|
"""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.inceptionv4 import InceptionV4, BasicConv2d |
|
from pretrainedmodels.models.inceptionv4 import pretrained_settings |
|
|
|
from ._base import EncoderMixin |
|
|
|
|
|
class InceptionV4Encoder(InceptionV4, EncoderMixin): |
|
def __init__(self, stage_idxs, out_channels, depth=5, **kwargs): |
|
super().__init__(**kwargs) |
|
self._stage_idxs = stage_idxs |
|
self._out_channels = out_channels |
|
self._depth = depth |
|
self._in_channels = 3 |
|
|
|
|
|
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) |
|
|
|
|
|
del self.last_linear |
|
|
|
def make_dilated(self, stage_list, dilation_list): |
|
raise ValueError( |
|
"InceptionV4 encoder does not support dilated mode " |
|
"due to pooling operation for downsampling!" |
|
) |
|
|
|
def get_stages(self): |
|
return [ |
|
nn.Identity(), |
|
self.features[: self._stage_idxs[0]], |
|
self.features[self._stage_idxs[0] : self._stage_idxs[1]], |
|
self.features[self._stage_idxs[1] : self._stage_idxs[2]], |
|
self.features[self._stage_idxs[2] : self._stage_idxs[3]], |
|
self.features[self._stage_idxs[3] :], |
|
] |
|
|
|
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) |
|
|
|
|
|
inceptionv4_encoders = { |
|
"inceptionv4": { |
|
"encoder": InceptionV4Encoder, |
|
"pretrained_settings": pretrained_settings["inceptionv4"], |
|
"params": { |
|
"stage_idxs": (3, 5, 9, 15), |
|
"out_channels": (3, 64, 192, 384, 1024, 1536), |
|
"num_classes": 1001, |
|
}, |
|
} |
|
} |
|
|
