def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('responses', nargs='+', type=str)
parser.add_argument('-t', '--title', default='', type=str)
parser.add_argument('-o', '--output', default='log-responses', type=str)
parser.add_argument('-l', '--layers', nargs='?', type=str)
parser.add_argument('-a', '--alpha', default=0.99, type=float)
args = parser.parse_args()
alpha = args.alpha
vz = VzLog(args.output)
vz.title(args.title)
vz.log('alpha =', alpha)
plt.figure()
layers = args.layers
for fn in args.responses:
data = dd.io.load(fn)
name = data['name']
if layers is None:
layers = data['layers']
y = []
ystd = []
for l in layers:
rs = []
for X in data['responses'][l]:
C = np.corrcoef(X.T)
rs.append(
np.where(
np.sort(np.linalg.eigvals(C))[::-1].cumsum() /
C.shape[0] > alpha)[0][0] / C.shape[0])
y.append(np.mean(rs))
ystd.append(np.std(rs))
plt.errorbar(np.arange(len(y)), y, yerr=ystd, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('<- Redundancy / Identity-like ->')
plt.legend(loc=4)
plt.ylim((0, 1))
plt.savefig(vz.impath('svg'))
plt.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('caffemodel', type=str)
parser.add_argument('-o', '--output', default='log-network', type=str)
args = parser.parse_args()
vz = VzLog(args.output)
mm = caffe.proto.caffe_pb2.NetParameter()
with open(args.caffemodel, 'rb') as f:
mm.ParseFromString(f.read())
vz.title('Network')
c = 0
for layer in mm.layers:
if layer.blobs:
blob = layer.blobs[0]
shape = _blob_shape(blob)
X = np.asarray(blob.data).reshape(shape)
vz.section(layer.name)
vz.log('min', X.min(), 'max', X.max(), 'mean', X.mean(), 'std', X.std())
vz.log('shape', X.shape)
if c == 0:
grid = dd.plot.ColorImageGrid(X.transpose(0, 2, 3, 1),
vmin=None, vmax=None)
else:
grid = dd.plot.ImageGrid(X, cmap=cm.rainbow,
vmin=None, vmax=None, vsym=True)
grid.save(vz.impath(), scale=4)
blob = layer.blobs[1]
shape = _blob_shape(blob)
X = np.asarray(blob.data).reshape(shape)
# Plot biases
vz.text('Bias')
vz.log('shape', X.shape)
plt.figure(figsize=(6, 1.5))
plt.plot(X.ravel())
plt.xlim((0, X.size-1))
plt.savefig(vz.impath('svg'))
plt.close()
c += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument("caffemodel", type=str)
parser.add_argument("-o", "--output", default="log-network", type=str)
args = parser.parse_args()
vz = VzLog(args.output)
mm = caffe.proto.caffe_pb2.NetParameter()
with open(args.caffemodel, "rb") as f:
mm.ParseFromString(f.read())
vz.title("Network")
c = 0
for layer in mm.layers:
if layer.blobs:
blob = layer.blobs[0]
shape = _blob_shape(blob)
X = np.asarray(blob.data).reshape(shape)
vz.section(layer.name)
vz.log("min", X.min(), "max", X.max(), "mean", X.mean(), "std", X.std())
vz.log("shape", X.shape)
if c == 0:
grid = dd.plot.ColorImageGrid(X.transpose(0, 2, 3, 1), vmin=None, vmax=None)
else:
grid = dd.plot.ImageGrid(X, cmap=cm.rainbow, vmin=None, vmax=None, vsym=True)
grid.save(vz.impath(), scale=4)
blob = layer.blobs[1]
shape = _blob_shape(blob)
X = np.asarray(blob.data).reshape(shape)
# Plot biases
vz.text("Bias")
vz.log("shape", X.shape)
plt.figure(figsize=(6, 1.5))
plt.plot(X.ravel())
plt.xlim((0, X.size - 1))
plt.savefig(vz.impath("svg"))
plt.close()
c += 1
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('responses', nargs='+', type=str)
parser.add_argument('-t', '--title', default='', type=str)
parser.add_argument('-o', '--output', default='log-responses', type=str)
parser.add_argument('-m', '--model', nargs='+', type=str)
parser.add_argument('-l', '--layers', nargs='?', type=str)
parser.add_argument('-a', '--alpha', default=0.99, type=float)
args = parser.parse_args()
alpha = args.alpha
vz = VzLog(args.output)
vz.title(args.title)
vz.log('alpha =', alpha)
mms = []
for fn in args.model:
mm = caffe.proto.caffe_pb2.NetParameter()
with open(fn, 'rb') as f:
mm.ParseFromString(f.read())
mms.append(mm)
layers = args.layers
plt.figure()
for fn in args.responses:
data = dd.io.load(fn)
name = data['name']
if layers is None:
layers = data['layers']
y = []
ystd = []
for l in layers:
rs = []
#for X in data['responses'][l]:
X = data['responses'][l]
C = np.corrcoef(X.T)
print(l, X.shape)
C[np.isnan(C)] = 0.0
try:
rs.append(np.where(np.sort(np.linalg.eigvals(C))[::-1].cumsum() / C.shape[0] > alpha)[0][0] / C.shape[0])
except:
break
y.append(np.mean(rs))
ystd.append(np.std(rs))
plt.errorbar(np.arange(len(y)), y, yerr=ystd, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('<- Redundancy / Identity-like ->')
plt.legend(loc=4)
plt.ylim((0, 1))
plt.savefig(vz.impath('svg'))
plt.close()
plt.figure()
for fn in args.responses:
data = dd.io.load(fn)
name = data['name']
if layers is None:
layers = data['layers']
y = []
ystd = []
for l in layers:
rs = []
#for X in data['responses'][l]:
X = data['responses'][l]
y.append(X.mean())
plt.plot(np.arange(len(y)), y, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('Mean')
plt.legend(loc=4)
plt.savefig(vz.impath('svg'))
plt.close()
plt.figure()
for fn in args.responses:
data = dd.io.load(fn)
name = data['name']
if layers is None:
layers = data['layers']
y = []
ystd = []
for l in layers:
rs = []
#for X in data['responses'][l]:
X = data['responses'][l]
y.append((X**2).mean())
plt.plot(np.arange(len(y)), y, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('Second moment')
plt.ylim((0, None))
plt.legend(loc=4)
plt.savefig(vz.impath('svg'))
plt.close()
plt.figure()
for fn in args.responses:
data = dd.io.load(fn)
name = data['name']
if layers is None:
layers = data['layers']
y = []
ystd = []
for l in layers:
rs = []
#for X in data['responses'][l]:
X = data['responses'][l]
y.append(X.std())
plt.plot(np.arange(len(y)), y, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('Standard deviation')
plt.ylim((0, None))
plt.legend(loc=4)
plt.savefig(vz.impath('svg'))
plt.close()
plt.figure()
for mm in mms:
y = []
for l in layers:
blobs = get_blobs(mm, l)
y.append(blobs[0].mean())
plt.plot(np.arange(len(y)), y, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('Weight mean')
plt.legend(loc=1)
plt.savefig(vz.impath('svg'))
plt.close()
plt.figure()
for mm in mms:
y = []
for l in layers:
blobs = get_blobs(mm, l)
y.append(blobs[0].std())
plt.plot(np.arange(len(y)), y, label='{}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('Weight s.d.')
plt.legend(loc=1)
plt.savefig(vz.impath('svg'))
plt.close()
plt.figure()
for mm in mms:
y = []
y2 = []
for l in layers:
blobs = get_blobs(mm, l)
b0 = blobs[0]
print(l, blobs[0].shape)
if l.startswith('conv'):
N = b0.shape[0] * b0.shape[2] * b0.shape[3]
M = b0.shape[1] * b0.shape[2] * b0.shape[3]
else:
N = b0.shape[2]
M = b0.shape[3]
y.append(N)
y2.append(M)
plt.plot(np.arange(len(y)), y, label='n={}'.format(name))
plt.plot(np.arange(len(y2)), y2, label='m={}'.format(name))
plt.xticks(np.arange(len(y)), layers)
plt.ylabel('Size')
plt.legend(loc=1)
plt.savefig(vz.impath('svg'))
plt.close()
