def inference_roicnn(images, keep_prob, deconv = False, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_roicnn') assert isinstance(keep_prob, object) if not layer == len(feat): print('Make sure you have defined the feature map size for each layer.') return #images2 = rsvp_quick_inference.inference_augment_s_filter(images) # # add noise #images2 = tf.cond(keep_prob < .999999, lambda: images + tf.truncated_normal(images.get_shape(), mean = 0.0, stddev = (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * 1.0), lambda: images) # .8 was working well with .25 dropout and .992 or .994 decay # local st conv_tensor = rsvp_quick_inference.inference_local_st5_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, 2) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_local_st5_filter \ (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, 'pool' + str(l), 2) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) if deconv: for l in range(layer-1, 0, -1): conv_tensor = rsvp_quick_inference.inference_local_st5_unfilter \ (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l]) pool_tensor = rsvp_quick_inference.inference_unpooling_s_filter(conv_tensor, 'pool' + str(l), 2) else: logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_roicnn_1layer(images, keep_prob, feat=[2]): _print_tensor_size(images, 'inference_roicnn') assert isinstance(keep_prob, object) # local st conv_tensor = rsvp_quick_inference.inference_local_st5_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_roicnn_1layer(images, keep_prob, feat=[2]): _print_tensor_size(images, 'inference_roicnn') assert isinstance(keep_prob, object) # local st conv_tensor = rsvp_quick_inference.inference_local_st5_filter( images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor) logits = rsvp_quick_inference.inference_fully_connected_1layer( pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def test_roicnn(images, keep_prob, layer=2, feat=[2, 4]): for l in range(0, layer): if l == 0: conv_tensor = rsvp_quick_inference.inference_local_st5_filter(images, 'conv0', out_feat=feat[0]) else: conv_tensor = rsvp_quick_inference.inference_local_st5_filter \ (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_roicnn(images, keep_prob, deconv=False, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_roicnn') assert isinstance(keep_prob, object) if not layer == len(feat): print( 'Make sure you have defined the feature map size for each layer.') return #images2 = rsvp_quick_inference.inference_augment_s_filter(images) # # add noise #images2 = tf.cond(keep_prob < .999999, lambda: images + tf.truncated_normal(images.get_shape(), mean = 0.0, stddev = (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * 1.0), lambda: images) # .8 was working well with .25 dropout and .992 or .994 decay # local st conv_tensor = rsvp_quick_inference.inference_local_st5_filter( images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter( conv_tensor, 2) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_local_st5_filter \ (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter( conv_tensor, 'pool' + str(l), 2) logits = rsvp_quick_inference.inference_fully_connected_1layer( pool_tensor, keep_prob) if deconv: for l in range(layer - 1, 0, -1): conv_tensor = rsvp_quick_inference.inference_local_st5_unfilter \ (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l]) pool_tensor = rsvp_quick_inference.inference_unpooling_s_filter( conv_tensor, 'pool' + str(l), 2) else: logits = rsvp_quick_inference.inference_fully_connected_1layer( pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_roicnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_roicnn') assert isinstance(keep_prob, object) if not layer == len(feat): print('Make sure you have defined the feature map size for each layer.') return # local st conv_tensor = rsvp_quick_inference.inference_local_st5_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_local_st5_filter\ (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l]) pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits