update yolo deps

face_detector/.gitignore

@@ -1,5 +0,0 @@
-/dataset.zip
-/dataset
-/test.json
-/runs/*
-/yolov5s.pt

face_detector/hubconf.py

@@ -1,142 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
-
-Usage:
-    import torch
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-"""
-
-import torch
-
-
-def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    """Creates a specified YOLOv5 model
-
-    Arguments:
-        name (str): name of model, i.e. 'yolov5s'
-        pretrained (bool): load pretrained weights into the model
-        channels (int): number of input channels
-        classes (int): number of model classes
-        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
-        verbose (bool): print all information to screen
-        device (str, torch.device, None): device to use for model parameters
-
-    Returns:
-        YOLOv5 pytorch model
-    """
-    from pathlib import Path
-
-    from models.yolo import Model
-    from models.experimental import attempt_load
-    from utils.general import check_requirements, set_logging
-    from utils.downloads import attempt_download
-    from utils.torch_utils import select_device
-
-    file = Path(__file__).resolve()
-    check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
-    set_logging(verbose=verbose)
-
-    save_dir = Path('') if str(name).endswith('.pt') else file.parent
-    path = (save_dir / name).with_suffix('.pt')  # checkpoint path
-    try:
-        device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
-
-        if pretrained and channels == 3 and classes == 80:
-            model = attempt_load(path, map_location=device)  # download/load FP32 model
-        else:
-            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
-            model = Model(cfg, channels, classes)  # create model
-            if pretrained:
-                ckpt = torch.load(attempt_download(path), map_location=device)  # load
-                msd = model.state_dict()  # model state_dict
-                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-                csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
-                model.load_state_dict(csd, strict=False)  # load
-                if len(ckpt['model'].names) == classes:
-                    model.names = ckpt['model'].names  # set class names attribute
-        if autoshape:
-            model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
-        return model.to(device)
-
-    except Exception as e:
-        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
-        s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
-        raise Exception(s) from e
-
-
-def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
-    # YOLOv5 custom or local model
-    return _create(path, autoshape=autoshape, verbose=verbose, device=device)
-
-
-def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-nano model https://github.com/ultralytics/yolov5
-    return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-small model https://github.com/ultralytics/yolov5
-    return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-medium model https://github.com/ultralytics/yolov5
-    return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-large model https://github.com/ultralytics/yolov5
-    return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
-    return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-if __name__ == '__main__':
-    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)  # pretrained
-    # model = custom(path='path/to/model.pt')  # custom
-
-    # Verify inference
-    import cv2
-    import numpy as np
-    from PIL import Image
-    from pathlib import Path
-
-    imgs = ['data/images/zidane.jpg',  # filename
-            Path('data/images/zidane.jpg'),  # Path
-            'https://ultralytics.com/images/zidane.jpg',  # URI
-            cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
-            Image.open('data/images/bus.jpg'),  # PIL
-            np.zeros((320, 640, 3))]  # numpy
-
-    results = model(imgs)  # batched inference
-    results.print()
-    results.save()

face_detector/main.py

@@ -1,36 +0,0 @@
-import torch
-
-from IPython.display import Image, clear_output  # to display images
-#from utils.google_utils import gdrive_downl#ad  # to download models/datasets
-
-# clear_output()
-print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))
-
-
-dataset_base = "/content/drive/MyDrive/AI Playground/face_detector/dataset/faces/"
-
-
-dataset_yolo = dataset_base + "yolo/"
-
-data_yaml = dataset_yolo + "data.yaml"
-
-#pretrained = "/content/drive/MyDrive/AI Playground/face_detector/yolov5s.pt"
-
-#trained_custom = "/content/drive/MyDrive/AI Playground/face_detector/dataset/faces/best_l.pt"
-
-test_path = dataset_base + "yolo/test/images"
-
-# define number of classes based on YAML
-import yaml
-with open("dataset/yolo/data.yaml", 'r') as stream:
-    num_classes = str(yaml.safe_load(stream)['nc'])
-
-from IPython.core.magic import register_line_cell_magic
-
-@register_line_cell_magic
-def writetemplate(line, cell):
-    with open(line, 'w') as f:
-        f.write(cell.format(**globals()))
-        
-        
-        

face_detector/models/tf.py

@@ -1,463 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-TensorFlow, Keras and TFLite versions of YOLOv5
-Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
-
-Usage:
-    $ python models/tf.py --weights yolov5s.pt
-
-Export:
-    $ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
-"""
-
-import argparse
-import logging
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-# ROOT = ROOT.relative_to(Path.cwd())  # relative
-
-import numpy as np
-import tensorflow as tf
-import torch
-import torch.nn as nn
-from tensorflow import keras
-
-from models.common import Bottleneck, BottleneckCSP, Concat, Conv, C3, DWConv, Focus, SPP, SPPF, autopad
-from models.experimental import CrossConv, MixConv2d, attempt_load
-from models.yolo import Detect
-from utils.general import make_divisible, print_args, LOGGER
-from utils.activations import SiLU
-
-
-class TFBN(keras.layers.Layer):
-    # TensorFlow BatchNormalization wrapper
-    def __init__(self, w=None):
-        super().__init__()
-        self.bn = keras.layers.BatchNormalization(
-            beta_initializer=keras.initializers.Constant(w.bias.numpy()),
-            gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
-            moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
-            moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
-            epsilon=w.eps)
-
-    def call(self, inputs):
-        return self.bn(inputs)
-
-
-class TFPad(keras.layers.Layer):
-    def __init__(self, pad):
-        super().__init__()
-        self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
-
-    def call(self, inputs):
-        return tf.pad(inputs, self.pad, mode='constant', constant_values=0)
-
-
-class TFConv(keras.layers.Layer):
-    # Standard convolution
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
-        # ch_in, ch_out, weights, kernel, stride, padding, groups
-        super().__init__()
-        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
-        assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
-        # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
-        # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
-
-        conv = keras.layers.Conv2D(
-            c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True,
-            kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
-            bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
-        self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
-        self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
-
-        # YOLOv5 activations
-        if isinstance(w.act, nn.LeakyReLU):
-            self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity
-        elif isinstance(w.act, nn.Hardswish):
-            self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity
-        elif isinstance(w.act, (nn.SiLU, SiLU)):
-            self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity
-        else:
-            raise Exception(f'no matching TensorFlow activation found for {w.act}')
-
-    def call(self, inputs):
-        return self.act(self.bn(self.conv(inputs)))
-
-
-class TFFocus(keras.layers.Layer):
-    # Focus wh information into c-space
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
-        # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__()
-        self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
-
-    def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
-        # inputs = inputs / 255.  # normalize 0-255 to 0-1
-        return self.conv(tf.concat([inputs[:, ::2, ::2, :],
-                                    inputs[:, 1::2, ::2, :],
-                                    inputs[:, ::2, 1::2, :],
-                                    inputs[:, 1::2, 1::2, :]], 3))
-
-
-class TFBottleneck(keras.layers.Layer):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
-        self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
-        self.add = shortcut and c1 == c2
-
-    def call(self, inputs):
-        return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
-
-
-class TFConv2d(keras.layers.Layer):
-    # Substitution for PyTorch nn.Conv2D
-    def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
-        super().__init__()
-        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
-        self.conv = keras.layers.Conv2D(
-            c2, k, s, 'VALID', use_bias=bias,
-            kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),
-            bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, )
-
-    def call(self, inputs):
-        return self.conv(inputs)
-
-
-class TFBottleneckCSP(keras.layers.Layer):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
-        # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
-        self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
-        self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
-        self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
-        self.bn = TFBN(w.bn)
-        self.act = lambda x: keras.activations.relu(x, alpha=0.1)
-        self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
-
-    def call(self, inputs):
-        y1 = self.cv3(self.m(self.cv1(inputs)))
-        y2 = self.cv2(inputs)
-        return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
-
-
-class TFC3(keras.layers.Layer):
-    # CSP Bottleneck with 3 convolutions
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
-        # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
-        self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
-        self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
-        self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
-
-    def call(self, inputs):
-        return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
-
-
-class TFSPP(keras.layers.Layer):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
-    def __init__(self, c1, c2, k=(5, 9, 13), w=None):
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
-        self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
-        self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k]
-
-    def call(self, inputs):
-        x = self.cv1(inputs)
-        return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
-
-
-class TFSPPF(keras.layers.Layer):
-    # Spatial pyramid pooling-Fast layer
-    def __init__(self, c1, c2, k=5, w=None):
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
-        self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
-        self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')
-
-    def call(self, inputs):
-        x = self.cv1(inputs)
-        y1 = self.m(x)
-        y2 = self.m(y1)
-        return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
-
-
-class TFDetect(keras.layers.Layer):
-    def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):  # detection layer
-        super().__init__()
-        self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [tf.zeros(1)] * self.nl  # init grid
-        self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
-        self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
-                                      [self.nl, 1, -1, 1, 2])
-        self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
-        self.training = False  # set to False after building model
-        self.imgsz = imgsz
-        for i in range(self.nl):
-            ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
-            self.grid[i] = self._make_grid(nx, ny)
-
-    def call(self, inputs):
-        z = []  # inference output
-        x = []
-        for i in range(self.nl):
-            x.append(self.m[i](inputs[i]))
-            # x(bs,20,20,255) to x(bs,3,20,20,85)
-            ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
-            x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3])
-
-            if not self.training:  # inference
-                y = tf.sigmoid(x[i])
-                xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
-                wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
-                # Normalize xywh to 0-1 to reduce calibration error
-                xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
-                wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
-                y = tf.concat([xy, wh, y[..., 4:]], -1)
-                z.append(tf.reshape(y, [-1, 3 * ny * nx, self.no]))
-
-        return x if self.training else (tf.concat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-        xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
-        return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
-
-
-class TFUpsample(keras.layers.Layer):
-    def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
-        super().__init__()
-        assert scale_factor == 2, "scale_factor must be 2"
-        self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
-        # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
-        # with default arguments: align_corners=False, half_pixel_centers=False
-        # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
-        #                                                            size=(x.shape[1] * 2, x.shape[2] * 2))
-
-    def call(self, inputs):
-        return self.upsample(inputs)
-
-
-class TFConcat(keras.layers.Layer):
-    def __init__(self, dimension=1, w=None):
-        super().__init__()
-        assert dimension == 1, "convert only NCHW to NHWC concat"
-        self.d = 3
-
-    def call(self, inputs):
-        return tf.concat(inputs, self.d)
-
-
-def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
-    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
-    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
-    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
-        m_str = m
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except NameError:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
-            c1, c2 = ch[f], args[0]
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
-        elif m is Detect:
-            args.append([ch[x + 1] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-            args.append(imgsz)
-        else:
-            c2 = ch[f]
-
-        tf_m = eval('TF' + m_str.replace('nn.', ''))
-        m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
-            else tf_m(*args, w=model.model[i])  # module
-
-        torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum(x.numel() for x in torch_m_.parameters())  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10}  {t:<40}{str(args):<30}')  # print
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        ch.append(c2)
-    return keras.Sequential(layers), sorted(save)
-
-
-class TFModel:
-    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)):  # model, channels, classes
-        super().__init__()
-        if isinstance(cfg, dict):
-            self.yaml = cfg  # model dict
-        else:  # is *.yaml
-            import yaml  # for torch hub
-            self.yaml_file = Path(cfg).name
-            with open(cfg) as f:
-                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
-
-        # Define model
-        if nc and nc != self.yaml['nc']:
-            LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
-            self.yaml['nc'] = nc  # override yaml value
-        self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
-
-    def predict(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
-                conf_thres=0.25):
-        y = []  # outputs
-        x = inputs
-        for i, m in enumerate(self.model.layers):
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
-            x = m(x)  # run
-            y.append(x if m.i in self.savelist else None)  # save output
-
-        # Add TensorFlow NMS
-        if tf_nms:
-            boxes = self._xywh2xyxy(x[0][..., :4])
-            probs = x[0][:, :, 4:5]
-            classes = x[0][:, :, 5:]
-            scores = probs * classes
-            if agnostic_nms:
-                nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
-                return nms, x[1]
-            else:
-                boxes = tf.expand_dims(boxes, 2)
-                nms = tf.image.combined_non_max_suppression(
-                    boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False)
-                return nms, x[1]
-
-        return x[0]  # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
-        # x = x[0][0]  # [x(1,6300,85), ...] to x(6300,85)
-        # xywh = x[..., :4]  # x(6300,4) boxes
-        # conf = x[..., 4:5]  # x(6300,1) confidences
-        # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1))  # x(6300,1)  classes
-        # return tf.concat([conf, cls, xywh], 1)
-
-    @staticmethod
-    def _xywh2xyxy(xywh):
-        # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-        x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
-        return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
-
-
-class AgnosticNMS(keras.layers.Layer):
-    # TF Agnostic NMS
-    def call(self, input, topk_all, iou_thres, conf_thres):
-        # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
-        return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input,
-                         fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
-                         name='agnostic_nms')
-
-    @staticmethod
-    def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):  # agnostic NMS
-        boxes, classes, scores = x
-        class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
-        scores_inp = tf.reduce_max(scores, -1)
-        selected_inds = tf.image.non_max_suppression(
-            boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres)
-        selected_boxes = tf.gather(boxes, selected_inds)
-        padded_boxes = tf.pad(selected_boxes,
-                              paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
-                              mode="CONSTANT", constant_values=0.0)
-        selected_scores = tf.gather(scores_inp, selected_inds)
-        padded_scores = tf.pad(selected_scores,
-                               paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
-                               mode="CONSTANT", constant_values=-1.0)
-        selected_classes = tf.gather(class_inds, selected_inds)
-        padded_classes = tf.pad(selected_classes,
-                                paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
-                                mode="CONSTANT", constant_values=-1.0)
-        valid_detections = tf.shape(selected_inds)[0]
-        return padded_boxes, padded_scores, padded_classes, valid_detections
-
-
-def representative_dataset_gen(dataset, ncalib=100):
-    # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
-    for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
-        input = np.transpose(img, [1, 2, 0])
-        input = np.expand_dims(input, axis=0).astype(np.float32)
-        input /= 255.0
-        yield [input]
-        if n >= ncalib:
-            break
-
-
-def run(weights=ROOT / 'yolov5s.pt',  # weights path
-        imgsz=(640, 640),  # inference size h,w
-        batch_size=1,  # batch size
-        dynamic=False,  # dynamic batch size
-        ):
-    # PyTorch model
-    im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
-    model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
-    y = model(im)  # inference
-    model.info()
-
-    # TensorFlow model
-    im = tf.zeros((batch_size, *imgsz, 3))  # BHWC image
-    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-    y = tf_model.predict(im)  # inference
-
-    # Keras model
-    im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
-    keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
-    keras_model.summary()
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(FILE.stem, opt)
-    return opt
-
-
-def main(opt):
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

face_detector/params.yaml

@@ -1,13 +0,0 @@
-preparation:
-  train_percentage: 0.9
-  validation_percentage: 0.08  
-  test_percentage: 0.02  
-
-train:
-  epochs: 100
-  batch_size: 32
-  image_size: 320
-
-test:
-  conf: 0.4
-  image_size: 320

face_detector/prepare.py

@@ -1,84 +0,0 @@
-import shutil
-import os
-import random
-import math
-import yaml
-
-def split_dataset(dataset_path,train_percentage, validation_percentage,classes=[],mode="move",output_path=""):
-  def shift(source,destination,mode):
-    if mode == "copy":
-      shutil.copyfile(source,destination)
-    else:
-      shutil.move(source,destination) 
-       
-  def diff_lists(l1,l2):
-    from collections import Counter
-    return list((Counter(l1) - Counter(l2)).elements())
-  
-  if mode == "copy" and output_path == "":
-    raise Exception("Cannot copy files on the same directory")  
-      
-  if validation_percentage > train_percentage:
-    raise Exception("validation_percentage must be lower than train_percentage")  
-  
-  other_files = None
-  
-  if classes == []:
-    classes = os.listdir(dataset_path)
-  else: 
-    other_files = diff_lists(os.listdir(dataset_path),classes)      
-  
-  test_percentage = 1 - train_percentage
-      
-  if not dataset_path.endswith("/"):
-    dataset_path = dataset_path + "/"
-
-  if output_path == "":
-    output_path = dataset_path
-    
-  train_dir = output_path + "/train"
-  test_dir = output_path + "/test"
-  validation_dir = output_path + "/valid"
-  seed = 42
-    
-  for _class in classes:
-    os.makedirs(train_dir + "/" + _class,exist_ok=True)
-    os.makedirs(test_dir + "/" + _class,exist_ok=True)
-    os.makedirs(validation_dir + "/" + _class,exist_ok=True)    
-        
-    data = sorted(os.listdir(dataset_path + _class + "/"))
-    random.Random(seed).shuffle(data)
-    
-    data_length = len(data)
-    
-    test_size = math.floor(data_length * test_percentage)
-    validation_size = math.floor(data_length * validation_percentage)
-
-    for i,single_data in enumerate(data):
-          
-      single_data_path = dataset_path + _class + "/" + single_data    
-        
-      if i < test_size:
-        shift(single_data_path, test_dir +  "/" + _class + "/" + single_data, mode)
-                    
-      elif test_size < i <= test_size + validation_size:
-        shift(single_data_path, validation_dir + "/" + _class + "/" + single_data, mode)
-        
-      else:    
-        shift(single_data_path, train_dir + "/" + _class + "/" + single_data, mode)
-    if mode == "move":
-      shutil.rmtree(dataset_path + _class)      
-  
-  if other_files is not None:
-    for file in other_files:
-      shift(dataset_path + file, output_path + "/" + file, mode) 
-
-os.system("unzip -n dataset.zip")
-
-with open("params.yaml", 'r') as fd:
-    params = yaml.safe_load(fd)
-    
-    train_percentage = params['preparation']['train_percentage']
-    validation_percentage = params['preparation']['validation_percentage']
-       
-    split_dataset(dataset_path="dataset/yolo",train_percentage=train_percentage,validation_percentage=validation_percentage,classes=["images","labels"])

face_detector/train.py

@@ -1,686 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Train a YOLOv5 model on a custom dataset
-
-Usage:
-    $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
-"""
-
-import argparse
-import logging
-import math
-import os
-import random
-import sys
-import time
-from copy import deepcopy
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import yaml
-from torch.cuda import amp
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import Adam, SGD, lr_scheduler
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-import val  # for end-of-epoch mAP
-from models.experimental import attempt_load
-from models.yolo import Model
-from utils.autoanchor import check_anchors
-from utils.autobatch import check_train_batch_size
-from utils.datasets import create_dataloader
-from utils.general import labels_to_class_weights, increment_path,increment_path1, labels_to_image_weights, init_seeds, \
-    strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
-    check_file, check_yaml, check_suffix, print_args, print_mutation, one_cycle, colorstr, methods, LOGGER
-from utils.downloads import attempt_download
-from utils.loss import ComputeLoss
-from utils.plots import plot_labels, plot_evolve
-from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device, \
-    torch_distributed_zero_first
-from utils.loggers.wandb.wandb_utils import check_wandb_resume
-from utils.metrics import fitness
-from utils.loggers import Loggers
-from utils.callbacks import Callbacks
-
-
-import mlflow
-import yaml
-
-
-
-
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-
-
-def train(hyp,  # path/to/hyp.yaml or hyp dictionary
-          opt,
-          device,
-          callbacks
-          ):
-    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, = \
-        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
-        opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
-
-
-    params = None
-    with open("params.yaml", 'r') as fd:
-        params = yaml.safe_load(fd)
-    
-    
-    epochs = params['train']['epochs']
-    batch_size = params['train']['batch_size']
-    imgsz = params['train']['image_size']
-    opt.imgsz =imgsz 
-        
-
-
-    params = None
-    with open("params.yaml", 'r') as fd:
-        params = yaml.safe_load(fd)
-    
-    
-    epochs = params['train']['epochs']
-    batch_size = params['train']['batch_size']
-    imgsz = params['train']['image_size']
-    opt.imgsz =imgsz 
-        
-    mlflow.log_param("epochs", epochs)
-    mlflow.log_param("batch_size", batch_size)
-    mlflow.log_param("image_size", imgsz)
-    
-
-
-    # Directories
-    w = save_dir / 'weights'  # weights dir
-    (w.parent if evolve else w).mkdir(parents=True, exist_ok=True)  # make dir
-    last, best = w / 'last.pt', w / 'best.pt'
-
-    # Hyperparameters
-    if isinstance(hyp, str):
-        with open(hyp, errors='ignore') as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-    LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
-
-    # Save run settings
-    with open(save_dir / 'hyp.yaml', 'w') as f:
-        yaml.safe_dump(hyp, f, sort_keys=False)
-    with open(save_dir / 'opt.yaml', 'w') as f:
-        yaml.safe_dump(vars(opt), f, sort_keys=False)
-    data_dict = None
-
-    # Loggers
-    if RANK in [-1, 0]:
-        loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
-        if loggers.wandb:
-            data_dict = loggers.wandb.data_dict
-            if resume:
-                weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
-
-        # Register actions
-        for k in methods(loggers):
-            callbacks.register_action(k, callback=getattr(loggers, k))
-
-    # Config
-    plots = not evolve  # create plots
-    cuda = device.type != 'cpu'
-    init_seeds(1 + RANK)
-    with torch_distributed_zero_first(LOCAL_RANK):
-        data_dict = data_dict or check_dataset(data)  # check if None
-    train_path, val_path = data_dict['train'], data_dict['val']
-    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
-    names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
-    assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check
-    is_coco = data.endswith('coco.yaml') and nc == 80  # COCO dataset
-
-    # Model
-    check_suffix(weights, '.pt')  # check weights
-    pretrained = weights.endswith('.pt')
-    if pretrained:
-        with torch_distributed_zero_first(LOCAL_RANK):
-            weights = attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
-        model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-        exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys
-        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-        csd = intersect_dicts(csd, model.state_dict(), exclude=exclude)  # intersect
-        model.load_state_dict(csd, strict=False)  # load
-        LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report
-    else:
-        model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-
-    # Freeze
-    freeze = [f'model.{x}.' for x in range(freeze)]  # layers to freeze
-    for k, v in model.named_parameters():
-        v.requires_grad = True  # train all layers
-        if any(x in k for x in freeze):
-            LOGGER.info(f'freezing {k}')
-            v.requires_grad = False
-
-    # Image size
-    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
-    imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
-
-    # Batch size
-    if RANK == -1 and batch_size == -1:  # single-GPU only, estimate best batch size
-        batch_size = check_train_batch_size(model, imgsz)
-
-    # Optimizer
-    nbs = 64  # nominal batch size
-    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
-    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
-    LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
-
-    g0, g1, g2 = [], [], []  # optimizer parameter groups
-    for v in model.modules():
-        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias
-            g2.append(v.bias)
-        if isinstance(v, nn.BatchNorm2d):  # weight (no decay)
-            g0.append(v.weight)
-        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
-            g1.append(v.weight)
-
-    if opt.adam:
-        optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
-    else:
-        optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
-
-    optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']})  # add g1 with weight_decay
-    optimizer.add_param_group({'params': g2})  # add g2 (biases)
-    LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
-                f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias")
-    del g0, g1, g2
-
-    # Scheduler
-    if opt.linear_lr:
-        lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear
-    else:
-        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']
-    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)  # plot_lr_scheduler(optimizer, scheduler, epochs)
-
-    # EMA
-    ema = ModelEMA(model) if RANK in [-1, 0] else None
-
-    # Resume
-    start_epoch, best_fitness = 0, 0.0
-    if pretrained:
-        # Optimizer
-        if ckpt['optimizer'] is not None:
-            optimizer.load_state_dict(ckpt['optimizer'])
-            best_fitness = ckpt['best_fitness']
-
-        # EMA
-        if ema and ckpt.get('ema'):
-            ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
-            ema.updates = ckpt['updates']
-
-        # Epochs
-        start_epoch = ckpt['epoch'] + 1
-        if resume:
-            assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
-        if epochs < start_epoch:
-            LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
-            epochs += ckpt['epoch']  # finetune additional epochs
-
-        del ckpt, csd
-
-    # Image sizes
-    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
-    nl = model.model[-1].nl  # number of detection layers (used for scaling hyp['obj'])
-    #imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
-    imgsz = check_img_size(imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
-
-    # DP mode
-    if cuda and RANK == -1 and torch.cuda.device_count() > 1:
-        logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n'
-                        'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
-        model = torch.nn.DataParallel(model)
-
-    # SyncBatchNorm
-    if opt.sync_bn and cuda and RANK != -1:
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
-        LOGGER.info('Using SyncBatchNorm()')
-
-    # Trainloader
-    train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
-                                              hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
-                                              workers=workers, image_weights=opt.image_weights, quad=opt.quad,
-                                              prefix=colorstr('train: '))
-    mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max())  # max label class
-    nb = len(train_loader)  # number of batches
-    assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
-
-    # Process 0
-    if RANK in [-1, 0]:
-        val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
-                                       hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1,
-                                       workers=workers, pad=0.5,
-                                       prefix=colorstr('val: '))[0]
-
-        if not resume:
-            labels = np.concatenate(dataset.labels, 0)
-            # c = torch.tensor(labels[:, 0])  # classes
-            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
-            # model._initialize_biases(cf.to(device))
-            if plots:
-                plot_labels(labels, names, save_dir)
-
-            # Anchors
-            if not opt.noautoanchor:
-                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
-            model.half().float()  # pre-reduce anchor precision
-
-        callbacks.run('on_pretrain_routine_end')
-
-    # DDP mode
-    if cuda and RANK != -1:
-        model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
-
-    # Model parameters
-    nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)
-    hyp['box'] *= 3. / nl  # scale to layers
-    hyp['cls'] *= nc / 80. * 3. / nl  # scale to classes and layers
-    hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl  # scale to image size and layers
-    hyp['label_smoothing'] = opt.label_smoothing
-    model.nc = nc  # attach number of classes to model
-    model.hyp = hyp  # attach hyperparameters to model
-    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
-    model.names = names
-
-    # Start training
-    t0 = time.time()
-    nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)
-    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
-    last_opt_step = -1
-    maps = np.zeros(nc)  # mAP per class
-    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, [email protected], [email protected], val_loss(box, obj, cls)
-    scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = amp.GradScaler(enabled=cuda)
-    stopper = EarlyStopping(patience=opt.patience)
-    compute_loss = ComputeLoss(model)  # init loss class
-    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
-                f'Using {train_loader.num_workers} dataloader workers\n'
-                f"Logging results to {colorstr('bold', save_dir)}\n"
-                f'Starting training for {epochs} epochs...')
-    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
-        model.train()
-
-        # Update image weights (optional, single-GPU only)
-        if opt.image_weights:
-            cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
-            iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
-            dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
-
-        # Update mosaic border (optional)
-        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
-        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
-
-        mloss = torch.zeros(3, device=device)  # mean losses
-        if RANK != -1:
-            train_loader.sampler.set_epoch(epoch)
-        pbar = enumerate(train_loader)
-        LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
-        if RANK in [-1, 0]:
-            pbar = tqdm(pbar, total=nb)  # progress bar
-        optimizer.zero_grad()
-        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
-            ni = i + nb * epoch  # number integrated batches (since train start)
-            imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0
-
-            # Warmup
-            if ni <= nw:
-                xi = [0, nw]  # x interp
-                # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
-                accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
-                for j, x in enumerate(optimizer.param_groups):
-                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
-                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
-                    if 'momentum' in x:
-                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
-
-            # Multi-scale
-            if opt.multi_scale:
-                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
-                sf = sz / max(imgs.shape[2:])  # scale factor
-                if sf != 1:
-                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
-                    imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
-
-            # Forward
-            with amp.autocast(enabled=cuda):
-                pred = model(imgs)  # forward
-                loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
-                if RANK != -1:
-                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
-                if opt.quad:
-                    loss *= 4.
-
-            # Backward
-            scaler.scale(loss).backward()
-
-            # Optimize
-            if ni - last_opt_step >= accumulate:
-                scaler.step(optimizer)  # optimizer.step
-                scaler.update()
-                optimizer.zero_grad()
-                if ema:
-                    ema.update(model)
-                last_opt_step = ni
-
-            # Log
-            if RANK in [-1, 0]:
-                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
-                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
-                pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
-                    f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
-                callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
-            # end batch ------------------------------------------------------------------------------------------------
-
-        # Scheduler
-        lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
-        scheduler.step()
-
-        if RANK in [-1, 0]:
-            # mAP
-            callbacks.run('on_train_epoch_end', epoch=epoch)
-            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
-            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
-            if not noval or final_epoch:  # Calculate mAP
-                results, maps, _ = val.run(data_dict,
-                                           batch_size=batch_size // WORLD_SIZE * 2,
-                                           imgsz=imgsz,
-                                           model=ema.ema,
-                                           single_cls=single_cls,
-                                           dataloader=val_loader,
-                                           save_dir=save_dir,
-                                           plots=False,
-                                           callbacks=callbacks,
-                                           compute_loss=compute_loss)
-                
-                mlflow.log_metric("train_precision",results[0])
-                mlflow.log_metric("train_recall",results[1])
-                mlflow.log_metric("train_mAP0.5",results[2])
-                mlflow.log_metric("train_mAP0.5_.95",results[3])                                           
-
-            # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, [email protected], [email protected]]
-            if fi > best_fitness:
-                best_fitness = fi
-            log_vals = list(mloss) + list(results) + lr
-            callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
-
-            # Save model
-            if (not nosave) or (final_epoch and not evolve):  # if save
-                ckpt = {'epoch': epoch,
-                        'best_fitness': best_fitness,
-                        'model': deepcopy(de_parallel(model)).half(),
-                        'ema': deepcopy(ema.ema).half(),
-                        'updates': ema.updates,
-                        'optimizer': optimizer.state_dict(),
-                        'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None}
-
-                # Save last, best and delete
-                torch.save(ckpt, last)
-                if best_fitness == fi:
-                    torch.save(ckpt, best)
-                if (epoch > 0) and (opt.save_period > 0) and (epoch % opt.save_period == 0):
-                    torch.save(ckpt, w / f'epoch{epoch}.pt')
-                del ckpt
-                callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
-
-            # Stop Single-GPU
-            if RANK == -1 and stopper(epoch=epoch, fitness=fi):
-                break
-
-            # Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
-            # stop = stopper(epoch=epoch, fitness=fi)
-            # if RANK == 0:
-            #    dist.broadcast_object_list([stop], 0)  # broadcast 'stop' to all ranks
-
-        # Stop DPP
-        # with torch_distributed_zero_first(RANK):
-        # if stop:
-        #    break  # must break all DDP ranks
-
-        # end epoch ----------------------------------------------------------------------------------------------------
-    # end training -----------------------------------------------------------------------------------------------------
-    if RANK in [-1, 0]:
-        LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
-        for f in last, best:
-            if f.exists():
-                strip_optimizer(f)  # strip optimizers
-                if f is best:
-                    LOGGER.info(f'\nValidating {f}...')
-                    results, _, _ = val.run(data_dict,
-                                            batch_size=batch_size // WORLD_SIZE * 2,
-                                            imgsz=imgsz,
-                                            model=attempt_load(f, device).half(),
-                                            iou_thres=0.65 if is_coco else 0.60,  # best pycocotools results at 0.65
-                                            single_cls=single_cls,
-                                            dataloader=val_loader,
-                                            save_dir=save_dir,
-                                            save_json=is_coco,
-                                            verbose=True,
-                                            plots=True,
-                                            callbacks=callbacks,
-                                            compute_loss=compute_loss)  # val best model with plots
-                    if is_coco:
-                        callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
-
-        callbacks.run('on_train_end', last, best, plots, epoch, results)
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
-
-    torch.cuda.empty_cache()
-    
-   
-
-    return results
-
-
-def parse_opt(known=False):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
-    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
-    parser.add_argument('--epochs', type=int, default=300)
-    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
-    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
-    parser.add_argument('--rect', action='store_true', help='rectangular training')
-    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
-    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
-    parser.add_argument('--noval', action='store_true', help='only validate final epoch')
-    parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
-    parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
-    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
-    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
-    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
-    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
-    parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
-    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
-    parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
-    parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
-    parser.add_argument('--name', default='exp', help='save to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--quad', action='store_true', help='quad dataloader')
-    parser.add_argument('--linear-lr', action='store_true', help='linear LR')
-    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
-    parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
-    parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
-    parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
-    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
-
-    # Weights & Biases arguments
-    parser.add_argument('--entity', default=None, help='W&B: Entity')
-    parser.add_argument('--upload_dataset', action='store_true', help='W&B: Upload dataset as artifact table')
-    parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
-    parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
-
-    opt = parser.parse_known_args()[0] if known else parser.parse_args()
-    return opt
-
-
-def main(opt, callbacks=Callbacks()):
-    # Checks
-    if RANK in [-1, 0]:
-        print_args(FILE.stem, opt)
-        check_git_status()
-        check_requirements(exclude=['thop'])
-
-    # Resume
-    if opt.resume and not check_wandb_resume(opt) and not opt.evolve:  # resume an interrupted run
-        ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path
-        assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
-        with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
-            opt = argparse.Namespace(**yaml.safe_load(f))  # replace
-        opt.cfg, opt.weights, opt.resume = '', ckpt, True  # reinstate
-        LOGGER.info(f'Resuming training from {ckpt}')
-    else:
-        opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
-            check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks
-        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
-        if opt.evolve:
-            opt.project = str(ROOT / 'runs/evolve')
-            opt.exist_ok, opt.resume = opt.resume, False  # pass resume to exist_ok and disable resume
-        #opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
-        opt.save_dir = str(increment_path1(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
-
-    # DDP mode
-    device = select_device(opt.device, batch_size=opt.batch_size)
-    if LOCAL_RANK != -1:
-        assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
-        assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
-        assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
-        assert not opt.evolve, '--evolve argument is not compatible with DDP training'
-        torch.cuda.set_device(LOCAL_RANK)
-        device = torch.device('cuda', LOCAL_RANK)
-        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
-
-    # Train
-    if not opt.evolve:
-        train(opt.hyp, opt, device, callbacks)
-        if WORLD_SIZE > 1 and RANK == 0:
-            LOGGER.info('Destroying process group... ')
-            dist.destroy_process_group()
-
-    # Evolve hyperparameters (optional)
-    else:
-        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
-        meta = {'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
-                'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
-                'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
-                'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
-                'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
-                'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
-                'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
-                'box': (1, 0.02, 0.2),  # box loss gain
-                'cls': (1, 0.2, 4.0),  # cls loss gain
-                'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
-                'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
-                'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
-                'iou_t': (0, 0.1, 0.7),  # IoU training threshold
-                'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
-                'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
-                'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
-                'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
-                'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
-                'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
-                'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
-                'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
-                'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
-                'shear': (1, 0.0, 10.0),  # image shear (+/- deg)
-                'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
-                'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
-                'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
-                'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
-                'mixup': (1, 0.0, 1.0),  # image mixup (probability)
-                'copy_paste': (1, 0.0, 1.0)}  # segment copy-paste (probability)
-
-        with open(opt.hyp, errors='ignore') as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
-                hyp['anchors'] = 3
-        opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir)  # only val/save final epoch
-        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
-        if opt.bucket:
-            os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}')  # download evolve.csv if exists
-
-        for _ in range(opt.evolve):  # generations to evolve
-            if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate
-                # Select parent(s)
-                parent = 'single'  # parent selection method: 'single' or 'weighted'
-                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
-                n = min(5, len(x))  # number of previous results to consider
-                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
-                w = fitness(x) - fitness(x).min() + 1E-6  # weights (sum > 0)
-                if parent == 'single' or len(x) == 1:
-                    # x = x[random.randint(0, n - 1)]  # random selection
-                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection
-                elif parent == 'weighted':
-                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination
-
-                # Mutate
-                mp, s = 0.8, 0.2  # mutation probability, sigma
-                npr = np.random
-                npr.seed(int(time.time()))
-                g = np.array([meta[k][0] for k in hyp.keys()])  # gains 0-1
-                ng = len(meta)
-                v = np.ones(ng)
-                while all(v == 1):  # mutate until a change occurs (prevent duplicates)
-                    v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
-                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
-                    hyp[k] = float(x[i + 7] * v[i])  # mutate
-
-            # Constrain to limits
-            for k, v in meta.items():
-                hyp[k] = max(hyp[k], v[1])  # lower limit
-                hyp[k] = min(hyp[k], v[2])  # upper limit
-                hyp[k] = round(hyp[k], 5)  # significant digits
-
-            # Train mutation
-            results = train(hyp.copy(), opt, device, callbacks)
-
-            # Write mutation results
-
-
-            print_mutation(results, hyp.copy(), save_dir, opt.bucket)
-
-        # Plot results
-        plot_evolve(evolve_csv)
-        LOGGER.info(f'Hyperparameter evolution finished\n'
-                    f"Results saved to {colorstr('bold', save_dir)}\n"
-                    f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
-
-
-def run(**kwargs):
-    # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
-    opt = parse_opt(True)
-    for k, v in kwargs.items():
-        setattr(opt, k, v)
-    main(opt)
-
-
-if __name__ == "__main__":
-    import os
-    
-    os.system("databricks configure --host https://community.cloud.databricks.com")
-    mlflow.set_tracking_uri("databricks")
-    mlflow.set_experiment("/Users/[email protected]/Yolov5")
-    opt = parse_opt()
-    mlflow.start_run()
-    main(opt)
-    mlflow.end_run()
-

face_detector/utils/activations.py

@@ -1,101 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Activation functions
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
-class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
-    @staticmethod
-    def forward(x):
-        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
-        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
-
-
-# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
-class Mish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * F.softplus(x).tanh()
-
-
-class MemoryEfficientMish(nn.Module):
-    class F(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x):
-            ctx.save_for_backward(x)
-            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            x = ctx.saved_tensors[0]
-            sx = torch.sigmoid(x)
-            fx = F.softplus(x).tanh()
-            return grad_output * (fx + x * sx * (1 - fx * fx))
-
-    def forward(self, x):
-        return self.F.apply(x)
-
-
-# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
-class FReLU(nn.Module):
-    def __init__(self, c1, k=3):  # ch_in, kernel
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
-        self.bn = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        return torch.max(x, self.bn(self.conv(x)))
-
-
-# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
-class AconC(nn.Module):
-    r""" ACON activation (activate or not).
-    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
-    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
-    """
-
-    def __init__(self, c1):
-        super().__init__()
-        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
-
-    def forward(self, x):
-        dpx = (self.p1 - self.p2) * x
-        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
-
-
-class MetaAconC(nn.Module):
-    r""" ACON activation (activate or not).
-    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
-    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
-    """
-
-    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
-        super().__init__()
-        c2 = max(r, c1 // r)
-        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
-        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
-        # self.bn1 = nn.BatchNorm2d(c2)
-        # self.bn2 = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
-        # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
-        # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable
-        beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed
-        dpx = (self.p1 - self.p2) * x
-        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x

face_detector/utils/aws/resume.py

@@ -1,40 +0,0 @@
-# Resume all interrupted trainings in yolov5/ dir including DDP trainings
-# Usage: $ python utils/aws/resume.py
-
-import os
-import sys
-from pathlib import Path
-
-import torch
-import yaml
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[2]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-port = 0  # --master_port
-path = Path('').resolve()
-for last in path.rglob('*/**/last.pt'):
-    ckpt = torch.load(last)
-    if ckpt['optimizer'] is None:
-        continue
-
-    # Load opt.yaml
-    with open(last.parent.parent / 'opt.yaml', errors='ignore') as f:
-        opt = yaml.safe_load(f)
-
-    # Get device count
-    d = opt['device'].split(',')  # devices
-    nd = len(d)  # number of devices
-    ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1)  # distributed data parallel
-
-    if ddp:  # multi-GPU
-        port += 1
-        cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
-    else:  # single-GPU
-        cmd = f'python train.py --resume {last}'
-
-    cmd += ' > /dev/null 2>&1 &'  # redirect output to dev/null and run in daemon thread
-    print(cmd)
-    os.system(cmd)

face_detector/utils/flask_rest_api/README.md

@@ -1,73 +0,0 @@
-# Flask REST API
-
-[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are
-commonly used to expose Machine Learning (ML)  models to other services. This folder contains an example REST API
-created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
-
-## Requirements
-
-[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
-
-```shell
-$ pip install Flask
-```
-
-## Run
-
-After Flask installation run:
-
-```shell
-$ python3 restapi.py --port 5000
-```
-
-Then use [curl](https://curl.se/) to perform a request:
-
-```shell
-$ curl -X POST -F [email protected] 'http://localhost:5000/v1/object-detection/yolov5s'
-```
-
-The model inference results are returned as a JSON response:
-
-```json
-[
-  {
-    "class": 0,
-    "confidence": 0.8900438547,
-    "height": 0.9318675399,
-    "name": "person",
-    "width": 0.3264600933,
-    "xcenter": 0.7438579798,
-    "ycenter": 0.5207948685
-  },
-  {
-    "class": 0,
-    "confidence": 0.8440024257,
-    "height": 0.7155083418,
-    "name": "person",
-    "width": 0.6546785235,
-    "xcenter": 0.427829951,
-    "ycenter": 0.6334488392
-  },
-  {
-    "class": 27,
-    "confidence": 0.3771208823,
-    "height": 0.3902671337,
-    "name": "tie",
-    "width": 0.0696444362,
-    "xcenter": 0.3675483763,
-    "ycenter": 0.7991207838
-  },
-  {
-    "class": 27,
-    "confidence": 0.3527112305,
-    "height": 0.1540903747,
-    "name": "tie",
-    "width": 0.0336618312,
-    "xcenter": 0.7814827561,
-    "ycenter": 0.5065554976
-  }
-]
-```
-
-An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given
-in `example_request.py`

face_detector/utils/flask_rest_api/example_request.py

@@ -1,13 +0,0 @@
-"""Perform test request"""
-import pprint
-
-import requests
-
-DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-TEST_IMAGE = "zidane.jpg"
-
-image_data = open(TEST_IMAGE, "rb").read()
-
-response = requests.post(DETECTION_URL, files={"image": image_data}).json()
-
-pprint.pprint(response)

face_detector/utils/flask_rest_api/restapi.py

@@ -1,37 +0,0 @@
-"""
-Run a rest API exposing the yolov5s object detection model
-"""
-import argparse
-import io
-
-import torch
-from PIL import Image
-from flask import Flask, request
-
-app = Flask(__name__)
-
-DETECTION_URL = "/v1/object-detection/yolov5s"
-
-
-@app.route(DETECTION_URL, methods=["POST"])
-def predict():
-    if not request.method == "POST":
-        return
-
-    if request.files.get("image"):
-        image_file = request.files["image"]
-        image_bytes = image_file.read()
-
-        img = Image.open(io.BytesIO(image_bytes))
-
-        results = model(img, size=640)  # reduce size=320 for faster inference
-        return results.pandas().xyxy[0].to_json(orient="records")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
-    parser.add_argument("--port", default=5000, type=int, help="port number")
-    args = parser.parse_args()
-
-    model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force_reload to recache
-    app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat

face_detector/utils/google_app_engine/Dockerfile

@@ -1,25 +0,0 @@
-FROM gcr.io/google-appengine/python
-
-# Create a virtualenv for dependencies. This isolates these packages from
-# system-level packages.
-# Use -p python3 or -p python3.7 to select python version. Default is version 2.
-RUN virtualenv /env -p python3
-
-# Setting these environment variables are the same as running
-# source /env/bin/activate.
-ENV VIRTUAL_ENV /env
-ENV PATH /env/bin:$PATH
-
-RUN apt-get update && apt-get install -y python-opencv
-
-# Copy the application's requirements.txt and run pip to install all
-# dependencies into the virtualenv.
-ADD requirements.txt /app/requirements.txt
-RUN pip install -r /app/requirements.txt
-
-# Add the application source code.
-ADD . /app
-
-# Run a WSGI server to serve the application. gunicorn must be declared as
-# a dependency in requirements.txt.
-CMD gunicorn -b :$PORT main:app

face_detector/utils/google_app_engine/additional_requirements.txt

@@ -1,4 +0,0 @@
-# add these requirements in your app on top of the existing ones
-pip==19.2
-Flask==1.0.2
-gunicorn==19.9.0

face_detector/utils/google_app_engine/app.yaml

@@ -1,14 +0,0 @@
-runtime: custom
-env: flex
-
-service: yolov5app
-
-liveness_check:
-  initial_delay_sec: 600
-
-manual_scaling:
-  instances: 1
-resources:
-  cpu: 1
-  memory_gb: 4
-  disk_size_gb: 20

face_detector/utils/loggers/__init__.py

@@ -1,156 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Logging utils
-"""
-
-import os
-import warnings
-from threading import Thread
-
-import pkg_resources as pkg
-import torch
-from torch.utils.tensorboard import SummaryWriter
-
-from utils.general import colorstr, emojis
-from utils.loggers.wandb.wandb_utils import WandbLogger
-from utils.plots import plot_images, plot_results
-from utils.torch_utils import de_parallel
-
-LOGGERS = ('csv', 'tb', 'wandb')  # text-file, TensorBoard, Weights & Biases
-RANK = int(os.getenv('RANK', -1))
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')  # verify package import not local dir
-    if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in [0, -1]:
-        wandb_login_success = wandb.login(timeout=30)
-        if not wandb_login_success:
-            wandb = None
-except (ImportError, AssertionError):
-    wandb = None
-
-
-class Loggers():
-    # YOLOv5 Loggers class
-    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
-        self.save_dir = save_dir
-        self.weights = weights
-        self.opt = opt
-        self.hyp = hyp
-        self.logger = logger  # for printing results to console
-        self.include = include
-        self.keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
-                     'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',  # metrics
-                     'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
-                     'x/lr0', 'x/lr1', 'x/lr2']  # params
-        for k in LOGGERS:
-            setattr(self, k, None)  # init empty logger dictionary
-        self.csv = True  # always log to csv
-
-        # Message
-        if not wandb:
-            prefix = colorstr('Weights & Biases: ')
-            s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)"
-            print(emojis(s))
-
-        # TensorBoard
-        s = self.save_dir
-        if 'tb' in self.include and not self.opt.evolve:
-            prefix = colorstr('TensorBoard: ')
-            self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
-            self.tb = SummaryWriter(str(s))
-
-        # W&B
-        if wandb and 'wandb' in self.include:
-            wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
-            run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
-            self.opt.hyp = self.hyp  # add hyperparameters
-            self.wandb = WandbLogger(self.opt, run_id)
-        else:
-            self.wandb = None
-
-    def on_pretrain_routine_end(self):
-        # Callback runs on pre-train routine end
-        paths = self.save_dir.glob('*labels*.jpg')  # training labels
-        if self.wandb:
-            self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
-
-    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn):
-        # Callback runs on train batch end
-        if plots:
-            if ni == 0:
-                if not sync_bn:  # tb.add_graph() --sync known issue https://github.com/ultralytics/yolov5/issues/3754
-                    with warnings.catch_warnings():
-                        warnings.simplefilter('ignore')  # suppress jit trace warning
-                        self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
-            if ni < 3:
-                f = self.save_dir / f'train_batch{ni}.jpg'  # filename
-                Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
-            if self.wandb and ni == 10:
-                files = sorted(self.save_dir.glob('train*.jpg'))
-                self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
-
-    def on_train_epoch_end(self, epoch):
-        # Callback runs on train epoch end
-        if self.wandb:
-            self.wandb.current_epoch = epoch + 1
-
-    def on_val_image_end(self, pred, predn, path, names, im):
-        # Callback runs on val image end
-        if self.wandb:
-            self.wandb.val_one_image(pred, predn, path, names, im)
-
-    def on_val_end(self):
-        # Callback runs on val end
-        if self.wandb:
-            files = sorted(self.save_dir.glob('val*.jpg'))
-            self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
-
-    def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
-        # Callback runs at the end of each fit (train+val) epoch
-        x = {k: v for k, v in zip(self.keys, vals)}  # dict
-        if self.csv:
-            file = self.save_dir / 'results.csv'
-            n = len(x) + 1  # number of cols
-            s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n')  # add header
-            with open(file, 'a') as f:
-                f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
-
-        if self.tb:
-            for k, v in x.items():
-                self.tb.add_scalar(k, v, epoch)
-
-        if self.wandb:
-            self.wandb.log(x)
-            self.wandb.end_epoch(best_result=best_fitness == fi)
-
-    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
-        # Callback runs on model save event
-        if self.wandb:
-            if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
-                self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
-
-    def on_train_end(self, last, best, plots, epoch, results):
-        # Callback runs on training end
-        if plots:
-            plot_results(file=self.save_dir / 'results.csv')  # save results.png
-        files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
-        files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter
-
-        if self.tb:
-            import cv2
-            for f in files:
-                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
-        if self.wandb:
-            self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
-            # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
-            if not self.opt.evolve:
-                wandb.log_artifact(str(best if best.exists() else last), type='model',
-                                   name='run_' + self.wandb.wandb_run.id + '_model',
-                                   aliases=['latest', 'best', 'stripped'])
-                self.wandb.finish_run()
-            else:
-                self.wandb.finish_run()
-                self.wandb = WandbLogger(self.opt)

face_detector/utils/loggers/wandb/README.md

@@ -1,147 +0,0 @@
-📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021.
-* [About Weights & Biases](#about-weights-&-biases)
-* [First-Time Setup](#first-time-setup)
-* [Viewing runs](#viewing-runs)
-* [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
-* [Reports: Share your work with the world!](#reports)
-
-## About Weights & Biases
-Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
-
-Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
-
- * [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
- * [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
- * [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
- * [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
- * [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
- * [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
-
-## First-Time Setup
-<details open>
- <summary> Toggle Details </summary>
-When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.
-
-W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as:
-
- ```shell
- $ python train.py --project ... --name ...
- ```
-
-YOLOv5 notebook example: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-<img width="960" alt="Screen Shot 2021-09-29 at 10 23 13 PM" src="https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png">
-
-
- </details>
-
-## Viewing Runs
-<details open>
-  <summary> Toggle Details </summary>
-Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in <b>realtime</b> . All important information is logged:
-
- * Training & Validation losses
- * Metrics: Precision, Recall, [email protected], [email protected]:0.95
- * Learning Rate over time
- * A bounding box debugging panel, showing the training progress over time
- * GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**
- * System: Disk I/0, CPU utilization, RAM memory usage
- * Your trained model as W&B Artifact
- * Environment: OS and Python types, Git repository and state, **training command**
-
-<p align="center"><img width="900" alt="Weights & Biases dashboard" src="https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png"></p>
-
-
-</details>
-
-## Advanced Usage
-You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
-<details open>
- <h3>1. Visualize and Version Datasets</h3>
- Log, visualize, dynamically query, and understand your data with <a href='https://docs.wandb.ai/guides/data-vis/tables'>W&B Tables</a>. You can use the following command to log your dataset as a W&B Table. This will generate a <code>{dataset}_wandb.yaml</code> file which can be used to train from dataset artifact.
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
-
- ![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
- </details>
-
- <h3> 2: Train and Log Evaluation simultaneousy </h3>
-   This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
-   Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
-   so no images will be uploaded from your system more than once.
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data ..  --upload_data </code>
-
-![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
- </details>
-
- <h3> 3: Train using dataset artifact </h3>
-   When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
-   can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data {data}_wandb.yaml </code>
-
-![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
- </details>
-
-   <h3> 4: Save model checkpoints as artifacts </h3>
-  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
-  You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
-
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --save_period 1 </code>
-
-![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
- </details>
-
-</details>
-
- <h3> 5: Resume runs from checkpoint artifacts. </h3>
-Any run can be resumed using artifacts if the <code>--resume</code> argument starts with <code>wandb-artifact://</code> prefix followed by the run path, i.e, <code>wandb-artifact://username/project/runid </code>. This doesn't require the model checkpoint to be present on the local system.
-
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-
-![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
- </details>
-
-  <h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
- <b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
- The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
- train from <code>_wandb.yaml</code> file and set <code>--save_period</code>
-
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-
-![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
- </details>
-
-</details>
-
-
- <h3> Reports </h3>
-W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).
-
-<img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">
-
-
-## Environments
-
-YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
-- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
-- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-
-
-## Status
-
-![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
-
-If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

face_detector/utils/loggers/wandb/log_dataset.py

@@ -1,23 +0,0 @@
-import argparse
-
-from wandb_utils import WandbLogger
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
-    logger = WandbLogger(opt, None, job_type='Dataset Creation')  # TODO: return value unused
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
-    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
-    parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
-    parser.add_argument('--entity', default=None, help='W&B entity')
-    parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
-
-    opt = parser.parse_args()
-    opt.resume = False  # Explicitly disallow resume check for dataset upload job
-
-    create_dataset_artifact(opt)

face_detector/utils/loggers/wandb/sweep.py

@@ -1,41 +0,0 @@
-import sys
-from pathlib import Path
-
-import wandb
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-from train import train, parse_opt
-from utils.general import increment_path
-from utils.torch_utils import select_device
-from utils.callbacks import Callbacks
-
-
-def sweep():
-    wandb.init()
-    # Get hyp dict from sweep agent
-    hyp_dict = vars(wandb.config).get("_items")
-
-    # Workaround: get necessary opt args
-    opt = parse_opt(known=True)
-    opt.batch_size = hyp_dict.get("batch_size")
-    opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
-    opt.epochs = hyp_dict.get("epochs")
-    opt.nosave = True
-    opt.data = hyp_dict.get("data")
-    opt.weights = str(opt.weights)
-    opt.cfg = str(opt.cfg)
-    opt.data = str(opt.data)
-    opt.hyp = str(opt.hyp)
-    opt.project = str(opt.project)
-    device = select_device(opt.device, batch_size=opt.batch_size)
-
-    # train
-    train(hyp_dict, opt, device, callbacks=Callbacks())
-
-
-if __name__ == "__main__":
-    sweep()

face_detector/utils/loggers/wandb/sweep.yaml

@@ -1,143 +0,0 @@
-# Hyperparameters for training
-# To set range-
-# Provide min and max values as:
-#      parameter:
-#
-#         min: scalar
-#         max: scalar
-# OR
-#
-# Set a specific list of search space-
-#     parameter:
-#         values: [scalar1, scalar2, scalar3...]
-#
-# You can use grid, bayesian and hyperopt search strategy
-# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
-
-program: utils/loggers/wandb/sweep.py
-method: random
-metric:
-  name: metrics/mAP_0.5
-  goal: maximize
-
-parameters:
-  # hyperparameters: set either min, max range or values list
-  data:
-    value: "data/coco128.yaml"
-  batch_size:
-    values: [64]
-  epochs:
-    values: [10]
-
-  lr0:
-    distribution: uniform
-    min: 1e-5
-    max: 1e-1
-  lrf:
-    distribution: uniform
-    min: 0.01
-    max: 1.0
-  momentum:
-    distribution: uniform
-    min: 0.6
-    max: 0.98
-  weight_decay:
-    distribution: uniform
-    min: 0.0
-    max: 0.001
-  warmup_epochs:
-    distribution: uniform
-    min: 0.0
-    max: 5.0
-  warmup_momentum:
-    distribution: uniform
-    min: 0.0
-    max: 0.95
-  warmup_bias_lr:
-    distribution: uniform
-    min: 0.0
-    max: 0.2
-  box:
-    distribution: uniform
-    min: 0.02
-    max: 0.2
-  cls:
-    distribution: uniform
-    min: 0.2
-    max: 4.0
-  cls_pw:
-    distribution: uniform
-    min: 0.5
-    max: 2.0
-  obj:
-    distribution: uniform
-    min: 0.2
-    max: 4.0
-  obj_pw:
-    distribution: uniform
-    min: 0.5
-    max: 2.0
-  iou_t:
-    distribution: uniform
-    min: 0.1
-    max: 0.7
-  anchor_t:
-    distribution: uniform
-    min: 2.0
-    max: 8.0
-  fl_gamma:
-    distribution: uniform
-    min: 0.0
-    max: 0.1
-  hsv_h:
-    distribution: uniform
-    min: 0.0
-    max: 0.1
-  hsv_s:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  hsv_v:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  degrees:
-    distribution: uniform
-    min: 0.0
-    max: 45.0
-  translate:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  scale:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  shear:
-    distribution: uniform
-    min: 0.0
-    max: 10.0
-  perspective:
-    distribution: uniform
-    min: 0.0
-    max: 0.001
-  flipud:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  fliplr:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  mosaic:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  mixup:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  copy_paste:
-    distribution: uniform
-    min: 0.0
-    max: 1.0

face_detector/utils/loggers/wandb/wandb_utils.py

@@ -1,527 +0,0 @@
-"""Utilities and tools for tracking runs with Weights & Biases."""
-
-import logging
-import os
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Dict
-
-import pkg_resources as pkg
-import yaml
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-from utils.datasets import LoadImagesAndLabels
-from utils.datasets import img2label_paths
-from utils.general import check_dataset, check_file
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')  # verify package import not local dir
-except (ImportError, AssertionError):
-    wandb = None
-
-RANK = int(os.getenv('RANK', -1))
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
-    return from_string[len(prefix):]
-
-
-def check_wandb_config_file(data_config_file):
-    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
-    if Path(wandb_config).is_file():
-        return wandb_config
-    return data_config_file
-
-
-def check_wandb_dataset(data_file):
-    is_trainset_wandb_artifact = False
-    is_valset_wandb_artifact = False
-    if check_file(data_file) and data_file.endswith('.yaml'):
-        with open(data_file, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)
-        is_trainset_wandb_artifact = (isinstance(data_dict['train'], str) and
-                                      data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX))
-        is_valset_wandb_artifact = (isinstance(data_dict['val'], str) and
-                                    data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX))
-    if is_trainset_wandb_artifact or is_valset_wandb_artifact:
-        return data_dict
-    else:
-        return check_dataset(data_file)
-
-
-def get_run_info(run_path):
-    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
-    run_id = run_path.stem
-    project = run_path.parent.stem
-    entity = run_path.parent.parent.stem
-    model_artifact_name = 'run_' + run_id + '_model'
-    return entity, project, run_id, model_artifact_name
-
-
-def check_wandb_resume(opt):
-    process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
-    if isinstance(opt.resume, str):
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            if RANK not in [-1, 0]:  # For resuming DDP runs
-                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
-                api = wandb.Api()
-                artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
-                modeldir = artifact.download()
-                opt.weights = str(Path(modeldir) / "last.pt")
-            return True
-    return None
-
-
-def process_wandb_config_ddp_mode(opt):
-    with open(check_file(opt.data), errors='ignore') as f:
-        data_dict = yaml.safe_load(f)  # data dict
-    train_dir, val_dir = None, None
-    if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
-        api = wandb.Api()
-        train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
-        train_dir = train_artifact.download()
-        train_path = Path(train_dir) / 'data/images/'
-        data_dict['train'] = str(train_path)
-
-    if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
-        api = wandb.Api()
-        val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
-        val_dir = val_artifact.download()
-        val_path = Path(val_dir) / 'data/images/'
-        data_dict['val'] = str(val_path)
-    if train_dir or val_dir:
-        ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
-        with open(ddp_data_path, 'w') as f:
-            yaml.safe_dump(data_dict, f)
-        opt.data = ddp_data_path
-
-
-class WandbLogger():
-    """Log training runs, datasets, models, and predictions to Weights & Biases.
-
-    This logger sends information to W&B at wandb.ai. By default, this information
-    includes hyperparameters, system configuration and metrics, model metrics,
-    and basic data metrics and analyses.
-
-    By providing additional command line arguments to train.py, datasets,
-    models and predictions can also be logged.
-
-    For more on how this logger is used, see the Weights & Biases documentation:
-    https://docs.wandb.com/guides/integrations/yolov5
-    """
-
-    def __init__(self, opt, run_id=None, job_type='Training'):
-        """
-        - Initialize WandbLogger instance
-        - Upload dataset if opt.upload_dataset is True
-        - Setup trainig processes if job_type is 'Training'
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        run_id (str) -- Run ID of W&B run to be resumed
-        job_type (str) -- To set the job_type for this run
-
-       """
-        # Pre-training routine --
-        self.job_type = job_type
-        self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
-        self.val_artifact, self.train_artifact = None, None
-        self.train_artifact_path, self.val_artifact_path = None, None
-        self.result_artifact = None
-        self.val_table, self.result_table = None, None
-        self.bbox_media_panel_images = []
-        self.val_table_path_map = None
-        self.max_imgs_to_log = 16
-        self.wandb_artifact_data_dict = None
-        self.data_dict = None
-        # It's more elegant to stick to 1 wandb.init call,
-        #  but useful config data is overwritten in the WandbLogger's wandb.init call
-        if isinstance(opt.resume, str):  # checks resume from artifact
-            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
-                model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
-                assert wandb, 'install wandb to resume wandb runs'
-                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
-                self.wandb_run = wandb.init(id=run_id,
-                                            project=project,
-                                            entity=entity,
-                                            resume='allow',
-                                            allow_val_change=True)
-                opt.resume = model_artifact_name
-        elif self.wandb:
-            self.wandb_run = wandb.init(config=opt,
-                                        resume="allow",
-                                        project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
-                                        entity=opt.entity,
-                                        name=opt.name if opt.name != 'exp' else None,
-                                        job_type=job_type,
-                                        id=run_id,
-                                        allow_val_change=True) if not wandb.run else wandb.run
-        if self.wandb_run:
-            if self.job_type == 'Training':
-                if opt.upload_dataset:
-                    if not opt.resume:
-                        self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
-
-                if opt.resume:
-                    # resume from artifact
-                    if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-                        self.data_dict = dict(self.wandb_run.config.data_dict)
-                    else:  # local resume
-                        self.data_dict = check_wandb_dataset(opt.data)
-                else:
-                    self.data_dict = check_wandb_dataset(opt.data)
-                    self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
-
-                    # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
-                    self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},
-                                                 allow_val_change=True)
-                self.setup_training(opt)
-
-            if self.job_type == 'Dataset Creation':
-                self.data_dict = self.check_and_upload_dataset(opt)
-
-    def check_and_upload_dataset(self, opt):
-        """
-        Check if the dataset format is compatible and upload it as W&B artifact
-
-        arguments:
-        opt (namespace)-- Commandline arguments for current run
-
-        returns:
-        Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
-        """
-        assert wandb, 'Install wandb to upload dataset'
-        config_path = self.log_dataset_artifact(opt.data,
-                                                opt.single_cls,
-                                                'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
-        print("Created dataset config file ", config_path)
-        with open(config_path, errors='ignore') as f:
-            wandb_data_dict = yaml.safe_load(f)
-        return wandb_data_dict
-
-    def setup_training(self, opt):
-        """
-        Setup the necessary processes for training YOLO models:
-          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
-          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
-          - Setup log_dict, initialize bbox_interval
-
-        arguments:
-        opt (namespace) -- commandline arguments for this run
-
-        """
-        self.log_dict, self.current_epoch = {}, 0
-        self.bbox_interval = opt.bbox_interval
-        if isinstance(opt.resume, str):
-            modeldir, _ = self.download_model_artifact(opt)
-            if modeldir:
-                self.weights = Path(modeldir) / "last.pt"
-                config = self.wandb_run.config
-                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
-                    self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
-                                                                                                       config.hyp
-        data_dict = self.data_dict
-        if self.val_artifact is None:  # If --upload_dataset is set, use the existing artifact, don't download
-            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
-                                                                                           opt.artifact_alias)
-            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
-                                                                                       opt.artifact_alias)
-
-        if self.train_artifact_path is not None:
-            train_path = Path(self.train_artifact_path) / 'data/images/'
-            data_dict['train'] = str(train_path)
-        if self.val_artifact_path is not None:
-            val_path = Path(self.val_artifact_path) / 'data/images/'
-            data_dict['val'] = str(val_path)
-
-        if self.val_artifact is not None:
-            self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-            self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
-            self.val_table = self.val_artifact.get("val")
-            if self.val_table_path_map is None:
-                self.map_val_table_path()
-        if opt.bbox_interval == -1:
-            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
-        train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
-        # Update the the data_dict to point to local artifacts dir
-        if train_from_artifact:
-            self.data_dict = data_dict
-
-    def download_dataset_artifact(self, path, alias):
-        """
-        download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
-
-        arguments:
-        path -- path of the dataset to be used for training
-        alias (str)-- alias of the artifact to be download/used for training
-
-        returns:
-        (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
-        is found otherwise returns (None, None)
-        """
-        if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
-            artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
-            dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
-            assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
-            datadir = dataset_artifact.download()
-            return datadir, dataset_artifact
-        return None, None
-
-    def download_model_artifact(self, opt):
-        """
-        download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        """
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
-            assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
-            modeldir = model_artifact.download()
-            epochs_trained = model_artifact.metadata.get('epochs_trained')
-            total_epochs = model_artifact.metadata.get('total_epochs')
-            is_finished = total_epochs is None
-            assert not is_finished, 'training is finished, can only resume incomplete runs.'
-            return modeldir, model_artifact
-        return None, None
-
-    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
-        """
-        Log the model checkpoint as W&B artifact
-
-        arguments:
-        path (Path)   -- Path of directory containing the checkpoints
-        opt (namespace) -- Command line arguments for this run
-        epoch (int)  -- Current epoch number
-        fitness_score (float) -- fitness score for current epoch
-        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
-        """
-        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
-            'original_url': str(path),
-            'epochs_trained': epoch + 1,
-            'save period': opt.save_period,
-            'project': opt.project,
-            'total_epochs': opt.epochs,
-            'fitness_score': fitness_score
-        })
-        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
-        wandb.log_artifact(model_artifact,
-                           aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
-        print("Saving model artifact on epoch ", epoch + 1)
-
-    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
-        """
-        Log the dataset as W&B artifact and return the new data file with W&B links
-
-        arguments:
-        data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
-        single_class (boolean)  -- train multi-class data as single-class
-        project (str) -- project name. Used to construct the artifact path
-        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
-        file with _wandb postfix. Eg -> data_wandb.yaml
-
-        returns:
-        the new .yaml file with artifact links. it can be used to start training directly from artifacts
-        """
-        self.data_dict = check_dataset(data_file)  # parse and check
-        data = dict(self.data_dict)
-        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
-        names = {k: v for k, v in enumerate(names)}  # to index dictionary
-        self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
-            data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None
-        self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
-            data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
-        if data.get('train'):
-            data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
-        if data.get('val'):
-            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
-        path = Path(data_file).stem
-        path = (path if overwrite_config else path + '_wandb') + '.yaml'  # updated data.yaml path
-        data.pop('download', None)
-        data.pop('path', None)
-        with open(path, 'w') as f:
-            yaml.safe_dump(data, f)
-
-        if self.job_type == 'Training':  # builds correct artifact pipeline graph
-            self.wandb_run.use_artifact(self.val_artifact)
-            self.wandb_run.use_artifact(self.train_artifact)
-            self.val_artifact.wait()
-            self.val_table = self.val_artifact.get('val')
-            self.map_val_table_path()
-        else:
-            self.wandb_run.log_artifact(self.train_artifact)
-            self.wandb_run.log_artifact(self.val_artifact)
-        return path
-
-    def map_val_table_path(self):
-        """
-        Map the validation dataset Table like name of file -> it's id in the W&B Table.
-        Useful for - referencing artifacts for evaluation.
-        """
-        self.val_table_path_map = {}
-        print("Mapping dataset")
-        for i, data in enumerate(tqdm(self.val_table.data)):
-            self.val_table_path_map[data[3]] = data[0]
-
-    def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int,str], name: str = 'dataset'):
-        """
-        Create and return W&B artifact containing W&B Table of the dataset.
-
-        arguments:
-        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
-        class_to_id -- hash map that maps class ids to labels
-        name -- name of the artifact
-
-        returns:
-        dataset artifact to be logged or used
-        """
-        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
-        artifact = wandb.Artifact(name=name, type="dataset")
-        img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
-        img_files = tqdm(dataset.img_files) if not img_files else img_files
-        for img_file in img_files:
-            if Path(img_file).is_dir():
-                artifact.add_dir(img_file, name='data/images')
-                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
-                artifact.add_dir(labels_path, name='data/labels')
-            else:
-                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
-                label_file = Path(img2label_paths([img_file])[0])
-                artifact.add_file(str(label_file),
-                                  name='data/labels/' + label_file.name) if label_file.exists() else None
-        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
-        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
-        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
-            box_data, img_classes = [], {}
-            for cls, *xywh in labels[:, 1:].tolist():
-                cls = int(cls)
-                box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]},
-                                 "class_id": cls,
-                                 "box_caption": "%s" % (class_to_id[cls])})
-                img_classes[cls] = class_to_id[cls]
-            boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
-            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
-                           Path(paths).name)
-        artifact.add(table, name)
-        return artifact
-
-    def log_training_progress(self, predn, path, names):
-        """
-        Build evaluation Table. Uses reference from validation dataset table.
-
-        arguments:
-        predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image
-        names (dict(int, str)): hash map that maps class ids to labels
-        """
-        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
-        box_data = []
-        total_conf = 0
-        for *xyxy, conf, cls in predn.tolist():
-            if conf >= 0.25:
-                box_data.append(
-                    {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
-                     "class_id": int(cls),
-                     "box_caption": f"{names[cls]} {conf:.3f}",
-                     "scores": {"class_score": conf},
-                     "domain": "pixel"})
-                total_conf += conf
-        boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-        id = self.val_table_path_map[Path(path).name]
-        self.result_table.add_data(self.current_epoch,
-                                   id,
-                                   self.val_table.data[id][1],
-                                   wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
-                                   total_conf / max(1, len(box_data))
-                                   )
-
-    def val_one_image(self, pred, predn, path, names, im):
-        """
-        Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
-
-        arguments:
-        pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image
-        """
-        if self.val_table and self.result_table:  # Log Table if Val dataset is uploaded as artifact
-            self.log_training_progress(predn, path, names)
-
-        if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
-            if self.current_epoch % self.bbox_interval == 0:
-                box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
-                             "class_id": int(cls),
-                             "box_caption": f"{names[cls]} {conf:.3f}",
-                             "scores": {"class_score": conf},
-                             "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
-                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-                self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
-
-    def log(self, log_dict):
-        """
-        save the metrics to the logging dictionary
-
-        arguments:
-        log_dict (Dict) -- metrics/media to be logged in current step
-        """
-        if self.wandb_run:
-            for key, value in log_dict.items():
-                self.log_dict[key] = value
-
-    def end_epoch(self, best_result=False):
-        """
-        commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
-
-        arguments:
-        best_result (boolean): Boolean representing if the result of this evaluation is best or not
-        """
-        if self.wandb_run:
-            with all_logging_disabled():
-                if self.bbox_media_panel_images:
-                    self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
-                wandb.log(self.log_dict)
-                self.log_dict = {}
-                self.bbox_media_panel_images = []
-            if self.result_artifact:
-                self.result_artifact.add(self.result_table, 'result')
-                wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
-                                                                  ('best' if best_result else '')])
-
-                wandb.log({"evaluation": self.result_table})
-                self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
-                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-
-    def finish_run(self):
-        """
-        Log metrics if any and finish the current W&B run
-        """
-        if self.wandb_run:
-            if self.log_dict:
-                with all_logging_disabled():
-                    wandb.log(self.log_dict)
-            wandb.run.finish()
-
-
-@contextmanager
-def all_logging_disabled(highest_level=logging.CRITICAL):
-    """ source - https://gist.github.com/simon-weber/7853144
-    A context manager that will prevent any logging messages triggered during the body from being processed.
-    :param highest_level: the maximum logging level in use.
-      This would only need to be changed if a custom level greater than CRITICAL is defined.
-    """
-    previous_level = logging.root.manager.disable
-    logging.disable(highest_level)
-    try:
-        yield
-    finally:
-        logging.disable(previous_level)

face_detector/utils/plots.py

@@ -1,447 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Plotting utils
-"""
-
-import math
-import os
-from copy import copy
-from pathlib import Path
-
-import cv2
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sn
-import torch
-from PIL import Image, ImageDraw, ImageFont
-
-from utils.general import user_config_dir, is_ascii, is_chinese, xywh2xyxy, xyxy2xywh
-from utils.metrics import fitness
-
-# Settings
-CONFIG_DIR = user_config_dir()  # Ultralytics settings dir
-RANK = int(os.getenv('RANK', -1))
-matplotlib.rc('font', **{'size': 11})
-matplotlib.use('Agg')  # for writing to files only
-
-
-class Colors:
-    # Ultralytics color palette https://ultralytics.com/
-    def __init__(self):
-        # hex = matplotlib.colors.TABLEAU_COLORS.values()
-        hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
-               '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
-        self.palette = [self.hex2rgb('#' + c) for c in hex]
-        self.n = len(self.palette)
-
-    def __call__(self, i, bgr=False):
-        c = self.palette[int(i) % self.n]
-        return (c[2], c[1], c[0]) if bgr else c
-
-    @staticmethod
-    def hex2rgb(h):  # rgb order (PIL)
-        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
-
-
-colors = Colors()  # create instance for 'from utils.plots import colors'
-
-
-def check_font(font='Arial.ttf', size=10):
-    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
-    font = Path(font)
-    font = font if font.exists() else (CONFIG_DIR / font.name)
-    try:
-        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-    except Exception as e:  # download if missing
-        url = "https://ultralytics.com/assets/" + font.name
-        print(f'Downloading {url} to {font}...')
-        torch.hub.download_url_to_file(url, str(font), progress=False)
-        return ImageFont.truetype(str(font), size)
-
-
-class Annotator:
-    if RANK in (-1, 0):
-        check_font()  # download TTF if necessary
-
-    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
-    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
-        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
-        self.pil = pil or not is_ascii(example) or is_chinese(example)
-        if self.pil:  # use PIL
-            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-            self.draw = ImageDraw.Draw(self.im)
-            self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
-                                   size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
-        else:  # use cv2
-            self.im = im
-        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
-
-    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
-        # Add one xyxy box to image with label
-        if self.pil or not is_ascii(label):
-            self.draw.rectangle(box, width=self.lw, outline=color)  # box
-            if label:
-                w, h = self.font.getsize(label)  # text width, height
-                outside = box[1] - h >= 0  # label fits outside box
-                self.draw.rectangle([box[0],
-                                     box[1] - h if outside else box[1],
-                                     box[0] + w + 1,
-                                     box[1] + 1 if outside else box[1] + h + 1], fill=color)
-                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
-                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
-        else:  # cv2
-            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
-            if label:
-                tf = max(self.lw - 1, 1)  # font thickness
-                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
-                outside = p1[1] - h - 3 >= 0  # label fits outside box
-                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
-                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
-                cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
-                            thickness=tf, lineType=cv2.LINE_AA)
-
-    def rectangle(self, xy, fill=None, outline=None, width=1):
-        # Add rectangle to image (PIL-only)
-        self.draw.rectangle(xy, fill, outline, width)
-
-    def text(self, xy, text, txt_color=(255, 255, 255)):
-        # Add text to image (PIL-only)
-        w, h = self.font.getsize(text)  # text width, height
-        self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
-
-    def result(self):
-        # Return annotated image as array
-        return np.asarray(self.im)
-
-
-def hist2d(x, y, n=100):
-    # 2d histogram used in labels.png and evolve.png
-    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
-    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
-    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
-    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
-    return np.log(hist[xidx, yidx])
-
-
-def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
-    from scipy.signal import butter, filtfilt
-
-    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
-    def butter_lowpass(cutoff, fs, order):
-        nyq = 0.5 * fs
-        normal_cutoff = cutoff / nyq
-        return butter(order, normal_cutoff, btype='low', analog=False)
-
-    b, a = butter_lowpass(cutoff, fs, order=order)
-    return filtfilt(b, a, data)  # forward-backward filter
-
-
-def output_to_target(output):
-    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
-    targets = []
-    for i, o in enumerate(output):
-        for *box, conf, cls in o.cpu().numpy():
-            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
-    return np.array(targets)
-
-
-def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):
-    # Plot image grid with labels
-    if isinstance(images, torch.Tensor):
-        images = images.cpu().float().numpy()
-    if isinstance(targets, torch.Tensor):
-        targets = targets.cpu().numpy()
-    if np.max(images[0]) <= 1:
-        images *= 255.0  # de-normalise (optional)
-    bs, _, h, w = images.shape  # batch size, _, height, width
-    bs = min(bs, max_subplots)  # limit plot images
-    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
-
-    # Build Image
-    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
-    for i, im in enumerate(images):
-        if i == max_subplots:  # if last batch has fewer images than we expect
-            break
-        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
-        im = im.transpose(1, 2, 0)
-        mosaic[y:y + h, x:x + w, :] = im
-
-    # Resize (optional)
-    scale = max_size / ns / max(h, w)
-    if scale < 1:
-        h = math.ceil(scale * h)
-        w = math.ceil(scale * w)
-        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
-
-    # Annotate
-    fs = int((h + w) * ns * 0.01)  # font size
-    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True)
-    for i in range(i + 1):
-        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
-        annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
-        if paths:
-            annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
-        if len(targets) > 0:
-            ti = targets[targets[:, 0] == i]  # image targets
-            boxes = xywh2xyxy(ti[:, 2:6]).T
-            classes = ti[:, 1].astype('int')
-            labels = ti.shape[1] == 6  # labels if no conf column
-            conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
-
-            if boxes.shape[1]:
-                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
-                    boxes[[0, 2]] *= w  # scale to pixels
-                    boxes[[1, 3]] *= h
-                elif scale < 1:  # absolute coords need scale if image scales
-                    boxes *= scale
-            boxes[[0, 2]] += x
-            boxes[[1, 3]] += y
-            for j, box in enumerate(boxes.T.tolist()):
-                cls = classes[j]
-                color = colors(cls)
-                cls = names[cls] if names else cls
-                if labels or conf[j] > 0.25:  # 0.25 conf thresh
-                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
-                    annotator.box_label(box, label, color=color)
-    annotator.im.save(fname)  # save
-
-
-def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
-    # Plot LR simulating training for full epochs
-    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
-    y = []
-    for _ in range(epochs):
-        scheduler.step()
-        y.append(optimizer.param_groups[0]['lr'])
-    plt.plot(y, '.-', label='LR')
-    plt.xlabel('epoch')
-    plt.ylabel('LR')
-    plt.grid()
-    plt.xlim(0, epochs)
-    plt.ylim(0)
-    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
-    plt.close()
-
-
-def plot_val_txt():  # from utils.plots import *; plot_val()
-    # Plot val.txt histograms
-    x = np.loadtxt('val.txt', dtype=np.float32)
-    box = xyxy2xywh(x[:, :4])
-    cx, cy = box[:, 0], box[:, 1]
-
-    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
-    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
-    ax.set_aspect('equal')
-    plt.savefig('hist2d.png', dpi=300)
-
-    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
-    ax[0].hist(cx, bins=600)
-    ax[1].hist(cy, bins=600)
-    plt.savefig('hist1d.png', dpi=200)
-
-
-def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
-    # Plot targets.txt histograms
-    x = np.loadtxt('targets.txt', dtype=np.float32).T
-    s = ['x targets', 'y targets', 'width targets', 'height targets']
-    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
-    ax = ax.ravel()
-    for i in range(4):
-        ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
-        ax[i].legend()
-        ax[i].set_title(s[i])
-    plt.savefig('targets.jpg', dpi=200)
-
-
-def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_val_study()
-    # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
-    save_dir = Path(file).parent if file else Path(dir)
-    plot2 = False  # plot additional results
-    if plot2:
-        ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
-
-    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
-    # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
-    for f in sorted(save_dir.glob('study*.txt')):
-        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
-        x = np.arange(y.shape[1]) if x is None else np.array(x)
-        if plot2:
-            s = ['P', 'R', '[email protected]', '[email protected]:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
-            for i in range(7):
-                ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
-                ax[i].set_title(s[i])
-
-        j = y[3].argmax() + 1
-        ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
-                 label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
-
-    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
-             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
-
-    ax2.grid(alpha=0.2)
-    ax2.set_yticks(np.arange(20, 60, 5))
-    ax2.set_xlim(0, 57)
-    ax2.set_ylim(25, 55)
-    ax2.set_xlabel('GPU Speed (ms/img)')
-    ax2.set_ylabel('COCO AP val')
-    ax2.legend(loc='lower right')
-    f = save_dir / 'study.png'
-    print(f'Saving {f}...')
-    plt.savefig(f, dpi=300)
-
-
-def plot_labels(labels, names=(), save_dir=Path('')):
-    # plot dataset labels
-    print('Plotting labels... ')
-    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
-    nc = int(c.max() + 1)  # number of classes
-    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
-
-    # seaborn correlogram
-    sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
-    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
-    plt.close()
-
-    # matplotlib labels
-    matplotlib.use('svg')  # faster
-    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
-    y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195
-    ax[0].set_ylabel('instances')
-    if 0 < len(names) < 30:
-        ax[0].set_xticks(range(len(names)))
-        ax[0].set_xticklabels(names, rotation=90, fontsize=10)
-    else:
-        ax[0].set_xlabel('classes')
-    sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
-    sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
-
-    # rectangles
-    labels[:, 1:3] = 0.5  # center
-    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
-    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
-    for cls, *box in labels[:1000]:
-        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
-    ax[1].imshow(img)
-    ax[1].axis('off')
-
-    for a in [0, 1, 2, 3]:
-        for s in ['top', 'right', 'left', 'bottom']:
-            ax[a].spines[s].set_visible(False)
-
-    plt.savefig(save_dir / 'labels.jpg', dpi=200)
-    matplotlib.use('Agg')
-    plt.close()
-
-
-def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
-    # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
-    ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
-    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
-    files = list(Path(save_dir).glob('frames*.txt'))
-    for fi, f in enumerate(files):
-        try:
-            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows
-            n = results.shape[1]  # number of rows
-            x = np.arange(start, min(stop, n) if stop else n)
-            results = results[:, x]
-            t = (results[0] - results[0].min())  # set t0=0s
-            results[0] = x
-            for i, a in enumerate(ax):
-                if i < len(results):
-                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
-                    a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
-                    a.set_title(s[i])
-                    a.set_xlabel('time (s)')
-                    # if fi == len(files) - 1:
-                    #     a.set_ylim(bottom=0)
-                    for side in ['top', 'right']:
-                        a.spines[side].set_visible(False)
-                else:
-                    a.remove()
-        except Exception as e:
-            print(f'Warning: Plotting error for {f}; {e}')
-    ax[1].legend()
-    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
-
-
-def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *; plot_evolve()
-    # Plot evolve.csv hyp evolution results
-    evolve_csv = Path(evolve_csv)
-    data = pd.read_csv(evolve_csv)
-    keys = [x.strip() for x in data.columns]
-    x = data.values
-    f = fitness(x)
-    j = np.argmax(f)  # max fitness index
-    plt.figure(figsize=(10, 12), tight_layout=True)
-    matplotlib.rc('font', **{'size': 8})
-    for i, k in enumerate(keys[7:]):
-        v = x[:, 7 + i]
-        mu = v[j]  # best single result
-        plt.subplot(6, 5, i + 1)
-        plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
-        plt.plot(mu, f.max(), 'k+', markersize=15)
-        plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9})  # limit to 40 characters
-        if i % 5 != 0:
-            plt.yticks([])
-        print(f'{k:>15}: {mu:.3g}')
-    f = evolve_csv.with_suffix('.png')  # filename
-    plt.savefig(f, dpi=200)
-    plt.close()
-    print(f'Saved {f}')
-
-
-def plot_results(file='path/to/results.csv', dir=''):
-    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
-    save_dir = Path(file).parent if file else Path(dir)
-    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
-    ax = ax.ravel()
-    files = list(save_dir.glob('results*.csv'))
-    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
-    for fi, f in enumerate(files):
-        try:
-            data = pd.read_csv(f)
-            s = [x.strip() for x in data.columns]
-            x = data.values[:, 0]
-            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
-                y = data.values[:, j]
-                # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
-                ax[i].set_title(s[j], fontsize=12)
-                # if j in [8, 9, 10]:  # share train and val loss y axes
-                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
-        except Exception as e:
-            print(f'Warning: Plotting error for {f}: {e}')
-    ax[1].legend()
-    fig.savefig(save_dir / 'results.png', dpi=200)
-    plt.close()
-
-
-def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
-    """
-    x:              Features to be visualized
-    module_type:    Module type
-    stage:          Module stage within model
-    n:              Maximum number of feature maps to plot
-    save_dir:       Directory to save results
-    """
-    if 'Detect' not in module_type:
-        batch, channels, height, width = x.shape  # batch, channels, height, width
-        if height > 1 and width > 1:
-            f = f"stage{stage}_{module_type.split('.')[-1]}_features.png"  # filename
-
-            blocks = torch.chunk(x[0].cpu(), channels, dim=0)  # select batch index 0, block by channels
-            n = min(n, channels)  # number of plots
-            fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)  # 8 rows x n/8 cols
-            ax = ax.ravel()
-            plt.subplots_adjust(wspace=0.05, hspace=0.05)
-            for i in range(n):
-                ax[i].imshow(blocks[i].squeeze())  # cmap='gray'
-                ax[i].axis('off')
-
-            print(f'Saving {save_dir / f}... ({n}/{channels})')
-            plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')
-            plt.close()

face_detector/val.py

@@ -1,382 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Validate a trained YOLOv5 model accuracy on a custom dataset
-
-Usage:
-    $ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640
-"""
-
-import argparse
-import json
-import os
-import sys
-from pathlib import Path
-from threading import Thread
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.experimental import attempt_load
-from utils.datasets import create_dataloader
-from utils.general import box_iou, coco80_to_coco91_class, colorstr, check_dataset, check_img_size, \
-    check_requirements, check_suffix, check_yaml, increment_path, non_max_suppression, print_args, scale_coords, \
-    xyxy2xywh, xywh2xyxy, LOGGER
-from utils.metrics import ap_per_class, ConfusionMatrix
-from utils.plots import output_to_target, plot_images, plot_val_study
-from utils.torch_utils import select_device, time_sync
-from utils.callbacks import Callbacks
-
-
-def save_one_txt(predn, save_conf, shape, file):
-    # Save one txt result
-    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
-    for *xyxy, conf, cls in predn.tolist():
-        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
-        with open(file, 'a') as f:
-            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-
-def save_one_json(predn, jdict, path, class_map):
-    # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
-    image_id = int(path.stem) if path.stem.isnumeric() else path.stem
-    box = xyxy2xywh(predn[:, :4])  # xywh
-    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
-    for p, b in zip(predn.tolist(), box.tolist()):
-        jdict.append({'image_id': image_id,
-                      'category_id': class_map[int(p[5])],
-                      'bbox': [round(x, 3) for x in b],
-                      'score': round(p[4], 5)})
-
-
-def process_batch(detections, labels, iouv):
-    """
-    Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
-    Arguments:
-        detections (Array[N, 6]), x1, y1, x2, y2, conf, class
-        labels (Array[M, 5]), class, x1, y1, x2, y2
-    Returns:
-        correct (Array[N, 10]), for 10 IoU levels
-    """
-    correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
-    iou = box_iou(labels[:, 1:], detections[:, :4])
-    x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes match
-    if x[0].shape[0]:
-        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]
-        if x[0].shape[0] > 1:
-            matches = matches[matches[:, 2].argsort()[::-1]]
-            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-            # matches = matches[matches[:, 2].argsort()[::-1]]
-            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        matches = torch.Tensor(matches).to(iouv.device)
-        correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
-    return correct
-
-
-@torch.no_grad()
-def run(data,
-        weights=None,  # model.pt path(s)
-        batch_size=32,  # batch size
-        imgsz=640,  # inference size (pixels)
-        conf_thres=0.001,  # confidence threshold
-        iou_thres=0.6,  # NMS IoU threshold
-        task='val',  # train, val, test, speed or study
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        single_cls=False,  # treat as single-class dataset
-        augment=False,  # augmented inference
-        verbose=False,  # verbose output
-        save_txt=False,  # save results to *.txt
-        save_hybrid=False,  # save label+prediction hybrid results to *.txt
-        save_conf=False,  # save confidences in --save-txt labels
-        save_json=False,  # save a COCO-JSON results file
-        project=ROOT / 'runs/val',  # save to project/name
-        name='exp',  # save to project/name
-        exist_ok=False,  # existing project/name ok, do not increment
-        half=True,  # use FP16 half-precision inference
-        model=None,
-        dataloader=None,
-        save_dir=Path(''),
-        plots=True,
-        callbacks=Callbacks(),
-        compute_loss=None,
-        ):
-    # Initialize/load model and set device
-    training = model is not None
-    if training:  # called by train.py
-        device = next(model.parameters()).device  # get model device
-
-    else:  # called directly
-        device = select_device(device, batch_size=batch_size)
-
-        # Directories
-        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-        # Load model
-        check_suffix(weights, '.pt')
-        model = attempt_load(weights, map_location=device)  # load FP32 model
-        gs = max(int(model.stride.max()), 32)  # grid size (max stride)
-        imgsz = check_img_size(imgsz, s=gs)  # check image size
-
-        # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
-        # if device.type != 'cpu' and torch.cuda.device_count() > 1:
-        #     model = nn.DataParallel(model)
-
-        # Data
-        data = check_dataset(data)  # check
-
-    # Half
-    half &= device.type != 'cpu'  # half precision only supported on CUDA
-    model.half() if half else model.float()
-
-    # Configure
-    model.eval()
-    is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt')  # COCO dataset
-    nc = 1 if single_cls else int(data['nc'])  # number of classes
-    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for [email protected]:0.95
-    niou = iouv.numel()
-
-    # Dataloader
-    if not training:
-        if device.type != 'cpu':
-            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
-        pad = 0.0 if task == 'speed' else 0.5
-        task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
-        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=pad, rect=True,
-                                       prefix=colorstr(f'{task}: '))[0]
-
-    seen = 0
-    confusion_matrix = ConfusionMatrix(nc=nc)
-    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
-    class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
-    s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', '[email protected]', '[email protected]:.95')
-    dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
-    loss = torch.zeros(3, device=device)
-    jdict, stats, ap, ap_class = [], [], [], []
-    for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
-        t1 = time_sync()
-        img = img.to(device, non_blocking=True)
-        img = img.half() if half else img.float()  # uint8 to fp16/32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
-        targets = targets.to(device)
-        nb, _, height, width = img.shape  # batch size, channels, height, width
-        t2 = time_sync()
-        dt[0] += t2 - t1
-
-        # Run model
-        out, train_out = model(img, augment=augment)  # inference and training outputs
-        dt[1] += time_sync() - t2
-
-        # Compute loss
-        if compute_loss:
-            loss += compute_loss([x.float() for x in train_out], targets)[1]  # box, obj, cls
-
-        # Run NMS
-        targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
-        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
-        t3 = time_sync()
-        out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
-        dt[2] += time_sync() - t3
-
-        # Statistics per image
-        for si, pred in enumerate(out):
-            labels = targets[targets[:, 0] == si, 1:]
-            nl = len(labels)
-            tcls = labels[:, 0].tolist() if nl else []  # target class
-            path, shape = Path(paths[si]), shapes[si][0]
-            seen += 1
-
-            if len(pred) == 0:
-                if nl:
-                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
-                continue
-
-            # Predictions
-            if single_cls:
-                pred[:, 5] = 0
-            predn = pred.clone()
-            scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred
-
-            # Evaluate
-            if nl:
-                tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
-                scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
-                labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
-                correct = process_batch(predn, labelsn, iouv)
-                if plots:
-                    confusion_matrix.process_batch(predn, labelsn)
-            else:
-                correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
-            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))  # (correct, conf, pcls, tcls)
-
-            # Save/log
-            if save_txt:
-                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
-            if save_json:
-                save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
-            callbacks.run('on_val_image_end', pred, predn, path, names, img[si])
-
-        # Plot images
-        if plots and batch_i < 3:
-            f = save_dir / f'val_batch{batch_i}_labels.jpg'  # labels
-            Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
-            f = save_dir / f'val_batch{batch_i}_pred.jpg'  # predictions
-            Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
-
-    # Compute statistics
-    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
-    if len(stats) and stats[0].any():
-        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
-        ap50, ap = ap[:, 0], ap.mean(1)  # [email protected], [email protected]:0.95
-        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
-        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
-    else:
-        nt = torch.zeros(1)
-
-
-    # Print results
-    pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
-    LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
-
-    import json
-    import mlflow
-    
-
-    mlflow.log_metric("val_f1",round(f1.mean(),2))
-    mlflow.log_metric("val_precision",round(mp,2))
-    mlflow.log_metric("val_recall",round(mr,2))
-    mlflow.log_metric("val_mAP0.5",round(map50,2))
-    mlflow.log_metric("val_mAP0.5_.95",round(map,2))   
-
-    with open("test.csv","w") as f:
-      f.write("f1,P,R,[email protected],[email protected]:.95\n")
-
-      data = ','.join([str(i) for i in [f1.mean(),mp,mr,map50,map]])
-
-
-      f.write(data + "\n")
-
-      
-
-
-    # Print results per class
-    if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
-        for i, c in enumerate(ap_class):
-            LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
-
-
-    # Print speeds
-    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
-    if not training:
-        shape = (batch_size, 3, imgsz, imgsz)
-        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
-
-    # Plots
-    if plots:
-        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        callbacks.run('on_val_end')
-
-    # Save JSON
-    if save_json and len(jdict):
-        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
-        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
-        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
-        LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
-        with open(pred_json, 'w') as f:
-            json.dump(jdict, f)
-
-        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
-            check_requirements(['pycocotools'])
-            from pycocotools.coco import COCO
-            from pycocotools.cocoeval import COCOeval
-
-            anno = COCO(anno_json)  # init annotations api
-            pred = anno.loadRes(pred_json)  # init predictions api
-            eval = COCOeval(anno, pred, 'bbox')
-            if is_coco:
-                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate
-            eval.evaluate()
-            eval.accumulate()
-            eval.summarize()
-            map, map50 = eval.stats[:2]  # update results ([email protected]:0.95, [email protected])
-        except Exception as e:
-            LOGGER.info(f'pycocotools unable to run: {e}')
-
-    # Return results
-    model.float()  # for training
-    if not training:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    maps = np.zeros(nc) + map
-    for i, c in enumerate(ap_class):
-        maps[c] = ap[i]
-    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
-    parser.add_argument('--batch-size', type=int, default=32, help='batch size')
-    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
-    parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
-    parser.add_argument('--task', default='val', help='train, val, test, speed or study')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
-    parser.add_argument('--augment', action='store_true', help='augmented inference')
-    parser.add_argument('--verbose', action='store_true', help='report mAP by class')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
-    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
-    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
-    parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
-    parser.add_argument('--name', default='exp', help='save to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    opt = parser.parse_args()
-    opt.data = check_yaml(opt.data)  # check YAML
-    opt.save_json |= opt.data.endswith('coco.yaml')
-    opt.save_txt |= opt.save_hybrid
-    print_args(FILE.stem, opt)
-    return opt
-
-
-def main(opt):
-    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
-
-    if opt.task in ('train', 'val', 'test'):  # run normally
-        run(**vars(opt))
-
-    elif opt.task == 'speed':  # speed benchmarks
-        # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
-        for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
-            run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=opt.imgsz, conf_thres=.25, iou_thres=.45,
-                device=opt.device, save_json=False, plots=False)
-
-    elif opt.task == 'study':  # run over a range of settings and save/plot
-        # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
-        x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
-        for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
-            f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
-            y = []  # y axis
-            for i in x:  # img-size
-                LOGGER.info(f'\nRunning {f} point {i}...')
-                r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
-                              iou_thres=opt.iou_thres, device=opt.device, save_json=opt.save_json, plots=False)
-                y.append(r + t)  # results and times
-            np.savetxt(f, y, fmt='%10.4g')  # save
-        os.system('zip -r study.zip study_*.txt')
-        plot_val_study(x=x)  # plot
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

face_detector/validate.py

@@ -1,62 +0,0 @@
-import os, sys
-
-p = os.path.abspath('..')
-sys.path.insert(1, p)
-from data_expectations import create_dataset_file, dataset_validation
-import glob
-
-with open("dataset/yolo/state.txt", "r") as f:
-  uid = f.read()
-
-train_imgs = glob.glob("dataset/yolo/train/images/*")
-valid_imgs = glob.glob("dataset/yolo/valid/images/*")
-test_imgs = glob.glob("dataset/yolo/test/images/*")     
-
-splits = [{
-  "meta": "dataset/images_face_detection_train.csv",
-  "data": "dataset/yolo/train/images/*"
-  }, 
-  {
-  "meta": "dataset/images_face_detection_valid.csv",
-  "data": "dataset/yolo/valid/images/*"
-  }, 
-  {
-  "meta": "dataset/images_face_detection_test.csv",
-  "data": "dataset/yolo/test/images/*"
-  }, ]
-
-partial_success = True
-
-for split in splits:
-  imgs = glob.glob(split["data"])
-  create_dataset_file.create(split["meta"], imgs)
-  results = dataset_validation.test_ge(split["meta"])
-  
-  print(results)
-  
-  
-  for result in results:
-    print(result["success"])
-    partial_success = partial_success and result["success"]
-
-  if not partial_success:
-    break
-
-with open("dataset/data_valid_result.txt", "w") as f:
-  f.write(uid.strip() + "-" + str(partial_success) )
-
-assert partial_success
-
-"""
-images_face_detection_train = glob.glob("dataset/yolo/train/images/*")
-images_face_detection_valid = glob.glob("dataset/yolo/valid/images/*")
-images_face_detection_test = glob.glob("dataset/yolo/test/images/*")     
-                
-create("images_face_detection_train.csv",images_face_detection_train)                
-create("images_face_detection_valid.csv",images_face_detection_valid)                
-create("images_face_detection_test.csv",images_face_detection_test) 
-
-test_ge("images_face_detection_train.csv")
-test_ge("images_face_detection_valid.csv")
-test_ge("images_face_detection_test.csv")
-"""

util.py

@@ -0,0 +1,82 @@
+class Detection(object):
+
+
+    def __init__(self, id: int, xmin: int, ymin: int, xmax:int, ymax:int, conf: float, class_id:int, class_name:str, orig_img_sz: "tuple[int]") -> None:
+        
+        self.id = id
+
+        self.xmin = xmin
+        self.ymin = ymin
+        self.xmax = xmax
+        self.ymax = ymax
+
+        self.w = self.xmax - self.xmin
+        self.h = self.ymax - self.ymin
+
+        self.conf = conf
+        self.class_id = class_id
+        self.class_name = class_name
+        
+        self.orig_img_h = orig_img_sz[1]
+        self.orig_img_w = orig_img_sz[0]
+
+    def get_hw_ratio(self):
+
+        return self.h / self.w
+    
+    def get_height_proportion(self):
+
+        return self.h / self.orig_img_h
+    
+    def get_width_proportion(self):
+
+        return self.w / self.orig_img_w
+    
+    def contains(self, detection2: "Detection"):
+
+        if self.xmin <= detection2.xmin and self.xmax >= detection2.xmax and \
+            self.ymin <= detection2.ymin and self.ymax >= detection2.ymax:
+            return True
+        
+        return False
+
+    def get_iou(self, detection2: "Detection"):
+        """
+        Calculate the Intersection over Union (IoU) of two bounding boxes.
+
+        Returns
+        -------
+        float
+            in [0, 1]
+        """
+        assert self.xmin < self.xmax
+        assert self.ymin < self.ymax
+        assert detection2.xmin < detection2.xmax
+        assert detection2.ymin < detection2.ymax
+
+        # determine the coordinates of the intersection rectangle
+        x_left = max(self.xmin, detection2.xmin)
+        y_top = max(self.ymin, detection2.ymin)
+        x_right = min(self.xmax, detection2.xmax)
+        y_bottom = min(self.ymax, detection2.ymax)
+
+        if x_right < x_left or y_bottom < y_top:
+            return 0.0
+
+        # The intersection of two axis-aligned bounding boxes is always an
+        # axis-aligned bounding box
+        intersection_area = (x_right - x_left) * (y_bottom - y_top)
+
+        # compute the area of both AABBs
+        bb1_area = (self.xmax - self.xmin) * (self.ymax - self.ymin)
+        bb2_area = (detection2.xmax - detection2.xmin) * (detection2.ymax - detection2.ymin)
+
+        # compute the intersection over union by taking the intersection
+        # area and dividing it by the sum of prediction + ground-truth
+        # areas - the interesection area
+        iou = intersection_area / float(bb1_area + bb2_area - intersection_area)
+
+        return iou
+
+    def __str__(self) -> str:
+        return f"[{self.xmin}, {self.ymin}, {self.xmax}, {self.ymax}]"

{face_detector → yolov5}/.dockerignore

@@ -15,6 +15,7 @@ data/samples/*
 **/*.pt
 **/*.pth
 **/*.onnx
+**/*.engine
 **/*.mlmodel
 **/*.torchscript
 **/*.torchscript.pt
@@ -23,6 +24,7 @@ data/samples/*
 **/*.pb
 *_saved_model/
 *_web_model/
+*_openvino_model/
 
 # Below Copied From .gitignore -----------------------------------------------------------------------------------------
 # Below Copied From .gitignore -----------------------------------------------------------------------------------------

yolov5/.gitattributes

@@ -0,0 +1,2 @@
+# this drop notebooks from GitHub language stats
+*.ipynb linguist-vendored

yolov5/.gitignore

@@ -0,0 +1,256 @@
+# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tif
+*.tiff
+*.heic
+*.JPG
+*.JPEG
+*.PNG
+*.BMP
+*.TIF
+*.TIFF
+*.HEIC
+*.mp4
+*.mov
+*.MOV
+*.avi
+*.data
+*.json
+*.cfg
+!setup.cfg
+!cfg/yolov3*.cfg
+
+storage.googleapis.com
+runs/*
+data/*
+data/images/*
+!data/*.yaml
+!data/hyps
+!data/scripts
+!data/images
+!data/images/zidane.jpg
+!data/images/bus.jpg
+!data/*.sh
+
+results*.csv
+
+# Datasets -------------------------------------------------------------------------------------------------------------
+coco/
+coco128/
+VOC/
+
+# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
+*.m~
+*.mat
+!targets*.mat
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+darknet53.conv.74
+yolov3-tiny.conv.15
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+/wandb/
+.installed.cfg
+*.egg
+
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

{face_detector → yolov5}/.pre-commit-config.yaml

@@ -13,7 +13,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v4.1.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -24,18 +24,17 @@ repos:
       - id: check-docstring-first
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.23.1
+    rev: v2.31.0
     hooks:
       - id: pyupgrade
         args: [--py36-plus]
         name: Upgrade code
 
-  # TODO
-  #- repo: https://github.com/PyCQA/isort
-  #  rev: 5.9.3
-  #  hooks:
-  #    - id: isort
-  #      name: imports
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+        name: Sort imports
 
   # TODO
   #- repo: https://github.com/pre-commit/mirrors-yapf
@@ -61,7 +60,7 @@ repos:
   #    - id: yesqa
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 3.9.2
+    rev: 4.0.1
     hooks:
       - id: flake8
         name: PEP8

yolov5/CONTRIBUTING.md

@@ -0,0 +1,94 @@
+## Contributing to YOLOv5 🚀
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be
+helping push the frontiers of what's possible in AI 😃!
+
+## Submitting a Pull Request (PR) 🛠️
+
+Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
+
+### 1. Select File to Update
+
+Select `requirements.txt` to update by clicking on it in GitHub.
+<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
+
+### 2. Click 'Edit this file'
+
+Button is in top-right corner.
+<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
+
+### 3. Make Changes
+
+Change `matplotlib` version from `3.2.2` to `3.3`.
+<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
+
+### 4. Preview Changes and Submit PR
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
+for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
+changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
+<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
+
+### PR recommendations
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+- ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an
+  automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may
+  be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature'
+  with the name of your local branch:
+
+  ```bash
+  git remote add upstream https://github.com/ultralytics/yolov5.git
+  git fetch upstream
+  git checkout feature  # <----- replace 'feature' with local branch name
+  git merge upstream/master
+  git push -u origin -f
+  ```
+
+- ✅ Verify all Continuous Integration (CI) **checks are passing**.
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
+  but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
+
+## Submitting a Bug Report 🐛
+
+If you spot a problem with YOLOv5 please submit a Bug Report!
+
+For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
+short guidelines below to help users provide what we need in order to get started.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily
+understand and use to **reproduce** the problem. This is referred to by community members as creating
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
+the problem should be:
+
+* ✅ **Minimal** – Use as little code as possible that still produces the same problem
+* ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+* ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
+should be:
+
+* ✅ **Current** – Verify that your code is up-to-date with current
+  GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
+  copy to ensure your problem has not already been resolved by previous commits.
+* ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
+  repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **
+Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
+understand and diagnose your problem.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under
+the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)

{face_detector → yolov5}/Dockerfile

@@ -1,7 +1,7 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:21.05-py3
+FROM nvcr.io/nvidia/pytorch:21.10-py3
 
 # Install linux packages
 RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
@@ -10,9 +10,9 @@ RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
 COPY requirements.txt .
 RUN python -m pip install --upgrade pip
 RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
-RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook wandb>=0.12.2
-RUN pip install --no-cache -U torch torchvision numpy
-# RUN pip install --no-cache torch==1.9.1+cu111 torchvision==0.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
+RUN pip install --no-cache -r requirements.txt albumentations coremltools onnx gsutil notebook numpy Pillow wandb>=0.12.2
+RUN pip install --no-cache torch==1.10.1+cu113 torchvision==0.11.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+# RUN pip install --no-cache -U torch torchvision
 
 # Create working directory
 RUN mkdir -p /usr/src/app
@@ -59,3 +59,6 @@ ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/
 
 # DDP test
 # python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
+
+# GCP VM from Image
+# docker.io/ultralytics/yolov5:latest

yolov5/README.md

@@ -0,0 +1,304 @@
+<div align="center">
+<p>
+   <a align="left" href="https://ultralytics.com/yolov5" target="_blank">
+   <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
+</p>
+<br>
+<div>
+   <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
+   <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
+   <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+   <br>
+   <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+   <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+   <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
+</div>
+<br>
+<div align="center">
+   <a href="https://github.com/ultralytics">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
+   </a>
+   <img width="2%" />
+   <a href="https://www.linkedin.com/company/ultralytics">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
+   </a>
+   <img width="2%" />
+   <a href="https://twitter.com/ultralytics">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
+   </a>
+   <img width="2%" />
+   <a href="https://www.producthunt.com/@glenn_jocher">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
+   </a>
+   <img width="2%" />
+   <a href="https://youtube.com/ultralytics">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
+   </a>
+   <img width="2%" />
+   <a href="https://www.facebook.com/ultralytics">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
+   </a>
+   <img width="2%" />
+   <a href="https://www.instagram.com/ultralytics/">
+   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
+   </a>
+</div>
+
+<br>
+<p>
+YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
+ open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
+</p>
+
+<!--
+<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
+<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
+-->
+
+</div>
+
+## <div align="center">Documentation</div>
+
+See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
+
+## <div align="center">Quick Start Examples</div>
+
+<details open>
+<summary>Install</summary>
+
+Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
+[**Python>=3.7.0**](https://www.python.org/) environment, including
+[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+
+```bash
+git clone https://github.com/ultralytics/yolov5  # clone
+cd yolov5
+pip install -r requirements.txt  # install
+```
+
+</details>
+
+<details open>
+<summary>Inference</summary>
+
+Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
+. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
+YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
+
+```python
+import torch
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom
+
+# Images
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+
+
+<details>
+<summary>Inference with detect.py</summary>
+
+`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
+the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
+
+```bash
+python detect.py --source 0  # webcam
+                          img.jpg  # image
+                          vid.mp4  # video
+                          path/  # directory
+                          path/*.jpg  # glob
+                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+```
+
+</details>
+
+<details>
+<summary>Training</summary>
+
+The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
+results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
+and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
+YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
+1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
+largest `--batch-size` possible, or pass `--batch-size -1` for
+YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
+
+```bash
+python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
+                                       yolov5s                                64
+                                       yolov5m                                40
+                                       yolov5l                                24
+                                       yolov5x                                16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>Tutorials</summary>
+
+* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED
+* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️
+  RECOMMENDED
+* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
+* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)&nbsp; 🌟 NEW
+* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
+* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ NEW
+* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
+* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
+* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
+* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
+* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
+* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)&nbsp; ⭐ NEW
+* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
+
+</details>
+
+## <div align="center">Environments</div>
+
+Get started in seconds with our verified environments. Click each icon below for details.
+
+<div align="center">
+    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
+    </a>
+    <a href="https://www.kaggle.com/ultralytics/yolov5">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
+    </a>
+    <a href="https://hub.docker.com/r/ultralytics/yolov5">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
+    </a>
+    <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
+    </a>
+    <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
+    </a>
+</div>
+
+## <div align="center">Integrations</div>
+
+<div align="center">
+    <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
+    </a>
+    <a href="https://roboflow.com/?ref=ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
+    </a>
+</div>
+
+|Weights and Biases|Roboflow ⭐ NEW|
+|:-:|:-:|
+|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
+
+
+<!-- ## <div align="center">Compete and Win</div>
+
+We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
+
+<p align="center">
+  <a href="https://github.com/ultralytics/yolov5/discussions/3213">
+  <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
+</p> -->
+
+## <div align="center">Why YOLOv5</div>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p>
+</details>
+<details>
+  <summary>Figure Notes (click to expand)</summary>
+
+* **COCO AP val** denotes [email protected]:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
+* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
+* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
+* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+</details>
+
+### Pretrained Checkpoints
+
+[assets]: https://github.com/ultralytics/yolov5/releases
+
+[TTA]: https://github.com/ultralytics/yolov5/issues/303
+
+|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
+|---                    |---  |---    |---    |---    |---    |---    |---    |---
+|[YOLOv5n][assets]      |640  |28.4   |46.0   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
+|[YOLOv5s][assets]      |640  |37.2   |56.0   |98     |6.4    |0.9    |7.2    |16.5
+|[YOLOv5m][assets]      |640  |45.2   |63.9   |224    |8.2    |1.7    |21.2   |49.0
+|[YOLOv5l][assets]      |640  |48.8   |67.2   |430    |10.1   |2.7    |46.5   |109.1
+|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
+|                       |     |       |       |       |       |       |       |
+|[YOLOv5n6][assets]     |1280 |34.0   |50.7   |153    |8.1    |2.1    |3.2    |4.6
+|[YOLOv5s6][assets]     |1280 |44.5   |63.0   |385    |8.2    |3.6    |12.6   |16.8
+|[YOLOv5m6][assets]     |1280 |51.0   |69.0   |887    |11.1   |6.8    |35.7   |50.0
+|[YOLOv5l6][assets]     |1280 |53.6   |71.6   |1784   |15.8   |10.5   |76.7   |111.4
+|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
+
+<details>
+  <summary>Table Notes (click to expand)</summary>
+
+* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
+* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">Contribute</div>
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
+
+## <div align="center">Contact</div>
+
+For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
+professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
+
+<br>
+
+<div align="center">
+    <a href="https://github.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.linkedin.com/company/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://twitter.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.producthunt.com/@glenn_jocher">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://youtube.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.facebook.com/ultralytics">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
+    </a>
+    <img width="3%" />
+    <a href="https://www.instagram.com/ultralytics/">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
+    </a>
+</div>

{face_detector → yolov5}/data/Argoverse.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
 # Example usage: python train.py --data Argoverse.yaml
 # parent
 # ├── yolov5

{face_detector → yolov5}/data/GlobalWheat2020.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Global Wheat 2020 dataset http://www.global-wheat.com/
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
 # Example usage: python train.py --data GlobalWheat2020.yaml
 # parent
 # ├── yolov5

{face_detector → yolov5}/data/Objects365.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Objects365 dataset https://www.objects365.org/
+# Objects365 dataset https://www.objects365.org/ by Megvii
 # Example usage: python train.py --data Objects365.yaml
 # parent
 # ├── yolov5
@@ -10,7 +10,7 @@
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/Objects365  # dataset root dir
 train: images/train  # train images (relative to 'path') 1742289 images
-val: images/val # val images (relative to 'path') 5570 images
+val: images/val # val images (relative to 'path') 80000 images
 test:  # test images (optional)
 
 # Classes
@@ -63,7 +63,7 @@ download: |
   from pycocotools.coco import COCO
   from tqdm import tqdm
 
-  from utils.general import download, Path
+  from utils.general import Path, download, np, xyxy2xywhn
 
   # Make Directories
   dir = Path(yaml['path'])  # dataset root dir
@@ -72,33 +72,41 @@ download: |
       for q in 'train', 'val':
           (dir / p / q).mkdir(parents=True, exist_ok=True)
 
-  # Download
-  url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/"
-  download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir, delete=False)  # annotations json
-  download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train',
-           curl=True, delete=False, threads=8)
+  # Train, Val Splits
+  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+      print(f"Processing {split} in {patches} patches ...")
+      images, labels = dir / 'images' / split, dir / 'labels' / split
 
-  # Move
-  train = dir / 'images' / 'train'
-  for f in tqdm(train.rglob('*.jpg'), desc=f'Moving images'):
-      f.rename(train / f.name)  # move to /images/train
+      # Download
+      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+      if split == 'train':
+          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+      elif split == 'val':
+          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
+          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
 
-  # Labels
-  coco = COCO(dir / 'zhiyuan_objv2_train.json')
-  names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
-  for cid, cat in enumerate(names):
-      catIds = coco.getCatIds(catNms=[cat])
-      imgIds = coco.getImgIds(catIds=catIds)
-      for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
-          width, height = im["width"], im["height"]
-          path = Path(im["file_name"])  # image filename
-          try:
-              with open(dir / 'labels' / 'train' / path.with_suffix('.txt').name, 'a') as file:
-                  annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
-                  for a in coco.loadAnns(annIds):
-                      x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
-                      x, y = x + w / 2, y + h / 2  # xy to center
-                      file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
+      # Move
+      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+          f.rename(images / f.name)  # move to /images/{split}
 
-          except Exception as e:
-              print(e)
+      # Labels
+      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+              width, height = im["width"], im["height"]
+              path = Path(im["file_name"])  # image filename
+              try:
+                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)

{face_detector → yolov5}/data/SKU-110K.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
 # Example usage: python train.py --data SKU-110K.yaml
 # parent
 # ├── yolov5

{face_detector → yolov5}/data/VOC.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
 # Example usage: python train.py --data VOC.yaml
 # parent
 # ├── yolov5

{face_detector → yolov5}/data/VisDrone.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
 # Example usage: python train.py --data VisDrone.yaml
 # parent
 # ├── yolov5

{face_detector → yolov5}/data/coco.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org
+# COCO 2017 dataset http://cocodataset.org by Microsoft
 # Example usage: python train.py --data coco.yaml
 # parent
 # ├── yolov5
@@ -10,7 +10,7 @@
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/coco  # dataset root dir
 train: train2017.txt  # train images (relative to 'path') 118287 images
-val: val2017.txt  # train images (relative to 'path') 5000 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
 test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
 
 # Classes

{face_detector → yolov5}/data/coco128.yaml

@@ -1,5 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: python train.py --data coco128.yaml
 # parent
 # ├── yolov5
@@ -27,4 +27,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
 
 
 # Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
+download: https://ultralytics.com/assets/coco128.zip

{face_detector → yolov5}/data/hyps/hyp.scratch-high.yaml

@@ -31,4 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.1  # image mixup (probability)
-copy_paste: 0.1  # segment copy-paste (probability)
+copy_paste: 0.1  # segment copy-paste (probability)

{face_detector → yolov5}/data/hyps/hyp.scratch-low.yaml

@@ -31,4 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-med.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)