# CI Checks
%%shell
export PYTHONPATH="$PWD"  # to run *.py. files in subdirectories
rm -rf runs  # remove runs/
for m in yolov5s; do  # models
  python train.py --weights $m.pt --epochs 3 --img 320 --device 0  # train pretrained
  python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0  # train scratch
  for d in 0; do # cpu; do  # devices
    python detect.py --weights $m.pt --device $d  # detect official
    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom
    python val.py --weights $m.pt --device $d # val official
    python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom
  done
  python hubconf.py  # hub
  python models/yolo.py --cfg $m.yaml  # build PyTorch model
  python models/tf.py --weights $m.pt  # build TensorFlow model
  python export.py --img 128 --batch 1 --weights $m.pt --include tflite #torchscript onnx  # export
done

from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad, C3, **SPPF**

...

class TFSPPF(keras.layers.Layer):
    # Spatial pyramid pooling-Fast layer
    def __init__(self, c1, c2, k=5, w=None):
        super(TFSPPF, self).__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))

Eager execution of tf.constant with unsupported shape (value has 131072 elements, shape is (1, 1, 512, 512) with 262144 elements)