def test_crop():
img = _image()
augmentor = Crop(size=(100, 200), resize=(200, 200))
result = augmentor(**{'image': img})
image = result['image']
_show_image(image)
assert image.shape[0] == 100
assert image.shape[1] == 200
assert image.shape[2] == 3
},
]),
'weight_decay_rate':
0.0001,
}
NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = None
NETWORK.OPTIMIZER_KWARGS = {}
NETWORK.LEARNING_RATE_FUNC = None
NETWORK.LEARNING_RATE_KWARGS = {}
NETWORK.WEIGHT_DECAY_RATE = None
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
NETWORK.ACTIVATION_QUANTIZER_KWARGS = {'bit': 2, 'max_value': 2}
NETWORK.WEIGHT_QUANTIZER = binary_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
# dataset
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
Pad(2),
Crop(size=IMAGE_SIZE),
FlipLeftRight(),
])
"power": 4.0,
"end_learning_rate": 0.0
}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.WEIGHT_DECAY_RATE = 0.0005
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
NETWORK.ACTIVATION_QUANTIZER_KWARGS = {
'bit': 2,
'max_value': 2.0,
}
NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
NETWORK.QUANTIZE_FIRST_CONVOLUTION = True
NETWORK.QUANTIZE_LAST_CONVOLUTION = False
# dataset
DATASET = EasyDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
Crop(size=IMAGE_SIZE, resize=256),
FlipLeftRight(),
Brightness((0.75, 1.25)),
Color((0.75, 1.25)),
Contrast((0.75, 1.25)),
Hue((-10, 10)),
])