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test.py
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test.py
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import torch
import numpy as np
import torch_dct as dct
import time
from MRT.Models import Transformer
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import os
from data import TESTDATA
dataset_name='mupots'
test_dataset = TESTDATA(dataset=dataset_name)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
device='cpu'
batch_size=1
model = Transformer(d_word_vec=128, d_model=128, d_inner=1024,
n_layers=3, n_head=8, d_k=64, d_v=64,device=device).to(device)
plot=False
gt=False
model.load_state_dict(torch.load('./saved_model/29.model',map_location=device))
body_edges = np.array(
[[0,1], [1,2],[2,3],[0,4],
[4,5],[5,6],[0,7],[7,8],[7,9],[9,10],[10,11],[7,12],[12,13],[13,14]]
)
losses=[]
total_loss=0
loss_list1=[]
loss_list2=[]
loss_list3=[]
with torch.no_grad():
model.eval()
loss_list=[]
for jjj,data in enumerate(test_dataloader,0):
print(jjj)
#if jjj!=20:
# continue
input_seq,output_seq=data
input_seq=torch.tensor(input_seq,dtype=torch.float32).to(device)
output_seq=torch.tensor(output_seq,dtype=torch.float32).to(device)
n_joints=int(input_seq.shape[-1]/3)
use=[input_seq.shape[1]]
input_=input_seq.view(-1,15,input_seq.shape[-1])
output_=output_seq.view(output_seq.shape[0]*output_seq.shape[1],-1,input_seq.shape[-1])
input_ = dct.dct(input_)
output__ = dct.dct(output_[:,:,:])
rec_=model.forward(input_[:,1:15,:]-input_[:,:14,:],dct.idct(input_[:,-1:,:]),input_seq,use)
rec=dct.idct(rec_)
results=output_[:,:1,:]
for i in range(1,16):
results=torch.cat([results,output_[:,:1,:]+torch.sum(rec[:,:i,:],dim=1,keepdim=True)],dim=1)
results=results[:,1:,:]
new_input_seq=torch.cat([input_seq,results.reshape(input_seq.shape)],dim=-2)
new_input=dct.dct(new_input_seq.reshape(-1,30,45))
new_rec_=model.forward(new_input[:,1:,:]-new_input[:,:-1,:],dct.idct(new_input[:,-1:,:]),new_input_seq,use)
new_rec=dct.idct(new_rec_)
new_results=new_input_seq.reshape(-1,30,45)[:,-1:,:]
for i in range(1,16):
new_results=torch.cat([new_results,new_input_seq.reshape(-1,30,45)[:,-1:,:]+torch.sum(new_rec[:,:i,:],dim=1,keepdim=True)],dim=1)
new_results=new_results[:,1:,:]
results=torch.cat([results,new_results],dim=-2)
rec=torch.cat([rec,new_rec],dim=-2)
results=output_[:,:1,:]
for i in range(1,16+15):
results=torch.cat([results,output_[:,:1,:]+torch.sum(rec[:,:i,:],dim=1,keepdim=True)],dim=1)
results=results[:,1:,:]
new_new_input_seq=torch.cat([input_seq,results.unsqueeze(0)],dim=-2)
new_new_input=dct.dct(new_new_input_seq.reshape(-1,45,45))
new_new_rec_=model.forward(new_new_input[:,1:,:]-new_new_input[:,:-1,:],dct.idct(new_new_input[:,-1:,:]),new_new_input_seq,use)
new_new_rec=dct.idct(new_new_rec_)
rec=torch.cat([rec,new_new_rec],dim=-2)
results=output_[:,:1,:]
for i in range(1,31+15):
results=torch.cat([results,output_[:,:1,:]+torch.sum(rec[:,:i,:],dim=1,keepdim=True)],dim=1)
results=results[:,1:,:]
prediction_1=results[:,:15,:].view(results.shape[0],-1,n_joints,3)
prediction_2=results[:,:30,:].view(results.shape[0],-1,n_joints,3)
prediction_3=results[:,:45,:].view(results.shape[0],-1,n_joints,3)
gt_1=output_seq[0][:,1:16,:].view(results.shape[0],-1,n_joints,3)
gt_2=output_seq[0][:,1:31,:].view(results.shape[0],-1,n_joints,3)
gt_3=output_seq[0][:,1:46,:].view(results.shape[0],-1,n_joints,3)
if dataset_name=='mocap':
#match the scale with the paper, also see line 63 in mix_mocap.py
loss1=torch.sqrt(((prediction_1/1.8 - gt_1/1.8) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
loss2=torch.sqrt(((prediction_2/1.8 - gt_2/1.8) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
loss3=torch.sqrt(((prediction_3/1.8 - gt_3/1.8) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
#pose with align
# loss1=torch.sqrt((((prediction_1 - prediction_1[:,:,0:1,:] - gt_1 + gt_1[:,:,0:1,:])/1.8) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
# loss2=torch.sqrt((((prediction_2 - prediction_2[:,:,0:1,:] - gt_2 + gt_2[:,:,0:1,:])/1.8) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
# loss3=torch.sqrt((((prediction_3 - prediction_3[:,:,0:1,:] - gt_3 + gt_3[:,:,0:1,:])/1.8) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
if dataset_name=='mupots':
loss1=torch.sqrt(((prediction_1 - gt_1) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
loss2=torch.sqrt(((prediction_2 - gt_2) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
loss3=torch.sqrt(((prediction_3 - gt_3) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
#pose with align
# loss1=torch.sqrt(((prediction_1 - prediction_1[:,:,0:1,:] - gt_1 + gt_1[:,:,0:1,:]) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
# loss2=torch.sqrt(((prediction_2 - prediction_2[:,:,0:1,:] - gt_2 + gt_2[:,:,0:1,:]) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
# loss3=torch.sqrt(((prediction_3 - prediction_3[:,:,0:1,:] - gt_3 + gt_3[:,:,0:1,:]) ** 2).sum(dim=-1)).mean(dim=-1).mean(dim=-1).numpy().tolist()
loss_list1.append(np.mean(loss1))#+loss1
loss_list2.append(np.mean(loss2))#+loss2
loss_list3.append(np.mean(loss3))#+loss3
loss=torch.mean((rec[:,:,:]-(output_[:,1:46,:]-output_[:,:45,:]))**2)
losses.append(loss)
rec=results[:,:,:]
rec=rec.reshape(results.shape[0],-1,n_joints,3)
input_seq=input_seq.view(results.shape[0],15,n_joints,3)
pred=torch.cat([input_seq,rec],dim=1)
output_seq=output_seq.view(results.shape[0],-1,n_joints,3)[:,1:,:,:]
all_seq=torch.cat([input_seq,output_seq],dim=1)
pred=pred[:,:,:,:].cpu()
all_seq=all_seq[:,:,:,:].cpu()
if plot:
fig = plt.figure(figsize=(10, 4.5))
fig.tight_layout()
ax = fig.add_subplot(111, projection='3d')
plt.ion()
length=45+15
length_=45+15
i=0
p_x=np.linspace(-10,10,15)
p_y=np.linspace(-10,10,15)
X,Y=np.meshgrid(p_x,p_y)
while i < length_:
ax.lines = []
for x_i in range(p_x.shape[0]):
temp_x=[p_x[x_i],p_x[x_i]]
temp_y=[p_y[0],p_y[-1]]
z=[0,0]
ax.plot(temp_x,temp_y,z,color='black',alpha=0.1)
for y_i in range(p_x.shape[0]):
temp_x=[p_x[0],p_x[-1]]
temp_y=[p_y[y_i],p_y[y_i]]
z=[0,0]
ax.plot(temp_x,temp_y,z,color='black',alpha=0.1)
for j in range(results.shape[0]):
xs=pred[j,i,:,0].numpy()
ys=pred[j,i,:,1].numpy()
zs=pred[j,i,:,2].numpy()
alpha=1
ax.plot( zs,xs, ys, 'y.',alpha=alpha)
if gt:
x=all_seq[j,i,:,0].numpy()
y=all_seq[j,i,:,1].numpy()
z=all_seq[j,i,:,2].numpy()
ax.plot( z,x, y, 'y.')
plot_edge=True
if plot_edge:
for edge in body_edges:
x=[pred[j,i,edge[0],0],pred[j,i,edge[1],0]]
y=[pred[j,i,edge[0],1],pred[j,i,edge[1],1]]
z=[pred[j,i,edge[0],2],pred[j,i,edge[1],2]]
if i>=15:
ax.plot(z,x, y, zdir='z',c='blue',alpha=alpha)
else:
ax.plot(z,x, y, zdir='z',c='green',alpha=alpha)
if gt:
x=[all_seq[j,i,edge[0],0],all_seq[j,i,edge[1],0]]
y=[all_seq[j,i,edge[0],1],all_seq[j,i,edge[1],1]]
z=[all_seq[j,i,edge[0],2],all_seq[j,i,edge[1],2]]
if i>=15:
ax.plot( z,x, y, 'yellow',alpha=0.8)
else:
ax.plot( z, x, y, 'green')
ax.set_xlim3d([-3 , 3])
ax.set_ylim3d([-3 , 3])
ax.set_zlim3d([0,3])
# ax.set_xlim3d([-8 , 8])
# ax.set_ylim3d([-8 , 8])
# ax.set_zlim3d([0,5])
# ax.set_xticklabels([])
# ax.set_yticklabels([])
# ax.set_zticklabels([])
ax.set_axis_off()
#ax.patch.set_alpha(1)
#ax.set_aspect('equal')
#ax.set_xlabel("x")
#ax.set_ylabel("y")
#ax.set_zlabel("z")
plt.title(str(i),y=-0.1)
plt.pause(0.1)
i += 1
plt.ioff()
plt.show()
plt.close()
print('avg 1 second',np.mean(loss_list1))
print('avg 2 seconds',np.mean(loss_list2))
print('avg 3 seconds',np.mean(loss_list3))