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train_detection.py
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import torch
from PIL import Image
from skimage.transform import resize
import numpy as np
import ipdb
import os
import pickle
N = 500
retrain = True
do_reload = False
epoch = 60
subset = 'detection'
im_to_pre = pickle.load(open('storage/im_to_pre.pkl', 'rb'))
train_split = 0.8
test_split = 0.1
val_split = 0.1
def preprocess(batch, mean, std):
batch -= mean
batch /= std
return batch
image_folder = 'Animal_1/'
all_folders = ['cropped', 'non_synapse']
model = torch.load('../trials/resnext_50_pretrained.pt')
num_params = 0
for param in model.named_parameters():
if retrain:
#print(param[0])
param[1].requires_grad = True
else:
param[1].requires_grad = False
num_params += 1
print('num_params: ' + str(num_params))
#ipdb.set_trace()
model.fc = torch.nn.Linear(2048, len(all_folders), bias=False)
model.fc.requires_grad = True
if retrain:
#reload_path = 'trials/2D_pr_retrain=True_lr_0.0001_bs_4_epoch_100.pt'
reload_path = 'trials/2D_'+subset+'_trainvaltest_retrain=False_lr_0.0001_bs_4_epoch_135.pt'
#reload_path = 'trials/2D_inhibitory_excitatory_retrain=True_lr_0.0001_bs_4_epoch_150.pt'
model.load_state_dict(torch.load(reload_path))
dtype = torch.FloatTensor
if torch.cuda.is_available():
dtype = torch.cuda.FloatTensor
model.to('cuda')
print('using cuda')
else:
print('no cuda')
dtype = torch.FloatTensor
#x = []
#y = []
index = 0
batch_size = 2
#batch_size = 1
if retrain:
num_epochs = 300
else:
num_epochs = 200
if retrain:
print('retrain')
with open('storage/x_train_'+subset+'.pkl', 'rb') as f:
x_train = pickle.load(f)
with open('storage/y_train_'+subset+'.pkl', 'rb') as f:
y_train = pickle.load(f)
with open('storage/x_val_'+subset+'.pkl', 'rb') as f:
x_val = pickle.load(f)
with open('storage/y_val_'+subset+'.pkl', 'rb') as f:
y_val = pickle.load(f)
with open('storage/x_test_'+subset+'.pkl', 'rb') as f:
x_test = pickle.load(f)
with open('storage/y_test_'+subset+'.pkl', 'rb') as f:
y_test = pickle.load(f)
total_mean = np.load('storage/mean_'+subset+'.npy')
total_std = np.load('storage/std_'+subset+'.npy')
else:
im_to_synapse = {}
type_to_cell = {key:set() for key in all_folders}
x_train = []
y_train = []
x_val = []
y_val = []
x_test = []
y_test = []
f_lens = []
for folder in all_folders:
folder = os.path.join(image_folder, folder)
ind = 0
files_in_folder = [os.path.join(path, name) for path, subdirs, files in os.walk(folder) for name in files]
unique_names = set([f[:-6] for f in files_in_folder if not os.path.isdir(os.path.join(folder,f))])
f_len = len(unique_names)
f_lens.append(f_len)
#ipdb.set_trace()
f_len = min(f_lens)
prev_type = 'train'
for f in all_folders:
folder = os.path.join(image_folder, f)
files_in_folder = [os.path.join(path, name) for path, subdirs, files in os.walk(folder) for name in files]
inds = np.random.permutation(len(files_in_folder))
ind_1 = int(train_split*f_len)
ind_2 = ind_1 + int(test_split*f_len)
ind_3 = min(f_len, ind_2) + int(val_split*f_len)
train_inds = inds[0:ind_1]
test_inds = inds[ind_1:ind_2]
val_inds = inds[ind_2:ind_3]
for train_ind in train_inds:
imname = files_in_folder[train_ind]
x_train.append(imname)
y_temp = [0]*len(all_folders)
y_temp[index] = 1
y_train.append(y_temp)
for test_ind in test_inds:
imname = files_in_folder[test_ind]
x_test.append(imname)
y_temp = [0]*len(all_folders)
y_temp[index] = 1
y_test.append(y_temp)
for val_ind in val_inds:
imname = files_in_folder[val_ind]
x_val.append(imname)
y_temp = [0]*len(all_folders)
y_temp[index] = 1
y_val.append(y_temp)
index += 1
stats_stack = []
to_remove = []
for i in range(len(x_train)):
im_name = x_train[i]
img = np.array(Image.open(im_name))
#print(img.shape)
if len(img.shape) < 2:
print('image name: ' + im_name)
print('image shape: ' + str(img.shape))
to_remove.append(i)
elif len(img.shape) > 2:
img = img[:,:,0]
if not (img.shape[0] == 500 and img.shape[1] == 500):
print('image name: ' + im_name)
print('image shape: ' + str(img.shape))
to_remove.append(i)
#img = resize(img, output_shape = [N,N], preserve_range=True, mode = 'constant', order = 1)
else:
stats_stack.append(img)
for i in range(len(to_remove)):
count = to_remove[i]
del x_train[count]
del y_train[count]
for j in range(i, len(to_remove)):
to_remove[j] -= 1
to_remove = []
for i in range(len(x_val)):
im_name = x_val[i]
img = np.array(Image.open(im_name))
if len(img.shape) < 2:
print('image name: ' + im_name)
print('image shape: ' + str(img.shape))
to_remove.append(i)
elif len(img.shape) > 2:
img = img[:,:,0]
if not (img.shape[0] == 500 and img.shape[1] == 500):
print('image name: ' + im_name)
print('image shape: ' + str(img.shape))
to_remove.append(i)
for i in range(len(to_remove)):
count = to_remove[i]
del x_val[count]
del y_val[count]
for j in range(i, len(to_remove)):
to_remove[j] -= 1
to_remove = []
for i in range(len(x_test)):
im_name = x_test[i]
img = np.array(Image.open(im_name))
if len(img.shape) < 2:
print('image name: ' + im_name)
print('image shape: ' + str(img.shape))
to_remove.append(i)
elif len(img.shape) > 2:
img = img[:,:,0]
if not (img.shape[0] == 500 and img.shape[1] == 500):
print('image name: ' + im_name)
print('image shape: ' + str(img.shape))
to_remove.append(i)
for i in range(len(to_remove)):
count = to_remove[i]
del x_test[count]
del y_test[count]
for j in range(i, len(to_remove)):
to_remove[j] -= 1
with open('storage/x_train_'+subset+'.pkl', 'wb') as f:
pickle.dump(x_train, f)
with open('storage/y_train_'+subset+'.pkl', 'wb') as f:
pickle.dump(y_train, f)
with open('storage/x_val_'+subset+'.pkl', 'wb') as f:
pickle.dump(x_val, f)
with open('storage/y_val_'+subset+'.pkl', 'wb') as f:
pickle.dump(y_val, f)
with open('storage/x_test_'+subset+'.pkl', 'wb') as f:
pickle.dump(x_test, f)
with open('storage/y_test_'+subset+'.pkl', 'wb') as f:
pickle.dump(y_test, f)
with open('storage/synapse_trainvaltest_'+subset+'.pkl', 'wb') as f:
pickle.dump(im_to_synapse, f)
total_mean = np.mean(stats_stack)
total_std = np.std(stats_stack)
print('mean: ' + str(total_mean))
print('std: ' + str(total_std))
np.save('storage/mean_'+subset+'.npy', total_mean)
np.save('storage/std_'+subset+'.npy', total_std)
print('x_test len: ' + str(len(x_test)))
print('y_test len: ' + str(len(y_test)))
print('x_val len: ' + str(len(x_val)))
print('y_val len: ' + str(len(y_val)))
x = x_train
y = y_train
print('x_train len: ' + str(len(x)))
print('y_train len: ' + str(len(y)))
#ipdb.set_trace()
def ciona_data_gen():
N_train = len(x)
X = np.empty([batch_size,3,N,N])
Y = np.empty([batch_size, len(all_folders)])
while True:
#print(N_train)
inds = np.random.permutation(N_train)
i=0
while i <= (N_train - batch_size):
for j in range(batch_size):
im_name = x[inds[i+j]]
#print(im_name)
img = np.array(Image.open(im_name))
if len(img.shape)>2:
img = img[:,:,0]
X[j] = img
Y[j] = y[inds[i+j]]
X = preprocess(X, total_mean, total_std)
i+=batch_size
yield torch.from_numpy(X).type(dtype), torch.from_numpy(Y).type(dtype)
def ciona_data_gen_val():
N_train = len(x_val)
X = np.empty([batch_size,3,N,N])
Y = np.empty([batch_size, len(all_folders)])
while True:
#print(N_train)
inds = np.random.permutation(N_train)
i=0
while i <= (N_train - batch_size):
for j in range(batch_size):
im_name = x_val[inds[i+j]]
#print(im_name)
img = np.array(Image.open(im_name))
if len(img.shape)>2:
img = img[:,:,0]
X[j] = img
Y[j] = y_val[inds[i+j]]
X = preprocess(X, total_mean, total_std)
i+=batch_size
yield torch.from_numpy(X).type(dtype), torch.from_numpy(Y).type(dtype)
def ciona_data_gen_test():
N_train = len(x_test)
X = np.empty([batch_size,3,N,N])
Y = np.empty([batch_size, len(all_folders)])
while True:
#print(N_train)
inds = np.random.permutation(N_train)
i=0
while i <= (N_train - batch_size):
for j in range(batch_size):
im_name = x_test[inds[i+j]]
#print(im_name)
img = np.array(Image.open(im_name))
if len(img.shape)>2:
img = img[:,:,0]
X[j] = img
Y[j] = y_test[inds[i+j]]
X = preprocess(X, total_mean, total_std)
i+=batch_size
yield torch.from_numpy(X).type(dtype), torch.from_numpy(Y).type(dtype)
data_gen = ciona_data_gen()
data_gen_val = ciona_data_gen_val()
data_gen_test = ciona_data_gen_test()
''' Training '''
criterion = torch.nn.BCEWithLogitsLoss()
lr = 0.0001
optimizer = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True, weight_decay=0.0001)
r = range(num_epochs)
print_every = 5
if do_reload:
reload_path = 'trials/2D_'+subset+'_trainvaltest_retrain=False_lr_0.0001_bs_4_epoch_' + str(epoch) + '.pt'
#reload_path = 'trials/2D_inhibitory_excitatory_retrain=True_lr_0.0001_bs_4_epoch_150.pt'
model.load_state_dict(torch.load(reload_path))
r = range(epoch+1,num_epochs)
for l in r:
running_loss = 0.0
avg_loss = 0.0
print('epoch: ' + str(l))
for k in range(int(len(x)/batch_size)):
inputs, labels = next(data_gen)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs,labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
avg_loss += loss.item()
model.eval()
running_accuracy = 0
for m in range(int(len(x_val)/batch_size)):
inputs, labels = next(data_gen_val)
outputs = model(inputs)
labels_np = labels.cpu().numpy()
outputs_np = outputs.cpu().detach().numpy()
#print(labels_np)
#print(outputs_np)
accuracy = np.mean(np.argmax(outputs_np,1) == np.argmax(labels_np,1))
running_accuracy += accuracy
#print(accuracy)
avg_accuracy = running_accuracy / int(len(x_val)/batch_size)
print('avg validation accuracy: ' + str(avg_accuracy))
running_accuracy = 0
for m in range(int(len(x_test)/batch_size)):
inputs, labels = next(data_gen_test)
outputs = model(inputs)
labels_np = labels.cpu().numpy()
outputs_np = outputs.cpu().detach().numpy()
#print(labels_np)
#print(outputs_np)
accuracy = np.mean(np.argmax(outputs_np,1) == np.argmax(labels_np,1))
running_accuracy += accuracy
#print(accuracy)
avg_accuracy = running_accuracy / int(len(x_test)/batch_size)
print('avg test accuracy: ' + str(avg_accuracy))
model.train()
state = model.state_dict()
if l % 5 == 0:
torch.save(state, 'trials/2D_'+subset+'_trainvaltest_retrain='+str(retrain)+'_lr_' + str(lr) + '_bs_' + str(batch_size) + '_epoch_' + str(l)+'.pt')
print('avg loss this epoch: ' + str(avg_loss/(len(x)/batch_size)))
print('done')