def inference_stcnn_1layer(images, keep_prob, feat=[4]):
_print_tensor_size(images, 'inference_stcnn')
assert isinstance(keep_prob, object)
# global spatial local temporal
conv_tensor = rsvp_quick_inference.inference_global_st_filter(images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kheight=1)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_stcnn_1layer(images, keep_prob, feat=[4]):
_print_tensor_size(images, 'inference_stcnn')
assert isinstance(keep_prob, object)
# global spatial local temporal
conv_tensor = rsvp_quick_inference.inference_global_st_filter(
images, 'conv0', out_feat=feat[0])
logits = rsvp_quick_inference.inference_fully_connected_1layer(
conv_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_stcnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_stcnn')
assert isinstance(keep_prob, object)
# global spatial local temporal
conv_tensor = rsvp_quick_inference.inference_global_st_filter(images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kheight=1)
for l in range(1, layer):
# here use the 1*5 filter which go across channels
conv_tensor = rsvp_quick_inference.inference_temporal_filter\
(pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
# the pooling should have the width padding to 1 because we only consider channel correlation
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kheight=1)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_stcnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_stcnn')
assert isinstance(keep_prob, object)
# global spatial local temporal
conv_tensor = rsvp_quick_inference.inference_global_st_filter(images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kheight=1)
for l in range(1, layer):
# here use the 1*5 filter which go across channels
conv_tensor = rsvp_quick_inference.inference_temporal_filter\
(pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
# the pooling should have the width padding to 1 because we only consider channel correlation
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kheight=1)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
