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show.py
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import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import torch
import time
from data import build_val_transform
from datasets.cityscapes import Cityscapes
from model import RegSeg
from train import get_dataset_loaders
import yaml
from data_utils import get_dataloader_val
import torchvision.transforms as T
import torch.cuda.amp as amp
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
def get_colors():
palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
colors = torch.arange(255).view(-1, 1) * palette
colors = (colors % 255).numpy().astype("uint8")
return colors
def get_colors_cityscapes():
colors=np.zeros((256,3))
colors[255]=[255,255,255]
for c in Cityscapes.classes:
if 0<=c.train_id<=18:
colors[c.train_id]=c.color
return colors.astype("uint8")
def get_colors_cityscapes_labelid():
colors=np.zeros((256,3))
colors[255]=[255,255,255]
for c in Cityscapes.classes:
colors[c.id]=c.color
return colors.astype("uint8")
def get_colors_mapillary():
#colors=[[165, 42, 42], [0, 192, 0], [250, 170, 31], [250, 170, 32], [196, 196, 196], [190, 153, 153], [180, 165, 180], [90, 120, 150], [250, 170, 33], [250, 170, 34], [128, 128, 128], [250, 170, 35], [102, 102, 156], [128, 64, 255], [140, 140, 200], [170, 170, 170], [250, 170, 36], [250, 170, 160], [250, 170, 37], [96, 96, 96], [230, 150, 140], [128, 64, 128], [110, 110, 110], [110, 110, 110], [244, 35, 232], [128, 196, 128], [150, 100, 100], [70, 70, 70], [150, 150, 150], [150, 120, 90], [220, 20, 60], [220, 20, 60], [255, 0, 0], [255, 0, 100], [255, 0, 200], [255, 255, 255], [255, 255, 255], [250, 170, 29], [250, 170, 28], [250, 170, 26], [250, 170, 25], [250, 170, 24], [250, 170, 22], [250, 170, 21], [250, 170, 20], [255, 255, 255], [250, 170, 19], [250, 170, 18], [250, 170, 12], [250, 170, 11], [255, 255, 255], [255, 255, 255], [250, 170, 16], [250, 170, 15], [250, 170, 15], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [64, 170, 64], [230, 160, 50], [70, 130, 180], [190, 255, 255], [152, 251, 152], [107, 142, 35], [0, 170, 30], [255, 255, 128], [250, 0, 30], [100, 140, 180], [220, 128, 128], [222, 40, 40], [100, 170, 30], [40, 40, 40], [33, 33, 33], [100, 128, 160], [20, 20, 255], [142, 0, 0], [70, 100, 150], [250, 171, 30], [250, 172, 30], [250, 173, 30], [250, 174, 30], [250, 175, 30], [250, 176, 30], [210, 170, 100], [153, 153, 153], [153, 153, 153], [128, 128, 128], [0, 0, 80], [210, 60, 60], [250, 170, 30], [250, 170, 30], [250, 170, 30], [250, 170, 30], [250, 170, 30], [250, 170, 30], [192, 192, 192], [192, 192, 192], [192, 192, 192], [220, 220, 0], [220, 220, 0], [0, 0, 196], [192, 192, 192], [220, 220, 0], [140, 140, 20], [119, 11, 32], [150, 0, 255], [0, 60, 100], [0, 0, 142], [0, 0, 90], [0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110], [0, 0, 70], [0, 0, 142], [0, 0, 192], [170, 170, 170], [32, 32, 32], [111, 74, 0], [120, 10, 10], [81, 0, 81], [111, 111, 0], [0, 0, 0]]
colors=[[165, 42, 42], [0, 192, 0], [196, 196, 196], [190, 153, 153], [180, 165, 180], [90, 120, 150], [102, 102, 156], [128, 64, 255], [140, 140, 200], [170, 170, 170], [250, 170, 160], [96, 96, 96], [230, 150, 140], [128, 64, 128], [110, 110, 110], [244, 35, 232], [150, 100, 100], [70, 70, 70], [150, 120, 90], [220, 20, 60], [255, 0, 0], [255, 0, 100], [255, 0, 200], [200, 128, 128], [255, 255, 255], [64, 170, 64], [230, 160, 50], [70, 130, 180], [190, 255, 255], [152, 251, 152], [107, 142, 35], [0, 170, 30], [255, 255, 128], [250, 0, 30], [100, 140, 180], [220, 220, 220], [220, 128, 128], [222, 40, 40], [100, 170, 30], [40, 40, 40], [33, 33, 33], [100, 128, 160], [142, 0, 0], [70, 100, 150], [210, 170, 100], [153, 153, 153], [128, 128, 128], [0, 0, 80], [250, 170, 30], [192, 192, 192], [220, 220, 0], [140, 140, 20], [119, 11, 32], [150, 0, 255], [0, 60, 100], [0, 0, 142], [0, 0, 90], [0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110], [0, 0, 70], [0, 0, 192], [32, 32, 32], [120, 10, 10], [0, 0, 0]]
colors=np.array(colors).astype("uint8")
return colors
def get_colors_mapillary_reduced():
colors=[[165, 42, 42], [0, 192, 0], [196, 196, 196], [190, 153, 153], [180, 165, 180], [90, 120, 150], [102, 102, 156], [128, 64, 255], [140, 140, 200], [170, 170, 170], [250, 170, 160], [96, 96, 96], [230, 150, 140], [128, 64, 128], [110, 110, 110], [244, 35, 232], [150, 100, 100], [70, 70, 70], [150, 120, 90], [220, 20, 60], [255, 0, 0], [255, 0, 100], [255, 0, 200], [200, 128, 128], [255, 255, 255], [64, 170, 64], [230, 160, 50], [70, 130, 180], [190, 255, 255], [152, 251, 152], [107, 142, 35], [0, 170, 30], [255, 255, 128], [250, 0, 30], [100, 140, 180], [220, 220, 220], [220, 128, 128], [222, 40, 40], [100, 170, 30], [40, 40, 40], [33, 33, 33], [100, 128, 160], [142, 0, 0], [70, 100, 150], [210, 170, 100], [153, 153, 153], [128, 128, 128], [0, 0, 80], [250, 170, 30], [192, 192, 192], [220, 220, 0], [140, 140, 20], [119, 11, 32], [150, 0, 255], [0, 60, 100], [0, 0, 142], [0, 0, 90], [0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110], [0, 0, 70], [0, 0, 192], [32, 32, 32], [120, 10, 10], [0, 0, 0]]
colors=np.array(colors).astype("uint8")
ious=[0.0, 0.0, 57.68, 58.66, 63.16, 56.59, 50.9, 45.04, 39.82, 18.31, 22.22, 45.77, 49.91, 87.97, 43.31, 70.61, 76.67, 86.43, 41.84, 66.81, 46.77, 50.41, 0.0, 69.53, 57.07, 48.28, 4.99, 97.77, 76.83, 68.69, 88.77, 72.93, 17.02, 22.26, 5.05, 45.31, 29.76, 0.0, 20.38, 36.26, 2.43, 43.12, 4.4, 0.0, 37.03, 40.48, 52.69, 44.16, 60.96, 36.36, 66.61, 43.94, 47.44, 16.69, 73.89, 89.68, 0.0, 55.74, 46.28, 22.28, 6.71, 67.39, 8.41, 68.79, 91.75,0]
ious=np.array(ious)
colors=colors[ious>30]
all_colors=np.zeros((256,3)).astype("uint8")
all_colors[:len(colors)]=colors
return all_colors
def get_colors_camvid(color_to_class):
colors=np.zeros((256,3))
colors[255]=[255,255,255]
for color,cls in color_to_class.items():
colors[cls]=color
return colors.astype("uint8")
def show_image(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
def _open(filename):
image = Image.open(filename).convert("RGB")
preprocess = T.Compose([
T.Resize(1024),
T.ToTensor(),
T.Normalize(mean=mean, std=std),
])
input_tensor = preprocess(image)
images = input_tensor.unsqueeze(0)
return images
def show_mask(images):
colors=get_colors()
r = Image.fromarray(images.byte().cpu().numpy())
r.putpalette(colors)
plt.imshow(r)
def show_cityscapes_mask(images):
colors=get_colors_cityscapes()
r = Image.fromarray(images.byte().cpu().numpy())
r.putpalette(colors)
plt.imshow(r)
def show_camvid_mask(images,colors):
r = Image.fromarray(images.byte().cpu().numpy())
r.putpalette(colors)
plt.imshow(r)
def show_mapillary_mask(images,reduced=False):
colors=get_colors_mapillary()
if reduced:
colors=get_colors_mapillary_reduced()
r = Image.fromarray(images.byte().cpu().numpy())
r.putpalette(colors)
plt.imshow(r)
def display(data_loader,show_mask,num_images=5,skip=4,images_per_line=6):
images_so_far = 0
fig = plt.figure(figsize=(6, 4))
num_rows=int(np.ceil(num_images/images_per_line))
data_loader = iter(data_loader)
for _ in range(skip):
next(data_loader)
for images, targets in data_loader:
for image, target in zip(images, targets):
print(image.size(), target.size())
plt.subplot(num_rows, 2*images_per_line, images_so_far + 1)
plt.axis('off')
show_image(image)
plt.subplot(num_rows, 2*images_per_line, images_so_far + 2)
plt.axis('off')
show_mask(target)
images_so_far += 2
if images_so_far == 2 * num_images:
plt.tight_layout()
plt.show()
return
plt.tight_layout()
plt.show()
def show(model,data_loader,device,show_mask,num_images=5,skip=4,images_per_line=2,mixed_precision=False):
images_so_far=0
model.eval()
model.to(device)
num_rows = int(np.ceil(num_images / images_per_line))
fig=plt.figure(figsize=(8,4))
data_loader=iter(data_loader)
for _ in range(skip):
next(data_loader)
with torch.no_grad():
for images, targets in data_loader:
images, targets = images.to(device), targets.to(device)
start=time.time()
if torch.cuda.is_available():
with amp.autocast(enabled=mixed_precision):
outputs = model(images)
else:
outputs = model(images)
outputs=outputs.argmax(1)
end=time.time()
print(end-start)
outputs=outputs.cpu()
images=images.cpu()
targets=targets.cpu()
for image,target,output in zip(images,targets,outputs):
print(image.size(),target.size(),output.size())
plt.subplot(num_rows, 3*images_per_line, images_so_far+1)
plt.axis('off')
show_image(image)
plt.subplot(num_rows, 3*images_per_line, images_so_far+2)
plt.axis('off')
show_mask(target)
plt.subplot(num_rows,3*images_per_line,images_so_far+3)
plt.axis('off')
show_mask(output)
images_so_far+=3
if images_so_far==3*num_images:
plt.tight_layout()
plt.show()
return
plt.tight_layout()
plt.show()
def show_files(model,files,device,show_mask,num_images=5,images_per_line=2,mixed_precision=False):
images_so_far=0
model.eval()
model.to(device)
num_rows = int(np.ceil(num_images / images_per_line))
fig=plt.figure(figsize=(8,4))
with torch.no_grad():
for filename in files:
images=_open(filename)
images= images.to(device)
start=time.time()
if torch.cuda.is_available():
with amp.autocast(enabled=mixed_precision):
outputs = model(images)
else:
outputs = model(images)
outputs=outputs.argmax(1)
end=time.time()
print(end-start)
outputs=outputs.cpu()
images=images.cpu()
for image,output in zip(images,outputs):
print(image.size(),output.size())
plt.subplot(num_rows, 2*images_per_line, images_so_far+1)
plt.axis('off')
show_image(image)
plt.subplot(num_rows, 2*images_per_line, images_so_far+2)
plt.axis('off')
show_mask(output)
images_so_far+=2
if images_so_far==2*num_images:
plt.tight_layout()
plt.show()
return
plt.tight_layout()
plt.show()
def display_cityscapes():
import random
num_images=16
images_per_line=4
skip=0
seed=0
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
config_filename="configs/cityscapes_500epochs.yaml"
with open(config_filename) as file:
config=yaml.full_load(file)
config["class_uniform_pct"]=0.5
config["dataset_dir"]="cityscapes_dataset"
config["val_split"]="val"
config["train_split"]="train"
config["train_min_size"]=400
config["train_max_size"]=1600
config["train_crop_size"]=[1024,1024]
config["aug_mode"]="randaug_reduced"
config["num_workers"]=0
train_loader,val_loader,train_set=get_dataset_loaders(config)
display(val_loader,show_cityscapes_mask,num_images=num_images,skip=skip,images_per_line=images_per_line)
def display_camvid():
import random
num_images=9
images_per_line=3
skip=0
seed=0
torch.manual_seed(seed)
random.seed(seed)
config_filename= "configs/camvid_200epochs.yaml"
with open(config_filename) as file:
config=yaml.full_load(file)
config["train_split"]="trainval"
config["val_split"]="test"
config["dataset_dir"]="CamVid3"
train_loader,val_loader,train_set=get_dataset_loaders(config)
colours=get_colors_camvid(train_set.color_to_class)
_show_mask = lambda image : show_camvid_mask(image,colours)
display(train_loader,_show_mask,num_images=num_images,skip=skip,images_per_line=images_per_line)
def display_coco():
import random
num_images=9
images_per_line=3
skip=0
seed=0
torch.manual_seed(seed)
random.seed(seed)
config_filename="configs/coco_exp30_decoder12_5epochs.yaml"
with open(config_filename) as file:
config=yaml.full_load(file)
config["aug_mode"]="baseline"
config["dataset_name"]="coco2"
config["dataset_dir"]="coco"
train_loader,val_loader,train_set=get_dataset_loaders(config)
display(val_loader,show_mask,num_images=num_images,skip=skip,images_per_line=images_per_line)
def dispay_mapillary():
import random
num_images=16
images_per_line=4
skip=16
seed=0
torch.manual_seed(seed)
random.seed(seed)
config_filename="configs/mapillary_240epochs.yaml"
with open(config_filename) as file:
config=yaml.full_load(file)
train_loader,val_loader,train_set=get_dataset_loaders(config)
display(train_loader,lambda images:show_mapillary_mask(images,False),num_images=num_images,skip=skip,images_per_line=images_per_line)
def show_mapillary_model():
import random
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model=RegSeg(
name="exp49_decoder14",
num_classes=65,
pretrained="checkpoints/mapillary_exp49_decoder14_300_epochs_run1"
)
num_images=10
images_per_line=2
skip=0
config_filename="configs/mapillary_240epochs.yaml"
with open(config_filename) as file:
config=yaml.full_load(file)
config["num_workers"]=0
config["batch_size"]=1
seed=0
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
train_loader,val_loader,train_set=get_dataset_loaders(config)
show(model,train_loader,device,show_mapillary_mask,num_images=num_images,skip=skip,images_per_line=images_per_line)
def show_cityscapes_test():
import os
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model=RegSeg(
name="exp48_decoder26",
num_classes=19,
pretrained="checkpoints/cityscapes_exp48_decoder26_train_1000_epochs_run2"
)
files=["munich/munich_000203_000019_leftImg8bit.png",
"munich/munich_000235_000019_leftImg8bit.png",
"munich/munich_000292_000019_leftImg8bit.png",
"munich/munich_000298_000019_leftImg8bit.png"
]
# files=["berlin/berlin_000129_000019_leftImg8bit.png",
# "berlin/berlin_000019_000019_leftImg8bit.png",
# "berlin/berlin_000035_000019_leftImg8bit.png",
# "berlin/berlin_000043_000019_leftImg8bit.png",
# "berlin/berlin_000087_000019_leftImg8bit.png",
# "berlin/berlin_000117_000019_leftImg8bit.png"
# ]
new_files=[]
for file in files:
file=os.path.join("cityscapes_dataset/leftImg8bit/test",file)
new_files.append(file)
num_images=len(files)
images_per_line=2
show_files(model,new_files,device,show_cityscapes_mask,num_images=num_images,images_per_line=images_per_line)
def show_cityscapes_model():
import random
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model=RegSeg(
name="exp48_decoder26",
num_classes=19,
pretrained="checkpoints/cityscapes_exp48_decoder26_train_1000_epochs_run2"
)
num_images=4
images_per_line=1
skip=8
config_filename="configs/cityscapes_1000epochs.yaml"
with open(config_filename) as file:
config=yaml.full_load(file)
config["num_workers"]=0
config["batch_size"]=1
config["class_uniform_pct"]=0
config["train_crop_size"]=[1024,1024*2]
config["train_max_size"]=1024
config["train_min_size"]=1024
config["dataset_dir"]="cityscapes_dataset"
seed=0
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
train_loader,val_loader,train_set=get_dataset_loaders(config)
show(model,val_loader,device,show_cityscapes_mask,num_images=num_images,skip=skip,images_per_line=images_per_line)
def show_cityscapes_failure_modes():
num_images=4
images_per_line=1
skip=0
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model=RegSeg(
name="exp48_decoder26",
num_classes=19,
pretrained="checkpoints/cityscapes_exp48_decoder26_train_1000_epochs_run2"
)
val_transform=build_val_transform(1024,1024)
indices=[312, 84, 54, 161, 230, 319, 303, 297, 262, 310, 263, 318, 360, 11, 321, 290, 257, 283, 121, 101, 276, 100, 317, 124, 175, 267, 278, 178, 231, 228]
# indices=[312, 84, 161, 228, 54, 319, 313, 310, 230, 317, 262, 276, 318, 66, 301, 296, 321, 360, 140, 257, 290, 101, 83, 178, 263, 278, 121, 147, 26, 125]
val = Cityscapes("cityscapes_dataset", split="val", target_type="semantic",
transforms=val_transform, class_uniform_pct=0)
val=torch.utils.data.Subset(val,indices)
val_loader = get_dataloader_val(val, 0)
show(model,val_loader,device,show_cityscapes_mask,num_images=num_images,skip=skip,images_per_line=images_per_line)
if __name__=="__main__":
show_cityscapes_model()