import Evaluation as E
import DP1 as TD
import scipy.io as io
all_dice = np.array([])
with np.load(
'/home/xvt131/Biomediq/Functions/Adhish_copy/Exp101/cheat.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)
Dice = []
for img in DP.get_paths("/home/xvt131/Biomediq/Data/kneeData/Validating-Rand"):
A, B, C = TD.get_indeces(img)
B1 = B.reshape(np.prod(B.shape))
batch = 5000
num_batches = A.shape[0] / batch
Sha = B.shape
preds = np.zeros(shape=(len(B1), NC))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M, N, O = TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M, N, O)
if num_batches * batch < A.shape[0]:
tot = num_batches * batch
K = A[tot:]
M, N, O = TD.Patch_gen(K, 29, C)
f_vali = theano.function([input_var, input2_var, input3_var, target_var], [costV])
from confusionmatrix import ConfusionMatrix
batch_size = 10
num_epochs = 175
train_acc= []
valid_acc = []
cur_loss = 0
loss = []
valid_loss = []
Train = DP.get_paths("/home/xvt131/Biomediq/Validating-Rand")
Test = DP.get_paths("/home/xvt131/Biomediq/Validating-Rand")
import gc
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
for im in Train:
XY, XZ, YZ, Y_train = DP.Patch_triplanar_para(im, PS)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i*batch_size, (i+1)*batch_size)
f_vali = theano.function([input_var, target_var], [costV])
from confusionmatrix import ConfusionMatrix
batch_size = 100
num_epochs = 10
train_acc = []
valid_acc = []
cur_loss = 0
loss = []
valid_loss = []
Train = DP.get_paths("/home/xvt131/Biomediq/Validating-Rand")
Test = DP.get_paths("/home/xvt131/Biomediq/Validating-Rand")
import gc
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
for im in Train:
XY, XZ, Y_train = DP.Patch_3D_para(im, PS, P2)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
f_vali = theano.function([ input2_var, target_var], [costV])
from confusionmatrix import ConfusionMatrix
batch_size = 10
num_epochs = 5
train_acc= []
valid_acc = []
cur_loss = 0
loss = []
valid_loss = []
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Training-Rand")
Test = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand")
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(classes)
confusion_train = ConfusionMatrix(classes)
B = np.array([])
Pos = np.empty((0, 1,3))
for im in Train:
Scan, Y_train, Post = DP.Sampling(im)
B = np.int32(np.append(B, Y_train))
Pos = np.float32(np.vstack((Pos, Post)))
f_vali = theano.function([input_var, input2_var, input3_var, target_var],
[costV])
import Evaluation as E
import DP1 as TD
import scipy.io as io
all_dice = np.array([])
with np.load('/home/xvt131/Functions/Adhish_copy/Exp101/AD.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)
Dice = []
for img in DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand"):
A, B, C = TD.get_indeces(img)
B1 = B.reshape(np.prod(B.shape))
batch = 12000
num_batches = A.shape[0] / batch
Sha = B.shape
preds = np.zeros(shape=(A.shape[0], NC))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M, N, O = TD.Patch_gen(K, 29, C)
preds[idx] = f_eval(M, N, O)
if num_batches * batch < A.shape[0]:
tot = num_batches * batch
f_train = theano.function([input_var, input2_var, target_var], [cost],
updates=updates)
import Evaluation as E
import DP1 as TD
import scipy.io as io
with np.load(
"/home/xvt131/Biomediq/Functions/Adhish_copy/Exp101/3dpop.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)
Dice = []
for img in DP.get_paths("/home/xvt131/Biomediq/Data/kneeData/Validating-Rand"):
A, B, C = TD.get_indeces(img)
B1 = B.reshape(np.prod(B.shape))
batch = 10000
num_batches = A.shape[0] / batch
Sha = B.shape
print Sha
preds = np.zeros(shape=(A.shape[0], NC))
for i in range(num_batches):
idx = range(i * batch, (i + 1) * batch)
K = A[idx]
M, N = TD.Patch_gen_three(K, PS, PS2, C)
preds[idx] = f_eval(M, N)
if num_batches * batch < A.shape[0]:
tot = num_batches * batch
K = A[tot:]
M, N = TD.Patch_gen_three(K, PS, PS2, C)
f_vali = theano.function([input_var, input2_var, input3_var, target_var],
[costV])
from confusionmatrix import ConfusionMatrix
batch_size = 500
num_epochs = 25
train_acc = []
valid_acc = []
cur_loss = 0
loss = []
valid_loss = []
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/all_train")
Test = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand")
import gc
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
for im in Train:
XY, XZ, YZ, Y_train = DP.Patch_triplanar_para(im, PS)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
f_train = theano.function([input_var, input2_var, target_var], [cost],
updates=updates)
from confusionmatrix import ConfusionMatrix
batch_size = 100
num_epochs = 100
train_acc = []
valid_loss = []
valid_acc = []
train_loss = []
cur_loss = 0
loss = []
Train = DP.get_paths("/home/xvt131/Biomediq/Data/kneeData/allTrain")
Test = DP.get_paths("/home/xvt131/Biomediq/Data/kneeData/Validating-Rand")
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
# Train = np.random.choice(Train_all, 5)
for im in Train:
X_train, XX, Y_train = DP.Patch_3D_para(im, PS, PS2)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
[cost],
updates=updates)
from confusionmatrix import ConfusionMatrix
batch_size = 100
num_epochs = 50
train_acc, train_loss = [], []
valid_acc, valid_loss = [], []
test_acc, test_loss = [], []
cur_loss = 0
loss = []
valid_loss = []
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Training-Rand")
Test = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand")
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
for im in Train:
XY, XZ, YZ, Y_train = DP.Patch_gen_mix(im)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
xy_batch = XY[idx]