Include training for SimCLR and auto-encoder (#17)

include simclr and ae

.gitignore

@@ -17,6 +17,8 @@ data/*
 *.log
 *.pt
 *.mdl
+*.png
+*.ipynb
 
 # Org-mode
 .org-id-locations

.pre-commit-config.yaml

@@ -8,5 +8,5 @@ repos:
     rev: v1.2.3
     hooks:
     - id: flake8
-      args: ['--ignore=E203,W503']
+      args: ['--ignore=E203,W503,E731']
       exclude: mtl.py

conf/evaluation/flip_net_ae.yaml

@@ -0,0 +1,25 @@
+## baselines
+## Pre-trained flip-net fine-tuned fine tune all layers
+
+feat_hand_crafted: false
+feat_random_cnn: false
+
+# trained networks
+model_path: /data/UKBB/SSL/day_sec_10k/logs/models
+flip_net: false
+flip_net_ft: true
+flip_net_random_mlp: false
+load_weights: true
+freeze_weight: false
+flip_net_path: "/data/UKBB/SSL/final_models/ae.mdl"
+input_size: 300  # input size after resampling the raw data
+subR: 1
+
+
+# hyper-parameters
+learning_rate: 0.0001
+num_workers: 6
+patience: 5
+num_epoch: 200
+
+evaluation_name: flip_net_ae

conf/evaluation/flip_net_simclr.yaml

@@ -0,0 +1,26 @@
+## baselines
+## Pre-trained flip-net fine-tuned fine tune all layers
+
+feat_hand_crafted: false
+feat_random_cnn: false
+
+# trained networks
+model_path: /data/UKBB/SSL/day_sec_10k/logs/models
+flip_net: false
+flip_net_ft: true
+flip_net_random_mlp: false
+load_weights: true
+freeze_weight: false
+# flip_net_path: "/data/UKBB/SSL/final_models/simclr_first.mdl"
+flip_net_path: "/data/UKBB/SSL/day_sec_1k/logs/models/simclr_200e.mdl"
+input_size: 300  # input size after resampling the raw data
+subR: 1
+
+
+# hyper-parameters
+learning_rate: 0.0001
+num_workers: 6
+patience: 5
+num_epoch: 200
+
+evaluation_name: flip_net_simclr_100k

conf/model/resnet.yaml

@@ -6,4 +6,4 @@ resnet_version: 1
 warm_up_step: 5
 lr_scale: true
 patience: 5
-
+is_ae: false

conf/task/ae.yaml

@@ -0,0 +1,9 @@
+rotation: false
+switch_axis: false
+time_reversal: false
+permutation: false
+scale: false
+time_warped: false
+positive_ratio: 0.5
+task_name: 'ae'
+multi: false

conf/task/permutation.yaml

@@ -5,4 +5,5 @@ permutation: true
 scale: false
 time_warped: false
 positive_ratio: 0.5
-task_name: 'permutation'
+task_name: 'permutation'
+multi: false

conf/task/scale.yaml

@@ -8,3 +8,4 @@ scale_sigma: 0.25
 min_scale_sigma: 0.05
 positive_ratio: 0.5
 task_name: 'scale'
+multi: false

conf/task/simclr.yaml

@@ -0,0 +1,9 @@
+rotation: false
+switch_axis: false
+time_reversal: false
+permutation: false
+scale: false
+time_warped: false
+positive_ratio: 0.5
+task_name: 'simclr'
+multi: false

conf/task/time_reversal.yaml

@@ -5,4 +5,5 @@ permutation: false
 scale: false
 time_warped: false
 positive_ratio: 0.5
-task_name: 'aot'
+task_name: 'aot'
+multi: false

downstream_task_evaluation.py

@@ -15,7 +15,7 @@
 import pathlib
 
 # SSL net
-from sslearning.models.accNet import cnn1, SSLNET, Resnet
+from sslearning.models.accNet import cnn1, SSLNET, Resnet, EncoderMLP
 from sslearning.scores import classification_scores, classification_report
 import copy
 from sklearn import preprocessing
@@ -282,7 +282,9 @@ def mlp_predict(model, data_loader, my_device, cfg):
 
 
 def init_model(cfg, my_device):
-    if cfg.model.resnet_version > 0:
+    if cfg.model.is_ae:
+        model = EncoderMLP(cfg.data.output_size)
+    elif cfg.model.resnet_version > 0:
         model = Resnet(
             output_size=cfg.data.output_size,
             is_eva=True,
@@ -297,6 +299,7 @@ def init_model(cfg, my_device):
     if cfg.multi_gpu:
         model = nn.DataParallel(model, device_ids=cfg.gpu_ids)
 
+    print(model)
     model.to(my_device, dtype=torch.float)
     return model
 
@@ -305,11 +308,8 @@ def setup_model(cfg, my_device):
     model = init_model(cfg, my_device)
 
     if cfg.evaluation.load_weights:
-        load_weights(
-            cfg.evaluation.flip_net_path,
-            model,
-            my_device
-        )
+        print("Loading weights from %s" % cfg.evaluation.flip_net_path)
+        load_weights(cfg.evaluation.flip_net_path, model, my_device)
     if cfg.evaluation.freeze_weight:
         freeze_weights(model)
     return model
@@ -503,11 +503,11 @@ def handcraft_features(xyz, sample_rate):
     feats["std"] = np.std(m)
     feats["range"] = np.ptp(m)
     feats["mad"] = stats.median_abs_deviation(m)
-    if feats['std'] > .01:
-        feats['skew'] = np.nan_to_num(stats.skew(m))
-        feats['kurt'] = np.nan_to_num(stats.kurtosis(m))
+    if feats["std"] > 0.01:
+        feats["skew"] = np.nan_to_num(stats.skew(m))
+        feats["kurt"] = np.nan_to_num(stats.kurtosis(m))
     else:
-        feats['skew'] = feats['kurt'] = 0
+        feats["skew"] = feats["kurt"] = 0
     feats["enmomean"] = np.mean(np.abs(m - 1))
 
     # Spectrum using Welch's method with 3s segment length
@@ -620,9 +620,7 @@ def get_data_with_subject_count(subject_count, X, y, pid):
     return filter_X, filter_y, filter_pid
 
 
-def load_weights(
-    weight_path, model, my_device
-):
+def load_weights(weight_path, model, my_device):
     # only need to change weights name when
     # the model is trained in a distributed manner
 
@@ -632,12 +630,17 @@ def load_weights(
     )  # v2 has the right para names
 
     # distributed pretraining can be inferred from the keys' module. prefix
-    head = next(iter(pretrained_dict_v2)).split('.')[0]  # get head of first key
-    if head == 'module':
+    head = next(iter(pretrained_dict_v2)).split(".")[
+        0
+    ]  # get head of first key
+    if head == "module":
         # remove module. prefix from dict keys
-        pretrained_dict_v2 = {k.partition('module.')[2]: pretrained_dict_v2[k] for k in pretrained_dict_v2.keys()}
+        pretrained_dict_v2 = {
+            k.partition("module.")[2]: pretrained_dict_v2[k]
+            for k in pretrained_dict_v2.keys()
+        }
 
-    if hasattr(model, 'module'):
+    if hasattr(model, "module"):
         model_dict = model.module.state_dict()
         multi_gpu_ft = True
     else:

mtl.py

@@ -576,7 +576,7 @@ def main_worker(rank, cfg):
             task_losses.append(task_loss)
 
         train_task_losses = np.array(task_losses)
-        if epoch < cfg.model.warm_up_step:
+        if epoch >= cfg.model.warm_up_step:
             scheduler.step()
 
         train_losses = np.array(train_losses)

sslearning/data/data_loader.py

@@ -55,10 +55,48 @@ def subject_collate(batch):
     return [data, aot_y, scale_y, permutation_y, time_w_y]
 
 
+def simclr_subject_collate(batch):
+    x1 = [item[0] for item in batch]
+    x1 = torch.cat(x1)
+    x2 = [item[1] for item in batch]
+    x2 = torch.cat(x2)
+    return [x1, x2]
+
+
 def worker_init_fn(worker_id):
     np.random.seed(int(time.time()))
 
 
+def augment_view(X, cfg):
+    new_X = []
+    X = X.numpy()
+
+    for i in range(len(X)):
+        current_x = X[i, :, :]
+
+        # choice = np.random.choice(
+        #     2, 1, p=[cfg.task.positive_ratio, 1 - cfg.task.positive_ratio]
+        # )[0]
+        # current_x = my_transforms.flip(current_x, choice)
+        # choice = np.random.choice(
+        #     2, 1, p=[cfg.task.positive_ratio, 1 - cfg.task.positive_ratio]
+        # )[0]
+        # current_x = my_transforms.permute(current_x, choice)
+        # choice = np.random.choice(
+        #     2, 1, p=[cfg.task.positive_ratio, 1 - cfg.task.positive_ratio]
+        # )[0]
+        # current_x = my_transforms.time_warp(current_x, choice)
+        choice = np.random.choice(
+            2, 1, p=[cfg.task.positive_ratio, 1 - cfg.task.positive_ratio]
+        )[0]
+        current_x = my_transforms.rotation(current_x, choice)
+        new_X.append(current_x)
+
+    new_X = np.array(new_X)
+    new_X = torch.Tensor(new_X)
+    return new_X
+
+
 def generate_labels(X, shuffle, cfg):
     labels = []
     new_X = []
@@ -314,6 +352,80 @@ def __getitem__(self, idx):
         )
 
 
+class SIMCLR_dataset:
+    def __init__(
+        self,
+        data_root,
+        file_list_path,
+        cfg,
+        transform=None,
+        shuffle=False,
+        is_epoch_data=False,
+    ):
+        """
+        Args:
+            data_root (string): directory containing all data files
+            file_list_path (string): file list
+            cfg (dict): config
+            shuffle (bool): whether permute epoches within one subject
+            is_epoch_data (bool): whether each sample is one
+            second of data or 10 seconds of data
+
+
+        Returns:
+            data : transformed sample
+            labels (dict) : labels for avalaible transformations
+        """
+        check_file_list(file_list_path, data_root, cfg)
+        file_list_df = pd.read_csv(file_list_path)
+        self.file_list = file_list_df["file_list"].to_list()
+        self.data_root = data_root
+        self.cfg = cfg
+        self.is_epoch_data = is_epoch_data
+        self.ratio2keep = cfg.data.ratio2keep
+        self.shuffle = shuffle
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.file_list)
+
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+        print(idx)
+
+        # idx starts from zero
+        file_to_load = self.file_list[idx]
+        X = np.load(file_to_load, allow_pickle=True)
+
+        # to help select a percentage of data per subject
+        subject_data_count = int(len(X) * self.ratio2keep)
+        assert subject_data_count >= self.cfg.dataloader.num_sample_per_subject
+        if self.ratio2keep != 1:
+            X = X[:subject_data_count, :]
+
+        if self.is_epoch_data:
+            X = weighted_epoch_sample(
+                X, num_sample=self.cfg.dataloader.num_sample_per_subject
+            )
+        else:
+            X = weighted_sample(
+                X,
+                num_sample=self.cfg.dataloader.num_sample_per_subject,
+                epoch_len=self.cfg.dataloader.epoch_len,
+                sample_rate=self.cfg.dataloader.sample_rate,
+                is_weighted_sample=self.cfg.data.weighted_sample,
+            )
+
+        X = torch.from_numpy(X)
+        if self.transform:
+            X = self.transform(X)
+
+        X1 = augment_view(X, self.cfg)
+        X2 = augment_view(X, self.cfg)
+        return (X1, X2)
+
+
 # Return:
 # x: batch_size * feature size (125)
 # y: batch_size * label_size (5)

sslearning/data/data_transformation.py

@@ -1,7 +1,6 @@
 import numpy as np
 from transforms3d.axangles import axangle2mat  # for rotation
 from scipy.interpolate import CubicSpline  # for warping
-import math
 
 """
 This file implements a list of transforms for tri-axial raw-accelerometry
@@ -29,17 +28,18 @@ def rotation(sample, choice):
         choice (float): [0, 9] for each axis,
         we can do 4 rotations 0, 90 180, 270
     """
-    if choice == 9:
-        return sample
+    if choice == 1:
+        # angle_choices = [1 / 4 * np.pi, 1 / 2 * np.pi, 3 / 4 * np.pi]
+        # angle = angle_choices[choice % 3]
+        # axis = axis_choices[math.floor(choice / 3)]
 
-    axis_choices = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]
-    angle_choices = [1 / 4 * np.pi, 1 / 2 * np.pi, 3 / 4 * np.pi]
-    axis = axis_choices[math.floor(choice / 3)]
-    angle = angle_choices[choice % 3]
+        axes = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]
+        sample = np.swapaxes(sample, 0, 1)
+        for i in range(3):
+            angle = np.random.uniform(low=-np.pi, high=np.pi)
+            sample = np.matmul(sample, axangle2mat(axes[i], angle))
 
-    sample = np.swapaxes(sample, 0, 1)
-    sample = np.matmul(sample, axangle2mat(axis, angle))
-    sample = np.swapaxes(sample, 0, 1)
+        sample = np.swapaxes(sample, 0, 1)
     return sample