/
intonet.py
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/
intonet.py
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import ipt
import mxnet as mx
import HeartDeepLearning.my_utils as mu
from collections import OrderedDict
import math
import matplotlib.pyplot as plt
def show_layer(layers, *args):
if isinstance(layers, str):
layers = [layers]
N = len(args)
MAX_COL = 4
row = math.ceil(N / MAX_COL)
fig, subs = plt.subplots(row, MAX_COL)
for layer in layers:
print '-' * 30, layer
for idx, output in enumerate(args):
L = len(output[layer].shape)
if L == 4:
subs[idx].imshow(output[layer][0, 0], cmap='gray')
elif L == 2:
subs[idx].imshow(output[layer], cmap='gray')
else:
print 'Abandoned', idx, output[layer].shape
plt.show()
fig.clear()
plt.close()
def show_filter(layer, *args):
print layer
N = len(args)
MAX_COL = 4
row = math.ceil(N / MAX_COL)
fig, subs = plt.subplots(row, MAX_COL)
shape = args[0][layer].shape
H = shape[1]
L = len(shape)
if L == 2:
for idx, output in enumerate(args):
print '_' * 30, h
subs[idx].imshow(rout[layer][0, h], cmap='gray')
plt.show()
fig.clear()
plt.close()
return
for h in range(H):
for idx, output in enumerate(args):
print '_' * 30, h
subs[idx].imshow(rout[layer][0, h], cmap='gray')
plt.show()
fig.clear()
plt.close()
def fetch_internal(net, val, perfix, epoch, is_rnn=False):
def verify(name):
exclude = ['weight', 'bias', 'gamma',
'beta', 'blockgrad', 'data', 'label']
for _ in exclude:
if _ in name:
print 'Abandoned:', name
return False
# if name.startswith('_'):
# return False
for _ in ['c', 'h']:
if name == _:
print 'Abandoned:', name
return False
return True
net = net.get_internals()
print '\n', net.list_outputs(), '\n'
features = [net[i]
for i in range(len(net.list_outputs())) if verify(net[i].name)]
names = [_.name for _ in features]
net = mx.sym.Group(features)
from mxnet.model import load_checkpoint
sym, arg, aux = load_checkpoint(perfix, epoch)
if not is_rnn:
model = mx.model.FeedForward(
net, ctx=mu.gpu(1), num_epoch=1, begin_epoch=0)
else:
from HeartDeepLearning.RNN import rnn_feed
model = rnn_feed.Feed(net, ctx=mu.gpu(1), num_epoch=1, begin_epoch=0)
shape = OrderedDict(val.provide_data + val.provide_label)
model._init_params(shape)
model.arg_params.update(arg)
model.aux_params.update(aux)
print '\nStart Predict'
outputs, img, label = mu.predict_draw(model, val)
outputs = dict(zip(names, outputs))
arg = { k:v.asnumpy() for k,v in arg.items() }
aux = { k:v.asnumpy() for k,v in aux.items() }
for _ in ['outputs', 'arg', 'aux']:
print '\nKey of %s' % _
o = locals()[_]
for k in o:
print k, o[k].shape, o[k].mean(), o[k].std()
print 'Done'
return outputs, img, label, arg, aux
if __name__ == '__main__':
perfix = '/home/zijia/HeartDeepLearning/CNN/Result/[ACC-0.93164 E29]'
epoch = 29
from CNN.cnn import cnn_net
net = cnn_net()
iters = mu.get(3, small=True)
fetch_internal(net, iters['val'], perfix, epoch)