def __init__(self, kernel_size, strides, expand_ratio, input_filters,
output_filters, se_ratio):
oup = expand_ratio * input_filters
if expand_ratio != 1:
self._expand_conv = Tensor.zeros(oup, input_filters, 1, 1)
self._bn0 = BatchNorm2D(oup)
else:
self._expand_conv = None
self.strides = strides
if strides == (2, 2):
self.pad = [(kernel_size - 1) // 2 - 1, (kernel_size - 1) // 2] * 2
else:
self.pad = [(kernel_size - 1) // 2] * 4
self._depthwise_conv = Tensor.zeros(oup, 1, kernel_size, kernel_size)
self._bn1 = BatchNorm2D(oup)
num_squeezed_channels = max(1, int(input_filters * se_ratio))
self._se_reduce = Tensor.zeros(num_squeezed_channels, oup, 1, 1)
self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
self._se_expand = Tensor.zeros(oup, num_squeezed_channels, 1, 1)
self._se_expand_bias = Tensor.zeros(oup)
self._project_conv = Tensor.zeros(output_filters, oup, 1, 1)
self._bn2 = BatchNorm2D(output_filters)
def __init__(self, number=0):
self.number = number
global_params = [
# width, depth
(1.0, 1.0), # b0
(1.0, 1.1), # b1
(1.1, 1.2), # b2
(1.2, 1.4), # b3
(1.4, 1.8), # b4
(1.6, 2.2), # b5
(1.8, 2.6), # b6
(2.0, 3.1), # b7
(2.2, 3.6), # b8
(4.3, 5.3), # l2
][number]
def round_filters(filters):
multiplier = global_params[0]
divisor = 8
filters *= multiplier
new_filters = max(divisor,
int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
new_filters += divisor
return int(new_filters)
def round_repeats(repeats):
return int(math.ceil(global_params[1] * repeats))
out_channels = round_filters(32)
self._conv_stem = Tensor.zeros(out_channels, 3, 3, 3)
self._bn0 = BatchNorm2D(out_channels)
blocks_args = [
[1, 3, (1, 1), 1, 32, 16, 0.25],
[2, 3, (2, 2), 6, 16, 24, 0.25],
[2, 5, (2, 2), 6, 24, 40, 0.25],
[3, 3, (2, 2), 6, 40, 80, 0.25],
[3, 5, (1, 1), 6, 80, 112, 0.25],
[4, 5, (2, 2), 6, 112, 192, 0.25],
[1, 3, (1, 1), 6, 192, 320, 0.25],
]
self._blocks = []
# num_repeats, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio
for b in blocks_args:
args = b[1:]
args[3] = round_filters(args[3])
args[4] = round_filters(args[4])
for n in range(round_repeats(b[0])):
self._blocks.append(MBConvBlock(*args))
args[3] = args[4]
args[1] = (1, 1)
in_channels = round_filters(320)
out_channels = round_filters(1280)
self._conv_head = Tensor.zeros(out_channels, in_channels, 1, 1)
self._bn1 = BatchNorm2D(out_channels)
self._fc = Tensor.zeros(out_channels, 1000)
self._fc_bias = Tensor.zeros(1000)
def __init__(self, h, w, inp, filters=128, conv=3): self.h, self.w = h, w self.inp = inp #init weights self.cweights = [Tensor.uniform(filters, inp if i==0 else filters, conv, conv) for i in range(3)] self.cbiases = [Tensor.uniform(1, filters, 1, 1) for i in range(3)] #init layers self._bn = BatchNorm2D(128, training=True) self._seb = SqueezeExciteBlock2D(filters)
def __init__(self):
self.blocks = 3
self.block_convs = 3
# TODO: raise back to 128 when it's fast
self.chans = 32
self.convs = [
Tensor.uniform(self.chans, self.chans if i > 0 else 1, 3, 3)
for i in range(self.blocks * self.block_convs)
]
self.cbias = [
Tensor.uniform(1, self.chans, 1, 1)
for i in range(self.blocks * self.block_convs)
]
self.bn = [BatchNorm2D(self.chans, training=True) for i in range(3)]
self.fc1 = Tensor.uniform(self.chans, 10)
self.fc2 = Tensor.uniform(self.chans, 10)
def create_modules(self, blocks):
net_info = blocks[0] # Info about model hyperparameters
prev_filters = 3
filters = None
output_filters = []
module_list = []
## module
for index, x in enumerate(blocks[1:]):
module_type = x["type"]
module = []
if module_type == "convolutional":
try:
batch_normalize = int(x["batch_normalize"])
bias = False
except:
batch_normalize = 0
bias = True
# layer
activation = x["activation"]
filters = int(x["filters"])
padding = int(x["pad"])
if padding:
pad = (int(x["size"]) - 1) // 2
else:
pad = 0
conv = Conv2d(prev_filters, filters, int(x["size"]), int(x["stride"]), pad, bias = bias)
module.append(conv)
# BatchNorm2d
if batch_normalize:
bn = BatchNorm2D(filters, eps=1e-05, training=True, track_running_stats=True)
module.append(bn)
# LeakyReLU activation
if activation == "leaky":
module.append(LeakyReLU(0.1))
# TODO: Add tiny model
elif module_type == "maxpool":
size = int(x["size"])
stride = int(x["stride"])
maxpool = MaxPool2d(size, stride)
module.append(maxpool)
elif module_type == "upsample":
upsample = Upsample(scale_factor = 2, mode = "nearest")
module.append(upsample)
elif module_type == "route":
x["layers"] = x["layers"].split(",")
# Start of route
start = int(x["layers"][0])
# End if it exists
try:
end = int(x["layers"][1])
except:
end = 0
if start > 0: start = start - index
if end > 0: end = end - index
route = EmptyLayer()
module.append(route)
if end < 0:
filters = output_filters[index + start] + output_filters[index + end]
else:
filters = output_filters[index + start]
# Shortcut corresponds to skip connection
elif module_type == "shortcut":
module.append(EmptyLayer())
elif module_type == "yolo":
mask = x["mask"].split(",")
mask = [int(x) for x in mask]
anchors = x["anchors"].split(",")
anchors = [int(a) for a in anchors]
anchors = [(anchors[i], anchors[i+1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in mask]
detection = DetectionLayer(anchors)
module.append(detection)
# Append to module_list
module_list.append(module)
if filters is not None:
prev_filters = filters
output_filters.append(filters)
return (net_info, module_list)
