HW1: Implementing Tiny YOLOv2 using tensorflow API
Hw1 (#1) * Fill out blanked codes I didn't test yet, but I wrote most of logical flows that the homework requires. I wrote draw function and main logic. However, I didn't understand 4th requirements. * Change indentation unit to tab In previous commit, two spaces and four spaces are mixed for indentation. So, I changed all to tab. * Create layers, not yet weights I created YOLO-v2-tiny model, but pretrained model is delivered in pickle format, so I haven't understood how to use it. But soon I'll load it * Refactor duplicated codes into a function * Fix bugs in opening video file and resizing function * Set batch to 1 because it only proceeds inference * (Incomplete) Consume Too much(more than 100GB) memory * Fix a typo * Fix memory allocation error I tried to make filters from 16 until 1024, but I accidently did from 16 to pow(16, 9) ~ 6e10 ~ 60G. That was why my code cannot allocate enough memory. Plus, I fixed professor's wrong code. sess.run evaluates only last layer. * Set fixed precision on throughput * Add some information about yolov2tiny architecture * (Incomplete) Find a bottleneck on non-max-suppression To find out, I made a tracer and set on some functions * Fix wrongly indented codes in nms function * Reshape bounding boxes from (416, 416) to original video resolution * Add parameters * Show attributes * Store every layer on first frame into intermediate folder * WIP: Make a room for bias, but not yet implemented cuz still figuring out to use weights * Change video codec to mp4v * WIP: create layers hierarchically YOLO-v2-tiny consists of nine composit layers. And each layer consists of smaller layers such as conv, batch_norm, bias, maxpool, leakyReLU. Therefore, I mimicked its hierarchy. * Infer objects correctly * Add explicit bias layers right after each conv layer. * Load weights by using tf.Variable and attentive layers in tf.nn. * Use leftupper coordinate and rightbottom coordinate in draw function since coordinates are already shaped in restore_shape function. * Remove unused comments and debug lines * Use original image in draw function. * Measure inference, end-to-end, fps and total time * Update for LaTeX * Add report template * Limit yolo takes only 70% of GPU VRAM In small VRAM environment, allow_grouth option is not enough for preventing out of memory error. So, referencing some information, I forced not to take more than 70% VRAM * Clarify which values we save * Update yolov2tiny.py delete out_chan, default value of stride 통일성 위해서 maxpool도 그냥 max_pool2d로 하면 어떤지? (일단 주석해놈) * Update yolov2tiny.py * Update __init__.py Add start time of "end-to-end time", beg_start and change previous beg -> beg_infer Does change to name of "beg" in obj_detection effect to measure function? (I'm not sure) * Update yolov2tiny.py put back n_... values to post processing * Create consider.txt * Update yolov2tiny.py confirm "tf.nn.max_pool2d" working well * Update consider.txt * Update __init__.py move down the saving intermediate first frame result(tensor) part for measuring necessary time * Update consider.txt * Update __init__.py add printing total time * Add some details * Add comment about inference FPS * Write detailed info of why I chose tf.nn functions * Upload whole model visualization I visualized the whole tf graph by using tf.train.Saver. The only catch is, it is too verbose to see the main logic. But I decided to save the visualized graph just in case. * Add GPU benchmark * Add cpu benchmark * Add first draft of the report * Update report.tex Some change * Update report.tex * Change code location and table * Edit figures Co-authored-by: jehoon315 <[email protected]>