def eval(model, split, seq_length, n_cpu, disp):
dataset = GolfDB(data_file='data/val_split_{}.pkl'.format(split),
vid_dir='data/videos_160/',
seq_length=seq_length,
transform=transforms.Compose([ToTensor(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
train=False)
data_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=n_cpu,
drop_last=False)
correct = []
delta = []
tolerance = []
predictions = []
predictions_original = []
ground_truth = []
for i, sample in enumerate(data_loader):
images, labels = sample['images'][:,::step,:,:,:], sample['labels']
# full samples do not fit into GPU memory so evaluate sample in 'seq_length' batches
seq_length_new = int(np.ceil(seq_length/step))
batch = 0
while batch * (seq_length_new-1) < images.shape[1]-1:
if (batch + 1) * (seq_length_new-1) + 1 > images.shape[1]:
image_batch = images[:, batch * (seq_length_new-1):, :, :, :]
else:
image_batch = images[:, batch * (seq_length_new-1):(batch + 1) * (seq_length_new-1)+1, :, :, :]
logits = model(image_batch.to(device))
if batch == 0:
probs = F.softmax(logits.data, dim=1).cpu().numpy()
else:
probs = np.append(probs[:-1], F.softmax(logits.data, dim=1).cpu().numpy(), 0)
batch += 1
gt, pp, deltas, tol, c, original = correct_preds(probs, labels.squeeze())
if disp:
print(i, c)
correct.append(c)
tolerance.append(tol)
delta.append(deltas)
predictions.append(pp)
ground_truth.append(gt)
predictions_original.append(original)
np.savez_compressed('results/' + version_name + '.npz', array1=np.array(delta), array2=np.array(predictions),
array3=np.array(tolerance), array4=np.array(ground_truth),
array5=np.array(predictions_original))
print(np.round(np.mean(np.array(correct),0),3))
print(np.round(np.sqrt(np.mean(np.square(np.array(delta)/np.array(tolerance)[:,np.newaxis]),0)),3))
print(np.round(np.std(np.array(delta)/np.array(tolerance)[:,np.newaxis],0),3))
PCE = np.mean(correct)
return PCE
def eval(model, split, seq_length, n_cpu, disp):
dataset = StsqDB(data_file='val_split_{}.pkl'.format(split),
vid_dir='data/videos_40/',
seq_length=seq_length,
transform=transforms.Compose([
ToTensor(),
Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]),
train=False)
data_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=n_cpu,
drop_last=False)
correct = []
for i, sample in enumerate(data_loader):
images, labels = sample['images'], sample['labels']
# full samples do not fit into GPU memory so evaluate sample in 'seq_length' batches
batch = 0
while batch * seq_length < images.shape[1]:
if (batch + 1) * seq_length > images.shape[1]:
image_batch = images[:, batch * seq_length:, :, :, :]
else:
image_batch = images[:, batch * seq_length:(batch + 1) *
seq_length, :, :, :]
logits = model(image_batch.to(device))
if batch == 0:
probs = F.softmax(logits.data, dim=1).to(device).numpy()
else:
probs = np.append(
probs,
F.softmax(logits.data, dim=1).to(device).numpy(), 0)
batch += 1
_, _, _, _, c = correct_preds(probs, labels.squeeze())
if disp:
print(i, c)
correct.append(c)
PCE = np.mean(correct)
return PCE
def eval(model, split, seq_length, bs, n_cpu, disp):
if use_no_element == False:
dataset = StsqDB(
data_file='data/no_ele/seq_length_{}/val_split_{}.pkl'.format(
int(seq_length), split),
vid_dir='data/videos_40/',
seq_length=int(seq_length),
transform=transforms.Compose([
ToTensor(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
train=False)
else:
dataset = StsqDB(
data_file='data/seq_length_{}/train_split_{}.pkl'.format(
args.seq_length, args.split),
vid_dir='data/videos_40/',
seq_length=int(seq_length),
transform=transforms.Compose([
ToTensor(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
train=True)
data_loader = DataLoader(dataset,
batch_size=int(bs),
shuffle=False,
num_workers=n_cpu,
drop_last=True)
correct = []
if use_no_element == False:
element_correct = [[] for i in range(12)]
element_sum = [[] for i in range(12)]
confusion_matrix = np.zeros([12, 12], int)
else:
element_correct = [[] for i in range(13)]
element_sum = [[] for i in range(13)]
confusion_matrix = np.zeros([13, 13], int)
for i, sample in enumerate(data_loader):
images, labels = sample['images'].to(device), sample['labels'].to(
device)
logits = model(images)
probs = F.softmax(logits.data, dim=1) ##確率
labels = labels.view(int(bs) * int(seq_length))
_, c, element_c, element_s, conf = correct_preds(
probs, labels.squeeze())
if disp:
print(i, c)
correct.append(c)
for j in range(len(element_c)):
element_correct[j].append(element_c[j])
for j in range(len(element_s)):
element_sum[j].append(element_s[j])
confusion_matrix = confusion_matrix + conf
PCE = np.mean(correct)
all_element_correct = np.sum(element_correct, axis=1)
all_element_sum = np.sum(element_sum, axis=1)
element_PCE = all_element_correct / all_element_sum
return PCE, element_PCE, all_element_correct, all_element_sum, confusion_matrix
def myeval(model, split, seq_length, n_cpu, disp, stream_choice=0):
# summaryFile = open("summary_opt_{}.txt".format(split),"w")
videosNum = 0 # 统计验证集的视频数量
if cfg.FRAME_13_OPEN:
dataset = GolfDB_13(data_file=cfg.OUR_PKL_FILE_PATH,
json_dir=cfg.VAL_JSON_PATH,
dataloader_opt=cfg.DATAOPT,
seq_length=64,
train=False)
else:
if stream_choice == 1:
# 评价非光流法
dataset = GolfDB(
data_file='/home/zqr/codes/GolfDB/data/val_split_{}.pkl'.
format(split),
vid_dir=cfg.VIDEO_160_PATH,
seq_length=seq_length,
transform=None,
myMean=[0.485, 0.456, 0.406],
myStd=[0.229, 0.224, 0.225],
train=False)
else: # 评价光流法
dataset = GolfDB_T(
data_file='/home/zqr/codes/GolfDB/data/val_split_{}.pkl'.
format(split),
vid_dir=cfg.OPT_RESIZE_FILE_PATH,
seq_length=seq_length,
train=False)
data_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=n_cpu,
drop_last=False)
correct = []
# 这三个是为了做融合用
all_probs = []
all_tols = []
all_events = []
for i, sample in enumerate(data_loader):
videosNum += 1
images, labels = sample['images'], sample['labels']
# full samples do not fit into GPU memory so evaluate sample in 'seq_length' batches
batch = 0
while batch * seq_length < images.shape[1]:
if (batch + 1) * seq_length > images.shape[1]:
image_batch = images[:, batch * seq_length:, :, :, :]
else:
image_batch = images[:, batch * seq_length:(batch + 1) *
seq_length, :, :, :]
logits = model(image_batch.cuda())
if batch == 0:
probs = F.softmax(logits.data, dim=1).cpu().numpy()
else:
probs = np.append(probs,
F.softmax(logits.data, dim=1).cpu().numpy(),
0)
batch += 1
events, preds, _, tol, c = correct_preds(probs, labels.squeeze())
all_probs.append(probs)
all_tols.append(tol)
all_events.append(events)
# 统计信息
# for i, item in enumerate(c):
# if c[i] == 0:
# summary[i] += 1
# if c[0] ==0 or c[7]==0:
# info = str((preds - events).tolist())
# summaryFile.write(info)
# summaryFile.write(' ')
# summaryFile.write(tol)
# summaryFile.write('\n')
# else:
# summaryFile.write('\n')
if disp:
# print(i, c)
print("ground truth:")
print(events)
print("preds:")
print(preds)
correct.append(c)
PFCR = np.mean(correct, axis=0)
PCE = np.mean(correct)
print("PCE:")
print(PCE)
# summaryFile.close()
return PCE, videosNum, all_probs, all_tols, all_events, PFCR
def eval(model, split, seq_length, n_cpu, disp, steps=1):
print("------in function")
if not _video_interpolation:
dataset = GolfDB(data_file='data/val_split_{}.pkl'.format(split),
vid_dir='data/videos_160/',
seq_length=seq_length,
transform=transforms.Compose([
ToTensor(),
Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]),
train=False)
else:
dataset = GolfDB(data_file='data/val_split_{}.pkl'.format(split),
vid_dir='data/videos_160/'.replace(
'videos_160',
'videos_downsampled_' + str(steps) + 'x'),
seq_length=seq_length,
transform=transforms.Compose([
ToTensor(),
Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]),
train=False)
steps = 1
data_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=n_cpu,
drop_last=False)
idx_keep = np.arange(0, seq_length, steps)
idx_erase = np.delete(np.arange(0, seq_length), idx_keep)
correct = []
for i, sample in enumerate(data_loader):
images, labels = sample['images'], sample['labels']
if steps > 1:
#### Downsample video (temporally)
images[:,
idx_erase, :, :, :] = images[:,
np.repeat(idx_keep, steps -
1)[:len(idx_erase
)], :, :, :]
# full samples do not fit into GPU memory so evaluate sample in 'seq_length' batches
batch = 0
while batch * seq_length < images.shape[1]:
if (batch + 1) * seq_length > images.shape[1]:
image_batch = images[:, batch * seq_length:, :, :, :]
else:
image_batch = images[:, batch * seq_length:(batch + 1) *
seq_length, :, :, :]
logits = model(image_batch.cuda())
if batch == 0:
probs = F.softmax(logits.data, dim=1).cpu().numpy()
else:
probs = np.append(probs,
F.softmax(logits.data, dim=1).cpu().numpy(),
0)
batch += 1
if i == 176:
print('hello')
_, _, _, _, c = correct_preds(probs, labels.squeeze())
if disp:
print(i, c)
correct.append(c)
PCE = np.mean(correct)
return PCE
