Deep Learning - Inference¶
Loading model¶
To load a pretrained network one should use atlalign.ml_utils.load_model.
Note that it loads all possible custom layers in the background so that the user does not have
to worry about it.
from atlalign.ml_utils import load_model
path_1 = 'path/to/a/folder' # inside of this folder .json (architecture) and .h5 (weights)
path_2 = 'path/to/a/file.h5' # architecture and weights not separated + additional info (loss and optimizer)
model_path_1 = load_model(path_1)
model_path_2 = load_model(path_2, compile=True)
Merging¶
To merge a global and a local alignment network one can perform the composition via the __call__ method
of atlalign.base.DisplacementField on a per sample basis. A better approach is to use a custom keras layer
implementing composition. For the latter option we provide a utility function atlalign.ml_utils.merge_global_local.
from atlalign.ml_utils import load_model, merge_global_local
path_global = 'global_model.h5'
path_local = 'local_model.h5'
model_global = load_model(path_global)
model_local = load_model(path_local)
model_merged = merge_global_local(model_global, model_local)
Forward pass¶
Performing the actual inference is extremely simple. Please review the SupervisedGenerator to understand the shape of the expected input. To quickly summarize (for non-inverse models) the user only needs to create a 4D array of the following shape
(batch_size, height=320, width=456, depth=2)
The last dimension is simply a stack of the img_ref and img_mov.
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1' # to enable GPU
import numpy as np
from atlalign.base import DisplacementField
from atlalign.ml_utils import load_model
batch_size = 32 # how many samples are grouped together at inference time
model = load_model('path/to/model.h5')
X = np.random.random((200, 320, 456, 2))
[reg_images, deltas_xy] = model.predict(X, batch_size=batch_size)
# one can also create instances of DisplacementFields to perform many other tasks
dfs = [DisplacementField(deltas_xy[i, ..., 0], deltas_xy[i, ..., 1]) for i in range(len(X))]