From cc057cbe680376d96487c86026b528f24b695e1e Mon Sep 17 00:00:00 2001 From: Talmo Pereira Date: Sun, 17 Jan 2021 19:45:13 -0500 Subject: [PATCH] Inference CLI fixes (#459) Fix single instance inference and RGB detection. --- sleap/nn/data/confidence_maps.py | 4 +- sleap/nn/data/pipelines.py | 5 ++ sleap/nn/data/providers.py | 2 +- sleap/nn/inference.py | 25 +++++--- .../best_model.h5 | Bin 2598232 -> 0 bytes .../labels_pr.val.slp | Bin 23949 -> 0 bytes .../metrics.train.npz | Bin 1899 -> 0 bytes .../metrics.val.npz | Bin 1899 -> 0 bytes .../training_log.csv | 11 ---- .../best_model.h5 | Bin 0 -> 371352 bytes .../initial_config.json | 48 ++++++++------- .../labels_gt.train.slp | Bin 15864 -> 15864 bytes .../labels_gt.val.slp | Bin 15864 -> 15864 bytes .../training_config.json | 57 ++++++++++-------- .../training_log.csv | 13 ++++ .../small_robot_minimal.slp} | Bin 23739 -> 15864 bytes tests/fixtures/datasets.py | 5 ++ tests/fixtures/models.py | 4 ++ tests/nn/data/test_confidence_maps.py | 23 +++++++ tests/nn/test_inference.py | 19 +++++- tests/nn/test_training.py | 33 ++++++++++ 21 files changed, 178 insertions(+), 71 deletions(-) delete mode 100644 tests/data/models/minimal_instance.UNet.centroid2/best_model.h5 delete mode 100644 tests/data/models/minimal_instance.UNet.centroid2/labels_pr.val.slp delete mode 100644 tests/data/models/minimal_instance.UNet.centroid2/metrics.train.npz delete mode 100644 tests/data/models/minimal_instance.UNet.centroid2/metrics.val.npz delete mode 100644 tests/data/models/minimal_instance.UNet.centroid2/training_log.csv create mode 100644 tests/data/models/minimal_robot.UNet.single_instance/best_model.h5 rename tests/data/models/{minimal_instance.UNet.centroid2 => minimal_robot.UNet.single_instance}/initial_config.json (80%) rename tests/data/models/{minimal_instance.UNet.centroid2 => minimal_robot.UNet.single_instance}/labels_gt.train.slp (74%) rename tests/data/models/{minimal_instance.UNet.centroid2 => minimal_robot.UNet.single_instance}/labels_gt.val.slp (74%) rename tests/data/models/{minimal_instance.UNet.centroid2 => minimal_robot.UNet.single_instance}/training_config.json (84%) create mode 100644 tests/data/models/minimal_robot.UNet.single_instance/training_log.csv rename tests/data/{models/minimal_instance.UNet.centroid2/labels_pr.train.slp => slp_hdf5/small_robot_minimal.slp} (52%) diff --git a/sleap/nn/data/confidence_maps.py b/sleap/nn/data/confidence_maps.py index fd565177f..e4cac3e1b 100644 --- a/sleap/nn/data/confidence_maps.py +++ b/sleap/nn/data/confidence_maps.py @@ -536,9 +536,9 @@ def generate_confmaps(example): if self.with_offsets: example["offsets"] = mask_offsets( make_offsets( - example["center_instance"], xv, yv, stride=self.output_stride + example["points"], xv, yv, stride=self.output_stride ), - example["instance_confidence_maps"], + example["confidence_maps"], self.offsets_threshold, ) if self.flatten_offsets: diff --git a/sleap/nn/data/pipelines.py b/sleap/nn/data/pipelines.py index a87e550ed..cc218d74c 100644 --- a/sleap/nn/data/pipelines.py +++ b/sleap/nn/data/pipelines.py @@ -331,11 +331,14 @@ class SingleInstanceConfmapsPipeline: optimization_config: Optimization-related configuration. single_instance_confmap_head: Instantiated head describing the output confidence maps tensor. + offsets_head: Optional head describing the offset refinement maps. """ data_config: DataConfig optimization_config: OptimizationConfig single_instance_confmap_head: SingleInstanceConfmapsHead + offsets_head: Optional[OffsetRefinementHead] = None + def make_base_pipeline(self, data_provider: Provider) -> Pipeline: """Create base pipeline with input data only. @@ -394,6 +397,8 @@ def make_training_pipeline(self, data_provider: Provider) -> Pipeline: pipeline += SingleInstanceConfidenceMapGenerator( sigma=self.single_instance_confmap_head.sigma, output_stride=self.single_instance_confmap_head.output_stride, + with_offsets=self.offsets_head is not None, + offsets_threshold=self.offsets_head.sigma_threshold if self.offsets_head is not None else 1.0 ) if len(data_provider) >= self.optimization_config.batch_size: diff --git a/sleap/nn/data/providers.py b/sleap/nn/data/providers.py index cc21bdffe..3fc7eff3d 100644 --- a/sleap/nn/data/providers.py +++ b/sleap/nn/data/providers.py @@ -285,7 +285,7 @@ def make_dataset(self) -> tf.data.Dataset: grid in order to properly map points to image coordinates. """ # Grab an image to test for the dtype. - test_image = tf.convert_to_tensor(self.video.get_frame(0)) + test_image = tf.convert_to_tensor(self.video.get_frame(self.video.last_frame_idx)) image_dtype = test_image.dtype def py_fetch_frame(ind): diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py index 9751d7084..3ef9b292a 100644 --- a/sleap/nn/inference.py +++ b/sleap/nn/inference.py @@ -764,6 +764,14 @@ def call(self, data): threshold=self.peak_threshold, ) + # Adjust for stride and scale. + peaks = peaks * self.output_stride + if self.input_scale != 1.0: + # Note: We add 0.5 here to offset TensorFlow's weird image resizing. This + # may not always(?) be the most correct approach. + # See: https://github.com/tensorflow/tensorflow/issues/6720 + peaks = (peaks / self.input_scale) + 0.5 + out = {"peaks": peaks, "peak_vals": peak_vals} if self.return_confmaps: out["confmaps"] = cms @@ -961,13 +969,16 @@ def _predict_generator( # Run inference on current batch. preds = self.inference_model.predict(ex) - # Convert to numpy arrays if not already. - if isinstance(preds["video_ind"], tf.Tensor): - preds["video_ind"] = preds["video_ind"].numpy().flatten() - if isinstance(preds["frame_ind"], tf.Tensor): - preds["frame_ind"] = preds["frame_ind"].numpy().flatten() + ex["peaks"] = preds["peaks"] + ex["peak_vals"] = preds["peak_vals"] - yield preds + # Convert to numpy arrays if not already. + if isinstance(ex["video_ind"], tf.Tensor): + ex["video_ind"] = ex["video_ind"].numpy().flatten() + if isinstance(ex["frame_ind"], tf.Tensor): + ex["frame_ind"] = ex["frame_ind"].numpy().flatten() + + yield ex def _make_labeled_frames_from_generator( self, generator: Iterator[Dict[str, np.ndarray]], data_provider: Provider @@ -2775,7 +2786,7 @@ def get_relevant_args(key): ) elif "single_instance" in trained_model_paths: predictor = SingleInstancePredictor.from_trained_models( - confmap_model_path=trained_model_paths["single_instance"], + trained_model_paths["single_instance"], **get_relevant_args("single"), **kwargs, ) diff --git a/tests/data/models/minimal_instance.UNet.centroid2/best_model.h5 b/tests/data/models/minimal_instance.UNet.centroid2/best_model.h5 deleted file mode 100644 index d1387a293db1daed49467e01b52ee30b5684f81c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2598232 zcmeEv30#fa8uv~~C6Q86l#nqEq}uO#HX#ZTg_KG((xizL8Z~LqBr1v`MH!;>t|#-H z;mADC^N?AFe7oA4IHq&%x!-rcd)q%+`(5jKpS9M1J;R!JjLGm3ZKOI$aq2%35*#s3 zyN0jAKc562tNfk~QNm|o`ZU285kVL&2n$*?ei7lc;?$>g6vVd^@K+OUjBpjaXgR^y zn8T+O@)w4J*El%>#zH>A_~!6`0|66bBlG&2S_!JFEeH<^ 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"filters": 8, + "filters_rate": 2.0, "middle_block": true, - "up_interpolate": false, + "up_interpolate": true, "stacks": 1 }, "hourglass": null, @@ -39,23 +39,23 @@ "pretrained_encoder": null }, "heads": { - "single_instance": null, - "centroid": { - "anchor_part": null, + "single_instance": { + "part_names": null, "sigma": 5.0, - "output_stride": 2, - "offset_refinement": true + "output_stride": 4, + "offset_refinement": false }, + "centroid": null, "centered_instance": null, "multi_instance": null } }, "optimization": { - "preload_data": false, + "preload_data": true, "augmentation_config": { "rotate": false, - "rotation_min_angle": -180, - "rotation_max_angle": 180, + "rotation_min_angle": -180.0, + "rotation_max_angle": 180.0, "translate": false, "translate_min": -5, "translate_max": 5, @@ -83,12 +83,12 @@ "prefetch": true, "batch_size": 4, "batches_per_epoch": null, - "min_batches_per_epoch": 100, + "min_batches_per_epoch": 200, "val_batches_per_epoch": null, - "min_val_batches_per_epoch": 1, - "epochs": 10, + "min_val_batches_per_epoch": 10, + "epochs": 100, "optimizer": "adam", - "initial_learning_rate": 0.0001, + "initial_learning_rate": 0.001, "learning_rate_schedule": { "reduce_on_plateau": true, "reduction_factor": 0.5, @@ -112,11 +112,13 @@ }, "outputs": { "save_outputs": true, - "run_name": "minimal_instance.UNet.centroid", + "run_name": "minimal_robot.UNet.single_instance", "run_name_prefix": "", - "run_name_suffix": null, - "runs_folder": "models", - "tags": [], + "run_name_suffix": "", + "runs_folder": "", + "tags": [ + "" + ], "save_visualizations": false, "log_to_csv": true, "checkpointing": { @@ -134,10 +136,10 @@ "visualizations": true }, "zmq": { - "subscribe_to_controller": false, + "subscribe_to_controller": true, "controller_address": "tcp://127.0.0.1:9000", "controller_polling_timeout": 10, - "publish_updates": false, + "publish_updates": true, "publish_address": "tcp://127.0.0.1:9001" } } diff --git a/tests/data/models/minimal_instance.UNet.centroid2/labels_gt.train.slp b/tests/data/models/minimal_robot.UNet.single_instance/labels_gt.train.slp similarity index 74% rename from tests/data/models/minimal_instance.UNet.centroid2/labels_gt.train.slp rename to tests/data/models/minimal_robot.UNet.single_instance/labels_gt.train.slp index 153baa1546cd56a2afc366acfffc122c78801ccf..6cbc97f1a27edd8cc553cd0b6ad09f52c8012cd5 100644 GIT binary patch delta 320 zcmexS{iAxr9d^c^$#>bcfTV%E2#d;A=7h-#94i=mCTX)PzmlW%#B$g!Vmu04;<`?T1=O*Ul#24i!<(KH?7MLjM zD5V!ARu(5G=A!0}#019#tt^+)$=hC5_jSYv7(p6S5Im~-vmh7VlH z4qF~wtLZzN3>4XDlg4m(Z>EEt;e~hQHR(VRY34Q8G_wjF{x0Iv*_sX1HTj{S*yN>x z3P4&ENDFW>F+c#z#Ers&j8G1YV%p5exsYx01Bo86Pd67x@-RYJ4N@M=j4Yc^$|f-~ zvP{;Imw*Jt24Dzy0E0rXKoDjI)cDPv@`pJgiUKqhCZCWM+T5h+$;1k>-xLv+AOo^M f2JApHfN8U)&TOawKt+=ibcfTV%E2#d;A=7h-#94i=mCTX)PzmlW%#B$g!Vmu04;<`?T1=O*Ul#24i!<(KH?7MLjM zD5V!ARu(5G=AgSqk@}%nf_x308vkEs{jB1 delta 393 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z3L;?vD@hL?*{=g!OC=N)V0^!nnu@=;UkA96NOyZ_Ix>8p4sa8J@ugI1YW>wV z9pDW_!Xbo`RDODa4)7yH!Z%q-+WT&j1NfAe4)7)d^{pR8c9pGjn z;rFLX(pmeo4sZ*RuxBVqgNG060BP6A`|RBC#y@fi;AP9oueOG!GgWjhBrN+j&K zO44t8bsgXsksczcB)$ILE)H;lz}Tjhq@R8Dbq=saq)&KhTK)D?9blWl*xQwwzP3K4 z1Kdj_I*L^L<@*kn>HzO15^e}eO%Gi@!2$j(kyevbl9sJI#sS_#VBAHNq%&`n>Ht4Q zB>Ag1nUtjA)f;qx2OX&VOG$20l8)c}JRc;`UXp7_DoLRqU*Z7oC-7P?Qpwpq4)6g2 z<8)W4>5XUJh12;Pqalt1UBh zfR7Ql$&0l9;9DHv;{<-#i}aH_QXJrC3B19J^o!mT9N?2_u=~o?b2L$z;Uvr-9(@bg ziu5Zvi$xomVI68#DT+i*p;KGOoVUZ$c_qbpE$RlRx-6{^-_}`bno~1Y-uHq4SHfJicdM?A`7K2`V(dXNn)5Lh6vKwyEu L0)YjtLks)|U(VxL diff --git a/tests/fixtures/datasets.py b/tests/fixtures/datasets.py index 0242b8655..fadb79504 100644 --- a/tests/fixtures/datasets.py +++ b/tests/fixtures/datasets.py @@ -18,6 +18,7 @@ TEST_JSON_MIN_LABELS = "tests/data/json_format_v2/minimal_instance.json" TEST_SLP_MIN_LABELS = "tests/data/slp_hdf5/minimal_instance.slp" TEST_MAT_LABELS = "tests/data/mat/labels.mat" +TEST_SLP_MIN_LABELS_ROBOT = "tests/data/slp_hdf5/small_robot_minimal.slp" @pytest.fixture @@ -40,6 +41,10 @@ def min_labels_slp(): return Labels.load_file(TEST_SLP_MIN_LABELS) +@pytest.fixture +def min_labels_robot(): + return Labels.load_file(TEST_SLP_MIN_LABELS_ROBOT) + @pytest.fixture def mat_labels(): return Labels.load_leap_matlab(TEST_MAT_LABELS, gui=False) diff --git a/tests/fixtures/models.py b/tests/fixtures/models.py index fde949dc7..d46cd894a 100644 --- a/tests/fixtures/models.py +++ b/tests/fixtures/models.py @@ -15,3 +15,7 @@ def min_centered_instance_model_path(): @pytest.fixture def min_bottomup_model_path(): return "tests/data/models/minimal_instance.UNet.bottomup" + +@pytest.fixture +def min_single_instance_robot_model_path(): + return "tests/data/models/minimal_robot.UNet.single_instance" diff --git a/tests/nn/data/test_confidence_maps.py b/tests/nn/data/test_confidence_maps.py index 31a2db535..750600c58 100644 --- a/tests/nn/data/test_confidence_maps.py +++ b/tests/nn/data/test_confidence_maps.py @@ -10,6 +10,7 @@ make_confmaps, make_multi_confmaps, make_multi_confmaps_with_offsets, + SingleInstanceConfidenceMapGenerator, MultiConfidenceMapGenerator, InstanceConfidenceMapGenerator, ) @@ -108,6 +109,28 @@ def test_make_multi_confmaps_with_offsets(): assert offsets.shape == (4, 5, 2, 2) +def test_single_instance_confidence_map_generator(min_labels_robot): + labels_reader = providers.LabelsReader(min_labels_robot) + confmap_generator = SingleInstanceConfidenceMapGenerator( + sigma=5, output_stride=2, with_offsets=False + ) + ds = labels_reader.make_dataset() + ds = confmap_generator.transform_dataset(ds) + example = next(iter(ds)) + assert example["confidence_maps"].shape == (320 // 2, 560 // 2, 2) + assert example["confidence_maps"].dtype == tf.float32 + + confmap_generator = SingleInstanceConfidenceMapGenerator( + sigma=5, output_stride=2, with_offsets=True + ) + ds = labels_reader.make_dataset() + ds = confmap_generator.transform_dataset(ds) + example = next(iter(ds)) + assert example["confidence_maps"].shape == (320 // 2, 560 // 2, 2) + assert example["confidence_maps"].dtype == tf.float32 + assert example["offsets"].shape == (320 // 2, 560 // 2, 4) + assert example["offsets"].dtype == tf.float32 + def test_multi_confidence_map_generator(min_labels): labels_reader = providers.LabelsReader(min_labels) multi_confmap_generator = MultiConfidenceMapGenerator( diff --git a/tests/nn/test_inference.py b/tests/nn/test_inference.py index 67fb13a5c..91d38ca9a 100644 --- a/tests/nn/test_inference.py +++ b/tests/nn/test_inference.py @@ -17,6 +17,7 @@ find_head, SingleInstanceInferenceLayer, SingleInstanceInferenceModel, + SingleInstancePredictor, CentroidCropGroundTruth, CentroidCrop, FindInstancePeaksGroundTruth, @@ -469,6 +470,19 @@ def test_single_instance_inference(): assert "confmaps" in preds +def test_single_instance_predictor(min_labels_robot, min_single_instance_robot_model_path): + predictor = SingleInstancePredictor.from_trained_models( + min_single_instance_robot_model_path + ) + labels_pr = predictor.predict(min_labels_robot) + assert len(labels_pr) == 2 + assert len(labels_pr[0].instances) == 1 + + points_gt = np.concatenate([min_labels_robot[0][0].numpy(), min_labels_robot[1][0].numpy()], axis=0) + points_pr = np.concatenate([labels_pr[0][0].numpy(), labels_pr[1][0].numpy()], axis=0) + assert_allclose(points_gt, points_pr, atol=10.) + + def test_topdown_predictor_centroid(min_labels, min_centroid_model_path): predictor = TopdownPredictor.from_trained_models( centroid_model_path=min_centroid_model_path @@ -511,7 +525,10 @@ def test_topdown_predictor_bottomup(min_labels, min_bottomup_model_path): assert_allclose(points_gt[inds1.numpy()], points_pr[inds2.numpy()], atol=1.75) -def test_load_model(min_centroid_model_path, min_centered_instance_model_path, min_bottomup_model_path): +def test_load_model(min_single_instance_robot_model_path, min_centroid_model_path, min_centered_instance_model_path, min_bottomup_model_path): + predictor = load_model(min_single_instance_robot_model_path) + assert isinstance(predictor, SingleInstancePredictor) + predictor = load_model([min_centroid_model_path, min_centered_instance_model_path]) assert isinstance(predictor, TopdownPredictor) diff --git a/tests/nn/test_training.py b/tests/nn/test_training.py index 645e2d6dc..38be58953 100644 --- a/tests/nn/test_training.py +++ b/tests/nn/test_training.py @@ -30,6 +30,39 @@ def cfg(): return cfg +def test_train_single_instance(min_labels_robot, cfg): + cfg.model.heads.single_instance = ( + sleap.nn.config.SingleInstanceConfmapsHeadConfig( + sigma=1.5, output_stride=1, offset_refinement=False + ) + ) + trainer = sleap.nn.training.SingleInstanceModelTrainer.from_config( + cfg, training_labels=min_labels_robot + ) + trainer.setup() + trainer.train() + assert trainer.keras_model.output_names[0] == "SingleInstanceConfmapsHead_0" + assert tuple(trainer.keras_model.outputs[0].shape) == (None, 320, 560, 2) + + +def test_train_single_instance_with_offset(min_labels_robot, cfg): + cfg.model.heads.single_instance = ( + sleap.nn.config.SingleInstanceConfmapsHeadConfig( + sigma=1.5, output_stride=1, offset_refinement=True + ) + ) + trainer = sleap.nn.training.SingleInstanceModelTrainer.from_config( + cfg, training_labels=min_labels_robot + ) + trainer.setup() + trainer.train() + assert trainer.keras_model.output_names[0] == "SingleInstanceConfmapsHead_0" + assert tuple(trainer.keras_model.outputs[0].shape) == (None, 320, 560, 2) + + assert trainer.keras_model.output_names[1] == "OffsetRefinementHead_0" + assert tuple(trainer.keras_model.outputs[1].shape) == (None, 320, 560, 4) + + def test_train_centroids(training_labels, cfg): cfg.model.heads.centroid = ( sleap.nn.config.CentroidsHeadConfig(

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