commit 2330c5f1a899e33ca896ee91713c9c7072a45f12
Author: BlenderNeko <126974546+BlenderNeko@users.noreply.github.com>
Date: Mon May 29 00:13:11 2023 +0200
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+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
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+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+
+ Copyright (C)
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..14a2cd6
--- /dev/null
+++ b/README.md
@@ -0,0 +1,27 @@
+# ComfyUI SeeCoder nodes
+
+This repo contains 2 experimental WIP nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) that let's you use [SeeCoders](https://github.com/SHI-Labs/Prompt-Free-Diffusion).
+
+## getting SeeCoders
+You can find the seecoders [here](https://huggingface.co/shi-labs/prompt-free-diffusion). They have to be placed at `models/seecoders`
+
+## nodes:
+
+### SEECoderImageEncode
+
+this node can be used to create an embedding from an image
+
+- **image**: the image to encode
+- **seecoder_name**: the name of the seecoder
+
+### ConcatConditioning
+
+this node can be used to concat different embeddings together, so you can e.g. create both a text and a visual embedding and concat them together.
+
+- **conditioning_to**: a set of embeddings to concat something to
+- **conditioning_from**: the embedding to concat behind those in **conditioning_to**
+
+## TODO:
+
+ - [ ] support for non safetensor formats
+ - [ ] bring attention layers in line with ones used in comfy
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..e74c2f2
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS']
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..ac4814e
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,117 @@
+
+import folder_paths
+import torch
+from seecoder.seecoder import SemanticExtractionEncoder, QueryTransformer, Decoder
+from seecoder.swin import SwinTransformer
+import safetensors.torch
+import os
+import sys
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
+import comfy.model_management
+
+folder_paths.folder_names_and_paths["seecoder"] = ([os.path.join(folder_paths.models_dir, "seecoders")], folder_paths.supported_ckpt_extensions)
+
+_swine_config = {
+ "embed_dim" : 192,
+ "depths" : [ 2, 2, 18, 2 ],
+ "num_heads" : [ 6, 12, 24, 48 ],
+ "window_size" : 12,
+ "ape" : False,
+ "drop_path_rate" : 0.3,
+ "patch_norm" : True,
+}
+
+_decoder_config = {
+ "inchannels" : {'res3' : 384, 'res4' : 768, 'res5' : 1536},
+ "trans_input_tags" : ['res3', 'res4', 'res5'],
+ "trans_dim" : 768,
+ "trans_dropout" : 0.1,
+ "trans_nheads" : 8,
+ "trans_feedforward_dim" : 1024,
+ "trans_num_layers" : 6,
+}
+
+_qt_config = {
+ "in_channels":768,
+ "hidden_dim":768,
+ "num_queries":[4, 144],
+ "nheads":8,
+ "num_layers":9,
+ "feedforward_dim":2048,
+ "pre_norm":False,
+ "num_feature_levels":3,
+ "enforce_input_project":False,
+ "with_fea2d_pos":False
+}
+
+class SEECoderImageEncode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "seecoder_name": (folder_paths.get_filename_list("seecoder"), ),
+ "image": ("IMAGE",),
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "SEECoderEncode"
+
+ CATEGORY = "conditioning"
+
+ def SEECoderEncode(self, seecoder_name, image):
+ device = comfy.model_management.get_torch_device()
+ path = folder_paths.get_full_path("seecoder", seecoder_name)
+ sd = safetensors.torch.load_file(path, device="cpu")
+ sd = {k[10:] if k.startswith('ctx.image.') else k: v for k,v in sd.items()}
+ is_pa = any([x.startswith("qtransformer.pe_layer") for x in sd.keys()])
+
+ swine_config = _swine_config.copy()
+ decoder_config = _decoder_config.copy()
+ qt_config = _qt_config.copy()
+ if is_pa:
+ qt_config['with_fea2d_pos'] = True
+
+ swine = SwinTransformer(**swine_config)
+ decoder = Decoder(**decoder_config)
+ queryTransformer = QueryTransformer(**qt_config)
+
+ SEE_encoder = SemanticExtractionEncoder(swine, decoder, queryTransformer)
+ SEE_encoder.load_state_dict(sd)
+ SEE_encoder = SEE_encoder.to(device)
+ SEE_encoder.eval()
+ encoding = SEE_encoder(image.movedim(-1,1).to(device)).cpu()
+
+ return ([[encoding, {}]], )
+
+class ConcatConditioning:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "conditioning_to": ("CONDITIONING",),
+ "conditioning_from": ("CONDITIONING",),
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "SEECoderEncode"
+
+ CATEGORY = "_for_testing"
+
+ def SEECoderEncode(self, conditioning_to, conditioning_from):
+ out = []
+
+ if len(conditioning_from) > 1:
+ print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+
+ cond_from = conditioning_from[0][0]
+
+ for i in range(len(conditioning_to)):
+ t1 = conditioning_to[i][0]
+ tw = torch.cat((t1, cond_from),1)
+ n = [tw, conditioning_to[i][1].copy()]
+ out.append(n)
+
+ return (out, )
+
+NODE_CLASS_MAPPINGS = {
+ "SEECoderImageEncode": SEECoderImageEncode,
+ "ConcatConditioning": ConcatConditioning,
+}
\ No newline at end of file
diff --git a/seecoder.py b/seecoder.py
new file mode 100644
index 0000000..b4330b3
--- /dev/null
+++ b/seecoder.py
@@ -0,0 +1,570 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import copy
+
+from .seecoder_utils import with_pos_embed
+
+###########
+# helpers #
+###########
+
+def _get_clones(module, N):
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+def c2_xavier_fill(module):
+ # Caffe2 implementation of XavierFill in fact
+ nn.init.kaiming_uniform_(module.weight, a=1)
+ if module.bias is not None:
+ nn.init.constant_(module.bias, 0)
+
+def with_pos_embed(x, pos):
+ return x if pos is None else x + pos
+
+###########
+# Modules #
+###########
+
+class Conv2d_Convenience(nn.Conv2d):
+ def __init__(self, *args, **kwargs):
+ norm = kwargs.pop("norm", None)
+ activation = kwargs.pop("activation", None)
+ super().__init__(*args, **kwargs)
+ self.norm = norm
+ self.activation = activation
+
+ def forward(self, x):
+ x = F.conv2d(
+ x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
+ if self.norm is not None:
+ x = self.norm(x)
+ if self.activation is not None:
+ x = self.activation(x)
+ return x
+
+class DecoderLayer(nn.Module):
+ def __init__(self,
+ dim=256,
+ feedforward_dim=1024,
+ dropout=0.1,
+ activation="relu",
+ n_heads=8,):
+
+ super().__init__()
+
+ self.self_attn = nn.MultiheadAttention(dim, n_heads, dropout=dropout)
+ self.dropout1 = nn.Dropout(dropout)
+ self.norm1 = nn.LayerNorm(dim)
+
+ self.linear1 = nn.Linear(dim, feedforward_dim)
+ self.activation = _get_activation_fn(activation)
+ self.dropout2 = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(feedforward_dim, dim)
+ self.dropout3 = nn.Dropout(dropout)
+ self.norm2 = nn.LayerNorm(dim)
+
+ def forward(self, x):
+ h = x
+ h1 = self.self_attn(x, x, x, attn_mask=None)[0]
+ h = h + self.dropout1(h1)
+ h = self.norm1(h)
+
+ h2 = self.linear2(self.dropout2(self.activation(self.linear1(h))))
+ h = h + self.dropout3(h2)
+ h = self.norm2(h)
+ return h
+
+class DecoderLayerStacked(nn.Module):
+ def __init__(self, layer, num_layers, norm=None):
+ super().__init__()
+ self.layers = _get_clones(layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+
+ def forward(self, x):
+ h = x
+ for _, layer in enumerate(self.layers):
+ h = layer(h)
+ if self.norm is not None:
+ h = self.norm(h)
+ return h
+
+class SelfAttentionLayer(nn.Module):
+ def __init__(self, channels, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(channels)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward_post(self,
+ qkv,
+ qk_pos = None,
+ mask = None,):
+ h = qkv
+ qk = with_pos_embed(qkv, qk_pos).transpose(0, 1)
+ v = qkv.transpose(0, 1)
+ h1 = self.self_attn(qk, qk, v, attn_mask=mask)[0]
+ h1 = h1.transpose(0, 1)
+ h = h + self.dropout(h1)
+ h = self.norm(h)
+ return h
+
+ def forward_pre(self, tgt,
+ tgt_mask = None,
+ tgt_key_padding_mask = None,
+ query_pos = None):
+ # deprecated
+ assert False
+ tgt2 = self.norm(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+ return tgt
+
+ def forward(self, *args, **kwargs):
+ if self.normalize_before:
+ return self.forward_pre(*args, **kwargs)
+ return self.forward_post(*args, **kwargs)
+
+class CrossAttentionLayer(nn.Module):
+ def __init__(self, channels, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.multihead_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(channels)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward_post(self,
+ q,
+ kv,
+ q_pos = None,
+ k_pos = None,
+ mask = None,):
+ h = q
+ q = with_pos_embed(q, q_pos).transpose(0, 1)
+ k = with_pos_embed(kv, k_pos).transpose(0, 1)
+ v = kv.transpose(0, 1)
+ h1 = self.multihead_attn(q, k, v, attn_mask=mask)[0]
+ h1 = h1.transpose(0, 1)
+ h = h + self.dropout(h1)
+ h = self.norm(h)
+ return h
+
+ def forward_pre(self, tgt, memory,
+ memory_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None):
+ # Deprecated
+ assert False
+ tgt2 = self.norm(tgt)
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory, attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+ return tgt
+
+ def forward(self, *args, **kwargs):
+ if self.normalize_before:
+ return self.forward_pre(*args, **kwargs)
+ return self.forward_post(*args, **kwargs)
+
+class FeedForwardLayer(nn.Module):
+ def __init__(self, channels, hidden_channels=2048, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.linear1 = nn.Linear(channels, hidden_channels)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(hidden_channels, channels)
+ self.norm = nn.LayerNorm(channels)
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward_post(self, x):
+ h = x
+ h1 = self.linear2(self.dropout(self.activation(self.linear1(h))))
+ h = h + self.dropout(h1)
+ h = self.norm(h)
+ return h
+
+ def forward_pre(self, x):
+ xn = self.norm(x)
+ h = x
+ h1 = self.linear2(self.dropout(self.activation(self.linear1(xn))))
+ h = h + self.dropout(h1)
+ return h
+
+ def forward(self, *args, **kwargs):
+ if self.normalize_before:
+ return self.forward_pre(*args, **kwargs)
+ return self.forward_post(*args, **kwargs)
+
+class MLP(nn.Module):
+ def __init__(self, in_channels, channels, out_channels, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ h = [channels] * (num_layers - 1)
+ self.layers = nn.ModuleList(
+ nn.Linear(n, k)
+ for n, k in zip([in_channels]+h, h+[out_channels]))
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+class PPE_MLP(nn.Module):
+ def __init__(self, freq_num=20, freq_max=None, out_channel=768, mlp_layer=3):
+ import math
+ super().__init__()
+ self.freq_num = freq_num
+ self.freq_max = freq_max
+ self.out_channel = out_channel
+ self.mlp_layer = mlp_layer
+ self.twopi = 2 * math.pi
+
+ mlp = []
+ in_channel = freq_num*4
+ for idx in range(mlp_layer):
+ linear = nn.Linear(in_channel, out_channel, bias=True)
+ nn.init.xavier_normal_(linear.weight)
+ nn.init.constant_(linear.bias, 0)
+ mlp.append(linear)
+ if idx != mlp_layer-1:
+ mlp.append(nn.SiLU())
+ in_channel = out_channel
+ self.mlp = nn.Sequential(*mlp)
+ nn.init.constant_(self.mlp[-1].weight, 0)
+
+ def forward(self, x, mask=None):
+ assert mask is None, "Mask not implemented"
+ h, w = x.shape[-2:]
+ minlen = min(h, w)
+
+ h_embed, w_embed = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
+ if self.training:
+ import numpy.random as npr
+ pertube_h, pertube_w = npr.uniform(-0.5, 0.5), npr.uniform(-0.5, 0.5)
+ else:
+ pertube_h, pertube_w = 0, 0
+
+ h_embed = (h_embed+0.5 - h/2 + pertube_h) / (minlen) * self.twopi
+ w_embed = (w_embed+0.5 - w/2 + pertube_w) / (minlen) * self.twopi
+ h_embed, w_embed = h_embed.to(x.device).to(x.dtype), w_embed.to(x.device).to(x.dtype)
+
+ dim_t = torch.linspace(0, 1, self.freq_num, dtype=torch.float32, device=x.device)
+ freq_max = self.freq_max if self.freq_max is not None else minlen/2
+ dim_t = freq_max ** dim_t.to(x.dtype)
+
+ pos_h = h_embed[:, :, None] * dim_t
+ pos_w = w_embed[:, :, None] * dim_t
+ pos = torch.cat((pos_h.sin(), pos_h.cos(), pos_w.sin(), pos_w.cos()), dim=-1)
+ pos = self.mlp(pos)
+ pos = pos.permute(2, 0, 1)[None]
+ return pos
+
+ def __repr__(self, _repr_indent=4):
+ head = "Positional encoding " + self.__class__.__name__
+ body = [
+ "num_pos_feats: {}".format(self.num_pos_feats),
+ "temperature: {}".format(self.temperature),
+ "normalize: {}".format(self.normalize),
+ "scale: {}".format(self.scale),
+ ]
+ # _repr_indent = 4
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
+
+###########
+# Decoder #
+###########
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ inchannels,
+ trans_input_tags,
+ trans_num_layers,
+ trans_dim,
+ trans_nheads,
+ trans_dropout,
+ trans_feedforward_dim,):
+
+ super().__init__()
+ trans_inchannels = {
+ k: v for k, v in inchannels.items() if k in trans_input_tags}
+ fpn_inchannels = {
+ k: v for k, v in inchannels.items() if k not in trans_input_tags}
+
+ self.trans_tags = sorted(list(trans_inchannels.keys()))
+ self.fpn_tags = sorted(list(fpn_inchannels.keys()))
+ self.all_tags = sorted(list(inchannels.keys()))
+
+ if len(self.trans_tags)==0:
+ assert False # Not allowed
+
+ self.num_trans_lvls = len(self.trans_tags)
+
+ self.inproj_layers = nn.ModuleDict()
+ for tagi in self.trans_tags:
+ layeri = nn.Sequential(
+ nn.Conv2d(trans_inchannels[tagi], trans_dim, kernel_size=1),
+ nn.GroupNorm(32, trans_dim),)
+ nn.init.xavier_uniform_(layeri[0].weight, gain=1)
+ nn.init.constant_(layeri[0].bias, 0)
+ self.inproj_layers[tagi] = layeri
+
+ tlayer = DecoderLayer(
+ dim = trans_dim,
+ n_heads = trans_nheads,
+ dropout = trans_dropout,
+ feedforward_dim = trans_feedforward_dim,
+ activation = 'relu',)
+
+ self.transformer = DecoderLayerStacked(tlayer, trans_num_layers)
+ for p in self.transformer.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+ self.level_embed = nn.Parameter(torch.Tensor(len(self.trans_tags), trans_dim))
+ nn.init.normal_(self.level_embed)
+
+ self.lateral_layers = nn.ModuleDict()
+ self.output_layers = nn.ModuleDict()
+ for tagi in self.all_tags:
+ lateral_conv = Conv2d_Convenience(
+ inchannels[tagi], trans_dim, kernel_size=1,
+ bias=False, norm=nn.GroupNorm(32, trans_dim))
+ c2_xavier_fill(lateral_conv)
+ self.lateral_layers[tagi] = lateral_conv
+
+ for tagi in self.fpn_tags:
+ output_conv = Conv2d_Convenience(
+ trans_dim, trans_dim, kernel_size=3, stride=1, padding=1,
+ bias=False, norm=nn.GroupNorm(32, trans_dim), activation=F.relu,)
+ c2_xavier_fill(output_conv)
+ self.output_layers[tagi] = output_conv
+
+ def forward(self, features):
+ x = []
+ spatial_shapes = {}
+ for idx, tagi in enumerate(self.trans_tags[::-1]):
+ xi = features[tagi]
+ xi = self.inproj_layers[tagi](xi)
+ bs, _, h, w = xi.shape
+ spatial_shapes[tagi] = (h, w)
+ xi = xi.flatten(2).transpose(1, 2) + self.level_embed[idx].view(1, 1, -1)
+ x.append(xi)
+
+ x_length = [xi.shape[1] for xi in x]
+ x_concat = torch.cat(x, 1)
+ y_concat = self.transformer(x_concat)
+ y = torch.split(y_concat, x_length, dim=1)
+
+ out = {}
+ for idx, tagi in enumerate(self.trans_tags[::-1]):
+ h, w = spatial_shapes[tagi]
+ yi = y[idx].transpose(1, 2).view(bs, -1, h, w)
+ out[tagi] = yi
+
+ for idx, tagi in enumerate(self.all_tags[::-1]):
+ lconv = self.lateral_layers[tagi]
+ if tagi in self.trans_tags:
+ out[tagi] = out[tagi] + lconv(features[tagi])
+ tag_save = tagi
+ else:
+ oconv = self.output_layers[tagi]
+ h = lconv(features[tagi])
+ oprev = out[tag_save]
+ h = h + F.interpolate(oconv(oprev), size=h.shape[-2:], mode="bilinear", align_corners=False)
+ out[tagi] = h
+
+ return out
+
+#####################
+# Query Transformer #
+#####################
+
+class QueryTransformer(nn.Module):
+ def __init__(self,
+ in_channels,
+ hidden_dim,
+ num_queries = [8, 144],
+ nheads = 8,
+ num_layers = 9,
+ feedforward_dim = 2048,
+ mask_dim = 256,
+ pre_norm = False,
+ num_feature_levels = 3,
+ enforce_input_project = False,
+ with_fea2d_pos = True):
+
+ super().__init__()
+
+ if with_fea2d_pos:
+ self.pe_layer = PPE_MLP(freq_num=20, freq_max=None, out_channel=hidden_dim, mlp_layer=3)
+ else:
+ self.pe_layer = None
+
+ if in_channels!=hidden_dim or enforce_input_project:
+ self.input_proj = nn.ModuleList()
+ for _ in range(num_feature_levels):
+ self.input_proj.append(nn.Conv2d(in_channels, hidden_dim, kernel_size=1))
+ c2_xavier_fill(self.input_proj[-1])
+ else:
+ self.input_proj = None
+
+ self.num_heads = nheads
+ self.num_layers = num_layers
+ self.transformer_selfatt_layers = nn.ModuleList()
+ self.transformer_crossatt_layers = nn.ModuleList()
+ self.transformer_feedforward_layers = nn.ModuleList()
+
+ for _ in range(self.num_layers):
+ self.transformer_selfatt_layers.append(
+ SelfAttentionLayer(
+ channels=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm, ))
+
+ self.transformer_crossatt_layers.append(
+ CrossAttentionLayer(
+ channels=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm, ))
+
+ self.transformer_feedforward_layers.append(
+ FeedForwardLayer(
+ channels=hidden_dim,
+ hidden_channels=feedforward_dim,
+ dropout=0.0,
+ normalize_before=pre_norm, ))
+
+ self.num_queries = num_queries
+ num_gq, num_lq = self.num_queries
+ self.init_query = nn.Embedding(num_gq+num_lq, hidden_dim)
+ self.query_pos_embedding = nn.Embedding(num_gq+num_lq, hidden_dim)
+
+ self.num_feature_levels = num_feature_levels
+ self.level_embed = nn.Embedding(num_feature_levels, hidden_dim)
+
+ def forward(self, x):
+ # x is a list of multi-scale feature
+ assert len(x) == self.num_feature_levels
+ fea2d = []
+ fea2d_pos = []
+ size_list = []
+
+ for i in range(self.num_feature_levels):
+ size_list.append(x[i].shape[-2:])
+ if self.pe_layer is not None:
+ pi = self.pe_layer(x[i], None).flatten(2)
+ pi = pi.transpose(1, 2)
+ else:
+ pi = None
+ xi = self.input_proj[i](x[i]) if self.input_proj is not None else x[i]
+ xi = xi.flatten(2) + self.level_embed.weight[i][None, :, None]
+ xi = xi.transpose(1, 2)
+ fea2d.append(xi)
+ fea2d_pos.append(pi)
+
+ bs, _, _ = fea2d[0].shape
+ num_gq, num_lq = self.num_queries
+ gquery = self.init_query.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
+ lquery = self.init_query.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
+
+ gquery_pos = self.query_pos_embedding.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
+ lquery_pos = self.query_pos_embedding.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
+
+ for i in range(self.num_layers):
+ level_index = i % self.num_feature_levels
+
+ qout = self.transformer_crossatt_layers[i](
+ q = lquery,
+ kv = fea2d[level_index],
+ q_pos = lquery_pos,
+ k_pos = fea2d_pos[level_index],
+ mask = None,)
+ lquery = qout
+
+ qout = self.transformer_selfatt_layers[i](
+ qkv = torch.cat([gquery, lquery], dim=1),
+ qk_pos = torch.cat([gquery_pos, lquery_pos], dim=1),)
+
+ qout = self.transformer_feedforward_layers[i](qout)
+
+ gquery = qout[:, :num_gq]
+ lquery = qout[:, num_gq:]
+
+ output = torch.cat([gquery, lquery], dim=1)
+
+ return output
+
+##################
+# Main structure #
+##################
+
+class SemanticExtractionEncoder(nn.Module):
+ def __init__(self,
+ imencoder_cfg,
+ imdecoder_cfg,
+ qtransformer_cfg):
+ super().__init__()
+ self.imencoder = imencoder_cfg
+ self.imdecoder = imdecoder_cfg
+ self.qtransformer = qtransformer_cfg
+
+ def forward(self, x):
+ fea = self.imencoder(x)
+ hs = {'res3' : fea['res3'],
+ 'res4' : fea['res4'],
+ 'res5' : fea['res5'], }
+ hs = self.imdecoder(hs)
+ hs = [hs['res3'], hs['res4'], hs['res5']]
+ q = self.qtransformer(hs)
+ return q
+
+ def encode(self, x):
+ return self(x)
diff --git a/seecoder_utils.py b/seecoder_utils.py
new file mode 100644
index 0000000..40acf4d
--- /dev/null
+++ b/seecoder_utils.py
@@ -0,0 +1,108 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
+import math
+import copy
+
+def _get_clones(module, N):
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+def _is_power_of_2(n):
+ if (not isinstance(n, int)) or (n < 0):
+ raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+ return (n & (n-1) == 0) and n != 0
+
+def c2_xavier_fill(module):
+ # Caffe2 implementation of XavierFill in fact
+ nn.init.kaiming_uniform_(module.weight, a=1)
+ if module.bias is not None:
+ nn.init.constant_(module.bias, 0)
+
+def with_pos_embed(x, pos):
+ return x if pos is None else x + pos
+
+class PositionEmbeddingSine(nn.Module):
+ def __init__(self, num_pos_feats=64, temperature=256, normalize=False, scale=None):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, x, mask=None):
+ if mask is None:
+ mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+ not_mask = ~mask
+ h, w = not_mask.shape[-2:]
+ minlen = min(h, w)
+ h_embed = not_mask.cumsum(1, dtype=torch.float32)
+ w_embed = not_mask.cumsum(2, dtype=torch.float32)
+ if self.normalize:
+ eps = 1e-6
+ h_embed = (h_embed - h/2) / (minlen + eps) * self.scale
+ w_embed = (w_embed - w/2) / (minlen + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_w = w_embed[:, :, :, None] / dim_t
+ pos_h = h_embed[:, :, :, None] / dim_t
+ pos_w = torch.stack(
+ (pos_w[:, :, :, 0::2].sin(), pos_w[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos_h = torch.stack(
+ (pos_h[:, :, :, 0::2].sin(), pos_h[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos = torch.cat((pos_h, pos_w), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+ def __repr__(self, _repr_indent=4):
+ head = "Positional encoding " + self.__class__.__name__
+ body = [
+ "num_pos_feats: {}".format(self.num_pos_feats),
+ "temperature: {}".format(self.temperature),
+ "normalize: {}".format(self.normalize),
+ "scale: {}".format(self.scale),
+ ]
+ # _repr_indent = 4
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
+
+class Conv2d_Convenience(nn.Conv2d):
+ def __init__(self, *args, **kwargs):
+ norm = kwargs.pop("norm", None)
+ activation = kwargs.pop("activation", None)
+ super().__init__(*args, **kwargs)
+ self.norm = norm
+ self.activation = activation
+
+ def forward(self, x):
+ if not torch.jit.is_scripting():
+ if x.numel() == 0 and self.training:
+ assert not isinstance(
+ self.norm, torch.nn.SyncBatchNorm
+ ), "SyncBatchNorm does not support empty inputs!"
+ x = F.conv2d(
+ x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
+ )
+ if self.norm is not None:
+ x = self.norm(x)
+ if self.activation is not None:
+ x = self.activation(x)
+ return x
+
diff --git a/seet_tdecoder.py b/seet_tdecoder.py
new file mode 100644
index 0000000..3d5585a
--- /dev/null
+++ b/seet_tdecoder.py
@@ -0,0 +1,697 @@
+#import fvcore.nn.weight_init as weight_init #dependency only needed for training?
+from typing import Optional
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .seecoder_utils import PositionEmbeddingSine, _get_clones, _get_activation_fn
+
+##########
+# helper #
+##########
+
+def with_pos_embed(x, pos):
+ return x if pos is None else x + pos
+
+##############
+# One Former #
+##############
+
+class Transformer(nn.Module):
+ def __init__(self,
+ d_model=512,
+ nhead=8,
+ num_encoder_layers=6,
+ num_decoder_layers=6,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ return_intermediate_dec=False,):
+
+ super().__init__()
+ encoder_layer = TransformerEncoderLayer(
+ d_model, nhead, dim_feedforward, dropout, activation, normalize_before)
+ encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+ self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
+
+ decoder_layer = TransformerDecoderLayer(
+ d_model, nhead, dim_feedforward, dropout, activation, normalize_before)
+ decoder_norm = nn.LayerNorm(d_model)
+ self.decoder = TransformerDecoder(
+ decoder_layer,
+ num_decoder_layers,
+ decoder_norm,
+ return_intermediate=return_intermediate_dec,)
+
+ self._reset_parameters()
+
+ self.d_model = d_model
+ self.nhead = nhead
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward(self, src, mask, query_embed, pos_embed, task_token=None):
+ # flatten NxCxHxW to HWxNxC
+ bs, c, h, w = src.shape
+ src = src.flatten(2).permute(2, 0, 1)
+ pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+ query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
+ if mask is not None:
+ mask = mask.flatten(1)
+
+ if task_token is None:
+ tgt = torch.zeros_like(query_embed)
+ else:
+ tgt = task_token.repeat(query_embed.shape[0], 1, 1)
+
+ memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) # src = memory
+ hs = self.decoder(
+ tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed
+ )
+ return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
+
+class TransformerEncoder(nn.Module):
+ def __init__(self, encoder_layer, num_layers, norm=None):
+ super().__init__()
+ self.layers = _get_clones(encoder_layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+
+ def forward(self, src, mask=None, src_key_padding_mask=None, pos=None,):
+ output = src
+ for layer in self.layers:
+ output = layer(
+ output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos
+ )
+ if self.norm is not None:
+ output = self.norm(output)
+ return output
+
+class TransformerDecoder(nn.Module):
+ def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
+ super().__init__()
+ self.layers = _get_clones(decoder_layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+ self.return_intermediate = return_intermediate
+
+ def forward(
+ self,
+ tgt,
+ memory,
+ tgt_mask=None,
+ memory_mask=None,
+ tgt_key_padding_mask=None,
+ memory_key_padding_mask=None,
+ pos=None,
+ query_pos=None,):
+
+ output = tgt
+ intermediate = []
+ for layer in self.layers:
+ output = layer(
+ output,
+ memory,
+ tgt_mask=tgt_mask,
+ memory_mask=memory_mask,
+ tgt_key_padding_mask=tgt_key_padding_mask,
+ memory_key_padding_mask=memory_key_padding_mask,
+ pos=pos,
+ query_pos=query_pos,
+ )
+ if self.return_intermediate:
+ intermediate.append(self.norm(output))
+
+ if self.norm is not None:
+ output = self.norm(output)
+ if self.return_intermediate:
+ intermediate.pop()
+ intermediate.append(output)
+
+ if self.return_intermediate:
+ return torch.stack(intermediate)
+
+ return output.unsqueeze(0)
+
+class TransformerEncoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False, ):
+
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, x, pos):
+ return x if pos is None else x + pos
+
+ def forward_post(
+ self,
+ src,
+ src_mask = None,
+ src_key_padding_mask = None,
+ pos = None,):
+
+ q = k = self.with_pos_embed(src, pos)
+ src2 = self.self_attn(
+ q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+ )[0]
+ src = src + self.dropout1(src2)
+ src = self.norm1(src)
+ src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+ src = src + self.dropout2(src2)
+ src = self.norm2(src)
+ return src
+
+ def forward_pre(
+ self,
+ src,
+ src_mask = None,
+ src_key_padding_mask = None,
+ pos = None,):
+
+ src2 = self.norm1(src)
+ q = k = self.with_pos_embed(src2, pos)
+ src2 = self.self_attn(
+ q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+ )[0]
+ src = src + self.dropout1(src2)
+ src2 = self.norm2(src)
+ src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
+ src = src + self.dropout2(src2)
+ return src
+
+ def forward(
+ self,
+ src,
+ src_mask = None,
+ src_key_padding_mask = None,
+ pos = None,):
+ if self.normalize_before:
+ return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
+ return self.forward_post(src, src_mask, src_key_padding_mask, pos)
+
+class TransformerDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,):
+
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.norm3 = nn.LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+ self.dropout3 = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, x, pos):
+ return x if pos is None else x + pos
+
+ def forward_post(
+ self,
+ tgt,
+ memory,
+ tgt_mask = None,
+ memory_mask = None,
+ tgt_key_padding_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None,):
+
+ q = k = self.with_pos_embed(tgt, query_pos)
+ tgt2 = self.self_attn(
+ q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout1(tgt2)
+ tgt = self.norm1(tgt)
+ tgt2 = self.multihead_attn(
+ query=self.with_pos_embed(tgt, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,)[0]
+ tgt = tgt + self.dropout2(tgt2)
+ tgt = self.norm2(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+ tgt = tgt + self.dropout3(tgt2)
+ tgt = self.norm3(tgt)
+ return tgt
+
+ def forward_pre(
+ self,
+ tgt,
+ memory,
+ tgt_mask = None,
+ memory_mask = None,
+ tgt_key_padding_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None,):
+
+ tgt2 = self.norm1(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(
+ q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+ )[0]
+ tgt = tgt + self.dropout1(tgt2)
+ tgt2 = self.norm2(tgt)
+ tgt2 = self.multihead_attn(
+ query=self.with_pos_embed(tgt2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )[0]
+ tgt = tgt + self.dropout2(tgt2)
+ tgt2 = self.norm3(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout3(tgt2)
+ return tgt
+
+ def forward(
+ self,
+ tgt,
+ memory,
+ tgt_mask = None,
+ memory_mask = None,
+ tgt_key_padding_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None, ):
+
+ if self.normalize_before:
+ return self.forward_pre(
+ tgt,
+ memory,
+ tgt_mask,
+ memory_mask,
+ tgt_key_padding_mask,
+ memory_key_padding_mask,
+ pos,
+ query_pos,)
+ return self.forward_post(
+ tgt,
+ memory,
+ tgt_mask,
+ memory_mask,
+ tgt_key_padding_mask,
+ memory_key_padding_mask,
+ pos,
+ query_pos,)
+
+class SelfAttentionLayer(nn.Module):
+
+ def __init__(self, d_model, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt,
+ tgt_mask = None,
+ tgt_key_padding_mask = None,
+ query_pos = None):
+ q = k = self.with_pos_embed(tgt, query_pos).transpose(0 ,1)
+ tgt2 = self.self_attn(q, k, value=tgt.transpose(0 ,1), attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2.transpose(0 ,1))
+ tgt = self.norm(tgt)
+
+ return tgt
+
+ def forward_pre(self, tgt,
+ tgt_mask = None,
+ tgt_key_padding_mask = None,
+ query_pos = None):
+ tgt2 = self.norm(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+
+ return tgt
+
+ def forward(self, tgt,
+ tgt_mask = None,
+ tgt_key_padding_mask = None,
+ query_pos = None):
+ if self.normalize_before:
+ return self.forward_pre(tgt, tgt_mask,
+ tgt_key_padding_mask, query_pos)
+ return self.forward_post(tgt, tgt_mask,
+ tgt_key_padding_mask, query_pos)
+
+class CrossAttentionLayer(nn.Module):
+
+ def __init__(self, d_model, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt, memory,
+ memory_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None):
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos).transpose(0, 1),
+ key=self.with_pos_embed(memory, pos).transpose(0, 1),
+ value=memory.transpose(0, 1), attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2.transpose(0, 1))
+ tgt = self.norm(tgt)
+
+ return tgt
+
+ def forward_pre(self, tgt, memory,
+ memory_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None):
+ tgt2 = self.norm(tgt)
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory, attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+
+ return tgt
+
+ def forward(self, tgt, memory,
+ memory_mask = None,
+ memory_key_padding_mask = None,
+ pos = None,
+ query_pos = None):
+ if self.normalize_before:
+ return self.forward_pre(tgt, memory, memory_mask,
+ memory_key_padding_mask, pos, query_pos)
+ return self.forward_post(tgt, memory, memory_mask,
+ memory_key_padding_mask, pos, query_pos)
+
+class FFNLayer(nn.Module):
+
+ def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm = nn.LayerNorm(d_model)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt):
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+ return tgt
+
+ def forward_pre(self, tgt):
+ tgt2 = self.norm(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout(tgt2)
+ return tgt
+
+ def forward(self, tgt):
+ if self.normalize_before:
+ return self.forward_pre(tgt)
+ return self.forward_post(tgt)
+
+class MLP(nn.Module):
+ """ Very simple multi-layer perceptron (also called FFN)"""
+ def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+class Seet_OneFormer_TDecoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ mask_classification,
+ num_classes,
+ hidden_dim,
+ num_queries,
+ nheads,
+ dropout,
+ dim_feedforward,
+ enc_layers,
+ is_train,
+ dec_layers,
+ class_dec_layers,
+ pre_norm,
+ mask_dim,
+ enforce_input_project,
+ use_task_norm,):
+
+ super().__init__()
+
+ assert mask_classification, "Only support mask classification model"
+ self.mask_classification = mask_classification
+ self.is_train = is_train
+ self.use_task_norm = use_task_norm
+
+ # positional encoding
+ N_steps = hidden_dim // 2
+ self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+ self.class_transformer = Transformer(
+ d_model=hidden_dim,
+ dropout=dropout,
+ nhead=nheads,
+ dim_feedforward=dim_feedforward,
+ num_encoder_layers=enc_layers,
+ num_decoder_layers=class_dec_layers,
+ normalize_before=pre_norm,
+ return_intermediate_dec=False,
+ )
+
+ # define Transformer decoder here
+ self.num_heads = nheads
+ self.num_layers = dec_layers
+ self.transformer_self_attention_layers = nn.ModuleList()
+ self.transformer_cross_attention_layers = nn.ModuleList()
+ self.transformer_ffn_layers = nn.ModuleList()
+
+ for _ in range(self.num_layers):
+ self.transformer_self_attention_layers.append(
+ SelfAttentionLayer(
+ d_model=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.transformer_cross_attention_layers.append(
+ CrossAttentionLayer(
+ d_model=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.transformer_ffn_layers.append(
+ FFNLayer(
+ d_model=hidden_dim,
+ dim_feedforward=dim_feedforward,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.decoder_norm = nn.LayerNorm(hidden_dim)
+
+ self.num_queries = num_queries
+ # learnable query p.e.
+ self.query_embed = nn.Embedding(num_queries, hidden_dim)
+
+ # level embedding (we always use 3 scales)
+ self.num_feature_levels = 3
+ self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
+ self.input_proj = nn.ModuleList()
+ for _ in range(self.num_feature_levels):
+ if in_channels != hidden_dim or enforce_input_project:
+ self.input_proj.append(nn.Conv2d(in_channels, hidden_dim, kernel_size=1))
+ #weight_init.c2_xavier_fill(self.input_proj[-1])
+ else:
+ self.input_proj.append(nn.Sequential())
+
+ self.class_input_proj = nn.Conv2d(in_channels, hidden_dim, kernel_size=1)
+ #weight_init.c2_xavier_fill(self.class_input_proj)
+
+ # output FFNs
+ if self.mask_classification:
+ self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
+ self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+ def forward(self, x, mask_features, tasks):
+ # x is a list of multi-scale feature
+ assert len(x) == self.num_feature_levels
+ src = []
+ pos = []
+ size_list = []
+
+ for i in range(self.num_feature_levels):
+ size_list.append(x[i].shape[-2:])
+ pos.append(self.pe_layer(x[i], None).flatten(2))
+ src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])
+ pos[-1] = pos[-1].transpose(1, 2)
+ src[-1] = src[-1].transpose(1, 2)
+
+ bs, _, _ = src[0].shape
+
+ query_embed = self.query_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
+
+ tasks = tasks.unsqueeze(0)
+ if self.use_task_norm:
+ tasks = self.decoder_norm(tasks)
+
+ feats = self.pe_layer(mask_features, None)
+
+ out_t, _ = self.class_transformer(
+ feats, None,
+ self.query_embed.weight[:-1],
+ self.class_input_proj(mask_features),
+ tasks if self.use_task_norm else None)
+ out_t = out_t[0]
+
+ out = torch.cat([out_t, tasks], dim=1)
+
+ output = out.clone()
+
+ predictions_class = []
+ predictions_mask = []
+
+ # prediction heads on learnable query features
+ outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(
+ output, mask_features, attn_mask_target_size=size_list[0])
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+
+ for i in range(self.num_layers):
+ level_index = i % self.num_feature_levels
+ attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+
+ output = self.transformer_cross_attention_layers[i](
+ output, src[level_index],
+ memory_mask=attn_mask,
+ memory_key_padding_mask=None,
+ pos=pos[level_index], query_pos=query_embed, )
+
+ output = self.transformer_self_attention_layers[i](
+ output, tgt_mask=None,
+ tgt_key_padding_mask=None,
+ query_pos=query_embed, )
+
+ # FFN
+ output = self.transformer_ffn_layers[i](output)
+
+ outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(
+ output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels])
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+
+ assert len(predictions_class) == self.num_layers + 1
+
+ out = {
+ 'pred_logits': predictions_class[-1],
+ 'pred_masks': predictions_mask[-1],}
+
+ return out
+
+ def forward_prediction_heads(self, output, mask_features, attn_mask_target_size):
+ decoder_output = self.decoder_norm(output)
+ outputs_class = self.class_embed(decoder_output)
+ mask_embed = self.mask_embed(decoder_output)
+ outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
+
+ attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False)
+ attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()
+ attn_mask = attn_mask.detach()
+
+ return outputs_class, outputs_mask, attn_mask
diff --git a/swin.py b/swin.py
new file mode 100644
index 0000000..bbfb25c
--- /dev/null
+++ b/swin.py
@@ -0,0 +1,657 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+import numpy as np
+
+
+##############################
+# timm.models.layers helpers #
+##############################
+
+def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
+ if drop_prob == 0. or not training:
+ return x
+ keep_prob = 1 - drop_prob
+ shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
+ random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+ if keep_prob > 0.0 and scale_by_keep:
+ random_tensor.div_(keep_prob)
+ return x * random_tensor
+
+class DropPath(nn.Module):
+ """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+ """
+ def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
+ super(DropPath, self).__init__()
+ self.drop_prob = drop_prob
+ self.scale_by_keep = scale_by_keep
+
+ def forward(self, x):
+ return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+ def extra_repr(self):
+ return f'drop_prob={round(self.drop_prob,3):0.3f}'
+
+def _ntuple(n):
+ def parse(x):
+ from itertools import repeat
+ import collections.abc
+ if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+ return tuple(x)
+ return tuple(repeat(x, n))
+ return parse
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple
+
+def _trunc_normal_(tensor, mean, std, a, b):
+ import warnings
+ import math
+
+ def norm_cdf(x):
+ return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+ if (mean < a - 2 * std) or (mean > b + 2 * std):
+ warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+ "The distribution of values may be incorrect.",
+ stacklevel=2)
+
+ l = norm_cdf((a - mean) / std)
+ u = norm_cdf((b - mean) / std)
+ tensor.uniform_(2 * l - 1, 2 * u - 1)
+ tensor.erfinv_()
+ tensor.mul_(std * math.sqrt(2.))
+ tensor.add_(mean)
+ tensor.clamp_(min=a, max=b)
+ return tensor
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+ with torch.no_grad():
+ return _trunc_normal_(tensor, mean, std, a, b)
+
+#############
+# main swin #
+#############
+
+class Mlp(nn.Module):
+ """ Multilayer perceptron."""
+
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+
+class WindowAttention(nn.Module):
+ """ Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim ** -0.5
+
+ # define a parameter table of relative position bias
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ trunc_normal_(self.relative_position_bias_table, std=.02)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """ Forward function.
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = (q @ k.transpose(-2, -1))
+
+ relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+
+class SwinTransformerBlock(nn.Module):
+ """ Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, dim, num_heads, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ self.H = None
+ self.W = None
+
+ def forward(self, x, mask_matrix):
+ """ Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ mask_matrix: Attention mask for cyclic shift.
+ """
+ B, L, C = x.shape
+ H, W = self.H, self.W
+ assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = self.norm1(x)
+ x = x.view(B, H, W, C)
+
+ # pad feature maps to multiples of window size
+ pad_l = pad_t = 0
+ pad_r = (self.window_size - W % self.window_size) % self.window_size
+ pad_b = (self.window_size - H % self.window_size) % self.window_size
+ x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+ _, Hp, Wp, _ = x.shape
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ attn_mask = mask_matrix
+ else:
+ shifted_x = x
+ attn_mask = None
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA
+ attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+
+ if pad_r > 0 or pad_b > 0:
+ x = x[:, :H, :W, :].contiguous()
+
+ x = x.view(B, H * W, C)
+
+ # FFN
+ x = shortcut + self.drop_path(x)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+ return x
+
+
+class PatchMerging(nn.Module):
+ """ Patch Merging Layer
+ Args:
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+ def __init__(self, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def forward(self, x, H, W):
+ """ Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+
+ x = x.view(B, H, W, C)
+
+ # padding
+ pad_input = (H % 2 == 1) or (W % 2 == 1)
+ if pad_input:
+ x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.norm(x)
+ x = self.reduction(x)
+
+ return x
+
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of feature channels
+ depth (int): Depths of this stage.
+ num_heads (int): Number of attention head.
+ window_size (int): Local window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(self,
+ dim,
+ depth,
+ num_heads,
+ window_size=7,
+ mlp_ratio=4.,
+ qkv_bias=True,
+ qk_scale=None,
+ drop=0.,
+ attn_drop=0.,
+ drop_path=0.,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ use_checkpoint=False):
+ super().__init__()
+ self.window_size = window_size
+ self.shift_size = window_size // 2
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(
+ dim=dim,
+ num_heads=num_heads,
+ window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ drop=drop,
+ attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer)
+ for i in range(depth)])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, H, W):
+ """ Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+
+ # calculate attention mask for SW-MSA
+ Hp = int(np.ceil(H / self.window_size)) * self.window_size
+ Wp = int(np.ceil(W / self.window_size)) * self.window_size
+ img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device, dtype=x.dtype) # 1 Hp Wp 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ for blk in self.blocks:
+ blk.H, blk.W = H, W
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, attn_mask)
+ else:
+ x = blk(x, attn_mask)
+ if self.downsample is not None:
+ x_down = self.downsample(x, H, W)
+ Wh, Ww = (H + 1) // 2, (W + 1) // 2
+ return x, H, W, x_down, Wh, Ww
+ else:
+ return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+ """ Image to Patch Embedding
+ Args:
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ patch_size = to_2tuple(patch_size)
+ self.patch_size = patch_size
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ """Forward function."""
+ # padding
+ _, _, H, W = x.size()
+ if W % self.patch_size[1] != 0:
+ x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+ if H % self.patch_size[0] != 0:
+ x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+ x = self.proj(x) # B C Wh Ww
+ if self.norm is not None:
+ Wh, Ww = x.size(2), x.size(3)
+ x = x.flatten(2).transpose(1, 2)
+ x = self.norm(x)
+ x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+ return x
+
+
+class SwinTransformer(nn.Module):
+ """ Swin Transformer backbone.
+ A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
+ https://arxiv.org/pdf/2103.14030
+ Args:
+ pretrain_img_size (int): Input image size for training the pretrained model,
+ used in absolute postion embedding. Default 224.
+ patch_size (int | tuple(int)): Patch size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ depths (tuple[int]): Depths of each Swin Transformer stage.
+ num_heads (tuple[int]): Number of attention head of each stage.
+ window_size (int): Window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+ drop_rate (float): Dropout rate.
+ attn_drop_rate (float): Attention dropout rate. Default: 0.
+ drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+ out_indices (Sequence[int]): Output from which stages.
+ frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+ -1 means not freezing any parameters.
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(self,
+ pretrain_img_size=224,
+ patch_size=4,
+ in_chans=3,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ mlp_ratio=4.,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ drop_path_rate=0.2,
+ norm_layer=nn.LayerNorm,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ use_checkpoint=False):
+ super().__init__()
+
+ self.pretrain_img_size = pretrain_img_size
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.out_indices = out_indices
+ self.frozen_stages = frozen_stages
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ pretrain_img_size = to_2tuple(pretrain_img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
+
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
+ trunc_normal_(self.absolute_pos_embed, std=.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
+
+ # build layers
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = BasicLayer(
+ dim=int(embed_dim * 2 ** i_layer),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ drop=drop_rate,
+ attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+ norm_layer=norm_layer,
+ downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+ use_checkpoint=use_checkpoint)
+ self.layers.append(layer)
+
+ num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+ self.num_features = num_features
+
+ # add a norm layer for each output
+ for i_layer in out_indices:
+ layer = norm_layer(num_features[i_layer])
+ layer_name = f'norm{i_layer}'
+ self.add_module(layer_name, layer)
+
+ self._freeze_stages()
+
+ def _freeze_stages(self):
+ if self.frozen_stages >= 0:
+ self.patch_embed.eval()
+ for param in self.patch_embed.parameters():
+ param.requires_grad = False
+
+ if self.frozen_stages >= 1 and self.ape:
+ self.absolute_pos_embed.requires_grad = False
+
+ if self.frozen_stages >= 2:
+ self.pos_drop.eval()
+ for i in range(0, self.frozen_stages - 1):
+ m = self.layers[i]
+ m.eval()
+ for param in m.parameters():
+ param.requires_grad = False
+
+ def forward(self, x):
+ """Forward function."""
+ x = self.patch_embed(x)
+
+ Wh, Ww = x.size(2), x.size(3)
+ if self.ape:
+ # interpolate the position embedding to the corresponding size
+ absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
+ x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
+ else:
+ x = x.flatten(2).transpose(1, 2)
+ x = self.pos_drop(x)
+
+ outs = []
+ for i in range(self.num_layers):
+ layer = self.layers[i]
+ x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+ if i in self.out_indices:
+ norm_layer = getattr(self, f'norm{i}')
+ x_out = norm_layer(x_out)
+
+ out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+ outs.append(out)
+
+ outputs = {
+ 'res2' : outs[0],
+ 'res3' : outs[1],
+ 'res4' : outs[2],
+ 'res5' : outs[3],}
+ return outputs
+
+ def train(self, mode=True):
+ """Convert the model into training mode while keep layers freezed."""
+ super(SwinTransformer, self).train(mode)
+ self._freeze_stages()
+ return self