update hello.py.

hello.py

@@ -1,6 +1,14 @@
 import torch
 import torch.nn as nn
 from vit_pytorch import ViT,SimpleViT
+from vit_pytorch.distill import DistillableViT
+from vit_pytorch.deepvit import DeepViT
+from vit_pytorch.cait import CaiT
+from vit_pytorch.pit import PiT
+from vit_pytorch.regionvit import RegionViT
+from vit_pytorch.sep_vit import SepViT
+from vit_pytorch.crossformer import CrossFormer
+from vit_pytorch.nest import NesT
 
 from utils.data_utils import get_loader_new
 from utils.scheduler import  WarmupCosineSchedule
@@ -12,18 +20,108 @@ import numpy as np
 
 def net():
 
-    model = ViT(
+
+    model = NesT(
     image_size = 600,
     patch_size = 30,
-    num_classes = 5,
+    dim = 256,
-    dim = 768,
+    heads = 16,
-    depth = 12,
+    num_hierarchies = 3,        # number of hierarchies
-    heads = 12,
+    block_repeats = (2, 2, 12),  # the number of transformer blocks at each heirarchy, starting from the bottom
-    mlp_dim = 3072,
+    num_classes = 5
-    dropout = 0.1,
-    emb_dropout = 0.1
 )
 
+#     model = CrossFormer( #图片尺寸要是7的倍数，如448
+#     num_classes = 5,                # number of output classes
+#     dim = (64, 128, 256, 512),         # dimension at each stage
+#     depth = (2, 2, 8, 2),              # depth of transformer at each stage
+#     global_window_size = (8, 4, 2, 1), # global window sizes at each stage
+#     local_window_size = 7,             # local window size (can be customized for each stage, but in paper, held constant at 7 for all stages)
+# )
+
+#     model = RegionViT( #图片尺寸要是7的倍数，如448
+#     dim = (64, 128, 256, 512),      # tuple of size 4, indicating dimension at each stage
+#     depth = (2, 2, 8, 2),           # depth of the region to local transformer at each stage
+#     window_size = 7,                # window size, which should be either 7 or 14
+#     num_classes = 5,             # number of output classes
+#     tokenize_local_3_conv = False,  # whether to use a 3 layer convolution to encode the local tokens from the image. the paper uses this for the smaller models, but uses only 1 conv (set to False) for the larger models
+#     use_peg = False,                # whether to use positional generating module. they used this for object detection for a boost in performance
+# )
+
+#     model = SepViT( #图片尺寸要是7的倍数，如448
+#     num_classes = 5,
+#     dim = 32,               # dimensions of first stage, which doubles every stage (32, 64, 128, 256) for SepViT-Lite
+#     dim_head = 32,          # attention head dimension
+#     heads = (1, 2, 4, 8),   # number of heads per stage
+#     depth = (1, 2, 6, 2),   # number of transformer blocks per stage
+#     window_size = 7,        # window size of DSS Attention block
+#     dropout = 0.1           # dropout
+# )
+
+#     model = PiT(
+#     image_size = 600,
+#     patch_size = 30,
+#     dim = 1024,
+#     num_classes = 5,
+#     depth = (1, 1, 1),     # list of depths, indicating the number of rounds of each stage before a downsample
+#     heads = 8,
+#     mlp_dim = 3072,
+#     dropout = 0.1,
+#     emb_dropout = 0.1
+# )
+  
+
+#     model =   CaiT(
+#     image_size = 600,
+#     patch_size = 30,
+#     num_classes = 1000,
+#     dim = 1024,
+#     depth = 12,             # depth of transformer for patch to patch attention only
+#     cls_depth = 2,          # depth of cross attention of CLS tokens to patch
+#     heads = 16,
+#     mlp_dim = 2048,
+#     dropout = 0.1,
+#     emb_dropout = 0.1,
+#     layer_dropout = 0.05    # randomly dropout 5% of the layers
+# )
+
+# model =   DeepViT(
+#     image_size = 600,
+#     patch_size = 30,
+#     num_classes = 5,
+#     dim = 256,
+#     depth = 6,
+#     heads = 6,
+#     mlp_dim = 256,
+#     dropout = 0.1,
+#     emb_dropout = 0.1
+# )
+
+#     model =  DistillableViT(
+#     image_size = 600,
+#     patch_size = 30,
+#     num_classes = 5,
+#     dim = 1080,
+#     depth = 12,
+#     heads = 12,
+#     mlp_dim = 3072,
+#     dropout = 0.1,
+#     emb_dropout = 0.1
+# )
+
+    
+#     model = ViT(
+#     image_size = 600,
+#     patch_size = 30,
+#     num_classes = 5,
+#     dim = 768,
+#     depth = 12,
+#     heads = 12,
+#     mlp_dim = 3072,
+#     dropout = 0.1,
+#     emb_dropout = 0.1
+# )
+
 #     model = SimpleViT(
 #     image_size = 600,
 #     patch_size = 30,
@@ -36,18 +134,18 @@ def net():
 
 # model = ViT(
 #     #Vit-best
-    #     # image_size = 600,
+#     image_size = 600,
-    #     # patch_size = 30,
+#     patch_size = 30,
-    #     # num_classes = 5,
+#     num_classes = 5,
-    #     # dim = 512,
+#     dim = 512,
-    #     # depth = 6,
+#     depth = 6,
-    #     # heads = 8,
+#     heads = 8,
-    #     # mlp_dim = 512,
+#     mlp_dim = 512,
-    #     # pool = 'cls', 
+#     pool = 'cls', 
-    #     # channels = 3, 
+#     channels = 3, 
-    #     # dim_head = 12,
+#     dim_head = 12,
-    #     # dropout = 0.1,
+#     dropout = 0.1,
-    #     # emb_dropout = 0.1
+#     emb_dropout = 0.1
 
 #     #Vit-small
 #     image_size = 600,
@@ -64,23 +162,26 @@ def net():
 #     emb_dropout = 0.1
 
 #     #Vit-tiny
-    #     # image_size = 600,
+#     image_size = 600,
-    #     # patch_size = 30,
+#     patch_size = 30,
-    #     # num_classes = 5,
+#     num_classes = 5,
-    #     # dim = 256,
+#     dim = 256,
-    #     # depth = 4,
+#     depth = 4,
-    #     # heads = 6,
+#     heads = 6,
-    #     # mlp_dim = 256,
+#     mlp_dim = 256,
-    #     # pool = 'cls', 
+#     pool = 'cls', 
-    #     # channels = 3, 
+#     channels = 3, 
-    #     # dim_head = 6,
+#     dim_head = 6,
-    #     # dropout = 0.1,
+#     dropout = 0.1,
-    #     # emb_dropout = 0.1
+#     emb_dropout = 0.1
 # )
 
 
     return model
 
+# img = torch.randn(1, 3, 448, 448)
+# model = net()
+# preds = model(img) # (1, 1000)
 
 
 #计算模型参数数量