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# coding=utf-8 |
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""" |
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Tests for deepreg/model/backbone/local_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.local_net import AdditiveUpsampling, LocalNet |
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def test_additive_up_sampling(): |
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""" |
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Test AdditiveUpsampling. |
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""" |
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batch = 3 |
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filters = 4 |
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input_shape = (4, 5, 6) |
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outputs_shape = tuple(x * 2 for x in input_shape) |
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config = dict( |
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filters=filters, |
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output_padding=(1, 1, 1), |
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kernel_size=3, |
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padding="same", |
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strides=2, |
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output_shape=outputs_shape, |
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name="TestAdditiveUpsampling", |
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) |
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layer = AdditiveUpsampling(**config) |
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inputs = tf.ones(shape=(batch, *input_shape, filters * 2)) |
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output = layer.call(inputs) |
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assert output.shape == (batch, *outputs_shape, filters) |
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got = layer.get_config() |
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assert got == {"trainable": True, "dtype": "float32", **config} |
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class TestLocalNet: |
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""" |
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Test the backbone.local_net.LocalNet 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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[((11, 12, 13), (0, 1, 2, 4), 4), ((8, 8, 8), (0, 1, 2), 3)], |
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) |
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@pytest.mark.parametrize("use_additive_upsampling", [True, False]) |
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@pytest.mark.parametrize("pooling", [True, False]) |
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@pytest.mark.parametrize("concat_skip", [True, False]) |
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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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use_additive_upsampling: bool, |
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pooling: bool, |
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concat_skip: bool, |
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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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:param use_additive_upsampling: whether use additive up-sampling layer |
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for decoding. |
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:param pooling: for down-sampling, use non-parameterized |
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pooling if true, otherwise use conv3d |
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:param concat_skip: if concatenate skip or add it |
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""" |
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out_ch = 3 |
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network = LocalNet( |
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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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use_additive_upsampling=use_additive_upsampling, |
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pooling=pooling, |
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concat_skip=concat_skip, |
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) |
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inputs = tf.ones(shape=(5, *image_size, out_ch)) |
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output = network.call(inputs) |
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assert inputs.shape == output.shape |
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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=(0, 1), |
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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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use_additive_upsampling=True, |
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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 = LocalNet(**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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