From 4522d5d65af3d548395b91b3ac98ab3ca5f6e730 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Baris=20B=C3=BCy=C3=BCktas?= <baris.bueyuektas@tu-berlin.de>
Date: Fri, 13 Jan 2023 14:46:46 +0000
Subject: [PATCH] Update evaluation.py
---
evaluation.py | 120 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 120 insertions(+)
create mode 100644 evaluation.py
diff --git a/evaluation.py b/evaluation.py
new file mode 100644
index 0000000..9979f49
--- /dev/null
+++ b/evaluation.py
@@ -0,0 +1,120 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+import torch.nn as nn
+import torch
+from pytorch_datasets import Ben19Dataset
+from pytorch_datasets1 import Ben19Dataset1
+from pytorch_utils import start_cuda, get_classification_report, print_micro_macro
+from sklearn.metrics import classification_report
+from torch.utils.data import Dataset, DataLoader
+from tqdm import tqdm
+import numpy as np
+from model import ANN
+from model import ANN_S2
+# In[ ]:
+
+def change_sizes(labels):
+ new_labels=np.zeros((len(labels[0]),19))
+ for i in range(len(labels[0])): #128
+
+ for j in range(len(labels)): #19
+ new_labels[i,j] = int(labels[j][i])
+
+ return torch.from_numpy(new_labels)
+
+
+def test(ann,ann1,ann2):
+ ann.eval()
+ lmdb_path = '/faststorage/BigEarthNet_S1_S2/BEN_S1_S2.lmdb'
+ batch_size=256
+ num_workers=4
+ y_true = []
+ predicted_probs = []
+
+
+
+ csv_val_path='/data/all_test.csv'
+ val_set = Ben19Dataset(lmdb_path=lmdb_path, csv_path=csv_val_path, img_transform='default')
+ val_loader = DataLoader(val_set, batch_size=batch_size, num_workers=num_workers,shuffle=False, pin_memory=True)
+
+ csv_val_path='/data/all_test.csv'
+ val_set1 = Ben19Dataset1(lmdb_path=lmdb_path, csv_path=csv_val_path, img_transform='default')
+ val_loader1 = DataLoader(val_set1, batch_size=batch_size, num_workers=num_workers,shuffle=False, pin_memory=True)
+
+ y_true = []
+ s1 = torch.ones(1,2048).cuda()
+ with torch.no_grad():
+ for batch_idx, batch in enumerate(tqdm(val_loader, desc="test")):
+
+ data = batch['s1_bands'].cuda()
+ labels = batch['label']
+
+ labels=labels.detach().numpy()
+ y_true += list(labels)
+ logits,x = model1(data)
+
+ s1 = torch.cat((s1,x),0)
+
+ s2 = torch.ones(1,2048).cuda()
+ with torch.no_grad():
+ for batch_idx, batch in enumerate(tqdm(val_loader1, desc="test")):
+
+ data = batch['data'].cuda()
+
+ labels = batch['label']
+
+
+ labels=change_sizes(labels)
+ labels=labels.detach().numpy()
+
+ logits,x1 = model2(data)
+ s2 = torch.cat((s2,x1),0)
+
+ s1 = torch.cat((s1,s2),1)
+ s1 = s1[1:,:]
+ print(s1.shape)
+ new_classifier = nn.Sequential(*list(ann1.children()))
+ print(new_classifier)
+ predicted_probs=[]
+ for i in range(s1.shape[0]):
+
+ probs = torch.sigmoid( torch.matmul( s1[i,:] , torch.transpose( ann.state_dict()['FC.weight'],0,1) ) + ann.state_dict()['FC.bias'] ).cpu().numpy()
+ probs = probs.reshape(1,-1)
+
+ predicted_probs += list(probs)
+
+
+ predicted_probs = np.asarray(predicted_probs)
+ y_predicted = (predicted_probs >= 0.5).astype(np.float32)
+
+
+ y_true = np.asarray(y_true)
+
+ print(len(y_true),len(y_predicted))
+ print( classification_report(y_true, y_predicted) )
+ report = get_classification_report(y_true, y_predicted, predicted_probs, 'aaaa')
+ print_micro_macro(report)
+
+
+# In[ ]:
+
+
+net = torch.load('/data/scenario2-1.pth')
+model = ANN(name='server').to("cuda")
+model.load_state_dict(net)
+
+
+net1 = torch.load('/data/scenario2-2.pth')
+model1 = ANN(name='server1').to("cuda")
+model1.load_state_dict(net1)
+
+net2 = torch.load('/data/scenario2-3.pth')
+model2 = ANN_S2(name='server2').to("cuda")
+model2.load_state_dict(net2)
+
+
+test(model,model1,model2)
+
--
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