def _build_transform_train()

in Dassl.pytorch/dassl/data/transforms/transforms.py [0:0]

• 95 lines of code
• 19 McCabe index (conditional complexity)

def _build_transform_train(cfg, choices, target_size, normalize):
    print("Building transform_train")
    tfm_train = []

    interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
    input_size = cfg.INPUT.SIZE

    # Make sure the image size matches the target size
    conditions = []
    conditions += ["random_crop" not in choices]
    conditions += ["random_resized_crop" not in choices]
    if all(conditions):
        print(f"+ resize to {target_size}")
        tfm_train += [Resize(input_size, interpolation=interp_mode)]

    if "random_translation" in choices:
        print("+ random translation")
        tfm_train += [Random2DTranslation(input_size[0], input_size[1])]

    if "random_crop" in choices:
        crop_padding = cfg.INPUT.CROP_PADDING
        print(f"+ random crop (padding = {crop_padding})")
        tfm_train += [RandomCrop(input_size, padding=crop_padding)]

    if "random_resized_crop" in choices:
        s_ = cfg.INPUT.RRCROP_SCALE
        print(f"+ random resized crop (size={input_size}, scale={s_})")
        tfm_train += [
            RandomResizedCrop(input_size, scale=s_, interpolation=interp_mode)
        ]

    if "random_flip" in choices:
        print("+ random flip")
        tfm_train += [RandomHorizontalFlip()]

    if "imagenet_policy" in choices:
        print("+ imagenet policy")
        tfm_train += [ImageNetPolicy()]

    if "cifar10_policy" in choices:
        print("+ cifar10 policy")
        tfm_train += [CIFAR10Policy()]

    if "svhn_policy" in choices:
        print("+ svhn policy")
        tfm_train += [SVHNPolicy()]

    if "randaugment" in choices:
        n_ = cfg.INPUT.RANDAUGMENT_N
        m_ = cfg.INPUT.RANDAUGMENT_M
        print(f"+ randaugment (n={n_}, m={m_})")
        tfm_train += [RandAugment(n_, m_)]

    if "randaugment_fixmatch" in choices:
        n_ = cfg.INPUT.RANDAUGMENT_N
        print(f"+ randaugment_fixmatch (n={n_})")
        tfm_train += [RandAugmentFixMatch(n_)]

    if "randaugment2" in choices:
        n_ = cfg.INPUT.RANDAUGMENT_N
        print(f"+ randaugment2 (n={n_})")
        tfm_train += [RandAugment2(n_)]

    if "colorjitter" in choices:
        b_ = cfg.INPUT.COLORJITTER_B
        c_ = cfg.INPUT.COLORJITTER_C
        s_ = cfg.INPUT.COLORJITTER_S
        h_ = cfg.INPUT.COLORJITTER_H
        print(
            f"+ color jitter (brightness={b_}, "
            f"contrast={c_}, saturation={s_}, hue={h_})"
        )
        tfm_train += [
            ColorJitter(
                brightness=b_,
                contrast=c_,
                saturation=s_,
                hue=h_,
            )
        ]

    if "randomgrayscale" in choices:
        print("+ random gray scale")
        tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)]

    if "gaussian_blur" in choices:
        print(f"+ gaussian blur (kernel={cfg.INPUT.GB_K})")
        gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P
        tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)]

    print("+ to torch tensor of range [0, 1]")
    tfm_train += [ToTensor()]

    if "cutout" in choices:
        cutout_n = cfg.INPUT.CUTOUT_N
        cutout_len = cfg.INPUT.CUTOUT_LEN
        print(f"+ cutout (n_holes={cutout_n}, length={cutout_len})")
        tfm_train += [Cutout(cutout_n, cutout_len)]

    if "normalize" in choices:
        print(
            f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})"
        )
        tfm_train += [normalize]

    if "gaussian_noise" in choices:
        print(
            f"+ gaussian noise (mean={cfg.INPUT.GN_MEAN}, std={cfg.INPUT.GN_STD})"
        )
        tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)]

    if "instance_norm" in choices:
        print("+ instance normalization")
        tfm_train += [InstanceNormalization()]

    tfm_train = Compose(tfm_train)

    return tfm_train