-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_quantizedTF.py
465 lines (390 loc) · 16.8 KB
/
test_quantizedTF.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
import argparse
import baselineUtils
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import os
import time
from transformer.batch import subsequent_mask
from torch.optim import Adam,SGD,RMSprop,Adagrad
from transformer.noam_opt import NoamOpt
import numpy as np
import scipy.io
import json
import pickle
import matplotlib.pyplot as plt
from torch.utils.tensorboard import SummaryWriter
import quantized_TF
def diversity_metric(samples):
#print(len(samples))
final_norm = 0
c = 0
#print(samples)
for i in samples:
#print('know')
temp_min = np.inf
for j in samples:
if np.sum(i-j) == 0 :
pass
else:
norm = np.linalg.norm(i-j)
if norm < temp_min:
temp_min = norm
if temp_min == np.inf:
c = c+1
else:
#print(temp_min)
final_norm += temp_min
#print('final_norm_step', final_norm/(len(samples)))
return final_norm/(len(samples)) #you have to divide
def our_diversity_metric(preds_dict):
final_div = 0
for key,values in preds_dict.items() :
final_div += diversity_metric(values)
#print(final_div)
#print(final_div)
return final_div/len(preds_dict), final_div
def get_min_distance_ADE(list_arr1, list_arr2) :
#list_arr2 is the predicted
#list_arr1 is the GT
list_min = []
for arr in list_arr1 :
#print(arr.shape[0])
min_dist = np.inf
for arr2 in list_arr2 :
# print(np.sum((arr - arr2)**2))
#print(arr-arr2)
dist = np.sum((arr - arr2)**2)
if dist < min_dist:
min_dist = dist
list_min.append(min_dist)
#print(list_min)
#print(np.sum(list_min))
#print(len(list_min))
#print(np.sum(list_min) / (len(list_min) * arr.shape[0]))
return np.sum(list_min) / (len(list_min) * arr.shape[0])
'''
add comment
'''
def get_min_distance_FDE(list_arr1, list_arr2) :
list_min = []
for arr in list_arr1 :
#print(arr.shape[0])
min_dist = np.inf
for arr2 in list_arr2 :
dist = np.sum((arr[-1,:] - arr2[-1,:])**2)
if dist < min_dist:
min_dist = dist
list_min.append(min_dist)
return np.sum(list_min) / len(list_min)
'''
add comment
'''
def get_metrics_ADEandFDE(gts_dict, prs_dict):
dict_of_metrics = {}
FDE = 0
ADE = 0
for key,values in gts_dict.items() :
FDE += get_min_distance_ADE(values, prs_dict[key])
ADE += get_min_distance_FDE(values, prs_dict[key])
#now take average
scaling = len(gts_dict)
return FDE/scaling, ADE/scaling, FDE, ADE
def main():
parser=argparse.ArgumentParser(description='Train the individual Transformer model')
parser.add_argument('--dataset_folder',type=str,default='datasets')
parser.add_argument('--dataset_name',type=str,default='zara1')
parser.add_argument('--obs',type=int,default=8)
parser.add_argument('--preds',type=int,default=12)
parser.add_argument('--emb_size',type=int,default=512)
parser.add_argument('--heads',type=int, default=8)
parser.add_argument('--layers',type=int,default=6)
parser.add_argument('--cpu',action='store_true')
parser.add_argument('--verbose',action='store_true')
parser.add_argument('--batch_size',type=int,default=256)
parser.add_argument('--delim',type=str,default='\t')
parser.add_argument('--name', type=str, default="zara2")
parser.add_argument('--epoch',type=str,default="00018")
parser.add_argument('--num_samples', type=int, default="21")
args=parser.parse_args()
model_name=args.name
try:
os.mkdir('models')
except:
pass
try:
os.mkdir('output')
except:
pass
try:
os.mkdir('output/QuantizedTF')
except:
pass
try:
os.mkdir(f'models/QuantizedTF')
except:
pass
try:
os.mkdir(f'output/QuantizedTF/{args.name}')
except:
pass
try:
os.mkdir(f'models/QuantizedTF/{args.name}')
except:
pass
#log=SummaryWriter('logs/%s'%model_name)
# log.add_scalar('eval/mad', 0, 0)
# log.add_scalar('eval/fad', 0, 0)
device=torch.device("cuda")
if args.cpu or not torch.cuda.is_available():
device=torch.device("cpu")
args.verbose=True
## creation of the dataloaders for train and validation
test_dataset,_ = baselineUtils.create_dataset(args.dataset_folder,args.dataset_name,0,args.obs,args.preds,delim=args.delim,train=False,eval=True,verbose=args.verbose)
mat = scipy.io.loadmat(os.path.join(args.dataset_folder, args.dataset_name, "clusters.mat"))
clusters=mat['centroids']
model=quantized_TF.QuantizedTF(clusters.shape[0], clusters.shape[0]+1, clusters.shape[0], N=args.layers,
d_model=args.emb_size, d_ff=1024, h=args.heads).to(device)
model.load_state_dict(torch.load(f'models/QuantizedTF/{args.name}/{args.epoch}.pth'))
model.to(device)
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0)
#optim = SGD(list(a.parameters())+list(model.parameters())+list(generator.parameters()),lr=0.01)
#sched=torch.optim.lr_scheduler.StepLR(optim,0.0005)
# DETERMINISTIC MODE
with torch.no_grad():
model.eval()
gt=[]
pr=[]
inp_=[]
peds=[]
frames=[]
dt=[]
for id_b,batch in enumerate(test_dl):
print(f"batch {id_b:03d}/{len(test_dl)}")
#print('batch', batch)
peds.append(batch['peds'])
frames.append(batch['frames'])
dt.append(batch['dataset'])
scale = np.random.uniform(0.5, 2)
# rot_mat = np.array([[np.cos(r), np.sin(r)], [-np.sin(r), np.cos(r)]])
n_in_batch = batch['src'].shape[0]
speeds_inp = batch['src'][:, 1:, 2:4]
gt_b = batch['trg'][:, :, 0:2]
inp = torch.tensor(
scipy.spatial.distance.cdist(speeds_inp.reshape(-1, 2), clusters).argmin(axis=1).reshape(n_in_batch,
-1)).to(
device)
src_att = torch.ones((inp.shape[0], 1,inp.shape[1])).to(device)
start_of_seq = torch.tensor([clusters.shape[0]]).repeat(n_in_batch).unsqueeze(1).to(device)
dec_inp = start_of_seq
for i in range(args.preds):
trg_att = subsequent_mask(dec_inp.shape[1]).repeat(n_in_batch, 1, 1).to(device)
out = model(inp, dec_inp, src_att, trg_att)
print(out.shape)
#print('out', out)
#print('out-1', out[:,-1,:])
#print('out-1', out[:,-1,:].shape)
dec_inp=torch.cat((dec_inp,out[:,-1:].argmax(dim=2)),1)
#print('dec_inp', dec_inp)
#print("out -1argmaxxxxxxxxxxxxx", out[:,-1:].argmax(dim=2))
#print(out[:,-1:].argmax(dim=2).shape)
preds_tr_b=clusters[dec_inp[:,1:].cpu().numpy()].cumsum(1)+batch['src'][:,-1:,0:2].cpu().numpy()
# print('dec_inp[:,1:].cpu().numpy()', dec_inp[:,1:].cpu().numpy())
#print('dec_inp[:,1:].cpu().numpy()', dec_inp[:,1:].cpu().numpy().shape)
#print(' cluster dec_inp[:,1:].cpu().numpy()', clusters[dec_inp[:,1:].cpu().numpy()])
#print('dec_inp[:,1:].cpu().numpy()cumsum', dec_inp[:,1:].cpu().numpy().cumsum(1))
gt.append(gt_b)
pr.append(preds_tr_b)
#print("pr", pr)
peds=np.concatenate(peds,0)
frames=np.concatenate(frames,0)
dt=np.concatenate(dt,0)
gt=np.concatenate(gt,0)
dt_names=test_dataset.data['dataset_name']
pr=np.concatenate(pr,0)
mad,fad,errs=baselineUtils.distance_metrics(gt,pr)
#log.add_scalar('eval/DET_mad', mad, epoch)
#log.add_scalar('eval/DET_fad', fad, epoch)
scipy.io.savemat(f"output/QuantizedTF/{args.name}/MM_deterministic.mat",{'input':inp,'gt':gt,'pr':pr,'peds':peds,'frames':frames,'dt':dt,'dt_names':dt_names})
print("Determinitic:")
print("mad: %6.3f"%mad)
print("fad: %6.3f" % fad)
print('MULITMODALITY NOW')
# MULTI MODALITY
num_samples=args.num_samples
print("num_samples", num_samples)
#print("Entered multi modality")
model.eval()
gt=[]
pr_all={}
inp_=[]
peds=[]
frames=[]
dt=[]
cluster_num = {} ##added by nina
#fill an empty dictionary with the future predictions sample per sample
for sam in range(num_samples):
pr_all[sam]=[]
cluster_num[sam] = []
#now loop in the batches
for id_b,batch in enumerate(test_dl):
# print('batch', batch)
#print(f"batch {id_b:03d}/{len(test_dl)}")
peds.append(batch['peds'])
#print("peds", peds)
#print("peds shape", len(peds))
frames.append(batch['frames'])
dt.append(batch['dataset'])
scale = np.random.uniform(0.5, 2)
# rot_mat = np.array([[np.cos(r), np.sin(r)], [-np.sin(r), np.cos(r)]])
n_in_batch = batch['src'].shape[0]
#print("batch_src", batch['src'].shape)
speeds_inp = batch['src'][:, 1:, 2:4]
gt_b = batch['trg'][:, :, 0:2]
gt.append(gt_b)
inp__=batch['src'][:,:,0:2]
inp_.append(inp__)
inp = torch.tensor(
scipy.spatial.distance.cdist(speeds_inp.reshape(-1, 2), clusters).argmin(axis=1).reshape(n_in_batch,
-1)).to(
device)
src_att = torch.ones((inp.shape[0], 1,inp.shape[1])).to(device)
start_of_seq = torch.tensor([clusters.shape[0]]).repeat(n_in_batch).unsqueeze(1).to(device)
#print('start of seq' , start_of_seq.shape)
#a for in the samples
for sam in range(num_samples):
dec_inp = start_of_seq #random inizialization so dec_inp[:,1] is random!!!! dim 1024, 1
#print('start_of_seq', start_of_seq.shape)
#for i in 12(future points)
for i in range(args.preds):
#the mask for decoder
#print('i', i)
#print('first dec inpppppp', dec_inp.shape[1])
trg_att = subsequent_mask(dec_inp.shape[1]).repeat(n_in_batch, 1, 1).to(device)
#predict the outcome, that is iof dimension 1024, i, 1000
out = model.predict(inp, dec_inp, src_att, trg_att)
#print('out.shapeeeeeeeeeee', out)
#now We take only the last predicted probabilities
h=out[:,-1] #just take the class here
#h is a the probs for the future_time i of the batch
print('h.shape', h.shape)
print('out.shape', out.shape)
#print(torch.multinomial(h,1).shape)
dec_inp=torch.cat((dec_inp,torch.multinomial(h,1)),1)
#print('last dec inppppppppp', dec_inp )
#dec_inp=torch.cat((dec_inp,out[:,-1:].argmax(dim=2)),1)
print('torch.multinomial(h,1))', torch.multinomial(h,1).shape)
#print('last dec inppppppppp', dec_inp.shape )
# print('dec_inp', dec_inp.shape)
#print('dec_inp', dec_inp.shape)
#print('dec_inp[:,1:]',dec_inp[:,1:].shape)
#print('batch[\'src\']', batch['src'][:,-1:,0:2])
#print('hereeeeeeeeeeeeeeeeee' ,clusters[dec_inp[:,1:].cpu().numpy()])
preds_tr_b=clusters[dec_inp[:,1:].cpu().numpy()].cumsum(1)+batch['src'][:,-1:,0:2].cpu().numpy()
# print('preds_tr_b', preds_tr_b.shape)
#print("clusters" , clusters.shape)
pr_all[sam].append(preds_tr_b)
#print("pr_all", pr_all)
#print("pr_all_shape", pr_all[0][0].shape)
#print("cluster_num", cluster_num[0].shape)
#print(pr_all[0])
#print("pr_all_shape1", len(pr_all[0]))
#print('clusters.shape', clusters.shape)
peds=np.concatenate(peds,0)
frames=np.concatenate(frames,0)
dt=np.concatenate(dt,0)
#print('gt', gt)
gt=np.concatenate(gt,0)
#print('looooooooooooooooook hereeeeeeee')
# print('gt', gt)
# print('gt_shape' , gt.shape)
dt_names=test_dataset.data['dataset_name']
#pr=np.concatenate(pr,0)
inp=np.concatenate(inp_,0)
samp = {}
print('####################################################')
print('keys')
#print(pr_all.keys())
print('####################################################')
for k in pr_all.keys():
# print('pr', pr_all[k])
#print('pr_shape0', pr_all[k][0].shape)
#print('pr_shape0', pr_all[k][1].shape)
#print('pr_shape0', pr_all[k][2].shape)
#print('pr_shape', len(pr_all[k]))
#print(np.concatenate((pr_all[k][0],pr_all[k][1], pr_all[k][2])).shape)
samp[k] = {}
#samp[k]['pr'] = np.concatenate(pr_all[k], 0)
samp[k]['pr'] = np.concatenate((pr_all[k][0],pr_all[k][1], pr_all[k][2]))
#print(samp[k]['pr'])
#dict = {'horse' : [traj1, traj2, traj3]}
# fun (9f)
#GT = {'horse': [traj1]}
samp[k]['mad'], samp[k]['fad'], samp[k]['err'] = baselineUtils.distance_metrics(gt, samp[k]['pr'])
#print(samp[0].keys)
#print(len(samp[0]['pr']))
my_dict = {}
for i in range(len(samp[0]['pr'])):
my_dict[str(i)] = []
for item in range(len(samp[0]['pr'])):
for i in range(len(pr_all.keys())):
my_dict[str(item)].append(samp[i]['pr'][item])
dict_of_noisy_GT = {}
for i in range(n_in_batch):
dict_of_noisy_GT[str(i)]= [gt_b[i].numpy()]
How_many_to_noise = len(my_dict['0'])
#print('1 GT', dict_of_noisy_GT['33'])
for key, value in dict_of_noisy_GT.items():
for i in range(How_many_to_noise - 1):
noise = np.random.normal(0, 1, dict_of_noisy_GT[key][0].shape)
new_signal = dict_of_noisy_GT[key][0] + noise
dict_of_noisy_GT[key].append(new_signal)
ADE, FDE, ADE_unsc, FDE_unsc = get_metrics_ADEandFDE(dict_of_noisy_GT, my_dict)
print('##########################################')
print ('plots')
plt.plot(*dict_of_noisy_GT['33'][0].T, linestyle='-', c = 'g')
for i in range(len(my_dict['33'])) :
plt.plot(*my_dict['33'][i].T, linestyle='-', c = 'r')
plt.show()
print('temporary ADE', ADE)
print('temporary FDE', FDE)
#print(len(my_dict))
#print(my_dict['33'])
print('##########################################')
print('The final ADE in average is: ', ADE_unsc/len(my_dict) )
print('The final FDE in average is: ', FDE_unsc / len(my_dict) )
print('the average diversity is', our_diversity_metric(my_dict)[0])
print('##########################################')
ev = [samp[i]['err'] for i in range(num_samples)]
e20 = np.stack(ev, -1)
mad_samp=e20.mean(1).min(-1).mean()
fad_samp=e20[:,-1].min(-1).mean()
#mad,fad,errs=baselineUtils.distance_metrics(gt,pr)
#log.add_scalar('eval/MM_mad', mad_samp, epoch)
#log.add_scalar('eval/MM_fad', fad_samp, epoch)
preds_all_fin=np.stack(list([samp[i]['pr'] for i in range(num_samples)]),-1)
scipy.io.savemat(f"output/QuantizedTF/{args.name}/MM_{num_samples}.mat",{'input':inp,'gt':gt,'pr':preds_all_fin,'peds':peds,'frames':frames,'dt':dt,'dt_names':dt_names})
print("Determinitic:")
print("mad: %6.3f"%mad)
print("fad: %6.3f" % fad)
print("Multimodality:")
print("mad: %6.3f"%mad_samp)
print("fad: %6.3f" % fad_samp)
#here thee is our new and powerful code
#all rights reserved
'''
HERE DPP sampling (yea sure..)
'''
#1) take 1 probability vector (e.g. vec = [.2,.3,.5])
#2) define a trainable sampling function DSF (e.g. depends on some ***parameter***!)
#3) sample with the trainable DSF from vec
#4) the samples are the z1, ...zN in paper (e.g are the #num of cluster chosen)
#5) you transform z1,...zN in coordinates x1 =(x_1,y_1), ...xN=(x_N,y_N) as in paper notation
#6) Define S, r as in Paper, then L(***params***)
#7) compute loss
#8) with Gdsecent update ***parameters*** of DSF until convergence
#9) after convergence just sample stuff and enjoy free nap.
#10) hot chocolate!
if __name__=='__main__':
main()