add rel

data/icews14/relations.dict

@@ -12407,3 +12407,233 @@
 12406	Carry out roadside bombing[65]
 12407	Appeal for target to allow international involvement (non-mediation)[1]
 12408	Reject request for change in leadership[179]
+12409	Criticize or denounce
+12410	Express intent to meet or negotiate
+12411	Consult
+12412	Make an appeal or request
+12413	Abduct, hijack, or take hostage
+12414	Praise or endorse
+12415	Engage in negotiation
+12416	Use unconventional violence
+12417	Make statement
+12418	Arrest, detain, or charge with legal action
+12419	Use conventional military force
+12420	Complain officially
+12421	Impose administrative sanctions
+12422	Express intent to cooperate
+12423	Make a visit
+12424	Appeal for de-escalation of military engagement
+12425	Sign formal agreement
+12426	Attempt to assassinate
+12427	Host a visit
+12428	Increase military alert status
+12429	Impose embargo, boycott, or sanctions
+12430	Provide economic aid
+12431	Demonstrate or rally
+12432	Express intent to engage in diplomatic cooperation (such as policy support)
+12433	Appeal for intelligence
+12434	Demand
+12435	Carry out suicide bombing
+12436	Threaten
+12437	Express intent to provide material aid
+12438	Grant diplomatic recognition
+12439	Meet at a 'third' location
+12440	Accuse
+12441	Investigate
+12442	Reject
+12443	Appeal for diplomatic cooperation (such as policy support)
+12444	Engage in symbolic act
+12445	Defy norms, law
+12446	Consider policy option
+12447	Provide aid
+12448	Sexually assault
+12449	Make empathetic comment
+12450	Bring lawsuit against
+12451	Impose blockade, restrict movement
+12452	Make pessimistic comment
+12453	Protest violently, riot
+12454	Reduce or break diplomatic relations
+12455	Grant asylum
+12456	Engage in diplomatic cooperation
+12457	Make optimistic comment
+12458	Torture
+12459	Refuse to yield
+12460	Appeal for change in leadership
+12461	Cooperate militarily
+12462	Mobilize or increase armed forces
+12463	fight with small arms and light weapons
+12464	Ease administrative sanctions
+12465	Appeal for political reform
+12466	Return, release person(s)
+12467	Discuss by telephone
+12468	Demonstrate for leadership change
+12469	Impose restrictions on political freedoms
+12470	Reduce relations
+12471	Investigate crime, corruption
+12472	Engage in material cooperation
+12473	Appeal to others to meet or negotiate
+12474	Provide humanitarian aid
+12475	Use tactics of violent repression
+12476	Occupy territory
+12477	Demand humanitarian aid
+12478	Threaten non-force
+12479	Express intent to cooperate economically
+12480	Conduct suicide, car, or other non-military bombing
+12481	Demand diplomatic cooperation (such as policy support)
+12482	Demand meeting, negotiation
+12483	Deny responsibility
+12484	Express intent to change institutions, regime
+12485	Give ultimatum
+12486	Appeal for judicial cooperation
+12487	Rally support on behalf of
+12488	Obstruct passage, block
+12489	Share intelligence or information
+12490	Expel or deport individuals
+12491	Confiscate property
+12492	Accuse of aggression
+12493	Physically assault
+12494	Retreat or surrender militarily
+12495	Veto
+12496	Kill by physical assault
+12497	Assassinate
+12498	Appeal for change in institutions, regime
+12499	Forgive
+12500	Reject proposal to meet, discuss, or negotiate
+12501	Express intent to provide humanitarian aid
+12502	Appeal for release of persons or property
+12503	Acknowledge or claim responsibility
+12504	Ease economic sanctions, boycott, embargo
+12505	Express intent to cooperate militarily
+12506	Cooperate economically
+12507	Express intent to provide economic aid
+12508	Mobilize or increase police power
+12509	Employ aerial weapons
+12510	Accuse of human rights abuses
+12511	Conduct strike or boycott
+12512	Appeal for policy change
+12513	Demonstrate military or police power
+12514	Provide military aid
+12515	Reject plan, agreement to settle dispute
+12516	Yield
+12517	Appeal for easing of administrative sanctions
+12518	Mediate
+12519	Apologize
+12520	Express intent to release persons or property
+12521	Express intent to de-escalate military engagement
+12522	Accede to demands for rights
+12523	Demand economic aid
+12524	Impose state of emergency or martial law
+12525	Receive deployment of peacekeepers
+12526	Demand de-escalation of military engagement
+12527	Declare truce, ceasefire
+12528	Reduce or stop humanitarian assistance
+12529	Appeal to others to settle dispute
+12530	Reject request for military aid
+12531	Threaten with political dissent, protest
+12532	Appeal to engage in or accept mediation
+12533	Express intent to ease economic sanctions, boycott, or embargo
+12534	Coerce
+12535	fight with artillery and tanks
+12536	Express intent to cooperate on intelligence
+12537	Express intent to settle dispute
+12538	Express accord
+12539	Decline comment
+12540	Rally opposition against
+12541	Halt negotiations
+12542	Demand that target yields
+12543	Appeal for military aid
+12544	Threaten with military force
+12545	Express intent to provide military protection or peacekeeping
+12546	Threaten with sanctions, boycott, embargo
+12547	Express intent to provide military aid
+12548	Demand change in leadership
+12549	Appeal for economic aid
+12550	Refuse to de-escalate military engagement
+12551	Refuse to release persons or property
+12552	Increase police alert status
+12553	Return, release property
+12554	Ease military blockade
+12555	Appeal for material cooperation
+12556	Express intent to cooperate on judicial matters
+12557	Appeal for economic cooperation
+12558	Demand settling of dispute
+12559	Accuse of crime, corruption
+12560	Defend verbally
+12561	Provide military protection or peacekeeping
+12562	Accuse of espionage, treason
+12563	Seize or damage property
+12564	Accede to requests or demands for political reform
+12565	Appeal for easing of economic sanctions, boycott, or embargo
+12566	Threaten to reduce or stop aid
+12567	Engage in judicial cooperation
+12568	Appeal to yield
+12569	Demand military aid
+12570	Refuse to ease administrative sanctions
+12571	Demand release of persons or property
+12572	Accede to demands for change in leadership
+12573	Appeal for humanitarian aid
+12574	Threaten with repression
+12575	Demand change in institutions, regime
+12576	Demonstrate for policy change
+12577	Appeal for aid
+12578	Appeal for rights
+12579	Engage in violent protest for rights
+12580	Express intent to mediate
+12581	Expel or withdraw peacekeepers
+12582	Appeal for military protection or peacekeeping
+12583	Engage in mass killings
+12584	Accuse of war crimes
+12585	Reject military cooperation
+12586	Threaten to halt negotiations
+12587	Ban political parties or politicians
+12588	Express intent to change leadership
+12589	Demand material cooperation
+12590	Express intent to institute political reform
+12591	Demand easing of administrative sanctions
+12592	Express intent to engage in material cooperation
+12593	Reduce or stop economic assistance
+12594	Express intent to ease administrative sanctions
+12595	Demand intelligence cooperation
+12596	Ease curfew
+12597	Receive inspectors
+12598	Demand rights
+12599	Demand political reform
+12600	Demand judicial cooperation
+12601	Engage in political dissent
+12602	Detonate nuclear weapons
+12603	Violate ceasefire
+12604	Express intent to accept mediation
+12605	Refuse to ease economic sanctions, boycott, or embargo
+12606	Demand mediation
+12607	Obstruct passage to demand leadership change
+12608	Express intent to yield
+12609	Conduct hunger strike
+12610	Threaten to halt mediation
+12611	Reject judicial cooperation
+12612	Reduce or stop military assistance
+12613	Ease political dissent
+12614	Threaten to reduce or break relations
+12615	Demobilize armed forces
+12616	Use as human shield
+12617	Demand policy change
+12618	Accede to demands for change in institutions, regime
+12619	Reject economic cooperation
+12620	Reject material cooperation
+12621	Halt mediation
+12622	Accede to demands for change in policy
+12623	Investigate war crimes
+12624	Threaten with administrative sanctions
+12625	Reduce or stop material aid
+12626	Destroy property
+12627	Express intent to change policy
+12628	Use chemical, biological, or radiological weapons
+12629	Reject request for military protection or peacekeeping
+12630	Demand material aid
+12631	Engage in mass expulsion
+12632	Investigate human rights abuses
+12633	Carry out car bombing
+12634	Expel or withdraw
+12635	Ease state of emergency or martial law
+12636	Carry out roadside bombing
+12637	Appeal for target to allow international involvement (non-mediation)
+12638	Reject request for change in leadership

data/wikidata12k_both/relations.dict

@@ -421,3 +421,27 @@
 420	P551[36-69]
 421	P579[0-15]
 422	P102[54-62]
+423	P131
+424	P1435
+425	P39
+426	P54
+427	P31
+428	P463
+429	P512
+430	P190
+431	P150
+432	P1376
+433	P166
+434	P2962
+435	P108
+436	P17
+437	P793
+438	P69
+439	P26
+440	P579
+441	P1411
+442	P6
+443	P1346
+444	P102
+445	P27
+446	P551

main.py

@@ -91,9 +91,11 @@ class Main(object):
         for line in open('./data/{}/{}'.format(self.p.dataset, "relations.dict")):
             id, rel = map(str.lower, line.strip().split('\t'))
             self.rel2id[rel] = int(id)
+            rel_set.add(rel)
 
         # self.ent2id = {ent: idx for idx, ent in enumerate(ent_set)}
         # self.rel2id = {rel: idx for idx, rel in enumerate(rel_set)}
+        
         self.rel2id.update({rel+'_reverse': idx+len(self.rel2id)
                            for idx, rel in enumerate(rel_set)})
 
@@ -111,47 +113,48 @@ class Main(object):
         for split in ['train', 'test', 'valid']:
             for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)):
                 sub, rel, obj, *_ = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
-                sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj]
+                nt_rel = rel.split('[')[0]
-                self.data[split].append((sub, rel, obj))
+                sub, rel, obj, nt_rel = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj], self.rel2id[nt_rel]
+                self.data[split].append((sub, rel, obj, nt_rel))
 
                 if split == 'train':
-                    sr2o[(sub, rel)].add(obj)
+                    sr2o[(sub, rel, nt_rel)].add(obj)
-                    sr2o[(obj, rel+self.p.num_rel)].add(sub)
+                    sr2o[(obj, rel+self.p.num_rel, nt_rel + self.p.num_rel)].add(sub)
         self.data = dict(self.data)
 
         self.sr2o = {k: list(v) for k, v in sr2o.items()}
         for split in ['test', 'valid']:
-            for sub, rel, obj in self.data[split]:
+            for sub, rel, obj, nt_rel in self.data[split]:
-                sr2o[(sub, rel)].add(obj)
+                sr2o[(sub, rel, nt_rel)].add(obj)
-                sr2o[(obj, rel+self.p.num_rel)].add(sub)
+                sr2o[(obj, rel+self.p.num_rel, nt_rel + self.p.num_rel)].add(sub)
 
         self.sr2o_all = {k: list(v) for k, v in sr2o.items()}
 
         self.triples = ddict(list)
 
         if self.p.train_strategy == 'one_to_n':
-            for (sub, rel), obj in self.sr2o.items():
+            for (sub, rel, nt_rel), obj in self.sr2o.items():
                 self.triples['train'].append(
-                    {'triple': (sub, rel, -1), 'label': self.sr2o[(sub, rel)], 'sub_samp': 1})
+                    {'triple': (sub, rel, -1, nt_rel), 'label': self.sr2o[(sub, rel, nt_rel)], 'sub_samp': 1})
         else:
-            for sub, rel, obj in self.data['train']:
+            for sub, rel, obj, nt_rel in self.data['train']:
                 rel_inv = rel + self.p.num_rel
-                sub_samp = len(self.sr2o[(sub, rel)]) + \
+                sub_samp = len(self.sr2o[(sub, rel, nt_rel)]) + \
-                    len(self.sr2o[(obj, rel_inv)])
+                    len(self.sr2o[(obj, rel_inv, nt_rel + self.p.num_rel)])
                 sub_samp = np.sqrt(1/sub_samp)
 
                 self.triples['train'].append({'triple': (
-                    sub, rel, obj),     'label': self.sr2o[(sub, rel)],     'sub_samp': sub_samp})
+                    sub, rel, obj, nt_rel),     'label': self.sr2o[(sub, rel, nt_rel)],     'sub_samp': sub_samp})
                 self.triples['train'].append({'triple': (
-                    obj, rel_inv, sub), 'label': self.sr2o[(obj, rel_inv)], 'sub_samp': sub_samp})
+                    obj, rel_inv, sub, nt_rel + self.p.num_rel), 'label': self.sr2o[(obj, rel_inv, nt_rel + self.p.num_rel)], 'sub_samp': sub_samp})
 
         for split in ['test', 'valid']:
-            for sub, rel, obj in self.data[split]:
+            for sub, rel, obj, nt_rel in self.data[split]:
                 rel_inv = rel + self.p.num_rel
                 self.triples['{}_{}'.format(split, 'tail')].append(
-                    {'triple': (sub, rel, obj), 	   'label': self.sr2o_all[(sub, rel)]})
+                    {'triple': (sub, rel, obj, nt_rel), 	   'label': self.sr2o_all[(sub, rel, nt_rel)]})
                 self.triples['{}_{}'.format(split, 'head')].append(
-                    {'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]})
+                    {'triple': (obj, rel_inv, sub, nt_rel + self.p.num_rel), 'label': self.sr2o_all[(obj, rel_inv, nt_rel + self.p.num_rel)]})
 
         self.triples = dict(self.triples)
 
@@ -275,13 +278,13 @@ class Main(object):
             if self.p.train_strategy == 'one_to_x':
                 triple, label, neg_ent, sub_samp = [
                     _.to(self.device) for _ in batch]
-                return triple[:, 0], triple[:, 1], triple[:, 2], label, neg_ent, sub_samp
+                return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, neg_ent, sub_samp
             else:
                 triple, label = [_.to(self.device) for _ in batch]
-                return triple[:, 0], triple[:, 1], triple[:, 2], label, None, None
+                return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, None, None
         else:
             triple, label = [_.to(self.device) for _ in batch]
-            return triple[:, 0], triple[:, 1], triple[:, 2], label
+            return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label
 
     def save_model(self, save_path):
         """
@@ -416,8 +419,8 @@ class Main(object):
             obj_pred = []
             obj_pred_score = []
             for step, batch in enumerate(train_iter):
-                sub, rel, obj, label = self.read_batch(batch, split)
+                sub, rel, obj, nt_rel, label = self.read_batch(batch, split)
-                pred = self.model.forward(sub, rel, None, 'one_to_n')
+                pred = self.model.forward(sub, rel, nt_rel, None, 'one_to_n')
                 b_range = torch.arange(pred.size()[0], device=self.device)
                 target_pred = pred[b_range, obj]
                 pred = torch.where(label.byte(), torch.zeros_like(pred), pred)
@@ -474,10 +477,10 @@ class Main(object):
         for step, batch in enumerate(train_iter):
             self.optimizer.zero_grad()
 
-            sub, rel, obj, label, neg_ent, sub_samp = self.read_batch(
+            sub, rel, obj, nt_rel, label, neg_ent, sub_samp = self.read_batch(
                 batch, 'train')
 
-            pred = self.model.forward(sub, rel, neg_ent, self.p.train_strategy)
+            pred = self.model.forward(sub, rel, nt_rel, neg_ent, self.p.train_strategy)
             loss = self.model.loss(pred, label, sub_samp)
 
             loss.backward()
@@ -690,7 +693,7 @@ if __name__ == "__main__":
                 collate_fn=TrainDataset.collate_fn
             ))
         for step, batch in enumerate(dataloader):
-            sub, rel, obj, label, neg_ent, sub_samp = model.read_batch(
+            sub, rel, obj, nt_rel, label, neg_ent, sub_samp = model.read_batch(
                 batch, 'train')
             
             if (neg_ent is None):

models.py

@@ -435,50 +435,6 @@ class TuckER(torch.nn.Module):
 
         return pred
 
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        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(2 * dim)
-
-    def forward(self, x):
-        """
-        x: B, C, H, W
-        """
-        B, C, H, W = x.shape
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        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.reduction(x)
-        x = self.norm(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        flops += H * W * self.dim // 2
-        return flops
 
 class FouriER(torch.nn.Module):
     def __init__(self, params, hid_drop = None, embed_dim = None):
@@ -510,6 +466,10 @@ class FouriER(torch.nn.Module):
             self.p.ent_vec_dim, image_h*image_w)
         torch.nn.init.xavier_normal_(self.ent_fusion.weight)
 
+        self.ent_attn = torch.nn.Linear(
+            128, 128)
+        torch.nn.init.xavier_normal_(self.ent_attn.weight)
+
         self.rel_fusion = torch.nn.Linear(
             self.p.rel_vec_dim, image_h*image_w)
         torch.nn.init.xavier_normal_(self.rel_fusion.weight)
@@ -532,10 +492,9 @@ class FouriER(torch.nn.Module):
         self.patch_embed = PatchEmbed(in_chans=channels, patch_size=self.p.patch_size, 
                                       embed_dim=self.p.embed_dim, stride=4, padding=2)
         network = []
-        layers = [2, 2, 6, 2]
+        layers = [4, 4, 12, 4]
-        embed_dims = [self.p.embed_dim, 320, 256, 128]
+        embed_dims = [self.p.embed_dim, 128, 320, 128]
-        mlp_ratios = [4, 4, 8, 12]
+        mlp_ratios = [4, 4, 4, 4]
-        num_heads = [4, 4, 4, 4]
         downsamples = [True, True, True, True]
         pool_size=3
         act_layer=nn.GELU
@@ -547,7 +506,6 @@ class FouriER(torch.nn.Module):
         down_patch_size=3
         down_stride=2
         down_pad=1
-        window_size = 4
         num_classes=self.p.embed_dim
         for i in range(len(layers)):
             stage = basic_blocks(embed_dims[i], i, layers, 
@@ -556,9 +514,7 @@ class FouriER(torch.nn.Module):
                                  drop_rate=drop_rate, 
                                  drop_path_rate=drop_path_rate,
                                  use_layer_scale=use_layer_scale, 
-                                 layer_scale_init_value=layer_scale_init_value,
+                                 layer_scale_init_value=layer_scale_init_value)
-                                 num_heads=num_heads[i], input_resolution=(image_h // (2**i), image_w // (2**i)),
-                                 window_size=window_size, shift_size=0)
             network.append(stage)
             if i >= len(layers) - 1:
                 break
@@ -570,7 +526,6 @@ class FouriER(torch.nn.Module):
                         padding=down_pad, 
                         in_chans=embed_dims[i], embed_dim=embed_dims[i+1]
                         )
-                    # PatchMerging(dim=embed_dims[i+1])
                     )
 
         self.network = nn.ModuleList(network)
@@ -597,7 +552,14 @@ class FouriER(torch.nn.Module):
         # output only the features of last layer for image classification
         return x
     
-    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
+    def fuse_attention(self, s_embedding, l_embedding):
+        w1 = self.ent_attn(torch.tanh(s_embedding))
+        w2 = self.ent_attn(torch.tanh(l_embedding))
+        aff = F.softmax(torch.cat((w1,w2),1), 1)
+        en_embedding = aff[:,0].unsqueeze(1) * s_embedding + aff[:, 1].unsqueeze(1) * l_embedding
+        return en_embedding
+
+    def forward(self, sub, rel, nt_rel, neg_ents, strategy='one_to_x'):
         sub_emb = self.ent_fusion(self.ent_embed(sub))
         rel_emb = self.rel_fusion(self.rel_embed(rel))
         comb_emb = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
@@ -606,6 +568,17 @@ class FouriER(torch.nn.Module):
         z = self.forward_embeddings(y)
         z = self.forward_tokens(z)
         z = z.mean([-2, -1])
+
+        nt_rel_emb = self.rel_fusion(self.rel_embed(nt_rel))
+        comb_emb_1 = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), nt_rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
+        y_1 = comb_emb_1.view(-1, 2, self.p.image_h, self.p.image_w)
+        y_1 = self.bn0(y_1)
+        z_1 = self.forward_embeddings(y_1)
+        z_1 = self.forward_tokens(z_1)
+        z_1 = z_1.mean([-2, -1])
+
+        z = self.fuse_attention(z, z_1)
+
         z = self.norm(z)
         x = self.head(z)
         x = self.hidden_drop(x)
@@ -736,7 +709,7 @@ def basic_blocks(dim, index, layers,
                  pool_size=3, mlp_ratio=4., 
                  act_layer=nn.GELU, norm_layer=GroupNorm, 
                  drop_rate=.0, drop_path_rate=0., 
-                 use_layer_scale=True, layer_scale_init_value=1e-5, num_heads = 4, input_resolution = None, window_size = 4, shift_size = 2):
+                 use_layer_scale=True, layer_scale_init_value=1e-5):
     """
     generate PoolFormer blocks for a stage
     return: PoolFormer blocks 
@@ -751,8 +724,6 @@ def basic_blocks(dim, index, layers,
             drop=drop_rate, drop_path=block_dpr, 
             use_layer_scale=use_layer_scale, 
             layer_scale_init_value=layer_scale_init_value, 
-            num_heads=num_heads, input_resolution = input_resolution,
-            window_size=window_size, shift_size=shift_size
             ))
     blocks = nn.Sequential(*blocks)
 
@@ -872,12 +843,9 @@ class WindowAttention(nn.Module):
         attn = attn + relative_position_bias.unsqueeze(0)
 
         if mask is not None:
-            try:
+            nW = mask.shape[0]
-                nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(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 = attn.view(-1, self.num_heads, N, N)
-            except:
-                pass
             attn = self.softmax(attn)
         else:
             attn = self.softmax(attn)
@@ -938,18 +906,17 @@ class PoolFormerBlock(nn.Module):
     """
     def __init__(self, dim, pool_size=3, mlp_ratio=4., 
                  act_layer=nn.GELU, norm_layer=GroupNorm, 
-                 drop=0., drop_path=0., num_heads=4,
+                 drop=0., drop_path=0., 
-                 use_layer_scale=True, layer_scale_init_value=1e-5, input_resolution = None, window_size = 4, shift_size = 2):
+                 use_layer_scale=True, layer_scale_init_value=1e-5):
 
         super().__init__()
 
         self.norm1 = norm_layer(dim)
         #self.token_mixer = Pooling(pool_size=pool_size)
         # self.token_mixer = FNetBlock()
-        self.window_size = window_size
+        self.window_size = 4
-        self.shift_size = shift_size
+        self.attn_mask = None
-        self.input_resolution = input_resolution
+        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=4)
-        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, attn_drop=0.2, proj_drop=0.1)
         self.norm2 = norm_layer(dim)
         mlp_hidden_dim = int(dim * mlp_ratio)
         self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, 
@@ -965,31 +932,6 @@ class PoolFormerBlock(nn.Module):
             self.layer_scale_2 = nn.Parameter(
                 layer_scale_init_value * torch.ones((dim)), requires_grad=True)
 
-        if self.shift_size > 0:
-            # calculate attention mask for SW-MSA
-            H, W = self.input_resolution
-            img_mask = torch.zeros((1, 1, H, W))  # 1 H W 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))
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
     def forward(self, x):
         B, C, H, W = x.shape
         x_windows = window_partition(x, self.window_size)
@@ -997,10 +939,6 @@ class PoolFormerBlock(nn.Module):
         attn_windows = self.token_mixer(x_windows, mask=self.attn_mask)
         attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
         x_attn = window_reverse(attn_windows, self.window_size, H, W)
-        if self.shift_size > 0:
-            x = torch.roll(x_attn, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = x_attn
         if self.use_layer_scale:
             x = x + self.drop_path(
                 self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)