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Support block type strings in resnet #4701

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Support block type strings in resnet #4701

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10 changes: 9 additions & 1 deletion monai/networks/nets/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,7 +172,7 @@ class ResNet(nn.Module):
@deprecated_arg("n_classes", since="0.6")
def __init__(
self,
block: Type[Union[ResNetBlock, ResNetBottleneck]],
block: Union[Type[Union[ResNetBlock, ResNetBottleneck]], str],

@yiheng-wang-nv yiheng-wang-nv Jul 21, 2022

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Hi @KumoLiu , can you please also update the docstrings of arg block? Thanks

layers: List[int],
block_inplanes: List[int],
spatial_dims: int = 3,
Expand All @@ -192,6 +192,14 @@ def __init__(
if n_classes is not None and num_classes == 400:
num_classes = n_classes

if isinstance(block, str):
if block == "basic":
block = ResNetBlock
elif block == "bottleneck":
block = ResNetBottleneck
else:
raise ValueError("Unknown block '%s', use basic or bottleneck" % block)

conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]
norm_type: Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]
pool_type: Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]
Expand Down
50 changes: 49 additions & 1 deletion tests/test_resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,8 @@
from parameterized import parameterized

from monai.networks import eval_mode
from monai.networks.nets import resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200
from monai.networks.nets import ResNet, resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200
from monai.networks.nets.resnet import ResNetBlock
from monai.utils import optional_import
from tests.utils import test_script_save

Expand Down Expand Up @@ -95,10 +96,57 @@
((2, 512), (2, 2048)),
]

TEST_CASE_5 = [ # 1D, batch 1, 2 input channels
{
"block": "basic",
"layers": [1, 1, 1, 1],
"block_inplanes": [64, 128, 256, 512],
"spatial_dims": 1,
"n_input_channels": 2,
"num_classes": 3,
"conv1_t_size": [3],
"conv1_t_stride": 1,
},
(1, 2, 32),
(1, 3),
]

TEST_CASE_5_A = [ # 1D, batch 1, 2 input channels
{
"block": ResNetBlock,
"layers": [1, 1, 1, 1],
"block_inplanes": [64, 128, 256, 512],
"spatial_dims": 1,
"n_input_channels": 2,
"num_classes": 3,
"conv1_t_size": [3],
"conv1_t_stride": 1,
},
(1, 2, 32),
(1, 3),
]

TEST_CASE_6 = [ # 1D, batch 1, 2 input channels
{
"block": "bottleneck",
"layers": [3, 4, 6, 3],
"block_inplanes": [64, 128, 256, 512],
"spatial_dims": 1,
"n_input_channels": 2,
"num_classes": 3,
"conv1_t_size": [3],
"conv1_t_stride": 1,
},
(1, 2, 32),
(1, 3),
]

TEST_CASES = []
for case in [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_2_A, TEST_CASE_3_A]:
for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]:
TEST_CASES.append([model, *case])
for case in [TEST_CASE_5, TEST_CASE_5_A, TEST_CASE_6]:
TEST_CASES.append([ResNet, *case])

TEST_SCRIPT_CASES = [
[model, *TEST_CASE_1] for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]
Expand Down