id int64 0 458k | file_name stringlengths 4 119 | file_path stringlengths 14 227 | content stringlengths 24 9.96M | size int64 24 9.96M | language stringclasses 1
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values | total_lines int64 1 219k | avg_line_length float64 2.52 4.63M | max_line_length int64 5 9.91M | alphanum_fraction float64 0 1 | repo_name stringlengths 7 101 | repo_stars int64 100 139k | repo_forks int64 0 26.4k | repo_open_issues int64 0 2.27k | repo_license stringclasses 12
values | repo_extraction_date stringclasses 433
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,284,900 | setup.py | dajuric_dark/dark-1/setup.py | from setuptools import setup
setup(
name="dark",
description="",
version="1.0",
packages=["dark"],
package_dir={"dark": "dark"},
install_requires = []
)
| 182 | Python | .py | 9 | 16 | 33 | 0.598837 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,901 | eval.py | dajuric_dark/dark-1/samples/1.0-Func-derivation/torch/eval.py | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
import torch
import matplotlib.pyplot as plt
def calc_points(range = (-15, 15)):
x = torch.linspace(range[0], range[1], 50).reshape(1, -1)
x = torch.tensor(x, requires_grad=True)
fx = torch.add(torch.mul(2, torch.pow(x, 2)), 5) #2*x^2 + 5
#fx ... | 761 | Python | .py | 21 | 32.285714 | 105 | 0.621585 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,902 | eval.py | dajuric_dark/dark-1/samples/1.0-Func-derivation/dark/eval.py | import dark
import matplotlib.pyplot as plt
def calc_points(x):
x = dark.Parameter(x)
fx = dark.add(dark.mul(2, dark.pow(x, 2)), 5) #2*x^2 + 5
#fx = dark.div(dark.mul(dark.tanh(x), dark.pow(x, 2)), dark.sqrt(x)) #(tanh(x) * x^2) / sqrt(x)
fx.backward() #calc gradient
return x.data, \
... | 805 | Python | .py | 22 | 32.590909 | 127 | 0.625 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,903 | util.py | dajuric_dark/dark-1/samples/1.1-Func-optimization/torch/util.py | import numpy as np
import matplotlib.pyplot as plt
ax = None
def plot(eval_func, range, x_param):
global ax
if ax is None:
plt.ion()
plt.show()
X_curve = np.linspace(range[0], range[1], 100).reshape(-1, 1)
Y_curve = eval_func(X_curve).reshape(-1, 1)
plt.plot(X_c... | 434 | Python | .py | 14 | 25.214286 | 64 | 0.637056 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,904 | train.py | dajuric_dark/dark-1/samples/1.1-Func-optimization/torch/train.py | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
import torch
from util import *
def eval_func(x):
fx = torch.add(torch.mul(2, torch.pow(x, 2)), 5) #2*x^2 + 5
return fx
x = torch.tensor(10.0, requires_grad=True)
for _ in range(15):
print(f"Value of x is: {x.data.item():2.1f}")
fx = eval_fu... | 537 | Python | .py | 16 | 29.3125 | 71 | 0.634578 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,905 | util.py | dajuric_dark/dark-1/samples/1.1-Func-optimization/dark/util.py | import numpy as np
import matplotlib.pyplot as plt
ax = None
def plot(eval_func, range, x_param):
global ax
if ax is None:
plt.ion()
plt.show()
X_curve = np.linspace(range[0], range[1], 100)
Y_curve = [eval_func(x) for x in X_curve]
plt.plot(X_curve, Y_curve, co... | 414 | Python | .py | 14 | 23.5 | 49 | 0.637838 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,906 | train.py | dajuric_dark/dark-1/samples/1.1-Func-optimization/dark/train.py | import os
os.environ["USE_CPU"] = "True"
import dark
from util import *
def eval_func(x):
fx = dark.add(dark.mul(2, dark.pow(x, 2)), 5) #2*x^2 + 5
return fx
x = dark.Parameter(10.0)
for _ in range(15):
print(f"Value of x is: {x.data:2.1f}")
fx = eval_func(x)
fx.backward() #calc gradient
... | 480 | Python | .py | 16 | 25.75 | 61 | 0.617257 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,907 | autodiff.py | dajuric_dark/dark-1/dark/core/autodiff.py | class Node():
op = None
inputs = []
data = None
requires_grad = True
_grad = None
def __init__(self, val):
self.data = val
def backward(self):
if self.grad is None:
self.grad = 1
order = self._topological_sort()
for node in order:
... | 2,414 | Python | .py | 82 | 19.804878 | 46 | 0.528495 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,908 | operations.py | dajuric_dark/dark-1/dark/core/operations.py | from .autodiff import Operation
import math as m
# ------ scalar ------
class AbsoluteValue(Operation):
def forward(self, x):
return x if x >= 0 else -x
def backward(self, dldy, y, x):
gz = (x > 0)
lz = ~gz
return [dldy * gz - dldy * lz]
class Add(Operation):
def forward... | 2,740 | Python | .py | 101 | 21.059406 | 43 | 0.571986 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,909 | setup.py | dajuric_dark/dark-7/setup.py | from setuptools import setup
setup(
name="dark",
description="",
version="7.0",
packages=["dark"],
package_dir={"dark": "dark"},
install_requires = []
)
| 182 | Python | .py | 9 | 16 | 33 | 0.598837 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,910 | process_db.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/db/process_db.py | import os, shutil
import cv2
from rich.progress import track
import mediapipe as mp
from glob import glob
mp_face_detection = mp.solutions.face_detection
script_dir = os.path.dirname(os.path.realpath(__file__))
def get_bbox(det):
normalized_bb = det.location_data.relative_bounding_box
x = min(max(normalized_b... | 2,752 | Python | .py | 69 | 33.869565 | 96 | 0.62315 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,911 | config.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/config.py | import torch
import albumentations as A
from albumentations.pytorch import ToTensorV2
import cv2
import os
script_dir = os.path.dirname(os.path.realpath(__file__))
DB_PATH = f"{script_dir}/../db/"
MODEL_PATH = f"{script_dir}/model.pt"
C = 1
TEST_SIZE = 0.1
IM_SIZE = 128
ANCHORS = [
[(0.28, 0.22), (0.38, 0.48), ... | 1,538 | Python | .py | 46 | 29.152174 | 94 | 0.643537 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,912 | test.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/test.py | from config import *
from model import BlazeFace as YoloNet
from utils import *
import torch
import cv2
from glob import glob
import random
sAnchors = get_scaled_anchors()
def predict(model, im):
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = test_transforms(image=im, bboxes=[])["image"]
im = im.unsqueeze... | 995 | Python | .py | 32 | 25.5625 | 72 | 0.627513 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,913 | utils.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/utils.py | import torch
import numpy as np
import cv2
from rich.progress import BarColumn, Progress, TextColumn, TimeRemainingColumn, TaskProgressColumn
from config import *
def iou(boxesA, boxesB):
boxA_x1 = boxesA[..., 0:1] - boxesA[..., 2:3] / 2
boxA_y1 = boxesA[..., 1:2] - boxesA[..., 3:4] / 2
boxA_x2 = boxesA[..... | 4,530 | Python | .py | 110 | 31.718182 | 101 | 0.53461 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,914 | train.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/train.py | from torch.optim import *
from torch.optim.lr_scheduler import *
from config import *
from dataset import *
from model import BlazeFace as YoloNet
from loss import *
from utils import *
from dataset import *
import os
sAnchors = get_scaled_anchors().to(device)
def train_loop(dLoader: DataLoader, model: YoloNet, loss_... | 2,370 | Python | .py | 62 | 31.774194 | 97 | 0.622213 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,915 | model-blaze-full.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/model-blaze-full.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import *
# https://github.com/deepcam-cn/yolov5-face/blob/master/models/common.py
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pa... | 6,586 | Python | .py | 140 | 36.535714 | 137 | 0.605054 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,916 | loss.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/loss.py | import torch
import torch.nn as nn
from config import *
from utils import *
class YoloLoss(nn.Module):
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
self.bce = nn.BCEWithLogitsLoss()
self.entropy = nn.CrossEntropyLoss()
self.sigmoid = nn.Sigmoid()
s... | 2,172 | Python | .py | 50 | 34.4 | 121 | 0.476894 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,917 | dataset.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/dataset.py | import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from config import *
from utils import *
import glob
import random
class YoloDataset(Dataset):
def __init__(self, im_folder, imT=None):
super().__init__()
self.lblFiles = sorted(glob.glob(f"{im_fo... | 3,425 | Python | .py | 84 | 31.416667 | 85 | 0.572217 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,918 | model.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/torch/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import *
# https://github.com/deepcam-cn/yolov5-face/blob/master/models/common.py
# https://vincentblog.xyz/posts/face-detection-for-low-end-hardware-using-the-blaze-face-architecture
class Conv(nn.Module):
def __init__(self, c_in, c_o... | 4,245 | Python | .py | 109 | 28.688073 | 108 | 0.550772 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,919 | config.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/config.py | import dark.tensor as dt
import albumentations as A
import cv2
import os
import numpy as np
script_dir = os.path.dirname(os.path.realpath(__file__))
DB_PATH = f"{script_dir}/../db/"
MODEL_PATH = f"{script_dir}/model.pt"
C = 1
TEST_SIZE = 0.1
IM_SIZE = 128
ANCHORS = [
[(0.28, 0.22), (0.38, 0.48), (0.9, 0.78)],
... | 1,820 | Python | .py | 53 | 29.226415 | 94 | 0.609195 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,920 | test.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/test.py | import dark
from config import *
from model import BlazeFace as YoloNet
from utils import *
import cv2
from glob import glob
import random
import pickle
sAnchors = get_scaled_anchors()
def predict(model, im):
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = test_transforms(image=im, bboxes=[])["image"]
im =... | 1,043 | Python | .py | 31 | 28.354839 | 132 | 0.638384 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,921 | utils.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/utils.py | import dark.tensor as dt
import numpy as np
import cv2
from rich.progress import BarColumn, Progress, TextColumn, TimeRemainingColumn, TaskProgressColumn
from config import *
def iou(boxesA, boxesB):
boxA_x1 = boxesA[..., 0:1] - boxesA[..., 2:3] / 2
boxA_y1 = boxesA[..., 1:2] - boxesA[..., 3:4] / 2
boxA_x2... | 4,464 | Python | .py | 108 | 31.861111 | 98 | 0.533958 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,922 | train.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/train.py | import dark
from dark.optim import *
from config import *
from dataset import *
from model import BlazeFace as YoloNet
from loss import *
from utils import *
from dataset import *
import os
sAnchors = get_scaled_anchors()
def train_loop(dLoader: DataLoader, model: YoloNet, loss_fn: YoloLoss, optimizer: Optimizer):
... | 1,998 | Python | .py | 56 | 29.214286 | 97 | 0.617387 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,923 | loss.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/loss.py | import dark
import dark.nn as nn
from config import *
from utils import *
class YoloLoss(nn.Module):
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
self.bce = nn.BCEWithLogitsLoss()
self.entropy = nn.CrossEntropyLoss()
self.sigmoid = nn.Sigmoid()
sel... | 3,368 | Python | .py | 75 | 30.44 | 86 | 0.454147 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,924 | dataset.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/dataset.py | from dark.utils.data import Dataset, DataLoader
import numpy as np
from PIL import Image
from config import *
from utils import *
import glob
class YoloDataset(Dataset):
def __init__(self, im_folder, imT=None):
super().__init__()
self.lblFiles = sorted(glob.glob(f"{im_folder}/**/*.txt", recursive=T... | 3,333 | Python | .py | 81 | 31.530864 | 82 | 0.564943 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,925 | model.py | dajuric_dark/dark-7/samples/7.0-YOLOv3-detection/dark/model.py | import dark
import dark.nn as nn
from dark.nn.init import default_init_weights
from config import *
# https://github.com/deepcam-cn/yolov5-face/blob/master/models/common.py
# https://vincentblog.xyz/posts/face-detection-for-low-end-hardware-using-the-blaze-face-architecture
class Conv(nn.Module):
def __init__(sel... | 4,104 | Python | .py | 107 | 28.252336 | 101 | 0.549151 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,926 | util.py | dajuric_dark/dark-7/samples/2.0-Func-logistic-regression/torch/util.py | import numpy as np
import matplotlib.pyplot as plt
ax = None
def plot(X, Y, eval_func):
global ax
if ax is None:
plt.ion()
plt.show()
ax = plt.figure().add_subplot(projection='3d')
ax.cla()
X_curve = np.meshgrid(np.linspace(0, 1, 25), np.linspace(0, 1, 25)... | 555 | Python | .py | 17 | 26.352941 | 70 | 0.605578 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,927 | train.py | dajuric_dark/dark-7/samples/2.0-Func-logistic-regression/torch/train.py | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
import numpy as np
import torch
import matplotlib.pyplot as plt
from util import *
from dataset import *
def sigmoid(x):
den = torch.add(1, torch.exp(torch.neg(x)))
return torch.div(1, den)
def bce_loss(predictions, targets):
lossA = torch.mul(targets... | 1,249 | Python | .py | 34 | 33.117647 | 92 | 0.690058 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,928 | dataset.py | dajuric_dark/dark-7/samples/2.0-Func-logistic-regression/torch/dataset.py | import random
import torch
def get_data():
X = []; Y = []
for i in range(100):
x_1 = random.random()
x_2 = random.random()
y = 1 if x_1 < 0.5 else 0 #split
X.append((x_1, x_2))
Y.append([y])
X = torch.tensor(X, dtype=torch.float64)
Y = torch.tensor(Y,... | 357 | Python | .py | 13 | 20.923077 | 44 | 0.548193 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,929 | util.py | dajuric_dark/dark-7/samples/2.0-Func-logistic-regression/dark/util.py | import numpy as np
import matplotlib.pyplot as plt
ax = None
def plot(X, Y, eval_func):
global ax
if ax is None:
plt.ion()
plt.show()
ax = plt.figure().add_subplot(projection='3d')
ax.cla()
X_curve = np.meshgrid(np.linspace(0, 1, 25), np.linspace(0, 1, 25)... | 555 | Python | .py | 17 | 26.352941 | 70 | 0.605578 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,930 | train.py | dajuric_dark/dark-7/samples/2.0-Func-logistic-regression/dark/train.py | import os
os.environ["USE_CPU"] = "True"
import numpy as np
import dark
from dark import Parameter
import matplotlib.pyplot as plt
from util import *
from dataset import *
def sigmoid(x):
den = dark.add(1, dark.exp(dark.neg(x)))
return dark.div(1, den)
def bce_loss(predictions, targets):
lossA = dark.mul... | 1,184 | Python | .py | 35 | 30.285714 | 89 | 0.682582 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,931 | dataset.py | dajuric_dark/dark-7/samples/2.0-Func-logistic-regression/dark/dataset.py | import random
import numpy as np
def get_data():
X = []; Y = []
for i in range(100):
x_1 = random.random()
x_2 = random.random()
y = 1 if x_1 < 0.5 else 0 #split
X.append((x_1, x_2))
Y.append([y])
X = np.array(X); Y = np.array(Y)
return X, Y | 310 | Python | .py | 12 | 19.166667 | 40 | 0.503497 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,932 | eval.py | dajuric_dark/dark-7/samples/1.0-Func-derivation/torch/eval.py | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
import torch
import matplotlib.pyplot as plt
def calc_points(range = (-15, 15)):
x = torch.linspace(range[0], range[1], 50).reshape(1, -1)
x = torch.tensor(x, requires_grad=True)
fx = torch.add(torch.mul(2, torch.pow(x, 2)), 5) #2*x^2 + 5
#fx ... | 761 | Python | .py | 21 | 32.285714 | 105 | 0.621585 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,933 | eval.py | dajuric_dark/dark-7/samples/1.0-Func-derivation/dark/eval.py | import os
os.environ["USE_CPU"] = "True"
import dark
import dark.tensor as dt
import matplotlib.pyplot as plt
def calc_points(range = (-15, 15)):
x = dt.linspace(range[0], range[1], 50).reshape(1, -1)
x = dark.Parameter(x)
fx = dark.add(dark.mul(2, dark.pow(x, 2)), 5) #2*x^2 + 5
#fx = dark.div(da... | 743 | Python | .py | 22 | 29.954545 | 100 | 0.618513 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,934 | util.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/torch/util.py | import numpy as np
import cv2
import torch
from config import *
def _draw_label(im, correct, label):
color = (0, 255, 0) if correct == 1 else (0, 0, 255)
cv2.putText(im, label, (5, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
def save_samples(dataset, model, str_labels, filename, grid = (3, 5)):
h, w = g... | 1,175 | Python | .py | 28 | 32.535714 | 76 | 0.585077 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,935 | config.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/torch/config.py | import os
import torch
IM_SIZE = 32
BATCH_SIZE = 128
CLASS_COUNT = 2 # 10 for full dataset
EPOCHS = 5
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Running on: {device}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pickle" | 291 | Python | .py | 10 | 27.8 | 56 | 0.730216 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,936 | train.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/torch/train.py | import os
import torch
import torch.nn as nn
from torch.optim import *
from rich.progress import track
from model import Resnet9, get_net
from dataset import get_loaders
from config import *
from util import save_samples
def train_loop(dataloader, model, loss_fn, optimizer):
model.train()
size = len(dataloader... | 2,294 | Python | .py | 59 | 32.491525 | 126 | 0.610231 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,937 | dataset.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/torch/dataset.py | from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import *
from config import *
def get_loaders():
def label_transform(l):
one_hot = torch.zeros(CLASS_COUNT, dtype=torch.float32)
one_hot[l] = 1
return one_hot
trTransforms =... | 858 | Python | .py | 25 | 28.32 | 84 | 0.667874 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,938 | model.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/torch/model.py | import torch
import torch.nn as nn
import os
from config import *
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, pool_no=-1):
super().__init__()
list = [
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels)... | 1,766 | Python | .py | 50 | 26.3 | 76 | 0.574275 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,939 | util.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/dark/util.py | import numpy as np
import cv2
import dark.tensor as dt
def _draw_label(im, correct, label):
color = (0, 255, 0) if correct == 1 else (0, 0, 255)
cv2.putText(im, label, (5, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
def save_samples(dataset, model, str_labels, filename, grid = (3, 5)):
h, w = grid
i... | 1,113 | Python | .py | 26 | 33.230769 | 76 | 0.588462 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,940 | config.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/dark/config.py | import os
import dark.tensor as dt
IM_SIZE = 32
BATCH_SIZE = 128
CLASS_COUNT = 2 # 10 for full dataset
EPOCHS = 5
print(f"Running on: {'cuda' if dt.is_cuda() else 'cpu'}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pickle" | 272 | Python | .py | 9 | 29.111111 | 57 | 0.717557 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,941 | train.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/dark/train.py | import dark
import dark.nn as nn
from dark.optim import *
import dark.tensor as dt
from rich.progress import track
from model import Resnet9, get_net
from dataset import get_loaders
from config import *
from util import save_samples
def train_loop(dataloader, model, loss_fn, optimizer):
model.train()
size = le... | 2,214 | Python | .py | 56 | 33.071429 | 107 | 0.615747 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,942 | dataset.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/dark/dataset.py | from dark.utils.data import ImageFolder, DataLoader
from dark.utils.transforms import *
from config import *
def get_loaders():
def label_transform(l):
one_hot = dt.zeros(CLASS_COUNT, dtype=dt.float64)
one_hot[l] = 1
return one_hot
trTransforms = Compose(
Resize(IM_SIZE, IM_SIZ... | 830 | Python | .py | 24 | 28.375 | 84 | 0.655431 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,943 | model.py | dajuric_dark/dark-7/samples/5.0-ResNet-classification/dark/model.py | import dark
import dark.nn as nn
import dark.tensor as dt
from dark.nn.init import default_init_weights
from config import *
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, pool_no=-1):
super().__init__()
list = [
nn.Conv2d(in_channels, out_channels, kernel_si... | 1,860 | Python | .py | 51 | 27.509804 | 76 | 0.58864 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,944 | util.py | dajuric_dark/dark-7/samples/4.0-NN-classification/torch/util.py | import numpy as np
import cv2
import torch
from config import *
def _draw_label(im, correct):
color = (0, 255, 0) if correct == 1 else (0, 0, 255)
cv2.circle(im, (25, 25), 5, color, -1)
def save_samples(dataset, model, filename, grid = (3, 5)):
h, w = grid
i = 0
image_rows = []
for r in r... | 1,075 | Python | .py | 28 | 29.357143 | 72 | 0.568317 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,945 | config.py | dajuric_dark/dark-7/samples/4.0-NN-classification/torch/config.py | import os
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Running on: {device}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pth"
IM_SIZE = 28
BATCH_SIZE = 32
CLASS_COUNT = 10
EPOCHS = 5 | 265 | Python | .py | 10 | 25.3 | 56 | 0.727273 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,946 | test.py | dajuric_dark/dark-7/samples/4.0-NN-classification/torch/test.py | import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
from model import MyNNBlock, MyNN #for deserialization
from config import *
def get_random_sample():
images = glob.glob(f"{script_dir}/../db/test/**/*.png")
imPath = np.random.choice(images, 1)[0]
im = cv2.imread(imPath)
im = cv... | 877 | Python | .py | 33 | 22.69697 | 59 | 0.640382 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,947 | train.py | dajuric_dark/dark-7/samples/4.0-NN-classification/torch/train.py | import numpy as np
import torch.nn as nn
from torch.optim import *
from config import *
from rich.progress import track
from model import MyNN, get_net
from dataset import get_loaders
from util import save_samples
def train_loop(dataloader, model, loss_fn, optimizer):
model.train()
size = len(dataloader.datase... | 2,256 | Python | .py | 58 | 32.396552 | 86 | 0.605166 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,948 | dataset.py | dajuric_dark/dark-7/samples/4.0-NN-classification/torch/dataset.py |
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import *
from config import *
def get_loaders():
def label_transform(l):
one_hot = torch.zeros(CLASS_COUNT, dtype=torch.float32)
one_hot[l] = 1
return one_hot
trTransforms... | 924 | Python | .py | 27 | 27.925926 | 84 | 0.655056 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,949 | model.py | dajuric_dark/dark-7/samples/4.0-NN-classification/torch/model.py | import torch
import torch.nn as nn
import os
from config import *
class MyNNBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
self.activation = nn.ReLU()
def forward(self, x):
x = self.linear(x)
x = self.... | 935 | Python | .py | 34 | 20.764706 | 53 | 0.572717 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,950 | util.py | dajuric_dark/dark-7/samples/4.0-NN-classification/dark/util.py | import numpy as np
import cv2
import dark.tensor as dt
def _draw_label(im, correct):
color = (0, 255, 0) if correct == 1 else (0, 0, 255)
cv2.circle(im, (25, 25), 5, color, -1)
def save_samples(dataset, model, filename, grid = (3, 5)):
h, w = grid
i = 0
image_rows = []
for r in range(h):
... | 1,013 | Python | .py | 26 | 29.807692 | 72 | 0.570978 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,951 | config.py | dajuric_dark/dark-7/samples/4.0-NN-classification/dark/config.py | import os
import dark.tensor as dt
print(f"Running on: {'cuda' if dt.is_cuda() else 'cpu'}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pickle"
IM_SIZE = 28
BATCH_SIZE = 32
CLASS_COUNT = 10
EPOCHS = 5 | 250 | Python | .py | 9 | 26.666667 | 57 | 0.716667 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,952 | test.py | dajuric_dark/dark-7/samples/4.0-NN-classification/dark/test.py | import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import dark
from model import MyNNBlock, MyNN #for deserialization
from config import *
def get_random_sample():
images = glob.glob(f"{script_dir}/../db/test/**/*.png")
imPath = np.random.choice(images, 1)[0]
im = cv2.imread(imPath)... | 860 | Python | .py | 33 | 22.30303 | 59 | 0.643902 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,953 | train.py | dajuric_dark/dark-7/samples/4.0-NN-classification/dark/train.py | import numpy as np
import dark.tensor as dt
import dark
import dark.nn as nn
from dark.optim import *
from dark.utils.transforms import *
from config import *
from rich.progress import track
from model import MyNN, get_net
from dataset import get_loaders
from util import save_samples
def train_loop(dataloader, model, ... | 2,319 | Python | .py | 60 | 32.4 | 87 | 0.614177 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,954 | dataset.py | dajuric_dark/dark-7/samples/4.0-NN-classification/dark/dataset.py |
from dark.utils.data import ImageFolder, DataLoader
from dark.utils.transforms import *
from config import *
def get_loaders():
def label_transform(l):
one_hot = np.zeros(CLASS_COUNT, dtype=np.float32)
one_hot[l] = 1
return one_hot
trTransforms = Compose(
Resize(IM_SIZE, IM_S... | 874 | Python | .py | 26 | 27.115385 | 84 | 0.64566 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,955 | model.py | dajuric_dark/dark-7/samples/4.0-NN-classification/dark/model.py | import dark
import dark.nn as nn
from dark.nn.init import default_init_weights
import os
import pickle
from config import *
class MyNNBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
self.activation = nn.ReLU()
def forw... | 1,006 | Python | .py | 36 | 21.444444 | 53 | 0.593096 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,956 | modelD.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/torch/modelD.py | from torch.nn import *
from config import *
from util import init_weights
import os
class Discriminator(Module):
def __init__(self, ndf, nc):
super().__init__()
self.nn = Sequential(
#input: nc x 64 x 64
Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1),
... | 1,291 | Python | .py | 38 | 24.394737 | 73 | 0.530844 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,957 | util.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/torch/util.py | import torch
import torch.nn as nn
import cv2
def init_weights(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.normal_(m.weight.data, 0.0, 0.02)
nn.init.zeros_(m.bias.data)
if isinstance(m, nn.BatchNorm2d):
nn.init.normal_(m.weight.data, 1.0, 0.02)
... | 959 | Python | .py | 26 | 26.923077 | 69 | 0.586047 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,958 | config.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/torch/config.py | import torch
import os
device = "cuda" if torch.cuda.is_available() else "cpu"
script_dir = os.path.dirname(os.path.realpath(__file__))
modelD_path = f"{script_dir}/modelD.pth"
modelG_path = f"{script_dir}/modelG.pth"
nz = 100
BATCH_SIZE = 40
EPOCHS = 15 | 256 | Python | .py | 9 | 27.333333 | 56 | 0.731707 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,959 | train.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/torch/train.py | import torch
from torch.utils.data import DataLoader
from torch.nn import Module, BCEWithLogitsLoss
from torch.optim import Optimizer, Adam
from modelD import Discriminator, get_netD
from modelG import Generator, get_netG
from dataset import get_loader
from util import save_samples
from rich.progress import track
from... | 2,586 | Python | .py | 65 | 33.338462 | 73 | 0.66653 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,960 | modelG.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/torch/modelG.py | import torch
from torch.nn import *
from config import *
from util import init_weights
import os
class Generator(Module):
def __init__(self, nz, ngf, nc):
super().__init__()
self.nn = Sequential(
# input: Z
ConvTranspose2d(nz, ngf * 8, kernel_size=4, stride=1, paddi... | 1,392 | Python | .py | 42 | 23.690476 | 82 | 0.529367 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,961 | dataset.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/torch/dataset.py | from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import *
from config import *
def get_loader():
tr = Compose([
Resize((64, 64)),
ToTensor(),
Normalize(0.5, 0.5)
])
dataset = ImageFolder(f"{script_dir}/../db/", transfo... | 428 | Python | .py | 13 | 28.384615 | 78 | 0.699758 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,962 | modelD.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/dark/modelD.py | import dark
from dark.nn import *
import os
from util import init_weights
from config import *
class Discriminator(Module):
def __init__(self, ndf, nc):
super().__init__()
self.nn = Sequential(
#input: nc x 64 x 64
Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1)... | 1,271 | Python | .py | 38 | 24.105263 | 73 | 0.534653 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,963 | util.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/dark/util.py | import dark.tensor as dt
import dark.nn as nn
from dark.nn.init import default_init_weights
import cv2
def init_weights(m):
default_init_weights(m)
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
m.weights.data = dt.random.normal(0.0, 0.02, m.weights.data.shape)
m.bias.data =... | 1,102 | Python | .py | 28 | 29.857143 | 74 | 0.611 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,964 | config.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/dark/config.py | import dark.tensor as dt
import os
print(f"Running on: {'cuda' if dt.is_cuda() else 'cpu'}")
script_dir = os.path.dirname(os.path.realpath(__file__))
modelD_path = f"{script_dir}/modelD.pickle"
modelG_path = f"{script_dir}/modelG.pickle"
nz = 100
BATCH_SIZE = 40
EPOCHS = 15 | 276 | Python | .py | 9 | 29.555556 | 57 | 0.721805 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,965 | train.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/dark/train.py | import dark
import dark.tensor as dt
from dark.nn import Module, BCEWithLogitsLoss
from dark.optim import Optimizer, Adam
from dark.utils.data import DataLoader
from modelD import Discriminator, get_netD
from modelG import Generator, get_netG
from dataset import get_loader
from util import save_samples
from rich.progr... | 2,523 | Python | .py | 65 | 32.323077 | 80 | 0.667372 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,966 | modelG.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/dark/modelG.py | import dark
from dark.nn import *
import os
from util import init_weights
from config import *
class Generator(Module):
def __init__(self, nz, ngf, nc):
super().__init__()
self.nn = Sequential(
# input: Z
ConvTranspose2d(nz, ngf * 8, kernel_size=4, stride=1, padding... | 1,358 | Python | .py | 41 | 23.682927 | 82 | 0.528958 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,967 | dataset.py | dajuric_dark/dark-7/samples/6.0-GAN-image_gen/dark/dataset.py | from dark.utils.data import ImageFolder, DataLoader
from dark.utils.transforms import *
from config import *
def get_loader():
tr = Compose(
Resize(64, 64),
Normalize(0.5, 0.5),
ToTensorV2(),
)
dataset = ImageFolder(f"{script_dir}/../db/", imgT=tr)
data_loader = DataLoader(data... | 389 | Python | .py | 12 | 27.666667 | 79 | 0.675532 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,968 | process_db.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/db/process_db.py | import os
import cv2
from glob import glob
from rich.progress import track
script_dir = os.path.dirname(os.path.realpath(__file__))
def resize(img_folder):
im_files = sorted(glob(f"{img_folder}/*.*"))
for im_file in track(im_files, "Resizing..."):
im = cv2.imread(im_file)
im = cv2.resize(im, ... | 904 | Python | .py | 22 | 35.5 | 81 | 0.622426 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,969 | util.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/torch/util.py | import cv2
import torch
from config import *
def save_samples(dataset, model, filename, count = 5):
image_rows = []
for i in range(count):
im, true_mask = dataset[i]
im = torch.unsqueeze(im, 0).type(torch.float32)
pred_mask = model(im.to(device)).detach().cpu()
pred_mask = tor... | 847 | Python | .py | 18 | 40.444444 | 121 | 0.623786 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,970 | config.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/torch/config.py | import os
import torch
import albumentations as A
import albumentations.pytorch.transforms as AT
IM_SIZE = 64
BATCH_SIZE = 24
EPOCHS = 3
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using: {device}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pth"
... | 554 | Python | .py | 22 | 22.636364 | 56 | 0.705323 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,971 | train.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/torch/train.py | import torch.nn as nn
from torch.optim import Adam
from rich.progress import track
from dataset import get_loaders
from model import UNet, get_net
from util import save_samples
from config import *
def train_loop(dataloader, model, loss_fn, optimizer):
model.train()
size = len(dataloader.dataset)
for batc... | 1,848 | Python | .py | 49 | 31.081633 | 87 | 0.608032 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,972 | dataset.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/torch/dataset.py | import torch
from torch.utils.data import Dataset, DataLoader
from config import *
from glob import glob
from pathlib import Path
import cv2
class CarvanaDataset(Dataset):
def __init__(self, im_folder, transform=None):
self.im_files = sorted(glob(f"{im_folder}/*.jpg"))
self.transform = transform
... | 1,327 | Python | .py | 33 | 33.272727 | 78 | 0.648163 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,973 | model.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/torch/model.py | import torch
import torch.nn as nn
import os
from config import *
class UNet(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
features = [32, 64, 128, 256]
# down
self.down_blocks = nn.ModuleList()
self.down_reducs = nn.ModuleList()
for ... | 2,188 | Python | .py | 65 | 24.276923 | 90 | 0.545759 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,974 | util.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/dark/util.py | import dark.tensor as dt
import cv2
def save_samples(dataset, model, filename, count = 5):
image_rows = []
for i in range(count):
im, true_mask = dataset[i]
im, true_mask = dt.asarray(im), dt.asarray(true_mask)
im = dt.expand_dims(im, 0)
pred_mask = model(im).... | 899 | Python | .py | 19 | 39.263158 | 112 | 0.601399 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,975 | config.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/dark/config.py | import os
import dark.tensor as dt
from dark.utils.transforms import *
IM_SIZE = 64
BATCH_SIZE = 24
EPOCHS = 3
print(f"Running on: {'cuda' if dt.is_cuda() else 'cpu'}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pth"
trTransforms = Compose(
Resize(IM_SIZE, IM_SIZE)... | 545 | Python | .py | 22 | 22 | 57 | 0.680769 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,976 | train.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/dark/train.py | import dark
import dark.nn as nn
from dark.optim import *
from rich.progress import track
from dataset import get_loaders
from model import UNet, get_net
from util import save_samples
from config import *
def train_loop(dataloader, model, loss_fn, optimizer):
model.train()
size = len(dataloader.dataset)
f... | 1,761 | Python | .py | 48 | 30.25 | 87 | 0.61392 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,977 | dataset.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/dark/dataset.py | from dark.utils.data import Dataset, DataLoader
import numpy as np
import random
from datetime import datetime
import cv2
from config import *
from glob import glob
from pathlib import Path
import cv2
class CarvanaDataset(Dataset):
def __init__(self, im_folder, transform=None):
self.im_files = sorted(glob(... | 1,727 | Python | .py | 45 | 31.666667 | 78 | 0.641396 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,978 | model.py | dajuric_dark/dark-7/samples/6.1-UNet-segmentation/dark/model.py | import dark
import dark.nn as nn
from dark.nn.init import default_init_weights
import os
from config import *
# adopted from: https://medium.com/analytics-vidhya/creating-a-very-simple-u-net-model-with-pytorch-for-semantic-segmentation-of-satellite-images-223aa216e705
class UNet(nn.Module):
def __init__(self, in_c... | 2,446 | Python | .py | 68 | 26.808824 | 158 | 0.575771 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,979 | util.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/torch/util.py | import torch
import numpy as np
import matplotlib.pyplot as plt
ax = None
def plot(X, Y, eval_func):
global ax
if ax is None:
plt.ion()
plt.show()
ax = plt.figure().add_subplot(projection='3d')
ax.cla()
X_curve = torch.meshgrid(torch.linspace(0, 1, 25), to... | 641 | Python | .py | 18 | 29.611111 | 88 | 0.625213 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,980 | train.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/torch/train.py | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from model import get_net
from util import *
from dataset import *
X, y = get_data()
X = normalize_data(X); y = normalize_data(y)
X = torch.tensor... | 736 | Python | .py | 26 | 25.461538 | 84 | 0.67482 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,981 | dataset.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/torch/dataset.py | import random
import numpy as np
def get_data():
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
X = X.reshape(-1, 1); Y = Y.reshape(-1, 1)
data = np.concatenate((X, Y), axis=1)
va... | 523 | Python | .py | 18 | 24.222222 | 48 | 0.548387 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,982 | model.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/torch/model.py | import torch
import torch.nn as nn
class MyNNBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
self.activation = nn.LeakyReLU()
def forward(self, x):
x = self.linear(x)
x = self.activation(x)
retu... | 762 | Python | .py | 27 | 21.074074 | 48 | 0.557559 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,983 | util.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/dark/util.py | import dark.tensor as dt
import numpy as np
import matplotlib.pyplot as plt
ax = None
def plot(X, Y, eval_func):
global ax
if ax is None:
plt.ion()
plt.show()
ax = plt.figure().add_subplot(projection='3d')
ax.cla()
X_curve = np.meshgrid(np.linspace(0, 1, 2... | 611 | Python | .py | 19 | 26.263158 | 70 | 0.616906 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,984 | train.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/dark/train.py | import os
os.environ["USE_CPU"] = "True"
import dark
import dark.tensor as dt
import dark.nn as nn
import dark.optim as optim
import matplotlib.pyplot as plt
from model import get_net
from util import *
from dataset import *
X, y = get_data()
X = normalize_data(X); y = normalize_data(y)
model = get_net()
model.tra... | 636 | Python | .py | 26 | 21.730769 | 46 | 0.69398 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,985 | dataset.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/dark/dataset.py | import random
import numpy as np
def get_data():
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
X = X.reshape(-1, 1); Y = Y.reshape(-1, 1)
data = np.concatenate((X, Y), axis=1)
va... | 523 | Python | .py | 18 | 24.222222 | 48 | 0.548387 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,986 | model.py | dajuric_dark/dark-7/samples/3.0-Func-NN-regression/dark/model.py | import dark
import dark.nn as nn
from dark.nn.init import default_init_weights
class MyNNBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
self.activation = nn.LeakyReLU()
def forward(self, x):
x = self.linear(x)... | 841 | Python | .py | 29 | 22.172414 | 48 | 0.580726 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,987 | process_db.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/db/process_db.py | import os
from pathlib import Path
import json
import numpy as np
import cv2
from rich.progress import track
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
script_dir = os.path.dirname(os.path.realpath(__file__))
def read_keypoints(json_file):
data = json.load(open(json_file, "r"))
dat... | 2,689 | Python | .py | 68 | 33.367647 | 95 | 0.624951 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,988 | util.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/torch/util.py | import torch
import cv2
import numpy as np
def _draw_keypoints(im, keypoints, color):
imH, imW, _ = im.shape
for x, y in keypoints.reshape(-1, 2):
x = int(x * imW)
y = int(y * imH)
cv2.circle(im, (x, y), 3, color, -1)
def save_samples(dataset, model, filename, grid = (3, 5), device =... | 1,077 | Python | .py | 28 | 29.392857 | 81 | 0.56182 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,989 | config.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/torch/config.py | import os
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using: {device}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pth"
IM_SIZE = 96
KEYPOINT_COUNT = 68 * 2
BATCH_SIZE = 64
EPOCHS = 15 | 268 | Python | .py | 10 | 25.6 | 56 | 0.722656 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,990 | test_camera.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/torch/test_camera.py | import torch
from model import Resnet18
import numpy as np
import os
import cv2
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
device = 'cuda' if torch.cuda.is_available() else 'cpu'
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f... | 2,686 | Python | .py | 70 | 32.471429 | 95 | 0.613344 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,991 | train.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/torch/train.py | import torch
import torch.nn as nn
from torchvision.transforms import *
import torch.optim as optim
from rich.progress import track
from model import Resnet18, get_net
from util import save_samples
from dataset import get_loaders
from config import *
def train_loop(train_loader, model, criterion, optimizer):
trai... | 1,993 | Python | .py | 55 | 29.981818 | 97 | 0.620636 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,992 | dataset.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/torch/dataset.py | from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import json
import numpy as np
import cv2
import albumentations as A
import albumentations.pytorch.transforms as AT
from config import *
import random
from glob import glob
class KeypointDataset(Dataset):
def __init__(self, kp_files, tran... | 2,239 | Python | .py | 56 | 32.982143 | 77 | 0.643893 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,993 | model.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/torch/model.py | import torch
import torch.nn as nn
import os
from config import *
class Block(nn.Module):
def __init__(self, in_ch, out_ch, stride=1):
super(Block, self).__init__()
self.downsample = self.get_identity_downsample(in_ch, out_ch) if stride != 1 else None
self.conv1 = nn.Conv2d(in... | 2,923 | Python | .py | 78 | 27.115385 | 94 | 0.571535 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,994 | util.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/dark/util.py | import dark
import cv2
import numpy as np
def _draw_keypoints(im, keypoints, color):
imH, imW, _ = im.shape
for x, y in keypoints.reshape(-1, 2):
x = int(x * imW)
y = int(y * imH)
cv2.circle(im, (x, y), 3, color, -1)
def save_samples(dataset, model, filename, grid = (3, 5)):
h, w... | 1,034 | Python | .py | 28 | 27.857143 | 71 | 0.557851 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,995 | config.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/dark/config.py | import os
import dark.tensor as dt
import dark.utils.transforms as T
import dark.utils.point_transforms as P
print(f"Running on: {'cuda' if dt.is_cuda() else 'cpu'}")
script_dir = os.path.dirname(os.path.realpath(__file__))
model_path = f"{script_dir}/model.pth"
IM_SIZE = 96
KEYPOINT_COUNT = 68 * 2
BATCH_SIZE = 64
EP... | 929 | Python | .py | 34 | 24.264706 | 64 | 0.670045 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,996 | test_camera.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/dark/test_camera.py | import dark
import dark.tensor as dt
from model import Resnet18
import numpy as np
import os
import cv2
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
print(f"Running on: {'cuda' if dt.is_cuda() else 'cpu'}")
script_dir = os.path.dirname(os.path.realpath... | 2,643 | Python | .py | 70 | 31.857143 | 95 | 0.609524 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,997 | train.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/dark/train.py | from rich.progress import track
import dark
import dark.nn as nn
import dark.optim as optim
from model import Resnet18, get_net
from util import save_samples
from dataset import get_loaders
from config import *
def train_loop(train_loader, model, criterion, optimizer):
train_loss = 0.0
model.train()
for b... | 1,950 | Python | .py | 52 | 30.884615 | 84 | 0.622854 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,998 | dataset.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/dark/dataset.py | from dark.utils.data import Dataset, DataLoader
from config import *
import json
import numpy as np
import cv2
import random
from datetime import datetime
from glob import glob
class KeypointDataset(Dataset):
def __init__(self, kp_files, im_transform, kp_transform):
self.kp_files = kp_files
self.im... | 2,574 | Python | .py | 65 | 32.707692 | 90 | 0.625554 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |
2,284,999 | model.py | dajuric_dark/dark-7/samples/5.1-ResNet-regression/dark/model.py | import dark
import dark.nn as nn
from dark.nn.init import default_init_weights
import os
import pickle
from config import *
class Block(nn.Module):
def __init__(self, in_ch, out_ch, stride=1):
super(Block, self).__init__()
self.downsample = self.get_identity_downsample(in_ch, out_ch) if stride... | 3,042 | Python | .py | 81 | 27.518519 | 94 | 0.583006 | dajuric/dark | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:43 PM (Europe/Amsterdam) |