add autodl

AutoDL-Projects/xautodl/xmodels/transformers_quantum.py

@@ -0,0 +1,124 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
+#####################################################
+# Vision Transformer: arxiv.org/pdf/2010.11929.pdf  #
+#####################################################
+import copy, math
+from functools import partial
+from typing import Optional, Text, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from xautodl import spaces
+from xautodl import xlayers
+from xautodl.xlayers import weight_init
+
+
+class SuperQuaT(xlayers.SuperModule):
+    """The super transformer for transformer."""
+
+    def __init__(
+        self,
+        image_size,
+        patch_size,
+        num_classes,
+        dim,
+        depth,
+        heads,
+        mlp_multiplier=4,
+        channels=3,
+        dropout=0.0,
+        att_dropout=0.0,
+    ):
+        super(SuperQuaT, self).__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        if image_height % patch_height != 0 or image_width % patch_width != 0:
+            raise ValueError("Image dimensions must be divisible by the patch size.")
+
+        num_patches = (image_height // patch_height) * (image_width // patch_width)
+        patch_dim = channels * patch_height * patch_width
+        self.to_patch_embedding = xlayers.SuperSequential(
+            xlayers.SuperReArrange(
+                "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
+                p1=patch_height,
+                p2=patch_width,
+            ),
+            xlayers.SuperLinear(patch_dim, dim),
+        )
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(dropout)
+
+        # build the transformer encode layers
+        layers = []
+        for ilayer in range(depth):
+            layers.append(
+                xlayers.SuperTransformerEncoderLayer(
+                    dim,
+                    heads,
+                    False,
+                    mlp_multiplier,
+                    dropout=dropout,
+                    att_dropout=att_dropout,
+                )
+            )
+        self.backbone = xlayers.SuperSequential(*layers)
+        self.cls_head = xlayers.SuperSequential(
+            xlayers.SuperLayerNorm1D(dim), xlayers.SuperLinear(dim, num_classes)
+        )
+
+        weight_init.trunc_normal_(self.cls_token, std=0.02)
+        self.apply(_init_weights)
+
+    @property
+    def abstract_search_space(self):
+        raise NotImplementedError
+
+    def apply_candidate(self, abstract_child: spaces.VirtualNode):
+        super(SuperQuaT, self).apply_candidate(abstract_child)
+        raise NotImplementedError
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        tensors = self.to_patch_embedding(input)
+        batch, seq, _ = tensors.shape
+
+        cls_tokens = self.cls_token.expand(batch, -1, -1)
+        feats = torch.cat((cls_tokens, tensors), dim=1)
+        feats = feats + self.pos_embedding[:, : seq + 1, :]
+        feats = self.dropout(feats)
+
+        feats = self.backbone(feats)
+
+        x = feats[:, 0]  # the features for cls-token
+
+        return self.cls_head(x)
+
+
+def get_transformer(config):
+    if isinstance(config, str) and config.lower() in name2config:
+        config = name2config[config.lower()]
+    if not isinstance(config, dict):
+        raise ValueError("Invalid Configuration: {:}".format(config))
+    model_type = config.get("type", "vit").lower()
+    if model_type == "vit":
+        model = SuperQuaT(
+            image_size=config.get("image_size"),
+            patch_size=config.get("patch_size"),
+            num_classes=config.get("num_classes"),
+            dim=config.get("dim"),
+            depth=config.get("depth"),
+            heads=config.get("heads"),
+            dropout=config.get("dropout"),
+            att_dropout=config.get("att_dropout"),
+        )
+    else:
+        raise ValueError("Unknown model type: {:}".format(model_type))
+    return model