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test_ensemble_vote.py
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import pickle
import torch
import slowfast.utils.metrics as metrics
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import numpy as np
import csv
import os
def get_probs(pkl_path):
with open(pkl_path, 'rb') as f:
res = pickle.load(f)
res_labels = torch.from_numpy(res['video_labels']).numpy()
res_logits = torch.from_numpy(res['video_preds'])
res_probs = res_logits.softmax(1).numpy()
return res_labels,res_probs
def topk(matrix, K, axis=1):
if axis == 0:
row_index = np.arange(matrix.shape[1 - axis])
topk_index = np.argpartition(-matrix, K, axis=axis)[0:K, :]
topk_data = matrix[topk_index, row_index]
topk_index_sort = np.argsort(-topk_data,axis=axis)
topk_data_sort = topk_data[topk_index_sort,row_index]
topk_index_sort = topk_index[0:K,:][topk_index_sort,row_index]
else:
column_index = np.arange(matrix.shape[1 - axis])[:, None]
topk_index = np.argpartition(-matrix, K, axis=axis)[:, 0:K]
topk_data = matrix[column_index, topk_index]
topk_index_sort = np.argsort(-topk_data, axis=axis)
topk_data_sort = topk_data[column_index, topk_index_sort]
topk_index_sort = topk_index[:,0:K][column_index,topk_index_sort]
return topk_data_sort, topk_index_sort
def get_em_prob(ssv2_best_probs,uni_best_probs,mvit_best_probs, slowfast_best_probs,dense_best_probs,mode):
ssv2_probs = ssv2_best_probs
ssv2_preds = ssv2_probs.argmax(1)
indx_7_6 = np.bitwise_or((ssv2_preds==6),(ssv2_preds==7))
other_indx = np.bitwise_xor(np.ones(len(ssv2_preds)).astype(np.bool),indx_7_6)
new_probs = np.zeros(ssv2_probs.shape)
uni_probs = uni_best_probs
if mode == 0 or mode == 1:
new_probs[other_indx]=ssv2_probs[other_indx]*0.6+uni_probs[other_indx]*0.1+mvit_best_probs[other_indx]*0.1+slowfast_best_probs[other_indx]*0.1+dense_best_probs[other_indx]*0.1
elif mode == 2:
new_probs[other_indx]=ssv2_probs[other_indx]*0.4+uni_probs[other_indx]*0.3+mvit_best_probs[other_indx]*0.1+slowfast_best_probs[other_indx]*0.1+dense_best_probs[other_indx]*0.1
elif mode == 3 or mode == 4:
new_probs[other_indx]=ssv2_probs[other_indx]*0.2+uni_probs[other_indx]*0.2+mvit_best_probs[other_indx]*0.2+slowfast_best_probs[other_indx]*0.2+dense_best_probs[other_indx]*0.2
new_probs[indx_7_6] = ssv2_probs[indx_7_6]*0.1 + uni_probs[indx_7_6]*0.2
new_preds = new_probs.argmax(1)
for i,_ in enumerate(new_preds):
if new_preds[i]==5 or new_preds[i]==9:
new_probs[i] = slowfast_best_probs[i]
return new_probs
def get_em_prob_4(ssv2_best_probs,uni_best_probs,mvit_best_probs, slowfast_best_probs):
ssv2_probs = ssv2_best_probs
ssv2_preds = ssv2_probs.argmax(1)
indx_7_6 = np.bitwise_or((ssv2_preds==6),(ssv2_preds==7))
other_indx = np.bitwise_xor(np.ones(len(ssv2_preds)).astype(np.bool),indx_7_6)
new_probs = np.zeros(ssv2_probs.shape)
uni_probs = uni_best_probs
new_probs[other_indx]=ssv2_probs[other_indx]*0.25+uni_probs[other_indx]*0.25+mvit_best_probs[other_indx]*0.25+slowfast_best_probs[other_indx]*0.25
new_probs[indx_7_6] = ssv2_probs[indx_7_6]*0.8 + uni_probs[indx_7_6]*0.2
new_preds = new_probs.argmax(1)
for i,_ in enumerate(new_preds):
if new_preds[i]==5 or new_preds[i]==9:
new_probs[i] = slowfast_best_probs[i]
return new_probs
def deal_preds(preds,probs):
for i in range(len(preds)):
if preds[i]==10 and probs[i][10]<=0.84:
if (probs[i][2]>=0.2): #0.28
preds[i] = 2
elif( probs[i][3]>=0.1): # 0.10
preds[i] = 3
elif (probs[i][1]>=0.1): #20
preds[i] = 1
elif (probs[i][8]>=0.2): #17 0.03
preds[i] = 8
elif (probs[i][0]>=0.01): #17 0.03
preds[i] = 0
for i in range(len(preds)):
if preds[i]==9 and probs[i][9]<=0.94:
if( probs[i][5]>=0.2): #0.12
preds[i] = 5
elif( probs[i][8]>=0.1): #0.1
preds[i] = 8
for i in range(len(preds)):
if preds[i]==8 and probs[i][9]<=0.73: #bug
if( probs[i][1]>=0.2): #0.32
preds[i] = 1
elif( probs[i][4]>=0.1): #.0.1
preds[i] = 4
elif( probs[i][0]>=0.1): #0.08
preds[i] = 0
elif( probs[i][10]>=0.7): #0.4
preds[i] = 10
for i in range(len(preds)):
if preds[i]==7 and probs[i][7]<=0.26:
if( probs[i][2]>=0.05): #0.02
preds[i] = 2
## for i in range(len(preds)):
## if preds[i]==5 and probs[i][5]<=0.6:
## if( probs[i][9]>=0.05):
## preds[i] = 9
## if( probs[i][8]>=0.01):
## preds[i] = 1
for i in range(len(preds)):
if preds[i]==4 and probs[i][4]<=0.83:
if( probs[i][5]>=0.2): #0.11
preds[i] = 5
elif (probs[i][1]>=0.1): #0.05
preds[i] = 1
elif (probs[i][10]>=0.1): #0.05
preds[i] = 10
elif (probs[i][3]>=0.05): #0.05
preds[i] = 3
for i in range(len(preds)):
if preds[i]==3 and probs[i][3]<=0.7:
if( probs[i][2]>=0.2): #0.11
preds[i] = 2
elif (probs[i][0]>=0.03): #0.05
preds[i] = 0
# for i in range(len(preds)):
# if preds[i]==2 and probs[i][2]<=0.68:
# if( probs[i][3]>=0.5): #0.11
# preds[i] = 3
# find_idx = 8
# idx_walk = (preds==find_idx)
# probs_walk = probs[idx_walk]
# labels_walk = labels[idx_walk]
# M = {}
# other = []
# for label_walk,prob_walk in zip(labels_walk,probs_walk):
# if(label_walk!=find_idx):
# other.append(prob_walk[find_idx])
# if label_walk not in M:
# M[label_walk] = []
# M[label_walk].append(prob_walk[label_walk])
# else:
# M[label_walk].append(prob_walk[label_walk])
# for k,v in M.items():
# print(k,np.mean(v))
# print(np.mean(other))
return preds
def deal_preds_v2(preds,probs):
for i in range(len(preds)):
if preds[i]==4 and probs[i][4]<=0.83:
if( probs[i][5]>=0.1): #0.12
preds[i] = 5
elif (probs[i][1]>=0.3): #0.10
preds[i] = 1
elif (probs[i][3]>=0.03): #0.03
preds[i] = 3
for i in range(len(preds)):
if preds[i]==7 and probs[i][7]<=0.26:
if( probs[i][2]>=0.05): #0.02
preds[i] = 2
for i in range(len(preds)):
if preds[i]==8 and probs[i][8]<=0.73:
if( probs[i][10]>=0.2): #0.32
preds[i] = 10
elif( probs[i][4]>=0.3): #0.22
preds[i] = 4
elif( probs[i][0]>=0.1): #0.06
preds[i] = 0
for i in range(len(preds)):
if preds[i]==9 and probs[i][9]<=0.94:
if( probs[i][5]>=0.3): #0.12
preds[i] = 5
elif( probs[i][8]>=0.05): #0.1
preds[i] = 8
for i in range(len(preds)):
if preds[i]==10 and probs[i][10]<=0.84:
if (probs[i][9]>=0.01): #0.01
preds[i] = 9
elif( probs[i][3]>=0.07): # 0.10
preds[i] = 3
elif (probs[i][1]>=0.1): #20
preds[i] = 1
elif (probs[i][0]>=0.008): #17 0.03
preds[i] = 0
# find_idx = 10
# idx_walk = (preds==find_idx)
# probs_walk = probs[idx_walk]
# labels_walk = labels[idx_walk]
# M = {}
# other = []
# for label_walk,prob_walk in zip(labels_walk,probs_walk):
# if(label_walk!=find_idx):
# other.append(prob_walk[find_idx])
# if label_walk not in M:
# M[label_walk] = []
# M[label_walk].append(prob_walk[label_walk])
# else:
# M[label_walk].append(prob_walk[label_walk])
# for k,v in M.items():
# print(k,np.mean(v))
# print(np.mean(other))
return preds
def deal_preds_v3(preds,probs):
for i in range(len(preds)):
if preds[i]==10 and probs[i][10]<=0.8:
if( probs[i][3]>=0.15): # 0.10
preds[i] = 3
elif (probs[i][1]>=0.05): #15
preds[i] = 1
elif (probs[i][8]>=0.3): #17 0.03
preds[i] = 8
elif (probs[i][0]>=0.01): #17 0.06
preds[i] = 0
for i in range(len(preds)):
if preds[i]==8 and probs[i][8]<=0.8: #bug
if( probs[i][4]>=0.05): #.0.1
preds[i] = 4
elif( probs[i][0]>=0.2): #0.08
preds[i] = 0
# for i in range(len(preds)):
# if preds[i]==7 and probs[i][7]<=0.26:
# if( probs[i][2]>=0.05): #0.02
# preds[i] = 2
for i in range(len(preds)):
if preds[i]==4 and probs[i][4]<=0.83:
if( probs[i][5]>=0.2): #0.11
preds[i] = 5
elif (probs[i][1]>=0.2): #0.05
preds[i] = 1
elif (probs[i][3]>=0.1): #0.005
preds[i] = 3
for i in range(len(preds)):
if preds[i]==3 and probs[i][3]<=0.7:
if (probs[i][0]>=0.01): #0.05
preds[i] = 0
for i in range(len(preds)):
if preds[i]==9 and probs[i][9]<=0.96:
if( probs[i][4]>=0.2): #0.01
preds[i] = 4
# elif( probs[i][4]>=0.1): #0.01
# preds[i] = 4
# elif( probs[i][8]>=0.1): #0.01
# preds[i] = 8
return preds
def deal_preds_v4(preds,probs):
for i in range(len(preds)):
if preds[i]==10 and probs[i][10]<=0.9:
if( probs[i][3]>=0.2): # 0.10
preds[i] = 3
elif (probs[i][1]>=0.05): #15
preds[i] = 1
elif (probs[i][8]>=0.3): #17 0.03
preds[i] = 8
elif (probs[i][0]>=0.008): #17 0.06
preds[i] = 0
for i in range(len(preds)):
if preds[i]==8 and probs[i][8]<=0.8: #bug
if( probs[i][4]>=0.05): #.0.1
preds[i] = 4
elif( probs[i][0]>=0.2): #0.08
preds[i] = 0
# for i in range(len(preds)):
# if preds[i]==7 and probs[i][7]<=0.26:
# if( probs[i][2]>=0.05): #0.02
# preds[i] = 2
for i in range(len(preds)):
if preds[i]==4 and probs[i][4]<=0.9:
if( probs[i][5]>=0.2): #0.11
preds[i] = 5
elif (probs[i][1]>=0.2): #0.05
preds[i] = 1
elif (probs[i][3]>=0.2): #0.005
preds[i] = 3
for i in range(len(preds)):
if preds[i]==3 and probs[i][3]<=0.7:
if (probs[i][0]>=0.001): #0.05
preds[i] = 0
for i in range(len(preds)):
if preds[i]==9 and probs[i][9]<=0.8:
if( probs[i][4]>=0.01): #0.01
preds[i] = 4
# elif( probs[i][4]>=0.1): #0.01
# preds[i] = 4
# elif( probs[i][8]>=0.1): #0.01
# preds[i] = 8
return preds
def select_thres(mode):
if mode == 0 or mode == 1 or mode == 2:
ssv2_best_path = "././exp_pseudo_stage4/uniformer_b32_ssv2/testx32x224x1x3.pkl"
uni_best_path = "./exp_pseudo_stage4/uniformer_b32_k600/testx32x224x1x3.pkl"
mvit_best_path = "./exp_pseudo_stage4/mvit_b32_k600/testx32x224x1x3.pkl"
slowfast_best_path = "./exp_pseudo_stage4/sf32_k700/testx32x224x1x3.pkl"
dense_best_path = "./exp_experts/uniformer_b32_ssv2/testx32x224x1x1.pkl"
elif mode == 3 or mode == 4:
ssv2_best_path = "./exp_pseudo_stage4/uniformer_b32_ssv2/testx32x224x3x3.pkl"
uni_best_path = "./exp_pseudo_stage4/uniformer_b32_k600/testx32x224x3x3.pkl"
mvit_best_path = "./exp_pseudo_stage4/mvit_b32_k600/testx32x224x3x3.pkl"
slowfast_best_path = "./exp_pseudo_stage4/sf32_k700/testx32x224x3x3.pkl"
dense_best_path = "./exp_experts/uniformer_b32_ssv2/testx32x224x3x3.pkl"
_, ssv2_best_probs = get_probs(ssv2_best_path)
_, uni_best_probs = get_probs(uni_best_path)
_, mvit_best_probs = get_probs(mvit_best_path)
_, slowfast_best_probs = get_probs(slowfast_best_path)
_, dense_best_probs = get_probs(dense_best_path)
probs = get_em_prob(ssv2_best_probs, uni_best_probs, mvit_best_probs, slowfast_best_probs,dense_best_probs,mode)
# probs = ssv2_best_probs
preds = probs.argmax(1)
if mode == 1:
preds = deal_preds(preds,probs)
elif mode == 2:
preds = deal_preds_v2(preds,probs)
elif mode == 3:
preds = deal_preds_v3(preds,probs)
elif mode == 4:
preds = deal_preds_v4(preds,probs)
probs = torch.from_numpy(probs).softmax(1).to(torch.float32)
return preds,probs
# print(confusion_matrix(labels, newpreds))
# print(accuracy_score(labels, newpreds))
def generate_final_sub(preds,probs,mode):
test_path = 'data/test.csv'
with open(test_path, 'r') as f:
test_lines = f.readlines()
out = open("submission/arid_pred_{}.csv".format(mode), "w", newline = "")
csv_writer = csv.writer(out, dialect = "excel")
csv_writer.writerow(['VideoID', 'Video', 'ClassID', 'Probability'])
for i in range(len(test_lines)):
file_name = test_lines[i].rstrip().split(',')[0]
csv_writer.writerow([i, file_name, preds[i], probs[i].unsqueeze(0).data])
def vote_for_sub():
prefix = './submission'
file_list = [
# 'arid_pred_0.csv',
'arid_pred_1.csv',
# 'arid_pred_2.csv',
# 'arid_pred_3.csv',
'arid_pred_4.csv', # best
]
pred_data = []
for file in file_list:
with open(os.path.join(prefix, file)) as csvfile:
pred = []
csv_reader = csv.reader(csvfile)
birth_header = next(csv_reader)
for row in csv_reader:
pred.append(row)
pred_data.append(pred)
vote_pred = []
for i in range(len(pred_data[0])):
pred = []
for j in range(len(pred_data)):
pred.append(int(pred_data[j][i][2]))
most_label = np.argmax(np.bincount(np.array(pred))) # most frequent
vote_pred.append(most_label)
out = open("arid_pred.csv", "w", newline = "")
csv_writer = csv.writer(out, dialect = "excel")
csv_writer.writerow(['VideoID', 'Video', 'ClassID', 'Probability'])
for i, label in enumerate(vote_pred):
for j in range(len(pred_data)):
if int(pred_data[j][i][2]) == label:
tmp = pred_data[j][i]
break
csv_writer.writerow(tmp)
if __name__ == '__main__':
for i in range(4):
preds,probs = select_thres(mode=i)
generate_final_sub(preds,probs,mode=i)
vote_for_sub()