def get_transformations(p):
""" Return transformations for training and evaluationg """
from data import custom_transforms as tr
db_name = p['train_db_name']
__imagenet_pca = {
'eigval':
torch.Tensor([0.2175, 0.0188, 0.0045]),
'eigvec':
torch.Tensor([
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203],
])
}
# Training transformations
# Horizontal flips with probability of 0.5
transforms_tr = [tr.RandomHorizontalFlip()]
# Fixed Resize to input resolution
transforms_tr.extend([
tr.FixedResize(resolutions={
x: tuple(p.TRAIN.SCALE)
for x in p.ALL_TASKS.FLAGVALS
},
flagvals={
x: p.ALL_TASKS.FLAGVALS[x]
for x in p.ALL_TASKS.FLAGVALS
})
])
transforms_tr.extend([tr.AddIgnoreRegions(), tr.ToTensor()])
transforms_tr.extend(
[tr.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
transforms_tr = transforms.Compose(transforms_tr)
# Testing (during training transforms)
transforms_ts = []
transforms_ts.extend([
tr.FixedResize(
resolutions={
x: tuple(p.TRAIN.SCALE)
for x in p.ALL_TASKS.FLAGVALS
},
flagvals={x: p.TASKS.FLAGVALS[x]
for x in p.TASKS.FLAGVALS})
])
transforms_ts.extend([
tr.AddIgnoreRegions(),
tr.ToTensor(),
tr.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transforms_ts = transforms.Compose(transforms_ts)
return transforms_tr, transforms_ts
def get_transformations(p):
""" Return transformations for training and evaluationg """
from data import custom_transforms as tr
# Training transformations
# Horizontal flips with probability of 0.5
transforms_tr = [tr.RandomHorizontalFlip()]
# Rotations and scaling
transforms_tr.extend([
tr.ScaleNRotate(rots=(-20, 20),
scales=(.75, 1.25),
flagvals={
x: p.ALL_TASKS.FLAGVALS[x]
for x in p.ALL_TASKS.FLAGVALS
})
])
# Fixed Resize to input resolution
transforms_tr.extend([
tr.FixedResize(resolutions={
x: tuple(p.TRAIN.SCALE)
for x in p.ALL_TASKS.FLAGVALS
},
flagvals={
x: p.ALL_TASKS.FLAGVALS[x]
for x in p.ALL_TASKS.FLAGVALS
})
])
transforms_tr.extend([
tr.AddIgnoreRegions(),
tr.ToTensor(),
tr.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transforms_tr = transforms.Compose(transforms_tr)
# Testing (during training transforms)
transforms_ts = []
transforms_ts.extend([
tr.FixedResize(
resolutions={x: tuple(p.TEST.SCALE)
for x in p.TASKS.FLAGVALS},
flagvals={x: p.TASKS.FLAGVALS[x]
for x in p.TASKS.FLAGVALS})
])
transforms_ts.extend([
tr.AddIgnoreRegions(),
tr.ToTensor(),
tr.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transforms_ts = transforms.Compose(transforms_ts)
return transforms_tr, transforms_ts
def test_mt():
import torch
import data.custom_transforms as tr
import matplotlib.pyplot as plt
from torchvision import transforms
transform = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.ScaleNRotate(rots=(-2, 2),
scales=(.75, 1.25),
flagvals={
'image': cv2.INTER_CUBIC,
'edge': cv2.INTER_NEAREST,
'semseg': cv2.INTER_NEAREST,
'normals': cv2.INTER_LINEAR,
'depth': cv2.INTER_LINEAR
}),
tr.FixedResize(resolutions={
'image': (512, 512),
'edge': (512, 512),
'semseg': (512, 512),
'normals': (512, 512),
'depth': (512, 512)
},
flagvals={
'image': cv2.INTER_CUBIC,
'edge': cv2.INTER_NEAREST,
'semseg': cv2.INTER_NEAREST,
'normals': cv2.INTER_LINEAR,
'depth': cv2.INTER_LINEAR
}),
tr.AddIgnoreRegions(),
tr.ToTensor()
])
dataset = NYUD_MT(split='train',
transform=transform,
retname=True,
do_edge=True,
do_semseg=True,
do_normals=True,
do_depth=True)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=5,
shuffle=False,
num_workers=5)
for i, sample in enumerate(dataloader):
print(i)
for j in range(sample['image'].shape[0]):
f, ax_arr = plt.subplots(5)
for k in range(len(ax_arr)):
ax_arr[k].cla()
ax_arr[0].imshow(np.transpose(sample['image'][j], (1, 2, 0)))
ax_arr[1].imshow(sample['edge'][j, 0])
ax_arr[2].imshow(sample['semseg'][j, 0] / 40)
ax_arr[3].imshow(np.transpose(sample['normals'][j], (1, 2, 0)))
max_depth = torch.max(
sample['depth'][j, 0][sample['depth'][j, 0] != 255]).item()
ax_arr[4].imshow(
sample['depth'][j, 0] /
max_depth) # Not ideal. Better is to show inverse depth.
plt.show()
break
def test_all():
import matplotlib.pyplot as plt
import torch
import data.custom_transforms as tr
from torchvision import transforms
from utils.custom_collate import collate_mil
transform = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.ScaleNRotate(rots=(-90, 90),
scales=(1., 1.),
flagvals={
'image': cv2.INTER_CUBIC,
'edge': cv2.INTER_NEAREST,
'semseg': cv2.INTER_NEAREST,
'human_parts': cv2.INTER_NEAREST,
'normals': cv2.INTER_CUBIC,
'sal': cv2.INTER_NEAREST
}),
tr.FixedResize(resolutions={
'image': (512, 512),
'edge': (512, 512),
'semseg': (512, 512),
'human_parts': (512, 512),
'normals': (512, 512),
'sal': (512, 512)
},
flagvals={
'image': cv2.INTER_CUBIC,
'edge': cv2.INTER_NEAREST,
'semseg': cv2.INTER_NEAREST,
'human_parts': cv2.INTER_NEAREST,
'normals': cv2.INTER_CUBIC,
'sal': cv2.INTER_NEAREST
}),
tr.AddIgnoreRegions(),
tr.ToTensor()
])
dataset = PASCALContext(split='train',
transform=transform,
retname=True,
do_edge=True,
do_semseg=True,
do_human_parts=True,
do_normals=True,
do_sal=True)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=2,
shuffle=False,
num_workers=0)
for i, sample in enumerate(dataloader):
print(i)
for j in range(sample['image'].shape[0]):
f, ax_arr = plt.subplots(2, 3)
for k in range(len(ax_arr)):
for l in range(len(ax_arr[k])):
ax_arr[k][l].cla()
ax_arr[0][0].imshow(np.transpose(sample['image'][j], (1, 2, 0)))
ax_arr[0][1].imshow(
np.transpose(sample['edge'][j], (1, 2, 0))[:, :, 0])
ax_arr[0][2].imshow(
np.transpose(sample['semseg'][j], (1, 2, 0))[:, :, 0] / 20.)
ax_arr[1][0].imshow(
np.transpose(sample['human_parts'][j],
(1, 2, 0))[:, :, 0] / 6.)
ax_arr[1][1].imshow(np.transpose(sample['normals'][j], (1, 2, 0)))
ax_arr[1][2].imshow(
np.transpose(sample['sal'][j], (1, 2, 0))[:, :, 0])
plt.show()
break