def inference_cvcnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_cvcnn') 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 image_shape = images.get_shape().as_list() conv_tensor = rsvp_quick_inference.inference_5x5_filter( images, 'conv0', in_feat=image_shape[3], out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_5x5_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) logits = rsvp_quick_inference.inference_fully_connected_1layer( pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_cvcnn_1layer(images, keep_prob, feat=[2]): _print_tensor_size(images, 'inference_cvcnn') assert isinstance(keep_prob, object) # local st conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_cvcnn_1layer(images, keep_prob, feat=[2]): _print_tensor_size(images, 'inference_cvcnn') assert isinstance(keep_prob, object) # local st conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_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_5x5_filter(images, 'conv0', out_feat=feat[0]) else: conv_tensor = rsvp_quick_inference.inference_5x5_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=2, kwidth=2) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def test_cvcnn(images, keep_prob, layer=2, feat=[2, 4]): for l in range(0, layer): if l == 0: conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', in_feat=feat[l - 1], out_feat=feat[0]) else: conv_tensor = rsvp_quick_inference.inference_5x5_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) # was 1 x 4 logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_cvcnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_cvcnn') 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_5x5_filter(images, 'conv0', keep_prob, in_feat=1, out_feat=feat[0]) pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool0', kheight=poolh, kwidth=poolw) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_5x5_filter\ (pool_tensor, 'conv'+str(l), keep_prob, in_feat=feat[l-1], out_feat=feat[l]) pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_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_cvcnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_cvcnn') 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_5x5_filter(images, 'conv0', keep_prob, in_feat=1, out_feat=feat[0]) pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool0', kheight=poolh, kwidth=poolw) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_5x5_filter\ (pool_tensor, 'conv'+str(l), keep_prob, in_feat=feat[l-1], out_feat=feat[l]) pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_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_cvcnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_cvcnn') 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 image_shape = images.get_shape().as_list() conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', in_feat=image_shape[3], out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor) for l in range(1, layer): conv_tensor = rsvp_quick_inference.inference_5x5_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) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits