def create_pca_ckpt(ckpt_path):
"""
Given a checkpoint file it generates
train.CONV_KERNEL_SIZE * train.CONV_KERNEL_SIZE = n new checkpoint files. Each
with same values except for the weights and biases in the first convolutional
layer. To these values pca is applied, with variance in the dimensions:
1 .. n. These new checkpoint files are placed in a new dir, which is created
at the same location as ckpt_path. These new checkpoints file are named after
the original checkpoint file plus the pca dimensions and the total variance
that was kept in them.
:param ckpt_path: path to checkpoint file to which pca must be applied.
"""
print('Apply PCA to CKPT: ' + ckpt_path)
with tf.Graph().as_default(), tf.Session() as sess:
dir_name = ckpt_path.split('/')
dir_name = dir_name[-2]
_experiment_id = ExperimentID()
_experiment_id.init_string(dir_name)
eval.load_model(ckpt_path, _experiment_id, sess)
saver = tf.train.Saver(max_to_keep=None)
with tf.variable_scope('conv1') as scope:
scope.reuse_variables()
variable_w = tf.get_variable('weights')
variable_b = tf.get_variable('biases')
original_w = variable_w.eval()
original_b = variable_b.eval()
# Dir to save pca ckpts
pca_ckpt_dir = ckpt_path + '-pca/'
if not tf.gfile.Exists(pca_ckpt_dir):
tf.gfile.MakeDirs(pca_ckpt_dir)
ckpt_path_splitted = ckpt_path.split('.')
for i in xrange(25):
n_components = i + 1
pca_weights, pca_biases, cumsum_variance = pca_reduction(
original_w, original_b, n_components)
sess.run(variable_w.assign(pca_weights))
sess.run(variable_b.assign(pca_biases))
# <..>/<..>.ckpt-xxx to # <..>/<..>.pca-n_components.ckpt-xxx
pca_ckpt_path = '{}pca-{}_v-{:.2f}_.{}'.format(
pca_ckpt_dir, n_components, cumsum_variance,
ckpt_path_splitted[-1])
pca_ckpt_file_path = saver.save(sess, pca_ckpt_path)
print('Saved PCA checkpoint file: {}'.format(pca_ckpt_file_path))
def main(args: argparse.Namespace):
with_gpu = True if torch.cuda.is_available() else False
save_path = "demo"
model_path = args.model
img_path = args.image
model = load_model(model_path, with_gpu)
demo(model, img_path, save_path, with_gpu)
def main(args):
model_path = args.model
with_gpu = True if torch.cuda.is_available() else False
model = load_model(model_path, with_gpu)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_AUTOFOCUS, 1)
ret, img = cap.read()
with torch.no_grad():
while ret:
ret, img = cap.read()
if ret:
img = demo(model, img, with_gpu)
cv2.imshow('img', img)
cv2.waitKey(3)
from PIL import Image
import numpy as np
import os
from eval import load_model, get_pred, eval_inspect_cam
from utils import get_patinfo, dicom_checker, fundus_laterality
model_path_ret = '../models/step_23300_acc_0.892063558102/model'
sess_ret_ops = load_model(model_path_ret)
sess_ret, pred_op_ret, x_ret, y_ret, is_training_ret, top_conv_ret, cam_ret, cam_ind_ret, logits_ret = sess_ret_ops
f_path = './5217696_20141224_L.png'
if dicom_checker(f_path):
pat_id, exam_date, exam_time, img = get_patinfo(f_path)
else: # PNG , JPEG , JPG ...etc
img = Image.open(f_path)
# LR_checker
LR = fundus_laterality(img) # 0 : LEFT , 1 L RIGHT
print LR
np.shape(img)
np_img = np.asarray(img).reshape([1] + list(np.shape(img)))
actmap_path = eval_inspect_cam(sess_ret, cam_ret, cam_ind_ret, top_conv_ret,
np_img, x_ret, y_ret, is_training_ret,
logits_ret, '../media/actmap')
VOCAB_DIR = 'data/chs'
CKPT_DIR = 'output/chs'
MAX_SEQ_LEN = 10
N_LAYER = 2
EMBED_DIM = 500
HIDDEN_DIM = 500
word_to_ix = load_word_dict(Path(VOCAB_DIR))
vocab = len(word_to_ix)
embedding = nn.Embedding(vocab, EMBED_DIM, padding_idx=word_to_ix['[PAD]'])
encoder = GRUEncoder(embedding, HIDDEN_DIM, N_LAYER)
decoder = AttnGRUDecoder(embedding, HIDDEN_DIM, vocab, N_LAYER)
device = torch.device('cpu')
load_model(encoder, decoder, CKPT_DIR, device)
searcher = GreedySearchDecoder(encoder, decoder, word_to_ix['[SOS]'])
searcher.eval()
searcher.to(device)
ix_to_word = {idx: word for word, idx in word_to_ix.items()}
class ChatBot(Resource):
def predict(self, query):
output_tokens, scores = evaluate(searcher, word_to_ix, ix_to_word,
query, MAX_SEQ_LEN, False)
scores = scores.cpu().tolist()
output_tokens = [
x for x in output_tokens if not (x == '[EOS]' or x == '[PAD]')
]
reply = ''.join(output_tokens)
import model_utils
import os
model = model_utils.Ensamble(
['model_small'],
[
os.path.join(model_utils.repo_root, 'log', 'protagonist',
'model_small_18071_9943114')
],
)
model.init_session()
m1 = model.basecall_sample(
'/home/lpp/Downloads/minion/pass/26075_ch161_read656_strand.fast5')
model.close_session()
m1_ld = model.log_dir
import eval
model = eval.load_model(
'model_small',
os.path.join(model_utils.repo_root, 'log', 'protagonist',
'model_small_18071_9943114'))
model.init_session()
model.restore()
m2 = model.basecall_sample(
'/home/lpp/Downloads/minion/pass/26075_ch161_read656_strand.fast5')
model.close_session()
print(m1, m1_ld)
print(m2)