class Classifier(object):
def __init__(self, config, **kwargs):
self.config = config
self.net = Net(config, **kwargs)
self.shape = np.array(self.config.input_shape, dtype=np.uint32)
def forward_iter(self, data):
data = np.array(data, dtype=np.float32)
assert(np.all(data.shape[1:] == self.shape[1:]))
n_row = self.config.input_shape[0]
net_input = np.zeros(self.shape, dtype=np.float32)
s = 0
e = data.shape[0]
while s < e:
n_data = min(e - s, n_row)
net_input[:n_data, :, :, :] = data[s:s + n_data, :, :, :]
out = self.net.forward(blobs=[self.net.outputs[0]], **{self.net.inputs[0]: net_input})[self.net.outputs[0]]
assert(n_row == len(out))
for i in range(n_data):
yield(NetOutput(out[i].flatten()))
s += n_row
net_input = np.zeros(self.shape, dtype=np.float32)
def forward(self, data):
ret = []
for out in self.forward_iter(data):
ret.append(out)
return ret
class Classifier(object):
def __init__(self, config, **kwargs):
self.config = config
self.net = Net(config, **kwargs)
self.shape = np.array(self.config.input_shape, dtype=np.uint32)
def forward_iter(self, data):
data = np.array(data, dtype=np.float32)
assert (np.all(data.shape[1:] == self.shape[1:]))
n_row = self.config.input_shape[0]
net_input = np.zeros(self.shape, dtype=np.float32)
s = 0
e = data.shape[0]
while s < e:
n_data = min(e - s, n_row)
net_input[:n_data, :, :, :] = data[s:s + n_data, :, :, :]
out = self.net.forward(blobs=[self.net.outputs[0]],
**{self.net.inputs[0]:
net_input})[self.net.outputs[0]]
assert (n_row == len(out))
for i in range(n_data):
yield (NetOutput(out[i].flatten()))
s += n_row
net_input = np.zeros(self.shape, dtype=np.float32)
def forward(self, data):
ret = []
for out in self.forward_iter(data):
ret.append(out)
return ret
def __init__(self, config):
self.config = config
self.net = Net(config)
self.shape = np.array(self.config.input_shape, dtype=np.uint32)
def __init__(self, config, **kwargs):
self.config = config
self.net = Net(config, **kwargs)
self.shape = np.array(self.config.input_shape, dtype=np.uint32)
