recon = tf.squeeze(recon) ######################################################################### # Start TF session (respecting OMP_NUM_THREADS) nthr = os.getenv('OMP_NUM_THREADS') if nthr is None: sess = tf.Session() else: sess = tf.Session(config=tf.ConfigProto( intra_op_parallelism_threads=int(nthr))) sess.run(tf.global_variables_initializer()) ######################################################################### # Load latest model print("Restoring model from " + mfile) ut.loadNet(mfile, model.weights, sess) print("Done!") mses, psnrs = [], [] for nm in open(VLIST, 'r'): nm = nm.strip() if 'foreman' in nm or 'Parrots' in nm: image = np.float32(imread(nm)[:, :, 0]) / 65535. h, w = image.shape[:2] else: image = np.float32(imread(nm)) / 255. h, w = image.shape h_pad, w_pad = (PSZ - h % PSZ) % PSZ, (PSZ - w % PSZ) % PSZ image = np.pad(image, ((0, h_pad), (0, w_pad)), 'constant')
nthr = os.getenv('OMP_NUM_THREADS') if nthr is None: sess = tf.Session() else: sess = tf.Session(config=tf.ConfigProto( intra_op_parallelism_threads=int(nthr))) sess.run(tf.global_variables_initializer()) ######################################################################### # Load saved weights if any if niter > 0: mfn = wts+"/iter_%06d.model.npz" % niter sfn = wts+"/iter_%06d.state.npz" % niter ut.mprint("Restoring model from " + mfn ) ut.loadNet(mfn,model.weights,sess) ut.mprint("Restoring state from " + sfn ) ut.loadAdam(sfn,opt,model.weights,sess) ut.mprint("Done!") ######################################################################### # Main Training loop stop=False ut.mprint("Starting from Iteration %d" % niter) sess.run(tset.fetchOp,feed_dict=tset.fdict()) while niter < MAXITER and not ut.stop: ## Validate model every so often if niter % VALFREQ == 0: