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"""Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
Attributes:
_out_channels (list of int): specify number of channels for each encoder feature tensor
_depth (int): specify number of stages in decoder (in other words number of downsampling operations)
_in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
Methods:
forward(self, x: torch.Tensor)
produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
shape NCHW (features should be sorted in descending order according to spatial resolution, starting
with resolution same as input `x` tensor).
Input: `x` with shape (1, 3, 64, 64)
Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
[(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
(1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
also should support number of features according to specified depth, e.g. if depth = 5,
number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
"""
import torch.nn as nn
from pretrainedmodels.models.inceptionresnetv2 import InceptionResNetV2
from pretrainedmodels.models.inceptionresnetv2 import pretrained_settings
from ._base import EncoderMixin
class InceptionResNetV2Encoder(InceptionResNetV2, EncoderMixin):
def __init__(self, out_channels, depth=5, **kwargs):
super().__init__(**kwargs)
self._out_channels = out_channels
self._depth = depth
self._in_channels = 3
# correct paddings
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.kernel_size == (3, 3):
m.padding = (1, 1)
if isinstance(m, nn.MaxPool2d):
m.padding = (1, 1)
# remove linear layers
del self.avgpool_1a
del self.last_linear
def make_dilated(self, *args, **kwargs):
raise ValueError(
"InceptionResnetV2 encoder does not support dilated mode "
"due to pooling operation for downsampling!"
)
def get_stages(self):
return [
nn.Identity(),
nn.Sequential(self.conv2d_1a, self.conv2d_2a, self.conv2d_2b),
nn.Sequential(self.maxpool_3a, self.conv2d_3b, self.conv2d_4a),
nn.Sequential(self.maxpool_5a, self.mixed_5b, self.repeat),
nn.Sequential(self.mixed_6a, self.repeat_1),
nn.Sequential(self.mixed_7a, self.repeat_2, self.block8, self.conv2d_7b),
]
def forward(self, x):
stages = self.get_stages()
features = []
for i in range(self._depth + 1):
x = stages[i](x)
features.append(x)
return features
def load_state_dict(self, state_dict, **kwargs):
state_dict.pop("last_linear.bias", None)
state_dict.pop("last_linear.weight", None)
super().load_state_dict(state_dict, **kwargs)
inceptionresnetv2_encoders = {
"inceptionresnetv2": {
"encoder": InceptionResNetV2Encoder,
"pretrained_settings": pretrained_settings["inceptionresnetv2"],
"params": {"out_channels": (3, 64, 192, 320, 1088, 1536), "num_classes": 1000},
}
}
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