mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-29 03:00:14 -04:00
247 lines
8.1 KiB
Python
247 lines
8.1 KiB
Python
# load weights from
|
|
# https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth
|
|
# a rough copy of
|
|
# https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py
|
|
import os
|
|
GPU = os.getenv("GPU", None) is not None
|
|
import sys
|
|
import math
|
|
import io
|
|
import time
|
|
import numpy as np
|
|
np.set_printoptions(suppress=True)
|
|
|
|
from tinygrad.tensor import Tensor
|
|
from tinygrad.utils import fetch
|
|
from tinygrad.nn import BatchNorm2D
|
|
|
|
class MBConvBlock:
|
|
def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio):
|
|
oup = expand_ratio * input_filters
|
|
if expand_ratio != 1:
|
|
self._expand_conv = Tensor.zeros(oup, input_filters, 1, 1)
|
|
self._bn0 = BatchNorm2D(oup)
|
|
else:
|
|
self._expand_conv = None
|
|
|
|
self.strides = strides
|
|
if strides == (2,2):
|
|
self.pad = [(kernel_size-1)//2-1, (kernel_size-1)//2]*2
|
|
else:
|
|
self.pad = [(kernel_size-1)//2]*4
|
|
|
|
self._depthwise_conv = Tensor.zeros(oup, 1, kernel_size, kernel_size)
|
|
self._bn1 = BatchNorm2D(oup)
|
|
|
|
num_squeezed_channels = max(1, int(input_filters * se_ratio))
|
|
self._se_reduce = Tensor.zeros(num_squeezed_channels, oup, 1, 1)
|
|
self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
|
|
self._se_expand = Tensor.zeros(oup, num_squeezed_channels, 1, 1)
|
|
self._se_expand_bias = Tensor.zeros(oup)
|
|
|
|
self._project_conv = Tensor.zeros(output_filters, oup, 1, 1)
|
|
self._bn2 = BatchNorm2D(output_filters)
|
|
|
|
def __call__(self, inputs):
|
|
x = inputs
|
|
if self._expand_conv:
|
|
x = self._bn0(x.conv2d(self._expand_conv)).swish()
|
|
x = x.pad2d(padding=self.pad)
|
|
x = x.conv2d(self._depthwise_conv, stride=self.strides, groups=self._depthwise_conv.shape[0])
|
|
x = self._bn1(x).swish()
|
|
|
|
# has_se
|
|
x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
|
|
x_squeezed = x_squeezed.conv2d(self._se_reduce).add(self._se_reduce_bias.reshape(shape=[1, -1, 1, 1])).swish()
|
|
x_squeezed = x_squeezed.conv2d(self._se_expand).add(self._se_expand_bias.reshape(shape=[1, -1, 1, 1]))
|
|
x = x.mul(x_squeezed.sigmoid())
|
|
|
|
x = self._bn2(x.conv2d(self._project_conv))
|
|
if x.shape == inputs.shape:
|
|
x = x.add(inputs)
|
|
return x
|
|
|
|
class EfficientNet:
|
|
def __init__(self, number=0):
|
|
self.number = number
|
|
global_params = [
|
|
# width, depth
|
|
(1.0, 1.0), # b0
|
|
(1.0, 1.1), # b1
|
|
(1.1, 1.2), # b2
|
|
(1.2, 1.4), # b3
|
|
(1.4, 1.8), # b4
|
|
(1.6, 2.2), # b5
|
|
(1.8, 2.6), # b6
|
|
(2.0, 3.1), # b7
|
|
(2.2, 3.6), # b8
|
|
(4.3, 5.3), # l2
|
|
][number]
|
|
|
|
def round_filters(filters):
|
|
multiplier = global_params[0]
|
|
divisor = 8
|
|
filters *= multiplier
|
|
new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
|
|
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
|
|
new_filters += divisor
|
|
return int(new_filters)
|
|
|
|
def round_repeats(repeats):
|
|
return int(math.ceil(global_params[1] * repeats))
|
|
|
|
out_channels = round_filters(32)
|
|
self._conv_stem = Tensor.zeros(out_channels, 3, 3, 3)
|
|
self._bn0 = BatchNorm2D(out_channels)
|
|
blocks_args = [
|
|
[1, 3, (1,1), 1, 32, 16, 0.25],
|
|
[2, 3, (2,2), 6, 16, 24, 0.25],
|
|
[2, 5, (2,2), 6, 24, 40, 0.25],
|
|
[3, 3, (2,2), 6, 40, 80, 0.25],
|
|
[3, 5, (1,1), 6, 80, 112, 0.25],
|
|
[4, 5, (2,2), 6, 112, 192, 0.25],
|
|
[1, 3, (1,1), 6, 192, 320, 0.25],
|
|
]
|
|
self._blocks = []
|
|
# num_repeats, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio
|
|
for b in blocks_args:
|
|
args = b[1:]
|
|
args[3] = round_filters(args[3])
|
|
args[4] = round_filters(args[4])
|
|
for n in range(round_repeats(b[0])):
|
|
self._blocks.append(MBConvBlock(*args))
|
|
args[3] = args[4]
|
|
args[1] = (1,1)
|
|
|
|
in_channels = round_filters(320)
|
|
out_channels = round_filters(1280)
|
|
self._conv_head = Tensor.zeros(out_channels, in_channels, 1, 1)
|
|
self._bn1 = BatchNorm2D(out_channels)
|
|
self._fc = Tensor.zeros(out_channels, 1000)
|
|
self._fc_bias = Tensor.zeros(1000)
|
|
|
|
def forward(self, x):
|
|
x = x.pad2d(padding=(0,1,0,1))
|
|
x = self._bn0(x.conv2d(self._conv_stem, stride=2)).swish()
|
|
for block in self._blocks:
|
|
#print(x.shape)
|
|
x = block(x)
|
|
x = self._bn1(x.conv2d(self._conv_head)).swish()
|
|
x = x.avg_pool2d(kernel_size=x.shape[2:4])
|
|
x = x.reshape(shape=(-1, x.shape[1]))
|
|
#x = x.dropout(0.2)
|
|
return x.dot(self._fc).add(self._fc_bias.reshape(shape=[1,-1]))
|
|
|
|
def load_weights_from_torch(self):
|
|
# load b0
|
|
import torch
|
|
# https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/utils.py#L551
|
|
if self.number == 0:
|
|
b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth")
|
|
elif self.number == 2:
|
|
b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth")
|
|
elif self.number == 4:
|
|
b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth")
|
|
elif self.number == 7:
|
|
b0 = fetch("https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth")
|
|
else:
|
|
raise Exception("no pretrained weights")
|
|
b0 = torch.load(io.BytesIO(b0))
|
|
|
|
for k,v in b0.items():
|
|
if '_blocks.' in k:
|
|
k = "%s[%s].%s" % tuple(k.split(".", 2))
|
|
mk = "self."+k
|
|
#print(k, v.shape)
|
|
try:
|
|
mv = eval(mk)
|
|
except AttributeError:
|
|
try:
|
|
mv = eval(mk.replace(".weight", ""))
|
|
except AttributeError:
|
|
mv = eval(mk.replace(".bias", "_bias"))
|
|
vnp = v.numpy().astype(np.float32)
|
|
mv.data[:] = vnp if k != '_fc.weight' else vnp.T
|
|
if GPU:
|
|
mv.cuda_()
|
|
|
|
def infer(model, img):
|
|
# preprocess image
|
|
aspect_ratio = img.size[0] / img.size[1]
|
|
img = img.resize((int(224*max(aspect_ratio,1.0)), int(224*max(1.0/aspect_ratio,1.0))))
|
|
|
|
img = np.array(img)
|
|
y0,x0=(np.asarray(img.shape)[:2]-224)//2
|
|
retimg = img = img[y0:y0+224, x0:x0+224]
|
|
|
|
# if you want to look at the image
|
|
"""
|
|
import matplotlib.pyplot as plt
|
|
plt.imshow(img)
|
|
plt.show()
|
|
"""
|
|
|
|
# low level preprocess
|
|
img = np.moveaxis(img, [2,0,1], [0,1,2])
|
|
img = img.astype(np.float32)[:3].reshape(1,3,224,224)
|
|
img /= 255.0
|
|
img -= np.array([0.485, 0.456, 0.406]).reshape((1,-1,1,1))
|
|
img /= np.array([0.229, 0.224, 0.225]).reshape((1,-1,1,1))
|
|
|
|
# run the net
|
|
if GPU:
|
|
out = model.forward(Tensor(img).cuda()).cpu()
|
|
else:
|
|
out = model.forward(Tensor(img))
|
|
|
|
# if you want to look at the outputs
|
|
"""
|
|
import matplotlib.pyplot as plt
|
|
plt.plot(out.data[0])
|
|
plt.show()
|
|
"""
|
|
return out, retimg
|
|
|
|
if __name__ == "__main__":
|
|
# instantiate my net
|
|
model = EfficientNet(int(os.getenv("NUM", "0")))
|
|
model.load_weights_from_torch()
|
|
|
|
# category labels
|
|
import ast
|
|
lbls = fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
|
|
lbls = ast.literal_eval(lbls.decode('utf-8'))
|
|
|
|
# load image and preprocess
|
|
from PIL import Image
|
|
url = sys.argv[1]
|
|
if url == 'webcam':
|
|
import cv2
|
|
cap = cv2.VideoCapture(0)
|
|
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
|
|
while 1:
|
|
_ = cap.grab() # discard one frame to circumvent capture buffering
|
|
ret, frame = cap.read()
|
|
img = Image.fromarray(frame[:, :, [2,1,0]])
|
|
out, retimg = infer(model, img)
|
|
print(np.argmax(out.data), np.max(out.data), lbls[np.argmax(out.data)])
|
|
SCALE = 3
|
|
simg = cv2.resize(retimg, (224*SCALE, 224*SCALE))
|
|
retimg = cv2.cvtColor(simg, cv2.COLOR_RGB2BGR)
|
|
cv2.imshow('capture', retimg)
|
|
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
break
|
|
cap.release()
|
|
cv2.destroyAllWindows()
|
|
else:
|
|
if url.startswith('http'):
|
|
img = Image.open(io.BytesIO(fetch(url)))
|
|
else:
|
|
img = Image.open(url)
|
|
st = time.time()
|
|
out, _ = infer(model, img)
|
|
print(np.argmax(out.data), np.max(out.data), lbls[np.argmax(out.data)])
|
|
print("did inference in %.2f s" % (time.time()-st))
|
|
#print("NOT", np.argmin(out.data), np.min(out.data), lbls[np.argmin(out.data)])
|
|
|