wangxiaolong_S21009
3 years ago
parent
982b5027b9
commit
a35086dc02
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seed: 0 |
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mixed_precision: false |
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base_lr: 4.0e-4 |
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nr_gpus: 8 |
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batch_size: 4 |
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n_total_epoch: 600 |
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minibatch_per_epoch: 500 |
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loadmodel: ~ |
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log_dir: "./train_log" |
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model_save_freq_epoch: 1 |
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max_disp: 256 |
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image_width: 512 |
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image_height: 384 |
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training_data_path: "./stereo_trainset/crestereo" |
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log_level: "logging.INFO" |
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@ -0,0 +1,215 @@ |
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import os |
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import cv2 |
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import glob |
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import numpy as np |
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from PIL import Image, ImageEnhance |
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from megengine.data.dataset import Dataset |
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class Augmentor: |
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def __init__( |
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self, |
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image_height=384, |
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image_width=512, |
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max_disp=256, |
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scale_min=0.6, |
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scale_max=1.0, |
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seed=0, |
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): |
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super().__init__() |
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self.image_height = image_height |
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self.image_width = image_width |
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self.max_disp = max_disp |
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self.scale_min = scale_min |
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self.scale_max = scale_max |
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self.rng = np.random.RandomState(seed) |
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|
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def chromatic_augmentation(self, img): |
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random_brightness = np.random.uniform(0.8, 1.2) |
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random_contrast = np.random.uniform(0.8, 1.2) |
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random_gamma = np.random.uniform(0.8, 1.2) |
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img = Image.fromarray(img) |
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enhancer = ImageEnhance.Brightness(img) |
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img = enhancer.enhance(random_brightness) |
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enhancer = ImageEnhance.Contrast(img) |
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img = enhancer.enhance(random_contrast) |
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gamma_map = [ |
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255 * 1.0 * pow(ele / 255.0, random_gamma) for ele in range(256) |
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] * 3 |
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img = img.point(gamma_map) # use PIL's point-function to accelerate this part |
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img_ = np.array(img) |
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return img_ |
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def __call__(self, left_img, right_img, left_disp): |
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# 1. chromatic augmentation |
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left_img = self.chromatic_augmentation(left_img) |
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right_img = self.chromatic_augmentation(right_img) |
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|
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# 2. spatial augmentation |
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# 2.1) rotate & vertical shift for right image |
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if self.rng.binomial(1, 0.5): |
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angle, pixel = 0.1, 2 |
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px = self.rng.uniform(-pixel, pixel) |
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ag = self.rng.uniform(-angle, angle) |
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image_center = ( |
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self.rng.uniform(0, right_img.shape[0]), |
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self.rng.uniform(0, right_img.shape[1]), |
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) |
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rot_mat = cv2.getRotationMatrix2D(image_center, ag, 1.0) |
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right_img = cv2.warpAffine( |
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right_img, rot_mat, right_img.shape[1::-1], flags=cv2.INTER_LINEAR |
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) |
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trans_mat = np.float32([[1, 0, 0], [0, 1, px]]) |
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right_img = cv2.warpAffine( |
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right_img, trans_mat, right_img.shape[1::-1], flags=cv2.INTER_LINEAR |
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) |
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# 2.2) random resize |
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resize_scale = self.rng.uniform(self.scale_min, self.scale_max) |
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left_img = cv2.resize( |
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left_img, |
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None, |
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fx=resize_scale, |
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fy=resize_scale, |
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interpolation=cv2.INTER_LINEAR, |
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) |
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right_img = cv2.resize( |
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right_img, |
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None, |
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fx=resize_scale, |
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fy=resize_scale, |
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interpolation=cv2.INTER_LINEAR, |
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) |
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disp_mask = (left_disp < float(self.max_disp / resize_scale)) & (left_disp > 0) |
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disp_mask = disp_mask.astype("float32") |
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disp_mask = cv2.resize( |
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disp_mask, |
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None, |
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fx=resize_scale, |
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fy=resize_scale, |
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interpolation=cv2.INTER_LINEAR, |
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) |
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left_disp = ( |
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cv2.resize( |
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left_disp, |
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None, |
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fx=resize_scale, |
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fy=resize_scale, |
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interpolation=cv2.INTER_LINEAR, |
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) |
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* resize_scale |
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) |
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# 2.3) random crop |
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h, w, c = left_img.shape |
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dx = w - self.image_width |
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dy = h - self.image_height |
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dy = self.rng.randint(min(0, dy), max(0, dy) + 1) |
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dx = self.rng.randint(min(0, dx), max(0, dx) + 1) |
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M = np.float32([[1.0, 0.0, -dx], [0.0, 1.0, -dy]]) |
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left_img = cv2.warpAffine( |
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left_img, |
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M, |
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(self.image_width, self.image_height), |
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flags=cv2.INTER_LINEAR, |
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borderValue=0, |
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) |
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right_img = cv2.warpAffine( |
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right_img, |
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M, |
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(self.image_width, self.image_height), |
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flags=cv2.INTER_LINEAR, |
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borderValue=0, |
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) |
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left_disp = cv2.warpAffine( |
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left_disp, |
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M, |
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(self.image_width, self.image_height), |
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flags=cv2.INTER_LINEAR, |
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borderValue=0, |
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) |
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disp_mask = cv2.warpAffine( |
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disp_mask, |
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M, |
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(self.image_width, self.image_height), |
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flags=cv2.INTER_LINEAR, |
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borderValue=0, |
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) |
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# 3. add random occlusion to right image |
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if self.rng.binomial(1, 0.5): |
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sx = int(self.rng.uniform(50, 100)) |
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sy = int(self.rng.uniform(50, 100)) |
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cx = int(self.rng.uniform(sx, right_img.shape[0] - sx)) |
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cy = int(self.rng.uniform(sy, right_img.shape[1] - sy)) |
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right_img[cx - sx : cx + sx, cy - sy : cy + sy] = np.mean( |
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np.mean(right_img, 0), 0 |
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)[np.newaxis, np.newaxis] |
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return left_img, right_img, left_disp, disp_mask |
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class CREStereoDataset(Dataset): |
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def __init__(self, root): |
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super().__init__() |
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self.imgs = glob.glob(os.path.join(root, "**/*_left.jpg"), recursive=True) |
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self.augmentor = Augmentor( |
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image_height=384, |
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image_width=512, |
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max_disp=256, |
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scale_min=0.6, |
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scale_max=1.0, |
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seed=0, |
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) |
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self.rng = np.random.RandomState(0) |
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def get_disp(self, path): |
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disp = cv2.imread(path, cv2.IMREAD_UNCHANGED) |
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return disp.astype(np.float32) / 32 |
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def __getitem__(self, index): |
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# find path |
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left_path = self.imgs[index] |
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prefix = left_path[: left_path.rfind("_")] |
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right_path = prefix + "_right.jpg" |
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left_disp_path = prefix + "_left.disp.png" |
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right_disp_path = prefix + "_right.disp.png" |
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# read img, disp |
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left_img = cv2.imread(left_path, cv2.IMREAD_COLOR) |
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right_img = cv2.imread(right_path, cv2.IMREAD_COLOR) |
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left_disp = self.get_disp(left_disp_path) |
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right_disp = self.get_disp(right_disp_path) |
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if self.rng.binomial(1, 0.5): |
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left_img, right_img = np.fliplr(right_img), np.fliplr(left_img) |
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left_disp, right_disp = np.fliplr(right_disp), np.fliplr(left_disp) |
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left_disp[left_disp == np.inf] = 0 |
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# augmentaion |
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left_img, right_img, left_disp, disp_mask = self.augmentor( |
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left_img, right_img, left_disp |
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) |
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left_img = left_img.transpose(2, 0, 1).astype("uint8") |
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right_img = right_img.transpose(2, 0, 1).astype("uint8") |
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return { |
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"left": left_img, |
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"right": right_img, |
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"disparity": left_disp, |
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"mask": disp_mask, |
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} |
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def __len__(self): |
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return len(self.imgs) |
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Binary file not shown.
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import os |
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import sys |
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import time |
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import logging |
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from collections import namedtuple |
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import yaml |
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from tensorboardX import SummaryWriter |
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from nets import Model |
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from dataset import CREStereoDataset |
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import torch |
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import torch.nn as nn |
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import torch.optim as optim |
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from torch.utils.data import DataLoader |
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def parse_yaml(file_path: str) -> namedtuple: |
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"""Parse yaml configuration file and return the object in `namedtuple`.""" |
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with open(file_path, "rb") as f: |
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cfg: dict = yaml.safe_load(f) |
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args = namedtuple("train_args", cfg.keys())(*cfg.values()) |
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return args |
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def format_time(elapse): |
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elapse = int(elapse) |
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hour = elapse // 3600 |
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minute = elapse % 3600 // 60 |
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seconds = elapse % 60 |
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return "{:02d}:{:02d}:{:02d}".format(hour, minute, seconds) |
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def ensure_dir(path): |
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if not os.path.exists(path): |
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os.makedirs(path, exist_ok=True) |
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def adjust_learning_rate(optimizer, epoch): |
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warm_up = 0.02 |
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const_range = 0.6 |
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min_lr_rate = 0.05 |
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if epoch <= args.n_total_epoch * warm_up: |
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lr = (1 - min_lr_rate) * args.base_lr / ( |
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args.n_total_epoch * warm_up |
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) * epoch + min_lr_rate * args.base_lr |
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elif args.n_total_epoch * warm_up < epoch <= args.n_total_epoch * const_range: |
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lr = args.base_lr |
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else: |
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lr = (min_lr_rate - 1) * args.base_lr / ( |
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(1 - const_range) * args.n_total_epoch |
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) * epoch + (1 - min_lr_rate * const_range) / (1 - const_range) * args.base_lr |
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for param_group in optimizer.param_groups: |
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param_group['lr'] = lr |
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def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8): |
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''' |
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valid: (2, 384, 512) (B, H, W) -> (B, 1, H, W) |
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flow_preds[0]: (B, 2, H, W) |
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flow_gt: (B, 2, H, W) |
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''' |
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n_predictions = len(flow_preds) |
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flow_loss = 0.0 |
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for i in range(n_predictions): |
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i_weight = gamma ** (n_predictions - i - 1) |
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i_loss = torch.abs(flow_preds[i] - flow_gt) |
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flow_loss += i_weight * (valid.unsqueeze(1) * i_loss).mean() |
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return flow_loss |
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def main(args): |
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# initial info |
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torch.manual_seed(args.seed) |
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torch.cuda.manual_seed(args.seed) |
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# rank, world_size = dist.get_rank(), dist.get_world_size() |
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world_size = torch.cuda.device_count() # number of GPU(s) |
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# directory check |
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log_model_dir = os.path.join(args.log_dir, "models") |
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ensure_dir(log_model_dir) |
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# model / optimizer |
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model = Model( |
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max_disp=args.max_disp, mixed_precision=args.mixed_precision, test_mode=False |
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) |
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model = nn.DataParallel(model,device_ids=[i for i in range(world_size)]) |
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model.cuda() |
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optimizer = optim.Adam(model.parameters(), lr=0.1, betas=(0.9, 0.999)) |
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# model = nn.DataParallel(model,device_ids=[0]) |
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tb_log = SummaryWriter(os.path.join(args.log_dir, "train.events")) |
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# worklog |
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logging.basicConfig(level=eval(args.log_level)) |
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worklog = logging.getLogger("train_logger") |
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worklog.propagate = False |
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fileHandler = logging.FileHandler( |
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os.path.join(args.log_dir, "worklog.txt"), mode="a", encoding="utf8" |
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) |
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formatter = logging.Formatter( |
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fmt="%(asctime)s %(message)s", datefmt="%Y/%m/%d %H:%M:%S" |
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) |
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fileHandler.setFormatter(formatter) |
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consoleHandler = logging.StreamHandler(sys.stdout) |
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formatter = logging.Formatter( |
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fmt="\x1b[32m%(asctime)s\x1b[0m %(message)s", datefmt="%Y/%m/%d %H:%M:%S" |
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) |
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consoleHandler.setFormatter(formatter) |
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worklog.handlers = [fileHandler, consoleHandler] |
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# params stat |
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worklog.info(f"Use {world_size} GPU(s)") |
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worklog.info("Params: %s" % sum([p.numel() for p in model.parameters()])) |
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# load pretrained model if exist |
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chk_path = os.path.join(log_model_dir, "latest.pth") |
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if args.loadmodel is not None: |
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chk_path = args.loadmodel |
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elif not os.path.exists(chk_path): |
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chk_path = None |
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if chk_path is not None: |
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# if rank == 0: |
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worklog.info(f"loading model: {chk_path}") |
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state_dict = torch.load(chk_path) |
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model.load_state_dict(state_dict['state_dict']) |
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optimizer.load_state_dict(state_dict['optim_state_dict']) |
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resume_epoch_idx = state_dict["epoch"] |
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resume_iters = state_dict["iters"] |
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start_epoch_idx = resume_epoch_idx + 1 |
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start_iters = resume_iters |
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else: |
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start_epoch_idx = 1 |
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start_iters = 0 |
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# datasets |
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dataset = CREStereoDataset(args.training_data_path) |
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# if rank == 0: |
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worklog.info(f"Dataset size: {len(dataset)}") |
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dataloader = DataLoader(dataset, args.batch_size, shuffle=True, |
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num_workers=0, drop_last=True, persistent_workers=False, pin_memory=True) |
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# counter |
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cur_iters = start_iters |
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total_iters = args.minibatch_per_epoch * args.n_total_epoch |
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t0 = time.perf_counter() |
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for epoch_idx in range(start_epoch_idx, args.n_total_epoch + 1): |
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# adjust learning rate |
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epoch_total_train_loss = 0 |
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adjust_learning_rate(optimizer, epoch_idx) |
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model.train() |
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t1 = time.perf_counter() |
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# batch_idx = 0 |
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# for mini_batch_data in dataloader: |
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for batch_idx, mini_batch_data in enumerate(dataloader): |
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if batch_idx % args.minibatch_per_epoch == 0 and batch_idx != 0: |
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break |
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# batch_idx += 1 |
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cur_iters += 1 |
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# parse data |
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left, right, gt_disp, valid_mask = ( |
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mini_batch_data["left"], |
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mini_batch_data["right"], |
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mini_batch_data["disparity"].cuda(), |
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mini_batch_data["mask"].cuda(), |
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) |
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t2 = time.perf_counter() |
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optimizer.zero_grad() |
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# pre-process |
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gt_disp = torch.unsqueeze(gt_disp, dim=1) # [2, 384, 512] -> [2, 1, 384, 512] |
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gt_flow = torch.cat([gt_disp, gt_disp * 0], dim=1) # [2, 2, 384, 512] |
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# forward |
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flow_predictions = model(left.cuda(), right.cuda()) |
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# loss & backword |
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loss = sequence_loss( |
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flow_predictions, gt_flow, valid_mask, gamma=0.8 |
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) |
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# loss stats |
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loss_item = loss.data.item() |
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epoch_total_train_loss += loss_item |
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loss.backward() |
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optimizer.step() |
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t3 = time.perf_counter() |
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if cur_iters % 10 == 0: |
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tdata = t2 - t1 |
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time_train_passed = t3 - t0 |
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time_iter_passed = t3 - t1 |
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step_passed = cur_iters - start_iters |
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eta = ( |
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(total_iters - cur_iters) |
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/ max(step_passed, 1e-7) |
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* time_train_passed |
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) |
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meta_info = list() |
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meta_info.append("{:.2g} b/s".format(1.0 / time_iter_passed)) |
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meta_info.append("passed:{}".format(format_time(time_train_passed))) |
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meta_info.append("eta:{}".format(format_time(eta))) |
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meta_info.append( |
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"data_time:{:.2g}".format(tdata / time_iter_passed) |
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) |
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meta_info.append( |
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"lr:{:.5g}".format(optimizer.param_groups[0]["lr"]) |
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) |
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meta_info.append( |
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"[{}/{}:{}/{}]".format( |
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epoch_idx, |
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args.n_total_epoch, |
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batch_idx, |
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args.minibatch_per_epoch, |
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) |
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) |
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loss_info = [" ==> {}:{:.4g}".format("loss", loss_item)] |
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# exp_name = ['\n' + os.path.basename(os.getcwd())] |
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info = [",".join(meta_info)] + loss_info |
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worklog.info("".join(info)) |
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# minibatch loss |
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tb_log.add_scalar("train/loss_batch", loss_item, cur_iters) |
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tb_log.add_scalar( |
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"train/lr", optimizer.param_groups[0]["lr"], cur_iters |
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) |
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tb_log.flush() |
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t1 = time.perf_counter() |
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tb_log.add_scalar( |
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"train/loss", |
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epoch_total_train_loss / args.minibatch_per_epoch, |
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epoch_idx, |
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) |
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tb_log.flush() |
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# save model params |
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ckp_data = { |
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"epoch": epoch_idx, |
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"iters": cur_iters, |
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"batch_size": args.batch_size, |
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"epoch_size": args.minibatch_per_epoch, |
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"train_loss": epoch_total_train_loss / args.minibatch_per_epoch, |
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"state_dict": model.state_dict(), |
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"optim_state_dict": optimizer.state_dict(), |
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} |
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torch.save(ckp_data, os.path.join(log_model_dir, "latest.pth")) |
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if epoch_idx % args.model_save_freq_epoch == 0: |
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save_path = os.path.join(log_model_dir, "epoch-%d.pth" % epoch_idx) |
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worklog.info(f"Model params saved: {save_path}") |
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torch.save(ckp_data, save_path) |
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worklog.info("Training is done, exit.") |
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if __name__ == "__main__": |
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# train configuration |
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args = parse_yaml("cfgs/train.yaml") |
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main(args) |
||||
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Reference in new issue