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# coding=utf-8 |
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""" |
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Tests for deepreg/model/backbone/global_net.py |
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""" |
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from typing import Tuple |
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import pytest |
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import tensorflow as tf |
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from deepreg.model.backbone.global_net import AffineHead, GlobalNet |
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def test_affine_head(): |
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""" |
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Test AffineHead. |
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""" |
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batch = 3 |
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input_shape = (4, 5, 6) |
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config = dict(image_size=input_shape, name="TestAffineHead") |
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layer = AffineHead(**config) |
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inputs = tf.ones(shape=(batch, *input_shape, 2)) |
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ddf, theta = layer.call(inputs) |
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assert ddf.shape == (batch, *input_shape, 3) |
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assert theta.shape == (batch, 4, 3) |
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got = layer.get_config() |
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assert got == {"trainable": True, "dtype": "float32", **config} |
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class TestGlobalNet: |
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""" |
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Test the backbone.global_net.GlobalNet class |
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""" |
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@pytest.mark.parametrize( |
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"image_size,extract_levels,depth", |
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[ |
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((11, 12, 13), (0, 1, 2, 4), 4), |
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((11, 12, 13), None, 4), |
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((11, 12, 13), (0, 1, 2, 4), None), |
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((8, 8, 8), (0, 1, 2), 3), |
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], |
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) |
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def test_call( |
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self, |
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image_size: tuple, |
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extract_levels: Tuple[int, ...], |
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depth: int, |
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): |
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""" |
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:param image_size: (dim1, dim2, dim3), dims of input image. |
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:param extract_levels: from which depths the output will be built. |
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:param depth: input is at level 0, bottom is at level depth |
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""" |
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batch_size = 5 |
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out_ch = 3 |
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network = GlobalNet( |
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image_size=image_size, |
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num_channel_initial=2, |
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extract_levels=extract_levels, |
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depth=depth, |
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out_kernel_initializer="he_normal", |
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out_activation="softmax", |
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out_channels=out_ch, |
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) |
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inputs = tf.ones(shape=(batch_size, *image_size, out_ch)) |
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ddf, theta = network.call(inputs) |
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assert ddf.shape == inputs.shape |
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assert theta.shape == (batch_size, 4, 3) |
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def test_err(self): |
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with pytest.raises(ValueError) as err_info: |
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GlobalNet( |
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image_size=(4, 5, 6), |
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out_channels=3, |
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num_channel_initial=2, |
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depth=None, |
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extract_levels=None, |
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out_kernel_initializer="he_normal", |
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out_activation="softmax", |
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pooling=False, |
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concat_skip=False, |
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encode_kernel_sizes=[7, 3, 3], |
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decode_kernel_sizes=3, |
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strides=2, |
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padding="same", |
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name="Test", |
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) |
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assert "GlobalNet requires `depth` or `extract_levels`" in str(err_info.value) |
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View Code Duplication |
def test_get_config(self): |
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config = dict( |
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image_size=(4, 5, 6), |
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out_channels=3, |
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num_channel_initial=2, |
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depth=2, |
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extract_levels=(2,), |
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out_kernel_initializer="he_normal", |
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out_activation="softmax", |
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pooling=False, |
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concat_skip=False, |
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encode_kernel_sizes=[7, 3, 3], |
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decode_kernel_sizes=3, |
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encode_num_channels=[2, 4, 8], |
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decode_num_channels=[2, 4, 8], |
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strides=2, |
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padding="same", |
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name="Test", |
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) |
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network = GlobalNet(**config) |
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got = network.get_config() |
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assert got == config |
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DeepRegNet /
DeepReg
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