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test_masked_autoencoder_vit.py
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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
import torch
from parameterized import parameterized
from monai.networks import eval_mode
from monai.networks.nets.masked_autoencoder_vit import MaskedAutoEncoderViT
from tests.test_utils import skip_if_quick
TEST_CASE_MaskedAutoEncoderViT = []
for masking_ratio in [0.5]:
for dropout_rate in [0.6]:
for in_channels in [4]:
for hidden_size in [768]:
for img_size in [96, 128]:
for patch_size in [16]:
for num_heads in [12]:
for mlp_dim in [3072]:
for num_layers in [4]:
for decoder_hidden_size in [384]:
for decoder_mlp_dim in [512]:
for decoder_num_layers in [4]:
for decoder_num_heads in [16]:
for pos_embed_type in ["sincos", "learnable"]:
for proj_type in ["conv", "perceptron"]:
for nd in (2, 3):
test_case = [
{
"in_channels": in_channels,
"img_size": (img_size,) * nd,
"patch_size": (patch_size,) * nd,
"hidden_size": hidden_size,
"mlp_dim": mlp_dim,
"num_layers": num_layers,
"decoder_hidden_size": decoder_hidden_size,
"decoder_mlp_dim": decoder_mlp_dim,
"decoder_num_layers": decoder_num_layers,
"decoder_num_heads": decoder_num_heads,
"pos_embed_type": pos_embed_type,
"masking_ratio": masking_ratio,
"decoder_pos_embed_type": pos_embed_type,
"num_heads": num_heads,
"proj_type": proj_type,
"dropout_rate": dropout_rate,
},
(2, in_channels, *([img_size] * nd)),
(
2,
(img_size // patch_size) ** nd,
in_channels * (patch_size**nd),
),
]
if nd == 2:
test_case[0]["spatial_dims"] = 2 # type: ignore
TEST_CASE_MaskedAutoEncoderViT.append(test_case)
TEST_CASE_ill_args = [
[{"in_channels": 1, "img_size": (128, 128, 128), "patch_size": (16, 16, 16), "dropout_rate": 5.0}],
[{"in_channels": 1, "img_size": (128, 128, 128), "patch_size": (64, 64, 64), "pos_embed_type": "sin"}],
[{"in_channels": 1, "img_size": (128, 128, 128), "patch_size": (64, 64, 64), "decoder_pos_embed_type": "sin"}],
[{"in_channels": 1, "img_size": (32, 32, 32), "patch_size": (64, 64, 64)}],
[{"in_channels": 1, "img_size": (128, 128, 128), "patch_size": (64, 64, 64), "num_layers": 12, "num_heads": 14}],
[{"in_channels": 1, "img_size": (97, 97, 97), "patch_size": (16, 16, 16)}],
[{"in_channels": 1, "img_size": (128, 128, 128), "patch_size": (64, 64, 64), "masking_ratio": 1.1}],
[{"in_channels": 1, "img_size": (128, 128, 128), "patch_size": (64, 64, 64), "masking_ratio": -0.1}],
]
@skip_if_quick
class TestMaskedAutoencoderViT(unittest.TestCase):
@parameterized.expand(TEST_CASE_MaskedAutoEncoderViT)
def test_shape(self, input_param, input_shape, expected_shape):
net = MaskedAutoEncoderViT(**input_param)
with eval_mode(net):
result, _ = net(torch.randn(input_shape))
self.assertEqual(result.shape, expected_shape)
def test_frozen_pos_embedding(self):
net = MaskedAutoEncoderViT(in_channels=1, img_size=(96, 96, 96), patch_size=(16, 16, 16))
self.assertEqual(net.decoder_pos_embedding.requires_grad, False)
@parameterized.expand(TEST_CASE_ill_args)
def test_ill_arg(self, input_param):
with self.assertRaises(ValueError):
MaskedAutoEncoderViT(**input_param)
def test_access_attn_matrix(self):
# input format
in_channels = 1
img_size = (96, 96, 96)
patch_size = (16, 16, 16)
in_shape = (1, in_channels, img_size[0], img_size[1], img_size[2])
# no data in the matrix
no_matrix_acess_blk = MaskedAutoEncoderViT(in_channels=in_channels, img_size=img_size, patch_size=patch_size)
no_matrix_acess_blk(torch.randn(in_shape))
assert isinstance(no_matrix_acess_blk.blocks[0].attn.att_mat, torch.Tensor)
# no of elements is zero
assert no_matrix_acess_blk.blocks[0].attn.att_mat.nelement() == 0
# be able to acess the attention matrix
matrix_acess_blk = MaskedAutoEncoderViT(
in_channels=in_channels, img_size=img_size, patch_size=patch_size, save_attn=True
)
matrix_acess_blk(torch.randn(in_shape))
assert matrix_acess_blk.blocks[0].attn.att_mat.shape == (in_shape[0], 12, 55, 55)
def test_masking_ratio(self):
# input format
in_channels = 1
img_size = (96, 96, 96)
patch_size = (16, 16, 16)
in_shape = (1, in_channels, img_size[0], img_size[1], img_size[2])
# masking ratio 0.25
masking_ratio_blk = MaskedAutoEncoderViT(
in_channels=in_channels, img_size=img_size, patch_size=patch_size, masking_ratio=0.25, save_attn=True
)
masking_ratio_blk(torch.randn(in_shape))
desired_num_tokens = int(
(img_size[0] // patch_size[0])
* (img_size[1] // patch_size[1])
* (img_size[2] // patch_size[2])
* (1 - 0.25)
)
assert masking_ratio_blk.blocks[0].attn.att_mat.shape[-1] - 1 == desired_num_tokens
# masking ratio 0.33
masking_ratio_blk = MaskedAutoEncoderViT(
in_channels=in_channels, img_size=img_size, patch_size=patch_size, masking_ratio=0.33, save_attn=True
)
masking_ratio_blk(torch.randn(in_shape))
desired_num_tokens = int(
(img_size[0] // patch_size[0])
* (img_size[1] // patch_size[1])
* (img_size[2] // patch_size[2])
* (1 - 0.33)
)
assert masking_ratio_blk.blocks[0].attn.att_mat.shape[-1] - 1 == desired_num_tokens
if __name__ == "__main__":
unittest.main()