Commit 2d9f3013 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
Browse files

fixed shaping error

parent 758c8a5d

nn_ds_neibs1_tmp.py

+7 −7
Original line number Diff line number Diff line
@@ -55,7 +55,7 @@ ONLY_TILE = None # 4 # None # 0 # 4# None # (remove all but center tile 
ZIP_LHVAR =        True # combine _lvar and _hvar as odd/even elements



#DEBUG_PACK_TILES = True

WLOSS_LAMBDA =       0.001 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss

WLOSS_LAMBDA =       0.1 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss

SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY])

# CLUSTER_RADIUS should match input data

CLUSTER_RADIUS =     1 # 1 - 3x3, 2 - 5x5 tiles
@@ -476,7 +476,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope 
                wt = tf.transpose(w,[1,0])

                wt = wt[:,:-1]

                tmp1 = []

                for i in range(layout[index]):

                for i in range(out_shape):

                    

                    # reset when even

                    if i%2==0:
@@ -511,7 +511,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope 
                        tmp1.append(ts)

                

                imsum1 = tf.concat(tmp1,axis=0)

                imsum1_1 = tf.reshape(imsum1,[1,layout[index]*(TILE_SIDE+1)//2,2*TILE_LAYERS*(TILE_SIDE+1),3])

                imsum1_1 = tf.reshape(imsum1,[1,out_shape*(TILE_SIDE+1)//2,2*TILE_LAYERS*(TILE_SIDE+1),3])



                tf.summary.image("sub_w8s",imsum1_1)
@@ -547,7 +547,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope 
                    missing_in_block = math.pow(block_side,2) - block_size

                

                tmp1 = []

                for i in range(layout[index]):

                for i in range(out_shape):

                    

                    # reset when even

                    if i%4==0:
@@ -600,7 +600,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope 
                        tmp1.append(ts)

            

                imsum2 = tf.concat(tmp1,axis=0)

                tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index]*cluster_side*(block_side+1)//4,4*cluster_side*(block_side+1),3]))

                tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,out_shape*cluster_side*(block_side+1)//4,4*cluster_side*(block_side+1),3]))
@@ -627,7 +627,7 @@ def network_sub(input, layout, reuse, sym8 = False): 
                       if not reuse_this:

                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):

                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))

                           network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub')

                           network_summary_w_b(scp, inp.shape[1], num_sym8, layout, i, 'sub')

                   if num_non_sum > 0:

                       reuse_this = reuse

                       scp = 'g_fc_sub'+str(i)+"r"
@@ -635,7 +635,7 @@ def network_sub(input, layout, reuse, sym8 = False): 
                       if not reuse_this:

                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):

                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))

                           network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub') 

                           network_summary_w_b(scp, inp.shape[1], num_non_sum, layout, i, 'sub') 

                   fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))

               else:

                   scp = 'g_fc_sub'+str(i)