# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ TensorFlow/Keras and TFLite versions of YOLOv5 Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127 Usage: $ python models/tf.py --weights yolov5s.pt --cfg yolov5s.yaml Export int8 TFLite models: $ python models/tf.py --weights yolov5s.pt --cfg models/yolov5s.yaml --tfl-int8 \ --source path/to/images/ --ncalib 100 Detection: $ python detect.py --weights yolov5s.pb --img 320 $ python detect.py --weights yolov5s_saved_model --img 320 $ python detect.py --weights yolov5s-fp16.tflite --img 320 $ python detect.py --weights yolov5s-int8.tflite --img 320 --tfl-int8 For TensorFlow.js: $ python models/tf.py --weights yolov5s.pt --cfg models/yolov5s.yaml --img 320 --tf-nms --agnostic-nms $ pip install tensorflowjs $ tensorflowjs_converter \ --input_format=tf_frozen_model \ --output_node_names='Identity,Identity_1,Identity_2,Identity_3' \ yolov5s.pb \ web_model $ # Edit web_model/model.json to sort Identity* in ascending order $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example $ npm install $ ln -s ../../yolov5/web_model public/web_model $ npm start """ import argparse import logging import os import sys import traceback from copy import deepcopy from pathlib import Path sys.path.append('./') # to run '$ python *.py' files in subdirectories import numpy as np import tensorflow as tf import torch import torch.nn as nn import yaml from tensorflow import keras from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad, C3 from models.experimental import MixConv2d, CrossConv, attempt_load from models.yolo import Detect from utils.datasets import LoadImages from utils.general import check_dataset, check_yaml, make_divisible logger = logging.getLogger(__name__) class tf_BN(keras.layers.Layer): # TensorFlow BatchNormalization wrapper def __init__(self, w=None): super(tf_BN, self).__init__() self.bn = keras.layers.BatchNormalization( beta_initializer=keras.initializers.Constant(w.bias.numpy()), gamma_initializer=keras.initializers.Constant(w.weight.numpy()), moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), epsilon=w.eps) def call(self, inputs): return self.bn(inputs) class tf_Pad(keras.layers.Layer): def __init__(self, pad): super(tf_Pad, self).__init__() self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) def call(self, inputs): return tf.pad(inputs, self.pad, mode='constant', constant_values=0) class tf_Conv(keras.layers.Layer): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups super(tf_Conv, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert isinstance(k, int), "Convolution with multiple kernels are not allowed." # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch conv = keras.layers.Conv2D( c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False, kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) self.conv = conv if s == 1 else keras.Sequential([tf_Pad(autopad(k, p)), conv]) self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity # YOLOv5 activations if isinstance(w.act, nn.LeakyReLU): self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity elif isinstance(w.act, nn.Hardswish): self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity elif isinstance(w.act, nn.SiLU): self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity def call(self, inputs): return self.act(self.bn(self.conv(inputs))) class tf_Focus(keras.layers.Layer): # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, kernel, stride, padding, groups super(tf_Focus, self).__init__() self.conv = tf_Conv(c1 * 4, c2, k, s, p, g, act, w.conv) def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) # inputs = inputs / 255. # normalize 0-255 to 0-1 return self.conv(tf.concat([inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]], 3)) class tf_Bottleneck(keras.layers.Layer): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion super(tf_Bottleneck, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) self.cv2 = tf_Conv(c_, c2, 3, 1, g=g, w=w.cv2) self.add = shortcut and c1 == c2 def call(self, inputs): return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) class tf_Conv2d(keras.layers.Layer): # Substitution for PyTorch nn.Conv2D def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): super(tf_Conv2d, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" self.conv = keras.layers.Conv2D( c2, k, s, 'VALID', use_bias=bias, kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, ) def call(self, inputs): return self.conv(inputs) class tf_BottleneckCSP(keras.layers.Layer): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion super(tf_BottleneckCSP, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) self.cv2 = tf_Conv2d(c1, c_, 1, 1, bias=False, w=w.cv2) self.cv3 = tf_Conv2d(c_, c_, 1, 1, bias=False, w=w.cv3) self.cv4 = tf_Conv(2 * c_, c2, 1, 1, w=w.cv4) self.bn = tf_BN(w.bn) self.act = lambda x: keras.activations.relu(x, alpha=0.1) self.m = keras.Sequential([tf_Bottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) def call(self, inputs): y1 = self.cv3(self.m(self.cv1(inputs))) y2 = self.cv2(inputs) return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) class tf_C3(keras.layers.Layer): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion super(tf_C3, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) self.cv2 = tf_Conv(c1, c_, 1, 1, w=w.cv2) self.cv3 = tf_Conv(2 * c_, c2, 1, 1, w=w.cv3) self.m = keras.Sequential([tf_Bottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) def call(self, inputs): return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) class tf_SPP(keras.layers.Layer): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13), w=None): super(tf_SPP, self).__init__() c_ = c1 // 2 # hidden channels self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) self.cv2 = tf_Conv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] def call(self, inputs): x = self.cv1(inputs) return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) class tf_Detect(keras.layers.Layer): def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer super(tf_Detect, self).__init__() self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [tf.zeros(1)] * self.nl # init grid self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) self.anchor_grid = tf.reshape(tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), [self.nl, 1, -1, 1, 2]) self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] self.export = False # onnx export self.training = True # set to False after building model for i in range(self.nl): ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] self.grid[i] = self._make_grid(nx, ny) def call(self, inputs): # x = x.copy() # for profiling z = [] # inference output self.training |= self.export x = [] for i in range(self.nl): x.append(self.m[i](inputs[i])) # x(bs,20,20,255) to x(bs,3,20,20,85) ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3]) if not self.training: # inference y = tf.sigmoid(x[i]) xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # Normalize xywh to 0-1 to reduce calibration error xy /= tf.constant([[opt.img_size[1], opt.img_size[0]]], dtype=tf.float32) wh /= tf.constant([[opt.img_size[1], opt.img_size[0]]], dtype=tf.float32) y = tf.concat([xy, wh, y[..., 4:]], -1) z.append(tf.reshape(y, [-1, 3 * ny * nx, self.no])) return x if self.training else (tf.concat(z, 1), x) @staticmethod def _make_grid(nx=20, ny=20): # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) class tf_Upsample(keras.layers.Layer): def __init__(self, size, scale_factor, mode, w=None): super(tf_Upsample, self).__init__() assert scale_factor == 2, "scale_factor must be 2" # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) if opt.tf_raw_resize: # with default arguments: align_corners=False, half_pixel_centers=False self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, size=(x.shape[1] * 2, x.shape[2] * 2)) else: self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode) def call(self, inputs): return self.upsample(inputs) class tf_Concat(keras.layers.Layer): def __init__(self, dimension=1, w=None): super(tf_Concat, self).__init__() assert dimension == 1, "convert only NCHW to NHWC concat" self.d = 3 def call(self, inputs): return tf.concat(inputs, self.d) def parse_model(d, ch, model): # model_dict, input_channels(3) logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args m_str = m m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: c1, c2 = ch[f], args[0] c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 args = [c1, c2, *args[1:]] if m in [BottleneckCSP, C3]: args.insert(2, n) n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) elif m is Detect: args.append([ch[x + 1] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) else: c2 = ch[f] tf_m = eval('tf_' + m_str.replace('nn.', '')) m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ else tf_m(*args, w=model.model[i]) # module torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type np = sum([x.numel() for x in torch_m_.parameters()]) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) ch.append(c2) return keras.Sequential(layers), sorted(save) class tf_Model(): def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, input channels, number of classes super(tf_Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict # Define model if nc and nc != self.yaml['nc']: print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) self.yaml['nc'] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out def predict(self, inputs, profile=False): y = [] # outputs x = inputs for i, m in enumerate(self.model.layers): if m.f != -1: # if not from previous layer x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers x = m(x) # run y.append(x if m.i in self.savelist else None) # save output # Add TensorFlow NMS if opt.tf_nms: boxes = xywh2xyxy(x[0][..., :4]) probs = x[0][:, :, 4:5] classes = x[0][:, :, 5:] scores = probs * classes if opt.agnostic_nms: nms = agnostic_nms_layer()((boxes, classes, scores)) return nms, x[1] else: boxes = tf.expand_dims(boxes, 2) nms = tf.image.combined_non_max_suppression( boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, clip_boxes=False) return nms, x[1] return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] # x = x[0][0] # [x(1,6300,85), ...] to x(6300,85) # xywh = x[..., :4] # x(6300,4) boxes # conf = x[..., 4:5] # x(6300,1) confidences # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes # return tf.concat([conf, cls, xywh], 1) class agnostic_nms_layer(keras.layers.Layer): # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450 def call(self, input): return tf.map_fn(agnostic_nms, input, fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), name='agnostic_nms') def agnostic_nms(x): boxes, classes, scores = x class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) scores_inp = tf.reduce_max(scores, -1) selected_inds = tf.image.non_max_suppression( boxes, scores_inp, max_output_size=opt.topk_all, iou_threshold=opt.iou_thres, score_threshold=opt.score_thres) selected_boxes = tf.gather(boxes, selected_inds) padded_boxes = tf.pad(selected_boxes, paddings=[[0, opt.topk_all - tf.shape(selected_boxes)[0]], [0, 0]], mode="CONSTANT", constant_values=0.0) selected_scores = tf.gather(scores_inp, selected_inds) padded_scores = tf.pad(selected_scores, paddings=[[0, opt.topk_all - tf.shape(selected_boxes)[0]]], mode="CONSTANT", constant_values=-1.0) selected_classes = tf.gather(class_inds, selected_inds) padded_classes = tf.pad(selected_classes, paddings=[[0, opt.topk_all - tf.shape(selected_boxes)[0]]], mode="CONSTANT", constant_values=-1.0) valid_detections = tf.shape(selected_inds)[0] return padded_boxes, padded_scores, padded_classes, valid_detections def xywh2xyxy(xywh): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) def representative_dataset_gen(): # Representative dataset for use with converter.representative_dataset n = 0 for path, img, im0s, vid_cap in dataset: # Get sample input data as a numpy array in a method of your choosing. n += 1 input = np.transpose(img, [1, 2, 0]) input = np.expand_dims(input, axis=0).astype(np.float32) input /= 255.0 yield [input] if n >= opt.ncalib: break if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--dynamic-batch-size', action='store_true', help='dynamic batch size') parser.add_argument('--source', type=str, default='../data/coco128.yaml', help='dir of images or data.yaml file') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--tf-raw-resize', action='store_true', dest='tf_raw_resize', help='use tf.raw_ops.ResizeNearestNeighbor for resize') parser.add_argument('--topk-per-class', type=int, default=100, help='topk per class to keep in NMS') parser.add_argument('--topk-all', type=int, default=100, help='topk for all classes to keep in NMS') parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--score-thres', type=float, default=0.4, help='score threshold for NMS') opt = parser.parse_args() opt.cfg = check_yaml(opt.cfg) # check YAML opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) # Input img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection # Load PyTorch model model = attempt_load(opt.weights, map_location=torch.device('cpu'), inplace=True, fuse=False) model.model[-1].export = False # set Detect() layer export=True y = model(img) # dry run nc = y[0].shape[-1] - 5 # TensorFlow saved_model export try: print('\nStarting TensorFlow saved_model export with TensorFlow %s...' % tf.__version__) tf_model = tf_Model(opt.cfg, model=model, nc=nc) img = tf.zeros((opt.batch_size, *opt.img_size, 3)) # NHWC Input for TensorFlow m = tf_model.model.layers[-1] assert isinstance(m, tf_Detect), "the last layer must be Detect" m.training = False y = tf_model.predict(img) inputs = keras.Input(shape=(*opt.img_size, 3), batch_size=None if opt.dynamic_batch_size else opt.batch_size) keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) keras_model.summary() path = opt.weights.replace('.pt', '_saved_model') # filename keras_model.save(path, save_format='tf') print('TensorFlow saved_model export success, saved as %s' % path) except Exception as e: print('TensorFlow saved_model export failure: %s' % e) traceback.print_exc(file=sys.stdout) # TensorFlow GraphDef export try: print('\nStarting TensorFlow GraphDef export with TensorFlow %s...' % tf.__version__) # https://github.com/leimao/Frozen_Graph_TensorFlow full_model = tf.function(lambda x: keras_model(x)) full_model = full_model.get_concrete_function( tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) frozen_func = convert_variables_to_constants_v2(full_model) frozen_func.graph.as_graph_def() f = opt.weights.replace('.pt', '.pb') # filename tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=os.path.dirname(f), name=os.path.basename(f), as_text=False) print('TensorFlow GraphDef export success, saved as %s' % f) except Exception as e: print('TensorFlow GraphDef export failure: %s' % e) traceback.print_exc(file=sys.stdout) # TFLite model export if not opt.tf_nms: try: print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) # fp32 TFLite model export --------------------------------------------------------------------------------- # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] # converter.allow_custom_ops = False # converter.experimental_new_converter = True # tflite_model = converter.convert() # f = opt.weights.replace('.pt', '.tflite') # filename # open(f, "wb").write(tflite_model) # fp16 TFLite model export --------------------------------------------------------------------------------- converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] # converter.representative_dataset = representative_dataset_gen # converter.target_spec.supported_types = [tf.float16] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.allow_custom_ops = False converter.experimental_new_converter = True tflite_model = converter.convert() f = opt.weights.replace('.pt', '-fp16.tflite') # filename open(f, "wb").write(tflite_model) print('\nTFLite export success, saved as %s' % f) # int8 TFLite model export --------------------------------------------------------------------------------- if opt.tfl_int8: # Representative Dataset if opt.source.endswith('.yaml'): with open(check_yaml(opt.source)) as f: data = yaml.load(f, Loader=yaml.FullLoader) # data dict check_dataset(data) # check opt.source = data['train'] dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_dataset_gen converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 # or tf.int8 converter.inference_output_type = tf.uint8 # or tf.int8 converter.allow_custom_ops = False converter.experimental_new_converter = True converter.experimental_new_quantizer = False tflite_model = converter.convert() f = opt.weights.replace('.pt', '-int8.tflite') # filename open(f, "wb").write(tflite_model) print('\nTFLite (int8) export success, saved as %s' % f) except Exception as e: print('\nTFLite export failure: %s' % e) traceback.print_exc(file=sys.stdout)