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