Datasets:
Razvan27
/
ML4SE-Python

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util.py
dajuric_dark/dark-7/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...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,001
train.py
dajuric_dark/dark-7/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...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,002
util.py
dajuric_dark/dark-7/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).reshape(-1, 1) Y_curve = eval_func(X_curve).reshape(-1, 1) plt.plot(X_c...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,003
train.py
dajuric_dark/dark-7/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.item():2.1f}") fx = eval_func(x) fx.backward() #calc grad...
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dajuric/dark
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test_conv2d.py
dajuric_dark/dark-7/tests/test_conv2d.py
import os os.environ["USE_CPU"] = "True" import dark import dark.tensor as dt def approx_grad(X, function, eps=1e-6): grad = dt.zeros_like(X) for i in range(len(X)): X[i] += eps fn_2 = function(X) X[i] -= 2 * eps fn_1 = function(X) grad[i] = (fn_2...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,005
testConv2d.py
dajuric_dark/dark-7/tests/testConv2d.py
import numpy as np import random import os random.seed(0) np.random.seed(0) im = np.random.randint(-16, +16, size=(2, 2, 9, 9)).astype(np.float64) k = np.random.randint(-16, +16, size=(3, 2, 3, 3)).astype(np.float64) #--------------------------- import torch as tt import torch.nn as nn st = tt.tensor(im, requ...
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dajuric/dark
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2,285,006
testConv2dtranspose.py
dajuric_dark/dark-7/tests/testConv2dtranspose.py
import numpy as np import random import os random.seed(0) np.random.seed(0) im = np.random.randint(-16, +16, size=(4, 1, 32, 32)).astype(np.float64) k = np.random.randint(-16, +16, size=(2, 1, 3, 3)).astype(np.float64); k = k.transpose(1, 0, 2, 3) #--------------------------- import torch as tt import torch.nn...
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dajuric/dark
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2,285,007
testUpsample2d.py
dajuric_dark/dark-7/tests/testUpsample2d.py
import numpy as np import random import os random.seed(0) np.random.seed(0) im = np.random.randint(-16, +16, size=(1, 3, 2, 2)).astype(np.float64) #--------------------------- import torch as tt import torch.nn as nn st = tt.tensor(im, requires_grad=True) tRes = nn.functional.interpolate(st, scale_factor=3, mode=...
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dajuric/dark
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2,285,008
testCNN.py
dajuric_dark/dark-7/tests/testCNN.py
import numpy as np import random random.seed(1) np.random.seed(1) X = np.random.randint(-16, +16, size=(1, 3, 9, 9)).astype(np.float32) # ----------------------------------------------------------------------- #import os #os.environ["USE_CPU"] = "True" import dark import dark.nn as nn import dark.optim as optim imp...
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dajuric/dark
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2,285,009
test_conv2dtranspose.py
dajuric_dark/dark-7/tests/test_conv2dtranspose.py
#import os #os.environ["USE_CPU"] = "True" import dark import dark.tensor as dt def approx_grad(X, function, eps=1e-6): grad = dt.zeros_like(X) for i in range(len(X)): X[i] += eps fn_2 = function(X) X[i] -= 2 * eps fn_1 = function(X) grad[i] = (fn...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,010
testMaxPool2d.py
dajuric_dark/dark-7/tests/testMaxPool2d.py
import numpy as np import random import os random.seed(0) np.random.seed(0) im = np.random.randint(-16, +16, size=(1, 3, 2, 2)).astype(np.float64) #--------------------------- import torch as tt import torch.nn as nn st = tt.tensor(im, requires_grad=True) tRes = nn.functional.max_pool2d(st, kernel_size=2) tRes = ...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,011
__init__.py
dajuric_dark/dark-7/dark/__init__.py
from .core.operations import * from .core.autodiff import * from .utils.serialization import save, load
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,012
optimizers.py
dajuric_dark/dark-7/dark/optim/optimizers.py
import dark.tensor as dt class Optimizer(): def __init__(self, parameters): self.parameters = parameters def step(self): raise NotImplementedError() def zero_grad(self): for p in self.parameters: p.zero_grad() class SGD(Optimizer): def __init__(self, parameters,...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,013
transforms.py
dajuric_dark/dark-7/dark/utils/transforms.py
import cv2 import numpy as np import random from .util import max_inscribed_rect class Transform(): def __init__(self): pass def __call__(self, im): raise Exception("Not implemented") class Compose(): def __init__(self, *transforms): self.transforms = transforms def __call__(...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,014
util.py
dajuric_dark/dark-7/dark/utils/util.py
import math # https://stackoverflow.com/questions/5789239/calculate-largest-inscribed-rectangle-in-a-rotated-rectangle def max_inscribed_rect(w, h, angle): angle = math.radians(angle) quadrant = int(math.floor(angle / (math.pi / 2))) & 3 sign_alpha = angle if ((quadrant & 1) == 0) else math.pi - angle ...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,015
point_transforms.py
dajuric_dark/dark-7/dark/utils/point_transforms.py
import random import numpy as np from .util import max_inscribed_rect class PointTransform(): def __call__(self, pts, im_shape): pass class Compose(): def __init__(self, *transforms): self.transforms = transforms def __call__(self, pts, im_shape): t_pts = pts t_shape =...
1,897
Python
.py
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dajuric/dark
8
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,016
serialization.py
dajuric_dark/dark-7/dark/utils/serialization.py
import os import pickle from copy import deepcopy from .. import tensor as dt def _dt_to_numpy(obj): if "cupy.ndarray" in str(obj.__class__): return obj.get() return obj def _numpy_to_dt(obj): if "numpy.ndarray" in str(obj.__class__): return dt.asarray(obj) return obj def ...
1,243
Python
.py
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,017
data.py
dajuric_dark/dark-7/dark/utils/data.py
import os from glob import glob import math import numpy as np import cv2 class Dataset(): def __init__(self): pass def __len__(self): raise NotImplementedError() def __getitem__(self, index): raise NotImplementedError() class ImageFolder(Dataset): def __init__(self, root_fo...
3,615
Python
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dajuric/dark
8
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,018
ops.py
dajuric_dark/dark-7/dark/tensor/ops.py
import os use_cpu = os.environ.get("USE_CPU") in ["1", "True"] _is_cuda = False try: if use_cpu: raise Exception("CPU forced") from cupy import * from .gpu.conv import conv2d, conv2d_grad from .gpu.pool import max_pool2d, max_unpool2d _is_cuda = True except: from numpy import * from .c...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,019
pool.py
dajuric_dark/dark-7/dark/tensor/cpu/pool.py
# https://agustinus.kristia.de/techblog/2016/07/18/convnet-maxpool-layer/ # https://hackmd.io/@machine-learning/blog-post-cnnumpy-slow import numpy as np from numba import njit, prange @njit() def _max_pool2d_im(X, size, stride, out, out_idx): oh, ow = out.shape for h in range(oh): h_start = h * ...
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Python
.py
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,020
conv.py
dajuric_dark/dark-7/dark/tensor/cpu/conv.py
import numpy as np from numba import njit, prange @njit() def _pad(tensor, padding): sb, sc, sh, sw = tensor.shape padded = np.zeros((sb, sc, sh + padding*2, sw + padding*2)) padded[:, :, padding: sh + padding, padding: sw + padding] = tensor return padded @njit() def _conv2d_im(X, W, stride, ou...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,021
util.py
dajuric_dark/dark-7/dark/tensor/gpu/util.py
import cupy as cp def get_indices(x_shape, field_height, field_width, padding=1, stride=1): N, C, H, W = x_shape #assert (H + 2 * padding - field_height) % stride == 0 #assert (W + 2 * padding - field_height) % stride == 0 out_height = int((H + 2 * padding - field_height) / stride + 1) out_width =...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,022
pool.py
dajuric_dark/dark-7/dark/tensor/gpu/pool.py
# refs: https://agustinus.kristia.de/techblog/2016/07/18/convnet-maxpool-layer/ import cupy as cp from .util import im2col, col2im def max_pool2d(X, size, stride): n, d, h, w = X.shape h_out = (h - size) / stride + 1 w_out = (w - size) / stride + 1 if not w_out.is_integer() or not h_out.is_integer():...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,023
conv.py
dajuric_dark/dark-7/dark/tensor/gpu/conv.py
import cupy as cp from .util import im2col, col2im def conv2d(X, W, stride, padding): n_filters, d_filter, h_filter, w_filter = W.shape n_x, d_x, h_x, w_x = X.shape h_out = (h_x - h_filter + 2 * padding) // stride + 1 w_out = (w_x - w_filter + 2 * padding) // stride + 1 # if not h_out.is_integer(...
1,180
Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,024
autodiff.py
dajuric_dark/dark-7/dark/core/autodiff.py
import dark.tensor as dt class Node(): op = None inputs = [] data = None requires_grad = True _grad = None def __init__(self, val): self.data = dt.asarray(val) if len(self.data.shape) == 0: self.data = self.data.reshape((1, 1)) def backward(self): if self.grad is ...
2,807
Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,025
utils.py
dajuric_dark/dark-7/dark/core/utils.py
import dark.tensor as dt import numpy as np def reduce_sum(result, target_shape): dim_diff = result.ndim - len(target_shape) if dim_diff < 0: return result.reshape(target_shape) # sum all leading dimensions # except leading dimensions, sum all axis that are equal to 1, count from right ...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,026
operations.py
dajuric_dark/dark-7/dark/core/operations.py
from .autodiff import Operation from .utils import * import dark.tensor as dt # ------ scalar ------ class AbsoluteValue(Operation): def forward(self, x): return dt.abs(x) def backward(self, dldy, y, x): gz = (x > 0) lz = dt.logical_not(gz) return [dldy * gz - dldy * lz] clas...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,027
module.py
dajuric_dark/dark-7/dark/nn/module.py
from dark import Parameter, Node, Constant class Module(): def __init__(self): self._modules = {} self._parameters = {} def forward(self, *x): pass def __call__(self, *x): x = [(Constant(p) if not isinstance(p, Node) else p) for p in x] return self.forward(*x) ...
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dajuric/dark
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2,285,028
init.py
dajuric_dark/dark-7/dark/nn/init.py
import dark.tensor as dt import dark.nn as nn from dark import * def _calc_in_out_dims(t_shape): assert len(t_shape) > 1 in_maps = t_shape[1] out_maps = t_shape[0] if len(t_shape) > 2: s = dt.prod(dt.array(t_shape[2:])) in_maps *= s out_maps *= s return in_maps, out...
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dajuric/dark
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2,285,029
__init__.py
dajuric_dark/dark-7/dark/nn/__init__.py
from .init import * from .module import * from .modules import * from .losses import *
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dajuric/dark
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2,285,030
losses.py
dajuric_dark/dark-7/dark/nn/losses.py
import dark from .module import Module from .modules import Sigmoid, Softmax class MSELoss(Module): def __init__(self): super().__init__() def forward(self, predictions, targets): loss = dark.subtract(predictions, targets) loss = dark.pow(loss, 2) loss = dark.mean(loss) ...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,031
modules.py
dajuric_dark/dark-7/dark/nn/modules.py
import dark.tensor as dt from dark import Parameter, Node, Constant import dark from .module import * class ZeroParam(Parameter): def __init__(self, *shape): val = dt.zeros(shape) super().__init__(val) class Flatten(Module): def __init__(self): super().__init__() pass def...
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dajuric/dark
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2,285,032
setup.py
dajuric_dark/dark-4/setup.py
from setuptools import setup setup( name="dark", description="", version="4.0", packages=["dark"], package_dir={"dark": "dark"}, install_requires = [] )
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dajuric/dark
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2,285,033
util.py
dajuric_dark/dark-4/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)...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,034
train.py
dajuric_dark/dark-4/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...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,035
dataset.py
dajuric_dark/dark-4/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
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,036
util.py
dajuric_dark/dark-4/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
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dajuric/dark
8
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,037
train.py
dajuric_dark/dark-4/samples/2.0-Func-logistic-regression/dark/train.py
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(targets, dark.log(predictions)) lossB...
1,142
Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,038
dataset.py
dajuric_dark/dark-4/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
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Python
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dajuric/dark
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2,285,039
eval.py
dajuric_dark/dark-4/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 ...
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dajuric/dark
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2,285,040
eval.py
dajuric_dark/dark-4/samples/1.0-Func-derivation/dark/eval.py
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(dark.mul(dark.tanh(x), dark.pow(x, 2)), dark...
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dajuric/dark
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2,285,041
util.py
dajuric_dark/dark-4/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...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,042
config.py
dajuric_dark/dark-4/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
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,043
test.py
dajuric_dark/dark-4/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...
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dajuric/dark
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2,285,044
train.py
dajuric_dark/dark-4/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...
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dajuric/dark
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2,285,045
dataset.py
dajuric_dark/dark-4/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...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,046
model.py
dajuric_dark/dark-4/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....
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dajuric/dark
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2,285,047
util.py
dajuric_dark/dark-4/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): ...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,048
config.py
dajuric_dark/dark-4/samples/4.0-NN-classification/dark/config.py
import os import dark.tensor as dt 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
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,049
test.py
dajuric_dark/dark-4/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
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,050
train.py
dajuric_dark/dark-4/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, ...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,051
dataset.py
dajuric_dark/dark-4/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...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,052
model.py
dajuric_dark/dark-4/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...
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Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,053
util.py
dajuric_dark/dark-4/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...
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Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,054
train.py
dajuric_dark/dark-4/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...
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Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,055
dataset.py
dajuric_dark/dark-4/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
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dajuric/dark
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0
0
GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,056
model.py
dajuric_dark/dark-4/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
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dajuric/dark
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0
GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,057
util.py
dajuric_dark/dark-4/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
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dajuric/dark
8
0
0
GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,058
train.py
dajuric_dark/dark-4/samples/3.0-Func-NN-regression/dark/train.py
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.train() loss_fn = nn.MSELoss() opt = optim....
594
Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,059
dataset.py
dajuric_dark/dark-4/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
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,060
model.py
dajuric_dark/dark-4/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
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,061
util.py
dajuric_dark/dark-4/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...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,062
train.py
dajuric_dark/dark-4/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...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,063
util.py
dajuric_dark/dark-4/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).reshape(-1, 1) Y_curve = eval_func(X_curve).reshape(-1, 1) plt.plot(X_c...
438
Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,064
train.py
dajuric_dark/dark-4/samples/1.1-Func-optimization/dark/train.py
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.item():2.1f}") fx = eval_func(x) fx.backward() #calc gradient plot(lambda x: eval_func(x).dat...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,065
__init__.py
dajuric_dark/dark-4/dark/__init__.py
from .core.operations import * from .core.autodiff import * from .utils.serialization import save, load
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Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,066
optimizers.py
dajuric_dark/dark-4/dark/optim/optimizers.py
import dark.tensor as dt class Optimizer(): def __init__(self, parameters): self.parameters = parameters def step(self): raise NotImplementedError() def zero_grad(self): for p in self.parameters: p.zero_grad() class SGD(Optimizer): def __init__(self, parameters,...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,067
transforms.py
dajuric_dark/dark-4/dark/utils/transforms.py
import cv2 import numpy as np import random from .util import max_inscribed_rect class Transform(): def __init__(self): pass def __call__(self, im): raise Exception("Not implemented") class Compose(): def __init__(self, *transforms): self.transforms = transforms def __call__(...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,068
util.py
dajuric_dark/dark-4/dark/utils/util.py
import math # https://stackoverflow.com/questions/5789239/calculate-largest-inscribed-rectangle-in-a-rotated-rectangle def max_inscribed_rect(w, h, angle): angle = math.radians(angle) quadrant = int(math.floor(angle / (math.pi / 2))) & 3 sign_alpha = angle if ((quadrant & 1) == 0) else math.pi - angle ...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,069
serialization.py
dajuric_dark/dark-4/dark/utils/serialization.py
import os import pickle def load(filename): obj = pickle.load(open(filename, "rb")) return obj def save(obj, filename): dirname = os.path.dirname(filename) os.makedirs(dirname, exist_ok=True) pickle.dump(obj, open(filename, "wb"))
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,070
data.py
dajuric_dark/dark-4/dark/utils/data.py
import os from glob import glob import math import numpy as np import cv2 class Dataset(): def __init__(self): pass def __len__(self): raise NotImplementedError() def __getitem__(self, index): raise NotImplementedError() class ImageFolder(Dataset): def __init__(self, root_fo...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,071
ops.py
dajuric_dark/dark-4/dark/tensor/ops.py
from numpy import * seterr(over='raise') def unsqueeze(x, axis): return expand_dims(x, axis) def sigmoid(x): return 1 / (1 + exp(-x))
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,072
autodiff.py
dajuric_dark/dark-4/dark/core/autodiff.py
import dark.tensor as dt class Node(): op = None inputs = [] data = None requires_grad = True _grad = None def __init__(self, val): self.data = dt.asarray(val) if len(self.data.shape) == 0: self.data = self.data.reshape((1, 1)) def backward(self): if self.grad is ...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,073
utils.py
dajuric_dark/dark-4/dark/core/utils.py
import dark.tensor as dt import numpy as np def reduce_sum(result, target_shape): dim_diff = result.ndim - len(target_shape) if dim_diff < 0: return result.reshape(target_shape) # sum all leading dimensions # except leading dimensions, sum all axis that are equal to 1, count from right ...
553
Python
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0.70566
dajuric/dark
8
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,074
operations.py
dajuric_dark/dark-4/dark/core/operations.py
from .autodiff import Operation from .utils import * import dark.tensor as dt # ------ scalar ------ class AbsoluteValue(Operation): def forward(self, x): return dt.abs(x) def backward(self, dldy, y, x): gz = (x > 0) lz = dt.logical_not(gz) return [dldy * gz - dldy * lz] clas...
5,357
Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,075
module.py
dajuric_dark/dark-4/dark/nn/module.py
from dark import Parameter, Node, Constant class Module(): def __init__(self): self._modules = {} self._parameters = {} def forward(self, *x): pass def __call__(self, *x): x = [(Constant(p) if not isinstance(p, Node) else p) for p in x] return self.forward(*x) ...
2,248
Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,076
init.py
dajuric_dark/dark-4/dark/nn/init.py
import dark.tensor as dt import dark.nn as nn from dark import * def random_uniform_(tensor, a, b): t_shape = tensor.data.shape tensor.data = (b - a) * dt.random.rand(*t_shape) + a def default_init_weights(m): if isinstance(m, nn.Linear): stdv = 1. / dt.sqrt(m.weights.data.shape[1]) rando...
399
Python
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dajuric/dark
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0
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,077
__init__.py
dajuric_dark/dark-4/dark/nn/__init__.py
from .init import * from .module import * from .modules import * from .losses import *
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Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,078
losses.py
dajuric_dark/dark-4/dark/nn/losses.py
import dark from .module import Module from .modules import Sigmoid, Softmax class MSELoss(Module): def __init__(self): super().__init__() def forward(self, predictions, targets): loss = dark.subtract(predictions, targets) loss = dark.pow(loss, 2) loss = dark.mean(loss) ...
1,191
Python
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,079
modules.py
dajuric_dark/dark-4/dark/nn/modules.py
import dark.tensor as dt from dark import Parameter, Node, Constant import dark from .module import * class ZeroParam(Parameter): def __init__(self, *shape): val = dt.zeros(shape) super().__init__(val) class Flatten(Module): def __init__(self): super().__init__() pass def...
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dajuric/dark
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2,285,080
setup.py
dajuric_dark/dark-3/setup.py
from setuptools import setup setup( name="dark", description="", version="3.0", packages=["dark"], package_dir={"dark": "dark"}, install_requires = [] )
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2,285,081
util.py
dajuric_dark/dark-3/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)...
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2,285,082
train.py
dajuric_dark/dark-3/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...
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dajuric/dark
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2,285,083
dataset.py
dajuric_dark/dark-3/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,...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,084
util.py
dajuric_dark/dark-3/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)...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,085
train.py
dajuric_dark/dark-3/samples/2.0-Func-logistic-regression/dark/train.py
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(targets, dark.log(predictions)) lossB...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,086
dataset.py
dajuric_dark/dark-3/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
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dajuric/dark
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2,285,087
eval.py
dajuric_dark/dark-3/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 ...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,088
eval.py
dajuric_dark/dark-3/samples/1.0-Func-derivation/dark/eval.py
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(dark.mul(dark.tanh(x), dark.pow(x, 2)), dark...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,089
util.py
dajuric_dark/dark-3/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...
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,090
train.py
dajuric_dark/dark-3/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...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,091
dataset.py
dajuric_dark/dark-3/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...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,092
model.py
dajuric_dark/dark-3/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...
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dajuric/dark
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,093
util.py
dajuric_dark/dark-3/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...
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dajuric/dark
8
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,094
train.py
dajuric_dark/dark-3/samples/3.0-Func-NN-regression/dark/train.py
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.train() loss_fn = nn.MSELoss() opt = optim....
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dajuric/dark
8
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,095
dataset.py
dajuric_dark/dark-3/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...
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dajuric/dark
8
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GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,096
model.py
dajuric_dark/dark-3/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)...
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Python
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dajuric/dark
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9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,097
util.py
dajuric_dark/dark-3/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...
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dajuric/dark
8
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0
GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,098
train.py
dajuric_dark/dark-3/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
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dajuric/dark
8
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0
GPL-3.0
9/5/2024, 10:47:43 PM (Europe/Amsterdam)
2,285,099
util.py
dajuric_dark/dark-3/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).reshape(-1, 1) Y_curve = eval_func(X_curve).reshape(-1, 1) plt.plot(X_c...
438
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)