HALF_PRECISION, PARAM_INIT_FN, TRAINABLE_STEM, BATCHNORM_MOMENTUM) # load pretrained attention part # LOAD_TIMESTAMP, LOAD_EPOCH, LOAD_STEP = "21_May_2020_00:02:57.265", 3, 1349 # inpath = os.path.join(SNAPSHOT_DIR, "{}_epoch{}_step{}".format( # LOAD_TIMESTAMP, LOAD_EPOCH, LOAD_STEP)) # student.load_state_dicts(inpath) hm_parser = HeatmapParser(num_joints=NUM_HEATMAPS, **HM_PARSER_PARAMS) # LOG MODEL AND HYPERPARAMETERS student_summary = ModuleSummary.get_model_summary(student, as_string=True) txt_logger.info(student_summary) tb_logger.add_text("Architecture summary", student_summary, 0) # tb_logger.add_graph(student, DUMMY_INPUT) HPARS_DICT = {"num heatmaps": NUM_HEATMAPS, # model arch "AE dimensions": AE_DIMENSIONS, "pretrained HRNet path": MODEL_PATH, "half precision": HALF_PRECISION, "param init fn": PARAM_INIT_FN, **HM_PARSER_PARAMS, # preprocessing/augmentation "img norm mean": IMG_NORM_MEAN,
img_transform=IMG_NORMALIZE_TRANSFORM, whitelist_ids=MINIVAL_IDS) val_augm_dataset = CocoDistillationDatasetAugmented( COCO_DIR, "val2017", os.path.join(COCO_DIR, "hrnet_predictions", "val2017"), gt_stddevs_pix=[20.0, 9.0, 2.0], img_transform=IMG_NORMALIZE_TRANSFORM, overall_transform=OVERALL_HHRNET_TRANSFORM) hm_parser = HeatmapParser(num_joints=17, max_num_people=30, detection_threshold=0.1, tag_threshold=1.0, use_detection_val=True, ignore_too_much=False, tag_per_joint=True, nms_ksize=5, nms_padding=2) # print("Due to PLT bug type in this line and press c:\n", # "import matplotlib; matplotlib.use('TkAgg')") # breakpoint() # "TRAINING" SET for i in range(NUM_TRAIN_PLOTS): print("TRAIN >>>", i) img_id, img, mask, hms, teach_hms, teach_ae = val_augm_dataset[i] # plot: img, mask, ground truths, teacher detection matplotlib.use('TkAgg')