From 1afc0d6617c44bca4c6eebc95c6b1123aa151ecd Mon Sep 17 00:00:00 2001
From: Brainway <brainway@protonmail.com>
Date: Thu, 29 Dec 2022 06:19:17 +0000
Subject: [PATCH] update hello.py.

---
 hello.py | 47 +++++++++++++++++++++++++++++++++++++++--------
 1 file changed, 39 insertions(+), 8 deletions(-)

diff --git a/hello.py b/hello.py
index 8b5f78f..3a6f262 100644
--- a/hello.py
+++ b/hello.py
@@ -1,6 +1,6 @@
 import torch
 import torch.nn as nn
-from vit_pytorch import ViT,SimpleViT
+from vit_pytorch import ViT,SimpleViT,MAE
 from vit_pytorch.distill import DistillableViT
 from vit_pytorch.deepvit import DeepViT
 from vit_pytorch.cait import CaiT
@@ -9,6 +9,9 @@ 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 vit_pytorch.mobile_vit import MobileViT
+from vit_pytorch.simmim import SimMIM
+from vit_pytorch.ats_vit import ViT
 
 from utils.data_utils import get_loader_new
 from utils.scheduler import  WarmupCosineSchedule
@@ -20,17 +23,45 @@ import numpy as np
 
 def net():
 
-
-    model = NesT(
+    model =  ViT(
     image_size = 600,
     patch_size = 30,
-    dim = 256,
+    num_classes = 5,
+    dim = 1024,
+    depth = 6,
+    max_tokens_per_depth = (256, 128, 64, 32, 16, 8), # a tuple that denotes the maximum number of tokens that any given layer should have. if the layer has greater than this amount, it will undergo adaptive token sampling
     heads = 16,
-    num_hierarchies = 3,        # number of hierarchies
-    block_repeats = (2, 2, 12),  # the number of transformer blocks at each heirarchy, starting from the bottom
-    num_classes = 5
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1
 )
 
+#     modelv = ViT(
+#     image_size = 600,
+#     patch_size = 30,
+#     num_classes = 5,
+#     dim = 1024,
+#     depth = 6,
+#     heads = 8,
+#     mlp_dim = 2048
+# )
+
+#     model = MAE(
+#     encoder = modelv,
+#     masking_ratio = 0.5  # they found 50% to yield the best results
+# )
+
+   
+#     model = NesT(
+#     image_size = 600,
+#     patch_size = 30,
+#     dim = 256,
+#     heads = 16,
+#     num_hierarchies = 3,        # number of hierarchies
+#     block_repeats = (2, 2, 12),  # the number of transformer blocks at each heirarchy, starting from the bottom
+#     num_classes = 5
+# )
+
 #     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
@@ -296,7 +327,7 @@ def train(model,train_loader,device,train_NUM_STEPS,LEARNING_RATE,WEIGHT_DECAY,W
             batch = tuple(t.to(device) for t in batch)
             x, y = batch
             logits = model(x)
-         
+
             loss = criterion(logits,y)
             
             loss.backward()