Update utils.py

Boyiliee · web-flow · commit 9d063b126f1b · 2022-03-05T22:43:22.000-08:00
diff --git a/utils.py b/utils.py
@@ -7,7 +7,7 @@
 import torch
 import pytorch_lightning as pl
 import numpy as np
-#import cv2
+import cv2
 import random
 import math
 from torchvision import transforms
@@ -33,9 +33,14 @@ def do_training(hparams, model_constructor):
 
     hparams.sync_batchnorm = True
 
+    ttlogger = pl.loggers.TestTubeLogger(
+        "checkpoints", name=hparams.exp_name, version=hparams.version
+    )
+
     hparams.callbacks = make_checkpoint_callbacks(hparams.exp_name, hparams.version)
 
-    hparams.logger = pl.loggers.TensorBoardLogger("logs/")
+    wblogger = get_wandb_logger(hparams)
+    hparams.logger = [wblogger, ttlogger]
 
     trainer = pl.Trainer.from_argparse_args(hparams)
     trainer.fit(model)
@@ -160,4 +165,204 @@ def set_resume_parameters(hparams):
     else:
         version = 0
 
-    return hparams
+    return hparams
+
+
+def get_wandb_logger(hparams):
+    exp_dir = f"checkpoints/{hparams.exp_name}/version_{hparams.version}/"
+    id_file = f"{exp_dir}/wandb_id"
+
+    if os.path.exists(id_file):
+        with open(id_file) as f:
+            hparams.wandb_id = f.read()
+    else:
+        hparams.wandb_id = None
+
+    logger = pl.loggers.WandbLogger(
+        save_dir="checkpoints",
+        project=hparams.project_name,
+        name=hparams.exp_name,
+        id=hparams.wandb_id,
+    )
+
+    if hparams.wandb_id is None:
+        _ = logger.experiment
+
+    if not os.path.exists(exp_dir):
+        os.makedirs(exp_dir)
+
+        with open(id_file, "w") as f:
+            f.write(logger.version)
+
+    return logger
+
+
+class Resize(object):
+    """Resize sample to given size (width, height)."""
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+        letter_box=False,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+        self.__letter_box = letter_box
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def make_letter_box(self, sample):
+        top = bottom = (self.__height - sample.shape[0]) // 2
+        left = right = (self.__width - sample.shape[1]) // 2
+        sample = cv2.copyMakeBorder(
+            sample, top, bottom, left, right, cv2.BORDER_CONSTANT, None, 0
+        )
+        return sample
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__letter_box:
+            sample["image"] = self.make_letter_box(sample["image"])
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+                if self.__letter_box:
+                    sample["disparity"] = self.make_letter_box(sample["disparity"])
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+                if self.__letter_box:
+                    sample["depth"] = self.make_letter_box(sample["depth"])
+
+            sample["mask"] = cv2.resize(
+                sample["mask"].astype(np.float32),
+                (width, height),
+                interpolation=cv2.INTER_NEAREST,
+            )
+
+            if self.__letter_box:
+                sample["mask"] = self.make_letter_box(sample["mask"])
+
+            sample["mask"] = sample["mask"].astype(bool)
+
+        return sample
