Upload encoders/dpn.py

encoders/dpn.py

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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
+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,
+        },
+    },
+}