efee1712aa
* fix.. speed_limit error... * draw tpms settings. * fix.. traffic light stopping only.. * fix.. waze cam * fix.. waze... * add setting (Enable comma connect ) * auto detect LFA2 * fix.. cruisespeed1 * vff2 driving model. * fix.. * agnos 12.3 * fix.. * ff * ff * test * ff * fix.. drawTurnInfo.. * Update drive_helpers.py * fix.. support eng voice eng sounds fix settings... english fix.. mph.. fix.. roadlimit speed bug.. * new vff model.. 250608 * fix soundd.. * fix safe exit speed.. * fix.. sounds. * fix.. radar timeStep.. * KerryGold model * Update drive_helpers.py * fix.. model. * fix.. * fix.. * Revert "fix.." This reverts commit b09ec459afb855c533d47fd7e8a1a6b1a09466e7. * Revert "fix.." This reverts commit 290bec6b83a4554ca232d531a911edccf94a2156. * fix esim * add more acc table. 10kph * kg update.. * fix cruisebutton mode3 * test atc..cond. * fix.. canfd * fix.. angle control limit
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
# for imagenet download prepare.sh and run it
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import glob, random, json, math
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import numpy as np
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from PIL import Image
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import functools, pathlib
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from tinygrad.helpers import diskcache, getenv
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@functools.cache
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def get_imagenet_categories():
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ci = json.load(open(BASEDIR / "imagenet_class_index.json"))
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return {v[0]: int(k) for k,v in ci.items()}
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if getenv("MNISTMOCK"):
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BASEDIR = pathlib.Path(__file__).parent / "mnist"
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@functools.cache
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def get_train_files():
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if not BASEDIR.exists():
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from extra.datasets.fake_imagenet_from_mnist import create_fake_mnist_imagenet
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create_fake_mnist_imagenet(BASEDIR)
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if not (files:=glob.glob(p:=str(BASEDIR / "train/*/*"))): raise FileNotFoundError(f"No training files in {p}")
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return files
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else:
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BASEDIR = pathlib.Path(__file__).parent / "imagenet"
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@diskcache
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def get_train_files():
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if not (files:=glob.glob(p:=str(BASEDIR / "train/*/*"))): raise FileNotFoundError(f"No training files in {p}")
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return files
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@functools.cache
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def get_val_files():
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if not (files:=glob.glob(p:=str(BASEDIR / "val/*/*"))): raise FileNotFoundError(f"No validation files in {p}")
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return files
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def image_resize(img, size, interpolation):
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w, h = img.size
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w_new = int((w / h) * size) if w > h else size
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h_new = int((h / w) * size) if h > w else size
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return img.resize([w_new, h_new], interpolation)
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def rand_flip(img):
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if random.random() < 0.5:
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img = np.flip(img, axis=1).copy()
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return img
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def center_crop(img):
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rescale = min(img.size) / 256
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crop_left = (img.width - 224 * rescale) / 2.0
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crop_top = (img.height - 224 * rescale) / 2.0
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img = img.resize((224, 224), Image.BILINEAR, box=(crop_left, crop_top, crop_left + 224 * rescale, crop_top + 224 * rescale))
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return img
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# we don't use supplied imagenet bounding boxes, so scale min is just min_object_covered
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# https://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/kernels/image/sample_distorted_bounding_box_op.cc
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def random_resized_crop(img, size, scale=(0.10, 1.0), ratio=(3/4, 4/3)):
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w, h = img.size
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area = w * h
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# Crop
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random_solution_found = False
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for _ in range(100):
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aspect_ratio = random.uniform(ratio[0], ratio[1])
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max_scale = min(min(w * aspect_ratio / h, h / aspect_ratio / w), scale[1])
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target_area = area * random.uniform(scale[0], max_scale)
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w_new = int(round(math.sqrt(target_area * aspect_ratio)))
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h_new = int(round(math.sqrt(target_area / aspect_ratio)))
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if 0 < w_new <= w and 0 < h_new <= h:
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crop_left = random.randint(0, w - w_new)
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crop_top = random.randint(0, h - h_new)
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img = img.crop((crop_left, crop_top, crop_left + w_new, crop_top + h_new))
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random_solution_found = True
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break
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if not random_solution_found:
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# Center crop
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img = center_crop(img)
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else:
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# Resize
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img = img.resize([size, size], Image.BILINEAR)
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return img
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def preprocess_train(img):
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img = random_resized_crop(img, 224)
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img = rand_flip(np.array(img))
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return img
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