56 KiB
56 KiB
%%time
import os
if not os.path.exists('CSRNet-pytorch/'):
!pip install -U scipy torch_snippets torch_summary
!git clone https://github.com/sizhky/CSRNet-pytorch.git
from google.colab import files
files.upload() # upload kaggle.json
!mkdir -p ~/.kaggle
!mv kaggle.json ~/.kaggle/
!ls ~/.kaggle
!chmod 600 /root/.kaggle/kaggle.json
print('downloading data...')
!kaggle datasets download -d tthien/shanghaitech-with-people-density-map/
print('unzipping data...')
!unzip -qq shanghaitech-with-people-density-map.zip
%cd CSRNet-pytorch
!ln -s ../shanghaitech_with_people_density_map
from torch_snippets import *
import h5py
from scipy import ioCollecting scipy [?25l Downloading https://files.pythonhosted.org/packages/c8/89/63171228d5ced148f5ced50305c89e8576ffc695a90b58fe5bb602b910c2/scipy-1.5.4-cp36-cp36m-manylinux1_x86_64.whl (25.9MB) [K |████████████████████████████████| 25.9MB 119kB/s [?25hCollecting torch_snippets Downloading https://files.pythonhosted.org/packages/e5/57/7d513a66ffc00d1495c8a8eeac8754b42233d8a68aa565077db8939b0452/torch_snippets-0.235-py3-none-any.whl Collecting torch_summary Downloading https://files.pythonhosted.org/packages/83/49/f9db57bcad7246591b93519fd8e5166c719548c45945ef7d2fc9fcba46fb/torch_summary-1.4.3-py3-none-any.whl Requirement already satisfied, skipping upgrade: numpy>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from scipy) (1.18.5) Collecting opencv-python-headless [?25l Downloading https://files.pythonhosted.org/packages/08/e9/57d869561389884136be65a2d1bc038fe50171e2ba348fda269a4aab8032/opencv_python_headless-4.4.0.46-cp36-cp36m-manylinux2014_x86_64.whl (36.7MB) [K |████████████████████████████████| 36.7MB 89kB/s [?25hRequirement already satisfied, skipping upgrade: Pillow in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (7.0.0) Requirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (3.2.2) Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (4.41.1) Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (1.1.4) Collecting loguru [?25l Downloading https://files.pythonhosted.org/packages/6d/48/0a7d5847e3de329f1d0134baf707b689700b53bd3066a5a8cfd94b3c9fc8/loguru-0.5.3-py3-none-any.whl (57kB) [K |████████████████████████████████| 61kB 9.5MB/s [?25hRequirement already satisfied, skipping upgrade: dill in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (0.3.3) Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (2.4.7) Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (1.3.1) Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (0.10.0) Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (2.8.1) Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->torch_snippets) (2018.9) Collecting aiocontextvars>=0.2.0; python_version < "3.7" Downloading https://files.pythonhosted.org/packages/db/c1/7a723e8d988de0a2e623927396e54b6831b68cb80dce468c945b849a9385/aiocontextvars-0.2.2-py2.py3-none-any.whl Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->torch_snippets) (1.15.0) Collecting contextvars==2.4; python_version < "3.7" Downloading https://files.pythonhosted.org/packages/83/96/55b82d9f13763be9d672622e1b8106c85acb83edd7cc2fa5bc67cd9877e9/contextvars-2.4.tar.gz Collecting immutables>=0.9 [?25l Downloading https://files.pythonhosted.org/packages/99/e0/ea6fd4697120327d26773b5a84853f897a68e33d3f9376b00a8ff96e4f63/immutables-0.14-cp36-cp36m-manylinux1_x86_64.whl (98kB) [K |████████████████████████████████| 102kB 14.1MB/s [?25hBuilding wheels for collected packages: contextvars Building wheel for contextvars (setup.py) ... [?25l[?25hdone Created wheel for contextvars: filename=contextvars-2.4-cp36-none-any.whl size=7666 sha256=5976ad63e4d7be29f86455820048fd851c169bc1671ad4627743b2b16053a1e3 Stored in directory: /root/.cache/pip/wheels/a5/7d/68/1ebae2668bda2228686e3c1cf16f2c2384cea6e9334ad5f6de Successfully built contextvars [31mERROR: tensorflow 2.3.0 has requirement scipy==1.4.1, but you'll have scipy 1.5.4 which is incompatible.[0m [31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.[0m Installing collected packages: scipy, opencv-python-headless, immutables, contextvars, aiocontextvars, loguru, torch-snippets, torch-summary Found existing installation: scipy 1.4.1 Uninstalling scipy-1.4.1: Successfully uninstalled scipy-1.4.1 Successfully installed aiocontextvars-0.2.2 contextvars-2.4 immutables-0.14 loguru-0.5.3 opencv-python-headless-4.4.0.46 scipy-1.5.4 torch-snippets-0.235 torch-summary-1.4.3 Cloning into 'CSRNet-pytorch'... remote: Enumerating objects: 6, done.[K remote: Counting objects: 100% (6/6), done.[K remote: Compressing objects: 100% (6/6), done.[K remote: Total 92 (delta 1), reused 0 (delta 0), pack-reused 86[K Unpacking objects: 100% (92/92), done.
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Saving kaggle.json to kaggle.json kaggle.json downloading data... Downloading shanghaitech-with-people-density-map.zip to /content 100% 4.78G/4.79G [01:37<00:00, 41.5MB/s] 100% 4.79G/4.79G [01:37<00:00, 52.6MB/s] unzipping data... /content/CSRNet-pytorch CPU times: user 1.64 s, sys: 431 ms, total: 2.07 s Wall time: 5min 34s
part_A = Glob('shanghaitech_with_people_density_map/ShanghaiTech/part_A/train_data/');
image_folder = 'shanghaitech_with_people_density_map/ShanghaiTech/part_A/train_data/images/'
heatmap_folder = 'shanghaitech_with_people_density_map/ShanghaiTech/part_A/train_data/ground-truth-h5/'
gt_folder = 'shanghaitech_with_people_density_map/ShanghaiTech/part_A/train_data/ground-truth/'2020-11-08 10:55:51.544 | INFO | torch_snippets.loader:Glob:181 - 3 files found at shanghaitech_with_people_density_map/ShanghaiTech/part_A/train_data/
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tfm = T.Compose([
T.ToTensor()
])
class Crowds(Dataset):
def __init__(self, stems):
self.stems = stems
def __len__(self):
return len(self.stems)
def __getitem__(self, ix):
_stem = self.stems[ix]
image_path = f'{image_folder}/{_stem}.jpg'
heatmap_path = f'{heatmap_folder}/{_stem}.h5'
gt_path = f'{gt_folder}/GT_{_stem}.mat'
pts = io.loadmat(gt_path)
pts = len(pts['image_info'][0,0][0,0][0])
image = read(image_path, 1)
with h5py.File(heatmap_path, 'r') as hf:
gt = hf['density'][:]
gt = resize(gt, 1/8)*64
return image.copy(), gt.copy(), pts
def collate_fn(self, batch):
ims, gts, pts = list(zip(*batch))
ims = torch.cat([tfm(im)[None] for im in ims]).to(device)
gts = torch.cat([tfm(gt)[None] for gt in gts]).to(device)
return ims, gts, torch.tensor(pts).to(device)
def choose(self):
return self[randint(len(self))]
from sklearn.model_selection import train_test_split
trn_stems, val_stems = train_test_split(stems(Glob(image_folder)), random_state=10)
trn_ds = Crowds(trn_stems)
val_ds = Crowds(val_stems)
trn_dl = DataLoader(trn_ds, batch_size=1, shuffle=True, collate_fn=trn_ds.collate_fn)
val_dl = DataLoader(val_ds, batch_size=1, shuffle=True, collate_fn=val_ds.collate_fn)2020-11-08 10:55:53.547 | INFO | torch_snippets.loader:Glob:181 - 300 files found at shanghaitech_with_people_density_map/ShanghaiTech/part_A/train_data/images/
import torch.nn as nn
import torch
from torchvision import models
from utils import save_net,load_net
def make_layers(cfg, in_channels = 3,batch_norm=False,dilation = False):
if dilation:
d_rate = 2
else:
d_rate = 1
layers = []
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=d_rate, dilation=d_rate)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)class CSRNet(nn.Module):
def __init__(self, load_weights=False):
super(CSRNet, self).__init__()
self.seen = 0
self.frontend_feat = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512]
self.backend_feat = [512, 512, 512, 256, 128, 64]
self.frontend = make_layers(self.frontend_feat)
self.backend = make_layers(self.backend_feat,in_channels = 512,dilation = True)
self.output_layer = nn.Conv2d(64, 1, kernel_size=1)
if not load_weights:
mod = models.vgg16(pretrained = True)
self._initialize_weights()
items = list(self.frontend.state_dict().items())
_items = list(mod.state_dict().items())
for i in range(len(self.frontend.state_dict().items())):
items[i][1].data[:] = _items[i][1].data[:]
def forward(self,x):
x = self.frontend(x)
x = self.backend(x)
x = self.output_layer(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, std=0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)def train_batch(model, data, optimizer, criterion):
model.train()
optimizer.zero_grad()
ims, gts, pts = data
_gts = model(ims)
loss = criterion(_gts, gts)
loss.backward()
optimizer.step()
pts_loss = nn.L1Loss()(_gts.sum(), gts.sum())
return loss.item(), pts_loss.item()
@torch.no_grad()
def validate_batch(model, data, criterion):
model.eval()
ims, gts, pts = data
_gts = model(ims)
loss = criterion(_gts, gts)
pts_loss = nn.L1Loss()(_gts.sum(), gts.sum())
return loss.item(), pts_loss.item()model = CSRNet().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=1e-6)
n_epochs = 20
log = Report(n_epochs)
for ex in range(n_epochs):
N = len(trn_dl)
for bx, data in enumerate(trn_dl):
loss, pts_loss = train_batch(model, data, optimizer, criterion)
log.record(ex+(bx+1)/N, trn_loss=loss, trn_pts_loss=pts_loss, end='\r')
N = len(val_dl)
for bx, data in enumerate(val_dl):
loss, pts_loss = validate_batch(model, data, criterion)
log.record(ex+(bx+1)/N, val_loss=loss, val_pts_loss=pts_loss, end='\r')
log.report_avgs(ex+1)Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth
HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))
EPOCH: 1.000 trn_loss: 0.048 trn_pts_loss: 427.918 val_loss: 0.053 val_pts_loss: 347.524 (77.39s - 1470.45s remaining) EPOCH: 2.000 trn_loss: 0.042 trn_pts_loss: 306.686 val_loss: 0.048 val_pts_loss: 262.405 (154.97s - 1394.71s remaining) EPOCH: 3.000 trn_loss: 0.036 trn_pts_loss: 204.923 val_loss: 0.043 val_pts_loss: 190.447 (232.47s - 1317.31s remaining) EPOCH: 4.000 trn_loss: 0.033 trn_pts_loss: 159.764 val_loss: 0.042 val_pts_loss: 184.419 (310.06s - 1240.24s remaining) EPOCH: 5.000 trn_loss: 0.032 trn_pts_loss: 145.682 val_loss: 0.041 val_pts_loss: 241.129 (387.48s - 1162.44s remaining) EPOCH: 6.000 trn_loss: 0.031 trn_pts_loss: 139.674 val_loss: 0.039 val_pts_loss: 172.444 (464.97s - 1084.93s remaining) EPOCH: 7.000 trn_loss: 0.031 trn_pts_loss: 143.573 val_loss: 0.039 val_pts_loss: 154.602 (542.31s - 1007.15s remaining) EPOCH: 8.000 trn_loss: 0.030 trn_pts_loss: 126.261 val_loss: 0.038 val_pts_loss: 152.646 (619.65s - 929.47s remaining) EPOCH: 9.000 trn_loss: 0.030 trn_pts_loss: 121.418 val_loss: 0.036 val_pts_loss: 168.236 (696.97s - 851.86s remaining) EPOCH: 10.000 trn_loss: 0.029 trn_pts_loss: 122.989 val_loss: 0.039 val_pts_loss: 183.355 (774.30s - 774.30s remaining) EPOCH: 11.000 trn_loss: 0.029 trn_pts_loss: 121.840 val_loss: 0.038 val_pts_loss: 198.861 (851.56s - 696.73s remaining) EPOCH: 12.000 trn_loss: 0.029 trn_pts_loss: 123.117 val_loss: 0.032 val_pts_loss: 131.696 (928.88s - 619.26s remaining) EPOCH: 13.000 trn_loss: 0.029 trn_pts_loss: 117.252 val_loss: 0.041 val_pts_loss: 145.208 (1006.07s - 541.73s remaining) EPOCH: 14.000 trn_loss: 0.028 trn_pts_loss: 110.343 val_loss: 0.036 val_pts_loss: 134.451 (1083.44s - 464.33s remaining) EPOCH: 15.000 trn_loss: 0.028 trn_pts_loss: 113.773 val_loss: 0.036 val_pts_loss: 182.355 (1160.97s - 386.99s remaining) EPOCH: 16.000 trn_loss: 0.028 trn_pts_loss: 109.048 val_loss: 0.036 val_pts_loss: 152.386 (1238.67s - 309.67s remaining) EPOCH: 17.000 trn_loss: 0.028 trn_pts_loss: 107.314 val_loss: 0.035 val_pts_loss: 139.919 (1316.50s - 232.32s remaining) EPOCH: 18.000 trn_loss: 0.028 trn_pts_loss: 111.054 val_loss: 0.036 val_pts_loss: 133.333 (1394.37s - 154.93s remaining) EPOCH: 19.000 trn_loss: 0.027 trn_pts_loss: 110.610 val_loss: 0.036 val_pts_loss: 141.666 (1472.28s - 77.49s remaining) EPOCH: 20.000 trn_loss: 0.028 trn_pts_loss: 113.707 val_loss: 0.034 val_pts_loss: 167.978 (1550.17s - 0.00s remaining)
from matplotlib import cm as c
from torchvision import datasets, transforms
from PIL import Image
transform=transforms.Compose([
transforms.ToTensor(),transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
test_folder = 'shanghaitech_with_people_density_map/ShanghaiTech/part_A/test_data/'
imgs = Glob(f'{test_folder}/images')
f = choose(imgs)
print(f)
img = transform(Image.open(f).convert('RGB')).to(device)2020-11-08 11:30:25.780 | INFO | torch_snippets.loader:Glob:181 - 182 files found at shanghaitech_with_people_density_map/ShanghaiTech/part_A/test_data//images
shanghaitech_with_people_density_map/ShanghaiTech/part_A/test_data//images/IMG_4.jpg
output = model(img[None])
print("Predicted Count : ",int(output.detach().cpu().sum().numpy()))
temp = np.asarray(output.detach().cpu().reshape(output.detach().cpu().shape[2],output.detach().cpu().shape[3]))
plt.imshow(temp,cmap = c.jet)
plt.show()Predicted Count : 183