f_eval = theano.function([input_var, input2_var, input3_var, input4_var], eval_out)
f_train = theano.function([input_var, input2_var, input3_var, input4_var, target_var], [cost], updates=updates)
f_vali = theano.function([input_var, input2_var, input3_var,input4_var ,target_var], [costV])
import try_DP as TD
import scipy.io as io
import Evaluation as E
with np.load("/home/xvt131/Network_adapt/triplanar_Params_WI.npz") as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
for img in DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand"):
A, B, C = TD.Tri_Image_Load(img)
B1 = B.reshape(np.prod(B.shape))
batch = 100
num_batches = A.shape[0] / batch
Sha = B.shape
print Sha
TibiaD = []
FemoralD = []
preds = np.zeros(shape = ( len(B1), 2 ))
for i in range(num_batches):
idx = range(i*batch, (i+1)*batch)
K = A[idx]
M, N, O, P= TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M,N,O, P)
Final_pred = np.argmax(preds, axis = -1)
print Final_pred.shape
f_eval = theano.function([input2_var], eval_out)
f_train = theano.function([input2_var, target_var], [cost], updates=updates)
f_vali = theano.function([input2_var, target_var], [costV])
import Evaluation as E
import try_DP as TD
import scipy.io as io
with np.load("/home/xvt131/Network_adapt/WI_Params.npz") as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
for img in DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand"):
A, B, C, D, E = TD.Tri_Image_Load(img)
B1 = B.reshape(np.prod(B.shape))
batch = 10000
num_batches = A.shape[0] / batch
Sha = B.shape
print Sha
TibiaD = []
FemoralD = []
preds = np.zeros(shape=(len(B1), 2))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = D[idx]
preds[idx] = f_eval(K)
MM = np.ravel_multi_index(A.T, np.asarray(B.shape))
Final_pred = np.zeros(B1.shape)
Final_pred[MM] = preds
