239 lines
7.6 KiB
Python
239 lines
7.6 KiB
Python
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import pytest
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import torch
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmdet.models.necks import FPN, ChannelMapper
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def test_fpn():
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"""Tests fpn."""
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s = 64
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in_channels = [8, 16, 32, 64]
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feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8]
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out_channels = 8
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# `num_outs` is not equal to len(in_channels) - start_level
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with pytest.raises(AssertionError):
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FPN(in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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num_outs=2)
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# `end_level` is larger than len(in_channels) - 1
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with pytest.raises(AssertionError):
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FPN(in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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end_level=4,
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num_outs=2)
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# `num_outs` is not equal to end_level - start_level
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with pytest.raises(AssertionError):
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FPN(in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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end_level=3,
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num_outs=1)
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# Invalid `add_extra_convs` option
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with pytest.raises(AssertionError):
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FPN(in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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add_extra_convs='on_xxx',
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num_outs=5)
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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add_extra_convs=True,
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num_outs=5)
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# FPN expects a multiple levels of features per image
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feats = [
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torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
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for i in range(len(in_channels))
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]
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outs = fpn_model(feats)
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assert fpn_model.add_extra_convs == 'on_input'
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# Tests for fpn with no extra convs (pooling is used instead)
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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add_extra_convs=False,
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num_outs=5)
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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assert not fpn_model.add_extra_convs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# Tests for fpn with lateral bns
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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add_extra_convs=True,
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no_norm_on_lateral=False,
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norm_cfg=dict(type='BN', requires_grad=True),
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num_outs=5)
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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assert fpn_model.add_extra_convs == 'on_input'
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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bn_exist = False
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for m in fpn_model.modules():
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if isinstance(m, _BatchNorm):
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bn_exist = True
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assert bn_exist
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# Bilinear upsample
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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add_extra_convs=True,
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upsample_cfg=dict(mode='bilinear', align_corners=True),
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num_outs=5)
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fpn_model(feats)
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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assert fpn_model.add_extra_convs == 'on_input'
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# Scale factor instead of fixed upsample size upsample
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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start_level=1,
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add_extra_convs=True,
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upsample_cfg=dict(scale_factor=2),
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num_outs=5)
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# Extra convs source is 'inputs'
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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add_extra_convs='on_input',
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start_level=1,
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num_outs=5)
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assert fpn_model.add_extra_convs == 'on_input'
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# Extra convs source is 'laterals'
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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add_extra_convs='on_lateral',
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start_level=1,
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num_outs=5)
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assert fpn_model.add_extra_convs == 'on_lateral'
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# Extra convs source is 'outputs'
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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add_extra_convs='on_output',
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start_level=1,
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num_outs=5)
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assert fpn_model.add_extra_convs == 'on_output'
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# extra_convs_on_inputs=False is equal to extra convs source is 'on_output'
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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add_extra_convs=True,
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extra_convs_on_inputs=False,
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start_level=1,
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num_outs=5,
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)
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assert fpn_model.add_extra_convs == 'on_output'
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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# extra_convs_on_inputs=True is equal to extra convs source is 'on_input'
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fpn_model = FPN(
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in_channels=in_channels,
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out_channels=out_channels,
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add_extra_convs=True,
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extra_convs_on_inputs=True,
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start_level=1,
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num_outs=5,
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)
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assert fpn_model.add_extra_convs == 'on_input'
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outs = fpn_model(feats)
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assert len(outs) == fpn_model.num_outs
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for i in range(fpn_model.num_outs):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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def test_channel_mapper():
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"""Tests ChannelMapper."""
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s = 64
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in_channels = [8, 16, 32, 64]
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feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8]
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out_channels = 8
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kernel_size = 3
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feats = [
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torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
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for i in range(len(in_channels))
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]
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# in_channels must be a list
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with pytest.raises(AssertionError):
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channel_mapper = ChannelMapper(
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in_channels=10, out_channels=out_channels, kernel_size=kernel_size)
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# the length of channel_mapper's inputs must be equal to the length of
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# in_channels
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with pytest.raises(AssertionError):
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channel_mapper = ChannelMapper(
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in_channels=in_channels[:-1],
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out_channels=out_channels,
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kernel_size=kernel_size)
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channel_mapper(feats)
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channel_mapper = ChannelMapper(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size)
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outs = channel_mapper(feats)
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assert len(outs) == len(feats)
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for i in range(len(feats)):
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outs[i].shape[1] == out_channels
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outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
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