| """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 | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from pretrainedmodels.models.dpn import DPN | |
| from pretrainedmodels.models.dpn import pretrained_settings | |
| from ._base import EncoderMixin | |
| class DPNEncoder(DPN, EncoderMixin): | |
| def __init__(self, stage_idxs, out_channels, depth=5, **kwargs): | |
| super().__init__(**kwargs) | |
| self._stage_idxs = stage_idxs | |
| self._depth = depth | |
| self._out_channels = out_channels | |
| self._in_channels = 3 | |
| del self.last_linear | |
| def get_stages(self): | |
| return [ | |
| nn.Identity(), | |
| nn.Sequential( | |
| self.features[0].conv, self.features[0].bn, self.features[0].act | |
| ), | |
| nn.Sequential( | |
| self.features[0].pool, self.features[1 : 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]], | |
| ] | |
| def forward(self, x): | |
| stages = self.get_stages() | |
| features = [] | |
| for i in range(self._depth + 1): | |
| x = stages[i](x) | |
| if isinstance(x, (list, tuple)): | |
| features.append(F.relu(torch.cat(x, dim=1), inplace=True)) | |
| else: | |
| 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) | |
| dpn_encoders = { | |
| "dpn68": { | |
| "encoder": DPNEncoder, | |
| "pretrained_settings": pretrained_settings["dpn68"], | |
| "params": { | |
| "stage_idxs": (4, 8, 20, 24), | |
| "out_channels": (3, 10, 144, 320, 704, 832), | |
| "groups": 32, | |
| "inc_sec": (16, 32, 32, 64), | |
| "k_r": 128, | |
| "k_sec": (3, 4, 12, 3), | |
| "num_classes": 1000, | |
| "num_init_features": 10, | |
| "small": True, | |
| "test_time_pool": True, | |
| }, | |
| }, | |
| "dpn68b": { | |
| "encoder": DPNEncoder, | |
| "pretrained_settings": pretrained_settings["dpn68b"], | |
| "params": { | |
| "stage_idxs": (4, 8, 20, 24), | |
| "out_channels": (3, 10, 144, 320, 704, 832), | |
| "b": True, | |
| "groups": 32, | |
| "inc_sec": (16, 32, 32, 64), | |
| "k_r": 128, | |
| "k_sec": (3, 4, 12, 3), | |
| "num_classes": 1000, | |
| "num_init_features": 10, | |
| "small": True, | |
| "test_time_pool": True, | |
| }, | |
| }, | |
| "dpn92": { | |
| "encoder": DPNEncoder, | |
| "pretrained_settings": pretrained_settings["dpn92"], | |
| "params": { | |
| "stage_idxs": (4, 8, 28, 32), | |
| "out_channels": (3, 64, 336, 704, 1552, 2688), | |
| "groups": 32, | |
| "inc_sec": (16, 32, 24, 128), | |
| "k_r": 96, | |
| "k_sec": (3, 4, 20, 3), | |
| "num_classes": 1000, | |
| "num_init_features": 64, | |
| "test_time_pool": True, | |
| }, | |
| }, | |
| "dpn98": { | |
| "encoder": DPNEncoder, | |
| "pretrained_settings": pretrained_settings["dpn98"], | |
| "params": { | |
| "stage_idxs": (4, 10, 30, 34), | |
| "out_channels": (3, 96, 336, 768, 1728, 2688), | |
| "groups": 40, | |
| "inc_sec": (16, 32, 32, 128), | |
| "k_r": 160, | |
| "k_sec": (3, 6, 20, 3), | |
| "num_classes": 1000, | |
| "num_init_features": 96, | |
| "test_time_pool": True, | |
| }, | |
| }, | |
| "dpn107": { | |
| "encoder": DPNEncoder, | |
| "pretrained_settings": pretrained_settings["dpn107"], | |
| "params": { | |
| "stage_idxs": (5, 13, 33, 37), | |
| "out_channels": (3, 128, 376, 1152, 2432, 2688), | |
| "groups": 50, | |
| "inc_sec": (20, 64, 64, 128), | |
| "k_r": 200, | |
| "k_sec": (4, 8, 20, 3), | |
| "num_classes": 1000, | |
| "num_init_features": 128, | |
| "test_time_pool": True, | |
| }, | |
| }, | |
| "dpn131": { | |
| "encoder": DPNEncoder, | |
| "pretrained_settings": pretrained_settings["dpn131"], | |
| "params": { | |
| "stage_idxs": (5, 13, 41, 45), | |
| "out_channels": (3, 128, 352, 832, 1984, 2688), | |
| "groups": 40, | |
| "inc_sec": (16, 32, 32, 128), | |
| "k_r": 160, | |
| "k_sec": (4, 8, 28, 3), | |
| "num_classes": 1000, | |
| "num_init_features": 128, | |
| "test_time_pool": True, | |
| }, | |
| }, | |
| } | |