v flows output shape [H, W, 2]
'train_mode': 'fine-tune', # in ['train', 'fine-tune']
'adapt_info':
None, # if predicted flows are padded by the model, crop them back by to this size
'sparse_gt_flow':
False, # if gt flows are sparse (KITTI), only compute average EPE where gt flows aren't (0., 0.)
# Logging/Snapshot params
'display_step': 100, # show progress every 100 training batches
'snapshot_step': 500, # save trained model every 1000 training batches
'val_step':
500, # Test trained model on validation split every 1000 training batches
'val_batch_size':
20, # Use -1 to use entire validation split, or set number of val samples (0 disables it)
'tb_val_imgs':
'top_flow', # None, 'top_flow', or 'pyramid'; runs model on batch_size val images, log results
'tb_test_imgs':
None, # None, 'top_flow', or 'pyramid'; runs trained model on batch_size test images, log results
# Multi-GPU config
# list devices on which to run the model's train ops (can be more than one GPU)
'gpu_devices': gpu_devices,
# controller device to put the model's variables on (usually, /cpu:0 or /gpu:0 -> try both!)
'controller': controller,
# Training config and hyper-params
'use_tf_data':
False, # Set to True to get data from tf.data.Dataset; otherwise, use feed_dict with numpy
'use_mixed_precision':
False, # Set to True to use mixed precision training (fp16 inputs)
'loss_scaler':
128., # Loss scaler (only used in mixed precision training)
'batch_size': batch_size * len(gpu_devices),
'lr_policy':
'multisteps', # choose between None, 'multisteps', and 'cyclic'; adjust the max_steps below too
# Multistep lr schedule
'init_lr': 1e-05, # initial learning rate
'max_steps':
25000, # max number of training iterations (i.e., batches to run)
'lr_boundaries': [5000, 10000, 15000,
20000], # step schedule boundaries
'lr_values': [1e-05, 5e-06, 2.5e-06, 1.25e-06,
6.25e-07], # step schedule values
# Cyclic lr schedule
'cyclic_lr_max': 2e-05, # maximum bound
'cyclic_lr_base': 1e-06, # min bound
'cyclic_lr_stepsize':
20000 * 8 / ds_opts['batch_size'], # step schedule values
# 'max_steps': 200000, # max number of training iterations
# Loss functions hyper-params
'loss_fn':
'loss_multiscale', # 'loss_robust' doesn't really work; the loss goes down but the EPE doesn't
'alphas': [0.32, 0.08, 0.02, 0.01,
0.005], # See 'Implementation details" on page 5 of ref PDF
'gamma': 0.0004, # See 'Implementation details" on page 5 of ref PDF
'q': 0.4, # See 'Implementation details" on page 5 of ref PDF
'epsilon': 0.01, # See 'Implementation details" on page 5 of ref PDF
# Model hyper-params
'pyr_lvls': 6, # number of feature levels in the flow pyramid
'flow_pred_lvl':
2, # which level to upsample to generate the final optical flow prediction
'search_range': 4, # cost volume search range
# if True, use model with dense connections (4705064 params w/o, 9374274 params with (no residual conn.))
'use_dense_cx': False,
# if True, use model with residual connections (4705064 params w/o, 6774064 params with (+2069000) (no dense conn.))
'use_res_cx': False,
}
# Instantiate the model and display the model configuration
nn = ModelPWCNet(mode='train_with_val', options=nn_opts, dataset=ds)
nn.print_config()
# Train the model
nn.train()
(float(ds_opts['batch_size']) / 8))
nn_opts['max_steps'] = int(nn_opts['max_steps'] /
(float(ds_opts['batch_size']) / 8))
# In[7]:
# Instantiate the model and display the model configuration
nn = ModelPWCNet(mode='train_with_val', options=nn_opts, dataset=ds)
nn.print_config()
# ## Train the model
# In[8]:
# Train the model
nn.train()
# ## Training log
# Here are the training curves for the run above:
#
# 
# 
# 
# Here are the predictions issued by the model for a few validation samples:
#
# 
# 
# 
# 
