Upload encoders/efficientnet.py

encoders/efficientnet.py

@@ -0,0 +1,178 @@
+""" 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 efficientnet_pytorch import EfficientNet
+from efficientnet_pytorch.utils import url_map, url_map_advprop, get_model_params
+
+from ._base import EncoderMixin
+
+
+class EfficientNetEncoder(EfficientNet, EncoderMixin):
+    def __init__(self, stage_idxs, out_channels, model_name, depth=5):
+
+        blocks_args, global_params = get_model_params(model_name, override_params=None)
+        super().__init__(blocks_args, global_params)
+
+        self._stage_idxs = stage_idxs
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        del self._fc
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self._conv_stem, self._bn0, self._swish),
+            self._blocks[:self._stage_idxs[0]],
+            self._blocks[self._stage_idxs[0]:self._stage_idxs[1]],
+            self._blocks[self._stage_idxs[1]:self._stage_idxs[2]],
+            self._blocks[self._stage_idxs[2]:],
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        block_number = 0.
+        drop_connect_rate = self._global_params.drop_connect_rate
+
+        features = []
+        for i in range(self._depth + 1):
+
+            # Identity and Sequential stages
+            if i < 2:
+                x = stages[i](x)
+
+            # Block stages need drop_connect rate
+            else:
+                for module in stages[i]:
+                    drop_connect = drop_connect_rate * block_number / len(self._blocks)
+                    block_number += 1.
+                    x = module(x, drop_connect)
+
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("_fc.bias", None)
+        state_dict.pop("_fc.weight", None)
+        super().load_state_dict(state_dict, **kwargs)
+
+
+def _get_pretrained_settings(encoder):
+    pretrained_settings = {
+        "imagenet": {
+            "mean": [0.485, 0.456, 0.406],
+            "std": [0.229, 0.224, 0.225],
+            "url": url_map[encoder],
+            "input_space": "RGB",
+            "input_range": [0, 1],
+        },
+        "advprop": {
+            "mean": [0.5, 0.5, 0.5],
+            "std": [0.5, 0.5, 0.5],
+            "url": url_map_advprop[encoder],
+            "input_space": "RGB",
+            "input_range": [0, 1],
+        }
+    }
+    return pretrained_settings
+
+
+efficient_net_encoders = {
+    "efficientnet-b0": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b0"),
+        "params": {
+            "out_channels": (3, 32, 24, 40, 112, 320),
+            "stage_idxs": (3, 5, 9, 16),
+            "model_name": "efficientnet-b0",
+        },
+    },
+    "efficientnet-b1": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b1"),
+        "params": {
+            "out_channels": (3, 32, 24, 40, 112, 320),
+            "stage_idxs": (5, 8, 16, 23),
+            "model_name": "efficientnet-b1",
+        },
+    },
+    "efficientnet-b2": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b2"),
+        "params": {
+            "out_channels": (3, 32, 24, 48, 120, 352),
+            "stage_idxs": (5, 8, 16, 23),
+            "model_name": "efficientnet-b2",
+        },
+    },
+    "efficientnet-b3": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b3"),
+        "params": {
+            "out_channels": (3, 40, 32, 48, 136, 384),
+            "stage_idxs": (5, 8, 18, 26),
+            "model_name": "efficientnet-b3",
+        },
+    },
+    "efficientnet-b4": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b4"),
+        "params": {
+            "out_channels": (3, 48, 32, 56, 160, 448),
+            "stage_idxs": (6, 10, 22, 32),
+            "model_name": "efficientnet-b4",
+        },
+    },
+    "efficientnet-b5": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b5"),
+        "params": {
+            "out_channels": (3, 48, 40, 64, 176, 512),
+            "stage_idxs": (8, 13, 27, 39),
+            "model_name": "efficientnet-b5",
+        },
+    },
+    "efficientnet-b6": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b6"),
+        "params": {
+            "out_channels": (3, 56, 40, 72, 200, 576),
+            "stage_idxs": (9, 15, 31, 45),
+            "model_name": "efficientnet-b6",
+        },
+    },
+    "efficientnet-b7": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b7"),
+        "params": {
+            "out_channels": (3, 64, 48, 80, 224, 640),
+            "stage_idxs": (11, 18, 38, 55),
+            "model_name": "efficientnet-b7",
+        },
+    },
+}