def build_model(self, mobile=False, show=False): if mobile: self.model = create_hourglass_network(self.num_classes, self.num_stacks, self.inres, self.outres, bottleneck_mobile) else: self.model = create_hourglass_network(self.num_classes, self.num_stacks, self.inres, self.outres, bottleneck_block) # show model summary and layer name if show : self.model.summary()
def main(): model = create_hourglass_network(16, 2, (256, 256), (64, 64)) model.summary() print(len(model.output_layers)) plot_model(model, 'hg_s2.png', show_shapes=True) for layer in model.output_layers: print(layer.output_shape)
def main(): model = create_hourglass_network(16, 8, (256, 256), (64, 64), bottleneck_mobile) model.summary() print len(model.output_layers) #plot_model(model, 'hg_s2.png', show_shapes=True) for layer in model.output_layers: print layer.output_shape
def build_model(self, mobile=False, show=True): if mobile: self.model = create_hourglass_network(self.num_classes, self.num_stacks, self.num_channels, self.inres, self.outres, bottleneck_mobile) else: self.model = create_hourglass_network(self.num_classes, self.num_stacks, self.num_channels, self.inres, self.outres, bottleneck_block) # show model summary and layer name if show: self.model.summary() model_json = self.model.to_json() with open("HourglassNet.json", "w") as json_file: json_file.write(model_json) return self.model
wandb.config.downsample_ratio), # 100ms 'min_peak_height': 0.3, }) mit_loader_train = MITLoader('train', wandb.config) mit_loader_val = MITLoader('valid', wandb.config) training_set = DataGenerator(mit_loader_train, wandb.config.batch_size) validation_set = DataGenerator(mit_loader_val, wandb.config.batch_size) print('#batch_train, #batch_valid: {}, {}'.format(len(training_set), len(validation_set))) model = create_hourglass_network( len(wandb.config.labels), wandb.config.number_hourglass_modules, wandb.config.number_inner_channels, (int(wandb.config.sampling_rate * wandb.config.length_s), 1), bottleneck_block, wandb.config.hourglass_module_layers) model.summary() model.compile(optimizer='adam', loss=focal_loss( length_head_ignore=int(wandb.config.sampling_rate * wandb.config.head_ignore_s * wandb.config.downsample_ratio), length_tail_ignore=int(wandb.config.sampling_rate * wandb.config.tail_ignore_s * wandb.config.downsample_ratio)), run_eagerly=False) model.fit( training_set,