def pooling(self, inputs, pool_size, ignore_border, stride, pad, mode):
if pool_size == [1, 1]:
return inputs
if mode == "avg":
mode = "average_exc_pad"
if mode == "fmp":
height = inputs.shape[2]
width = inputs.shape[3]
batch = inputs.shape[0]
X = inputs.dimshuffle(2, 3, 0, 1) # (row, col, batches, filters)
sizes = T.zeros((batch, 2))
sizes = T.set_subtensor(sizes[:, 0], height)
sizes = T.set_subtensor(sizes[:, 1], width)
pooled_out, _ = fmp(X, sizes, pool_size[0])
return pooled_out.dimshuffle(2, 3, 0, 1)
pool_out = pool.pool_2d(
input=inputs,
ds=pool_size, # TODO(theano 0.9): change to ws
ignore_border=ignore_border,
st=stride, # TODO(theano 0.9): change to stride
padding=pad, # TODO(theano 0.9): change to pad
mode=mode)
pool_out.name = "pool_out_" + self.name
return pool_out
def pooling(self, inputs, pool_size, ignore_border, stride, pad, mode):
if pool_size == [1, 1]:
return inputs
if mode == "avg":
mode = "average_exc_pad"
if mode == "fmp":
height = inputs.shape[2]
width = inputs.shape[3]
batch = inputs.shape[0]
X = inputs.dimshuffle(2, 3, 0, 1) # (row, col, batches, filters)
sizes = T.zeros((batch, 2))
sizes = T.set_subtensor(sizes[:, 0], height)
sizes = T.set_subtensor(sizes[:, 1], width)
pooled_out, _ = fmp(X, sizes, pool_size[0])
return pooled_out.dimshuffle(2, 3, 0, 1)
pool_out = pool.pool_2d(
input=inputs,
ds=pool_size,
ignore_border=ignore_border,
st=stride,
padding=pad,
mode=mode
)
pool_out.name = "pool_out_"+self.name
return pool_out
def __init__(self, factor=numpy.sqrt(2), decay=1.0, min_factor=None, padding=False, **kwargs):
super(ConvFMPLayer, self).__init__(**kwargs)
if min_factor is None:
min_factor = factor
factor = T.maximum(factor * (decay ** self.network.epoch), numpy.float32(min_factor))
sizes_raw = self.source.output_sizes
# handle size problems
if not padding:
padding = T.min(self.source.output_sizes / factor) <= 0
padding = theano.printing.Print(global_fn=maybe_print_pad_warning)(padding)
fixed_sizes = T.maximum(sizes_raw, T.cast(T.as_tensor(
[factor + self.filter_height - 1, factor + self.filter_width - 1]), 'float32'))
sizes = ifelse(padding, fixed_sizes, sizes_raw)
X_size = T.cast(T.max(sizes, axis=0), "int32")
def pad_fn(x_t, s):
x = T.alloc(numpy.cast["float32"](0), X_size[0], X_size[1], self.X.shape[3])
x = T.set_subtensor(x[:s[0], :s[1]], x_t[:s[0], :s[1]])
return x
fixed_X, _ = theano.scan(pad_fn, [self.X.dimshuffle(2, 0, 1, 3), T.cast(sizes_raw, "int32")])
fixed_X = fixed_X.dimshuffle(1, 2, 0, 3)
self.X = ifelse(padding, T.unbroadcast(fixed_X, 3), self.X)
conv_out = CuDNNConvHWBCOpValidInstance(self.X, self.W, self.b)
conv_out_sizes = self.conv_output_size_from_input_size(sizes)
self.output, self.output_sizes = fmp(conv_out, conv_out_sizes, T.cast(factor,'float32'))
def __init__(self, factor=numpy.sqrt(2), decay=1.0, min_factor=None, padding=False, **kwargs):
super(ConvFMPLayer, self).__init__(**kwargs)
if min_factor is None:
min_factor = factor
factor = T.maximum(factor * (decay ** self.network.epoch), numpy.float32(min_factor))
sizes_raw = self.source.output_sizes
# handle size problems
if not padding:
padding = T.min(self.source.output_sizes / factor) <= 0
padding = theano.printing.Print(global_fn=maybe_print_pad_warning)(padding)
fixed_sizes = T.maximum(sizes_raw, T.cast(T.as_tensor(
[factor + self.filter_height - 1, factor + self.filter_width - 1]), 'float32'))
sizes = ifelse(padding, fixed_sizes, sizes_raw)
X_size = T.cast(T.max(sizes, axis=0), "int32")
def pad_fn(x_t, s):
x = T.alloc(numpy.cast["float32"](0), X_size[0], X_size[1], self.X.shape[3])
x = T.set_subtensor(x[:s[0], :s[1]], x_t[:s[0], :s[1]])
return x
fixed_X, _ = theano.scan(pad_fn, [self.X.dimshuffle(2, 0, 1, 3), T.cast(sizes_raw, "int32")])
fixed_X = fixed_X.dimshuffle(1, 2, 0, 3)
self.X = ifelse(padding, T.unbroadcast(fixed_X, 3), self.X)
conv_out = CuDNNConvHWBCOpValidInstance(self.X, self.W, self.b)
conv_out_sizes = self.conv_output_size_from_input_size(sizes)
self.output, self.output_sizes = fmp(conv_out, conv_out_sizes, T.cast(factor,'float32'))
def __init__(self, factor=numpy.sqrt(2), **kwargs): super(ConvFMPLayer, self).__init__(**kwargs) conv_out = CuDNNConvHWBCOpValidInstance(self.X, self.W, self.b) conv_out_sizes = self.conv_output_size_from_input_size(self.source.output_sizes) self.output, self.output_sizes = fmp(conv_out, conv_out_sizes, numpy.cast["float32"](factor))
